repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15 values |
|---|---|---|---|---|---|
iproduct/course-social-robotics | 11-dnn-keras/venv/Lib/site-packages/pandas/tests/indexes/numeric/test_astype.py | 5 | 2943 | import re
import numpy as np
import pytest
from pandas.core.dtypes.common import pandas_dtype
from pandas import Float64Index, Index, Int64Index
import pandas._testing as tm
class TestAstype:
def test_astype_float64_to_object(self):
float_index = Float64Index([0.0, 2.5, 5.0, 7.5, 10.0])
result = float_index.astype(object)
assert result.equals(float_index)
assert float_index.equals(result)
assert isinstance(result, Index) and not isinstance(result, Float64Index)
def test_astype_float64_mixed_to_object(self):
# mixed int-float
idx = Float64Index([1.5, 2, 3, 4, 5])
idx.name = "foo"
result = idx.astype(object)
assert result.equals(idx)
assert idx.equals(result)
assert isinstance(result, Index) and not isinstance(result, Float64Index)
@pytest.mark.parametrize("dtype", ["int16", "int32", "int64"])
def test_astype_float64_to_int_dtype(self, dtype):
# GH#12881
# a float astype int
idx = Float64Index([0, 1, 2])
result = idx.astype(dtype)
expected = Int64Index([0, 1, 2])
tm.assert_index_equal(result, expected)
idx = Float64Index([0, 1.1, 2])
result = idx.astype(dtype)
expected = Int64Index([0, 1, 2])
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("dtype", ["float32", "float64"])
def test_astype_float64_to_float_dtype(self, dtype):
# GH#12881
# a float astype int
idx = Float64Index([0, 1, 2])
result = idx.astype(dtype)
expected = idx
tm.assert_index_equal(result, expected)
idx = Float64Index([0, 1.1, 2])
result = idx.astype(dtype)
expected = Index(idx.values.astype(dtype))
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
def test_cannot_cast_to_datetimelike(self, dtype):
idx = Float64Index([0, 1.1, 2])
msg = (
f"Cannot convert Float64Index to dtype {pandas_dtype(dtype)}; "
f"integer values are required for conversion"
)
with pytest.raises(TypeError, match=re.escape(msg)):
idx.astype(dtype)
@pytest.mark.parametrize("dtype", [int, "int16", "int32", "int64"])
@pytest.mark.parametrize("non_finite", [np.inf, np.nan])
def test_cannot_cast_inf_to_int(self, non_finite, dtype):
# GH#13149
idx = Float64Index([1, 2, non_finite])
msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
with pytest.raises(ValueError, match=msg):
idx.astype(dtype)
def test_astype_from_object(self):
index = Index([1.0, np.nan, 0.2], dtype="object")
result = index.astype(float)
expected = Float64Index([1.0, np.nan, 0.2])
assert result.dtype == expected.dtype
tm.assert_index_equal(result, expected)
| gpl-2.0 |
Averroes/statsmodels | statsmodels/datasets/ccard/data.py | 25 | 1635 | """Bill Greene's credit scoring data."""
__docformat__ = 'restructuredtext'
COPYRIGHT = """Used with express permission of the original author, who
retains all rights."""
TITLE = __doc__
SOURCE = """
William Greene's `Econometric Analysis`
More information can be found at the web site of the text:
http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm
"""
DESCRSHORT = """William Greene's credit scoring data"""
DESCRLONG = """More information on this data can be found on the
homepage for Greene's `Econometric Analysis`. See source.
"""
NOTE = """::
Number of observations - 72
Number of variables - 5
Variable name definitions - See Source for more information on the
variables.
"""
from numpy import recfromtxt, column_stack, array
from statsmodels.datasets import utils as du
from os.path import dirname, abspath
def load():
"""Load the credit card data and returns a Dataset class.
Returns
-------
Dataset instance:
See DATASET_PROPOSAL.txt for more information.
"""
data = _get_data()
return du.process_recarray(data, endog_idx=0, dtype=float)
def load_pandas():
"""Load the credit card data and returns a Dataset class.
Returns
-------
Dataset instance:
See DATASET_PROPOSAL.txt for more information.
"""
data = _get_data()
return du.process_recarray_pandas(data, endog_idx=0)
def _get_data():
filepath = dirname(abspath(__file__))
data = recfromtxt(open(filepath + '/ccard.csv', 'rb'), delimiter=",",
names=True, dtype=float)
return data
| bsd-3-clause |
dhruv13J/scikit-learn | examples/model_selection/plot_roc.py | 146 | 3697 | """
=======================================
Receiver Operating Characteristic (ROC)
=======================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality.
ROC curves typically feature true positive rate on the Y axis, and false
positive rate on the X axis. This means that the top left corner of the plot is
the "ideal" point - a false positive rate of zero, and a true positive rate of
one. This is not very realistic, but it does mean that a larger area under the
curve (AUC) is usually better.
The "steepness" of ROC curves is also important, since it is ideal to maximize
the true positive rate while minimizing the false positive rate.
ROC curves are typically used in binary classification to study the output of
a classifier. In order to extend ROC curve and ROC area to multi-class
or multi-label classification, it is necessary to binarize the output. One ROC
curve can be drawn per label, but one can also draw a ROC curve by considering
each element of the label indicator matrix as a binary prediction
(micro-averaging).
.. note::
See also :func:`sklearn.metrics.roc_auc_score`,
:ref:`example_model_selection_plot_roc_crossval.py`.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
# Import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Binarize the output
y = label_binarize(y, classes=[0, 1, 2])
n_classes = y.shape[1]
# Add noisy features to make the problem harder
random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,
random_state=0)
# Learn to predict each class against the other
classifier = OneVsRestClassifier(svm.SVC(kernel='linear', probability=True,
random_state=random_state))
y_score = classifier.fit(X_train, y_train).decision_function(X_test)
# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
# Plot of a ROC curve for a specific class
plt.figure()
plt.plot(fpr[2], tpr[2], label='ROC curve (area = %0.2f)' % roc_auc[2])
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()
# Plot ROC curve
plt.figure()
plt.plot(fpr["micro"], tpr["micro"],
label='micro-average ROC curve (area = {0:0.2f})'
''.format(roc_auc["micro"]))
for i in range(n_classes):
plt.plot(fpr[i], tpr[i], label='ROC curve of class {0} (area = {1:0.2f})'
''.format(i, roc_auc[i]))
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.show()
| bsd-3-clause |
suraj-jayakumar/capstoneproject | sentiment-analysis/supervised_learning/archive/classifier_articles/lstm_d2v/lstm_d2v.py | 2 | 2580 | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 23 15:53:29 2016
@author: suraj
"""
# gensim modules
from gensim.models import Doc2Vec
# np
import numpy as np
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Sequential, model_from_json
from keras.layers.recurrent import LSTM
# classifier
from sklearn.linear_model import LogisticRegression
model2 = Doc2Vec.load('./imdb.d2v')
model2.most_similar('good')
train_arrays = np.zeros((25000,1, 100))
train_labels = np.zeros(25000)
for i in range(12500):
prefix_train_pos = 'TRAIN_POS_' + str(i)
prefix_train_neg = 'TRAIN_NEG_' + str(i)
train_arrays[i][0] = model2.docvecs[i]
train_arrays[12500 + i] [0]= model2.docvecs[i+12500]
train_labels[i] = 1
train_labels[12500 + i] = 0
test_arrays = np.zeros((25000,1, 100))
test_labels = np.zeros(25000)
for i in range(12500):
prefix_test_pos = 'TEST_POS_' + str(i)
prefix_test_neg = 'TEST_NEG_' + str(i)
test_arrays[i][0] = model2.docvecs[25000+i]
test_arrays[12500 + i][0] = model2.docvecs[25000+12500+i]
test_labels[i] = 1
test_labels[12500 + i] = 0
'''
model = Sequential()
model.add(LSTM(100,batch_input_shape=(1,1,100)))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam')
model.fit(train_arrays, train_labels, batch_size=1, nb_epoch=10,show_accuracy=True)
score, acc = model.evaluate(np.array(test_arrays), np.array(test_labels),
batch_size=1,
show_accuracy=True)
print('Test score:', score)
print('Test accuracy:', acc)
open('lstm_new.json','w').write(model.to_json())
model.save_weights('lstm_new.h5',overwrite=True)
'''
model = model_from_json(open('lstm_new.json').read())
model.load_weights('lstm_new.h5')
#
#score, acc = model.evaluate(np.array(test_arrays), np.array(test_labels),
# batch_size=1,
# show_accuracy=True)
#
#
#print('Test score:', score)
#print('Test accuracy:', acc)
#
#
#
#test_str = "this is a stupid film really pointless and bad"
#mytemp =np.reshape( np.sum( model2[test_str.split()],axis=0)/len(test_str.split()) ,(1,100))
#
#output = model.predict(np.array([mytemp]))
#print output[0][0]
#
#if(output[0][0]>0.5):
# print "Positive"
#else:
# print "Negative"
print model.predict(np.array([test_arrays[0]]))
print model.predict(np.array([test_arrays[-1]])) | mit |
dsquareindia/scikit-learn | sklearn/utils/setup.py | 77 | 2993 | import os
from os.path import join
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
import numpy
from numpy.distutils.misc_util import Configuration
config = Configuration('utils', parent_package, top_path)
config.add_subpackage('sparsetools')
cblas_libs, blas_info = get_blas_info()
cblas_compile_args = blas_info.pop('extra_compile_args', [])
cblas_includes = [join('..', 'src', 'cblas'),
numpy.get_include(),
blas_info.pop('include_dirs', [])]
libraries = []
if os.name == 'posix':
libraries.append('m')
cblas_libs.append('m')
config.add_extension('sparsefuncs_fast', sources=['sparsefuncs_fast.pyx'],
libraries=libraries)
config.add_extension('arrayfuncs',
sources=['arrayfuncs.pyx'],
depends=[join('src', 'cholesky_delete.h')],
libraries=cblas_libs,
include_dirs=cblas_includes,
extra_compile_args=cblas_compile_args,
**blas_info
)
config.add_extension('murmurhash',
sources=['murmurhash.pyx', join(
'src', 'MurmurHash3.cpp')],
include_dirs=['src'])
config.add_extension('lgamma',
sources=['lgamma.pyx', join('src', 'gamma.c')],
include_dirs=['src'],
libraries=libraries)
config.add_extension('graph_shortest_path',
sources=['graph_shortest_path.pyx'],
include_dirs=[numpy.get_include()])
config.add_extension('fast_dict',
sources=['fast_dict.pyx'],
language="c++",
include_dirs=[numpy.get_include()],
libraries=libraries)
config.add_extension('seq_dataset',
sources=['seq_dataset.pyx'],
include_dirs=[numpy.get_include()])
config.add_extension('weight_vector',
sources=['weight_vector.pyx'],
include_dirs=cblas_includes,
libraries=cblas_libs,
**blas_info)
config.add_extension("_random",
sources=["_random.pyx"],
include_dirs=[numpy.get_include()],
libraries=libraries)
config.add_extension("_logistic_sigmoid",
sources=["_logistic_sigmoid.pyx"],
include_dirs=[numpy.get_include()],
libraries=libraries)
config.add_subpackage('tests')
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(**configuration(top_path='').todict())
| bsd-3-clause |
cpcloud/vbench | vbench/db.py | 3 | 5573 | from pandas import DataFrame
from sqlalchemy import Table, Column, MetaData, create_engine, ForeignKey
from sqlalchemy import types as sqltypes
from sqlalchemy import sql
import logging
log = logging.getLogger('vb.db')
class BenchmarkDB(object):
"""
Persist vbench results in a sqlite3 database
"""
def __init__(self, dbpath):
log.info("Initializing DB at %s" % dbpath)
self.dbpath = dbpath
self._engine = create_engine('sqlite:///%s' % dbpath)
self._metadata = MetaData()
self._metadata.bind = self._engine
self._benchmarks = Table('benchmarks', self._metadata,
Column('checksum', sqltypes.String(32), primary_key=True),
Column('name', sqltypes.String(200), nullable=False),
Column('description', sqltypes.Text)
)
self._results = Table('results', self._metadata,
Column('checksum', sqltypes.String(32),
ForeignKey('benchmarks.checksum'), primary_key=True),
Column('revision', sqltypes.String(50), primary_key=True),
Column('timestamp', sqltypes.DateTime, nullable=False),
Column('ncalls', sqltypes.String(50)),
Column('timing', sqltypes.Float),
Column('traceback', sqltypes.Text),
)
self._blacklist = Table('blacklist', self._metadata,
Column('revision', sqltypes.String(50), primary_key=True)
)
self._ensure_tables_created()
_instances = {}
@classmethod
def get_instance(cls, dbpath):
if dbpath not in cls._instances:
cls._instances[dbpath] = BenchmarkDB(dbpath)
return cls._instances[dbpath]
def _ensure_tables_created(self):
log.debug("Ensuring DB tables are created")
self._benchmarks.create(self._engine, checkfirst=True)
self._results.create(self._engine, checkfirst=True)
self._blacklist.create(self._engine, checkfirst=True)
def update_name(self, benchmark):
"""
benchmarks : list
"""
table = self._benchmarks
stmt = (table.update().
where(table.c.checksum == benchmark.checksum).
values(checksum=benchmark.checksum))
self.conn.execute(stmt)
def restrict_to_benchmarks(self, benchmarks):
"""
benchmarks : list
"""
checksums = set([b.checksum for b in benchmarks])
ex_benchmarks = self.get_benchmarks()
to_delete = set(ex_benchmarks.index) - checksums
t = self._benchmarks
for chksum in to_delete:
log.info('Deleting %s\n%s' % (chksum, ex_benchmarks.xs(chksum)))
stmt = t.delete().where(t.c.checksum == chksum)
self.conn.execute(stmt)
@property
def conn(self):
return self._engine.connect()
def write_benchmark(self, bm, overwrite=False):
"""
"""
ins = self._benchmarks.insert()
ins = ins.values(name=bm.name, checksum=bm.checksum,
description=bm.description)
self.conn.execute(ins) # XXX: return the result?
def delete_benchmark(self, checksum):
"""
"""
pass
def write_result(self, checksum, revision, timestamp, ncalls,
timing, traceback=None, overwrite=False):
"""
"""
ins = self._results.insert()
ins = ins.values(checksum=checksum, revision=revision,
timestamp=timestamp,
ncalls=ncalls, timing=timing, traceback=traceback)
self.conn.execute(ins) # XXX: return the result?
def delete_result(self, checksum, revision):
"""
"""
pass
def delete_error_results(self):
tab = self._results
ins = tab.delete()
ins = ins.where(tab.c.timing == None)
self.conn.execute(ins)
def get_benchmarks(self):
stmt = sql.select([self._benchmarks])
result = self.conn.execute(stmt)
return _sqa_to_frame(result).set_index('checksum')
def get_rev_results(self, rev):
tab = self._results
stmt = sql.select([tab],
sql.and_(tab.c.revision == rev))
results = list(self.conn.execute(stmt))
return dict((v.checksum, v) for v in results)
def delete_rev_results(self, rev):
tab = self._results
stmt = tab.delete().where(tab.c.revision == rev)
self.conn.execute(stmt)
def add_rev_blacklist(self, rev):
"""
Don't try running this revision again
"""
stmt = self._blacklist.insert().values(revision=rev)
self.conn.execute(stmt)
def get_rev_blacklist(self):
stmt = self._blacklist.select()
return [x['revision'] for x in self.conn.execute(stmt)]
def clear_blacklist(self):
stmt = self._blacklist.delete()
self.conn.execute(stmt)
def get_benchmark_results(self, checksum):
"""
"""
tab = self._results
stmt = sql.select([tab.c.timestamp, tab.c.revision, tab.c.ncalls,
tab.c.timing, tab.c.traceback],
sql.and_(tab.c.checksum == checksum))
results = self.conn.execute(stmt)
df = _sqa_to_frame(results).set_index('timestamp')
return df.sort_index()
def _sqa_to_frame(result):
rows = [tuple(x) for x in result]
if not rows:
return DataFrame(columns=result.keys())
return DataFrame.from_records(rows, columns=result.keys())
| mit |
Aasmi/scikit-learn | sklearn/externals/joblib/parallel.py | 36 | 34375 | """
Helpers for embarrassingly parallel code.
"""
# Author: Gael Varoquaux < gael dot varoquaux at normalesup dot org >
# Copyright: 2010, Gael Varoquaux
# License: BSD 3 clause
from __future__ import division
import os
import sys
import gc
import warnings
from math import sqrt
import functools
import time
import threading
import itertools
from numbers import Integral
try:
import cPickle as pickle
except:
import pickle
from ._multiprocessing_helpers import mp
if mp is not None:
from .pool import MemmapingPool
from multiprocessing.pool import ThreadPool
from .format_stack import format_exc, format_outer_frames
from .logger import Logger, short_format_time
from .my_exceptions import TransportableException, _mk_exception
from .disk import memstr_to_kbytes
from ._compat import _basestring
VALID_BACKENDS = ['multiprocessing', 'threading']
# Environment variables to protect against bad situations when nesting
JOBLIB_SPAWNED_PROCESS = "__JOBLIB_SPAWNED_PARALLEL__"
# In seconds, should be big enough to hide multiprocessing dispatching
# overhead.
# This settings was found by running benchmarks/bench_auto_batching.py
# with various parameters on various platforms.
MIN_IDEAL_BATCH_DURATION = .2
# Should not be too high to avoid stragglers: long jobs running alone
# on a single worker while other workers have no work to process any more.
MAX_IDEAL_BATCH_DURATION = 2
class BatchedCalls(object):
"""Wrap a sequence of (func, args, kwargs) tuples as a single callable"""
def __init__(self, iterator_slice):
self.items = list(iterator_slice)
self._size = len(self.items)
def __call__(self):
return [func(*args, **kwargs) for func, args, kwargs in self.items]
def __len__(self):
return self._size
###############################################################################
# CPU count that works also when multiprocessing has been disabled via
# the JOBLIB_MULTIPROCESSING environment variable
def cpu_count():
""" Return the number of CPUs.
"""
if mp is None:
return 1
return mp.cpu_count()
###############################################################################
# For verbosity
def _verbosity_filter(index, verbose):
""" Returns False for indices increasingly apart, the distance
depending on the value of verbose.
We use a lag increasing as the square of index
"""
if not verbose:
return True
elif verbose > 10:
return False
if index == 0:
return False
verbose = .5 * (11 - verbose) ** 2
scale = sqrt(index / verbose)
next_scale = sqrt((index + 1) / verbose)
return (int(next_scale) == int(scale))
###############################################################################
class WorkerInterrupt(Exception):
""" An exception that is not KeyboardInterrupt to allow subprocesses
to be interrupted.
"""
pass
###############################################################################
class SafeFunction(object):
""" Wraps a function to make it exception with full traceback in
their representation.
Useful for parallel computing with multiprocessing, for which
exceptions cannot be captured.
"""
def __init__(self, func):
self.func = func
def __call__(self, *args, **kwargs):
try:
return self.func(*args, **kwargs)
except KeyboardInterrupt:
# We capture the KeyboardInterrupt and reraise it as
# something different, as multiprocessing does not
# interrupt processing for a KeyboardInterrupt
raise WorkerInterrupt()
except:
e_type, e_value, e_tb = sys.exc_info()
text = format_exc(e_type, e_value, e_tb, context=10,
tb_offset=1)
if issubclass(e_type, TransportableException):
raise
else:
raise TransportableException(text, e_type)
###############################################################################
def delayed(function, check_pickle=True):
"""Decorator used to capture the arguments of a function.
Pass `check_pickle=False` when:
- performing a possibly repeated check is too costly and has been done
already once outside of the call to delayed.
- when used in conjunction `Parallel(backend='threading')`.
"""
# Try to pickle the input function, to catch the problems early when
# using with multiprocessing:
if check_pickle:
pickle.dumps(function)
def delayed_function(*args, **kwargs):
return function, args, kwargs
try:
delayed_function = functools.wraps(function)(delayed_function)
except AttributeError:
" functools.wraps fails on some callable objects "
return delayed_function
###############################################################################
class ImmediateComputeBatch(object):
"""Sequential computation of a batch of tasks.
This replicates the async computation API but actually does not delay
the computations when joblib.Parallel runs in sequential mode.
"""
def __init__(self, batch):
# Don't delay the application, to avoid keeping the input
# arguments in memory
self.results = batch()
def get(self):
return self.results
###############################################################################
class BatchCompletionCallBack(object):
"""Callback used by joblib.Parallel's multiprocessing backend.
This callable is executed by the parent process whenever a worker process
has returned the results of a batch of tasks.
It is used for progress reporting, to update estimate of the batch
processing duration and to schedule the next batch of tasks to be
processed.
"""
def __init__(self, dispatch_timestamp, batch_size, parallel):
self.dispatch_timestamp = dispatch_timestamp
self.batch_size = batch_size
self.parallel = parallel
def __call__(self, out):
self.parallel.n_completed_tasks += self.batch_size
this_batch_duration = time.time() - self.dispatch_timestamp
if (self.parallel.batch_size == 'auto'
and self.batch_size == self.parallel._effective_batch_size):
# Update the smoothed streaming estimate of the duration of a batch
# from dispatch to completion
old_duration = self.parallel._smoothed_batch_duration
if old_duration == 0:
# First record of duration for this batch size after the last
# reset.
new_duration = this_batch_duration
else:
# Update the exponentially weighted average of the duration of
# batch for the current effective size.
new_duration = 0.8 * old_duration + 0.2 * this_batch_duration
self.parallel._smoothed_batch_duration = new_duration
self.parallel.print_progress()
if self.parallel._original_iterator is not None:
self.parallel.dispatch_next()
###############################################################################
class Parallel(Logger):
''' Helper class for readable parallel mapping.
Parameters
-----------
n_jobs: int, default: 1
The maximum number of concurrently running jobs, such as the number
of Python worker processes when backend="multiprocessing"
or the size of the thread-pool when backend="threading".
If -1 all CPUs are used. If 1 is given, no parallel computing code
is used at all, which is useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all
CPUs but one are used.
backend: str or None, default: 'multiprocessing'
Specify the parallelization backend implementation.
Supported backends are:
- "multiprocessing" used by default, can induce some
communication and memory overhead when exchanging input and
output data with the with the worker Python processes.
- "threading" is a very low-overhead backend but it suffers
from the Python Global Interpreter Lock if the called function
relies a lot on Python objects. "threading" is mostly useful
when the execution bottleneck is a compiled extension that
explicitly releases the GIL (for instance a Cython loop wrapped
in a "with nogil" block or an expensive call to a library such
as NumPy).
verbose: int, optional
The verbosity level: if non zero, progress messages are
printed. Above 50, the output is sent to stdout.
The frequency of the messages increases with the verbosity level.
If it more than 10, all iterations are reported.
pre_dispatch: {'all', integer, or expression, as in '3*n_jobs'}
The number of batches (of tasks) to be pre-dispatched.
Default is '2*n_jobs'. When batch_size="auto" this is reasonable
default and the multiprocessing workers shoud never starve.
batch_size: int or 'auto', default: 'auto'
The number of atomic tasks to dispatch at once to each
worker. When individual evaluations are very fast, multiprocessing
can be slower than sequential computation because of the overhead.
Batching fast computations together can mitigate this.
The ``'auto'`` strategy keeps track of the time it takes for a batch
to complete, and dynamically adjusts the batch size to keep the time
on the order of half a second, using a heuristic. The initial batch
size is 1.
``batch_size="auto"`` with ``backend="threading"`` will dispatch
batches of a single task at a time as the threading backend has
very little overhead and using larger batch size has not proved to
bring any gain in that case.
temp_folder: str, optional
Folder to be used by the pool for memmaping large arrays
for sharing memory with worker processes. If None, this will try in
order:
- a folder pointed by the JOBLIB_TEMP_FOLDER environment variable,
- /dev/shm if the folder exists and is writable: this is a RAMdisk
filesystem available by default on modern Linux distributions,
- the default system temporary folder that can be overridden
with TMP, TMPDIR or TEMP environment variables, typically /tmp
under Unix operating systems.
Only active when backend="multiprocessing".
max_nbytes int, str, or None, optional, 1M by default
Threshold on the size of arrays passed to the workers that
triggers automated memory mapping in temp_folder. Can be an int
in Bytes, or a human-readable string, e.g., '1M' for 1 megabyte.
Use None to disable memmaping of large arrays.
Only active when backend="multiprocessing".
Notes
-----
This object uses the multiprocessing module to compute in
parallel the application of a function to many different
arguments. The main functionality it brings in addition to
using the raw multiprocessing API are (see examples for details):
* More readable code, in particular since it avoids
constructing list of arguments.
* Easier debugging:
- informative tracebacks even when the error happens on
the client side
- using 'n_jobs=1' enables to turn off parallel computing
for debugging without changing the codepath
- early capture of pickling errors
* An optional progress meter.
* Interruption of multiprocesses jobs with 'Ctrl-C'
* Flexible pickling control for the communication to and from
the worker processes.
* Ability to use shared memory efficiently with worker
processes for large numpy-based datastructures.
Examples
--------
A simple example:
>>> from math import sqrt
>>> from sklearn.externals.joblib import Parallel, delayed
>>> Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10))
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
Reshaping the output when the function has several return
values:
>>> from math import modf
>>> from sklearn.externals.joblib import Parallel, delayed
>>> r = Parallel(n_jobs=1)(delayed(modf)(i/2.) for i in range(10))
>>> res, i = zip(*r)
>>> res
(0.0, 0.5, 0.0, 0.5, 0.0, 0.5, 0.0, 0.5, 0.0, 0.5)
>>> i
(0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0)
The progress meter: the higher the value of `verbose`, the more
messages::
>>> from time import sleep
>>> from sklearn.externals.joblib import Parallel, delayed
>>> r = Parallel(n_jobs=2, verbose=5)(delayed(sleep)(.1) for _ in range(10)) #doctest: +SKIP
[Parallel(n_jobs=2)]: Done 1 out of 10 | elapsed: 0.1s remaining: 0.9s
[Parallel(n_jobs=2)]: Done 3 out of 10 | elapsed: 0.2s remaining: 0.5s
[Parallel(n_jobs=2)]: Done 6 out of 10 | elapsed: 0.3s remaining: 0.2s
[Parallel(n_jobs=2)]: Done 9 out of 10 | elapsed: 0.5s remaining: 0.1s
[Parallel(n_jobs=2)]: Done 10 out of 10 | elapsed: 0.5s finished
Traceback example, note how the line of the error is indicated
as well as the values of the parameter passed to the function that
triggered the exception, even though the traceback happens in the
child process::
>>> from heapq import nlargest
>>> from sklearn.externals.joblib import Parallel, delayed
>>> Parallel(n_jobs=2)(delayed(nlargest)(2, n) for n in (range(4), 'abcde', 3)) #doctest: +SKIP
#...
---------------------------------------------------------------------------
Sub-process traceback:
---------------------------------------------------------------------------
TypeError Mon Nov 12 11:37:46 2012
PID: 12934 Python 2.7.3: /usr/bin/python
...........................................................................
/usr/lib/python2.7/heapq.pyc in nlargest(n=2, iterable=3, key=None)
419 if n >= size:
420 return sorted(iterable, key=key, reverse=True)[:n]
421
422 # When key is none, use simpler decoration
423 if key is None:
--> 424 it = izip(iterable, count(0,-1)) # decorate
425 result = _nlargest(n, it)
426 return map(itemgetter(0), result) # undecorate
427
428 # General case, slowest method
TypeError: izip argument #1 must support iteration
___________________________________________________________________________
Using pre_dispatch in a producer/consumer situation, where the
data is generated on the fly. Note how the producer is first
called a 3 times before the parallel loop is initiated, and then
called to generate new data on the fly. In this case the total
number of iterations cannot be reported in the progress messages::
>>> from math import sqrt
>>> from sklearn.externals.joblib import Parallel, delayed
>>> def producer():
... for i in range(6):
... print('Produced %s' % i)
... yield i
>>> out = Parallel(n_jobs=2, verbose=100, pre_dispatch='1.5*n_jobs')(
... delayed(sqrt)(i) for i in producer()) #doctest: +SKIP
Produced 0
Produced 1
Produced 2
[Parallel(n_jobs=2)]: Done 1 jobs | elapsed: 0.0s
Produced 3
[Parallel(n_jobs=2)]: Done 2 jobs | elapsed: 0.0s
Produced 4
[Parallel(n_jobs=2)]: Done 3 jobs | elapsed: 0.0s
Produced 5
[Parallel(n_jobs=2)]: Done 4 jobs | elapsed: 0.0s
[Parallel(n_jobs=2)]: Done 5 out of 6 | elapsed: 0.0s remaining: 0.0s
[Parallel(n_jobs=2)]: Done 6 out of 6 | elapsed: 0.0s finished
'''
def __init__(self, n_jobs=1, backend='multiprocessing', verbose=0,
pre_dispatch='2 * n_jobs', batch_size='auto', temp_folder=None,
max_nbytes='1M', mmap_mode='r'):
self.verbose = verbose
self._mp_context = None
if backend is None:
# `backend=None` was supported in 0.8.2 with this effect
backend = "multiprocessing"
elif hasattr(backend, 'Pool') and hasattr(backend, 'Lock'):
# Make it possible to pass a custom multiprocessing context as
# backend to change the start method to forkserver or spawn or
# preload modules on the forkserver helper process.
self._mp_context = backend
backend = "multiprocessing"
if backend not in VALID_BACKENDS:
raise ValueError("Invalid backend: %s, expected one of %r"
% (backend, VALID_BACKENDS))
self.backend = backend
self.n_jobs = n_jobs
if (batch_size == 'auto'
or isinstance(batch_size, Integral) and batch_size > 0):
self.batch_size = batch_size
else:
raise ValueError(
"batch_size must be 'auto' or a positive integer, got: %r"
% batch_size)
self.pre_dispatch = pre_dispatch
self._pool = None
self._temp_folder = temp_folder
if isinstance(max_nbytes, _basestring):
self._max_nbytes = 1024 * memstr_to_kbytes(max_nbytes)
else:
self._max_nbytes = max_nbytes
self._mmap_mode = mmap_mode
# Not starting the pool in the __init__ is a design decision, to be
# able to close it ASAP, and not burden the user with closing it.
self._output = None
self._jobs = list()
# A flag used to abort the dispatching of jobs in case an
# exception is found
self._aborting = False
def _dispatch(self, batch):
"""Queue the batch for computing, with or without multiprocessing
WARNING: this method is not thread-safe: it should be only called
indirectly via dispatch_one_batch.
"""
if self._pool is None:
job = ImmediateComputeBatch(batch)
self._jobs.append(job)
self.n_dispatched_batches += 1
self.n_dispatched_tasks += len(batch)
self.n_completed_tasks += len(batch)
if not _verbosity_filter(self.n_dispatched_batches, self.verbose):
self._print('Done %3i tasks | elapsed: %s',
(self.n_completed_tasks,
short_format_time(time.time() - self._start_time)
))
else:
# If job.get() catches an exception, it closes the queue:
if self._aborting:
return
dispatch_timestamp = time.time()
cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
job = self._pool.apply_async(SafeFunction(batch), callback=cb)
self._jobs.append(job)
self.n_dispatched_tasks += len(batch)
self.n_dispatched_batches += 1
def dispatch_next(self):
"""Dispatch more data for parallel processing
This method is meant to be called concurrently by the multiprocessing
callback. We rely on the thread-safety of dispatch_one_batch to protect
against concurrent consumption of the unprotected iterator.
"""
if not self.dispatch_one_batch(self._original_iterator):
self._iterating = False
self._original_iterator = None
def dispatch_one_batch(self, iterator):
"""Prefetch the tasks for the next batch and dispatch them.
The effective size of the batch is computed here.
If there are no more jobs to dispatch, return False, else return True.
The iterator consumption and dispatching is protected by the same
lock so calling this function should be thread safe.
"""
if self.batch_size == 'auto' and self.backend == 'threading':
# Batching is never beneficial with the threading backend
batch_size = 1
elif self.batch_size == 'auto':
old_batch_size = self._effective_batch_size
batch_duration = self._smoothed_batch_duration
if (batch_duration > 0 and
batch_duration < MIN_IDEAL_BATCH_DURATION):
# The current batch size is too small: the duration of the
# processing of a batch of task is not large enough to hide
# the scheduling overhead.
ideal_batch_size = int(
old_batch_size * MIN_IDEAL_BATCH_DURATION / batch_duration)
# Multiply by two to limit oscilations between min and max.
batch_size = max(2 * ideal_batch_size, 1)
self._effective_batch_size = batch_size
if self.verbose >= 10:
self._print("Batch computation too fast (%.4fs.) "
"Setting batch_size=%d.", (
batch_duration, batch_size))
elif (batch_duration > MAX_IDEAL_BATCH_DURATION and
old_batch_size >= 2):
# The current batch size is too big. If we schedule overly long
# running batches some CPUs might wait with nothing left to do
# while a couple of CPUs a left processing a few long running
# batches. Better reduce the batch size a bit to limit the
# likelihood of scheduling such stragglers.
self._effective_batch_size = batch_size = old_batch_size // 2
if self.verbose >= 10:
self._print("Batch computation too slow (%.2fs.) "
"Setting batch_size=%d.", (
batch_duration, batch_size))
else:
# No batch size adjustment
batch_size = old_batch_size
if batch_size != old_batch_size:
# Reset estimation of the smoothed mean batch duration: this
# estimate is updated in the multiprocessing apply_async
# CallBack as long as the batch_size is constant. Therefore
# we need to reset the estimate whenever we re-tune the batch
# size.
self._smoothed_batch_duration = 0
else:
# Fixed batch size strategy
batch_size = self.batch_size
with self._lock:
tasks = BatchedCalls(itertools.islice(iterator, batch_size))
if not tasks:
# No more tasks available in the iterator: tell caller to stop.
return False
else:
self._dispatch(tasks)
return True
def _print(self, msg, msg_args):
"""Display the message on stout or stderr depending on verbosity"""
# XXX: Not using the logger framework: need to
# learn to use logger better.
if not self.verbose:
return
if self.verbose < 50:
writer = sys.stderr.write
else:
writer = sys.stdout.write
msg = msg % msg_args
writer('[%s]: %s\n' % (self, msg))
def print_progress(self):
"""Display the process of the parallel execution only a fraction
of time, controlled by self.verbose.
"""
if not self.verbose:
return
elapsed_time = time.time() - self._start_time
# This is heuristic code to print only 'verbose' times a messages
# The challenge is that we may not know the queue length
if self._original_iterator:
if _verbosity_filter(self.n_dispatched_batches, self.verbose):
return
self._print('Done %3i tasks | elapsed: %s',
(self.n_completed_tasks,
short_format_time(elapsed_time),
))
else:
index = self.n_dispatched_batches
# We are finished dispatching
total_tasks = self.n_dispatched_tasks
# We always display the first loop
if not index == 0:
# Display depending on the number of remaining items
# A message as soon as we finish dispatching, cursor is 0
cursor = (total_tasks - index + 1
- self._pre_dispatch_amount)
frequency = (total_tasks // self.verbose) + 1
is_last_item = (index + 1 == total_tasks)
if (is_last_item or cursor % frequency):
return
remaining_time = (elapsed_time / (index + 1) *
(self.n_dispatched_tasks - index - 1.))
self._print('Done %3i out of %3i | elapsed: %s remaining: %s',
(index + 1,
total_tasks,
short_format_time(elapsed_time),
short_format_time(remaining_time),
))
def retrieve(self):
self._output = list()
while self._iterating or len(self._jobs) > 0:
if len(self._jobs) == 0:
# Wait for an async callback to dispatch new jobs
time.sleep(0.01)
continue
# We need to be careful: the job queue can be filling up as
# we empty it
if hasattr(self, '_lock'):
self._lock.acquire()
job = self._jobs.pop(0)
if hasattr(self, '_lock'):
self._lock.release()
try:
self._output.extend(job.get())
except tuple(self.exceptions) as exception:
try:
self._aborting = True
self._lock.acquire()
if isinstance(exception,
(KeyboardInterrupt, WorkerInterrupt)):
# We have captured a user interruption, clean up
# everything
if hasattr(self, '_pool'):
self._pool.close()
self._pool.terminate()
# We can now allow subprocesses again
os.environ.pop('__JOBLIB_SPAWNED_PARALLEL__', 0)
raise exception
elif isinstance(exception, TransportableException):
# Capture exception to add information on the local
# stack in addition to the distant stack
this_report = format_outer_frames(context=10,
stack_start=1)
report = """Multiprocessing exception:
%s
---------------------------------------------------------------------------
Sub-process traceback:
---------------------------------------------------------------------------
%s""" % (
this_report,
exception.message,
)
# Convert this to a JoblibException
exception_type = _mk_exception(exception.etype)[0]
raise exception_type(report)
raise exception
finally:
self._lock.release()
def __call__(self, iterable):
if self._jobs:
raise ValueError('This Parallel instance is already running')
n_jobs = self.n_jobs
if n_jobs == 0:
raise ValueError('n_jobs == 0 in Parallel has no meaning')
if n_jobs < 0 and mp is not None:
n_jobs = max(mp.cpu_count() + 1 + n_jobs, 1)
# The list of exceptions that we will capture
self.exceptions = [TransportableException]
self._lock = threading.Lock()
# Whether or not to set an environment flag to track
# multiple process spawning
set_environ_flag = False
if (n_jobs is None or mp is None or n_jobs == 1):
n_jobs = 1
self._pool = None
elif self.backend == 'threading':
self._pool = ThreadPool(n_jobs)
elif self.backend == 'multiprocessing':
if mp.current_process().daemon:
# Daemonic processes cannot have children
n_jobs = 1
self._pool = None
warnings.warn(
'Multiprocessing-backed parallel loops cannot be nested,'
' setting n_jobs=1',
stacklevel=2)
elif threading.current_thread().name != 'MainThread':
# Prevent posix fork inside in non-main posix threads
n_jobs = 1
self._pool = None
warnings.warn(
'Multiprocessing backed parallel loops cannot be nested'
' below threads, setting n_jobs=1',
stacklevel=2)
else:
already_forked = int(os.environ.get('__JOBLIB_SPAWNED_PARALLEL__', 0))
if already_forked:
raise ImportError('[joblib] Attempting to do parallel computing '
'without protecting your import on a system that does '
'not support forking. To use parallel-computing in a '
'script, you must protect your main loop using "if '
"__name__ == '__main__'"
'". Please see the joblib documentation on Parallel '
'for more information'
)
# Make sure to free as much memory as possible before forking
gc.collect()
# Set an environment variable to avoid infinite loops
set_environ_flag = True
poolargs = dict(
max_nbytes=self._max_nbytes,
mmap_mode=self._mmap_mode,
temp_folder=self._temp_folder,
verbose=max(0, self.verbose - 50),
context_id=0, # the pool is used only for one call
)
if self._mp_context is not None:
# Use Python 3.4+ multiprocessing context isolation
poolargs['context'] = self._mp_context
self._pool = MemmapingPool(n_jobs, **poolargs)
# We are using multiprocessing, we also want to capture
# KeyboardInterrupts
self.exceptions.extend([KeyboardInterrupt, WorkerInterrupt])
else:
raise ValueError("Unsupported backend: %s" % self.backend)
if self.batch_size == 'auto':
self._effective_batch_size = 1
iterator = iter(iterable)
pre_dispatch = self.pre_dispatch
if pre_dispatch == 'all' or n_jobs == 1:
# prevent further dispatch via multiprocessing callback thread
self._original_iterator = None
self._pre_dispatch_amount = 0
else:
self._original_iterator = iterator
if hasattr(pre_dispatch, 'endswith'):
pre_dispatch = eval(pre_dispatch)
self._pre_dispatch_amount = pre_dispatch = int(pre_dispatch)
# The main thread will consume the first pre_dispatch items and
# the remaining items will later be lazily dispatched by async
# callbacks upon task completions.
iterator = itertools.islice(iterator, pre_dispatch)
self._start_time = time.time()
self.n_dispatched_batches = 0
self.n_dispatched_tasks = 0
self.n_completed_tasks = 0
self._smoothed_batch_duration = 0.0
try:
if set_environ_flag:
# Set an environment variable to avoid infinite loops
os.environ[JOBLIB_SPAWNED_PROCESS] = '1'
self._iterating = True
while self.dispatch_one_batch(iterator):
pass
if pre_dispatch == "all" or n_jobs == 1:
# The iterable was consumed all at once by the above for loop.
# No need to wait for async callbacks to trigger to
# consumption.
self._iterating = False
self.retrieve()
# Make sure that we get a last message telling us we are done
elapsed_time = time.time() - self._start_time
self._print('Done %3i out of %3i | elapsed: %s finished',
(len(self._output),
len(self._output),
short_format_time(elapsed_time)
))
finally:
if n_jobs > 1:
self._pool.close()
self._pool.terminate() # terminate does a join()
if self.backend == 'multiprocessing':
os.environ.pop(JOBLIB_SPAWNED_PROCESS, 0)
self._jobs = list()
output = self._output
self._output = None
return output
def __repr__(self):
return '%s(n_jobs=%s)' % (self.__class__.__name__, self.n_jobs)
| bsd-3-clause |
pressel/mpi4py | demo/mandelbrot/mandelbrot-master.py | 11 | 1466 | from mpi4py import MPI
import numpy as np
x1 = -2.0
x2 = 1.0
y1 = -1.0
y2 = 1.0
w = 600
h = 400
maxit = 255
import os
dirname = os.path.abspath(os.path.dirname(__file__))
executable = os.path.join(dirname, 'mandelbrot-worker.exe')
# spawn worker
worker = MPI.COMM_SELF.Spawn(executable, maxprocs=7)
size = worker.Get_remote_size()
# send parameters
rmsg = np.array([x1, x2, y1, y2], dtype='f')
imsg = np.array([w, h, maxit], dtype='i')
worker.Bcast([rmsg, MPI.REAL], root=MPI.ROOT)
worker.Bcast([imsg, MPI.INTEGER], root=MPI.ROOT)
# gather results
counts = np.empty(size, dtype='i')
indices = np.empty(h, dtype='i')
cdata = np.empty([h, w], dtype='i')
worker.Gather(sendbuf=None,
recvbuf=[counts, MPI.INTEGER],
root=MPI.ROOT)
worker.Gatherv(sendbuf=None,
recvbuf=[indices, (counts, None), MPI.INTEGER],
root=MPI.ROOT)
worker.Gatherv(sendbuf=None,
recvbuf=[cdata, (counts * w, None), MPI.INTEGER],
root=MPI.ROOT)
# disconnect worker
worker.Disconnect()
# reconstruct full result
M = np.zeros([h, w], dtype='i')
M[indices, :] = cdata
# eye candy (requires matplotlib)
try:
from matplotlib import pyplot as plt
plt.imshow(M, aspect='equal')
plt.spectral()
try:
import signal
def action(*args): raise SystemExit
signal.signal(signal.SIGALRM, action)
signal.alarm(2)
except:
pass
plt.show()
except:
pass
| bsd-2-clause |
wathen/PhD | MHD/FEniCS/MHD/Stabilised/SaddlePointForm/Test/ShiftedMassApprox/MHDfluid3d.py | 1 | 10795 | #!/usr/bin/python
# interpolate scalar gradient onto nedelec space
from dolfin import *
import petsc4py
import sys
petsc4py.init(sys.argv)
from petsc4py import PETSc
Print = PETSc.Sys.Print
# from MatrixOperations import *
import numpy as np
#import matplotlib.pylab as plt
import PETScIO as IO
import common
import scipy
import scipy.io
import time
import BiLinear as forms
import IterOperations as Iter
import MatrixOperations as MO
import CheckPetsc4py as CP
import ExactSol
import Solver as S
import MHDmatrixPrecondSetup as PrecondSetup
import NSprecondSetup
import MHDallatonce as MHDpreconditioner
m = 4
errL2u =np.zeros((m-1,1))
errH1u =np.zeros((m-1,1))
errL2p =np.zeros((m-1,1))
errL2b =np.zeros((m-1,1))
errCurlb =np.zeros((m-1,1))
errL2r =np.zeros((m-1,1))
errH1r =np.zeros((m-1,1))
l2uorder = np.zeros((m-1,1))
H1uorder =np.zeros((m-1,1))
l2porder = np.zeros((m-1,1))
l2border = np.zeros((m-1,1))
Curlborder =np.zeros((m-1,1))
l2rorder = np.zeros((m-1,1))
H1rorder = np.zeros((m-1,1))
NN = np.zeros((m-1,1))
DoF = np.zeros((m-1,1))
Velocitydim = np.zeros((m-1,1))
Magneticdim = np.zeros((m-1,1))
Pressuredim = np.zeros((m-1,1))
Lagrangedim = np.zeros((m-1,1))
Wdim = np.zeros((m-1,1))
iterations = np.zeros((m-1,1))
SolTime = np.zeros((m-1,1))
udiv = np.zeros((m-1,1))
MU = np.zeros((m-1,1))
level = np.zeros((m-1,1))
NSave = np.zeros((m-1,1))
Mave = np.zeros((m-1,1))
TotalTime = np.zeros((m-1,1))
nn = 2
dim = 2
ShowResultPlots = 'yes'
split = 'Linear'
MU[0]= 1e0
for xx in xrange(1,m):
print xx
level[xx-1] = xx+0
nn = 2**(level[xx-1])
# Create mesh and define function space
nn = int(nn)
NN[xx-1] = nn/2
# parameters["form_compiler"]["quadrature_degree"] = 6
# parameters = CP.ParameterSetup()
mesh = UnitCubeMesh(nn,nn,nn)
order = 1
parameters['reorder_dofs_serial'] = False
Velocity = VectorFunctionSpace(mesh, "CG", order)
Pressure = FunctionSpace(mesh, "DG", order-1)
Magnetic = FunctionSpace(mesh, "N1curl", order)
Lagrange = FunctionSpace(mesh, "CG", order)
W = MixedFunctionSpace([Velocity,Magnetic, Pressure, Lagrange])
# W = Velocity*Pressure*Magnetic*Lagrange
Velocitydim[xx-1] = Velocity.dim()
Pressuredim[xx-1] = Pressure.dim()
Magneticdim[xx-1] = Magnetic.dim()
Lagrangedim[xx-1] = Lagrange.dim()
Wdim[xx-1] = W.dim()
print "\n\nW: ",Wdim[xx-1],"Velocity: ",Velocitydim[xx-1],"Pressure: ",Pressuredim[xx-1],"Magnetic: ",Magneticdim[xx-1],"Lagrange: ",Lagrangedim[xx-1],"\n\n"
dim = [Velocity.dim(), Magnetic.dim(), Pressure.dim(), Lagrange.dim()]
def boundary(x, on_boundary):
return on_boundary
u0, p0,b0, r0, Laplacian, Advection, gradPres,CurlCurl, gradR, NS_Couple,M_Couple = ExactSol.MHD3D(4,1)
bcu = DirichletBC(W.sub(0),u0, boundary)
bcb = DirichletBC(W.sub(1),b0, boundary)
bcr = DirichletBC(W.sub(3),r0, boundary)
# bc = [u0,p0,b0,r0]
bcs = [bcu,bcb,bcr]
FSpaces = [Velocity,Pressure,Magnetic,Lagrange]
(u, b, p, r) = TrialFunctions(W)
(v, c, q, s) = TestFunctions(W)
kappa = 1.0
Mu_m =1e1
MU = 1.0/1
IterType = 'Full'
F_NS = -MU*Laplacian+Advection+gradPres-kappa*NS_Couple
if kappa == 0:
F_M = Mu_m*CurlCurl+gradR -kappa*M_Couple
else:
F_M = Mu_m*kappa*CurlCurl+gradR -kappa*M_Couple
params = [kappa,Mu_m,MU]
# MO.PrintStr("Preconditioning MHD setup",5,"+","\n\n","\n\n")
HiptmairMatrices = PrecondSetup.MagneticSetup(Magnetic, Lagrange, b0, r0, 1e-4, params)
MO.PrintStr("Setting up MHD initial guess",5,"+","\n\n","\n\n")
u_k,p_k,b_k,r_k = common.InitialGuess(FSpaces,[u0,p0,b0,r0],[F_NS,F_M],params,HiptmairMatrices,1e-6,Neumann=Expression(("0","0")),options ="New", FS = "DG")
#plot(p_k, interactive = True)
b_t = TrialFunction(Velocity)
c_t = TestFunction(Velocity)
#print assemble(inner(b,c)*dx).array().shape
#print mat
#ShiftedMass = assemble(inner(mat*b,c)*dx)
#as_vector([inner(b,c)[0]*b_k[0],inner(b,c)[1]*(-b_k[1])])
ones = Function(Pressure)
ones.vector()[:]=(0*ones.vector().array()+1)
# pConst = - assemble(p_k*dx)/assemble(ones*dx)
p_k.vector()[:] += - assemble(p_k*dx)/assemble(ones*dx)
x = Iter.u_prev(u_k,b_k,p_k,r_k)
KSPlinearfluids, MatrixLinearFluids = PrecondSetup.FluidLinearSetup(Pressure, MU)
kspFp, Fp = PrecondSetup.FluidNonLinearSetup(Pressure, MU, u_k)
#plot(b_k)
ns,maxwell,CoupleTerm,Lmaxwell,Lns = forms.MHD2D(mesh, W,F_M,F_NS, u_k,b_k,params,IterType,"DG", SaddlePoint = "Yes")
RHSform = forms.PicardRHS(mesh, W, u_k, p_k, b_k, r_k, params,"DG",SaddlePoint = "Yes")
bcu = DirichletBC(W.sub(0),Expression(("0.0","0.0","0.0")), boundary)
bcb = DirichletBC(W.sub(1),Expression(("0.0","0.0","0.0")), boundary)
bcr = DirichletBC(W.sub(3),Expression(("0.0")), boundary)
bcs = [bcu,bcb,bcr]
eps = 1.0 # error measure ||u-u_k||
tol = 1.0E-4 # tolerance
iter = 0 # iteration counter
maxiter = 40 # max no of iterations allowed
SolutionTime = 0
outer = 0
# parameters['linear_algebra_backend'] = 'uBLAS'
# FSpaces = [Velocity,Magnetic,Pressure,Lagrange]
if IterType == "CD":
AA, bb = assemble_system(maxwell+ns, (Lmaxwell + Lns) - RHSform, bcs)
A,b = CP.Assemble(AA,bb)
# u = b.duplicate()
# P = CP.Assemble(PP)
u_is = PETSc.IS().createGeneral(range(Velocity.dim()))
NS_is = PETSc.IS().createGeneral(range(Velocity.dim()+Pressure.dim()))
M_is = PETSc.IS().createGeneral(range(Velocity.dim()+Pressure.dim(),W.dim()))
OuterTol = 1e-5
InnerTol = 1e-3
NSits =0
Mits =0
TotalStart =time.time()
SolutionTime = 0
while eps > tol and iter < maxiter:
iter += 1
MO.PrintStr("Iter "+str(iter),7,"=","\n\n","\n\n")
tic()
if IterType == "CD":
bb = assemble((Lmaxwell + Lns) - RHSform)
for bc in bcs:
bc.apply(bb)
FF = AA.sparray()[0:dim[0],0:dim[0]]
A,b = CP.Assemble(AA,bb)
# if iter == 1
if iter == 1:
u = b.duplicate()
F = A.getSubMatrix(u_is,u_is)
kspF = NSprecondSetup.LSCKSPnonlinear(F)
else:
AA, bb = assemble_system(maxwell+ns+CoupleTerm, (Lmaxwell + Lns) - RHSform, bcs)
A,b = CP.Assemble(AA,bb)
F = A.getSubMatrix(u_is,u_is)
n = FacetNormal(mesh)
mat = as_matrix([[b_k[2]*b_k[2]+b[1]*b[1],-b_k[1]*b_k[0],-b_k[0]*b_k[2]],
[-b_k[1]*b_k[0],b_k[0]*b_k[0]+b_k[2]*b_k[2],-b_k[2]*b_k[1]],
[-b_k[0]*b_k[2],-b_k[1]*b_k[2],b_k[0]*b_k[0]+b_k[1]*b_k[1]]])
a = params[2]*inner(grad(b_t), grad(c_t))*dx(W.mesh()) + inner((grad(b_t)*u_k),c_t)*dx(W.mesh()) +(1/2)*div(u_k)*inner(c_t,b_t)*dx(W.mesh()) - (1/2)*inner(u_k,n)*inner(c_t,b_t)*ds(W.mesh())+kappa/Mu_m*inner(mat*b_t,c_t)*dx(W.mesh())
ShiftedMass = assemble(a)
bcu.apply(ShiftedMass)
#MO.StoreMatrix(AA.sparray()[0:dim[0],0:dim[0]]+ShiftedMass.sparray(),"A")
FF = CP.Assemble(ShiftedMass)
kspF = NSprecondSetup.LSCKSPnonlinear(FF)
# if iter == 1:
if iter == 1:
u = b.duplicate()
print ("{:40}").format("MHD assemble, time: "), " ==> ",("{:4f}").format(toc()), ("{:9}").format(" time: "), ("{:4}").format(time.strftime('%X %x %Z')[0:5])
kspFp, Fp = PrecondSetup.FluidNonLinearSetup(Pressure, MU, u_k)
print "Inititial guess norm: ", u.norm()
ksp = PETSc.KSP()
ksp.create(comm=PETSc.COMM_WORLD)
pc = ksp.getPC()
ksp.setType('gmres')
pc.setType('python')
pc.setType(PETSc.PC.Type.PYTHON)
# FSpace = [Velocity,Magnetic,Pressure,Lagrange]
reshist = {}
def monitor(ksp, its, fgnorm):
reshist[its] = fgnorm
print its," OUTER:", fgnorm
# ksp.setMonitor(monitor)
ksp.max_it = 1000
FFSS = [Velocity,Magnetic,Pressure,Lagrange]
pc.setPythonContext(MHDpreconditioner.InnerOuterMAGNETICapprox(FFSS,kspF, KSPlinearfluids[0], KSPlinearfluids[1],Fp, HiptmairMatrices[3], HiptmairMatrices[4], HiptmairMatrices[2], HiptmairMatrices[0], HiptmairMatrices[1], HiptmairMatrices[6],1e-6,FF))
# OptDB = PETSc.Options()
# OptDB['pc_factor_mat_solver_package'] = "mumps"
# OptDB['pc_factor_mat_ordering_type'] = "rcm"
# ksp.setFromOptions()
scale = b.norm()
b = b/scale
ksp.setOperators(A,A)
stime = time.time()
ksp.solve(b,u)
Soltime = time.time()- stime
NSits += ksp.its
# Mits +=dodim
u = u*scale
SolutionTime = SolutionTime +Soltime
MO.PrintStr("Number of iterations ="+str(ksp.its),60,"+","\n\n","\n\n")
u1, p1, b1, r1, eps= Iter.PicardToleranceDecouple(u,x,FSpaces,dim,"2",iter, SaddlePoint = "Yes")
p1.vector()[:] += - assemble(p1*dx)/assemble(ones*dx)
u_k.assign(u1)
p_k.assign(p1)
b_k.assign(b1)
r_k.assign(r1)
uOld= np.concatenate((u_k.vector().array(),b_k.vector().array(),p_k.vector().array(),r_k.vector().array()), axis=0)
x = IO.arrayToVec(uOld)
XX= np.concatenate((u_k.vector().array(),p_k.vector().array(),b_k.vector().array(),r_k.vector().array()), axis=0)
SolTime[xx-1] = SolutionTime/iter
NSave[xx-1] = (float(NSits)/iter)
Mave[xx-1] = (float(Mits)/iter)
iterations[xx-1] = iter
TotalTime[xx-1] = time.time() - TotalStart
print SolTime
import pandas as pd
print "\n\n Iteration table"
if IterType == "Full":
IterTitles = ["l","DoF","AV solve Time","Total picard time","picard iterations","Av Outer its","Av Inner its",]
else:
IterTitles = ["l","DoF","AV solve Time","Total picard time","picard iterations","Av NS iters","Av M iters"]
IterValues = np.concatenate((level,Wdim,SolTime,TotalTime,iterations,NSave,Mave),axis=1)
IterTable= pd.DataFrame(IterValues, columns = IterTitles)
if IterType == "Full":
IterTable = MO.PandasFormat(IterTable,'Av Outer its',"%2.1f")
IterTable = MO.PandasFormat(IterTable,'Av Inner its',"%2.1f")
else:
IterTable = MO.PandasFormat(IterTable,'Av NS iters',"%2.1f")
IterTable = MO.PandasFormat(IterTable,'Av M iters',"%2.1f")
print IterTable.to_latex()
print " \n Outer Tol: ",OuterTol, "Inner Tol: ", InnerTol
# # # if (ShowResultPlots == 'yes'):
# plot(u_k)
# plot(interpolate(ue,Velocity))
# plot(p_k)
# plot(interpolate(pe,Pressure))
# plot(b_k)
# plot(interpolate(be,Magnetic))
# plot(r_k)
# plot(interpolate(re,Lagrange))
# interactive()
interactive()
| mit |
yuruofeifei/mxnet | example/kaggle-ndsb1/training_curves.py | 52 | 1879 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
## based on https://github.com/dmlc/mxnet/issues/1302
## Parses the model fit log file and generates a train/val vs epoch plot
import matplotlib.pyplot as plt
import numpy as np
import re
import argparse
parser = argparse.ArgumentParser(description='Parses log file and generates train/val curves')
parser.add_argument('--log-file', type=str,default="log_tr_va",
help='the path of log file')
args = parser.parse_args()
TR_RE = re.compile('.*?]\sTrain-accuracy=([\d\.]+)')
VA_RE = re.compile('.*?]\sValidation-accuracy=([\d\.]+)')
log = open(args.log_file).read()
log_tr = [float(x) for x in TR_RE.findall(log)]
log_va = [float(x) for x in VA_RE.findall(log)]
idx = np.arange(len(log_tr))
plt.figure(figsize=(8, 6))
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.plot(idx, log_tr, 'o', linestyle='-', color="r",
label="Train accuracy")
plt.plot(idx, log_va, 'o', linestyle='-', color="b",
label="Validation accuracy")
plt.legend(loc="best")
plt.xticks(np.arange(min(idx), max(idx)+1, 5))
plt.yticks(np.arange(0, 1, 0.2))
plt.ylim([0,1])
plt.show()
| apache-2.0 |
eadgarchen/tensorflow | tensorflow/contrib/learn/python/learn/estimators/debug_test.py | 46 | 32817 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for Debug estimators."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import functools
import operator
import tempfile
import numpy as np
from tensorflow.contrib.layers.python.layers import feature_column
from tensorflow.contrib.layers.python.layers import feature_column_ops
from tensorflow.contrib.learn.python.learn import experiment
from tensorflow.contrib.learn.python.learn.datasets import base
from tensorflow.contrib.learn.python.learn.estimators import _sklearn
from tensorflow.contrib.learn.python.learn.estimators import debug
from tensorflow.contrib.learn.python.learn.estimators import estimator_test_utils
from tensorflow.contrib.learn.python.learn.estimators import run_config
from tensorflow.contrib.learn.python.learn.estimators import test_data
from tensorflow.contrib.learn.python.learn.metric_spec import MetricSpec
from tensorflow.contrib.metrics.python.ops import metric_ops
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
from tensorflow.python.training import input as input_lib
NUM_EXAMPLES = 100
N_CLASSES = 5 # Cardinality of multiclass labels.
LABEL_DIMENSION = 3 # Dimensionality of regression labels.
def _train_test_split(features_and_labels):
features, labels = features_and_labels
train_set = (features[:int(len(features) / 2)], labels[:int(len(features) / 2)])
test_set = (features[int(len(features) / 2):], labels[int(len(features) / 2):])
return train_set, test_set
def _input_fn_builder(features, labels):
def input_fn():
feature_dict = {'features': constant_op.constant(features)}
my_labels = labels
if my_labels is not None:
my_labels = constant_op.constant(my_labels)
return feature_dict, my_labels
return input_fn
class DebugClassifierTest(test.TestCase):
def setUp(self):
np.random.seed(100)
self.features = np.random.rand(NUM_EXAMPLES, 5)
self.labels = np.random.choice(
range(N_CLASSES), p=[0.1, 0.3, 0.4, 0.1, 0.1], size=NUM_EXAMPLES)
self.binary_labels = np.random.choice(
range(2), p=[0.2, 0.8], size=NUM_EXAMPLES)
self.binary_float_labels = np.random.choice(
range(2), p=[0.2, 0.8], size=NUM_EXAMPLES)
def testPredict(self):
"""Tests that DebugClassifier outputs the majority class."""
(train_features, train_labels), (test_features,
test_labels) = _train_test_split(
[self.features, self.labels])
majority_class, _ = max(collections.Counter(train_labels).items(),
key=operator.itemgetter(1))
expected_prediction = np.vstack(
[[majority_class] for _ in range(test_labels.shape[0])])
classifier = debug.DebugClassifier(n_classes=N_CLASSES)
classifier.fit(input_fn=_input_fn_builder(train_features, train_labels),
steps=50)
pred = classifier.predict_classes(input_fn=_input_fn_builder(test_features,
None))
self.assertAllEqual(expected_prediction, np.vstack(pred))
def testPredictBinary(self):
"""Same as above for binary predictions."""
(train_features, train_labels), (test_features,
test_labels) = _train_test_split(
[self.features, self.binary_labels])
majority_class, _ = max(collections.Counter(train_labels).items(),
key=operator.itemgetter(1))
expected_prediction = np.vstack(
[[majority_class] for _ in range(test_labels.shape[0])])
classifier = debug.DebugClassifier(n_classes=2)
classifier.fit(input_fn=_input_fn_builder(train_features, train_labels),
steps=50)
pred = classifier.predict_classes(input_fn=_input_fn_builder(test_features,
None))
self.assertAllEqual(expected_prediction, np.vstack(pred))
(train_features, train_labels), (
test_features, test_labels) = _train_test_split(
[self.features, self.binary_float_labels])
majority_class, _ = max(collections.Counter(train_labels).items(),
key=operator.itemgetter(1))
expected_prediction = np.vstack(
[[majority_class] for _ in range(test_labels.shape[0])])
classifier = debug.DebugClassifier(n_classes=2)
classifier.fit(input_fn=_input_fn_builder(train_features, train_labels),
steps=50)
pred = classifier.predict_classes(input_fn=_input_fn_builder(test_features,
None))
self.assertAllEqual(expected_prediction, np.vstack(pred))
def testPredictProba(self):
"""Tests that DebugClassifier outputs observed class distribution."""
(train_features, train_labels), (test_features,
test_labels) = _train_test_split(
[self.features, self.labels])
class_distribution = np.zeros((1, N_CLASSES))
for label in train_labels:
class_distribution[0, label] += 1
class_distribution /= len(train_labels)
expected_prediction = np.vstack(
[class_distribution for _ in range(test_labels.shape[0])])
classifier = debug.DebugClassifier(n_classes=N_CLASSES)
classifier.fit(input_fn=_input_fn_builder(train_features, train_labels),
steps=50)
pred = classifier.predict_proba(
input_fn=_input_fn_builder(test_features, None))
self.assertAllClose(expected_prediction, np.vstack(pred), atol=0.1)
def testPredictProbaBinary(self):
"""Same as above but for binary classification."""
(train_features, train_labels), (test_features,
test_labels) = _train_test_split(
[self.features, self.binary_labels])
class_distribution = np.zeros((1, 2))
for label in train_labels:
class_distribution[0, label] += 1
class_distribution /= len(train_labels)
expected_prediction = np.vstack(
[class_distribution for _ in range(test_labels.shape[0])])
classifier = debug.DebugClassifier(n_classes=2)
classifier.fit(input_fn=_input_fn_builder(train_features, train_labels),
steps=50)
pred = classifier.predict_proba(
input_fn=_input_fn_builder(test_features, None))
self.assertAllClose(expected_prediction, np.vstack(pred), atol=0.1)
(train_features, train_labels), (
test_features, test_labels) = _train_test_split(
[self.features, self.binary_float_labels])
class_distribution = np.zeros((1, 2))
for label in train_labels:
class_distribution[0, int(label)] += 1
class_distribution /= len(train_labels)
expected_prediction = np.vstack(
[class_distribution for _ in range(test_labels.shape[0])])
classifier = debug.DebugClassifier(n_classes=2)
classifier.fit(input_fn=_input_fn_builder(train_features, train_labels),
steps=50)
pred = classifier.predict_proba(
input_fn=_input_fn_builder(test_features, None))
self.assertAllClose(expected_prediction, np.vstack(pred), atol=0.1)
def testExperimentIntegration(self):
exp = experiment.Experiment(
estimator=debug.DebugClassifier(n_classes=3),
train_input_fn=test_data.iris_input_multiclass_fn,
eval_input_fn=test_data.iris_input_multiclass_fn)
exp.test()
def _assertInRange(self, expected_min, expected_max, actual):
self.assertLessEqual(expected_min, actual)
self.assertGreaterEqual(expected_max, actual)
def testEstimatorContract(self):
estimator_test_utils.assert_estimator_contract(self, debug.DebugClassifier)
def testLogisticRegression_MatrixData(self):
"""Tests binary classification using matrix data as input."""
classifier = debug.DebugClassifier(
config=run_config.RunConfig(tf_random_seed=1))
input_fn = test_data.iris_input_logistic_fn
classifier.fit(input_fn=input_fn, steps=5)
scores = classifier.evaluate(input_fn=input_fn, steps=1)
self._assertInRange(0.0, 1.0, scores['accuracy'])
self.assertIn('loss', scores)
def testLogisticRegression_MatrixData_Labels1D(self):
"""Same as the last test, but label shape is [100] instead of [100, 1]."""
def _input_fn():
iris = test_data.prepare_iris_data_for_logistic_regression()
return {
'feature': constant_op.constant(
iris.data, dtype=dtypes.float32)
}, constant_op.constant(
iris.target, shape=[100], dtype=dtypes.int32)
classifier = debug.DebugClassifier(config=run_config.RunConfig(
tf_random_seed=1))
classifier.fit(input_fn=_input_fn, steps=5)
scores = classifier.evaluate(input_fn=_input_fn, steps=1)
self.assertIn('loss', scores)
def testLogisticRegression_NpMatrixData(self):
"""Tests binary classification using numpy matrix data as input."""
iris = test_data.prepare_iris_data_for_logistic_regression()
train_x = iris.data
train_y = iris.target
classifier = debug.DebugClassifier(
config=run_config.RunConfig(tf_random_seed=1))
classifier.fit(x=train_x, y=train_y, steps=5)
scores = classifier.evaluate(x=train_x, y=train_y, steps=1)
self._assertInRange(0.0, 1.0, scores['accuracy'])
def _assertBinaryPredictions(self, expected_len, predictions):
self.assertEqual(expected_len, len(predictions))
for prediction in predictions:
self.assertIn(prediction, (0, 1))
def _assertProbabilities(self, expected_batch_size, expected_n_classes,
probabilities):
self.assertEqual(expected_batch_size, len(probabilities))
for b in range(expected_batch_size):
self.assertEqual(expected_n_classes, len(probabilities[b]))
for i in range(expected_n_classes):
self._assertInRange(0.0, 1.0, probabilities[b][i])
def testLogisticRegression_TensorData(self):
"""Tests binary classification using tensor data as input."""
def _input_fn(num_epochs=None):
features = {
'age':
input_lib.limit_epochs(
constant_op.constant([[.8], [0.2], [.1]]),
num_epochs=num_epochs),
'language':
sparse_tensor.SparseTensor(
values=input_lib.limit_epochs(
['en', 'fr', 'zh'], num_epochs=num_epochs),
indices=[[0, 0], [0, 1], [2, 0]],
dense_shape=[3, 2])
}
return features, constant_op.constant([[1], [0], [0]], dtype=dtypes.int32)
classifier = debug.DebugClassifier(n_classes=2)
classifier.fit(input_fn=_input_fn, steps=50)
scores = classifier.evaluate(input_fn=_input_fn, steps=1)
self._assertInRange(0.0, 1.0, scores['accuracy'])
self.assertIn('loss', scores)
predict_input_fn = functools.partial(_input_fn, num_epochs=1)
predictions = list(classifier.predict_classes(input_fn=predict_input_fn))
self._assertBinaryPredictions(3, predictions)
def testLogisticRegression_FloatLabel(self):
"""Tests binary classification with float labels."""
def _input_fn_float_label(num_epochs=None):
features = {
'age':
input_lib.limit_epochs(
constant_op.constant([[50], [20], [10]]),
num_epochs=num_epochs),
'language':
sparse_tensor.SparseTensor(
values=input_lib.limit_epochs(
['en', 'fr', 'zh'], num_epochs=num_epochs),
indices=[[0, 0], [0, 1], [2, 0]],
dense_shape=[3, 2])
}
labels = constant_op.constant([[0.8], [0.], [0.2]], dtype=dtypes.float32)
return features, labels
classifier = debug.DebugClassifier(n_classes=2)
classifier.fit(input_fn=_input_fn_float_label, steps=50)
predict_input_fn = functools.partial(_input_fn_float_label, num_epochs=1)
predictions = list(classifier.predict_classes(input_fn=predict_input_fn))
self._assertBinaryPredictions(3, predictions)
predictions_proba = list(
classifier.predict_proba(input_fn=predict_input_fn))
self._assertProbabilities(3, 2, predictions_proba)
def testMultiClass_MatrixData(self):
"""Tests multi-class classification using matrix data as input."""
classifier = debug.DebugClassifier(n_classes=3)
input_fn = test_data.iris_input_multiclass_fn
classifier.fit(input_fn=input_fn, steps=200)
scores = classifier.evaluate(input_fn=input_fn, steps=1)
self._assertInRange(0.0, 1.0, scores['accuracy'])
self.assertIn('loss', scores)
def testMultiClass_MatrixData_Labels1D(self):
"""Same as the last test, but label shape is [150] instead of [150, 1]."""
def _input_fn():
iris = base.load_iris()
return {
'feature': constant_op.constant(
iris.data, dtype=dtypes.float32)
}, constant_op.constant(
iris.target, shape=[150], dtype=dtypes.int32)
classifier = debug.DebugClassifier(n_classes=3)
classifier.fit(input_fn=_input_fn, steps=200)
scores = classifier.evaluate(input_fn=_input_fn, steps=1)
self._assertInRange(0.0, 1.0, scores['accuracy'])
def testMultiClass_NpMatrixData(self):
"""Tests multi-class classification using numpy matrix data as input."""
iris = base.load_iris()
train_x = iris.data
train_y = iris.target
classifier = debug.DebugClassifier(n_classes=3)
classifier.fit(x=train_x, y=train_y, steps=200)
scores = classifier.evaluate(x=train_x, y=train_y, steps=1)
self._assertInRange(0.0, 1.0, scores['accuracy'])
def testMultiClass_StringLabel(self):
"""Tests multi-class classification with string labels."""
def _input_fn_train():
labels = constant_op.constant([['foo'], ['bar'], ['baz'], ['bar']])
features = {
'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
}
return features, labels
classifier = debug.DebugClassifier(
n_classes=3, label_keys=['foo', 'bar', 'baz'])
classifier.fit(input_fn=_input_fn_train, steps=5)
scores = classifier.evaluate(input_fn=_input_fn_train, steps=1)
self.assertIn('loss', scores)
def testLoss(self):
"""Tests loss calculation."""
def _input_fn_train():
# Create 4 rows, one of them (y = x), three of them (y=Not(x))
# The logistic prediction should be (y = 0.25).
labels = constant_op.constant([[1], [0], [0], [0]])
features = {'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),}
return features, labels
classifier = debug.DebugClassifier(n_classes=2)
classifier.fit(input_fn=_input_fn_train, steps=5)
scores = classifier.evaluate(input_fn=_input_fn_train, steps=1)
self.assertIn('loss', scores)
def testLossWithWeights(self):
"""Tests loss calculation with weights."""
def _input_fn_train():
# 4 rows with equal weight, one of them (y = x), three of them (y=Not(x))
# The logistic prediction should be (y = 0.25).
labels = constant_op.constant([[1.], [0.], [0.], [0.]])
features = {
'x': array_ops.ones(
shape=[4, 1], dtype=dtypes.float32),
'w': constant_op.constant([[1.], [1.], [1.], [1.]])
}
return features, labels
def _input_fn_eval():
# 4 rows, with different weights.
labels = constant_op.constant([[1.], [0.], [0.], [0.]])
features = {
'x': array_ops.ones(
shape=[4, 1], dtype=dtypes.float32),
'w': constant_op.constant([[7.], [1.], [1.], [1.]])
}
return features, labels
classifier = debug.DebugClassifier(
weight_column_name='w',
n_classes=2,
config=run_config.RunConfig(tf_random_seed=1))
classifier.fit(input_fn=_input_fn_train, steps=5)
scores = classifier.evaluate(input_fn=_input_fn_eval, steps=1)
self.assertIn('loss', scores)
def testTrainWithWeights(self):
"""Tests training with given weight column."""
def _input_fn_train():
# Create 4 rows, one of them (y = x), three of them (y=Not(x))
# First row has more weight than others. Model should fit (y=x) better
# than (y=Not(x)) due to the relative higher weight of the first row.
labels = constant_op.constant([[1], [0], [0], [0]])
features = {
'x': array_ops.ones(
shape=[4, 1], dtype=dtypes.float32),
'w': constant_op.constant([[100.], [3.], [2.], [2.]])
}
return features, labels
def _input_fn_eval():
# Create 4 rows (y = x)
labels = constant_op.constant([[1], [1], [1], [1]])
features = {
'x': array_ops.ones(
shape=[4, 1], dtype=dtypes.float32),
'w': constant_op.constant([[1.], [1.], [1.], [1.]])
}
return features, labels
classifier = debug.DebugClassifier(weight_column_name='w')
classifier.fit(input_fn=_input_fn_train, steps=5)
scores = classifier.evaluate(input_fn=_input_fn_eval, steps=1)
self._assertInRange(0.0, 1.0, scores['accuracy'])
def testCustomMetrics(self):
"""Tests custom evaluation metrics."""
def _input_fn(num_epochs=None):
# Create 4 rows, one of them (y = x), three of them (y=Not(x))
labels = constant_op.constant([[1], [0], [0], [0]])
features = {
'x':
input_lib.limit_epochs(
array_ops.ones(
shape=[4, 1], dtype=dtypes.float32),
num_epochs=num_epochs),
}
return features, labels
def _my_metric_op(predictions, labels):
# For the case of binary classification, the 2nd column of "predictions"
# denotes the model predictions.
labels = math_ops.to_float(labels)
predictions = array_ops.strided_slice(
predictions, [0, 1], [-1, 2], end_mask=1)
labels = math_ops.cast(labels, predictions.dtype)
return math_ops.reduce_sum(math_ops.multiply(predictions, labels))
classifier = debug.DebugClassifier(
config=run_config.RunConfig(tf_random_seed=1))
classifier.fit(input_fn=_input_fn, steps=5)
scores = classifier.evaluate(
input_fn=_input_fn,
steps=5,
metrics={
'my_accuracy':
MetricSpec(
metric_fn=metric_ops.streaming_accuracy,
prediction_key='classes'),
'my_precision':
MetricSpec(
metric_fn=metric_ops.streaming_precision,
prediction_key='classes'),
'my_metric':
MetricSpec(
metric_fn=_my_metric_op, prediction_key='probabilities')
})
self.assertTrue(
set(['loss', 'my_accuracy', 'my_precision', 'my_metric']).issubset(
set(scores.keys())))
predict_input_fn = functools.partial(_input_fn, num_epochs=1)
predictions = np.array(
list(classifier.predict_classes(input_fn=predict_input_fn)))
self.assertEqual(
_sklearn.accuracy_score([1, 0, 0, 0], predictions),
scores['my_accuracy'])
# Test the case where the 2nd element of the key is neither "classes" nor
# "probabilities".
with self.assertRaisesRegexp(KeyError, 'bad_type'):
classifier.evaluate(
input_fn=_input_fn,
steps=5,
metrics={
'bad_name':
MetricSpec(
metric_fn=metric_ops.streaming_auc,
prediction_key='bad_type')
})
def testTrainSaveLoad(self):
"""Tests that insures you can save and reload a trained model."""
def _input_fn(num_epochs=None):
features = {
'age':
input_lib.limit_epochs(
constant_op.constant([[.8], [.2], [.1]]),
num_epochs=num_epochs),
'language':
sparse_tensor.SparseTensor(
values=input_lib.limit_epochs(
['en', 'fr', 'zh'], num_epochs=num_epochs),
indices=[[0, 0], [0, 1], [2, 0]],
dense_shape=[3, 2])
}
return features, constant_op.constant([[1], [0], [0]], dtype=dtypes.int32)
model_dir = tempfile.mkdtemp()
classifier = debug.DebugClassifier(
model_dir=model_dir,
n_classes=3,
config=run_config.RunConfig(tf_random_seed=1))
classifier.fit(input_fn=_input_fn, steps=5)
predict_input_fn = functools.partial(_input_fn, num_epochs=1)
predictions1 = classifier.predict_classes(input_fn=predict_input_fn)
del classifier
classifier2 = debug.DebugClassifier(
model_dir=model_dir,
n_classes=3,
config=run_config.RunConfig(tf_random_seed=1))
predictions2 = classifier2.predict_classes(input_fn=predict_input_fn)
self.assertEqual(list(predictions1), list(predictions2))
def testExport(self):
"""Tests export model for servo."""
def input_fn():
return {
'age':
constant_op.constant([1]),
'language':
sparse_tensor.SparseTensor(
values=['english'], indices=[[0, 0]], dense_shape=[1, 1])
}, constant_op.constant([[1]])
language = feature_column.sparse_column_with_hash_bucket('language', 100)
feature_columns = [
feature_column.real_valued_column('age'),
feature_column.embedding_column(
language, dimension=1)
]
classifier = debug.DebugClassifier(config=run_config.RunConfig(
tf_random_seed=1))
classifier.fit(input_fn=input_fn, steps=5)
def default_input_fn(unused_estimator, examples):
return feature_column_ops.parse_feature_columns_from_examples(
examples, feature_columns)
export_dir = tempfile.mkdtemp()
classifier.export(export_dir, input_fn=default_input_fn)
class DebugRegressorTest(test.TestCase):
def setUp(self):
np.random.seed(100)
self.features = np.random.rand(NUM_EXAMPLES, 5)
self.targets = np.random.rand(NUM_EXAMPLES, LABEL_DIMENSION)
def testPredictScores(self):
"""Tests that DebugRegressor outputs the mean target."""
(train_features, train_labels), (test_features,
test_labels) = _train_test_split(
[self.features, self.targets])
mean_target = np.mean(train_labels, 0)
expected_prediction = np.vstack(
[mean_target for _ in range(test_labels.shape[0])])
classifier = debug.DebugRegressor(label_dimension=LABEL_DIMENSION)
classifier.fit(
input_fn=_input_fn_builder(train_features, train_labels), steps=50)
pred = classifier.predict_scores(input_fn=_input_fn_builder(test_features,
None))
self.assertAllClose(expected_prediction, np.vstack(pred), atol=0.1)
def testExperimentIntegration(self):
exp = experiment.Experiment(
estimator=debug.DebugRegressor(),
train_input_fn=test_data.iris_input_logistic_fn,
eval_input_fn=test_data.iris_input_logistic_fn)
exp.test()
def testEstimatorContract(self):
estimator_test_utils.assert_estimator_contract(self, debug.DebugRegressor)
def testRegression_MatrixData(self):
"""Tests regression using matrix data as input."""
regressor = debug.DebugRegressor(
config=run_config.RunConfig(tf_random_seed=1))
input_fn = test_data.iris_input_logistic_fn
regressor.fit(input_fn=input_fn, steps=200)
scores = regressor.evaluate(input_fn=input_fn, steps=1)
self.assertIn('loss', scores)
def testRegression_MatrixData_Labels1D(self):
"""Same as the last test, but label shape is [100] instead of [100, 1]."""
def _input_fn():
iris = test_data.prepare_iris_data_for_logistic_regression()
return {
'feature': constant_op.constant(iris.data, dtype=dtypes.float32)
}, constant_op.constant(
iris.target, shape=[100], dtype=dtypes.int32)
regressor = debug.DebugRegressor(
config=run_config.RunConfig(tf_random_seed=1))
regressor.fit(input_fn=_input_fn, steps=200)
scores = regressor.evaluate(input_fn=_input_fn, steps=1)
self.assertIn('loss', scores)
def testRegression_NpMatrixData(self):
"""Tests binary classification using numpy matrix data as input."""
iris = test_data.prepare_iris_data_for_logistic_regression()
train_x = iris.data
train_y = iris.target
regressor = debug.DebugRegressor(
config=run_config.RunConfig(tf_random_seed=1))
regressor.fit(x=train_x, y=train_y, steps=200)
scores = regressor.evaluate(x=train_x, y=train_y, steps=1)
self.assertIn('loss', scores)
def testRegression_TensorData(self):
"""Tests regression using tensor data as input."""
def _input_fn(num_epochs=None):
features = {
'age':
input_lib.limit_epochs(
constant_op.constant([[.8], [.15], [0.]]),
num_epochs=num_epochs),
'language':
sparse_tensor.SparseTensor(
values=input_lib.limit_epochs(
['en', 'fr', 'zh'], num_epochs=num_epochs),
indices=[[0, 0], [0, 1], [2, 0]],
dense_shape=[3, 2])
}
return features, constant_op.constant([1., 0., 0.2], dtype=dtypes.float32)
regressor = debug.DebugRegressor(
config=run_config.RunConfig(tf_random_seed=1))
regressor.fit(input_fn=_input_fn, steps=200)
scores = regressor.evaluate(input_fn=_input_fn, steps=1)
self.assertIn('loss', scores)
def testLoss(self):
"""Tests loss calculation."""
def _input_fn_train():
# Create 4 rows, one of them (y = x), three of them (y=Not(x))
# The algorithm should learn (y = 0.25).
labels = constant_op.constant([[1.], [0.], [0.], [0.]])
features = {'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),}
return features, labels
regressor = debug.DebugRegressor(
config=run_config.RunConfig(tf_random_seed=1))
regressor.fit(input_fn=_input_fn_train, steps=5)
scores = regressor.evaluate(input_fn=_input_fn_train, steps=1)
self.assertIn('loss', scores)
def testLossWithWeights(self):
"""Tests loss calculation with weights."""
def _input_fn_train():
# 4 rows with equal weight, one of them (y = x), three of them (y=Not(x))
# The algorithm should learn (y = 0.25).
labels = constant_op.constant([[1.], [0.], [0.], [0.]])
features = {
'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
'w': constant_op.constant([[1.], [1.], [1.], [1.]])
}
return features, labels
def _input_fn_eval():
# 4 rows, with different weights.
labels = constant_op.constant([[1.], [0.], [0.], [0.]])
features = {
'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
'w': constant_op.constant([[7.], [1.], [1.], [1.]])
}
return features, labels
regressor = debug.DebugRegressor(
weight_column_name='w', config=run_config.RunConfig(tf_random_seed=1))
regressor.fit(input_fn=_input_fn_train, steps=5)
scores = regressor.evaluate(input_fn=_input_fn_eval, steps=1)
self.assertIn('loss', scores)
def testTrainWithWeights(self):
"""Tests training with given weight column."""
def _input_fn_train():
# Create 4 rows, one of them (y = x), three of them (y=Not(x))
# First row has more weight than others. Model should fit (y=x) better
# than (y=Not(x)) due to the relative higher weight of the first row.
labels = constant_op.constant([[1.], [0.], [0.], [0.]])
features = {
'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
'w': constant_op.constant([[100.], [3.], [2.], [2.]])
}
return features, labels
def _input_fn_eval():
# Create 4 rows (y = x)
labels = constant_op.constant([[1.], [1.], [1.], [1.]])
features = {
'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
'w': constant_op.constant([[1.], [1.], [1.], [1.]])
}
return features, labels
regressor = debug.DebugRegressor(
weight_column_name='w', config=run_config.RunConfig(tf_random_seed=1))
regressor.fit(input_fn=_input_fn_train, steps=5)
scores = regressor.evaluate(input_fn=_input_fn_eval, steps=1)
self.assertIn('loss', scores)
def testCustomMetrics(self):
"""Tests custom evaluation metrics."""
def _input_fn(num_epochs=None):
# Create 4 rows, one of them (y = x), three of them (y=Not(x))
labels = constant_op.constant([[1.], [0.], [0.], [0.]])
features = {
'x':
input_lib.limit_epochs(
array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
num_epochs=num_epochs),
}
return features, labels
def _my_metric_op(predictions, labels):
return math_ops.reduce_sum(math_ops.multiply(predictions, labels))
regressor = debug.DebugRegressor(
config=run_config.RunConfig(tf_random_seed=1))
regressor.fit(input_fn=_input_fn, steps=5)
scores = regressor.evaluate(
input_fn=_input_fn,
steps=1,
metrics={
'my_error':
MetricSpec(
metric_fn=metric_ops.streaming_mean_squared_error,
prediction_key='scores'),
'my_metric':
MetricSpec(metric_fn=_my_metric_op, prediction_key='scores')
})
self.assertIn('loss', set(scores.keys()))
self.assertIn('my_error', set(scores.keys()))
self.assertIn('my_metric', set(scores.keys()))
predict_input_fn = functools.partial(_input_fn, num_epochs=1)
predictions = np.array(
list(regressor.predict_scores(input_fn=predict_input_fn)))
self.assertAlmostEqual(
_sklearn.mean_squared_error(np.array([1, 0, 0, 0]), predictions),
scores['my_error'])
# Tests the case where the prediction_key is not "scores".
with self.assertRaisesRegexp(KeyError, 'bad_type'):
regressor.evaluate(
input_fn=_input_fn,
steps=1,
metrics={
'bad_name':
MetricSpec(
metric_fn=metric_ops.streaming_auc,
prediction_key='bad_type')
})
def testTrainSaveLoad(self):
"""Tests that insures you can save and reload a trained model."""
def _input_fn(num_epochs=None):
features = {
'age':
input_lib.limit_epochs(
constant_op.constant([[0.8], [0.15], [0.]]),
num_epochs=num_epochs),
'language':
sparse_tensor.SparseTensor(
values=input_lib.limit_epochs(
['en', 'fr', 'zh'], num_epochs=num_epochs),
indices=[[0, 0], [0, 1], [2, 0]],
dense_shape=[3, 2])
}
return features, constant_op.constant([1., 0., 0.2], dtype=dtypes.float32)
model_dir = tempfile.mkdtemp()
regressor = debug.DebugRegressor(
model_dir=model_dir, config=run_config.RunConfig(tf_random_seed=1))
regressor.fit(input_fn=_input_fn, steps=5)
predict_input_fn = functools.partial(_input_fn, num_epochs=1)
predictions = list(regressor.predict_scores(input_fn=predict_input_fn))
del regressor
regressor2 = debug.DebugRegressor(
model_dir=model_dir, config=run_config.RunConfig(tf_random_seed=1))
predictions2 = list(regressor2.predict_scores(input_fn=predict_input_fn))
self.assertAllClose(predictions, predictions2)
if __name__ == '__main__':
test.main()
| apache-2.0 |
rgerkin/pyNeuroML | examples/quick_plot.py | 1 | 1359 | '''
Example showing use of pynml.generate_plot()
'''
from pyneuroml import pynml
import math
import random
from matplotlib import pyplot as plt
######## Some example data
ts = [t*0.01 for t in range(20000)]
siny = [math.sin(t/10) for t in ts]
cosey = [ math.exp(t/-80)*math.cos(t/5) for t in ts]
######## Generate a plot for this quickly with generate_plot
ax = pynml.generate_plot([ts,ts], # Add 2 sets of x values
[siny,cosey], # Add 2 sets of y values
"Some traces", # Title
xaxis = 'Time (ms)', # x axis legend
yaxis = 'Arbitrary units...', # y axis legend
linewidths = [2,3], # Thicknesses of each trace
show_plot_already=False, # Show or wait for plt.show()?
font_size = 10, # Font
bottom_left_spines_only = True, # Box or just x & y axes
save_figure_to='quick.png') # Save figure
######## Add another trace
ts_ = [t*0.1 for t in range(2000)]
randy = [ random.random() for t in ts_]
ax.plot(ts_,randy,'.') # Won't be included in saved PNG!
######## Show complete plot
plt.show()
| lgpl-3.0 |
glwagner/py2Periodic | build/lib/py2Periodic/nearInertialWavesInXY.py | 1 | 7790 | import doublyPeriodic
import numpy as np; from numpy import pi
import time
class model(doublyPeriodicModel):
def __init__(
self,
name = "linearizedNIWEquationExample",
# Grid parameters
nx = 128,
Lx = 2.0*pi,
ny = None,
Ly = None,
# Solver parameters
t = 0.0,
dt = 1.0e-2, # Numerical timestep
step = 0,
timeStepper = "ETDRK4", # Time-stepping method
nThreads = 1, # Number of threads for FFTW
#
# Near-inertial equation params: rotating and gravitating Earth
f0 = 1.0,
kappa = 64.0,
# Friction: 4th order hyperviscosity
waveVisc = 1.0e-4,
waveViscOrder = 2.0,
meanVisc = 1.0e-4,
meanViscOrder = 2.0,
):
# Initialize super-class.
doublyPeriodicModel.__init__(self,
physics = "two-dimensional turbulence and the" + \
" near-inertial wave equation",
nVars = 2,
realVars = False,
# Grid parameters
nx = nx,
ny = ny,
Lx = Lx,
Ly = Ly,
# Solver parameters
t = t,
dt = dt, # Numerical timestep
step = step, # Current step
timeStepper = timeStepper, # Time-stepping method
nThreads = nThreads, # Number of threads for FFTW
)
# Physical parameters specific to the Physical Problem
self.name = name
self.f0 = f0
self.kappa = kappa
self.meanVisc = meanVisc
self.meanViscOrder = meanViscOrder
self.waveVisc = waveVisc
self.waveViscOrder = waveViscOrder
# Initial routines
## Initialize variables and parameters specific to this problem
self._init_parameters()
self._set_linear_coeff()
self._init_time_stepper()
## Default vorticity initial condition: Gaussian vortex
rVortex = self.Lx/20
q0 = 0.1*self.f0 * np.exp( \
- ( (self.XX-self.Lx/2.0)**2.0 + (self.YY-self.Ly/2.0)**2.0 ) \
/ (2*rVortex**2.0) \
)
self.set_q(q0)
# Default wave initial condition: uniform velocity.
A0 = np.ones(self.physVarShape)
self.set_A(A0)
# Methods - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
def describe_physics(self):
print("""
This model solves the linearized near-inertial wave equation, also \n
known as the YBJ equation, and the \n
two-dimensional vorticity equation simulataneously. \n
Arbitrary-order hyperdissipation can be specified for both. \n
There are two prognostic variables: wave amplitude, and mean vorticity.
""")
def _set_linear_coeff(self):
""" Calculate the coefficient that multiplies the linear left hand
side of the equation """
# Two-dimensional turbulent part.
self.linearCoeff[:, :, 0] = self.meanVisc \
* (self.KK**2.0 + self.LL**2.0)**(self.meanViscOrder/2.0)
waveDissipation = self.waveVisc \
* (self.KK**2.0 + self.LL**2.0)**(self.waveViscOrder/2.0)
waveDispersion = 1j*self.f0/(2.0*self.kappa**2.0) \
* ( self.KK**2.0 + self.LL**2.0)
self.linearCoeff[:, :, 1] = waveDissipation + waveDispersion
def _calc_right_hand_side(self, soln, t):
""" Calculate the nonlinear right hand side of PDE """
# Views for clarity:
qh = soln[:, :, 0]
Ah = soln[:, :, 1]
# Physical-space things
self.q = np.real(self.ifft2(qh))
self.A = self.ifft2(Ah)
# Calculate streamfunction
self.psih = -qh / self.divideSafeKay2
# Mean velocities
self.U = -np.real(self.ifft2(self.jLL*self.psih))
self.V = np.real(self.ifft2(self.jKK*self.psih))
# Views to clarify calculation of A's RHS
U = self.U
V = self.V
q = self.q
A = self.A
# Right hand side for q
self.RHS[:, :, 0] = -self.jKK*self.fft2(U*q) - self.jLL*self.fft2(V*q)
# Right hand side for A, in steps:
## 1. Advection term,
self.RHS[:, :, 1] = -self.jKK*self.fft2(U*A) - self.jLL*self.fft2(V*A) \
-1j/2.0*self.fft2(q*A)
self._dealias_RHS()
def _init_parameters(self):
""" Pre-allocate parameters in memory in addition to the solution """
# Divide-safe square wavenumber
self.divideSafeKay2 = self.KK**2.0 + self.LL**2.0
self.divideSafeKay2[0, 0] = float('Inf')
# Vorticity and wave-field amplitude
self.q = np.zeros(self.physVarShape, np.dtype('float64'))
self.A = np.zeros(self.physVarShape, np.dtype('complex128'))
# Streamfunction transform
self.psih = np.zeros(self.specVarShape, np.dtype('complex128'))
# Mean and wave velocity components
self.U = np.zeros(self.physVarShape, np.dtype('float64'))
self.V = np.zeros(self.physVarShape, np.dtype('float64'))
def update_state_variables(self):
""" Update diagnostic variables to current model state """
# Views for clarity:
qh = self.soln[:, :, 0]
Ah = self.soln[:, :, 1]
# Streamfunction
self.psih = - qh / self.divideSafeKay2
# Physical-space PV and velocity components
self.q = np.real(self.ifft2(qh))
self.A = self.ifft2(Ah)
self.U = -np.real(self.ifft2(self.jLL*self.psih))
self.V = np.real(self.ifft2(self.jKK*self.psih))
def set_q(self, q):
""" Set model vorticity """
self.soln[:, :, 0] = self.fft2(q)
self.soln = self._dealias_array(self.soln)
self.update_state_variables()
def set_A(self, A):
""" Set model wave field amplitude"""
self.soln[:, :, 1] = self.fft2(A)
self.soln = self._dealias_array(self.soln)
self.update_state_variables()
def plot_current_state(self):
""" Create a simple plot that shows the state of the model."""
# Figure out how to do this efficiently.
import matplotlib.pyplot as plt
self.update_state_variables()
# Initialize colorbar dictionary
colorbarProperties = {
'orientation' : 'vertical',
'shrink' : 0.8,
'extend' : 'neither',
}
self.fig = plt.figure('Hydrostatic wave equation',
figsize=(8, 4))
ax1 = plt.subplot(121)
plt.pcolormesh(self.xx, self.yy, self.q, cmap='RdBu_r')
plt.axis('square')
ax2 = plt.subplot(122)
plt.pcolormesh(self.xx, self.yy, np.sqrt(self.uu**2.0+self.vv**2.0))
plt.axis('square')
def describe_model(self):
""" Describe the current model state """
print("\nThis is a doubly-periodic spectral model for \n" + \
"{:s} \n".format(self.physics) + \
"with the following attributes:\n\n" + \
" Domain : {:.2e} X {:.2e} m\n".format(self.Lx, self.Ly) + \
" Resolution : {:d} X {:d}\n".format(self.nx, self.ny) + \
" Timestep : {:.2e} s\n".format(self.dt) + \
" Current time : {:.2e} s\n\n".format(self.t) + \
"The FFT scheme uses {:d} thread(s).\n".format(self.nThreads))
| mit |
poryfly/scikit-learn | examples/covariance/plot_sparse_cov.py | 300 | 5078 | """
======================================
Sparse inverse covariance estimation
======================================
Using the GraphLasso estimator to learn a covariance and sparse precision
from a small number of samples.
To estimate a probabilistic model (e.g. a Gaussian model), estimating the
precision matrix, that is the inverse covariance matrix, is as important
as estimating the covariance matrix. Indeed a Gaussian model is
parametrized by the precision matrix.
To be in favorable recovery conditions, we sample the data from a model
with a sparse inverse covariance matrix. In addition, we ensure that the
data is not too much correlated (limiting the largest coefficient of the
precision matrix) and that there a no small coefficients in the
precision matrix that cannot be recovered. In addition, with a small
number of observations, it is easier to recover a correlation matrix
rather than a covariance, thus we scale the time series.
Here, the number of samples is slightly larger than the number of
dimensions, thus the empirical covariance is still invertible. However,
as the observations are strongly correlated, the empirical covariance
matrix is ill-conditioned and as a result its inverse --the empirical
precision matrix-- is very far from the ground truth.
If we use l2 shrinkage, as with the Ledoit-Wolf estimator, as the number
of samples is small, we need to shrink a lot. As a result, the
Ledoit-Wolf precision is fairly close to the ground truth precision, that
is not far from being diagonal, but the off-diagonal structure is lost.
The l1-penalized estimator can recover part of this off-diagonal
structure. It learns a sparse precision. It is not able to
recover the exact sparsity pattern: it detects too many non-zero
coefficients. However, the highest non-zero coefficients of the l1
estimated correspond to the non-zero coefficients in the ground truth.
Finally, the coefficients of the l1 precision estimate are biased toward
zero: because of the penalty, they are all smaller than the corresponding
ground truth value, as can be seen on the figure.
Note that, the color range of the precision matrices is tweaked to
improve readability of the figure. The full range of values of the
empirical precision is not displayed.
The alpha parameter of the GraphLasso setting the sparsity of the model is
set by internal cross-validation in the GraphLassoCV. As can be
seen on figure 2, the grid to compute the cross-validation score is
iteratively refined in the neighborhood of the maximum.
"""
print(__doc__)
# author: Gael Varoquaux <gael.varoquaux@inria.fr>
# License: BSD 3 clause
# Copyright: INRIA
import numpy as np
from scipy import linalg
from sklearn.datasets import make_sparse_spd_matrix
from sklearn.covariance import GraphLassoCV, ledoit_wolf
import matplotlib.pyplot as plt
##############################################################################
# Generate the data
n_samples = 60
n_features = 20
prng = np.random.RandomState(1)
prec = make_sparse_spd_matrix(n_features, alpha=.98,
smallest_coef=.4,
largest_coef=.7,
random_state=prng)
cov = linalg.inv(prec)
d = np.sqrt(np.diag(cov))
cov /= d
cov /= d[:, np.newaxis]
prec *= d
prec *= d[:, np.newaxis]
X = prng.multivariate_normal(np.zeros(n_features), cov, size=n_samples)
X -= X.mean(axis=0)
X /= X.std(axis=0)
##############################################################################
# Estimate the covariance
emp_cov = np.dot(X.T, X) / n_samples
model = GraphLassoCV()
model.fit(X)
cov_ = model.covariance_
prec_ = model.precision_
lw_cov_, _ = ledoit_wolf(X)
lw_prec_ = linalg.inv(lw_cov_)
##############################################################################
# Plot the results
plt.figure(figsize=(10, 6))
plt.subplots_adjust(left=0.02, right=0.98)
# plot the covariances
covs = [('Empirical', emp_cov), ('Ledoit-Wolf', lw_cov_),
('GraphLasso', cov_), ('True', cov)]
vmax = cov_.max()
for i, (name, this_cov) in enumerate(covs):
plt.subplot(2, 4, i + 1)
plt.imshow(this_cov, interpolation='nearest', vmin=-vmax, vmax=vmax,
cmap=plt.cm.RdBu_r)
plt.xticks(())
plt.yticks(())
plt.title('%s covariance' % name)
# plot the precisions
precs = [('Empirical', linalg.inv(emp_cov)), ('Ledoit-Wolf', lw_prec_),
('GraphLasso', prec_), ('True', prec)]
vmax = .9 * prec_.max()
for i, (name, this_prec) in enumerate(precs):
ax = plt.subplot(2, 4, i + 5)
plt.imshow(np.ma.masked_equal(this_prec, 0),
interpolation='nearest', vmin=-vmax, vmax=vmax,
cmap=plt.cm.RdBu_r)
plt.xticks(())
plt.yticks(())
plt.title('%s precision' % name)
ax.set_axis_bgcolor('.7')
# plot the model selection metric
plt.figure(figsize=(4, 3))
plt.axes([.2, .15, .75, .7])
plt.plot(model.cv_alphas_, np.mean(model.grid_scores, axis=1), 'o-')
plt.axvline(model.alpha_, color='.5')
plt.title('Model selection')
plt.ylabel('Cross-validation score')
plt.xlabel('alpha')
plt.show()
| bsd-3-clause |
nvoron23/scikit-learn | sklearn/mixture/gmm.py | 68 | 31091 | """
Gaussian Mixture Models.
This implementation corresponds to frequentist (non-Bayesian) formulation
of Gaussian Mixture Models.
"""
# Author: Ron Weiss <ronweiss@gmail.com>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Bertrand Thirion <bertrand.thirion@inria.fr>
import warnings
import numpy as np
from scipy import linalg
from time import time
from ..base import BaseEstimator
from ..utils import check_random_state, check_array
from ..utils.extmath import logsumexp
from ..utils.validation import check_is_fitted
from .. import cluster
from sklearn.externals.six.moves import zip
EPS = np.finfo(float).eps
def log_multivariate_normal_density(X, means, covars, covariance_type='diag'):
"""Compute the log probability under a multivariate Gaussian distribution.
Parameters
----------
X : array_like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row corresponds to a
single data point.
means : array_like, shape (n_components, n_features)
List of n_features-dimensional mean vectors for n_components Gaussians.
Each row corresponds to a single mean vector.
covars : array_like
List of n_components covariance parameters for each Gaussian. The shape
depends on `covariance_type`:
(n_components, n_features) if 'spherical',
(n_features, n_features) if 'tied',
(n_components, n_features) if 'diag',
(n_components, n_features, n_features) if 'full'
covariance_type : string
Type of the covariance parameters. Must be one of
'spherical', 'tied', 'diag', 'full'. Defaults to 'diag'.
Returns
-------
lpr : array_like, shape (n_samples, n_components)
Array containing the log probabilities of each data point in
X under each of the n_components multivariate Gaussian distributions.
"""
log_multivariate_normal_density_dict = {
'spherical': _log_multivariate_normal_density_spherical,
'tied': _log_multivariate_normal_density_tied,
'diag': _log_multivariate_normal_density_diag,
'full': _log_multivariate_normal_density_full}
return log_multivariate_normal_density_dict[covariance_type](
X, means, covars)
def sample_gaussian(mean, covar, covariance_type='diag', n_samples=1,
random_state=None):
"""Generate random samples from a Gaussian distribution.
Parameters
----------
mean : array_like, shape (n_features,)
Mean of the distribution.
covar : array_like, optional
Covariance of the distribution. The shape depends on `covariance_type`:
scalar if 'spherical',
(n_features) if 'diag',
(n_features, n_features) if 'tied', or 'full'
covariance_type : string, optional
Type of the covariance parameters. Must be one of
'spherical', 'tied', 'diag', 'full'. Defaults to 'diag'.
n_samples : int, optional
Number of samples to generate. Defaults to 1.
Returns
-------
X : array, shape (n_features, n_samples)
Randomly generated sample
"""
rng = check_random_state(random_state)
n_dim = len(mean)
rand = rng.randn(n_dim, n_samples)
if n_samples == 1:
rand.shape = (n_dim,)
if covariance_type == 'spherical':
rand *= np.sqrt(covar)
elif covariance_type == 'diag':
rand = np.dot(np.diag(np.sqrt(covar)), rand)
else:
s, U = linalg.eigh(covar)
s.clip(0, out=s) # get rid of tiny negatives
np.sqrt(s, out=s)
U *= s
rand = np.dot(U, rand)
return (rand.T + mean).T
class GMM(BaseEstimator):
"""Gaussian Mixture Model
Representation of a Gaussian mixture model probability distribution.
This class allows for easy evaluation of, sampling from, and
maximum-likelihood estimation of the parameters of a GMM distribution.
Initializes parameters such that every mixture component has zero
mean and identity covariance.
Read more in the :ref:`User Guide <gmm>`.
Parameters
----------
n_components : int, optional
Number of mixture components. Defaults to 1.
covariance_type : string, optional
String describing the type of covariance parameters to
use. Must be one of 'spherical', 'tied', 'diag', 'full'.
Defaults to 'diag'.
random_state: RandomState or an int seed (None by default)
A random number generator instance
min_covar : float, optional
Floor on the diagonal of the covariance matrix to prevent
overfitting. Defaults to 1e-3.
tol : float, optional
Convergence threshold. EM iterations will stop when average
gain in log-likelihood is below this threshold. Defaults to 1e-3.
n_iter : int, optional
Number of EM iterations to perform.
n_init : int, optional
Number of initializations to perform. the best results is kept
params : string, optional
Controls which parameters are updated in the training
process. Can contain any combination of 'w' for weights,
'm' for means, and 'c' for covars. Defaults to 'wmc'.
init_params : string, optional
Controls which parameters are updated in the initialization
process. Can contain any combination of 'w' for weights,
'm' for means, and 'c' for covars. Defaults to 'wmc'.
verbose : int, default: 0
Enable verbose output. If 1 then it always prints the current
initialization and iteration step. If greater than 1 then
it prints additionally the change and time needed for each step.
Attributes
----------
weights_ : array, shape (`n_components`,)
This attribute stores the mixing weights for each mixture component.
means_ : array, shape (`n_components`, `n_features`)
Mean parameters for each mixture component.
covars_ : array
Covariance parameters for each mixture component. The shape
depends on `covariance_type`::
(n_components, n_features) if 'spherical',
(n_features, n_features) if 'tied',
(n_components, n_features) if 'diag',
(n_components, n_features, n_features) if 'full'
converged_ : bool
True when convergence was reached in fit(), False otherwise.
See Also
--------
DPGMM : Infinite gaussian mixture model, using the dirichlet
process, fit with a variational algorithm
VBGMM : Finite gaussian mixture model fit with a variational
algorithm, better for situations where there might be too little
data to get a good estimate of the covariance matrix.
Examples
--------
>>> import numpy as np
>>> from sklearn import mixture
>>> np.random.seed(1)
>>> g = mixture.GMM(n_components=2)
>>> # Generate random observations with two modes centered on 0
>>> # and 10 to use for training.
>>> obs = np.concatenate((np.random.randn(100, 1),
... 10 + np.random.randn(300, 1)))
>>> g.fit(obs) # doctest: +NORMALIZE_WHITESPACE
GMM(covariance_type='diag', init_params='wmc', min_covar=0.001,
n_components=2, n_init=1, n_iter=100, params='wmc',
random_state=None, thresh=None, tol=0.001, verbose=0)
>>> np.round(g.weights_, 2)
array([ 0.75, 0.25])
>>> np.round(g.means_, 2)
array([[ 10.05],
[ 0.06]])
>>> np.round(g.covars_, 2) #doctest: +SKIP
array([[[ 1.02]],
[[ 0.96]]])
>>> g.predict([[0], [2], [9], [10]]) #doctest: +ELLIPSIS
array([1, 1, 0, 0]...)
>>> np.round(g.score([[0], [2], [9], [10]]), 2)
array([-2.19, -4.58, -1.75, -1.21])
>>> # Refit the model on new data (initial parameters remain the
>>> # same), this time with an even split between the two modes.
>>> g.fit(20 * [[0]] + 20 * [[10]]) # doctest: +NORMALIZE_WHITESPACE
GMM(covariance_type='diag', init_params='wmc', min_covar=0.001,
n_components=2, n_init=1, n_iter=100, params='wmc',
random_state=None, thresh=None, tol=0.001, verbose=0)
>>> np.round(g.weights_, 2)
array([ 0.5, 0.5])
"""
def __init__(self, n_components=1, covariance_type='diag',
random_state=None, thresh=None, tol=1e-3, min_covar=1e-3,
n_iter=100, n_init=1, params='wmc', init_params='wmc',
verbose=0):
if thresh is not None:
warnings.warn("'thresh' has been replaced by 'tol' in 0.16 "
" and will be removed in 0.18.",
DeprecationWarning)
self.n_components = n_components
self.covariance_type = covariance_type
self.thresh = thresh
self.tol = tol
self.min_covar = min_covar
self.random_state = random_state
self.n_iter = n_iter
self.n_init = n_init
self.params = params
self.init_params = init_params
self.verbose = verbose
if covariance_type not in ['spherical', 'tied', 'diag', 'full']:
raise ValueError('Invalid value for covariance_type: %s' %
covariance_type)
if n_init < 1:
raise ValueError('GMM estimation requires at least one run')
self.weights_ = np.ones(self.n_components) / self.n_components
# flag to indicate exit status of fit() method: converged (True) or
# n_iter reached (False)
self.converged_ = False
def _get_covars(self):
"""Covariance parameters for each mixture component.
The shape depends on ``cvtype``::
(n_states, n_features) if 'spherical',
(n_features, n_features) if 'tied',
(n_states, n_features) if 'diag',
(n_states, n_features, n_features) if 'full'
"""
if self.covariance_type == 'full':
return self.covars_
elif self.covariance_type == 'diag':
return [np.diag(cov) for cov in self.covars_]
elif self.covariance_type == 'tied':
return [self.covars_] * self.n_components
elif self.covariance_type == 'spherical':
return [np.diag(cov) for cov in self.covars_]
def _set_covars(self, covars):
"""Provide values for covariance"""
covars = np.asarray(covars)
_validate_covars(covars, self.covariance_type, self.n_components)
self.covars_ = covars
def score_samples(self, X):
"""Return the per-sample likelihood of the data under the model.
Compute the log probability of X under the model and
return the posterior distribution (responsibilities) of each
mixture component for each element of X.
Parameters
----------
X: array_like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
-------
logprob : array_like, shape (n_samples,)
Log probabilities of each data point in X.
responsibilities : array_like, shape (n_samples, n_components)
Posterior probabilities of each mixture component for each
observation
"""
check_is_fitted(self, 'means_')
X = check_array(X)
if X.ndim == 1:
X = X[:, np.newaxis]
if X.size == 0:
return np.array([]), np.empty((0, self.n_components))
if X.shape[1] != self.means_.shape[1]:
raise ValueError('The shape of X is not compatible with self')
lpr = (log_multivariate_normal_density(X, self.means_, self.covars_,
self.covariance_type) +
np.log(self.weights_))
logprob = logsumexp(lpr, axis=1)
responsibilities = np.exp(lpr - logprob[:, np.newaxis])
return logprob, responsibilities
def score(self, X, y=None):
"""Compute the log probability under the model.
Parameters
----------
X : array_like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
-------
logprob : array_like, shape (n_samples,)
Log probabilities of each data point in X
"""
logprob, _ = self.score_samples(X)
return logprob
def predict(self, X):
"""Predict label for data.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
C : array, shape = (n_samples,) component memberships
"""
logprob, responsibilities = self.score_samples(X)
return responsibilities.argmax(axis=1)
def predict_proba(self, X):
"""Predict posterior probability of data under each Gaussian
in the model.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
responsibilities : array-like, shape = (n_samples, n_components)
Returns the probability of the sample for each Gaussian
(state) in the model.
"""
logprob, responsibilities = self.score_samples(X)
return responsibilities
def sample(self, n_samples=1, random_state=None):
"""Generate random samples from the model.
Parameters
----------
n_samples : int, optional
Number of samples to generate. Defaults to 1.
Returns
-------
X : array_like, shape (n_samples, n_features)
List of samples
"""
check_is_fitted(self, 'means_')
if random_state is None:
random_state = self.random_state
random_state = check_random_state(random_state)
weight_cdf = np.cumsum(self.weights_)
X = np.empty((n_samples, self.means_.shape[1]))
rand = random_state.rand(n_samples)
# decide which component to use for each sample
comps = weight_cdf.searchsorted(rand)
# for each component, generate all needed samples
for comp in range(self.n_components):
# occurrences of current component in X
comp_in_X = (comp == comps)
# number of those occurrences
num_comp_in_X = comp_in_X.sum()
if num_comp_in_X > 0:
if self.covariance_type == 'tied':
cv = self.covars_
elif self.covariance_type == 'spherical':
cv = self.covars_[comp][0]
else:
cv = self.covars_[comp]
X[comp_in_X] = sample_gaussian(
self.means_[comp], cv, self.covariance_type,
num_comp_in_X, random_state=random_state).T
return X
def fit_predict(self, X, y=None):
"""Fit and then predict labels for data.
Warning: due to the final maximization step in the EM algorithm,
with low iterations the prediction may not be 100% accurate
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
C : array, shape = (n_samples,) component memberships
"""
return self._fit(X, y).argmax(axis=1)
def _fit(self, X, y=None, do_prediction=False):
"""Estimate model parameters with the EM algorithm.
A initialization step is performed before entering the
expectation-maximization (EM) algorithm. If you want to avoid
this step, set the keyword argument init_params to the empty
string '' when creating the GMM object. Likewise, if you would
like just to do an initialization, set n_iter=0.
Parameters
----------
X : array_like, shape (n, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
-------
responsibilities : array, shape (n_samples, n_components)
Posterior probabilities of each mixture component for each
observation.
"""
# initialization step
X = check_array(X, dtype=np.float64, ensure_min_samples=2)
if X.shape[0] < self.n_components:
raise ValueError(
'GMM estimation with %s components, but got only %s samples' %
(self.n_components, X.shape[0]))
max_log_prob = -np.infty
if self.verbose > 0:
print('Expectation-maximization algorithm started.')
for init in range(self.n_init):
if self.verbose > 0:
print('Initialization ' + str(init + 1))
start_init_time = time()
if 'm' in self.init_params or not hasattr(self, 'means_'):
self.means_ = cluster.KMeans(
n_clusters=self.n_components,
random_state=self.random_state).fit(X).cluster_centers_
if self.verbose > 1:
print('\tMeans have been initialized.')
if 'w' in self.init_params or not hasattr(self, 'weights_'):
self.weights_ = np.tile(1.0 / self.n_components,
self.n_components)
if self.verbose > 1:
print('\tWeights have been initialized.')
if 'c' in self.init_params or not hasattr(self, 'covars_'):
cv = np.cov(X.T) + self.min_covar * np.eye(X.shape[1])
if not cv.shape:
cv.shape = (1, 1)
self.covars_ = \
distribute_covar_matrix_to_match_covariance_type(
cv, self.covariance_type, self.n_components)
if self.verbose > 1:
print('\tCovariance matrices have been initialized.')
# EM algorithms
current_log_likelihood = None
# reset self.converged_ to False
self.converged_ = False
# this line should be removed when 'thresh' is removed in v0.18
tol = (self.tol if self.thresh is None
else self.thresh / float(X.shape[0]))
for i in range(self.n_iter):
if self.verbose > 0:
print('\tEM iteration ' + str(i + 1))
start_iter_time = time()
prev_log_likelihood = current_log_likelihood
# Expectation step
log_likelihoods, responsibilities = self.score_samples(X)
current_log_likelihood = log_likelihoods.mean()
# Check for convergence.
# (should compare to self.tol when deprecated 'thresh' is
# removed in v0.18)
if prev_log_likelihood is not None:
change = abs(current_log_likelihood - prev_log_likelihood)
if self.verbose > 1:
print('\t\tChange: ' + str(change))
if change < tol:
self.converged_ = True
if self.verbose > 0:
print('\t\tEM algorithm converged.')
break
# Maximization step
self._do_mstep(X, responsibilities, self.params,
self.min_covar)
if self.verbose > 1:
print('\t\tEM iteration ' + str(i + 1) + ' took {0:.5f}s'.format(
time() - start_iter_time))
# if the results are better, keep it
if self.n_iter:
if current_log_likelihood > max_log_prob:
max_log_prob = current_log_likelihood
best_params = {'weights': self.weights_,
'means': self.means_,
'covars': self.covars_}
if self.verbose > 1:
print('\tBetter parameters were found.')
if self.verbose > 1:
print('\tInitialization ' + str(init + 1) + ' took {0:.5f}s'.format(
time() - start_init_time))
# check the existence of an init param that was not subject to
# likelihood computation issue.
if np.isneginf(max_log_prob) and self.n_iter:
raise RuntimeError(
"EM algorithm was never able to compute a valid likelihood " +
"given initial parameters. Try different init parameters " +
"(or increasing n_init) or check for degenerate data.")
if self.n_iter:
self.covars_ = best_params['covars']
self.means_ = best_params['means']
self.weights_ = best_params['weights']
else: # self.n_iter == 0 occurs when using GMM within HMM
# Need to make sure that there are responsibilities to output
# Output zeros because it was just a quick initialization
responsibilities = np.zeros((X.shape[0], self.n_components))
return responsibilities
def fit(self, X, y=None):
"""Estimate model parameters with the EM algorithm.
A initialization step is performed before entering the
expectation-maximization (EM) algorithm. If you want to avoid
this step, set the keyword argument init_params to the empty
string '' when creating the GMM object. Likewise, if you would
like just to do an initialization, set n_iter=0.
Parameters
----------
X : array_like, shape (n, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
-------
self
"""
self._fit(X, y)
return self
def _do_mstep(self, X, responsibilities, params, min_covar=0):
""" Perform the Mstep of the EM algorithm and return the class weights
"""
weights = responsibilities.sum(axis=0)
weighted_X_sum = np.dot(responsibilities.T, X)
inverse_weights = 1.0 / (weights[:, np.newaxis] + 10 * EPS)
if 'w' in params:
self.weights_ = (weights / (weights.sum() + 10 * EPS) + EPS)
if 'm' in params:
self.means_ = weighted_X_sum * inverse_weights
if 'c' in params:
covar_mstep_func = _covar_mstep_funcs[self.covariance_type]
self.covars_ = covar_mstep_func(
self, X, responsibilities, weighted_X_sum, inverse_weights,
min_covar)
return weights
def _n_parameters(self):
"""Return the number of free parameters in the model."""
ndim = self.means_.shape[1]
if self.covariance_type == 'full':
cov_params = self.n_components * ndim * (ndim + 1) / 2.
elif self.covariance_type == 'diag':
cov_params = self.n_components * ndim
elif self.covariance_type == 'tied':
cov_params = ndim * (ndim + 1) / 2.
elif self.covariance_type == 'spherical':
cov_params = self.n_components
mean_params = ndim * self.n_components
return int(cov_params + mean_params + self.n_components - 1)
def bic(self, X):
"""Bayesian information criterion for the current model fit
and the proposed data
Parameters
----------
X : array of shape(n_samples, n_dimensions)
Returns
-------
bic: float (the lower the better)
"""
return (-2 * self.score(X).sum() +
self._n_parameters() * np.log(X.shape[0]))
def aic(self, X):
"""Akaike information criterion for the current model fit
and the proposed data
Parameters
----------
X : array of shape(n_samples, n_dimensions)
Returns
-------
aic: float (the lower the better)
"""
return - 2 * self.score(X).sum() + 2 * self._n_parameters()
#########################################################################
# some helper routines
#########################################################################
def _log_multivariate_normal_density_diag(X, means, covars):
"""Compute Gaussian log-density at X for a diagonal model"""
n_samples, n_dim = X.shape
lpr = -0.5 * (n_dim * np.log(2 * np.pi) + np.sum(np.log(covars), 1)
+ np.sum((means ** 2) / covars, 1)
- 2 * np.dot(X, (means / covars).T)
+ np.dot(X ** 2, (1.0 / covars).T))
return lpr
def _log_multivariate_normal_density_spherical(X, means, covars):
"""Compute Gaussian log-density at X for a spherical model"""
cv = covars.copy()
if covars.ndim == 1:
cv = cv[:, np.newaxis]
if covars.shape[1] == 1:
cv = np.tile(cv, (1, X.shape[-1]))
return _log_multivariate_normal_density_diag(X, means, cv)
def _log_multivariate_normal_density_tied(X, means, covars):
"""Compute Gaussian log-density at X for a tied model"""
cv = np.tile(covars, (means.shape[0], 1, 1))
return _log_multivariate_normal_density_full(X, means, cv)
def _log_multivariate_normal_density_full(X, means, covars, min_covar=1.e-7):
"""Log probability for full covariance matrices."""
n_samples, n_dim = X.shape
nmix = len(means)
log_prob = np.empty((n_samples, nmix))
for c, (mu, cv) in enumerate(zip(means, covars)):
try:
cv_chol = linalg.cholesky(cv, lower=True)
except linalg.LinAlgError:
# The model is most probably stuck in a component with too
# few observations, we need to reinitialize this components
try:
cv_chol = linalg.cholesky(cv + min_covar * np.eye(n_dim),
lower=True)
except linalg.LinAlgError:
raise ValueError("'covars' must be symmetric, "
"positive-definite")
cv_log_det = 2 * np.sum(np.log(np.diagonal(cv_chol)))
cv_sol = linalg.solve_triangular(cv_chol, (X - mu).T, lower=True).T
log_prob[:, c] = - .5 * (np.sum(cv_sol ** 2, axis=1) +
n_dim * np.log(2 * np.pi) + cv_log_det)
return log_prob
def _validate_covars(covars, covariance_type, n_components):
"""Do basic checks on matrix covariance sizes and values
"""
from scipy import linalg
if covariance_type == 'spherical':
if len(covars) != n_components:
raise ValueError("'spherical' covars have length n_components")
elif np.any(covars <= 0):
raise ValueError("'spherical' covars must be non-negative")
elif covariance_type == 'tied':
if covars.shape[0] != covars.shape[1]:
raise ValueError("'tied' covars must have shape (n_dim, n_dim)")
elif (not np.allclose(covars, covars.T)
or np.any(linalg.eigvalsh(covars) <= 0)):
raise ValueError("'tied' covars must be symmetric, "
"positive-definite")
elif covariance_type == 'diag':
if len(covars.shape) != 2:
raise ValueError("'diag' covars must have shape "
"(n_components, n_dim)")
elif np.any(covars <= 0):
raise ValueError("'diag' covars must be non-negative")
elif covariance_type == 'full':
if len(covars.shape) != 3:
raise ValueError("'full' covars must have shape "
"(n_components, n_dim, n_dim)")
elif covars.shape[1] != covars.shape[2]:
raise ValueError("'full' covars must have shape "
"(n_components, n_dim, n_dim)")
for n, cv in enumerate(covars):
if (not np.allclose(cv, cv.T)
or np.any(linalg.eigvalsh(cv) <= 0)):
raise ValueError("component %d of 'full' covars must be "
"symmetric, positive-definite" % n)
else:
raise ValueError("covariance_type must be one of " +
"'spherical', 'tied', 'diag', 'full'")
def distribute_covar_matrix_to_match_covariance_type(
tied_cv, covariance_type, n_components):
"""Create all the covariance matrices from a given template"""
if covariance_type == 'spherical':
cv = np.tile(tied_cv.mean() * np.ones(tied_cv.shape[1]),
(n_components, 1))
elif covariance_type == 'tied':
cv = tied_cv
elif covariance_type == 'diag':
cv = np.tile(np.diag(tied_cv), (n_components, 1))
elif covariance_type == 'full':
cv = np.tile(tied_cv, (n_components, 1, 1))
else:
raise ValueError("covariance_type must be one of " +
"'spherical', 'tied', 'diag', 'full'")
return cv
def _covar_mstep_diag(gmm, X, responsibilities, weighted_X_sum, norm,
min_covar):
"""Performing the covariance M step for diagonal cases"""
avg_X2 = np.dot(responsibilities.T, X * X) * norm
avg_means2 = gmm.means_ ** 2
avg_X_means = gmm.means_ * weighted_X_sum * norm
return avg_X2 - 2 * avg_X_means + avg_means2 + min_covar
def _covar_mstep_spherical(*args):
"""Performing the covariance M step for spherical cases"""
cv = _covar_mstep_diag(*args)
return np.tile(cv.mean(axis=1)[:, np.newaxis], (1, cv.shape[1]))
def _covar_mstep_full(gmm, X, responsibilities, weighted_X_sum, norm,
min_covar):
"""Performing the covariance M step for full cases"""
# Eq. 12 from K. Murphy, "Fitting a Conditional Linear Gaussian
# Distribution"
n_features = X.shape[1]
cv = np.empty((gmm.n_components, n_features, n_features))
for c in range(gmm.n_components):
post = responsibilities[:, c]
mu = gmm.means_[c]
diff = X - mu
with np.errstate(under='ignore'):
# Underflow Errors in doing post * X.T are not important
avg_cv = np.dot(post * diff.T, diff) / (post.sum() + 10 * EPS)
cv[c] = avg_cv + min_covar * np.eye(n_features)
return cv
def _covar_mstep_tied(gmm, X, responsibilities, weighted_X_sum, norm,
min_covar):
# Eq. 15 from K. Murphy, "Fitting a Conditional Linear Gaussian
# Distribution"
avg_X2 = np.dot(X.T, X)
avg_means2 = np.dot(gmm.means_.T, weighted_X_sum)
out = avg_X2 - avg_means2
out *= 1. / X.shape[0]
out.flat[::len(out) + 1] += min_covar
return out
_covar_mstep_funcs = {'spherical': _covar_mstep_spherical,
'diag': _covar_mstep_diag,
'tied': _covar_mstep_tied,
'full': _covar_mstep_full,
}
| bsd-3-clause |
amas0/patools | patools/trial.py | 1 | 5966 | import os
import numpy as np
import pandas as pd
import patools.packing as pck
class Trial:
def __init__(self, directory=os.getcwd(), nbs=False):
self.packings = self.loadTrials(directory, nbs)
self.df = pd.DataFrame(index=self.packings.keys())
self._getSphereRads()
self._getParticleRads()
def __len__(self):
return sum([len(packing) for packing in self.packings.values()])
def _getSphereRads(self):
rads = {}
for key, val in self.packings.items():
rads[key] = val.sphereRad
radS = pd.Series(rads, name='rad')
self.df['rad'] = radS
def _getParticleRads(self):
pck = self.packings[list(self.packings.keys())[0]]
self.bigRad = pck.bigRad
self.littleRad = pck.littleRad
def loadTrials(self, directory, nbs=False):
"""
Loads a set of packings corresponding to one parameter set. The
subdirectories should contain packings files themselves.
ParamDirectory -> Trial Subdirs -> Packing Files
"""
subdirs = os.listdir(directory)
trialPackings = {}
for trial in subdirs:
trialDir = os.path.join(directory, trial)
if not os.path.isdir(trialDir): # Only look in dirs
continue
newPacking = pck.Packing(trialDir,nbs=nbs)
if len(newPacking) <= 1: # Remove broken packings
continue
trialPackings[trial] = newPacking
return trialPackings
def calculatePF_all(self):
"""
Calculates the packing fraction for each packing in a trial. The packing
fractions for each trial are stored in a dataframe. This function is
most often accessed from the dataset object, where different statistics
of the packing fractions are reported, such as the mean, median, max, or
min.
"""
pfs = {}
for key, val in self.packings.items():
pfs[key] = val.calculatePF()
pfS = pd.Series(pfs, name='pf')
self.df['pf'] = pfS
return pfs
def calculateOP_all(self, n=6):
"""
Calculates the n-atic order parameter for each packing in a trial. The
average order parameter for a packing is stored in a dataframe. This
function is most often accessed from the dataset object, where different
statistics of the packing fractions are reported, such as the mean,
median, max, or min.
"""
ops = {}
for key, val in self.packings.items():
val.calculateOP(n)
ops[key] = val.op[n]
opS = pd.Series(ops, name=str(n) + '-atic')
self.df[str(n) + '-atic'] = opS
return opS
def calculateSegOP_all(self, n=6):
"""
Calculates the segregated n-atic order parameter for each packing in a
trial. The average order parameter for big-big and little-little for a
packing is stored in a dataframe. This function is most often accessed
from the dataset object, where different statistics of the packing
fractions are reported, such as the mean, median, max, or min.
"""
opsBig = {}
opsLittle = {}
for key, val in self.packings.items():
val.calculateSegOP(n)
opsBig[key] = val.ops[str(n) + 'B']
opsLittle[key] = val.ops[str(n) + 'L']
opSBig = pd.Series(opsBig, name=str(n) + '-aticBig')
opSLittle = pd.Series(opsLittle, name=str(n) + '-aticLittle')
self.df[str(n) + '-aticBig'] = opSBig
self.df[str(n) + '-aticLittle'] = opSLittle
def calculateDFG_all(self):
"""
Calculuates the defect graph size for all of the packings in a given
trial.
"""
dfgs = {}
for key, val in self.packings.items():
val.calculateDefectGraphSize()
dfgs[key] = val.dfG
dfgS = pd.Series(dfgs, name='DFGS')
self.df['DFGS'] = dfgS
return dfgS
def calculateDFF_all(self):
"""
Calculates the defect fraction for all of the packings in a given trial.
"""
dffs = {}
for key, val in self.packings.items():
val.calculateDefectFraction()
dffs[key] = val.dfN
dffS = pd.Series(dffs, name='DFF')
self.df['DFF'] = dffS
return dffS
def calculateCM_all(self):
"""
Calculates the coordination matrix for all of the packings in a given
trial.
"""
cmsbb = {}; cmsbl = {}; cmslb = {}; cmsll = {}
for key, val in self.packings.items():
val.calculateCoordinationMatrix()
cmsbb[key] = val.cm[0]
cmsbl[key] = val.cm[1]
cmslb[key] = val.cm[2]
cmsll[key] = val.cm[3]
self.df['BB'] = pd.Series(cmsbb)
self.df['BL'] = pd.Series(cmsbl)
self.df['LB'] = pd.Series(cmslb)
self.df['LL'] = pd.Series(cmsll)
def calculateRDF_all(self, nBins=50, nTestPts=0):
"""
Computes the RDF over a full trial of one parameter.
"""
self.gsBB = np.zeros([len(self.df), nBins])
self.gsBL = np.zeros([len(self.df), nBins])
self.gsLB = np.zeros([len(self.df), nBins])
self.gsLL = np.zeros([len(self.df), nBins])
empty = []
for i, val in enumerate(self.packings.values()):
val.calculateRadialDF(nBins, nTestPts)
try:
self.gsBB[i] = val.gsBB
self.gsBL[i] = val.gsBL
self.gsLB[i] = val.gsLB
self.gsLL[i] = val.gsLL
except AttributeError:
empty.append(i)
pass
self.gsBB = np.delete(self.gsBB, empty, axis=0)
self.gsBL = np.delete(self.gsBL, empty, axis=0)
self.gsLB = np.delete(self.gsLB, empty, axis=0)
self.gsLL = np.delete(self.gsLL, empty, axis=0)
| mit |
Basil-M/AMGEN-Summer-Project-2017 | python/code/Random walker/test_niifti.py | 1 | 2531 | import numpy as np
import niipy as nii
import matplotlib.pyplot as plt
from skimage.segmentation import random_walker
from skimage.data import binary_blobs
from skimage.exposure import rescale_intensity
import skimage
FOLDER_PATH= "/scratch/python/datasets/ACDC/ACDC_challenge_20170617/"
patient = 1
frame = 1
debug = 0
#FILEPATHS
nii_4d_path = FOLDER_PATH + "patient" + '{0:0>3}'.format(patient) + "/patient" + '{0:0>3}'.format(patient) + "_4d.nii.gz"
frame_path = FOLDER_PATH + "patient" + '{0:0>3}'.format(patient) + "/patient" + '{0:0>3}'.format(patient) + "_frame" + '{0:0>2}'.format(frame) + ".nii.gz"
mask_path = FOLDER_PATH + "patient" + '{0:0>3}'.format(patient) + "/patient" + '{0:0>3}'.format(patient) + "_frame" + '{0:0>2}'.format(frame) + "_scribble.nii.gz"
output_mask_path = FOLDER_PATH + "patient" + '{0:0>3}'.format(patient) + "/patient" + '{0:0>3}'.format(patient) + "_frame" + '{0:0>2}'.format(frame) + "_regen.nii.gz"
print("Extracting data from " + frame_path)
frame, frame_aff, frame_hdr = nii.load_nii(frame_path)
frame = frame[:,:,:]
mask, mask_aff, mask_hdr = nii.load_nii(mask_path)
slice_count = mask.shape[2]
labels = np.unique(mask).astype(int)
print mask.shape
print labels
new_labels = np.zeros(mask.shape)
for sliceNo in range(slice_count):
#RandomWalkAlgorithm
new_labels[:,:,sliceNo] = random_walker(frame[:,:,sliceNo], mask[:,:,sliceNo].astype(int),beta=5, mode = 'bf')
#BETA: Penalization coefficient for random walker motion. Higher beta = more difficult diffusion
#MODE: bf = brute force (fast for small images), cg = conjugate gradient, cg_mg = conjugate gradient + multigrid preconditioner
#return_full_prob: Can return full probabilities instead of 'most likely'
#Tolerance
if debug == 1:
# Plot results
data = frame[:,:,sliceNo]
markers = mask[:,:,sliceNo]
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 3.2),
sharex=True, sharey=True)
ax1.imshow(data, cmap='gray', interpolation='nearest')
ax1.axis('off')
ax1.set_adjustable('box-forced')
ax1.set_title('MRI slice %s'% (sliceNo + 1))
ax2.imshow(markers, cmap='magma', interpolation='nearest')
ax2.axis('off')
ax2.set_adjustable('box-forced')
ax2.set_title('Markers')
ax3.imshow(new_labels[:,:,sliceNo], cmap='gray', interpolation='nearest')
ax3.axis('off')
ax3.set_adjustable('box-forced')
ax3.set_title('Segmentation')
fig.tight_layout()
plt.show()
if debug == 0:
#Save nifti data
nii.save_nii(output_mask_path, new_labels, mask_aff, mask_hdr)
| mit |
herilalaina/scikit-learn | examples/cluster/plot_dict_face_patches.py | 36 | 2737 | """
Online learning of a dictionary of parts of faces
==================================================
This example uses a large dataset of faces to learn a set of 20 x 20
images patches that constitute faces.
From the programming standpoint, it is interesting because it shows how
to use the online API of the scikit-learn to process a very large
dataset by chunks. The way we proceed is that we load an image at a time
and extract randomly 50 patches from this image. Once we have accumulated
500 of these patches (using 10 images), we run the `partial_fit` method
of the online KMeans object, MiniBatchKMeans.
The verbose setting on the MiniBatchKMeans enables us to see that some
clusters are reassigned during the successive calls to
partial-fit. This is because the number of patches that they represent
has become too low, and it is better to choose a random new
cluster.
"""
print(__doc__)
import time
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.image import extract_patches_2d
faces = datasets.fetch_olivetti_faces()
# #############################################################################
# Learn the dictionary of images
print('Learning the dictionary... ')
rng = np.random.RandomState(0)
kmeans = MiniBatchKMeans(n_clusters=81, random_state=rng, verbose=True)
patch_size = (20, 20)
buffer = []
t0 = time.time()
# The online learning part: cycle over the whole dataset 6 times
index = 0
for _ in range(6):
for img in faces.images:
data = extract_patches_2d(img, patch_size, max_patches=50,
random_state=rng)
data = np.reshape(data, (len(data), -1))
buffer.append(data)
index += 1
if index % 10 == 0:
data = np.concatenate(buffer, axis=0)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0)
kmeans.partial_fit(data)
buffer = []
if index % 100 == 0:
print('Partial fit of %4i out of %i'
% (index, 6 * len(faces.images)))
dt = time.time() - t0
print('done in %.2fs.' % dt)
# #############################################################################
# Plot the results
plt.figure(figsize=(4.2, 4))
for i, patch in enumerate(kmeans.cluster_centers_):
plt.subplot(9, 9, i + 1)
plt.imshow(patch.reshape(patch_size), cmap=plt.cm.gray,
interpolation='nearest')
plt.xticks(())
plt.yticks(())
plt.suptitle('Patches of faces\nTrain time %.1fs on %d patches' %
(dt, 8 * len(faces.images)), fontsize=16)
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
plt.show()
| bsd-3-clause |
aitatanit/gmes | gmes/file_io.py | 1 | 2075 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from sys import stderr
from os.path import exists
try:
import psyco
psyco.profile()
from psyco.classes import *
except ImportError:
pass
from sys import modules
if not 'matplotlib.backends' in modules:
import matplotlib
matplotlib.use('TkAgg')
import pylab
# from tables import openFile
# GMES modules
from pw_material import MaterialElectricReal, MaterialElectricCmplx
from pw_material import MaterialMagneticReal, MaterialMagneticCmplx
class Probe(object):
def __init__(self, idx, field, filename):
"""
idx: index of probing point. type: tuple-3
field: field to probe. type: numpy.array
filename: recording file name. type: str
"""
self.idx = tuple(idx)
self.field = field
f_name = str(filename)
if exists(f_name):
stderr.write('Warning: ' + f_name + ' already exists.\n')
try:
self.f = open(f_name, 'w')
except IOError:
self.f = None
print('Warning: Can\'t open file ' + f_name + '.\n')
def __del__(self):
self.f.close()
def write_header(self, p, dt):
"""Write some meta-data on the header of the recording file.
p: space coordinates. type: tuple-3
dt: time-step. type: float
"""
self.f.write('# location=' + str(p) + '\n')
self.f.write('# dt=' + str(dt) + '\n')
def write(self, n):
self.f.write(str(n) + ' ' + str(self.field[self.idx]) + '\n')
def write_hdf5(data, name, low_index, high_index):
h5file = openFile(name + '.h5', mode='w')
group = h5file.createGroup('/')
h5file.createArray(group, name,
data[low_index[0]:high_index[0],
low_index[1]:high_index[1],
low_index[2]:high_index[2]])
h5file.close()
def snapshot(data, filename, title):
pylab.title(title)
pylab.imshow(data, origin="lower")
pylab.savefig(filename)
| gpl-3.0 |
elijah513/scikit-learn | examples/classification/plot_lda.py | 164 | 2224 | """
====================================================================
Normal and Shrinkage Linear Discriminant Analysis for classification
====================================================================
Shows how shrinkage improves classification.
"""
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.lda import LDA
n_train = 20 # samples for training
n_test = 200 # samples for testing
n_averages = 50 # how often to repeat classification
n_features_max = 75 # maximum number of features
step = 4 # step size for the calculation
def generate_data(n_samples, n_features):
"""Generate random blob-ish data with noisy features.
This returns an array of input data with shape `(n_samples, n_features)`
and an array of `n_samples` target labels.
Only one feature contains discriminative information, the other features
contain only noise.
"""
X, y = make_blobs(n_samples=n_samples, n_features=1, centers=[[-2], [2]])
# add non-discriminative features
if n_features > 1:
X = np.hstack([X, np.random.randn(n_samples, n_features - 1)])
return X, y
acc_clf1, acc_clf2 = [], []
n_features_range = range(1, n_features_max + 1, step)
for n_features in n_features_range:
score_clf1, score_clf2 = 0, 0
for _ in range(n_averages):
X, y = generate_data(n_train, n_features)
clf1 = LDA(solver='lsqr', shrinkage='auto').fit(X, y)
clf2 = LDA(solver='lsqr', shrinkage=None).fit(X, y)
X, y = generate_data(n_test, n_features)
score_clf1 += clf1.score(X, y)
score_clf2 += clf2.score(X, y)
acc_clf1.append(score_clf1 / n_averages)
acc_clf2.append(score_clf2 / n_averages)
features_samples_ratio = np.array(n_features_range) / n_train
plt.plot(features_samples_ratio, acc_clf1, linewidth=2,
label="LDA with shrinkage", color='r')
plt.plot(features_samples_ratio, acc_clf2, linewidth=2,
label="LDA", color='g')
plt.xlabel('n_features / n_samples')
plt.ylabel('Classification accuracy')
plt.legend(loc=1, prop={'size': 12})
plt.suptitle('LDA vs. shrinkage LDA (1 discriminative feature)')
plt.show()
| bsd-3-clause |
ssaeger/scikit-learn | examples/gaussian_process/plot_compare_gpr_krr.py | 67 | 5191 | """
==========================================================
Comparison of kernel ridge and Gaussian process regression
==========================================================
Both kernel ridge regression (KRR) and Gaussian process regression (GPR) learn
a target function by employing internally the "kernel trick". KRR learns a
linear function in the space induced by the respective kernel which corresponds
to a non-linear function in the original space. The linear function in the
kernel space is chosen based on the mean-squared error loss with
ridge regularization. GPR uses the kernel to define the covariance of
a prior distribution over the target functions and uses the observed training
data to define a likelihood function. Based on Bayes theorem, a (Gaussian)
posterior distribution over target functions is defined, whose mean is used
for prediction.
A major difference is that GPR can choose the kernel's hyperparameters based
on gradient-ascent on the marginal likelihood function while KRR needs to
perform a grid search on a cross-validated loss function (mean-squared error
loss). A further difference is that GPR learns a generative, probabilistic
model of the target function and can thus provide meaningful confidence
intervals and posterior samples along with the predictions while KRR only
provides predictions.
This example illustrates both methods on an artificial dataset, which
consists of a sinusoidal target function and strong noise. The figure compares
the learned model of KRR and GPR based on a ExpSineSquared kernel, which is
suited for learning periodic functions. The kernel's hyperparameters control
the smoothness (l) and periodicity of the kernel (p). Moreover, the noise level
of the data is learned explicitly by GPR by an additional WhiteKernel component
in the kernel and by the regularization parameter alpha of KRR.
The figure shows that both methods learn reasonable models of the target
function. GPR correctly identifies the periodicity of the function to be
roughly 2*pi (6.28), while KRR chooses the doubled periodicity 4*pi. Besides
that, GPR provides reasonable confidence bounds on the prediction which are not
available for KRR. A major difference between the two methods is the time
required for fitting and predicting: while fitting KRR is fast in principle,
the grid-search for hyperparameter optimization scales exponentially with the
number of hyperparameters ("curse of dimensionality"). The gradient-based
optimization of the parameters in GPR does not suffer from this exponential
scaling and is thus considerable faster on this example with 3-dimensional
hyperparameter space. The time for predicting is similar; however, generating
the variance of the predictive distribution of GPR takes considerable longer
than just predicting the mean.
"""
print(__doc__)
# Authors: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD 3 clause
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.kernel_ridge import KernelRidge
from sklearn.model_selection import GridSearchCV
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import WhiteKernel, ExpSineSquared
rng = np.random.RandomState(0)
# Generate sample data
X = 15 * rng.rand(100, 1)
y = np.sin(X).ravel()
y += 3 * (0.5 - rng.rand(X.shape[0])) # add noise
# Fit KernelRidge with parameter selection based on 5-fold cross validation
param_grid = {"alpha": [1e0, 1e-1, 1e-2, 1e-3],
"kernel": [ExpSineSquared(l, p)
for l in np.logspace(-2, 2, 10)
for p in np.logspace(0, 2, 10)]}
kr = GridSearchCV(KernelRidge(), cv=5, param_grid=param_grid)
stime = time.time()
kr.fit(X, y)
print("Time for KRR fitting: %.3f" % (time.time() - stime))
gp_kernel = ExpSineSquared(1.0, 5.0, periodicity_bounds=(1e-2, 1e1)) \
+ WhiteKernel(1e-1)
gpr = GaussianProcessRegressor(kernel=gp_kernel)
stime = time.time()
gpr.fit(X, y)
print("Time for GPR fitting: %.3f" % (time.time() - stime))
# Predict using kernel ridge
X_plot = np.linspace(0, 20, 10000)[:, None]
stime = time.time()
y_kr = kr.predict(X_plot)
print("Time for KRR prediction: %.3f" % (time.time() - stime))
# Predict using kernel ridge
stime = time.time()
y_gpr = gpr.predict(X_plot, return_std=False)
print("Time for GPR prediction: %.3f" % (time.time() - stime))
stime = time.time()
y_gpr, y_std = gpr.predict(X_plot, return_std=True)
print("Time for GPR prediction with standard-deviation: %.3f"
% (time.time() - stime))
# Plot results
plt.figure(figsize=(10, 5))
lw = 2
plt.scatter(X, y, c='k', label='data')
plt.plot(X_plot, np.sin(X_plot), color='navy', lw=lw, label='True')
plt.plot(X_plot, y_kr, color='turquoise', lw=lw,
label='KRR (%s)' % kr.best_params_)
plt.plot(X_plot, y_gpr, color='darkorange', lw=lw,
label='GPR (%s)' % gpr.kernel_)
plt.fill_between(X_plot[:, 0], y_gpr - y_std, y_gpr + y_std, color='darkorange',
alpha=0.2)
plt.xlabel('data')
plt.ylabel('target')
plt.xlim(0, 20)
plt.ylim(-4, 4)
plt.title('GPR versus Kernel Ridge')
plt.legend(loc="best", scatterpoints=1, prop={'size': 8})
plt.show()
| bsd-3-clause |
mne-tools/mne-tools.github.io | stable/_downloads/8d8aff0c4421b0723c62746f25bd3a16/decoding_rsa_sgskip.py | 15 | 6873 | """
.. _ex-rsa-noplot:
====================================
Representational Similarity Analysis
====================================
Representational Similarity Analysis is used to perform summary statistics
on supervised classifications where the number of classes is relatively high.
It consists in characterizing the structure of the confusion matrix to infer
the similarity between brain responses and serves as a proxy for characterizing
the space of mental representations
:footcite:`Shepard1980,LaaksoCottrell2000,KriegeskorteEtAl2008`.
In this example, we perform RSA on responses to 24 object images (among
a list of 92 images). Subjects were presented with images of human, animal
and inanimate objects :footcite:`CichyEtAl2014`. Here we use the 24 unique
images of faces and body parts.
.. note:: this example will download a very large (~6GB) file, so we will not
build the images below.
"""
# Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Jaakko Leppakangas <jaeilepp@student.jyu.fi>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
from pandas import read_csv
import matplotlib.pyplot as plt
from sklearn.model_selection import StratifiedKFold
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.manifold import MDS
import mne
from mne.io import read_raw_fif, concatenate_raws
from mne.datasets import visual_92_categories
print(__doc__)
data_path = visual_92_categories.data_path()
# Define stimulus - trigger mapping
fname = op.join(data_path, 'visual_stimuli.csv')
conds = read_csv(fname)
print(conds.head(5))
##############################################################################
# Let's restrict the number of conditions to speed up computation
max_trigger = 24
conds = conds[:max_trigger] # take only the first 24 rows
##############################################################################
# Define stimulus - trigger mapping
conditions = []
for c in conds.values:
cond_tags = list(c[:2])
cond_tags += [('not-' if i == 0 else '') + conds.columns[k]
for k, i in enumerate(c[2:], 2)]
conditions.append('/'.join(map(str, cond_tags)))
print(conditions[:10])
##############################################################################
# Let's make the event_id dictionary
event_id = dict(zip(conditions, conds.trigger + 1))
event_id['0/human bodypart/human/not-face/animal/natural']
##############################################################################
# Read MEG data
n_runs = 4 # 4 for full data (use less to speed up computations)
fname = op.join(data_path, 'sample_subject_%i_tsss_mc.fif')
raws = [read_raw_fif(fname % block, verbose='error')
for block in range(n_runs)] # ignore filename warnings
raw = concatenate_raws(raws)
events = mne.find_events(raw, min_duration=.002)
events = events[events[:, 2] <= max_trigger]
##############################################################################
# Epoch data
picks = mne.pick_types(raw.info, meg=True)
epochs = mne.Epochs(raw, events=events, event_id=event_id, baseline=None,
picks=picks, tmin=-.1, tmax=.500, preload=True)
##############################################################################
# Let's plot some conditions
epochs['face'].average().plot()
epochs['not-face'].average().plot()
##############################################################################
# Representational Similarity Analysis (RSA) is a neuroimaging-specific
# appelation to refer to statistics applied to the confusion matrix
# also referred to as the representational dissimilarity matrices (RDM).
#
# Compared to the approach from Cichy et al. we'll use a multiclass
# classifier (Multinomial Logistic Regression) while the paper uses
# all pairwise binary classification task to make the RDM.
# Also we use here the ROC-AUC as performance metric while the
# paper uses accuracy. Finally here for the sake of time we use
# RSA on a window of data while Cichy et al. did it for all time
# instants separately.
# Classify using the average signal in the window 50ms to 300ms
# to focus the classifier on the time interval with best SNR.
clf = make_pipeline(StandardScaler(),
LogisticRegression(C=1, solver='liblinear',
multi_class='auto'))
X = epochs.copy().crop(0.05, 0.3).get_data().mean(axis=2)
y = epochs.events[:, 2]
classes = set(y)
cv = StratifiedKFold(n_splits=5, random_state=0, shuffle=True)
# Compute confusion matrix for each cross-validation fold
y_pred = np.zeros((len(y), len(classes)))
for train, test in cv.split(X, y):
# Fit
clf.fit(X[train], y[train])
# Probabilistic prediction (necessary for ROC-AUC scoring metric)
y_pred[test] = clf.predict_proba(X[test])
##############################################################################
# Compute confusion matrix using ROC-AUC
confusion = np.zeros((len(classes), len(classes)))
for ii, train_class in enumerate(classes):
for jj in range(ii, len(classes)):
confusion[ii, jj] = roc_auc_score(y == train_class, y_pred[:, jj])
confusion[jj, ii] = confusion[ii, jj]
##############################################################################
# Plot
labels = [''] * 5 + ['face'] + [''] * 11 + ['bodypart'] + [''] * 6
fig, ax = plt.subplots(1)
im = ax.matshow(confusion, cmap='RdBu_r', clim=[0.3, 0.7])
ax.set_yticks(range(len(classes)))
ax.set_yticklabels(labels)
ax.set_xticks(range(len(classes)))
ax.set_xticklabels(labels, rotation=40, ha='left')
ax.axhline(11.5, color='k')
ax.axvline(11.5, color='k')
plt.colorbar(im)
plt.tight_layout()
plt.show()
##############################################################################
# Confusion matrix related to mental representations have been historically
# summarized with dimensionality reduction using multi-dimensional scaling [1].
# See how the face samples cluster together.
fig, ax = plt.subplots(1)
mds = MDS(2, random_state=0, dissimilarity='precomputed')
chance = 0.5
summary = mds.fit_transform(chance - confusion)
cmap = plt.get_cmap('rainbow')
colors = ['r', 'b']
names = list(conds['condition'].values)
for color, name in zip(colors, set(names)):
sel = np.where([this_name == name for this_name in names])[0]
size = 500 if name == 'human face' else 100
ax.scatter(summary[sel, 0], summary[sel, 1], s=size,
facecolors=color, label=name, edgecolors='k')
ax.axis('off')
ax.legend(loc='lower right', scatterpoints=1, ncol=2)
plt.tight_layout()
plt.show()
##############################################################################
# References
# ----------
# .. footbibliography::
| bsd-3-clause |
kyleabeauchamp/HMCNotes | code/correctness/old/test_john_hmc.py | 1 | 1318 | import lb_loader
import pandas as pd
import simtk.openmm.app as app
import numpy as np
import simtk.openmm as mm
from simtk import unit as u
from openmmtools import hmc_integrators, testsystems
sysname = "ljbox"
system, positions, groups, temperature, timestep = lb_loader.load(sysname)
integrator = hmc_integrators.GHMCIntegratorOneStep(temperature, timestep=8*u.femtoseconds)
context = lb_loader.build(system, integrator, positions, temperature)
integrator.step(50000)
positions = context.getState(getPositions=True).getPositions()
collision_rate = 1.0 / u.picoseconds
n_steps = 25
Neff_cutoff = 2000.
grid = []
for itype in ["GHMCIntegratorOneStep"]:
for timestep_factor in [1.0, 2.0, 4.0]:
d = dict(itype=itype, timestep=timestep / timestep_factor)
grid.append(d)
for settings in grid:
itype = settings.pop("itype")
timestep = settings["timestep"]
integrator = hmc_integrators.GHMCIntegratorOneStep(temperature, timestep=timestep)
context = lb_loader.build(system, integrator, positions, temperature)
filename = "./data/%s_%s_%.3f_%d.csv" % (sysname, itype, timestep / u.femtoseconds, collision_rate * u.picoseconds)
print(filename)
data, start, g, Neff = lb_loader.converge(context, n_steps=n_steps, Neff_cutoff=Neff_cutoff)
data.to_csv(filename)
| gpl-2.0 |
yunfeilu/scikit-learn | examples/applications/plot_species_distribution_modeling.py | 254 | 7434 | """
=============================
Species distribution modeling
=============================
Modeling species' geographic distributions is an important
problem in conservation biology. In this example we
model the geographic distribution of two south american
mammals given past observations and 14 environmental
variables. Since we have only positive examples (there are
no unsuccessful observations), we cast this problem as a
density estimation problem and use the `OneClassSVM` provided
by the package `sklearn.svm` as our modeling tool.
The dataset is provided by Phillips et. al. (2006).
If available, the example uses
`basemap <http://matplotlib.sourceforge.net/basemap/doc/html/>`_
to plot the coast lines and national boundaries of South America.
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_ ,
the Brown-throated Sloth.
- `"Microryzomys minutus"
<http://www.iucnredlist.org/apps/redlist/details/13408/0>`_ ,
also known as the Forest Small Rice Rat, a rodent that lives in Peru,
Colombia, Ecuador, Peru, and Venezuela.
References
----------
* `"Maximum entropy modeling of species geographic distributions"
<http://www.cs.princeton.edu/~schapire/papers/ecolmod.pdf>`_
S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
190:231-259, 2006.
"""
# Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Jake Vanderplas <vanderplas@astro.washington.edu>
#
# License: BSD 3 clause
from __future__ import print_function
from time import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets.base import Bunch
from sklearn.datasets import fetch_species_distributions
from sklearn.datasets.species_distributions import construct_grids
from sklearn import svm, metrics
# if basemap is available, we'll use it.
# otherwise, we'll improvise later...
try:
from mpl_toolkits.basemap import Basemap
basemap = True
except ImportError:
basemap = False
print(__doc__)
def create_species_bunch(species_name, train, test, coverages, xgrid, ygrid):
"""Create a bunch with information about a particular organism
This will use the test/train record arrays to extract the
data specific to the given species name.
"""
bunch = Bunch(name=' '.join(species_name.split("_")[:2]))
species_name = species_name.encode('ascii')
points = dict(test=test, train=train)
for label, pts in points.items():
# choose points associated with the desired species
pts = pts[pts['species'] == species_name]
bunch['pts_%s' % label] = pts
# determine coverage values for each of the training & testing points
ix = np.searchsorted(xgrid, pts['dd long'])
iy = np.searchsorted(ygrid, pts['dd lat'])
bunch['cov_%s' % label] = coverages[:, -iy, ix].T
return bunch
def plot_species_distribution(species=("bradypus_variegatus_0",
"microryzomys_minutus_0")):
"""
Plot the species distribution.
"""
if len(species) > 2:
print("Note: when more than two species are provided,"
" only the first two will be used")
t0 = time()
# Load the compressed data
data = fetch_species_distributions()
# Set up the data grid
xgrid, ygrid = construct_grids(data)
# The grid in x,y coordinates
X, Y = np.meshgrid(xgrid, ygrid[::-1])
# create a bunch for each species
BV_bunch = create_species_bunch(species[0],
data.train, data.test,
data.coverages, xgrid, ygrid)
MM_bunch = create_species_bunch(species[1],
data.train, data.test,
data.coverages, xgrid, ygrid)
# background points (grid coordinates) for evaluation
np.random.seed(13)
background_points = np.c_[np.random.randint(low=0, high=data.Ny,
size=10000),
np.random.randint(low=0, high=data.Nx,
size=10000)].T
# We'll make use of the fact that coverages[6] has measurements at all
# land points. This will help us decide between land and water.
land_reference = data.coverages[6]
# Fit, predict, and plot for each species.
for i, species in enumerate([BV_bunch, MM_bunch]):
print("_" * 80)
print("Modeling distribution of species '%s'" % species.name)
# Standardize features
mean = species.cov_train.mean(axis=0)
std = species.cov_train.std(axis=0)
train_cover_std = (species.cov_train - mean) / std
# Fit OneClassSVM
print(" - fit OneClassSVM ... ", end='')
clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.5)
clf.fit(train_cover_std)
print("done.")
# Plot map of South America
plt.subplot(1, 2, i + 1)
if basemap:
print(" - plot coastlines using basemap")
m = Basemap(projection='cyl', llcrnrlat=Y.min(),
urcrnrlat=Y.max(), llcrnrlon=X.min(),
urcrnrlon=X.max(), resolution='c')
m.drawcoastlines()
m.drawcountries()
else:
print(" - plot coastlines from coverage")
plt.contour(X, Y, land_reference,
levels=[-9999], colors="k",
linestyles="solid")
plt.xticks([])
plt.yticks([])
print(" - predict species distribution")
# Predict species distribution using the training data
Z = np.ones((data.Ny, data.Nx), dtype=np.float64)
# We'll predict only for the land points.
idx = np.where(land_reference > -9999)
coverages_land = data.coverages[:, idx[0], idx[1]].T
pred = clf.decision_function((coverages_land - mean) / std)[:, 0]
Z *= pred.min()
Z[idx[0], idx[1]] = pred
levels = np.linspace(Z.min(), Z.max(), 25)
Z[land_reference == -9999] = -9999
# plot contours of the prediction
plt.contourf(X, Y, Z, levels=levels, cmap=plt.cm.Reds)
plt.colorbar(format='%.2f')
# scatter training/testing points
plt.scatter(species.pts_train['dd long'], species.pts_train['dd lat'],
s=2 ** 2, c='black',
marker='^', label='train')
plt.scatter(species.pts_test['dd long'], species.pts_test['dd lat'],
s=2 ** 2, c='black',
marker='x', label='test')
plt.legend()
plt.title(species.name)
plt.axis('equal')
# Compute AUC with regards to background points
pred_background = Z[background_points[0], background_points[1]]
pred_test = clf.decision_function((species.cov_test - mean)
/ std)[:, 0]
scores = np.r_[pred_test, pred_background]
y = np.r_[np.ones(pred_test.shape), np.zeros(pred_background.shape)]
fpr, tpr, thresholds = metrics.roc_curve(y, scores)
roc_auc = metrics.auc(fpr, tpr)
plt.text(-35, -70, "AUC: %.3f" % roc_auc, ha="right")
print("\n Area under the ROC curve : %f" % roc_auc)
print("\ntime elapsed: %.2fs" % (time() - t0))
plot_species_distribution()
plt.show()
| bsd-3-clause |
lenovor/scikit-learn | sklearn/__check_build/__init__.py | 345 | 1671 | """ Module to give helpful messages to the user that did not
compile the scikit properly.
"""
import os
INPLACE_MSG = """
It appears that you are importing a local scikit-learn source tree. For
this, you need to have an inplace install. Maybe you are in the source
directory and you need to try from another location."""
STANDARD_MSG = """
If you have used an installer, please check that it is suited for your
Python version, your operating system and your platform."""
def raise_build_error(e):
# Raise a comprehensible error and list the contents of the
# directory to help debugging on the mailing list.
local_dir = os.path.split(__file__)[0]
msg = STANDARD_MSG
if local_dir == "sklearn/__check_build":
# Picking up the local install: this will work only if the
# install is an 'inplace build'
msg = INPLACE_MSG
dir_content = list()
for i, filename in enumerate(os.listdir(local_dir)):
if ((i + 1) % 3):
dir_content.append(filename.ljust(26))
else:
dir_content.append(filename + '\n')
raise ImportError("""%s
___________________________________________________________________________
Contents of %s:
%s
___________________________________________________________________________
It seems that scikit-learn has not been built correctly.
If you have installed scikit-learn from source, please do not forget
to build the package before using it: run `python setup.py install` or
`make` in the source directory.
%s""" % (e, local_dir, ''.join(dir_content).strip(), msg))
try:
from ._check_build import check_build
except ImportError as e:
raise_build_error(e)
| bsd-3-clause |
Jacob-Barhak/SharingDiseaseModels | Example1.py | 1 | 1208 | #Example 1: Simple Markov Model
#
#
#The Model uses 2 disease states: Alive, Dead
#
#The Yearly Probability of transition between state Alive and State Dead is: 0.05
#
#Initial Conditions: 100 people start in state Alive, None are Dead
#
#Output requested: Amount of people in each state for years 1-10.
import tellurium as te
import matplotlib.pyplot as plt
r = te.loada ('''
J0: A -> D; A*0.05
A = 100
D = 0
''')
# This will create the SBML XML file
te.saveToFile ('Example1.xml', r.getSBML())
r.setIntegrator('gillespie')
r.integrator.variable_step_size = True
r.getIntegrator().setValue('seed', 0)
result = r.simulate(0,10)
axis1 = plt.subplot(111)
axis1.plot (result[:,0], result[:,1], linestyle='-', linewidth=2, color='r')
axis1.plot (result[:,0], result[:,2], linestyle='--', linewidth=2, color='b')
plt.title("Example 1", fontsize="xx-large")
plt.xlabel("Time", fontsize="xx-large")
plt.ylabel("Individuals", fontsize="xx-large")
box = axis1.get_position()
axis1.set_position([box.x0, box.y0 + box.height*0.2, box.width , box.height*0.8])
# Put a legend to the right of the current axis
axis1.legend(['A', 'D'], loc='upper center', bbox_to_anchor=(0.5, -0.225), ncol=2, fontsize=9)
plt.show()
| gpl-3.0 |
shenzebang/scikit-learn | examples/linear_model/plot_sgd_weighted_samples.py | 344 | 1458 | """
=====================
SGD: Weighted samples
=====================
Plot decision function of a weighted dataset, where the size of points
is proportional to its weight.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
# we create 20 points
np.random.seed(0)
X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]
y = [1] * 10 + [-1] * 10
sample_weight = 100 * np.abs(np.random.randn(20))
# and assign a bigger weight to the last 10 samples
sample_weight[:10] *= 10
# plot the weighted data points
xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))
plt.figure()
plt.scatter(X[:, 0], X[:, 1], c=y, s=sample_weight, alpha=0.9,
cmap=plt.cm.bone)
## fit the unweighted model
clf = linear_model.SGDClassifier(alpha=0.01, n_iter=100)
clf.fit(X, y)
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
no_weights = plt.contour(xx, yy, Z, levels=[0], linestyles=['solid'])
## fit the weighted model
clf = linear_model.SGDClassifier(alpha=0.01, n_iter=100)
clf.fit(X, y, sample_weight=sample_weight)
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
samples_weights = plt.contour(xx, yy, Z, levels=[0], linestyles=['dashed'])
plt.legend([no_weights.collections[0], samples_weights.collections[0]],
["no weights", "with weights"], loc="lower left")
plt.xticks(())
plt.yticks(())
plt.show()
| bsd-3-clause |
sergeyk/vislab | vislab/vw3.py | 4 | 22400 | import glob
import numpy as np
import re
import subprocess
import time
import logging
import os
import pandas as pd
import sklearn.grid_search
import vislab.util
vw_cmd = "vw --quiet --compressed"
def _feat_for_vw(id_, feat_name, feat, decimals=6):
"""
Parameters
----------
id_: string
Identifying this example.
feat_name: string
To be used as the vw namespace.
feat: ndarray
Must be of either bool, int, or float dtype.
decimals: int [6]
Values will be rounded to this number of decimals.
"""
if feat.dtype == 'bool':
s = ' '.join(str(x) for x in np.where(feat)[0])
elif feat.dtype in [float, np.float32, int]:
# Round to the specified number of decimals.
feat = np.around(feat, decimals)
# Set things that are almost 0 to 0.
tolerance = 10 ** -decimals
feat[np.abs(feat) <= tolerance] = 0
s = ' '.join(
'{}:{}'.format(i, x)
for i, x in zip(np.arange(feat.size)[feat != 0], feat[feat != 0])
)
else:
print feat
raise ValueError('Unsupported feature dtype.')
return ' id{} |{} {}'.format(id_, feat_name, s)
def write_data_in_vw_format(feat_df, feat_name, output_filename):
"""
Parameters
----------
feat_df: pandas.DataFrame
The indices will be written out in the data file.
The DataFrame should either:
- contain one ndarray per row (1 column).
- or have the feature values in the columns (> 1 columns).
feat_name: string
To use as the VW namespace for this feature.
output_filename: string
Make sure it's in a writeable directory.
If it ends in '.gz', then the file will be gzipped.
"""
fn_name = 'write_data_in_vw_format'
# Establish what form the features are in.
ndarray_feat = len(feat_df.columns) == 1
# If the filename ends in gzip, we will output to the filename
# without the '.gz', because the gzip command at the end of this
# function will modify the output file in place and append '.gz'.
gzip = False
if output_filename.endswith('.gz'):
output_filename = output_filename[:-3]
gzip = True
t = time.time()
with open(output_filename, 'w') as f:
for ix, row in feat_df.iterrows():
if ndarray_feat:
feat = row.values[0]
# Check if there is only a single value here, and wrap if so
try:
len(feat)
except:
feat = np.array([feat])
else:
feat = row.values
f.write(_feat_for_vw(ix, feat_name, feat) + '\n')
logging.info('{}: forming and writing lines took {:.3f} s'.format(
fn_name, time.time() - t))
if gzip:
t = time.time()
retcode = subprocess.call(['gzip', '-f', output_filename])
assert(retcode == 0)
logging.info('{}: gzip took {:.3f} s'.format(fn_name, time.time() - t))
def _cache_cmd(
label_df_filename, feat_filenames, output_dirname, num_labels,
bit_precision=18, verbose=False, force=False):
"""
Run the labels and feature data through VW once to output to cache.
Parameters
----------
label_df_filename: string
A DataFrame in pickle or HDF5 format whose index is image ids.
feat_filenames: sequence of string
These features will be horizontally concatenated, so they should
have different namespaces.
These files must end in '.txt' or '.gz'.
output_dirname: string
num_labels: int
bit_precision: int [18]
Number of bits used in the hashing function.
If the product of features and classes (for OAA mode) is greater
than 260K, should increase from the default of 18.
verbose: bool [False]
force: bool [False]
"""
assert(os.path.exists(label_df_filename))
cache_filename = output_dirname + '/cache.vw'
cache_preview_filename = output_dirname + '/cache_preview.txt'
if not force and os.path.exists(cache_filename):
print("Cache file exists, not doing anything.")
return None, None
# Concatenate the feature files horizontally.
for f in feat_filenames:
assert(os.path.exists(f))
cats = [
'<(gzcat {})'.format(f) if f.endswith('.gz') else '<(cat {})'.format(f)
for f in feat_filenames
]
paste_cmd = "paste -d'\\0' {}".format(' '.join(cats))
# The output of the above is piped through a filter that selects by id.
vw_filter_filename = vislab.repo_dirname + '/vw_filter.py'
filter_cmd = "python {} {}".format(vw_filter_filename, label_df_filename)
# Output a few lines of what is piped into VW to a preview file.
head_cmd = 'head -n 10'
cache_preview_cmd = "{} | {} | {} > {}".format(
paste_cmd, filter_cmd, head_cmd, cache_preview_filename)
# Run all data through VW to cache it.
cache_cmd = "{} | {} | {}".format(
paste_cmd, filter_cmd, vw_cmd)
cache_cmd += " -k --cache_file {} --bit_precision {}".format(
cache_filename, bit_precision)
if num_labels > 2:
cache_cmd += ' --oaa {}'.format(num_labels)
return cache_cmd, cache_preview_cmd
def _get_feat_filenames(feat_names, feat_dirname):
"""
Get actual feature filenames and confirm they exist.
"""
feat_filenames = []
for feat_name in feat_names:
normal_name = feat_dirname + '/' + feat_name + '.txt'
gz_name = normal_name + '.gz'
if os.path.exists(gz_name):
feat_filenames.append(gz_name)
elif os.path.exists(normal_name):
feat_filenames.append(normal_name)
else:
raise Exception("Feature filename not found for '{}'".format(
feat_name))
return feat_filenames
def _train_vw_cmd(
setting, dirname, num_labels, bit_precision, from_model=None):
"""
Return command to train VW.
Parameters
----------
setting: dict
Parameters for VW.
dirname: string
Directory containing cached data.
num_labels: int
from_model: string or None [None]
If given, begins training from this model.
"""
model_filename = '{}/{}_model.vw'.format(
dirname, _setting_to_name(setting))
# The basic command.
cmd = vw_cmd + " --cache_file={}/cache.vw -f {}".format(
dirname, model_filename)
cmd += " --l1={} --l2={} --passes={} --loss_function={}".format(
setting['l1'], setting['l2'], setting['num_passes'], setting['loss'])
cmd += " --bit_precision {}".format(bit_precision)
if 'quadratic' in setting:
cmd += ' -q {}'.format(setting['quadratic'])
if num_labels < 0:
assert(setting['loss'] not in ['hinge', 'logistic'])
if num_labels > 2 and from_model is None:
cmd += ' --oaa {}'.format(num_labels)
# If we are training from scratch, then we will use all data.
if from_model is None:
cmd += " --holdout_off --save_resume"
# If we are training with the val data starting with the best model,
# then we turn on early termination and set the holdout fraction to
# 1/6, so that we don't overfit.
else:
cmd += " --early_terminate=3 --holdout_period=6 -i {}".format(
from_model)
return cmd
def _pred_vw_cmd(setting, model_dirname, pred_dirname):
"""
Return command to predict with a VW model.
It is important to output raw predictions here.
Parameters
----------
setting: dict
VW settings.
model_dirname: string
Directory containing model file with the given settings.
pred_dirname: string
Directory containing cache files. Predictions will be output
here as well.
"""
name = _setting_to_name(setting)
model_filename = '{}/{}_model.vw'.format(model_dirname, name)
assert(os.path.exists(model_filename))
pred_filename = '{}/{}_pred.txt'.format(pred_dirname, name)
cmd = vw_cmd + " -t -i {} --cache_file={}/cache.vw -r {}".format(
model_filename, pred_dirname, pred_filename)
if 'quadratic' in setting:
cmd += ' -q {}'.format(setting['quadratic'])
return cmd
def _setting_to_name(setting):
return '_'.join(
'{}_{}'.format(k, v)
for k, v in sorted(setting.iteritems())
)
def _name_to_setting(name):
d = dict(
(key, re.search('{}_(.+?)(_|$)'.format(key), name).groups()[0])
for key in ['loss', 'l1', 'l2', 'num_passes']
)
q = re.search('quadratic_(.+?)(_|$)', name)
if q is not None:
d['quadratic'] = q.groups()[0]
return d
def _train_with_val(
dataset, output_dirnames, settings, bit_precision,
num_workers=1, verbose=False):
#train_df = dataset['train_df']
val_df = dataset['val_df']
test_df = dataset['test_df']
num_labels = dataset['num_labels']
train_dir = output_dirnames['train']
val_dir = output_dirnames['val']
test_dir = output_dirnames['test']
print("Running VW training for {} param settings, {} at a time".format(
len(settings), num_workers))
# Run training commands in parallel.
# Each VW uses at most two cores and very little memory, and so
# can run in parallel with other instances. Since the instances
# read data at roughly the same rate, OS caching should work.
vislab.util.run_through_bash_script(
[
_train_vw_cmd(
setting, train_dir, dataset['num_labels'], bit_precision)
for setting in settings
],
train_dir + '/_train_cmds.sh',
False, num_workers
)
# Run prediction commands on the validation data in parallel.
vislab.util.run_through_bash_script(
[_pred_vw_cmd(setting, train_dir, val_dir) for setting in settings],
val_dir + '/_val_pred_cmds.sh',
False, num_workers
)
# Run prediction commands on the test data in parallel.
vislab.util.run_through_bash_script(
[_pred_vw_cmd(setting, train_dir, test_dir) for setting in settings],
test_dir + '/_test_pred_cmds.sh',
False, num_workers
)
# Load all the model predictions and pick the best settings.
val_scores = []
test_scores = []
for setting in settings:
_, val_score = _score_predict(setting, val_dir, num_labels, val_df)
val_scores.append(val_score)
_, test_score = _score_predict(setting, test_dir, num_labels, test_df)
test_scores.append(test_score)
# test_pred_df = _read_preds(
# '{}/{}_pred.txt'.format(test_dir, _setting_to_name(setting)),
# num_labels, setting['loss'])
# test_scores.append(_score_preds(test_pred_df, num_labels))
df = pd.DataFrame(settings)
df['val_score'] = val_scores
df['test_score'] = test_scores
df_str = df.to_string(
formatters={
'val_score': lambda x: '%.3f' % x,
'test_score': lambda x: '%.3f' % x
}
)
print(df_str)
with open(val_dir + '/_results.txt', 'w') as f:
f.write(df_str)
# In case of ties of best val score, take a random setting.
best_score = df['val_score'].max()
best_ind = np.random.choice(
np.where(df['val_score'] == best_score)[0], 1)[0]
del df['val_score']
del df['test_score']
best_setting = dict(df.iloc[best_ind])
print('Best setting: {}'.format(best_setting))
print('Best score: {:.3f}'.format(best_score))
return best_setting
def cache_files(
dataset, feat_names, feat_dirname, dirname, bit_precision,
num_workers, force=False, name_only=False):
"""
Cache files for splits in the dataset.
"""
vislab.util.makedirs(dirname)
# Get actual feature filenames.
feat_filenames = _get_feat_filenames(feat_names, feat_dirname)
split_names = ['train', 'val', 'test']
output_dirnames = {}
cache_cmds = []
cache_preview_cmds = []
for split_name in split_names:
cache_dirname = '_'.join(feat_names) + \
'_{}'.format(dataset['num_labels'])
output_dirnames[split_name] = vislab.util.makedirs(
'{}/{}/{}'.format(dirname, cache_dirname, split_name))
# If name_only, we only want to get the folder names.
if name_only:
continue
# Save the dataframe with labels for use in filtering examples.
df_filename = dirname + '/{}_df.h5'.format(split_name)
if force or not os.path.exists(df_filename):
dataset[split_name + '_df'].to_hdf(
df_filename, 'df', mode='w')
else:
logging.info("Not writing out DataFrame for {}".format(
split_name))
# Cache data by running it through a filter.
cache_cmd, cache_preview_cmd = _cache_cmd(
df_filename, feat_filenames, output_dirnames[split_name],
dataset['num_labels'], bit_precision, verbose=False, force=force)
if cache_cmd is not None:
cache_cmds.append(cache_cmd)
if cache_preview_cmd is not None:
cache_preview_cmds.append(cache_preview_cmd)
logging.info("Caching data")
t = time.time()
vislab.util.run_through_bash_script(
cache_preview_cmds, dirname + '/_cache_preview_cmds.sh',
verbose=False, num_workers=num_workers)
vislab.util.run_through_bash_script(
cache_cmds, dirname + '/_cache_cmds.sh',
verbose=False, num_workers=num_workers)
logging.info('Caching data took {:.3f} s'.format(time.time() - t))
return output_dirnames
def _predict(setting, source_dirname, target_dirname, gt_df, num_labels):
pred_cmd = _pred_vw_cmd(
setting, source_dirname, target_dirname)
vislab.util.run_through_bash_script(
[pred_cmd], target_dirname + '/_test_cmd.sh', verbose=False)
return _score_predict(setting, target_dirname, num_labels, gt_df)
def _score_predict(setting, dirname, num_labels, gt_df):
pred_filename = '{}/{}_pred.txt'.format(dirname, _setting_to_name(setting))
pred_df = _read_preds(pred_filename, num_labels, setting['loss'])
# For multi-class evaluation, need to have column names.
if num_labels > 2:
pred_df.columns = [
'pred_' + x for x in gt_df.columns
if x not in ['label', 'importance']
]
pred_df = pred_df.join(gt_df)
if num_labels < 0:
metrics = vislab.results.regression_metrics(
pred_df, name='', balanced=False,
with_plot=False, with_print=False)
score = metrics['mse']
elif num_labels == 2:
metrics = vislab.results.binary_metrics(
pred_df, name='', balanced=True,
with_plot=False, with_print=False)
score = metrics['accuracy']
elif num_labels > 2:
metrics = vislab.results.multiclass_metrics(
pred_df, pred_prefix='pred', balanced=True, random_preds=False,
with_plot=False, with_print=False)
score = metrics['accuracy']
else:
raise ValueError("Illegal num_labels")
return pred_df, score
def _read_preds(filename, num_labels, loss_function):
# TODO: should multiclass names be passed into here?
try:
if num_labels > 2:
df = pd.read_csv(filename, sep=' ', index_col=-1, header=None)
df = df.apply(
lambda x: [float(y.split(':')[1]) for y in x], raw=True)
else:
df = pd.read_csv(
filename, sep=' ', index_col=1, header=None, names=['pred'])
except Exception as e:
raise Exception("Could not read predictions: {}".format(e))
df.index = df.index.astype(str)
# If using logistic loss, convert to [-1, 1].
if loss_function == 'logistic':
df = df.apply(lambda x: (2. / (1. + np.exp(-x)) - 1.), raw=True)
return df
class VW(object):
"""
Initialize with a dirname and three DataFrames (train, val, test),
each containing a 'label' column and an 'importance' column.
Additionally, provide sequences of parameters to do grid search over
during training.
TODO:
- take collection_name: store results into mongodb
- can calculate importance here, instead of relying on the dataframe
"""
def __init__(
self, dirname, dataset_name,
num_workers=6, bit_precision=18, num_passes=[25],
loss=['hinge', 'logistic'],
l1=['0', '1e-6', '1e-9'],
l2=['0', '1e-6', '1e-9'],
quadratic=None):
# Actual output directory will have bit_precision info in name,
# because cache files are dependent on the precision.
self.partial_dirname = '{}_b{}'.format(dataset_name, bit_precision)
self.dirname = vislab.util.makedirs(
os.path.join(dirname, self.partial_dirname))
self.bit_precision = bit_precision
self.num_workers = num_workers
# Set the parameter grid, ordering it such that same number of
# passes are ordered together, for more efficient parallelism.
self.param_grid = {
'loss': loss,
'num_passes': num_passes,
'l1': [str(x) for x in l1],
'l2': [str(x) for x in l2]
}
if quadratic is not None:
self.param_grid['quadratic'] = [quadratic]
settings_df = pd.DataFrame(
list(sklearn.grid_search.ParameterGrid(self.param_grid))
).sort(['num_passes', 'loss'])
self.settings = [dict(row) for ind, row in settings_df.iterrows()]
def fit_and_predict(self, dataset, feat_names, feat_dirname, force=False):
"""
Parameters
----------
feat_names: sequence of string
feat_dirname: string
Directory containing VW-format files for the feat_names.
Each file contains features in VW format, and can be gzippd.
Use write_data_in_vw_format() to output these.
dataset: dict
Contains 'train_df', 'val_df', 'test_df' label DataFrames,
and 'num_labels': an int.
"""
# Cache all splits to VW format.
output_dirnames = cache_files(
dataset, feat_names, feat_dirname, self.dirname,
self.bit_precision, self.num_workers, force)
# Train models in a grid search over params.
best_setting = _train_with_val(
dataset, output_dirnames, self.settings, self.bit_precision,
self.num_workers, verbose=False)
# Update the best model with validation data.
print("Updating best VW model with validation data")
best_model_filename = '{}/{}_model.vw'.format(
output_dirnames['train'], _setting_to_name(best_setting))
cmd = _train_vw_cmd(
best_setting, output_dirnames['val'],
dataset['num_labels'], self.bit_precision, best_model_filename
)
cmd_filename = output_dirnames['val'] + '/_train_cmd.sh'
vislab.util.run_through_bash_script(
[cmd], cmd_filename, verbose=False)
# Get predictions from all the splits. Need to predict on train.
print("Running VW prediction on all splits.")
train_pred_df, train_score = _predict(
best_setting, output_dirnames['val'], output_dirnames['train'],
dataset['train_df'], dataset['num_labels'])
val_pred_df, val_score = _predict(
best_setting, output_dirnames['val'], output_dirnames['val'],
dataset['val_df'], dataset['num_labels'])
test_pred_df, test_score = _predict(
best_setting, output_dirnames['val'], output_dirnames['test'],
dataset['test_df'], dataset['num_labels'])
# Combine all predictions into one DataFrame.
train_pred_df['split'] = 'train'
val_pred_df['split'] = 'val'
test_pred_df['split'] = 'test'
pred_df = train_pred_df.append(val_pred_df).append(test_pred_df)
return pred_df, test_score, val_score, train_score
def predict(
self, dataset, source_dataset, source_dirname,
feat_names, feat_dirname, force=False):
"""
Looks for an existing model in the val directory of
source_dataset, and predicts target_dataset.
"""
# Make sure the datasets are compatible.
assert(dataset['num_labels'] == source_dataset['num_labels'])
# Make sure that we find the model we need in paths derived
# from the source dataset.
# Get the dirnames of the source_dataset.
actual_source_dirname = '{}_b{}'.format(
source_dirname, self.bit_precision)
source_output_dirnames = cache_files(
source_dataset, feat_names, feat_dirname, actual_source_dirname,
self.bit_precision, self.num_workers, force=False, name_only=True)
model_filenames = glob.glob(
'{}/*_model.vw'.format(source_output_dirnames['val']))
assert(len(model_filenames) == 1)
best_name = re.search('/(.+)_model.vw', model_filenames[0]).groups()[0]
best_setting = _name_to_setting(best_name)
# Cache all splits of the target dataset to VW format.
output_dirnames = cache_files(
dataset, feat_names, feat_dirname, self.dirname,
self.bit_precision, self.num_workers, force)
print("Running VW prediction on all splits.")
train_pred_df, train_score = _predict(
best_setting, source_output_dirnames['val'],
output_dirnames['train'],
dataset['train_df'], dataset['num_labels'])
val_pred_df, val_score = _predict(
best_setting, source_output_dirnames['val'],
output_dirnames['val'],
dataset['val_df'], dataset['num_labels'])
test_pred_df, test_score = _predict(
best_setting, source_output_dirnames['val'],
output_dirnames['test'],
dataset['test_df'], dataset['num_labels'])
# Combine all predictions into one DataFrame.
train_pred_df['split'] = 'train'
val_pred_df['split'] = 'val'
test_pred_df['split'] = 'test'
pred_df = train_pred_df.append(val_pred_df).append(test_pred_df)
return pred_df, test_score, val_score, train_score
| bsd-2-clause |
fy2462/apollo | modules/tools/calibration/plot_grid.py | 2 | 1897 | #!/usr/bin/env python
###############################################################################
# Copyright 2017 The Apollo Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
###############################################################################
import math
import sys
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm as cmx
from matplotlib import colors as mcolors
markers = [
"o", "v", "^", "<", ">", "1", "2", "3", "4", "8", "s", "p", "*", "+", "x",
"d", "|", "_"
]
if len(sys.argv) < 2:
print "usage: python plot_results.py result.csv"
sys.exit()
fn = sys.argv[1]
speed_table = {}
f = open(fn, 'r')
for line in f:
items = line.split(',')
cmd = round(float(items[0]))
speed = float(items[1])
acc = round(float(items[2]), 2)
if speed in speed_table:
cmd_table = speed_table[speed]
if cmd in cmd_table:
cmd_table[cmd].append(acc)
else:
cmd_table[cmd] = [acc]
else:
cmd_table = {}
cmd_table[cmd] = [acc]
speed_table[speed] = cmd_table
f.close()
for speed, cmd_dict in speed_table.items():
speed_list = []
acc_list = []
for cmd, accs in cmd_dict.items():
for acc in accs:
speed_list.append(speed)
acc_list.append(acc)
plt.plot(speed_list, acc_list, 'b.')
plt.show()
| apache-2.0 |
whbpt/pepture | plot.py | 1 | 1500 | #!/usr/bin/env python
from __future__ import print_function
#import keras
#from keras.datasets import mnist
#from keras.models import Sequential
#from keras.layers import Dense, Dropout, Flatten
#from keras.layers import Conv2D, MaxPooling2D
#from keras import backend as K
import os, random
import numpy as np
from peptotensor import *
from loaddata import *
#from pyheatmap.heatmap import HeatMap
from matplotlib import pyplot as PLT
from matplotlib import cm as CM
from matplotlib import axes
############################dealing with peptide to images
def matrix_map(x_train,name):
sum_=x_train[0]
# for line in x_train[1:]:
# sum_+=line
train_matrix=open(name,'w')
for line in sum_[0:21]:
for ll in line:
train_matrix.write(str(float(ll))+" ")
#train_matrix.write(str(float(ll)/float(np.sum(line)))+" ")
train_matrix.write('\n')
train_matrix.close()
#(train,validation,test,num_classes)=loadmulticlassification()
(train,validation,test,num_classes)=loadtestdata()
#(train,validation,test,num_classes)=loadbinaryclassification()
#(x_train, y_train)=peptoblosum(train)
#(x_test, y_test)=peptoblosum(test)
(x_train, y_train)=peptovec(train)
(x_test, y_test)=peptovec(test)
list_name=['tests/a.dat','tests/b.dat','tests/c.dat']
a_list=[]
b_list=[]
c_list=[]
list_list=[a_list,b_list,c_list]
for i in range(0,len(x_train)):
for j in range(0,num_classes):
if int(y_train[i])==j:
list_list[j].append(x_train[i])
for i in range(0,num_classes):
matrix_map(list_list[i],list_name[i])
| mit |
CreativeMachinesLab/aracna | RobotPi/SVMStrategy.py | 2 | 12810 | #! /usr/bin/env python
#import math, pdb, sys
#from numpy import *
#from numpy.linalg import *
#import random
#from copy import copy
from random import choice
from numpy import array, random, ones, zeros, sin, vstack, hstack, argmax, diag, linalg, dot, exp
#from sg import sg # Import shogun
import string, os
import pickle
import pdb
from Strategy import Strategy, OneStepStrategy
from util import *
from SineModel import SineModel5
# NOT USING THIS ANY MORE
#
# '''
# A strategy that uses the Support Vector Machine regression to
# guess which parameter vector would be good to try next. Requires
# the installation of "pysvmlight", an interface to SVM Light by
# Thorsten Joachims (http://svmlight.joachims.org/). pysvmlight is
# available here:
#
# http://bitbucket.org/wcauchois/pysvmlight
# '''
class SVMLearningStrategy(OneStepStrategy):
'''
A strategy that uses the Support Vector Machine regression to
guess which parameter vector would be good to try next. Requires
the installation of shogun-python, a python machine learning
library offering, among other things, access to libsvr.
http://www.shogun-toolbox.org/
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
'''
def __init__(self, *args, **kwargs):
# call this only after popping 'ranges' arg
super(SVMLearningStrategy, self).__init__(*args, **kwargs)
self.pickle = True
if 'pickle' in kwargs:
self.pickle = kwargs['pickle']
if self.pickle:
self.randStr = ''.join(choice(string.ascii_letters) for ii in range(6))
filename = 'svmstate_%s_000.pkl' % (self.randStr)
print 'SVMLearningStrategy saving itself as files like', filename
########################
# Strategy parameters
########################
# Number of points to add to the initial point to try before learning
N_init_neighborhood = 7
# Initial noise to add to the inital point to generate the
# N_init_neighborhood points
initialNoise = .1
# How close to search around the best point, in terms of a
# fraction of the range in each dimension
self.exploreScale = .01
# How many nearby points to check
self.numNearby = 100
# How much random noise to add to the next trial (might
# prevent model collapse)
self.bumpBy = .01
# If the best predicted distance is below this, the bump by
# self.lowDistBump instead of self.bumpBy
self.lowDistThresh = 5.0
# How much random noise to add to the next trial if we're getting nowhere
self.lowDistBump = .1
# Only use the last trainOnLast runs for training, instead of
# training on all data.
self.trainOnLast = 6
########################
# SVR parameters
########################
self.width = 2.1
#self.width = .1
#self.width = .03
#self.C=1.2
#self.C=.5 # tried this a few times, repetitive runs
#self.C=.04
self.C=500. # barely gets training on 8 perfect
self.C=1000. # maybe try this?
# SVR termination criteria
self.tube_epsilon=1e-3
# self.current is defined in Strategy constructor
# Populate toTry with some points
self.toTry = array(self.current)
for ii in range(N_init_neighborhood):
#row = randUniformPoint(self.ranges)
row = randGaussianPoint(self.current, self.ranges, initialNoise)
self.toTry = vstack((self.toTry, row))
self.X = None
self.y = None
def _getNext(self):
'''Get the next point to try. The first few times this will
return a random point near the initialPoint, and after that it
will return the best predicted point by the learning model,
perhaps with some added noise.'''
if self.toTry.shape[0] == 0:
# We're out of things to try. Make more.
# 1. Learn
self.train()
# 2. Try some nearby values
for ii in xrange(self.numNearby):
row = array(randGaussianPoint(self.bestState, self.ranges, self.exploreScale))
if ii == 0:
nearbyPoints = row
else:
nearbyPoints = vstack((nearbyPoints, row))
predictions = self.predict(nearbyPoints)
#print 'nearbyPoints', nearbyPoints
#print 'predicitons', predictions
# 3. Pick best one
iiMax = argmax(predictions)
self.toTry = array([nearbyPoints[iiMax, :]])
# Testing hack... this should be disabled
#self.toTry = array([randUniformPoint(self.ranges)])
# Prints the most promising vector found and its predicted value
print ' value for best', prettyVec(self.bestState),
print 'p: %.2f, a: %.2f' % (self.predict(self.bestState),
self.bestDist)
print ' most promising', prettyVec(self.toTry[0,:]), 'pred: %.2f' % predictions[iiMax]
# 4. (optional) Add a little noise (or a lot of noise)
if predictions[iiMax] < self.lowDistThresh:
extraStr = '+'
bumpBy = self.lowDistBump
else:
extraStr = ' '
bumpBy = self.bumpBy
bump = randGaussianPoint(zeros(self.toTry.shape[1]),
self.ranges, bumpBy, crop=False)
self.toTry += bump
print ' %snoisy promising' % extraStr, prettyVec(self.toTry[0,:]),
print 'pred: %.2f' % self.predict(self.toTry[0,:])
self.current = self.toTry[0,:]
return self.current
def updateResults(self, dist):
'''This must be called for the last point that was handed out!
Once called, we remove the first point from self.toTry and add
it to self.X and add the distance to self.y
'''
# MAKE SURE TO CALL super().updateResults!
super(SVMLearningStrategy, self).updateResults(dist)
dist = float(dist)
justTried = self.toTry[0,:]
self.toTry = self.toTry[1:,:]
if self.X == None:
self.X = justTried
self.y = array(dist)
else:
self.X = vstack((self.X, justTried))
self.y = hstack((self.y, array(dist)))
if self.pickle:
self.saveAndCleanup()
def train(self):
'''Learn a model from self.X and self.y'''
# Constants pulled from <shogun>/examples/documented/python/regression_libsvr.py
size_cache=10
# map each dimension of self.X to [0,1]
unif = phys2unif(self.X, self.ranges)
train_X = unif.T
train_y = self.y
train_X = train_X[:,-self.trainOnLast:]
train_y = train_y[-self.trainOnLast:]
sg('set_features', 'TRAIN', train_X)
#sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, self.width)
sg('set_kernel', 'LINEAR', 'REAL', size_cache)
sg('set_labels', 'TRAIN', train_y)
sg('new_regression', 'LIBSVR')
sg('svr_tube_epsilon', self.tube_epsilon)
sg('c', self.C)
sg('train_regression')
def predict(self, testPoints):
'''Predicts performance using previously learned model.
self.train() must be called before this!'''
if len(testPoints.shape) < 2:
testPoints = array([testPoints])
sg('set_features', 'TEST', phys2unif(testPoints,self.ranges).T)
predictions = sg('classify')
return predictions
def plot(self):
from matplotlib.pyplot import plot, show, savefig, xlabel, ylabel
plot(self.y)
xlabel('Iteration')
ylabel('Fitness (arbitrary units)')
savefig('svm_sim_results.eps')
savefig('svm_sim_results.png')
show()
def saveAndCleanup(self):
filename = 'svmstate_%s_%03d.pkl' % (self.randStr, self.iterations)
ff = open(filename, 'w')
pickle.dump(self, ff)
ff.close()
if self.iterations > 1:
lastIt = self.iterations-1
if (lastIt & (lastIt - 1)) != 0:
# if last one wasn't a power of two
filenameLast = 'svmstate_%s_%03d.pkl' % (self.randStr, lastIt)
os.remove(filenameLast)
def logHeader(self):
filename = 'svmstate_%s_000.pkl' % (self.randStr)
return '# SVMLearningStrategy saving itself as files like %s\n' % filename
#
# [JBY] The following is just code for testing the SVM/SVR learning
# capabilities.
#
def dummyObjective(X):
'''A Dummy objective that can be used to test learning strategies.
Intended to be used for vector X where each X is in or close to
[-1, 1].
'''
# Promote to float64 datatype
X = X * ones(len(X))
ret = 0.0
ret += sum(X)
ret += sum(sin(X/20))
return ret
def dummyObjectiveGauss(X, center, ranges):
'''A Dummy objective that can be used to test learning strategies.
fitness is 100 * GaussianPdf(mean, cov)
'''
covar = diag([((x[1]-x[0])*.2) ** 2 for x in ranges])
cinv = linalg.inv(covar)
return 100. * exp(-dot(dot((X-center), cinv), (X-center)))
def syntheticData(points = 10, dim = 3, fn = dummyObjective):
'''Generate the requested number of data points from a function.
Returns of the form:
[
(<label>, [(<feature>, <value>), ...]),
(<label>, [(<feature>, <value>), ...]),
...
]
'''
ret = []
for ii in range(points):
X = random.randn(dim)
y = fn(X)
ret.append( (y, [(ii+1, X[ii]) for ii in range(len(X))]) )
return ret
def syntheticData2(points = 10, dim = 3, fn = dummyObjective):
'''Generate the requested number of data points from a function.
Returns of the form:
X, y both numpy arrays
'''
ret = []
X = []
y = []
for ii in range(points):
X.append(random.randn(dim))
y.append(fn(X[-1]))
return array(X), array(y)
def main_svmlight():
# copied:
import svmlight
import pdb
training_data = syntheticData(30, 1)
test_data = syntheticData(30, 1)
#training_data = __import__('data').train0
#test_data = __import__('data').test0
print 'HERE 0'
print 'training_data is', training_data
print 'test_data is', test_data
# train a model based on the data
#pdb.set_trace()
print 'HERE 1'
model = svmlight.learn(training_data, type='regression', kernelType=2, verbosity=3)
print 'HERE 2'
# model data can be stored in the same format SVM-Light uses, for interoperability
# with the binaries.
svmlight.write_model(model, 'my_model.dat')
print 'HERE 3'
# classify the test data. this function returns a list of numbers, which represent
# the classifications.
#predictions = svmlight.classify(model, test_data)
pdb.set_trace()
predictions = svmlight.classify(model, training_data)
print 'HERE 4'
for p,example in zip(predictions, test_data):
print 'pred %.8f, actual %.8f' % (p, example[0])
def main_libsvr():
import pdb
train_X, train_y = syntheticData2(30, 1)
test_X, test_y = syntheticData2(20, 1)
train_X = train_X.T
test_X = test_X.T
print 'Trying LibSVR'
size_cache=10
width=2.1
C=1.2
epsilon=1e-5
tube_epsilon=1e-2
from sg import sg
sg('set_features', 'TRAIN', train_X)
sg('set_kernel', 'GAUSSIAN', 'REAL', size_cache, width)
sg('set_labels', 'TRAIN', train_y)
sg('new_regression', 'LIBSVR')
sg('svr_tube_epsilon', tube_epsilon)
sg('c', C)
sg('train_regression')
sg('set_features', 'TEST', test_X)
predictions = sg('classify')
for pred,act in zip(predictions, test_y):
print 'pred %.8f, actual %.8f' % (pred, act)
def main():
random.seed(11)
initialPoint = randUniformPoint(SineModel5.typicalRanges)
strategy = SVMLearningStrategy(initialPoint, ranges = SineModel5.typicalRanges)
center = array([100, 2, 0, 0, 0])
obj = lambda x: dummyObjectiveGauss(x, center, SineModel5.typicalRanges)
for ii in range(120):
print
print
current = strategy.getNext()
print ' %3d trying' % ii, prettyVec(current),
simDist = obj(current)
print simDist
strategy.updateResults(simDist)
strategy.plot()
if __name__ == '__main__':
main()
| gpl-3.0 |
tbabej/astropy | astropy/visualization/wcsaxes/tests/test_display_world_coordinates.py | 4 | 4688 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from ..core import WCSAxes
import matplotlib.pyplot as plt
from matplotlib.backend_bases import KeyEvent
from ....wcs import WCS
from ....extern import six
from ....coordinates import FK5
from ....time import Time
from .test_images import BaseImageTests
class TestDisplayWorldCoordinate(BaseImageTests):
def test_overlay_coords(self, tmpdir):
wcs = WCS(self.msx_header)
fig = plt.figure(figsize=(4, 4))
canvas = fig.canvas
ax = WCSAxes(fig, [0.1, 0.1, 0.8, 0.8], wcs=wcs)
fig.add_axes(ax)
# On some systems, fig.canvas.draw is not enough to force a draw, so we
# save to a temporary file.
fig.savefig(tmpdir.join('test1.png').strpath)
# Testing default displayed world coordinates
string_world = ax._display_world_coords(0.523412, 0.518311)
assert string_world == six.u('0\xb029\'45" -0\xb029\'20" (world)')
# Test pixel coordinates
event1 = KeyEvent('test_pixel_coords', canvas, 'w')
fig.canvas.key_press_event(event1.key, guiEvent=event1)
string_pixel = ax._display_world_coords(0.523412, 0.523412)
assert string_pixel == "0.523412 0.523412 (pixel)"
event3 = KeyEvent('test_pixel_coords', canvas, 'w')
fig.canvas.key_press_event(event3.key, guiEvent=event3)
# Test that it still displays world coords when there are no overlay coords
string_world2 = ax._display_world_coords(0.523412, 0.518311)
assert string_world2 == six.u('0\xb029\'45" -0\xb029\'20" (world)')
overlay = ax.get_coords_overlay('fk5')
# Regression test for bug that caused format to always be taken from
# main world coordinates.
overlay[0].set_major_formatter('d.ddd')
# On some systems, fig.canvas.draw is not enough to force a draw, so we
# save to a temporary file.
fig.savefig(tmpdir.join('test2.png').strpath)
event4 = KeyEvent('test_pixel_coords', canvas, 'w')
fig.canvas.key_press_event(event4.key, guiEvent=event4)
# Test that it displays the overlay world coordinates
string_world3 = ax._display_world_coords(0.523412, 0.518311)
assert string_world3 == six.u('267.176 -28\xb045\'56" (world, overlay 1)')
overlay = ax.get_coords_overlay(FK5())
# Regression test for bug that caused format to always be taken from
# main world coordinates.
overlay[0].set_major_formatter('d.ddd')
# On some systems, fig.canvas.draw is not enough to force a draw, so we
# save to a temporary file.
fig.savefig(tmpdir.join('test3.png').strpath)
event5 = KeyEvent('test_pixel_coords', canvas, 'w')
fig.canvas.key_press_event(event4.key, guiEvent=event4)
# Test that it displays the overlay world coordinates
string_world4 = ax._display_world_coords(0.523412, 0.518311)
assert string_world4 == six.u('267.176 -28\xb045\'56" (world, overlay 2)')
overlay = ax.get_coords_overlay(FK5(equinox=Time("J2030")))
# Regression test for bug that caused format to always be taken from
# main world coordinates.
overlay[0].set_major_formatter('d.ddd')
# On some systems, fig.canvas.draw is not enough to force a draw, so we
# save to a temporary file.
fig.savefig(tmpdir.join('test4.png').strpath)
event6 = KeyEvent('test_pixel_coords', canvas, 'w')
fig.canvas.key_press_event(event5.key, guiEvent=event6)
# Test that it displays the overlay world coordinates
string_world5 = ax._display_world_coords(0.523412, 0.518311)
assert string_world5 == six.u('267.652 -28\xb046\'23" (world, overlay 3)')
def test_cube_coords(self, tmpdir):
wcs = WCS(self.cube_header)
fig = plt.figure(figsize=(4, 4))
canvas = fig.canvas
ax = WCSAxes(fig, [0.1, 0.1, 0.8, 0.8], wcs=wcs, slices=('y', 50, 'x'))
fig.add_axes(ax)
# On some systems, fig.canvas.draw is not enough to force a draw, so we
# save to a temporary file.
fig.savefig(tmpdir.join('test.png').strpath)
# Testing default displayed world coordinates
string_world = ax._display_world_coords(0.523412, 0.518311)
assert string_world == six.u('2563 51\xb043\'01" (world)')
# Test pixel coordinates
event1 = KeyEvent('test_pixel_coords', canvas, 'w')
fig.canvas.key_press_event(event1.key, guiEvent=event1)
string_pixel = ax._display_world_coords(0.523412, 0.523412)
assert string_pixel == "0.523412 0.523412 (pixel)"
| bsd-3-clause |
oscarxie/tushare | tushare/datayes/trading.py | 14 | 4741 | #!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Created on 2015年7月4日
@author: JimmyLiu
@QQ:52799046
"""
from tushare.datayes import vars as vs
import pandas as pd
from pandas.compat import StringIO
class Trading():
def __init__(self, client):
self.client = client
def dy_market_tickRT(self, securityID='000001.XSHG,000001.XSHE', field=vs.TICK_RT_DEFAULT_COLS):
"""
获取最新市场信息快照
获取一只或多只证券最新Level1股票信息。
输入一只或多只证券代码，如000001.XSHG (上证指数） 或000001.XSHE（平安银行），
还有所选字段， 得到证券的最新交易快照。
证券可以是股票，指数， 部分债券或 基金。
getTickRTSnapshot
"""
code, result = self.client.getData(vs.TICK_RT%(securityID, field))
return _ret_data(code, result)
def dy_market_tickRtIndex(self, securityID='', field=''):
"""
获取指数成份股的最新市场信息快照
获取一个指数的成份股的最新Level1股票信息。
输入一个指数的证券代码，如000001.XSHG (上证指数） 或000300.XSHG（沪深300），
还有所选字段， 得到指数成份股的最新交易快照。
getTickRTSnapshotIndex
"""
code, result = self.client.getData(vs.TICK_RT_INDEX%(securityID, field))
return _ret_data(code, result)
def dy_market_industry_rt(self, securityID='', field=''):
"""
获取行业（证监会行业标准）资金流向
内容包括小单成交金额、中单成交金额、大单成交金额、超大单成交金额、本次成交单总金额等。
getIndustryTickRTSnapshot
"""
code, result = self.client.getData(vs.INDUSTRY_TICK_RT%(securityID, field))
return _ret_data(code, result)
def dy_market_future_rt(self, instrumentID='', field=''):
"""
获取一只或多只期货的最新市场信息快照
getFutureTickRTSnapshot
"""
code, result = self.client.getData(vs.FUTURE_TICK_RT%(instrumentID, field))
return _ret_data(code, result)
def dy_market_equ_rtrank(self, exchangeCD='', pagesize='',
pagenum='', desc='', field=''):
"""
获取沪深股票涨跌幅排行
getEquRTRank
"""
code, result = self.client.getData(vs.EQU_RT_RANK%(exchangeCD, pagesize,
pagenum, desc, field))
return _ret_data(code, result)
def dy_market_option_rt(self, optionId='', field=''):
"""
获取期权最新市场信息快照
getOptionTickRTSnapshot
"""
code, result = self.client.getData(vs.OPTION_RT%(optionId, field))
return _ret_data(code, result)
def dy_market_sectips(self, tipsTypeCD='H', field=''):
"""
上海证券交易所、深圳证券交易所今日停复牌股票列表。数据更新频率：日。
getSecTips
"""
code, result = self.client.getData(vs.SEC_TIPS%(tipsTypeCD, field))
return _ret_data(code, result)
def dy_market_tickrt_intraday(self, securityID='000001.XSHE', startTime='',
endTime='', field=''):
"""
获取一只股票，指数，债券，基金在当日内时间段Level1信息
对应：getTickRTIntraDay
"""
code, result = self.client.getData(vs.TICK_RT_INTRADAY%(securityID, startTime,
endTime, field))
return _ret_data(code, result)
def dy_market_bar_rt(self, securityID='000001.XSHE', startTime='',
endTime='', unit='1', field=''):
"""
获取一只证券当日的分钟线信息。
输入一只证券代码，如000001.XSHE（平安银行）， 得到此证券的当日的分钟线。
证券目前是股票，指数，基金和部分债券。
分钟线的有效数据上午从09：30 到11：30，下午从13：01到15：00
对应：getBarRTIntraDay
"""
code, result = self.client.getData(vs.TICK_RT_INTRADAY%(securityID, startTime,
endTime, field))
return _ret_data(code, result)
def _ret_data(code, result):
if code==200:
result = result.decode('utf-8') if vs.PY3 else result
df = pd.read_csv(StringIO(result))
return df
else:
print(result)
return None
| bsd-3-clause |
peterdougstuart/PCWG | pcwg/gui/dataset.py | 2 | 52819 | # -*- coding: utf-8 -*-
"""
Created on Wed Aug 10 14:27:00 2016
@author: Stuart
"""
import Tkinter as tk
import tkFileDialog
import ttk
import tkMessageBox
import os.path
import re
import pandas as pd
import base_dialog
import validation
from grid_box import GridBox
from grid_box import DialogGridBox
from ..configuration.base_configuration import RelationshipFilter
from ..configuration.base_configuration import Filter
from ..configuration.dataset_configuration import Exclusion
from ..configuration.dataset_configuration import CalibrationSector
from ..configuration.dataset_configuration import ShearMeasurement
from ..configuration.dataset_configuration import DatasetConfiguration
from ..configuration.preferences_configuration import Preferences
from ..core.dataset import getSeparatorValue
from ..core.dataset import getDecimalValue
from ..exceptions.handling import ExceptionHandler
from ..core.status import Status
columnSeparator = "|"
def encodeRelationshipFilterValuesAsText(relationshipFilter):
text = ""
for clause in relationshipFilter.clauses:
text += encodeFilterValuesAsText(clause.column,clause.value, clause.filterType, clause.inclusive, "" )
text += " #" + relationshipFilter.conjunction + "# "
return text[:-5]
def encodeFilterValuesAsText(column, value, filterType, inclusive, active):
return "{column}{sep}{value}{sep}{FilterType}{sep}{inclusive}{sep}{active}".format(column = column, sep = columnSeparator,value = value, FilterType = filterType, inclusive =inclusive, active = active)
def extractRelationshipFilterFromText(text):
try:
clauses = []
for i, subFilt in enumerate(text.split(base_dialog.filterSeparator)):
if i%2 == 0:
items = subFilt.split(base_dialog.columnSeparator)
column = items[0].strip()
value = float(items[1].strip())
filterType = items[2].strip()
inclusive = base_dialog.getBoolFromText(items[3].strip())
clauses.append(Filter(True,column,filterType,inclusive,value))
else:
if len(subFilt.strip()) > 1:
conjunction = subFilt.strip()
return RelationshipFilter(True,conjunction,clauses)
except ExceptionHandler.ExceptionType as ex:
ExceptionHandler.add(ex, "Cannot parse values from filter text")
class FilterDialog(base_dialog.BaseDialog):
def __init__(self, master, parent_dialog, item = None):
self.parent_dialog = parent_dialog
self.isNew = (item == None)
if self.isNew:
self.item = Filter()
else:
self.item = item
base_dialog.BaseDialog.__init__(self, master)
def ShowColumnPicker(self, parentDialog, pick, selectedColumn):
return self.parent_dialog.ShowColumnPicker(parentDialog, pick, selectedColumn)
def body(self, master):
self.prepareColumns(master)
self.addTitleRow(master, "Filter Settings:")
self.column = self.addPickerEntry(master, "Column:", validation.ValidateNotBlank(master), self.item.column)
self.value = self.addEntry(master, "Value:", validation.ValidateFloat(master), self.item.value)
self.filterType = self.addOption(master, "Filter Type:", ["Below", "Above", "AboveOrBelow"], self.item.filterType)
if self.item.inclusive:
self.inclusive = self.addCheckBox(master, "Inclusive:", 1)
else:
self.inclusive = self.addCheckBox(master, "Inclusive:", 0)
if self.item.active:
self.active = self.addCheckBox(master, "Active:", 1)
else:
self.active = self.addCheckBox(master, "Active:", 0)
#dummy label to indent controls
tk.Label(master, text=" " * 5).grid(row = (self.row-1), sticky=tk.W, column=self.titleColumn)
def apply(self):
if int(self.active.get()) == 1:
self.item.active = True
else:
self.item.active = False
if int(self.inclusive.get()) == 1:
self.item.inclusive = True
else:
self.item.inclusive = False
self.item.column = self.column.get()
self.item.value = float(self.value.get())
self.item.filterType = self.filterType.get()
if self.isNew:
Status.add("Filter created")
else:
Status.add("Filter updated")
class ExclusionDialog(base_dialog.BaseDialog):
def __init__(self, master, parent_dialog, item = None):
self.isNew = (item == None)
if self.isNew:
self.item = Exclusion()
else:
self.item = item
base_dialog.BaseDialog.__init__(self, master)
def body(self, master):
self.prepareColumns(master)
#dummy label to force width
tk.Label(master, text=" " * 275).grid(row = self.row, sticky=tk.W, column=self.titleColumn, columnspan = 8)
self.row += 1
self.addTitleRow(master, "Exclusion Settings:")
self.startDate = self.addDatePickerEntry(master, "Start Date:", validation.ValidateNotBlank(master), self.item.startDate)
self.endDate = self.addDatePickerEntry(master, "End Date:", validation.ValidateNotBlank(master), self.item.endDate)
if self.item.active:
self.active = self.addCheckBox(master, "Active:", 1)
else:
self.active = self.addCheckBox(master, "Active:", 0)
#dummy label to indent controls
tk.Label(master, text=" " * 5).grid(row = (self.row-1), sticky=tk.W, column=self.titleColumn)
def apply(self):
if int(self.active.get()) == 1:
self.item.active = True
else:
self.item.active = False
self.item.startDate = pd.to_datetime(self.startDate.get().strip(), dayfirst =True)
self.item.endDate = pd.to_datetime(self.endDate.get().strip(), dayfirst =True)
if self.isNew:
Status.add("Exclusion created")
else:
Status.add("Exclusion updated")
class CalibrationDirectionDialog(base_dialog.BaseDialog):
def __init__(self, master, parent_dialog, item):
self.isNew = (item == None)
if self.isNew:
self.item = CalibrationSector()
else:
self.item = item
base_dialog.BaseDialog.__init__(self, master)
def body(self, master):
self.prepareColumns(master)
self.addTitleRow(master, "Calibration Direction Settings:")
self.direction = self.addEntry(master, "Direction:", validation.ValidateFloat(master), self.item.direction)
self.slope = self.addEntry(master, "Slope:", validation.ValidateFloat(master), self.item.slope)
self.offset = self.addEntry(master, "Offset:", validation.ValidateFloat(master), self.item.offset)
if self.item.active:
self.active = self.addCheckBox(master, "Active:", 1)
else:
self.active = self.addCheckBox(master, "Active:", 0)
#dummy label to indent controls
tk.Label(master, text=" " * 5).grid(row = (self.row-1), sticky=tk.W, column=self.titleColumn)
def apply(self):
if int(self.active.get()) == 1:
self.item.active = True
else:
self.item.active = False
self.item.direction = float(self.direction.get())
self.item.slope = float(self.slope.get().strip())
self.item.offset = float(self.offset.get().strip())
if self.isNew:
Status.add("Calibration direction created")
else:
Status.add("Calibration direction updated")
class ShearDialogBase(base_dialog.BaseDialog):
def __init__(self, master, parent_dialog, item):
self.parent_dialog = parent_dialog
self.isNew = (item == None)
if self.isNew:
self.item = ShearMeasurement()
else:
self.item = item
base_dialog.BaseDialog.__init__(self, master)
def ShowColumnPicker(self, parentDialog, pick, selectedColumn):
return self.parent_dialog.ShowColumnPicker(parentDialog, pick, selectedColumn)
def parse_height(self):
wind_speed_text = self.windSpeed.get()
if len(wind_speed_text) > 0:
numbers = re.findall(r"[-+]?\d*\.\d+|[-+]?\d+", wind_speed_text)
print numbers
if len(numbers) > 0:
try:
self.height.set("{0}".format(float(numbers[0])))
except:
Status.add("Cannot parse height")
class ShearMeasurementDialog(ShearDialogBase):
def __init__(self, master, parent_dialog, item):
ShearDialogBase.__init__(self, master, parent_dialog, item)
def body(self, master):
self.prepareColumns(master)
self.addTitleRow(master, "Shear measurement:")
self.height = self.addEntry(master, "Height:", validation.ValidatePositiveFloat(master), self.item.height)
self.windSpeed = self.addPickerEntry(master, "Wind Speed:", validation.ValidateNotBlank(master), self.item.wind_speed_column, width = 60)
#dummy label to indent controls
tk.Label(master, text=" " * 5).grid(row = (self.row-1), sticky=tk.W, column=self.titleColumn)
def apply(self):
self.item.height = float(self.height.get())
self.item.wind_speed_column = self.windSpeed.get().strip()
if self.isNew:
Status.add("Shear measurement created")
else:
Status.add("Shear measurement updated")
class REWSProfileLevelDialog(ShearDialogBase):
def __init__(self, master, parent_dialog, item):
ShearDialogBase.__init__(self, master, parent_dialog, item)
def body(self, master):
self.prepareColumns(master)
self.addTitleRow(master, "REWS Level Settings:")
self.height = self.addEntry(master, "Height:", validation.ValidatePositiveFloat(master), self.item.height)
parse_button = tk.Button(master, text="Parse", command = self.parse_height, width=3, height=1)
parse_button.grid(row=(self.row-1), sticky=tk.N, column=self.inputColumn, padx = 160)
self.windSpeed = self.addPickerEntry(master, "Wind Speed:", validation.ValidateNotBlank(master), self.item.wind_speed_column, width = 60)
self.windDirection = self.addPickerEntry(master, "Wind Direction:", None, self.item.wind_direction_column, width = 60)
self.upflow = self.addPickerEntry(master, "Upflow:", None, self.item.upflow_column, width = 60)
#dummy label to indent controls
tk.Label(master, text=" " * 5).grid(row = (self.row-1), sticky=tk.W, column=self.titleColumn)
def apply(self):
self.item.height = float(self.height.get())
self.item.wind_speed_column = self.windSpeed.get().strip()
self.item.wind_direction_column = self.windDirection.get().strip()
self.item.upflow_column = self.upflow.get().strip()
if self.isNew:
Status.add("Rotor level created")
else:
Status.add("Rotor level updated")
class ExclusionsGridBox(DialogGridBox):
def get_headers(self):
return ["StartDate", "EndDate", "Active"]
def get_item_values(self, item):
values_dict = {}
if item.startDate is None:
values_dict["StartDate"] = ""
else:
values_dict["StartDate"] = base_dialog.convertDateToText(item.startDate)
if item.endDate is None:
values_dict["EndDate"] = ""
else:
values_dict["EndDate"] = base_dialog.convertDateToText(item.endDate)
values_dict["Active"] = item.active
return values_dict
def new_dialog(self, master, parent_dialog, item):
return ExclusionDialog(master, self.parent_dialog, item)
class FiltersGridBox(DialogGridBox):
def get_headers(self):
return ["Column","Value","FilterType","Inclusive","Active"]
def get_item_values(self, item):
values_dict = {}
values_dict["Column"] = item.column
values_dict["Value"] = item.value
values_dict["FilterType"] = item.filterType
values_dict["Inclusive"] = item.inclusive
values_dict["Active"] = item.active
return values_dict
def new_dialog(self, master, parent_dialog, item):
return FilterDialog(master, self.parent_dialog, item)
class CalibrationSectorsGridBox(DialogGridBox):
def get_headers(self):
return ["Direction","Slope","Offset","Active"]
def get_item_values(self, item):
values_dict = {}
values_dict["Direction"] = item.direction
values_dict["Slope"] = item.slope
values_dict["Offset"] = item.offset
values_dict["Active"] = item.active
return values_dict
def new_dialog(self, master, parent_dialog, item):
return CalibrationDirectionDialog(master, self.parent_dialog, item)
class ShearGridBox(DialogGridBox):
def get_headers(self):
return ["Height","WindSpeed"]
def get_item_values(self, item):
values_dict = {}
values_dict["Height"] = item.height
values_dict["WindSpeed"] = item.wind_speed_column
return values_dict
def new_dialog(self, master, parent_dialog, item):
return ShearMeasurementDialog(master, self.parent_dialog, item)
class REWSGridBox(DialogGridBox):
def get_headers(self):
return ["Height","WindSpeed", "WindDirection"]
def get_item_values(self, item):
values_dict = {}
values_dict["Height"] = item.height
values_dict["WindSpeed"] = item.wind_speed_column
values_dict["WindDirection"] = item.wind_direction_column
return values_dict
def new_dialog(self, master, parent_dialog, item):
return REWSProfileLevelDialog(master, self.parent_dialog, item)
class DatasetConfigurationDialog(base_dialog.BaseConfigurationDialog):
def getInitialFileName(self):
return "Dataset"
def addFilePath(self, master, path):
pass
def file_path_update(self, *args):
self.config.input_time_series.set_base(self.filePath.get())
def time_series_path_update(self, *args):
self.config.input_time_series.absolute_path = self.inputTimeSeriesPath.get()
def add_general(self, master, path):
self.filePath = self.addFileSaveAsEntry(master, "Configuration XML File Path:", validation.ValidateDatasetFilePath(master), path)
self.filePath.variable.trace("w", self.file_path_update)
self.name = self.addEntry(master, "Dataset Name:", validation.ValidateNotBlank(master), self.config.name)
self.inputTimeSeriesPath = self.addFileOpenEntry(master, "Input Time Series Path:", validation.ValidateTimeSeriesFilePath(master), self.config.input_time_series.absolute_path, self.filePath)
self.inputTimeSeriesPath.variable.trace("w", self.time_series_path_update)
self.separator = self.addOption(master, "Separator:", ["TAB", "COMMA", "SPACE", "SEMI-COLON"], self.config.separator)
self.separator.trace("w", self.columnSeparatorChange)
self.decimal = self.addOption(master, "Decimal Mark:", ["FULL STOP", "COMMA"], self.config.decimal)
self.decimal.trace("w", self.decimalChange)
self.headerRows = self.addEntry(master, "Header Rows:", validation.ValidateNonNegativeInteger(master), self.config.headerRows)
self.startDate = self.addDatePickerEntry(master, "Start Date:", None, self.config.startDate)
self.endDate = self.addDatePickerEntry(master, "End Date:", None, self.config.endDate)
self.hubWindSpeedMode = self.addOption(master, "Hub Wind Speed Mode:", ["None", "Calculated", "Specified"], self.config.hubWindSpeedMode)
self.hubWindSpeedMode.trace("w", self.hubWindSpeedModeChange)
self.calibrationMethod = self.addOption(master, "Calibration Method:", ["None", "Specified", "LeastSquares"], self.config.calibrationMethod)
self.calibrationMethod.trace("w", self.calibrationMethodChange)
self.densityMode = self.addOption(master, "Density Mode:", ["None", "Calculated", "Specified"], self.config.densityMode)
self.densityMode.trace("w", self.densityMethodChange)
def add_measurements(self, master):
self.timeStepInSeconds = self.addEntry(master, "Time Step In Seconds:", validation.ValidatePositiveInteger(master), self.config.timeStepInSeconds)
self.badData = self.addEntry(master, "Bad Data Value:", validation.ValidateFloat(master), self.config.badData)
self.dateFormat = self.addEntry(master, "Date Format:", validation.ValidateNotBlank(master), self.config.dateFormat, width = 60)
pickDateFormatButton = tk.Button(master, text=".", command = base_dialog.DateFormatPicker(self, self.dateFormat, ['%Y-%m-%d %H:%M:%S', '%Y-%m-%dT%H:%M:%S', '%d-%m-%y %H:%M', '%y-%m-%d %H:%M', '%d/%m/%Y %H:%M', '%d/%m/%Y %H:%M:%S', '%d/%m/%y %H:%M', '%y/%m/%d %H:%M']), width=5, height=1)
pickDateFormatButton.grid(row=(self.row-1), sticky=tk.E+tk.N, column=self.buttonColumn)
self.timeStamp = self.addPickerEntry(master, "Time Stamp:", validation.ValidateNotBlank(master), self.config.timeStamp, width = 60)
self.turbineLocationWindSpeed = self.addPickerEntry(master, "Turbine Location Wind Speed:", None, self.config.turbineLocationWindSpeed, width = 60) #Should this be with reference wind speed?
self.hubWindSpeed = self.addPickerEntry(master, "Hub Wind Speed:", None, self.config.hubWindSpeed, width = 60)
self.hubTurbulence = self.addPickerEntry(master, "Hub Turbulence:", None, self.config.hubTurbulence, width = 60)
self.temperature = self.addPickerEntry(master, "Temperature:", None, self.config.temperature, width = 60)
self.pressure = self.addPickerEntry(master, "Pressure:", None, self.config.pressure, width = 60)
self.density = self.addPickerEntry(master, "Density:", None, self.config.density, width = 60)
self.inflowAngle = self.addPickerEntry(master, "Inflow Angle:", None, self.config.inflowAngle, width = 60)
self.inflowAngle.setTip('Not required')
def add_power(self, master):
self.power = self.addPickerEntry(master, "Power:", None, self.config.power, width = 60)
self.powerMin = self.addPickerEntry(master, "Power Min:", None, self.config.powerMin, width = 60)
self.powerMax = self.addPickerEntry(master, "Power Max:", None, self.config.powerMax, width = 60)
self.powerSD = self.addPickerEntry(master, "Power Std Dev:", None, self.config.powerSD, width = 60)
def add_reference(self, master):
self.referenceWindSpeed = self.addPickerEntry(master, "Reference Wind Speed:", None, self.config.referenceWindSpeed, width = 60)
self.referenceWindSpeedStdDev = self.addPickerEntry(master, "Reference Wind Speed Std Dev:", None, self.config.referenceWindSpeedStdDev, width = 60)
self.referenceWindDirection = self.addPickerEntry(master, "Reference Wind Direction:", None, self.config.referenceWindDirection, width = 60)
self.referenceWindDirectionOffset = self.addEntry(master, "Reference Wind Direction Offset:", validation.ValidateFloat(master), self.config.referenceWindDirectionOffset)
def add_reference_shear(self, master):
self.shearCalibrationMethod = self.addOption(master, "Shear Calibration Method:", ["None", "LeastSquares"], self.config.shearCalibrationMethod)
self.row += 1
label = tk.Label(master, text="Reference Shear Heights (Power Law):")
label.grid(row=self.row, sticky=tk.W, column=self.titleColumn, columnspan = 2)
self.row += 1
self.referenceShearGridBox = ShearGridBox(master, self, self.row, self.inputColumn)
self.referenceShearGridBox.add_items(self.config.referenceShearMeasurements)
self.copyToREWSButton = tk.Button(master, text="Copy To REWS", command = self.copyToREWSShearProfileLevels, width=12, height=1)
self.copyToREWSButton.grid(row=self.row, sticky=tk.E+tk.N, column=self.buttonColumn)
def add_turbine_shear(self, master):
label = tk.Label(master, text="Turbine Shear Heights (Power Law):")
label.grid(row=self.row, sticky=tk.W, column=self.titleColumn, columnspan = 2)
self.row += 1
self.turbineShearGridBox = ShearGridBox(master, self, self.row, self.inputColumn)
self.turbineShearGridBox.add_items(self.config.turbineShearMeasurements)
def add_rews(self, master):
self.addTitleRow(master, "REWS Settings:")
self.rewsDefined = self.addCheckBox(master, "REWS Active", self.config.rewsDefined)
self.numberOfRotorLevels = self.addEntry(master, "REWS Number of Rotor Levels:", validation.ValidateNonNegativeInteger(master), self.config.numberOfRotorLevels)
self.rotorMode = self.addOption(master, "REWS Rotor Mode:", ["EvenlySpacedLevels", "ProfileLevels"], self.config.rotorMode)
self.hubMode = self.addOption(master, "Hub Mode:", ["Interpolated", "PiecewiseExponent"], self.config.hubMode)
label = tk.Label(master, text="REWS Profile Levels:")
label.grid(row=self.row, sticky=tk.W, column=self.titleColumn, columnspan = 2)
self.row += 1
self.rewsGridBox = REWSGridBox(master, self, self.row, self.inputColumn)
self.rewsGridBox.add_items(self.config.rewsProfileLevels)
self.copyToShearButton = tk.Button(master, text="Copy To Shear", command = self.copyToShearREWSProfileLevels, width=12, height=1)
self.copyToShearButton.grid(row=self.row, sticky=tk.E+tk.N, column=self.buttonColumn)
def add_specified_calibration(self, master):
label = tk.Label(master, text="Calibration Sectors:")
label.grid(row=self.row, sticky=tk.W, column=self.titleColumn, columnspan = 2)
self.row += 1
self.calibrationSectorsGridBox = CalibrationSectorsGridBox(master, self, self.row, self.inputColumn)
self.calibrationSectorsGridBox.add_items(self.config.calibrationSectors)
def add_calculated_calibration(self, master):
self.calibrationStartDate = self.addDatePickerEntry(master, "Calibration Start Date:", None, self.config.calibrationStartDate)
self.calibrationEndDate = self.addDatePickerEntry(master, "Calibration End Date:", None, self.config.calibrationEndDate)
self.siteCalibrationNumberOfSectors = self.addEntry(master, "Number of Sectors:", None, self.config.siteCalibrationNumberOfSectors)
self.siteCalibrationCenterOfFirstSector = self.addEntry(master, "Center of First Sector:", None, self.config.siteCalibrationCenterOfFirstSector)
label = tk.Label(master, text="Calibration Filters:")
label.grid(row=self.row, sticky=tk.W, column=self.titleColumn, columnspan = 2)
self.row += 1
self.calibrationFiltersGridBox = FiltersGridBox(master, self, self.row, self.inputColumn)
self.calibrationFiltersGridBox.add_items(self.config.calibrationFilters)
def add_exclusions(self, master):
#Exclusions
label = tk.Label(master, text="Exclusions:")
label.grid(row=self.row, sticky=tk.W, column=self.titleColumn, columnspan = 2)
self.row += 1
self.exclusionsGridBox = ExclusionsGridBox(master, self, self.row, self.inputColumn)
self.exclusionsGridBox.add_items(self.config.exclusions)
def add_filters(self, master):
#Filters
label = tk.Label(master, text="Filters:")
label.grid(row=self.row, sticky=tk.W, column=self.titleColumn, columnspan = 2)
self.row += 1
self.filtersGridBox = FiltersGridBox(master, self, self.row, self.inputColumn)
self.filtersGridBox.add_items(self.config.filters)
def add_meta_data(self, master):
self.data_type = self.addOption(master, "Data Type:", ["Mast", "LiDAR", "SoDAR", "Mast & LiDAR", "Mast & SoDAR", "Spinner"], self.config.data_type)
self.outline_site_classification = self.addOption(master, "Outline Site Classification:", ["Flat", "Complex", "Offshore"], self.config.outline_site_classification)
self.outline_forestry_classification = self.addOption(master, "Outline Forestry Classification:", ["Forested", "Non-Forested"], self.config.outline_forestry_classification)
#IEC Site Classification [• As described by Annex B of IEC 61400-12-1 (2006)]
self.iec_terrain_classification = self.addEntry(master, "IEC Terrain Classification:", None, self.config.iec_terrain_classification)
self.latitude = self.addEntry(master, "Latitude:", validation.ValidateOptionalFloat(master), self.config.latitude)
self.longitude = self.addEntry(master, "Longitude:", validation.ValidateOptionalFloat(master), self.config.longitude)
self.continent = self.addOption(master, "Continent:", ["Europe", "North America", "Asia", "South America", "Africa", "Antarctica"], self.config.continent)
self.country = self.addOption(master, "Country:", self.get_countries(), self.config.country)
self.elevation_above_sea_level = self.addEntry(master, "Elevation Above Sea Level:", validation.ValidateOptionalFloat(master), self.config.elevation_above_sea_level)
self.measurement_compliance = self.addOption(master, "Measurement Compliance:", ["IEC 61400-12-1 (2006) Compliant", "None", "Unknown"], self.config.measurement_compliance)
self.anemometry_type = self.addOption(master, "Anemometry Type:", ["Sonic", "Cups", "Spinner", "Not Applicable"], self.config.anemometry_type)
self.anemometry_heating = self.addOption(master, "Anemometry Heating:", ["Heated", "Unheated", "Unknown", "Not Applicable"], self.config.anemometry_heating)
self.turbulence_measurement_type = self.addOption(master, "Turbulence Measurement Type", ["LiDAR", "SoDAR", "Cups", "Sonic"], self.config.turbulence_measurement_type)
self.power_measurement_type = self.addOption(master, "Power Measurement Type:", ["Transducer", "SCADA", "Unknown"], self.config.power_measurement_type)
self.turbine_control_type = self.addOption(master, "Turbine Control Type:", ["Pitch", "Stall", "Active Stall"], self.config.turbine_control_type)
self.turbine_technology_vintage = self.addEntry(master, "Turbine Technology Vintage:", validation.ValidateOptionalPositiveInteger(master), self.config.turbine_technology_vintage)
self.time_zone = self.addOption(master, "Time Zone:", ["Local", "UTC"], self.config.time_zone)
def get_countries(self):
return ["Åland Islands","Albania","Algeria","American Samoa","Andorra","Angola","Anguilla","Antarctica","Antigua and Barbuda","Argentina","Armenia","Aruba","Australia","Austria","Azerbaijan","Bahamas","Bahrain","Bangladesh","Barbados","Belarus","Belgium","Belize","Benin","Bermuda","Bhutan","Bolivia","Bosnia and Herzegovina","Botswana","Bouvet Island","Brazil","British Indian Ocean Territory","Brunei Darussalam","Bulgaria","Burkina Faso","Burundi","Cambodia","Cameroon","Canada","Cape Verde","Caribbean Netherlands ","Cayman Islands","Central African Republic","Chad","Chile","China","Christmas Island","Cocos (Keeling) Islands","Colombia","Comoros","Congo","Congo, Democratic Republic of","Cook Islands","Costa Rica","Côte d'Ivoire","Croatia","Cuba","Curaçao","Cyprus","Czech Republic","Denmark","Djibouti","Dominica","Dominican Republic","Ecuador","Egypt","El Salvador","English Name","Equatorial Guinea","Eritrea","Estonia","Ethiopia","Falkland Islands","Faroe Islands","Fiji","Finland","France","French Guiana","French Polynesia","French Southern Territories","Gabon","Gambia","Georgia","Germany","Ghana","Gibraltar","Greece","Greenland","Grenada","Guadeloupe","Guam","Guatemala","Guernsey","Guinea","Guinea-Bissau","Guyana","Haiti","Heard and McDonald Islands","Honduras","Hong Kong","Hungary","Iceland","India","Indonesia","Iran","Iraq","Ireland","Isle of Man","Israel","Italy","Jamaica","Japan","Jersey","Jordan","Kazakhstan","Kenya","Kiribati","Kuwait","Kyrgyzstan","Lao People's Democratic Republic","Latvia","Lebanon","Lesotho","Liberia","Libya","Liechtenstein","Lithuania","Luxembourg","Macau","Macedonia","Madagascar","Malawi","Malaysia","Maldives","Mali","Malta","Marshall Islands","Martinique","Mauritania","Mauritius","Mayotte","Mexico","Micronesia, Federated States of","Moldova","Monaco","Mongolia","Montenegro","Montserrat","Morocco","Mozambique","Myanmar","Namibia","Nauru","Nepal","New Caledonia","New Zealand","Nicaragua","Niger","Nigeria","Niue","Norfolk Island","North Korea","Northern Mariana Islands","Norway","Oman","Pakistan","Palau","Palestine, State of","Panama","Papua New Guinea","Paraguay","Peru","Philippines","Pitcairn","Poland","Portugal","Puerto Rico","Qatar","Réunion","Romania","Russian Federation","Rwanda","Saint Barthélemy","Saint Helena","Saint Kitts and Nevis","Saint Lucia","Saint Vincent and the Grenadines","Saint-Martin (France)","Samoa","San Marino","Sao Tome and Principe","Saudi Arabia","Senegal","Serbia","Seychelles","Sierra Leone","Singapore","Sint Maarten (Dutch part)","Slovakia","Slovenia","Solomon Islands","Somalia","South Africa","South Georgia and the South Sandwich Islands","South Korea","South Sudan","Spain","Sri Lanka","St. Pierre and Miquelon","Sudan","Suriname","Svalbard and Jan Mayen Islands","Swaziland","Sweden","Switzerland","Syria","Taiwan","Tajikistan","Tanzania","Thailand","The Netherlands","Timor-Leste","Togo","Tokelau","Tonga","Trinidad and Tobago","Tunisia","Turkey","Turkmenistan","Turks and Caicos Islands","Tuvalu","Uganda","Ukraine","United Arab Emirates","United Kingdom","United States","United States Minor Outlying Islands","Uruguay","Uzbekistan","Vanuatu","Vatican","Venezuela","Vietnam","Virgin Islands (British)","Virgin Islands (U.S.)","Wallis and Futuna Islands","Western Sahara","Yemen","Zambia","Zimbabwe"]
def add_turbine(self, master):
self.cutInWindSpeed = self.addEntry(master, "Cut In Wind Speed:", validation.ValidatePositiveFloat(master), self.config.cutInWindSpeed)
self.cutOutWindSpeed = self.addEntry(master, "Cut Out Wind Speed:", validation.ValidatePositiveFloat(master), self.config.cutOutWindSpeed)
self.ratedPower = self.addEntry(master, "Rated Power:", validation.ValidatePositiveFloat(master), self.config.ratedPower)
self.hubHeight = self.addEntry(master, "Hub Height:", validation.ValidatePositiveFloat(master), self.config.hubHeight)
self.diameter = self.addEntry(master, "Diameter:", validation.ValidatePositiveFloat(master), self.config.diameter)
self.rotor_tilt = self.addEntry(master, "Tilt:", validation.ValidateOptionalFloat(master), self.config.rotor_tilt)
def add_advanced(self, master):
self.addTitleRow(master, "Density Pre-Correction:")
label = tk.Label(master, text="Note: your are unlikely to need to complete the following settings.")
label.grid(row=self.row, sticky=tk.W, column=self.titleColumn, columnspan = 2)
self.row += 1
label = tk.Label(master, text="If your time series data file has already been corrected/normalised to a reference density please use the settings below.")
label.grid(row=self.row, sticky=tk.W, column=self.titleColumn, columnspan = 2)
self.row += 1
self.density_pre_correction_active = self.addCheckBox(master, "Density Pre-Correction Active:", self.config.density_pre_correction_active)
self.density_pre_correction_wind_speed = self.addPickerEntry(master, "Density Pre-Correction Wind Speed:", None, self.config.density_pre_correction_wind_speed)
self.density_pre_correction_reference_density = self.addEntry(master,
"Density Pre-Connection Reference Density:",
validation.ValidateOptionalFloat(master),
self.config.density_pre_correction_reference_density)
def addFormElements(self, master, path):
self.availableColumnsFile = None
self.columnsFileHeaderRows = None
self.availableColumns = []
self.shearWindSpeedHeights = []
self.shearWindSpeeds = []
nb = ttk.Notebook(master, height=400)
nb.pressed_index = None
general_tab = tk.Frame(nb)
turbines_tab = tk.Frame(nb)
measurements_tab = tk.Frame(nb)
power_tab = tk.Frame(nb)
reference_tab = tk.Frame(nb)
reference_shear_tab = tk.Frame(nb)
turbine_shear_tab = tk.Frame(nb)
rews_tab = tk.Frame(nb)
calculated_calibration_tab = tk.Frame(nb)
specified_calibration_tab = tk.Frame(nb)
exclusions_tab = tk.Frame(nb)
filters_tab = tk.Frame(nb)
meta_data_tab = tk.Frame(nb)
advanced_tab = tk.Frame(nb)
nb.add(general_tab, text='General', padding=3)
nb.add(turbines_tab, text='Turbine', padding=3)
nb.add(measurements_tab, text='Measurements', padding=3)
nb.add(power_tab, text='Power', padding=3)
nb.add(reference_tab, text='Reference', padding=3)
nb.add(reference_shear_tab, text='Reference Shear', padding=3)
nb.add(turbine_shear_tab, text='Turbine Shear', padding=3)
nb.add(rews_tab, text='REWS', padding=3)
nb.add(calculated_calibration_tab, text='Calibration (Calculated)', padding=3)
nb.add(specified_calibration_tab, text='Calibration (Specified)', padding=3)
nb.add(exclusions_tab, text='Exclusions', padding=3)
nb.add(filters_tab, text='Filters', padding=3)
nb.add(meta_data_tab, text='Meta Data', padding=3)
nb.add(advanced_tab, text='Advanced', padding=3)
nb.grid(row=self.row, sticky=tk.E+tk.W+tk.N+tk.S, column=self.titleColumn, columnspan=8)
master.grid_rowconfigure(self.row, weight=1)
self.row += 1
self.add_general(general_tab, path)
self.add_turbine(turbines_tab)
self.add_measurements(measurements_tab)
self.add_power(power_tab)
self.add_reference(reference_tab)
self.add_reference_shear(reference_shear_tab)
self.add_turbine_shear(turbine_shear_tab)
self.add_rews(rews_tab)
self.add_calculated_calibration(calculated_calibration_tab)
self.add_specified_calibration(specified_calibration_tab)
self.add_exclusions(exclusions_tab)
self.add_filters(filters_tab)
self.add_meta_data(meta_data_tab)
self.add_advanced(advanced_tab)
self.calibrationMethodChange()
self.densityMethodChange()
def densityMethodChange(self, *args):
if self.densityMode.get() == "Specified":
densityModeSpecifiedComment = "Not required when density mode is set to specified"
self.temperature.setTip(densityModeSpecifiedComment)
self.pressure.setTip(densityModeSpecifiedComment)
self.density.clearTip()
elif self.densityMode.get() == "Calculated":
densityModeCalculatedComment = "Not required when density mode is set to calculate"
self.temperature.clearTip()
self.pressure.clearTip()
self.density.setTip(densityModeCalculatedComment)
elif self.densityMode.get() == "None":
densityModeNoneComment = "Not required when density mode is set to none"
self.temperature.setTip(densityModeNoneComment)
self.pressure.setTip(densityModeNoneComment)
self.density.setTip(densityModeNoneComment)
else:
raise Exception("Unknown density methods: %s" % self.densityMode.get())
def columnSeparatorChange(self, *args):
Status.add('reading separator', verbosity=2)
sep = getSeparatorValue(self.separator.get())
self.read_dataset()
return sep
def decimalChange(self, *args):
Status.add('reading decimal', verbosity=2)
decimal = getDecimalValue(self.decimal.get())
self.read_dataset()
return decimal
def hubWindSpeedModeChange(self, *args):
self.calibrationMethodChange()
def calibrationMethodChange(self, *args):
if self.hubWindSpeedMode.get() == "Calculated":
hubWindSpeedModeCalculatedComment = "Not required for calculated hub wind speed mode"
specifiedCalibrationMethodComment = "Not required for Specified Calibration Method"
leastSquaresCalibrationMethodComment = "Not required for Least Squares Calibration Method"
self.hubWindSpeed.setTip(hubWindSpeedModeCalculatedComment)
self.hubTurbulence.setTip(hubWindSpeedModeCalculatedComment)
self.siteCalibrationNumberOfSectors.clearTip()
self.siteCalibrationCenterOfFirstSector.clearTip()
self.referenceWindSpeed.clearTip()
self.referenceWindSpeedStdDev.clearTip()
self.referenceWindDirection.clearTip()
self.referenceWindDirectionOffset.clearTip()
if self.calibrationMethod.get() in ("LeastSquares", "York"):
self.turbineLocationWindSpeed.clearTip()
self.calibrationStartDate.clearTip()
self.calibrationEndDate.clearTip()
self.calibrationSectorsGridBox.setTip(leastSquaresCalibrationMethodComment)
self.calibrationFiltersGridBox.clearTip()
elif self.calibrationMethod.get() == "Specified":
self.turbineLocationWindSpeed.setTipNotRequired()
self.calibrationStartDate.setTipNotRequired()
self.calibrationEndDate.setTipNotRequired()
self.calibrationSectorsGridBox.clearTip()
self.calibrationFiltersGridBox.setTip(specifiedCalibrationMethodComment)
else:
if len(self.calibrationMethod.get()) > 0:
raise Exception("Unknown calibration method: %s" % self.calibrationMethod.get())
elif self.hubWindSpeedMode.get() == "Specified":
hubWindSpeedModeSpecifiedComment = "Not required for specified hub wind speed mode"
self.hubWindSpeed.clearTip()
self.hubTurbulence.clearTip()
self.turbineLocationWindSpeed.setTip(hubWindSpeedModeSpecifiedComment)
self.calibrationStartDate.setTip(hubWindSpeedModeSpecifiedComment)
self.calibrationEndDate.setTip(hubWindSpeedModeSpecifiedComment)
self.siteCalibrationNumberOfSectors.setTip(hubWindSpeedModeSpecifiedComment)
self.siteCalibrationCenterOfFirstSector.setTip(hubWindSpeedModeSpecifiedComment)
self.referenceWindSpeed.setTip(hubWindSpeedModeSpecifiedComment)
self.referenceWindSpeedStdDev.setTip(hubWindSpeedModeSpecifiedComment)
self.referenceWindDirection.setTip(hubWindSpeedModeSpecifiedComment)
self.referenceWindDirectionOffset.setTip(hubWindSpeedModeSpecifiedComment)
elif self.hubWindSpeedMode.get() == "None":
hubWindSpeedModeNoneComment = "Not required when hub wind speed mode is set to none"
self.hubWindSpeed.setTip(hubWindSpeedModeNoneComment)
self.hubTurbulence.setTip(hubWindSpeedModeNoneComment)
self.turbineLocationWindSpeed.setTip(hubWindSpeedModeNoneComment)
self.calibrationStartDate.setTip(hubWindSpeedModeNoneComment)
self.calibrationEndDate.setTip(hubWindSpeedModeNoneComment)
self.siteCalibrationNumberOfSectors.setTip(hubWindSpeedModeNoneComment)
self.siteCalibrationCenterOfFirstSector.setTip(hubWindSpeedModeNoneComment)
self.referenceWindSpeed.setTip(hubWindSpeedModeNoneComment)
self.referenceWindSpeedStdDev.setTip(hubWindSpeedModeNoneComment)
self.referenceWindDirection.setTip(hubWindSpeedModeNoneComment)
self.referenceWindDirectionOffset.setTip(hubWindSpeedModeNoneComment)
else:
raise Exception("Unknown hub wind speed mode: %s" % self.hubWindSpeedMode.get())
def copyToREWSShearProfileLevels(self):
self.rewsGridBox.remove_all()
for item in self.referenceShearGridBox.get_items():
self.rewsGridBox.add_item(ShearMeasurement(item.height, item.wind_speed_column))
def copyToShearREWSProfileLevels(self):
self.referenceShearGridBox.remove_all()
for item in self.rewsGridBox.get_items():
self.referenceShearGridBox.add_item(ShearMeasurement(item.height, item.wind_speed_column))
def getHeaderRows(self):
headerRowsText = self.headerRows.get()
if len(headerRowsText) > 0:
return int(headerRowsText)
else:
return 0
def ShowColumnPicker(self, parentDialog, pick, selectedColumn):
if self.config.input_time_series.absolute_path == None:
tkMessageBox.showwarning(
"InputTimeSeriesPath Not Set",
"You must set the InputTimeSeriesPath before using the ColumnPicker"
)
return
inputTimeSeriesPath = self.config.input_time_series.absolute_path
headerRows = self.getHeaderRows()
if self.columnsFileHeaderRows != headerRows or self.availableColumnsFile != inputTimeSeriesPath:
try:
self.read_dataset()
except ExceptionHandler.ExceptionType as e:
tkMessageBox.showwarning(
"Column header error",
"It was not possible to read column headers using the provided inputs.\rPlease check and amend 'Input Time Series Path' and/or 'Header Rows'.\r"
)
ExceptionHandler.add(e, "ERROR reading columns from {0}".format(inputTimeSeriesPath))
self.columnsFileHeaderRows = headerRows
self.availableColumnsFile = inputTimeSeriesPath
try:
base_dialog.ColumnPickerDialog(parentDialog, pick, self.availableColumns, selectedColumn)
except ExceptionHandler.ExceptionType as e:
ExceptionHandler.add(e, "Error picking column")
def read_dataset(self):
Status.add('reading dataSet', verbosity=2)
inputTimeSeriesPath = self.config.input_time_series.absolute_path
headerRows = self.getHeaderRows()
dataFrame = pd.read_csv(inputTimeSeriesPath, sep = getSeparatorValue(self.separator.get()), skiprows = headerRows, decimal = getDecimalValue(self.decimal.get()))
self.availableColumns = []
for col in dataFrame:
self.availableColumns.append(col)
def setConfigValues(self):
self.config.name = self.name.get()
self.config.startDate = base_dialog.getDateFromEntry(self.startDate)
self.config.endDate = base_dialog.getDateFromEntry(self.endDate)
self.config.hubWindSpeedMode = self.hubWindSpeedMode.get()
self.config.calibrationMethod = self.calibrationMethod.get()
self.config.densityMode = self.densityMode.get()
self.config.input_time_series.absolute_path = self.inputTimeSeriesPath.get()
self.config.timeStepInSeconds = int(self.timeStepInSeconds.get())
self.config.badData = float(self.badData.get())
self.config.dateFormat = self.dateFormat.get()
self.config.separator = self.separator.get()
self.config.decimal = self.decimal.get()
self.config.headerRows = self.getHeaderRows()
self.config.timeStamp = self.timeStamp.get()
self.config.power = self.power.get()
self.config.powerMin = self.powerMin.get()
self.config.powerMax = self.powerMax.get()
self.config.powerSD = self.powerSD.get()
self.config.referenceWindSpeed = self.referenceWindSpeed.get()
self.config.referenceWindSpeedStdDev = self.referenceWindSpeedStdDev.get()
self.config.referenceWindDirection = self.referenceWindDirection.get()
self.config.referenceWindDirectionOffset = base_dialog.floatSafe(self.referenceWindDirectionOffset.get())
self.config.turbineLocationWindSpeed = self.turbineLocationWindSpeed.get()
self.config.inflowAngle = self.inflowAngle.get()
self.config.temperature = self.temperature.get()
self.config.pressure = self.pressure.get()
self.config.density = self.density.get()
self.config.hubWindSpeed = self.hubWindSpeed.get()
self.config.hubTurbulence = self.hubTurbulence.get()
#REWS
self.config.rewsDefined = bool(self.rewsDefined.get())
self.config.numberOfRotorLevels = base_dialog.intSafe(self.numberOfRotorLevels.get())
self.config.rotorMode = self.rotorMode.get()
self.config.hubMode = self.hubMode.get()
self.config.rewsProfileLevels = self.rewsGridBox.get_items()
#shear masurements
self.config.referenceShearMeasurements = self.referenceShearGridBox.get_items()
self.config.turbineShearMeasurements = self.turbineShearGridBox.get_items()
self.config.shearCalibrationMethod = self.shearCalibrationMethod.get()
#calibrations
self.config.calibrationStartDate = base_dialog.getDateFromEntry(self.calibrationStartDate)
self.config.calibrationEndDate = base_dialog.getDateFromEntry(self.calibrationEndDate)
self.config.siteCalibrationNumberOfSectors = base_dialog.intSafe(self.siteCalibrationNumberOfSectors.get())
self.config.siteCalibrationCenterOfFirstSector = base_dialog.intSafe(self.siteCalibrationCenterOfFirstSector.get())
#calbirations
self.config.calibrationSectors = self.calibrationSectorsGridBox.get_items()
#calibration filters
self.config.calibrationFilters = self.calibrationFiltersGridBox.get_items()
#exclusions
self.config.exclusions = self.exclusionsGridBox.get_items()
#filters
self.config.filters = self.filtersGridBox.get_items()
#turbines
self.config.cutInWindSpeed = float(self.cutInWindSpeed.get())
self.config.cutOutWindSpeed = float(self.cutOutWindSpeed.get())
self.config.ratedPower = float(self.ratedPower.get())
self.config.hubHeight = float(self.hubHeight.get())
self.config.diameter = float(self.diameter.get())
if len(self.rotor_tilt.get()) > 0:
self.config.rotor_tilt = float(self.rotor_tilt.get())
else:
self.config.rotor_tilt = None
#meta data
self.config.data_type = self.data_type.get()
self.config.outline_site_classification = self.outline_site_classification.get()
self.config.outline_forestry_classification = self.outline_forestry_classification.get()
self.config.iec_terrain_classification = self.iec_terrain_classification.get()
if len(self.latitude.get()) > 0:
self.config.latitude = float(self.latitude.get())
else:
self.config.latitude = None
if len(self.longitude.get()) > 0:
self.config.longitude = float(self.longitude.get())
else:
self.config.longitude = None
self.config.continent = self.continent.get()
self.config.country = self.country.get()
if len(self.elevation_above_sea_level.get()) > 0:
self.config.elevation_above_sea_level = float(self.elevation_above_sea_level.get())
else:
self.config.elevation_above_sea_level = None
self.config.measurement_compliance = self.measurement_compliance.get()
self.config.anemometry_type = self.anemometry_type.get()
self.config.anemometry_heating = self.anemometry_heating.get()
self.config.turbulence_measurement_type = self.turbulence_measurement_type.get()
self.config.power_measurement_type = self.power_measurement_type.get()
self.config.turbine_control_type = self.turbine_control_type.get()
if len(self.turbine_technology_vintage.get()) > 0:
self.config.turbine_technology_vintage = int(self.turbine_technology_vintage.get())
else:
self.config.turbine_technology_vintage = None
self.config.time_zone = self.time_zone.get()
#advanced
self.config.density_pre_correction_active = bool(self.density_pre_correction_active.get())
self.config.density_pre_correction_wind_speed = self.density_pre_correction_wind_speed.get()
if len(self.density_pre_correction_reference_density.get()) > 0:
self.config.density_pre_correction_reference_density = float(self.density_pre_correction_reference_density.get())
else:
self.config.density_pre_correction_reference_density = None
class DatasetGridBox(GridBox):
def __init__(self, master, parent_dialog, row, column, datasets_file_manager):
self.parent_dialog = parent_dialog
headers = ["Dataset", "Exists"]
GridBox.__init__(self, master, headers, row, column)
self.pop_menu.add_command(label="Add Existing", command=self.add)
self.pop_menu_add.add_command(label="Add Existing", command=self.add)
self.datasets_file_manager = datasets_file_manager
self.add_items(self.datasets_file_manager)
def size(self):
return self.item_count()
def get(self, index):
return self.get_items()[index].display_path
def get_item_values(self, item):
values_dict = {}
values_dict["Dataset"] = item.display_path
values_dict["Exists"] = os.path.isfile(item.absolute_path)
return values_dict
def get_header_scale(self):
return 10
def new(self):
try:
config = DatasetConfiguration()
DatasetConfigurationDialog(self.master, self.add_from_file_path, config)
except ExceptionHandler.ExceptionType as e:
ExceptionHandler.add(e, "ERROR creating dataset config")
def add(self):
preferences = Preferences.get()
file_name = tkFileDialog.askopenfilename(parent=self.master, initialdir=preferences.dataset_last_opened_dir(), defaultextension=".xml")
if len(file_name) > 0: self.add_from_file_path(file_name)
def add_from_file_path(self, path):
try:
preferences = Preferences.get()
preferences.datasetLastOpened = path
preferences.save()
except ExceptionHandler.ExceptionType as e:
ExceptionHandler.add(e, "Cannot save preferences")
dataset = self.datasets_file_manager.append_absolute(path)
self.add_item(dataset)
self.parent_dialog.validate_datasets.validate()
def edit_item(self, item):
try:
datasetConfig = DatasetConfiguration(item.absolute_path)
DatasetConfigurationDialog(self.master, None, datasetConfig, None)
except ExceptionHandler.ExceptionType as e:
ExceptionHandler.add(e, "ERROR editing")
def remove(self):
selected = self.get_selected()
self.datasets_file_manager.remove(selected)
GridBox.remove(self)
self.parent_dialog.validate_datasets.validate()
| mit |
myselfHimanshu/UdacityDSWork | Intro to Machine Learning/k_means/k_means_cluster.py | 2 | 2938 | #!/usr/bin/python
"""
skeleton code for k-means clustering mini-project
"""
import pickle
import numpy
import matplotlib.pyplot as plt
import sys
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit
def Draw(pred, features, poi, mark_poi=False, name="image.png", f1_name="feature 1", f2_name="feature 2"):
""" some plotting code designed to help you visualize your clusters """
### plot each cluster with a different color--add more colors for
### drawing more than 4 clusters
colors = ["b", "c", "k", "m", "g"]
for ii, pp in enumerate(pred):
plt.scatter(features[ii][0], features[ii][1], color = colors[pred[ii]])
### if you like, place red stars over points that are POIs (just for funsies)
if mark_poi:
for ii, pp in enumerate(pred):
if poi[ii]:
plt.scatter(features[ii][0], features[ii][1], color="r", marker="*")
plt.xlabel(f1_name)
plt.ylabel(f2_name)
plt.savefig(name)
plt.show()
### load in the dict of dicts containing all the data on each person in the dataset
data_dict = pickle.load( open("../final_project/final_project_dataset.pkl", "r") )
### there's an outlier--remove it!
data_dict.pop("TOTAL", 0)
stocks = []
for key, value in data_dict.iteritems():
if value['exercised_stock_options'] != 'NaN':
stocks.append(value['exercised_stock_options'])
print min(stocks), max(stocks)
### the input features we want to use
### can be any key in the person-level dictionary (salary, director_fees, etc.)
feature_1 = "salary"
feature_2 = "exercised_stock_options"
#feature_3 = "total_payments"
poi = "poi"
features_list = [poi, feature_1, feature_2]
data = featureFormat(data_dict, features_list )
poi, finance_features = targetFeatureSplit( data )
### in the "clustering with 3 features" part of the mini-project,
### you'll want to change this line to
### for f1, f2, _ in finance_features:
### (as it's currently written, line below assumes 2 features)
for f1, f2 in finance_features:
plt.scatter( f1, f2)
plt.show()
from sklearn.cluster import KMeans
features_list = ["poi", feature_1, feature_2]
data2 = featureFormat(data_dict, features_list )
poi, finance_features = targetFeatureSplit( data2 )
clf = KMeans(n_clusters=2)
pred = clf.fit_predict( finance_features )
Draw(pred, finance_features, poi, name="clusters_before_scaling.pdf", f1_name=feature_1, f2_name=feature_2)
### cluster here; create predictions of the cluster labels
### for the data and store them to a list called pred
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(finance_features)
print scaler.transform([200000., 1000000.])
try:
Draw(pred, finance_features, poi, mark_poi=False, name="clusters.pdf", f1_name=feature_1, f2_name=feature_2)
except NameError:
print "no predictions object named pred found, no clusters to plot"
| gpl-2.0 |
weinbe58/QuSpin | examples/scripts/outdated/boson_MBL.py | 1 | 2746 | from quspin.basis import boson_basis_1d,spin_basis_1d
from quspin.operators import hamiltonian,exp_op
from quspin.tools.measurements import obs_vs_time
import numpy as np
import matplotlib.pyplot as plt
N=10
L=2*N
start=0
stop=10
num=20
endpoint=True
basis = boson_basis_1d(L,Nb=N)
np.random.seed()
U_1 = [[1.0,i,i] for i in range(L)]
U_2 = [[-1.0,i] for i in range(L)]
t = [[1.0,i,(i+1)] for i in range(L-1)]
I_list = [[(-1.0)**i,i] for i in range(L)]
static = [["+-",t],["-+",t],["nn",U_1],["n",U_2]]
dynamic = []
static,dynamic = basis.expanded_form(static,dynamic)
H0=hamiltonian(static,dynamic,basis=basis,dtype=np.float32)
I = hamiltonian([["n",I_list]],[],basis=basis,dtype=np.float32)/N
psi_i = np.zeros((basis.Ns),dtype=np.float32)
N0 = sum((N+1)**i for i in range(0,L,2))
i = basis.index(N0)
psi_i[i] = 1.0
times = np.linspace(start,stop,num=num,endpoint=endpoint)
def realization(H0,basis,psi_i,w,I,start,stop,num,endpoint,i):
print i
disorder = [[np.random.uniform(-w,w),i] for i in range(L)]
Hl = hamiltonian([["n",disorder]],[],basis=H0.basis,dtype=np.float32,check_pcon=False,check_herm=False,check_symm=False)
expH = exp_op(H0+Hl,a=-1j,start=start,stop=stop,num=num,endpoint=endpoint,iterate=True)
times = expH.grid
psi_t = expH.dot(psi_i)
obs = obs_vs_time(psi_t,times,{"I":I},basis=basis)#,Sent_args=dict(sparse=True,sub_sys_A=[0]))
try:
return obs["I"],obs["Sent_time"]["Sent"]
except KeyError:
return obs["I"]
nbin=100
nbs=1000
n_data = [realization(H0,basis,psi_i,5.0,I,start,stop,num,endpoint,i) for i in range(nbin)]
#n_data,S_data = zip(*n_data)
n_data = np.vstack(n_data)
n_1 = n_data.mean(axis=0)
bootstraps = np.random.choice(n_data.shape[0],size=(nbs,))
dn_1 = np.sqrt(((n_data[bootstraps] - n_1)**2).sum(axis=0)/(nbs*nbin))
"""
S_data = np.vstack(S_data)
S_1 = S_data.mean(axis=0)
bootstraps = np.random.choice(S_data.shape[0],size=(nbs,))
dS_1 = np.sqrt(((S_data[bootstraps] - S_1)**2).sum(axis=0)/(nbs*nbin))
"""
nbin=100
nbs=1000
n_data = [realization(H0,basis,psi_i,1.0,I,start,stop,num,endpoint,i) for i in range(nbin)]
#n_data,S_data = zip(*n_data)
n_data = np.vstack(n_data)
n_2 = n_data.mean(axis=0)
bootstraps = np.random.choice(n_data.shape[0],size=(nbs,))
dn_2 = np.sqrt(((n_data[bootstraps] - n_2)**2).sum(axis=0)/(nbs*nbin))
"""
S_data = np.vstack(S_data)
S_2 = S_data.mean(axis=0)
bootstraps = np.random.choice(S_data.shape[0],size=(nbs,))
dS_2 = np.sqrt(((S_data[bootstraps] - S_2)**2).sum(axis=0)/(nbs*nbin))
"""
plt.errorbar(times,n_1,dn_1,marker=".",color="green")
plt.errorbar(times,n_2,dn_2,marker=".",color="blue")
#plt.figure()
#plt.errorbar(times,S_1,dS_1,marker=".",color="green")
#plt.errorbar(times,S_2,dS_2,marker=".",color="blue")
plt.show()
| bsd-3-clause |
LSSTDESC/Twinkles | utils/catalog_production/add_om10_properties.py | 2 | 15129 | import numpy as np
import urllib
import os
import argparse
from sklearn.cross_validation import train_test_split
from astroML.plotting import setup_text_plots
import empiriciSN
from MatchingLensGalaxies_utilities import *
from astropy.io import fits
import GCRCatalogs
import pandas as pd
from GCR import GCRQuery
sys.path.append('/global/homes/b/brycek/DC2/sims_GCRCatSimInterface/workspace/sed_cache/')
from SedFitter import sed_from_galacticus_mags
from lsst.sims.photUtils import Sed, Bandpass, BandpassDict
def get_sl2s_data():
filename = os.path.join(os.environ['TWINKLES_DIR'], 'data',
'SonnenfeldEtal2013_Table3.csv')
z = np.array([])
z_err = np.array([])
v_disp = np.array([])
v_disp_err = np.array([])
r_eff = np.array([])
r_eff_err = np.array([])
log_m = np.array([])
log_m_err = np.array([])
infile = open(filename, 'r')
inlines = infile.readlines()
for line1 in inlines:
if line1[0] == '#': continue
line = line1.split(',')
#Params
z = np.append(z, float(line[1]))
v_disp = np.append(v_disp, float(line[2]))
r_eff = np.append(r_eff, float(line[3]))
log_m = np.append(log_m, float(line[4]))
#Errors
z_err = np.append(z_err, float(line[5]))
v_disp_err = np.append(v_disp_err, float(line[6]))
r_eff_err = np.append(r_eff_err, float(line[7]))
log_m_err = np.append(log_m_err, float(line[8]))
#Build final arrays
X = np.vstack([z, v_disp, r_eff, log_m]).T
Xerr = np.zeros(X.shape + X.shape[-1:])
diag = np.arange(X.shape[-1])
Xerr[:, diag, diag] = np.vstack([z_err**2, v_disp_err**2,
r_eff_err**2, log_m_err**2]).T
return X, Xerr
#Write new conditioning function
def get_log_m(cond_indices, m_index, X, model_file, Xerr=None):
"""
Uses a subset of parameters in the given data to condition the
model and return a sample value for log(M/M_sun).
Parameters
----------
cond_indices: array_like
Array of indices indicating which parameters to use to
condition the model.
m_index: int
Index of log(M/M_sun) in the list of parameters that were used
to fit the model.
X: array_like, shape = (n < n_features,)
Input data.
Xerr: array_like, shape = (X.shape,) (optional)
Error on input data. If none, no error used to condition.
Returns
-------
log_m: float
Sample value of log(M/M_sun) taken from the conditioned model.
Notes
-----
The fit_params array specifies a list of indices to use to
condition the model. The model will be conditioned and then
a mass will be drawn from the conditioned model.
This is so that the mass can be used to find cosmoDC2 galaxies
to act as hosts for OM10 systems.
This does not make assumptions about what parameters are being
used in the model, but does assume that the model has been
fit already.
"""
if m_index in cond_indices:
raise ValueError("Cannot condition model on log(M/M_sun).")
cond_data = np.array([])
if Xerr is not None: cond_err = np.array([])
m_cond_idx = m_index
n_features = empiricist.XDGMM.mu.shape[1]
j = 0
for i in range(n_features):
if i in cond_indices:
cond_data = np.append(cond_data,X[j])
if Xerr is not None: cond_err = np.append(cond_err, Xerr[j])
j += 1
if i < m_index: m_cond_idx -= 1
else:
cond_data = np.append(cond_data,np.nan)
if Xerr is not None: cond_err = np.append(cond_err, 0.0)
if Xerr is not None:
cond_XDGMM = empiricist.XDGMM.condition(cond_data, cond_err)
else: cond_XDGMM = empiricist.XDGMM.condition(cond_data)
sample = cond_XDGMM.sample()
log_m = sample[0][m_cond_idx]
return log_m
def estimate_stellar_masses_om10():
# Instantiate an empiriciSN worker object:
empiricist = empiriciSN.Empiricist()
X, Xerr = get_sl2s_data()
# Load in cached om10 catalog
filename = filename = os.path.join(os.environ['TWINKLES_DIR'], 'data', 'om10_qso_mock.fits')
hdulist = fits.open(filename)
twinkles_lenses = hdulist[1].data
# Predict a mass for each galaxy:
np.random.seed(0)
cond_indices = np.array([0,1])
twinkles_log_m_1comp = np.array([])
model_file='demo_model.fit'
empiricist.fit_model(X, Xerr, filename = 'demo_model.fit', n_components=1)
twinkles_data = np.array([twinkles_lenses['ZLENS'], twinkles_lenses['VELDISP']]).T
for x in twinkles_data:
log_m = get_log_m(cond_indices, 2, x[cond_indices], model_file)
twinkles_log_m_1comp = np.append(twinkles_log_m_1comp,log_m)
return twinkles_lenses, log_m, twinkles_log_m_1comp
def get_catalog(catalog, twinkles_lenses, twinkles_log_m_1comp):
gcr_om10_match = []
err = 0
np.random.seed(10)
i = 0
z_cat_min = np.power(10, np.log10(np.min(twinkles_lenses['ZLENS'])) - .1)
z_cat_max = np.power(10, np.log10(np.max(twinkles_lenses['ZLENS'])) + .1)
stellar_mass_cat_min = np.min(np.power(10, twinkles_log_m_1comp))*0.9
stellar_mass_cat_max = np.max(np.power(10, twinkles_log_m_1comp))*1.1
data = catalog.get_quantities(['galaxy_id', 'redshift_true', 'stellar_mass', 'ellipticity_true', 'size_true', 'size_minor_true',
'stellar_mass_bulge', 'stellar_mass_disk', 'size_bulge_true', 'size_minor_bulge_true'],
filters=['stellar_mass > %f' % stellar_mass_cat_min, 'stellar_mass < %f' % stellar_mass_cat_max,
'redshift_true > %f' % z_cat_min, 'redshift_true < %f' % z_cat_max,
'stellar_mass_bulge/stellar_mass > 0.99'])
#### Important Note
# Twinkles issue #310 (https://github.com/LSSTDESC/Twinkles/issues/310) says OM10 defines ellipticity as 1 - b/a but
# gcr_catalogs defines ellipticity as (1-b/a)/(1+b/a) (https://github.com/LSSTDESC/gcr-catalogs/blob/master/GCRCatalogs/SCHEMA.md)
data['om10_ellipticity'] = (1-(data['size_minor_bulge_true']/data['size_bulge_true']))
data_df = pd.DataFrame(data)
data_df.to_csv('om10_matching_checkpoint_1.csv', index=False)
def match_to_cat(twinkles_lenses, twinkles_log_m_1comp, data_df):
row_num = -1
keep_rows = []
for zsrc, m_star, ellip in zip(twinkles_lenses['ZLENS'], np.power(10, twinkles_log_m_1comp), twinkles_lenses['ELLIP']):
row_num += 1
#print(zsrc, m_star, ellip)
if row_num % 1000 == 0:
print(row_num)
z_min, z_max = np.power(10, np.log10(zsrc) - .1), np.power(10, np.log10(zsrc) + .1)
m_star_min, m_star_max = m_star*.9, m_star*1.1
ellip_min, ellip_max = ellip*.9, ellip*1.1
data_subset = data_df.query('redshift_true > %f and redshift_true < %f and stellar_mass > %f and stellar_mass < %f and om10_ellipticity > %f and om10_ellipticity < %f' %
(z_min, z_max, m_star_min, m_star_max, ellip_min, ellip_max))
#data = catalog.get_quantities(['redshift_true', 'stellar_mass', 'ellipticity_true'])
#data_subset = (query).filter(data)
#print(data_subset)
num_matches = len(data_subset['redshift_true'])
if num_matches == 0:
err += 1
continue
elif num_matches == 1:
gcr_data = [data_subset['redshift_true'].values[0],
data_subset['stellar_mass_bulge'].values[0],
data_subset['om10_ellipticity'].values[0],
data_subset['size_bulge_true'].values[0],
data_subset['size_minor_bulge_true'].values[0],
data_subset['galaxy_id'].values[0]]
gcr_om10_match.append(gcr_data)
keep_rows.append(row_num)
elif num_matches > 1:
use_idx = np.random.choice(num_matches)
gcr_data = [data_subset['redshift_true'].values[use_idx],
data_subset['stellar_mass_bulge'].values[use_idx],
data_subset['om10_ellipticity'].values[use_idx],
data_subset['size_bulge_true'].values[use_idx],
data_subset['size_minor_bulge_true'].values[use_idx],
data_subset['galaxy_id'].values[use_idx]]
gcr_om10_match.append(gcr_data)
keep_rows.append(row_num)
print("Total Match Failures: ", err, " Percentage Match Failures: ", np.float(err)/len(twinkles_log_m_1comp))
np.savetxt('gcr_om10_match.dat', gcr_om10_match)
def get_catalog_mags(catalog):
H0 = catalog.cosmology.H0.value
Om0 = catalog.cosmology.Om0
sed_label = []
sed_min_wave = []
sed_wave_width = []
for quant_label in sorted(catalog.list_all_quantities()):
if (quant_label.startswith('sed') and quant_label.endswith('bulge')):
sed_label.append(quant_label)
label_split = quant_label.split('_')
sed_min_wave.append(int(label_split[1])/10)
sed_wave_width.append(int(label_split[2])/10)
bin_order = np.argsort(sed_min_wave)
sed_label = np.array(sed_label)[bin_order]
sed_min_wave = np.array(sed_min_wave)[bin_order]
sed_wave_width = np.array(sed_wave_width)[bin_order]
for i in zip(sed_label, sed_min_wave, sed_wave_width):
print(i)
columns = ['galaxy_id', 'redshift_true', 'mag_u_lsst', 'mag_g_lsst', 'mag_r_lsst',
'mag_i_lsst', 'mag_z_lsst', 'mag_y_lsst']
for sed_bin in sed_label:
columns.append(sed_bin)
data = catalog.get_quantities(columns,
filters=['stellar_mass > %f' % stellar_mass_cat_min, 'stellar_mass < %f' % stellar_mass_cat_max,
'redshift_true > %f' % z_cat_min, 'redshift_true < %f' % z_cat_max,
'stellar_mass_bulge/stellar_mass > 0.99'])
data_df = pd.DataFrame(data)
data_df.to_csv('om10_matching_checkpoint_2.csv', index=False)
def get_30_band_mags(gcr_om10_match, data_df, catalog):
H0 = catalog.cosmology.H0.value
Om0 = catalog.cosmology.Om0
sed_name_list = []
magNorm_list = []
i = 0
lsst_mags = []
mag_30_list = []
redshift_list = []
sed_label = []
sed_min_wave = []
sed_wave_width = []
for quant_label in sorted(catalog.list_all_quantities()):
if (quant_label.startswith('sed') and quant_label.endswith('bulge')):
sed_label.append(quant_label)
label_split = quant_label.split('_')
sed_min_wave.append(int(label_split[1])/10)
sed_wave_width.append(int(label_split[2])/10)
bin_order = np.argsort(sed_min_wave)
sed_label = np.array(sed_label)[bin_order]
sed_min_wave = np.array(sed_min_wave)[bin_order]
sed_wave_width = np.array(sed_wave_width)[bin_order]
lsst_mags = []
mag_30_list = []
redshift_list = []
i = 0
# Using 1-component model results
for gal_id, gal_z in zip(gcr_gal_id_1comp, gcr_z_1comp):
if i % 1000 == 0:
print(i)
i+=1
data_subset = data_df.query(str('galaxy_id == %i' % gal_id)) ## Galaxy Ids are not unique in cosmoDC2_v0.1
mag_array = []
lsst_mag_array = [data_subset['mag_%s_lsst' % band_name].values[0] for band_name in ['u', 'g', 'r', 'i', 'z', 'y']]
for sed_bin in sed_label:
mag_array.append(-2.5*np.log10(data_subset[sed_bin].values[0]))
mag_array = np.array(mag_array)
lsst_mag_array = np.array(lsst_mag_array)
lsst_mags.append(lsst_mag_array)
mag_30_list.append(mag_array)
redshift_list.append(gal_z)
mag_30_list = np.array(mag_30_list).T
lsst_mags = np.array(lsst_mags).T
redshift_list = np.array(redshift_list)
sed_name, magNorm, av, rv = sed_from_galacticus_mags(mag_30_list, redshift_list,
H0, Om0, sed_min_wave, sed_wave_width, lsst_mags)
return np.array(sed_name), np.array(magNorm), np.array(av), np.array(rv)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("cat_version", type=str,
help="CosmoDC2 catalog name from gcr-catalogs.")
args = parser.parse_args()
catalog_version = args.cat_version
twinkles_lenses, log_m, twinkles_log_m_1comp = estimate_stellar_masses_om10()
# Use _image for DC2
catalog = GCRCatalogs.load_catalog(str(catalog_version + '_small'))
data_df = pd.read_csv('om10_matching_checkpoint_1.csv')
match_to_cat(twinkles_lenses, twinkles_log_m_1comp, data_df)
gcr_om10_match = np.genfromtxt('gcr_om10_match.dat')
gcr_z = []
gcr_m_star = []
gcr_r_eff = []
gcr_gal_id = []
for row in gcr_om10_match:
gcr_z.append(row[0])
gcr_m_star.append(row[1])
gcr_r_eff.append(np.sqrt(row[3]*row[4]))
gcr_gal_id.append(row[5])
get_catalog_mags(catalog)
data_df = pd.read_csv('om10_matching_checkpoint_2.csv')
sed_name, magNorm, av, rv = get_30_band_mags(gcr_gal_id, gcr_z, data_df, catalog)
sed_name_array = np.array(sed_name)
magNorm_array = np.array(magNorm)
av_array = np.array(av)
rv_array = np.array(rv)
test_bandpassDict = BandpassDict.loadTotalBandpassesFromFiles()
imsimband = Bandpass()
imsimband.imsimBandpass()
mag_norm_om10 = []
for i, idx in list(enumerate(keep_rows)):
if i % 10000 == 0:
print(i, idx)
test_sed = Sed()
test_sed.readSED_flambda(os.path.join(str(os.environ['SIMS_SED_LIBRARY_DIR']), sed_name_array[i]))
fnorm = test_sed.calcFluxNorm(twinkles_lenses['APMAG_I'][idx], test_bandpassDict['i'])
test_sed.multiplyFluxNorm(fnorm)
magNorm_diff = magNorm_array[3, i] - test_sed.calcMag(imsimband)
mag_norm_om10.append(magNorm_array[:,i] - magNorm_diff)
col_list = []
for col in twinkles_lenses.columns:
if col.name != 'REFF':
col_list.append(fits.Column(name=col.name, format=col.format, array=twinkles_lenses[col.name][keep_rows]))
else:
col_list.append(fits.Column(name=col.name, format=col.format, array=gcr_r_eff_1comp))
col_list.append(fits.Column(name='lens_sed', format='40A', array=sed_name_array))
col_list.append(fits.Column(name='sed_magNorm', format='6D', array=mag_norm_om10))
col_list.append(fits.Column(name='lens_av', format='D', array=av_array))
col_list.append(fits.Column(name='lens_rv', format='D', array=rv_array))
cols = fits.ColDefs(col_list)
tbhdu = fits.BinTableHDU.from_columns(cols)
tbhdu.writeto('../../data/twinkles_lenses_%s.fits' % catalog_version)
| mit |
3manuek/scikit-learn | sklearn/linear_model/least_angle.py | 42 | 49357 | """
Least Angle Regression algorithm. See the documentation on the
Generalized Linear Model for a complete discussion.
"""
from __future__ import print_function
# Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux
#
# License: BSD 3 clause
from math import log
import sys
import warnings
from distutils.version import LooseVersion
import numpy as np
from scipy import linalg, interpolate
from scipy.linalg.lapack import get_lapack_funcs
from .base import LinearModel
from ..base import RegressorMixin
from ..utils import arrayfuncs, as_float_array, check_X_y
from ..cross_validation import check_cv
from ..utils import ConvergenceWarning
from ..externals.joblib import Parallel, delayed
from ..externals.six.moves import xrange
import scipy
solve_triangular_args = {}
if LooseVersion(scipy.__version__) >= LooseVersion('0.12'):
solve_triangular_args = {'check_finite': False}
def lars_path(X, y, Xy=None, Gram=None, max_iter=500,
alpha_min=0, method='lar', copy_X=True,
eps=np.finfo(np.float).eps,
copy_Gram=True, verbose=0, return_path=True,
return_n_iter=False):
"""Compute Least Angle Regression or Lasso path using LARS algorithm [1]
The optimization objective for the case method='lasso' is::
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
in the case of method='lars', the objective function is only known in
the form of an implicit equation (see discussion in [1])
Read more in the :ref:`User Guide <least_angle_regression>`.
Parameters
-----------
X : array, shape: (n_samples, n_features)
Input data.
y : array, shape: (n_samples)
Input targets.
max_iter : integer, optional (default=500)
Maximum number of iterations to perform, set to infinity for no limit.
Gram : None, 'auto', array, shape: (n_features, n_features), optional
Precomputed Gram matrix (X' * X), if ``'auto'``, the Gram
matrix is precomputed from the given X, if there are more samples
than features.
alpha_min : float, optional (default=0)
Minimum correlation along the path. It corresponds to the
regularization parameter alpha parameter in the Lasso.
method : {'lar', 'lasso'}, optional (default='lar')
Specifies the returned model. Select ``'lar'`` for Least Angle
Regression, ``'lasso'`` for the Lasso.
eps : float, optional (default=``np.finfo(np.float).eps``)
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems.
copy_X : bool, optional (default=True)
If ``False``, ``X`` is overwritten.
copy_Gram : bool, optional (default=True)
If ``False``, ``Gram`` is overwritten.
verbose : int (default=0)
Controls output verbosity.
return_path : bool, optional (default=True)
If ``return_path==True`` returns the entire path, else returns only the
last point of the path.
return_n_iter : bool, optional (default=False)
Whether to return the number of iterations.
Returns
--------
alphas : array, shape: [n_alphas + 1]
Maximum of covariances (in absolute value) at each iteration.
``n_alphas`` is either ``max_iter``, ``n_features`` or the
number of nodes in the path with ``alpha >= alpha_min``, whichever
is smaller.
active : array, shape [n_alphas]
Indices of active variables at the end of the path.
coefs : array, shape (n_features, n_alphas + 1)
Coefficients along the path
n_iter : int
Number of iterations run. Returned only if return_n_iter is set
to True.
See also
--------
lasso_path
LassoLars
Lars
LassoLarsCV
LarsCV
sklearn.decomposition.sparse_encode
References
----------
.. [1] "Least Angle Regression", Effron et al.
http://www-stat.stanford.edu/~tibs/ftp/lars.pdf
.. [2] `Wikipedia entry on the Least-angle regression
<http://en.wikipedia.org/wiki/Least-angle_regression>`_
.. [3] `Wikipedia entry on the Lasso
<http://en.wikipedia.org/wiki/Lasso_(statistics)#Lasso_method>`_
"""
n_features = X.shape[1]
n_samples = y.size
max_features = min(max_iter, n_features)
if return_path:
coefs = np.zeros((max_features + 1, n_features))
alphas = np.zeros(max_features + 1)
else:
coef, prev_coef = np.zeros(n_features), np.zeros(n_features)
alpha, prev_alpha = np.array([0.]), np.array([0.]) # better ideas?
n_iter, n_active = 0, 0
active, indices = list(), np.arange(n_features)
# holds the sign of covariance
sign_active = np.empty(max_features, dtype=np.int8)
drop = False
# will hold the cholesky factorization. Only lower part is
# referenced.
# We are initializing this to "zeros" and not empty, because
# it is passed to scipy linalg functions and thus if it has NaNs,
# even if they are in the upper part that it not used, we
# get errors raised.
# Once we support only scipy > 0.12 we can use check_finite=False and
# go back to "empty"
L = np.zeros((max_features, max_features), dtype=X.dtype)
swap, nrm2 = linalg.get_blas_funcs(('swap', 'nrm2'), (X,))
solve_cholesky, = get_lapack_funcs(('potrs',), (X,))
if Gram is None:
if copy_X:
# force copy. setting the array to be fortran-ordered
# speeds up the calculation of the (partial) Gram matrix
# and allows to easily swap columns
X = X.copy('F')
elif Gram == 'auto':
Gram = None
if X.shape[0] > X.shape[1]:
Gram = np.dot(X.T, X)
elif copy_Gram:
Gram = Gram.copy()
if Xy is None:
Cov = np.dot(X.T, y)
else:
Cov = Xy.copy()
if verbose:
if verbose > 1:
print("Step\t\tAdded\t\tDropped\t\tActive set size\t\tC")
else:
sys.stdout.write('.')
sys.stdout.flush()
tiny = np.finfo(np.float).tiny # to avoid division by 0 warning
tiny32 = np.finfo(np.float32).tiny # to avoid division by 0 warning
equality_tolerance = np.finfo(np.float32).eps
while True:
if Cov.size:
C_idx = np.argmax(np.abs(Cov))
C_ = Cov[C_idx]
C = np.fabs(C_)
else:
C = 0.
if return_path:
alpha = alphas[n_iter, np.newaxis]
coef = coefs[n_iter]
prev_alpha = alphas[n_iter - 1, np.newaxis]
prev_coef = coefs[n_iter - 1]
alpha[0] = C / n_samples
if alpha[0] <= alpha_min + equality_tolerance: # early stopping
if abs(alpha[0] - alpha_min) > equality_tolerance:
# interpolation factor 0 <= ss < 1
if n_iter > 0:
# In the first iteration, all alphas are zero, the formula
# below would make ss a NaN
ss = ((prev_alpha[0] - alpha_min) /
(prev_alpha[0] - alpha[0]))
coef[:] = prev_coef + ss * (coef - prev_coef)
alpha[0] = alpha_min
if return_path:
coefs[n_iter] = coef
break
if n_iter >= max_iter or n_active >= n_features:
break
if not drop:
##########################################################
# Append x_j to the Cholesky factorization of (Xa * Xa') #
# #
# ( L 0 ) #
# L -> ( ) , where L * w = Xa' x_j #
# ( w z ) and z = ||x_j|| #
# #
##########################################################
sign_active[n_active] = np.sign(C_)
m, n = n_active, C_idx + n_active
Cov[C_idx], Cov[0] = swap(Cov[C_idx], Cov[0])
indices[n], indices[m] = indices[m], indices[n]
Cov_not_shortened = Cov
Cov = Cov[1:] # remove Cov[0]
if Gram is None:
X.T[n], X.T[m] = swap(X.T[n], X.T[m])
c = nrm2(X.T[n_active]) ** 2
L[n_active, :n_active] = \
np.dot(X.T[n_active], X.T[:n_active].T)
else:
# swap does only work inplace if matrix is fortran
# contiguous ...
Gram[m], Gram[n] = swap(Gram[m], Gram[n])
Gram[:, m], Gram[:, n] = swap(Gram[:, m], Gram[:, n])
c = Gram[n_active, n_active]
L[n_active, :n_active] = Gram[n_active, :n_active]
# Update the cholesky decomposition for the Gram matrix
if n_active:
linalg.solve_triangular(L[:n_active, :n_active],
L[n_active, :n_active],
trans=0, lower=1,
overwrite_b=True,
**solve_triangular_args)
v = np.dot(L[n_active, :n_active], L[n_active, :n_active])
diag = max(np.sqrt(np.abs(c - v)), eps)
L[n_active, n_active] = diag
if diag < 1e-7:
# The system is becoming too ill-conditioned.
# We have degenerate vectors in our active set.
# We'll 'drop for good' the last regressor added.
# Note: this case is very rare. It is no longer triggered by the
# test suite. The `equality_tolerance` margin added in 0.16.0 to
# get early stopping to work consistently on all versions of
# Python including 32 bit Python under Windows seems to make it
# very difficult to trigger the 'drop for good' strategy.
warnings.warn('Regressors in active set degenerate. '
'Dropping a regressor, after %i iterations, '
'i.e. alpha=%.3e, '
'with an active set of %i regressors, and '
'the smallest cholesky pivot element being %.3e'
% (n_iter, alpha, n_active, diag),
ConvergenceWarning)
# XXX: need to figure a 'drop for good' way
Cov = Cov_not_shortened
Cov[0] = 0
Cov[C_idx], Cov[0] = swap(Cov[C_idx], Cov[0])
continue
active.append(indices[n_active])
n_active += 1
if verbose > 1:
print("%s\t\t%s\t\t%s\t\t%s\t\t%s" % (n_iter, active[-1], '',
n_active, C))
if method == 'lasso' and n_iter > 0 and prev_alpha[0] < alpha[0]:
# alpha is increasing. This is because the updates of Cov are
# bringing in too much numerical error that is greater than
# than the remaining correlation with the
# regressors. Time to bail out
warnings.warn('Early stopping the lars path, as the residues '
'are small and the current value of alpha is no '
'longer well controlled. %i iterations, alpha=%.3e, '
'previous alpha=%.3e, with an active set of %i '
'regressors.'
% (n_iter, alpha, prev_alpha, n_active),
ConvergenceWarning)
break
# least squares solution
least_squares, info = solve_cholesky(L[:n_active, :n_active],
sign_active[:n_active],
lower=True)
if least_squares.size == 1 and least_squares == 0:
# This happens because sign_active[:n_active] = 0
least_squares[...] = 1
AA = 1.
else:
# is this really needed ?
AA = 1. / np.sqrt(np.sum(least_squares * sign_active[:n_active]))
if not np.isfinite(AA):
# L is too ill-conditioned
i = 0
L_ = L[:n_active, :n_active].copy()
while not np.isfinite(AA):
L_.flat[::n_active + 1] += (2 ** i) * eps
least_squares, info = solve_cholesky(
L_, sign_active[:n_active], lower=True)
tmp = max(np.sum(least_squares * sign_active[:n_active]),
eps)
AA = 1. / np.sqrt(tmp)
i += 1
least_squares *= AA
if Gram is None:
# equiangular direction of variables in the active set
eq_dir = np.dot(X.T[:n_active].T, least_squares)
# correlation between each unactive variables and
# eqiangular vector
corr_eq_dir = np.dot(X.T[n_active:], eq_dir)
else:
# if huge number of features, this takes 50% of time, I
# think could be avoided if we just update it using an
# orthogonal (QR) decomposition of X
corr_eq_dir = np.dot(Gram[:n_active, n_active:].T,
least_squares)
g1 = arrayfuncs.min_pos((C - Cov) / (AA - corr_eq_dir + tiny))
g2 = arrayfuncs.min_pos((C + Cov) / (AA + corr_eq_dir + tiny))
gamma_ = min(g1, g2, C / AA)
# TODO: better names for these variables: z
drop = False
z = -coef[active] / (least_squares + tiny32)
z_pos = arrayfuncs.min_pos(z)
if z_pos < gamma_:
# some coefficients have changed sign
idx = np.where(z == z_pos)[0][::-1]
# update the sign, important for LAR
sign_active[idx] = -sign_active[idx]
if method == 'lasso':
gamma_ = z_pos
drop = True
n_iter += 1
if return_path:
if n_iter >= coefs.shape[0]:
del coef, alpha, prev_alpha, prev_coef
# resize the coefs and alphas array
add_features = 2 * max(1, (max_features - n_active))
coefs = np.resize(coefs, (n_iter + add_features, n_features))
alphas = np.resize(alphas, n_iter + add_features)
coef = coefs[n_iter]
prev_coef = coefs[n_iter - 1]
alpha = alphas[n_iter, np.newaxis]
prev_alpha = alphas[n_iter - 1, np.newaxis]
else:
# mimic the effect of incrementing n_iter on the array references
prev_coef = coef
prev_alpha[0] = alpha[0]
coef = np.zeros_like(coef)
coef[active] = prev_coef[active] + gamma_ * least_squares
# update correlations
Cov -= gamma_ * corr_eq_dir
# See if any coefficient has changed sign
if drop and method == 'lasso':
# handle the case when idx is not length of 1
[arrayfuncs.cholesky_delete(L[:n_active, :n_active], ii) for ii in
idx]
n_active -= 1
m, n = idx, n_active
# handle the case when idx is not length of 1
drop_idx = [active.pop(ii) for ii in idx]
if Gram is None:
# propagate dropped variable
for ii in idx:
for i in range(ii, n_active):
X.T[i], X.T[i + 1] = swap(X.T[i], X.T[i + 1])
# yeah this is stupid
indices[i], indices[i + 1] = indices[i + 1], indices[i]
# TODO: this could be updated
residual = y - np.dot(X[:, :n_active], coef[active])
temp = np.dot(X.T[n_active], residual)
Cov = np.r_[temp, Cov]
else:
for ii in idx:
for i in range(ii, n_active):
indices[i], indices[i + 1] = indices[i + 1], indices[i]
Gram[i], Gram[i + 1] = swap(Gram[i], Gram[i + 1])
Gram[:, i], Gram[:, i + 1] = swap(Gram[:, i],
Gram[:, i + 1])
# Cov_n = Cov_j + x_j * X + increment(betas) TODO:
# will this still work with multiple drops ?
# recompute covariance. Probably could be done better
# wrong as Xy is not swapped with the rest of variables
# TODO: this could be updated
residual = y - np.dot(X, coef)
temp = np.dot(X.T[drop_idx], residual)
Cov = np.r_[temp, Cov]
sign_active = np.delete(sign_active, idx)
sign_active = np.append(sign_active, 0.) # just to maintain size
if verbose > 1:
print("%s\t\t%s\t\t%s\t\t%s\t\t%s" % (n_iter, '', drop_idx,
n_active, abs(temp)))
if return_path:
# resize coefs in case of early stop
alphas = alphas[:n_iter + 1]
coefs = coefs[:n_iter + 1]
if return_n_iter:
return alphas, active, coefs.T, n_iter
else:
return alphas, active, coefs.T
else:
if return_n_iter:
return alpha, active, coef, n_iter
else:
return alpha, active, coef
###############################################################################
# Estimator classes
class Lars(LinearModel, RegressorMixin):
"""Least Angle Regression model a.k.a. LAR
Read more in the :ref:`User Guide <least_angle_regression>`.
Parameters
----------
n_nonzero_coefs : int, optional
Target number of non-zero coefficients. Use ``np.inf`` for no limit.
fit_intercept : boolean
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
verbose : boolean or integer, optional
Sets the verbosity amount
normalize : boolean, optional, default False
If ``True``, the regressors X will be normalized before regression.
precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
eps : float, optional
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems. Unlike the ``tol`` parameter in some iterative
optimization-based algorithms, this parameter does not control
the tolerance of the optimization.
fit_path : boolean
If True the full path is stored in the ``coef_path_`` attribute.
If you compute the solution for a large problem or many targets,
setting ``fit_path`` to ``False`` will lead to a speedup, especially
with a small alpha.
Attributes
----------
alphas_ : array, shape (n_alphas + 1,) | list of n_targets such arrays
Maximum of covariances (in absolute value) at each iteration. \
``n_alphas`` is either ``n_nonzero_coefs`` or ``n_features``, \
whichever is smaller.
active_ : list, length = n_alphas | list of n_targets such lists
Indices of active variables at the end of the path.
coef_path_ : array, shape (n_features, n_alphas + 1) \
| list of n_targets such arrays
The varying values of the coefficients along the path. It is not
present if the ``fit_path`` parameter is ``False``.
coef_ : array, shape (n_features,) or (n_targets, n_features)
Parameter vector (w in the formulation formula).
intercept_ : float | array, shape (n_targets,)
Independent term in decision function.
n_iter_ : array-like or int
The number of iterations taken by lars_path to find the
grid of alphas for each target.
Examples
--------
>>> from sklearn import linear_model
>>> clf = linear_model.Lars(n_nonzero_coefs=1)
>>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1.1111, 0, -1.1111])
... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
Lars(copy_X=True, eps=..., fit_intercept=True, fit_path=True,
n_nonzero_coefs=1, normalize=True, precompute='auto', verbose=False)
>>> print(clf.coef_) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
[ 0. -1.11...]
See also
--------
lars_path, LarsCV
sklearn.decomposition.sparse_encode
"""
def __init__(self, fit_intercept=True, verbose=False, normalize=True,
precompute='auto', n_nonzero_coefs=500,
eps=np.finfo(np.float).eps, copy_X=True, fit_path=True):
self.fit_intercept = fit_intercept
self.verbose = verbose
self.normalize = normalize
self.method = 'lar'
self.precompute = precompute
self.n_nonzero_coefs = n_nonzero_coefs
self.eps = eps
self.copy_X = copy_X
self.fit_path = fit_path
def _get_gram(self):
# precompute if n_samples > n_features
precompute = self.precompute
if hasattr(precompute, '__array__'):
Gram = precompute
elif precompute == 'auto':
Gram = 'auto'
else:
Gram = None
return Gram
def fit(self, X, y, Xy=None):
"""Fit the model using X, y as training data.
parameters
----------
X : array-like, shape (n_samples, n_features)
Training data.
y : array-like, shape (n_samples,) or (n_samples, n_targets)
Target values.
Xy : array-like, shape (n_samples,) or (n_samples, n_targets), \
optional
Xy = np.dot(X.T, y) that can be precomputed. It is useful
only when the Gram matrix is precomputed.
returns
-------
self : object
returns an instance of self.
"""
X, y = check_X_y(X, y, y_numeric=True, multi_output=True)
n_features = X.shape[1]
X, y, X_mean, y_mean, X_std = self._center_data(X, y,
self.fit_intercept,
self.normalize,
self.copy_X)
if y.ndim == 1:
y = y[:, np.newaxis]
n_targets = y.shape[1]
alpha = getattr(self, 'alpha', 0.)
if hasattr(self, 'n_nonzero_coefs'):
alpha = 0. # n_nonzero_coefs parametrization takes priority
max_iter = self.n_nonzero_coefs
else:
max_iter = self.max_iter
precompute = self.precompute
if not hasattr(precompute, '__array__') and (
precompute is True or
(precompute == 'auto' and X.shape[0] > X.shape[1]) or
(precompute == 'auto' and y.shape[1] > 1)):
Gram = np.dot(X.T, X)
else:
Gram = self._get_gram()
self.alphas_ = []
self.n_iter_ = []
if self.fit_path:
self.coef_ = []
self.active_ = []
self.coef_path_ = []
for k in xrange(n_targets):
this_Xy = None if Xy is None else Xy[:, k]
alphas, active, coef_path, n_iter_ = lars_path(
X, y[:, k], Gram=Gram, Xy=this_Xy, copy_X=self.copy_X,
copy_Gram=True, alpha_min=alpha, method=self.method,
verbose=max(0, self.verbose - 1), max_iter=max_iter,
eps=self.eps, return_path=True,
return_n_iter=True)
self.alphas_.append(alphas)
self.active_.append(active)
self.n_iter_.append(n_iter_)
self.coef_path_.append(coef_path)
self.coef_.append(coef_path[:, -1])
if n_targets == 1:
self.alphas_, self.active_, self.coef_path_, self.coef_ = [
a[0] for a in (self.alphas_, self.active_, self.coef_path_,
self.coef_)]
self.n_iter_ = self.n_iter_[0]
else:
self.coef_ = np.empty((n_targets, n_features))
for k in xrange(n_targets):
this_Xy = None if Xy is None else Xy[:, k]
alphas, _, self.coef_[k], n_iter_ = lars_path(
X, y[:, k], Gram=Gram, Xy=this_Xy, copy_X=self.copy_X,
copy_Gram=True, alpha_min=alpha, method=self.method,
verbose=max(0, self.verbose - 1), max_iter=max_iter,
eps=self.eps, return_path=False, return_n_iter=True)
self.alphas_.append(alphas)
self.n_iter_.append(n_iter_)
if n_targets == 1:
self.alphas_ = self.alphas_[0]
self.n_iter_ = self.n_iter_[0]
self._set_intercept(X_mean, y_mean, X_std)
return self
class LassoLars(Lars):
"""Lasso model fit with Least Angle Regression a.k.a. Lars
It is a Linear Model trained with an L1 prior as regularizer.
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
Read more in the :ref:`User Guide <least_angle_regression>`.
Parameters
----------
alpha : float
Constant that multiplies the penalty term. Defaults to 1.0.
``alpha = 0`` is equivalent to an ordinary least square, solved
by :class:`LinearRegression`. For numerical reasons, using
``alpha = 0`` with the LassoLars object is not advised and you
should prefer the LinearRegression object.
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
verbose : boolean or integer, optional
Sets the verbosity amount
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument.
max_iter : integer, optional
Maximum number of iterations to perform.
eps : float, optional
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems. Unlike the ``tol`` parameter in some iterative
optimization-based algorithms, this parameter does not control
the tolerance of the optimization.
fit_path : boolean
If ``True`` the full path is stored in the ``coef_path_`` attribute.
If you compute the solution for a large problem or many targets,
setting ``fit_path`` to ``False`` will lead to a speedup, especially
with a small alpha.
Attributes
----------
alphas_ : array, shape (n_alphas + 1,) | list of n_targets such arrays
Maximum of covariances (in absolute value) at each iteration. \
``n_alphas`` is either ``max_iter``, ``n_features``, or the number of \
nodes in the path with correlation greater than ``alpha``, whichever \
is smaller.
active_ : list, length = n_alphas | list of n_targets such lists
Indices of active variables at the end of the path.
coef_path_ : array, shape (n_features, n_alphas + 1) or list
If a list is passed it's expected to be one of n_targets such arrays.
The varying values of the coefficients along the path. It is not
present if the ``fit_path`` parameter is ``False``.
coef_ : array, shape (n_features,) or (n_targets, n_features)
Parameter vector (w in the formulation formula).
intercept_ : float | array, shape (n_targets,)
Independent term in decision function.
n_iter_ : array-like or int.
The number of iterations taken by lars_path to find the
grid of alphas for each target.
Examples
--------
>>> from sklearn import linear_model
>>> clf = linear_model.LassoLars(alpha=0.01)
>>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1, 0, -1])
... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
LassoLars(alpha=0.01, copy_X=True, eps=..., fit_intercept=True,
fit_path=True, max_iter=500, normalize=True, precompute='auto',
verbose=False)
>>> print(clf.coef_) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
[ 0. -0.963257...]
See also
--------
lars_path
lasso_path
Lasso
LassoCV
LassoLarsCV
sklearn.decomposition.sparse_encode
"""
def __init__(self, alpha=1.0, fit_intercept=True, verbose=False,
normalize=True, precompute='auto', max_iter=500,
eps=np.finfo(np.float).eps, copy_X=True, fit_path=True):
self.alpha = alpha
self.fit_intercept = fit_intercept
self.max_iter = max_iter
self.verbose = verbose
self.normalize = normalize
self.method = 'lasso'
self.precompute = precompute
self.copy_X = copy_X
self.eps = eps
self.fit_path = fit_path
###############################################################################
# Cross-validated estimator classes
def _check_copy_and_writeable(array, copy=False):
if copy or not array.flags.writeable:
return array.copy()
return array
def _lars_path_residues(X_train, y_train, X_test, y_test, Gram=None,
copy=True, method='lars', verbose=False,
fit_intercept=True, normalize=True, max_iter=500,
eps=np.finfo(np.float).eps):
"""Compute the residues on left-out data for a full LARS path
Parameters
-----------
X_train : array, shape (n_samples, n_features)
The data to fit the LARS on
y_train : array, shape (n_samples)
The target variable to fit LARS on
X_test : array, shape (n_samples, n_features)
The data to compute the residues on
y_test : array, shape (n_samples)
The target variable to compute the residues on
Gram : None, 'auto', array, shape: (n_features, n_features), optional
Precomputed Gram matrix (X' * X), if ``'auto'``, the Gram
matrix is precomputed from the given X, if there are more samples
than features
copy : boolean, optional
Whether X_train, X_test, y_train and y_test should be copied;
if False, they may be overwritten.
method : 'lar' | 'lasso'
Specifies the returned model. Select ``'lar'`` for Least Angle
Regression, ``'lasso'`` for the Lasso.
verbose : integer, optional
Sets the amount of verbosity
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
max_iter : integer, optional
Maximum number of iterations to perform.
eps : float, optional
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems. Unlike the ``tol`` parameter in some iterative
optimization-based algorithms, this parameter does not control
the tolerance of the optimization.
Returns
--------
alphas : array, shape (n_alphas,)
Maximum of covariances (in absolute value) at each iteration.
``n_alphas`` is either ``max_iter`` or ``n_features``, whichever
is smaller.
active : list
Indices of active variables at the end of the path.
coefs : array, shape (n_features, n_alphas)
Coefficients along the path
residues : array, shape (n_alphas, n_samples)
Residues of the prediction on the test data
"""
X_train = _check_copy_and_writeable(X_train, copy)
y_train = _check_copy_and_writeable(y_train, copy)
X_test = _check_copy_and_writeable(X_test, copy)
y_test = _check_copy_and_writeable(y_test, copy)
if fit_intercept:
X_mean = X_train.mean(axis=0)
X_train -= X_mean
X_test -= X_mean
y_mean = y_train.mean(axis=0)
y_train = as_float_array(y_train, copy=False)
y_train -= y_mean
y_test = as_float_array(y_test, copy=False)
y_test -= y_mean
if normalize:
norms = np.sqrt(np.sum(X_train ** 2, axis=0))
nonzeros = np.flatnonzero(norms)
X_train[:, nonzeros] /= norms[nonzeros]
alphas, active, coefs = lars_path(
X_train, y_train, Gram=Gram, copy_X=False, copy_Gram=False,
method=method, verbose=max(0, verbose - 1), max_iter=max_iter, eps=eps)
if normalize:
coefs[nonzeros] /= norms[nonzeros][:, np.newaxis]
residues = np.dot(X_test, coefs) - y_test[:, np.newaxis]
return alphas, active, coefs, residues.T
class LarsCV(Lars):
"""Cross-validated Least Angle Regression model
Read more in the :ref:`User Guide <least_angle_regression>`.
Parameters
----------
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
verbose : boolean or integer, optional
Sets the verbosity amount
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument.
max_iter: integer, optional
Maximum number of iterations to perform.
cv : cross-validation generator, optional
see :mod:`sklearn.cross_validation`. If ``None`` is passed, default to
a 5-fold strategy
max_n_alphas : integer, optional
The maximum number of points on the path used to compute the
residuals in the cross-validation
n_jobs : integer, optional
Number of CPUs to use during the cross validation. If ``-1``, use
all the CPUs
eps : float, optional
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems.
Attributes
----------
coef_ : array, shape (n_features,)
parameter vector (w in the formulation formula)
intercept_ : float
independent term in decision function
coef_path_ : array, shape (n_features, n_alphas)
the varying values of the coefficients along the path
alpha_ : float
the estimated regularization parameter alpha
alphas_ : array, shape (n_alphas,)
the different values of alpha along the path
cv_alphas_ : array, shape (n_cv_alphas,)
all the values of alpha along the path for the different folds
cv_mse_path_ : array, shape (n_folds, n_cv_alphas)
the mean square error on left-out for each fold along the path
(alpha values given by ``cv_alphas``)
n_iter_ : array-like or int
the number of iterations run by Lars with the optimal alpha.
See also
--------
lars_path, LassoLars, LassoLarsCV
"""
method = 'lar'
def __init__(self, fit_intercept=True, verbose=False, max_iter=500,
normalize=True, precompute='auto', cv=None,
max_n_alphas=1000, n_jobs=1, eps=np.finfo(np.float).eps,
copy_X=True):
self.fit_intercept = fit_intercept
self.max_iter = max_iter
self.verbose = verbose
self.normalize = normalize
self.precompute = precompute
self.copy_X = copy_X
self.cv = cv
self.max_n_alphas = max_n_alphas
self.n_jobs = n_jobs
self.eps = eps
def fit(self, X, y):
"""Fit the model using X, y as training data.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data.
y : array-like, shape (n_samples,)
Target values.
Returns
-------
self : object
returns an instance of self.
"""
self.fit_path = True
X, y = check_X_y(X, y, y_numeric=True)
# init cross-validation generator
cv = check_cv(self.cv, X, y, classifier=False)
Gram = 'auto' if self.precompute else None
cv_paths = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
delayed(_lars_path_residues)(
X[train], y[train], X[test], y[test], Gram=Gram, copy=False,
method=self.method, verbose=max(0, self.verbose - 1),
normalize=self.normalize, fit_intercept=self.fit_intercept,
max_iter=self.max_iter, eps=self.eps)
for train, test in cv)
all_alphas = np.concatenate(list(zip(*cv_paths))[0])
# Unique also sorts
all_alphas = np.unique(all_alphas)
# Take at most max_n_alphas values
stride = int(max(1, int(len(all_alphas) / float(self.max_n_alphas))))
all_alphas = all_alphas[::stride]
mse_path = np.empty((len(all_alphas), len(cv_paths)))
for index, (alphas, active, coefs, residues) in enumerate(cv_paths):
alphas = alphas[::-1]
residues = residues[::-1]
if alphas[0] != 0:
alphas = np.r_[0, alphas]
residues = np.r_[residues[0, np.newaxis], residues]
if alphas[-1] != all_alphas[-1]:
alphas = np.r_[alphas, all_alphas[-1]]
residues = np.r_[residues, residues[-1, np.newaxis]]
this_residues = interpolate.interp1d(alphas,
residues,
axis=0)(all_alphas)
this_residues **= 2
mse_path[:, index] = np.mean(this_residues, axis=-1)
mask = np.all(np.isfinite(mse_path), axis=-1)
all_alphas = all_alphas[mask]
mse_path = mse_path[mask]
# Select the alpha that minimizes left-out error
i_best_alpha = np.argmin(mse_path.mean(axis=-1))
best_alpha = all_alphas[i_best_alpha]
# Store our parameters
self.alpha_ = best_alpha
self.cv_alphas_ = all_alphas
self.cv_mse_path_ = mse_path
# Now compute the full model
# it will call a lasso internally when self if LassoLarsCV
# as self.method == 'lasso'
Lars.fit(self, X, y)
return self
@property
def alpha(self):
# impedance matching for the above Lars.fit (should not be documented)
return self.alpha_
class LassoLarsCV(LarsCV):
"""Cross-validated Lasso, using the LARS algorithm
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
Read more in the :ref:`User Guide <least_angle_regression>`.
Parameters
----------
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
verbose : boolean or integer, optional
Sets the verbosity amount
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument.
max_iter : integer, optional
Maximum number of iterations to perform.
cv : cross-validation generator, optional
see sklearn.cross_validation module. If None is passed, default to
a 5-fold strategy
max_n_alphas : integer, optional
The maximum number of points on the path used to compute the
residuals in the cross-validation
n_jobs : integer, optional
Number of CPUs to use during the cross validation. If ``-1``, use
all the CPUs
eps : float, optional
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems.
copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
Attributes
----------
coef_ : array, shape (n_features,)
parameter vector (w in the formulation formula)
intercept_ : float
independent term in decision function.
coef_path_ : array, shape (n_features, n_alphas)
the varying values of the coefficients along the path
alpha_ : float
the estimated regularization parameter alpha
alphas_ : array, shape (n_alphas,)
the different values of alpha along the path
cv_alphas_ : array, shape (n_cv_alphas,)
all the values of alpha along the path for the different folds
cv_mse_path_ : array, shape (n_folds, n_cv_alphas)
the mean square error on left-out for each fold along the path
(alpha values given by ``cv_alphas``)
n_iter_ : array-like or int
the number of iterations run by Lars with the optimal alpha.
Notes
-----
The object solves the same problem as the LassoCV object. However,
unlike the LassoCV, it find the relevant alphas values by itself.
In general, because of this property, it will be more stable.
However, it is more fragile to heavily multicollinear datasets.
It is more efficient than the LassoCV if only a small number of
features are selected compared to the total number, for instance if
there are very few samples compared to the number of features.
See also
--------
lars_path, LassoLars, LarsCV, LassoCV
"""
method = 'lasso'
class LassoLarsIC(LassoLars):
"""Lasso model fit with Lars using BIC or AIC for model selection
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
AIC is the Akaike information criterion and BIC is the Bayes
Information criterion. Such criteria are useful to select the value
of the regularization parameter by making a trade-off between the
goodness of fit and the complexity of the model. A good model should
explain well the data while being simple.
Read more in the :ref:`User Guide <least_angle_regression>`.
Parameters
----------
criterion : 'bic' | 'aic'
The type of criterion to use.
fit_intercept : boolean
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
verbose : boolean or integer, optional
Sets the verbosity amount
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
precompute : True | False | 'auto' | array-like
Whether to use a precomputed Gram matrix to speed up
calculations. If set to ``'auto'`` let us decide. The Gram
matrix can also be passed as argument.
max_iter : integer, optional
Maximum number of iterations to perform. Can be used for
early stopping.
eps : float, optional
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems. Unlike the ``tol`` parameter in some iterative
optimization-based algorithms, this parameter does not control
the tolerance of the optimization.
Attributes
----------
coef_ : array, shape (n_features,)
parameter vector (w in the formulation formula)
intercept_ : float
independent term in decision function.
alpha_ : float
the alpha parameter chosen by the information criterion
n_iter_ : int
number of iterations run by lars_path to find the grid of
alphas.
criterion_ : array, shape (n_alphas,)
The value of the information criteria ('aic', 'bic') across all
alphas. The alpha which has the smallest information criteria
is chosen.
Examples
--------
>>> from sklearn import linear_model
>>> clf = linear_model.LassoLarsIC(criterion='bic')
>>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1.1111, 0, -1.1111])
... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
LassoLarsIC(copy_X=True, criterion='bic', eps=..., fit_intercept=True,
max_iter=500, normalize=True, precompute='auto',
verbose=False)
>>> print(clf.coef_) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
[ 0. -1.11...]
Notes
-----
The estimation of the number of degrees of freedom is given by:
"On the degrees of freedom of the lasso"
Hui Zou, Trevor Hastie, and Robert Tibshirani
Ann. Statist. Volume 35, Number 5 (2007), 2173-2192.
http://en.wikipedia.org/wiki/Akaike_information_criterion
http://en.wikipedia.org/wiki/Bayesian_information_criterion
See also
--------
lars_path, LassoLars, LassoLarsCV
"""
def __init__(self, criterion='aic', fit_intercept=True, verbose=False,
normalize=True, precompute='auto', max_iter=500,
eps=np.finfo(np.float).eps, copy_X=True):
self.criterion = criterion
self.fit_intercept = fit_intercept
self.max_iter = max_iter
self.verbose = verbose
self.normalize = normalize
self.copy_X = copy_X
self.precompute = precompute
self.eps = eps
def fit(self, X, y, copy_X=True):
"""Fit the model using X, y as training data.
Parameters
----------
X : array-like, shape (n_samples, n_features)
training data.
y : array-like, shape (n_samples,)
target values.
copy_X : boolean, optional, default True
If ``True``, X will be copied; else, it may be overwritten.
Returns
-------
self : object
returns an instance of self.
"""
self.fit_path = True
X, y = check_X_y(X, y, multi_output=True, y_numeric=True)
X, y, Xmean, ymean, Xstd = LinearModel._center_data(
X, y, self.fit_intercept, self.normalize, self.copy_X)
max_iter = self.max_iter
Gram = self._get_gram()
alphas_, active_, coef_path_, self.n_iter_ = lars_path(
X, y, Gram=Gram, copy_X=copy_X, copy_Gram=True, alpha_min=0.0,
method='lasso', verbose=self.verbose, max_iter=max_iter,
eps=self.eps, return_n_iter=True)
n_samples = X.shape[0]
if self.criterion == 'aic':
K = 2 # AIC
elif self.criterion == 'bic':
K = log(n_samples) # BIC
else:
raise ValueError('criterion should be either bic or aic')
R = y[:, np.newaxis] - np.dot(X, coef_path_) # residuals
mean_squared_error = np.mean(R ** 2, axis=0)
df = np.zeros(coef_path_.shape[1], dtype=np.int) # Degrees of freedom
for k, coef in enumerate(coef_path_.T):
mask = np.abs(coef) > np.finfo(coef.dtype).eps
if not np.any(mask):
continue
# get the number of degrees of freedom equal to:
# Xc = X[:, mask]
# Trace(Xc * inv(Xc.T, Xc) * Xc.T) ie the number of non-zero coefs
df[k] = np.sum(mask)
self.alphas_ = alphas_
with np.errstate(divide='ignore'):
self.criterion_ = n_samples * np.log(mean_squared_error) + K * df
n_best = np.argmin(self.criterion_)
self.alpha_ = alphas_[n_best]
self.coef_ = coef_path_[:, n_best]
self._set_intercept(Xmean, ymean, Xstd)
return self
| bsd-3-clause |
taxpon/pyzxcvbn | pyzxcvbn/frequency_lists.py | 1 | 679035 | frequency_lists = {"male_names": "james,john,robert,michael,william,david,richard,charles,joseph,thomas,christopher,daniel,paul,mark,donald,george,kenneth,steven,edward,brian,ronald,anthony,kevin,jason,matthew,gary,timothy,jose,larry,jeffrey,frank,scott,eric,stephen,andrew,raymond,gregory,joshua,jerry,dennis,walter,patrick,peter,harold,douglas,henry,carl,arthur,ryan,roger,joe,juan,jack,albert,jonathan,justin,terry,gerald,keith,samuel,willie,ralph,lawrence,nicholas,roy,benjamin,bruce,brandon,adam,harry,fred,wayne,billy,steve,louis,jeremy,aaron,randy,eugene,carlos,russell,bobby,victor,ernest,phillip,todd,jesse,craig,alan,shawn,clarence,sean,philip,chris,johnny,earl,jimmy,antonio,danny,bryan,tony,luis,mike,stanley,leonard,nathan,dale,manuel,rodney,curtis,norman,marvin,vincent,glenn,jeffery,travis,jeff,chad,jacob,melvin,alfred,kyle,francis,bradley,jesus,herbert,frederick,ray,joel,edwin,don,eddie,ricky,troy,randall,barry,bernard,mario,leroy,francisco,marcus,micheal,theodore,clifford,miguel,oscar,jay,jim,tom,calvin,alex,jon,ronnie,bill,lloyd,tommy,leon,derek,darrell,jerome,floyd,leo,alvin,tim,wesley,dean,greg,jorge,dustin,pedro,derrick,dan,zachary,corey,herman,maurice,vernon,roberto,clyde,glen,hector,shane,ricardo,sam,rick,lester,brent,ramon,tyler,gilbert,gene,marc,reginald,ruben,brett,angel,nathaniel,rafael,edgar,milton,raul,ben,cecil,duane,andre,elmer,brad,gabriel,ron,roland,jared,adrian,karl,cory,claude,erik,darryl,neil,christian,javier,fernando,clinton,ted,mathew,tyrone,darren,lonnie,lance,cody,julio,kurt,allan,clayton,hugh,max,dwayne,dwight,armando,felix,jimmie,everett,ian,ken,bob,jaime,casey,alfredo,alberto,dave,ivan,johnnie,sidney,byron,julian,isaac,clifton,willard,daryl,virgil,andy,salvador,kirk,sergio,seth,kent,terrance,rene,eduardo,terrence,enrique,freddie,stuart,fredrick,arturo,alejandro,joey,nick,luther,wendell,jeremiah,evan,julius,donnie,otis,trevor,luke,homer,gerard,doug,kenny,hubert,angelo,shaun,lyle,matt,alfonso,orlando,rex,carlton,ernesto,pablo,lorenzo,omar,wilbur,blake,horace,roderick,kerry,abraham,rickey,ira,andres,cesar,johnathan,malcolm,rudolph,damon,kelvin,rudy,preston,alton,archie,marco,wm,pete,randolph,garry,geoffrey,jonathon,felipe,bennie,gerardo,ed,dominic,loren,delbert,colin,guillermo,earnest,benny,noel,rodolfo,myron,edmund,salvatore,cedric,lowell,gregg,sherman,devin,sylvester,roosevelt,israel,jermaine,forrest,wilbert,leland,simon,irving,owen,rufus,woodrow,kristopher,levi,marcos,gustavo,lionel,marty,gilberto,clint,nicolas,laurence,ismael,orville,drew,ervin,dewey,al,wilfred,josh,hugo,ignacio,caleb,tomas,sheldon,erick,frankie,darrel,rogelio,terence,alonzo,elias,bert,elbert,ramiro,conrad,noah,grady,phil,cornelius,lamar,rolando,clay,percy,dexter,bradford,merle,darin,amos,terrell,moses,irvin,saul,roman,darnell,randal,tommie,timmy,darrin,brendan,toby,van,abel,dominick,emilio,elijah,cary,domingo,aubrey,emmett,marlon,emanuel,jerald,edmond,emil,dewayne,otto,teddy,reynaldo,bret,jess,trent,humberto,emmanuel,stephan,louie,vicente,lamont,garland,micah,efrain,heath,rodger,demetrius,ethan,eldon,rocky,pierre,eli,bryce,antoine,robbie,kendall,royce,sterling,grover,elton,cleveland,dylan,chuck,damian,reuben,stan,leonardo,russel,erwin,benito,hans,monte,blaine,ernie,curt,quentin,agustin,jamal,devon,adolfo,tyson,wilfredo,bart,jarrod,vance,denis,damien,joaquin,harlan,desmond,elliot,darwin,gregorio,kermit,roscoe,esteban,anton,solomon,norbert,elvin,nolan,carey,rod,quinton,hal,brain,rob,elwood,kendrick,darius,moises,marlin,fidel,thaddeus,cliff,marcel,ali,raphael,bryon,armand,alvaro,jeffry,dane,joesph,thurman,ned,sammie,rusty,michel,monty,rory,fabian,reggie,kris,isaiah,gus,avery,loyd,diego,adolph,millard,rocco,gonzalo,derick,rodrigo,gerry,rigoberto,alphonso,ty,rickie,noe,vern,elvis,bernardo,mauricio,hiram,donovan,basil,nickolas,scot,vince,quincy,eddy,sebastian,federico,ulysses,heriberto,donnell,denny,gavin,emery,romeo,jayson,dion,dante,clement,coy,odell,jarvis,bruno,issac,dudley,sanford,colby,carmelo,nestor,hollis,stefan,donny,art,linwood,beau,weldon,galen,isidro,truman,delmar,johnathon,silas,frederic,irwin,merrill,charley,marcelino,carlo,trenton,kurtis,aurelio,winfred,vito,collin,denver,leonel,emory,pasquale,mohammad,mariano,danial,landon,dirk,branden,adan,numbers,clair,buford,german,bernie,wilmer,emerson,zachery,jacques,errol,josue,edwardo,wilford,theron,raymundo,daren,tristan,robby,lincoln,jame,genaro,octavio,cornell,hung,arron,antony,herschel,alva,giovanni,garth,cyrus,cyril,ronny,stevie,lon,kennith,carmine,augustine,erich,chadwick,wilburn,russ,myles,jonas,mitchel,mervin,zane,jamel,lazaro,alphonse,randell,major,johnie,jarrett,ariel,abdul,dusty,luciano,seymour,scottie,eugenio,mohammed,valentin,arnulfo,lucien,ferdinand,thad,ezra,aldo,rubin,royal,mitch,earle,abe,marquis,lanny,kareem,jamar,boris,isiah,emile,elmo,aron,leopoldo,everette,josef,eloy,dorian,rodrick,reinaldo,lucio,jerrod,weston,hershel,lemuel,lavern,burt,jules,gil,eliseo,ahmad,nigel,efren,antwan,alden,margarito,refugio,dino,osvaldo,les,deandre,normand,kieth,ivory,trey,norberto,napoleon,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| mit |
mmottahedi/neuralnilm_prototype | scripts/e444.py | 2 | 18520 | from __future__ import print_function, division
import matplotlib
import logging
from sys import stdout
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import (Net, RealApplianceSource,
BLSTMLayer, DimshuffleLayer,
BidirectionalRecurrentLayer)
from neuralnilm.source import standardise, discretize, fdiff, power_and_fdiff
from neuralnilm.experiment import run_experiment, init_experiment
from neuralnilm.net import TrainingError
from neuralnilm.layers import MixtureDensityLayer
from neuralnilm.objectives import (scaled_cost, mdn_nll,
scaled_cost_ignore_inactive, ignore_inactive,
scaled_cost3)
from neuralnilm.plot import MDNPlotter, CentralOutputPlotter, Plotter
from neuralnilm.updates import clipped_nesterov_momentum
from lasagne.nonlinearities import sigmoid, rectify, tanh, identity
from lasagne.objectives import mse, binary_crossentropy
from lasagne.init import Uniform, Normal, Identity
from lasagne.layers import (LSTMLayer, DenseLayer, Conv1DLayer,
ReshapeLayer, FeaturePoolLayer, RecurrentLayer)
from lasagne.layers.batch_norm import BatchNormLayer
from lasagne.updates import nesterov_momentum, momentum
from functools import partial
import os
import __main__
from copy import deepcopy
from math import sqrt
import numpy as np
import theano.tensor as T
import gc
"""
444: just testing new DimshuffleLayer.
"""
NAME = os.path.splitext(os.path.split(__main__.__file__)[1])[0]
#PATH = "/homes/dk3810/workspace/python/neuralnilm/figures"
PATH = "/data/dk3810/figures"
SAVE_PLOT_INTERVAL = 5000
N_SEQ_PER_BATCH = 64
SEQ_LENGTH = 1024
source_dict = dict(
filename='/data/dk3810/ukdale.h5',
appliances=[
['washer dryer', 'washing machine'],
'hair straighteners',
'television',
'dish washer',
['fridge freezer', 'fridge', 'freezer']
],
max_appliance_powers=[2400, 500, 200, 2500, 200],
# max_input_power=200,
max_diff=200,
on_power_thresholds=[5] * 5,
min_on_durations=[1800, 60, 60, 1800, 60],
min_off_durations=[600, 12, 12, 1800, 12],
window=("2013-06-01", "2014-07-01"),
seq_length=SEQ_LENGTH,
# random_window=64,
output_one_appliance=True,
boolean_targets=False,
train_buildings=[1],
validation_buildings=[1],
skip_probability=0.75,
skip_probability_for_first_appliance=0.2,
one_target_per_seq=False,
n_seq_per_batch=N_SEQ_PER_BATCH,
# subsample_target=4,
include_diff=False,
include_power=True,
clip_appliance_power=False,
target_is_prediction=False,
# independently_center_inputs=True,
standardise_input=True,
standardise_targets=True,
# unit_variance_targets=False,
# input_padding=2,
lag=0,
clip_input=False,
# two_pass=True,
# clock_type='ramp',
# clock_period=SEQ_LENGTH
# classification=True
# reshape_target_to_2D=True
# input_stats={'mean': np.array([ 0.05526326], dtype=np.float32),
# 'std': np.array([ 0.12636775], dtype=np.float32)},
# target_stats={
# 'mean': np.array([ 0.04066789, 0.01881946,
# 0.24639061, 0.17608672, 0.10273963],
# dtype=np.float32),
# 'std': np.array([ 0.11449792, 0.07338708,
# 0.26608968, 0.33463112, 0.21250485],
# dtype=np.float32)}
)
net_dict = dict(
save_plot_interval=SAVE_PLOT_INTERVAL,
# loss_function=partial(ignore_inactive, loss_func=mdn_nll, seq_length=SEQ_LENGTH),
# loss_function=lambda x, t: mdn_nll(x, t).mean(),
# loss_function=lambda x, t: (mse(x, t) * MASK).mean(),
loss_function=lambda x, t: mse(x, t).mean(),
# loss_function=lambda x, t: binary_crossentropy(x, t).mean(),
# loss_function=partial(scaled_cost, loss_func=mse),
# loss_function=ignore_inactive,
# loss_function=partial(scaled_cost3, ignore_inactive=False),
# updates_func=momentum,
updates_func=clipped_nesterov_momentum,
updates_kwargs={'clip_range': (0, 10)},
learning_rate=1e-2,
learning_rate_changes_by_iteration={
2000: 1e-3,
10000: 1e-4
},
do_save_activations=True,
auto_reshape=False,
# plotter=CentralOutputPlotter
plotter=Plotter(n_seq_to_plot=32)
)
def exp_a(name):
global source
source_dict_copy = deepcopy(source_dict)
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source
))
net_dict_copy['layers_config'] = [
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 16,
'filter_length': 4,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{ # MIDDLE LAYER
'type': DenseLayer,
'num_units': 32,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
def exp_b(name):
global source
source_dict_copy = deepcopy(source_dict)
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source
))
net_dict_copy['layers_config'] = [
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{ # MIDDLE LAYER
'type': DenseLayer,
'num_units': 32,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
def exp_c(name):
global source
source_dict_copy = deepcopy(source_dict)
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source
))
net_dict_copy['layers_config'] = [
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 16,
'filter_length': 4,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 16,
'filter_length': 4,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 16,
'filter_length': 4,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{ # MIDDLE LAYER
'type': DenseLayer,
'num_units': 32,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
def exp_d(name):
global source
source_dict_copy = deepcopy(source_dict)
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source
))
net_dict_copy['layers_config'] = [
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 32,
'filter_length': 4,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 32,
'filter_length': 4,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{ # MIDDLE LAYER
'type': DenseLayer,
'num_units': 32,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
def exp_e(name):
global source
source_dict_copy = deepcopy(source_dict)
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source
))
net_dict_copy['layers_config'] = [
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 16,
'filter_length': 2,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 16,
'filter_length': 2,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{ # MIDDLE LAYER
'type': DenseLayer,
'num_units': 32,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
def exp_f(name):
global source
source_dict_copy = deepcopy(source_dict)
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source
))
net_dict_copy['layers_config'] = [
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 16,
'filter_length': 3,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 16,
'filter_length': 3,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{ # MIDDLE LAYER
'type': DenseLayer,
'num_units': 32,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
def exp_g(name):
global source
source_dict_copy = deepcopy(source_dict)
source = RealApplianceSource(**source_dict_copy)
net_dict_copy = deepcopy(net_dict)
net_dict_copy.update(dict(
experiment_name=name,
source=source
))
net_dict_copy['layers_config'] = [
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # (batch, features, time)
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 16,
'filter_length': 8,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': Conv1DLayer, # convolve over the time axis
'num_filters': 16,
'filter_length': 8,
'stride': 1,
'nonlinearity': rectify,
'border_mode': 'same'
},
{
'type': DimshuffleLayer,
'pattern': (0, 2, 1) # back to (batch, time, features)
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{ # MIDDLE LAYER
'type': DenseLayer,
'num_units': 32,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 8,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH // 4,
'nonlinearity': rectify
},
{
'type': DenseLayer,
'num_units': SEQ_LENGTH,
'nonlinearity': None
}
]
net = Net(**net_dict_copy)
return net
def main():
EXPERIMENTS = list('defg')
for experiment in EXPERIMENTS:
full_exp_name = NAME + experiment
func_call = init_experiment(PATH, experiment, full_exp_name)
logger = logging.getLogger(full_exp_name)
try:
net = eval(func_call)
run_experiment(net, epochs=20000)
except KeyboardInterrupt:
logger.info("KeyboardInterrupt")
break
except Exception as exception:
logger.exception("Exception")
# raise
else:
del net.source.train_activations
gc.collect()
finally:
logging.shutdown()
if __name__ == "__main__":
main()
"""
Emacs variables
Local Variables:
compile-command: "cp /home/jack/workspace/python/neuralnilm/scripts/e443.py /mnt/sshfs/imperial/workspace/python/neuralnilm/scripts/"
End:
"""
| mit |
kushalbhola/MyStuff | Practice/PythonApplication/env/Lib/site-packages/pandas/tests/arithmetic/conftest.py | 2 | 5507 | import numpy as np
import pytest
import pandas as pd
import pandas.util.testing as tm
# ------------------------------------------------------------------
# Helper Functions
def id_func(x):
if isinstance(x, tuple):
assert len(x) == 2
return x[0].__name__ + "-" + str(x[1])
else:
return x.__name__
# ------------------------------------------------------------------
@pytest.fixture(params=[1, np.array(1, dtype=np.int64)])
def one(request):
# zero-dim integer array behaves like an integer
return request.param
zeros = [
box_cls([0] * 5, dtype=dtype)
for box_cls in [pd.Index, np.array]
for dtype in [np.int64, np.uint64, np.float64]
]
zeros.extend(
[box_cls([-0.0] * 5, dtype=np.float64) for box_cls in [pd.Index, np.array]]
)
zeros.extend([np.array(0, dtype=dtype) for dtype in [np.int64, np.uint64, np.float64]])
zeros.extend([np.array(-0.0, dtype=np.float64)])
zeros.extend([0, 0.0, -0.0])
@pytest.fixture(params=zeros)
def zero(request):
# For testing division by (or of) zero for Index with length 5, this
# gives several scalar-zeros and length-5 vector-zeros
return request.param
# ------------------------------------------------------------------
# Vector Fixtures
@pytest.fixture(
params=[
pd.Float64Index(np.arange(5, dtype="float64")),
pd.Int64Index(np.arange(5, dtype="int64")),
pd.UInt64Index(np.arange(5, dtype="uint64")),
pd.RangeIndex(5),
],
ids=lambda x: type(x).__name__,
)
def numeric_idx(request):
"""
Several types of numeric-dtypes Index objects
"""
return request.param
# ------------------------------------------------------------------
# Scalar Fixtures
@pytest.fixture(
params=[
pd.Timedelta("5m4s").to_pytimedelta(),
pd.Timedelta("5m4s"),
pd.Timedelta("5m4s").to_timedelta64(),
],
ids=lambda x: type(x).__name__,
)
def scalar_td(request):
"""
Several variants of Timedelta scalars representing 5 minutes and 4 seconds
"""
return request.param
@pytest.fixture(
params=[
pd.offsets.Day(3),
pd.offsets.Hour(72),
pd.Timedelta(days=3).to_pytimedelta(),
pd.Timedelta("72:00:00"),
np.timedelta64(3, "D"),
np.timedelta64(72, "h"),
],
ids=lambda x: type(x).__name__,
)
def three_days(request):
"""
Several timedelta-like and DateOffset objects that each represent
a 3-day timedelta
"""
return request.param
@pytest.fixture(
params=[
pd.offsets.Hour(2),
pd.offsets.Minute(120),
pd.Timedelta(hours=2).to_pytimedelta(),
pd.Timedelta(seconds=2 * 3600),
np.timedelta64(2, "h"),
np.timedelta64(120, "m"),
],
ids=lambda x: type(x).__name__,
)
def two_hours(request):
"""
Several timedelta-like and DateOffset objects that each represent
a 2-hour timedelta
"""
return request.param
_common_mismatch = [
pd.offsets.YearBegin(2),
pd.offsets.MonthBegin(1),
pd.offsets.Minute(),
]
@pytest.fixture(
params=[
pd.Timedelta(minutes=30).to_pytimedelta(),
np.timedelta64(30, "s"),
pd.Timedelta(seconds=30),
]
+ _common_mismatch
)
def not_hourly(request):
"""
Several timedelta-like and DateOffset instances that are _not_
compatible with Hourly frequencies.
"""
return request.param
@pytest.fixture(
params=[
np.timedelta64(4, "h"),
pd.Timedelta(hours=23).to_pytimedelta(),
pd.Timedelta("23:00:00"),
]
+ _common_mismatch
)
def not_daily(request):
"""
Several timedelta-like and DateOffset instances that are _not_
compatible with Daily frequencies.
"""
return request.param
@pytest.fixture(
params=[
np.timedelta64(365, "D"),
pd.Timedelta(days=365).to_pytimedelta(),
pd.Timedelta(days=365),
]
+ _common_mismatch
)
def mismatched_freq(request):
"""
Several timedelta-like and DateOffset instances that are _not_
compatible with Monthly or Annual frequencies.
"""
return request.param
# ------------------------------------------------------------------
@pytest.fixture(params=[pd.Index, pd.Series, pd.DataFrame], ids=id_func)
def box(request):
"""
Several array-like containers that should have effectively identical
behavior with respect to arithmetic operations.
"""
return request.param
@pytest.fixture(
params=[pd.Index, pd.Series, pytest.param(pd.DataFrame, marks=pytest.mark.xfail)],
ids=id_func,
)
def box_df_fail(request):
"""
Fixture equivalent to `box` fixture but xfailing the DataFrame case.
"""
return request.param
@pytest.fixture(
params=[
(pd.Index, False),
(pd.Series, False),
(pd.DataFrame, False),
pytest.param((pd.DataFrame, True), marks=pytest.mark.xfail),
],
ids=id_func,
)
def box_transpose_fail(request):
"""
Fixture similar to `box` but testing both transpose cases for DataFrame,
with the tranpose=True case xfailed.
"""
# GH#23620
return request.param
@pytest.fixture(params=[pd.Index, pd.Series, pd.DataFrame, tm.to_array], ids=id_func)
def box_with_array(request):
"""
Fixture to test behavior for Index, Series, DataFrame, and pandas Array
classes
"""
return request.param
# alias so we can use the same fixture for multiple parameters in a test
box_with_array2 = box_with_array
| apache-2.0 |
robertsj/ME701_examples | plots/animations/2D_imshow_ani.py | 1 | 1446 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
'''
This function will take data that is complete at the time of making the plot.
It generates an image for each timestep (or whatever you want to animate), and
creates the animation from the list of images.
'''
def make_data():
# randomly generate a 30x30 array of numbers between 0 and 1
return np.random.random((30,30))
def time_dep(f, t_max=100.0, dt=1.0):
'''
Creates an array that contains all time dependent data
f is a function that will generate the data for a given time step
'''
# Initialize a blank list
data = []
# Loop over the number of time steps
for i in np.linspace(0,t_max,t_max/float(dt)):
# Append the new time step data to our list
data.append(f())
# convert the list to an array and return it
return np.array(data)
def make_2D_imshow(data):
# create a new blank list to contain all the plots
im = []
# loop over the number of time steps
for i in range(len(data)):
# Create a new plot
plot = plt.imshow(data[i])
# Make a tuple out of the plot and append it to the list
im.append((plot,))
# create the figure for the animation
fig = plt.figure(0)
# create the animation
ani = animation.ArtistAnimation(fig, im, interval=100, blit=True)
plt.show()
make_2D_imshow(time_dep(make_data))
| mit |
SuperSaiyanSSS/SinaWeiboSpider | ml/svm_utils.py | 1 | 2502 | # coding=utf-8
from __future__ import print_function
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
import os
import re
import jieba
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn import svm
path_doc_root = 'H:\py\workplace\/a2\SogouC.reduced2\\Reduced' # 根目录 即存放按类分类好的问本纪
path_tmp = 'H:\py\workplace\/a2\SogouC.reduced2ss11\\temp1' # 存放中间结果的位置
path_dictionary = os.path.join(path_tmp, 'THUNews.dict')
path_tmp_tfidf = os.path.join(path_tmp, 'tfidf_corpus')
path_tmp_lsi = os.path.join(path_tmp, 'lsi_corpus')
path_tmp_lsimodel = os.path.join(path_tmp, 'lsi_model.pkl')
path_tmp_predictor = os.path.join(path_tmp, 'predictor.pkl')
def convert_doc_to_wordlist(str_doc,cut_all):
sent_list = str_doc.split('\n')
sent_list = map(rm_char, sent_list) # 去掉一些字符，例如\u3000
word_2dlist = [rm_tokens(jieba.cut(part,cut_all=cut_all)) for part in sent_list] # 分词
word_list = sum(word_2dlist,[])
return word_list
def rm_tokens(words): # 去掉一些停用次和数字
words_list = list(words)
stop_words = get_stop_words()
for i in range(words_list.__len__())[::-1]:
if words_list[i] in stop_words: # 去除停用词
words_list.pop(i)
elif words_list[i].isdigit():
words_list.pop(i)
return words_list
def get_stop_words(path='stopwords_cn.txt'):
file = open(path,'rb').read().split('\n')
return set(file)
def rm_char(text):
text = re.sub('\u3000','',text)
return text
def svm_classify(train_set, train_tag, test_set, test_tag):
clf = svm.LinearSVC()
clf_res = clf.fit(train_set, train_tag)
train_pred = clf_res.predict(train_set)
test_pred = clf_res.predict(test_set)
train_err_num, train_err_ratio = checkPred(train_tag, train_pred)
test_err_num, test_err_ratio = checkPred(test_tag, test_pred)
print('=== 分类训练完毕，分类结果如下 ===')
print('训练集误差: {e}'.format(e=train_err_ratio))
print('检验集误差: {e}'.format(e=test_err_ratio))
return clf_res
def checkPred(data_tag, data_pred):
if data_tag.__len__() != data_pred.__len__():
raise RuntimeError('The length of data tag and data pred should be the same')
err_count = 0
for i in range(data_tag.__len__()):
if data_tag[i]!=data_pred[i]:
err_count += 1
err_ratio = err_count / data_tag.__len__()
return [err_count, err_ratio] | mit |
adelomana/schema | conditionedFitness/figureClonal/clonal.3.3.py | 2 | 3270 | import matplotlib,numpy,sys,scipy,pickle
import matplotlib.pyplot
sys.path.append('../lib')
import calculateStatistics
### MAIN
matplotlib.rcParams.update({'font.size':36,'font.family':'Times New Roman','xtick.labelsize':28,'ytick.labelsize':28})
thePointSize=12
jarDir='/Users/adriandelomana/scratch/'
# clonal 2
xSignal=numpy.array([[175, 153, 186, 189, 157],[37, 59, 46, 67, 70]])
xNoSignal=numpy.array([[200, 202, 224, 194, 193],[71, 66, 71, 87, 60]])
cf_mu_0, cf_sd_0, pvalue_0 = calculateStatistics.main(xSignal, xNoSignal)
xSignal=numpy.array([[25, 28, 19, 18, 16],[0, 9, 4, 9, 1]])
xNoSignal=numpy.array([[24, 16, 29, 17, 23],[4, 7, 5, 3, 4]])
cf_mu_50, cf_sd_50, pvalue_50 = calculateStatistics.main(xSignal, xNoSignal)
xSignal=numpy.array([[96, 97, 94, 127, 80],[32, 36, 36, 42, 36]])
xNoSignal=numpy.array([[104, 137, 110, 128, 113],[52, 36, 32, 50, 41]])
cf_mu_100, cf_sd_100, pvalue_100 = calculateStatistics.main(xSignal, xNoSignal)
xSignal=numpy.array([[204, 223, 199, 249, 193],[141, 131, 125, 154, 139]])
xNoSignal=numpy.array([[171, 217, 240, 200, 168],[166, 192, 163, 196, 170]])
cf_mu_150, cf_sd_150, pvalue_150 = calculateStatistics.main(xSignal, xNoSignal)
xSignal=numpy.array([[197, 216, 224, 219, 208],[181, 182, 186, 179, 116]])
xNoSignal=numpy.array([[261, 227, 229, 188, 236],[179, 169, 174, 183, 164]])
cf_mu_200, cf_sd_200, pvalue_200 = calculateStatistics.main(xSignal, xNoSignal)
xSignal=numpy.array([[226, 214, 222, 224, 211],[235, 199, 177, 199, 184]])
xNoSignal=numpy.array([[223, 230, 215, 273, 245],[204, 199, 247, 220, 204]])
cf_mu_250, cf_sd_250, pvalue_250 = calculateStatistics.main(xSignal, xNoSignal)
xSignal=numpy.array([[222, 235, 253, 234, 189],[175, 160, 194, 156, 178]])
xNoSignal=numpy.array([[212, 222, 246, 228, 220],[191, 192, 198, 217, 199]])
cf_mu_300, cf_sd_300, pvalue_300 = calculateStatistics.main(xSignal, xNoSignal)
x = [0, 50, 100, 150, 200, 250, 300]
y = [cf_mu_0, cf_mu_50, cf_mu_100, cf_mu_150, cf_mu_200, cf_mu_250, cf_mu_300]
z = [cf_sd_0, cf_sd_50, cf_sd_100, cf_sd_150, cf_sd_200, cf_sd_250, cf_sd_300]
w = [pvalue_0, pvalue_50, pvalue_100, pvalue_150, pvalue_200, pvalue_250, pvalue_300]
matplotlib.pyplot.errorbar(x,y,yerr=z,fmt=':o',color='green',ecolor='green',markeredgecolor='green',capsize=0,ms=thePointSize,mew=0)
for i in range(len(w)):
if y[i] > 0.:
sp=y[i]+z[i]+0.02
else:
sp=y[i]-z[i]-0.02
if w[i] < 0.05 and w[i] >= 0.01:
matplotlib.pyplot.scatter(x[i], sp, s=75, c='black', marker=r"${*}$", edgecolors='none')
if w[i] < 0.01:
matplotlib.pyplot.scatter(x[i]-3, sp, s=75, c='black', marker=r"${*}$", edgecolors='none')
matplotlib.pyplot.scatter(x[i]+3, sp, s=75, c='black', marker=r"${*}$", edgecolors='none')
matplotlib.pyplot.plot([0,300],[0,0],'--',color='black')
matplotlib.pyplot.xlim([-25,325])
matplotlib.pyplot.ylim([-0.4,0.4])
matplotlib.pyplot.yticks([-0.4,-0.2,0,0.2,0.4])
matplotlib.pyplot.xlabel('Generation')
matplotlib.pyplot.ylabel('Conditioned Fitness')
matplotlib.pyplot.tight_layout(pad=0.5)
matplotlib.pyplot.savefig('figure.clonal.3.3.pdf')
# save processed data alternative plotting
trajectory=[x,y,z]
jarFile=jarDir+'clonal.3.3.pickle'
f=open(jarFile,'wb')
pickle.dump(trajectory,f)
f.close()
| gpl-3.0 |
rileymcdowell/genomic-neuralnet | genomic_neuralnet/analyses/plots/network_comparison/compare_networks.py | 1 | 5046 | from __future__ import print_function
import os
import numpy as np
import pandas as pd
import scipy.stats as sps
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.stats.multicomp import pairwise_tukeyhsd
from shelve import DbfilenameShelf
from contextlib import closing
from collections import defaultdict
from functools import partial
from sklearn.preprocessing import OneHotEncoder
from genomic_neuralnet.analyses.plots \
import get_nn_model_data, palette, out_dir \
, get_significance_letters
sns.set_style('dark')
sns.set_palette(palette)
def string_to_label((species, trait)):
trait_name = trait.replace('_', ' ').title()
species_name = species.title()
if trait_name.count(' ') > 1:
trait_name = trait_name.replace(' ', '\n')
return '{}\n{}'.format(species_name, trait_name)
def make_best_dataframe(shelf_data):
data_dict = defaultdict(partial(defaultdict, dict))
num_models = len(shelf_data)
for model_name, optimization in shelf_data.iteritems():
for species_trait, opt_result in optimization.iteritems():
species, trait, gpu = tuple(species_trait.split('|'))
max_fit_index = opt_result.df['mean'].idxmax()
best_fit = opt_result.df.loc[max_fit_index]
mean_acc = best_fit.loc['mean']
sd_acc = best_fit.loc['std_dev']
hidden = best_fit.loc['hidden']
count = opt_result.folds * opt_result.runs
raw_results = best_fit.loc['raw_results']
data_dict[species][trait][model_name] = (mean_acc, sd_acc, count, raw_results, hidden)
# Add species column. Repeat once per trait per model (2*num models).
accuracy_df = pd.DataFrame({'species': np.repeat(data_dict.keys(), num_models*2)})
# Add trait column.
flattened_data = []
for species, trait_dict in data_dict.iteritems():
for trait, model_dict in trait_dict.iteritems():
for model, (mean, sd, count, raw_res, hidden) in model_dict.iteritems():
flattened_data.append((trait, model, mean, sd, count, raw_res, hidden))
accuracy_df['trait'], accuracy_df['model'], accuracy_df['mean'], \
accuracy_df['sd'], accuracy_df['count'], accuracy_df['raw_results'], \
accuracy_df['hidden'] = zip(*flattened_data)
return accuracy_df
def make_plot(accuracy_df):
accuracy_df = accuracy_df.sort_values(by=['species', 'trait'], ascending=[1,0])
fig, ax = plt.subplots()
species_and_traits = accuracy_df[['species', 'trait']].drop_duplicates()
x = np.arange(len(species_and_traits))
models = sorted(accuracy_df['model'].unique())
width = 0.22
species_list = species_and_traits['species']
trait_list = species_and_traits['trait']
bar_sets = []
error_offsets = []
for idx, model in enumerate(models):
means = accuracy_df[accuracy_df['model'] == model]['mean'].values
std_devs = accuracy_df[accuracy_df['model'] == model]['sd'].values
counts = accuracy_df[accuracy_df['model'] == model]['count'].values
std_errs = std_devs / np.sqrt(counts) # SE = sigma / sqrt(N)
# Size of 95% CI is the SE multiplied by a constant from the t distribution
# with n-1 degrees of freedom. [0] is the positive interval direction.
#confidence_interval_mult = sps.t.interval(alpha=0.95, df=counts - 1)[0]
#confidence_interval = confidence_interval_mult * std_errs
offset = width * idx
color = palette[idx]
b = ax.bar(x + offset, means, width, color=color)
e = ax.errorbar(x + offset + width/2, means, yerr=std_errs, ecolor='black', fmt='none')
bar_sets.append((b, model))
error_offsets.append(std_errs)
significance_letter_lookup = get_significance_letters(accuracy_df, ordered_model_names=models)
def label(idx, rects, model):
errors = error_offsets[idx]
for error, rect, species, trait in zip(errors, rects, species_list, trait_list):
height = rect.get_height()
significance_letter = significance_letter_lookup[model][species][trait]
ax.text( rect.get_x() + rect.get_width()/2.
, height + error + 0.02
, significance_letter
, ha='center'
, va='bottom')
[label(idx, b, m) for idx, (b,m) in enumerate(bar_sets)]
# Axis labels (layer 1).
ax.set_ylabel('Accuracy')
ax.set_xticks(x + width / 2 * len(models))
ax.set_xticklabels(map(string_to_label, zip(species_list, trait_list)))
ax.set_xlim((0 - width / 2, len(trait_list)))
ax.set_ylim((0, 1))
# Legend
ax.legend(map(lambda x: x[0], bar_sets), list(models))
plt.tight_layout()
fig_path = os.path.join(out_dir, 'network_comparison.png')
plt.savefig(fig_path, dpi=500)
plt.show()
def main():
data = get_nn_model_data()
accuracy_df = make_best_dataframe(data)
make_plot(accuracy_df)
if __name__ == '__main__':
main()
| mit |
ARM-software/lisa | lisa/analysis/functions.py | 1 | 32293 | # SPDX-License-Identifier: Apache-2.0
#
# Copyright (C) 2015, ARM Limited and contributors.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
""" Functions Analysis Module """
import json
import os
from operator import itemgetter, attrgetter
from statistics import mean
from functools import reduce
from itertools import chain
from copy import copy
from collections.abc import Mapping
from enum import IntEnum
import pandas as pd
from pandas.api.types import is_numeric_dtype
import numpy as np
from lisa.utils import groupby, memoized, FrozenDict, unzip_into
from lisa.datautils import df_merge
from lisa.analysis.base import TraceAnalysisBase, AnalysisHelpers
from lisa.analysis.load_tracking import LoadTrackingAnalysis
from lisa.trace import requires_events, requires_one_event_of, MissingTraceEventError
from lisa.conf import ConfigKeyError
from lisa.stats import Stats
from lisa.pelt import PELT_SCALE
class FunctionsAnalysis(TraceAnalysisBase):
"""
Support for ftrace events-based kernel functions profiling and analysis
"""
name = 'functions'
def df_resolve_ksym(self, df, addr_col, name_col='func_name', addr_map=None, exact=True):
"""
Resolve the kernel function names.
.. note:: If the ``addr_col`` is not of a numeric dtype, it will be
assumed to be function names already and the content will be copied
to ``name_col``.
:param df: Dataframe to augment
:type df: pandas.DataFrame
:param addr_col: Name of the column containing a kernel address.
:type addr_col: str
:param name_col: Name of the column to create with symbol names
:param name_col: str
:param addr_map: If provided, the mapping of kernel addresses to symbol
names. If missing, the symbols addresses from the
:class:`lisa.platforms.platinfo.PlatformInfo` attached to the trace
will be used.
:type addr_map: dict(int, str)
:param exact: If ``True``, an exact symbol address is expected. If
``False``, symbol addresses are sorted and paired to form
intervals, which are then used to infer the name. This is suited to
resolve an instruction pointer that could point anywhere inside of
a function (but before the starting address of the next function).
:type exact: bool
"""
trace = self.trace
df = df.copy(deep=False)
# Names already resolved, we can just copy the address column to the
# name one
if not is_numeric_dtype(df[addr_col].dtype):
df[name_col] = df[addr_col]
return df
if addr_map is None:
addr_map = trace.plat_info['kernel']['symbols-address']
if exact:
df[name_col] = df[addr_col].map(addr_map)
# Not exact means the function addresses will be used as ranges, so
# we can find in which function any instruction point value is
else:
# Sort by address, so that each consecutive pair of address
# constitue a range of address belonging to a given function.
addr_list = sorted(
addr_map.items(),
key=itemgetter(0)
)
bins, labels = zip(*addr_list)
# "close" the last bucket with the highest value possible of that column
max_addr = np.iinfo(df[addr_col].dtype).max
bins = list(bins) + [max_addr]
name_i = pd.cut(
df[addr_col],
bins=bins,
# Since our labels are not unique, we cannot pass it here
# directly. Instead, use an index into the labels list
labels=range(len(labels)),
# Include the left boundary and exclude the right one
include_lowest=True,
right=False,
)
df[name_col] = name_i.apply(lambda x: labels[x])
return df
def _df_with_ksym(self, event, *args, **kwargs):
df = self.trace.df_event(event)
try:
return self.df_resolve_ksym(df, *args, **kwargs)
except ConfigKeyError:
self.get_logger().warning(f'Missing symbol addresses, function names will not be resolved: {e}')
return df
@requires_one_event_of('funcgraph_entry', 'funcgraph_exit')
@TraceAnalysisBase.cache
def df_funcgraph(self, event):
"""
Return augmented dataframe of the event with the following column:
* ``func_name``: Name of the calling function if it could be
resolved.
:param event: One of:
* ``entry`` (``funcgraph_entry`` event)
* ``exit`` (``funcgraph_exit`` event)
:type event: str
"""
event = f'funcgraph_{event}'
return self._df_with_ksym(event, 'func', 'func_name', exact=False)
@df_funcgraph.used_events
@LoadTrackingAnalysis.df_cpus_signal.used_events
def _get_callgraph(self, tag_df=None, thread_root_functions=None):
entry_df = self.df_funcgraph(event='entry').copy(deep=False)
entry_df['event'] = _CallGraph._EVENT.ENTRY
exit_df = self.df_funcgraph(event='exit').copy(deep=False)
exit_df['event'] = _CallGraph._EVENT.EXIT
# Attempt to get the CPU capacity signal to normalize the results
capacity_cols = ['__cpu', 'event', 'capacity']
try:
capacity_df = self.trace.analysis.load_tracking.df_cpus_signal('capacity')
except MissingTraceEventError:
capacity_df = pd.DataFrame(columns=capacity_cols)
else:
capacity_df = capacity_df.copy(deep=False)
capacity_df['__cpu'] = capacity_df['cpu']
capacity_df['event'] = _CallGraph._EVENT.SET_CAPACITY
capacity_df = capacity_df[capacity_cols]
# Set a reasonable initial capacity
try:
orig_capacities = self.trace.plat_info['cpu-capacities']['orig']
except KeyError:
pass
else:
orig_capacities_df = pd.DataFrame.from_records(
(
(-1 * cpu, cpu, _CallGraph._EVENT.SET_CAPACITY, cap)
for cpu, cap in orig_capacities.items()
),
columns=['Time', '__cpu', 'event', 'capacity'],
index='Time',
)
capacity_df = pd.concat((orig_capacities_df, capacity_df))
to_merge = [entry_df, exit_df, capacity_df]
if tag_df is not None:
cpu = tag_df['__cpu']
tag_df = tag_df.drop(columns=['__cpu'])
tag_df = pd.DataFrame(dict(
tags=tag_df.apply(pd.Series.to_dict, axis=1),
__cpu=cpu,
))
tag_df['event'] = _CallGraph._EVENT.SET_TAG
to_merge.append(tag_df)
df = df_merge(to_merge)
return _CallGraph.from_df(
df,
thread_root_functions=thread_root_functions
)
@_get_callgraph.used_events
def df_calls(self, tag_df=None, thread_root_functions=None, normalize=True):
"""
Return a :class:`pandas.DataFrame` with a row for each function call,
along some metrics:
* ``cum_time``: cumulative time spent in that function. This
includes the time spent in all children too.
* ``self_time``: time spent in that function only. This
excludes the time spent in all children.
:param tag_df: Dataframe containing the tag event, which is used to tag
paths in the callgraph. The ``__cpu`` column is mandatory in order
to know which CPU is to be tagged at any index. Other colunms will
be used as tag keys. Tags are inherited from from both parents and
children. This allows a leaf function to emit an event and use it
for the whole path that lead to there. Equally, if a function emits
a tag, all the children of this call will inherit the tag too. This
allows a top-level function to tag a whole subtree at once.
:type tag_df: pandas.DataFrame
:param thread_root_functions: Functions that are considered to be a
root of threads. When they appear in the callgraph, the profiler
will consider the current function to be preempted and will not
register the call as a child of it and will avoid to count it in
the cumulative time.
:type thread_root_functions: list(str) or None
:param normalize: Normalize metrics according to the current CPU
capacity so that they appear to have run on the fastest CPU at
maximum frequency. This allows merging calls regardless of their
origin (CPU and frequency).
.. note:: Normalization only currently takes into account the
capacity of the CPU when the function is entered. If it changes
during execution, the result will be somewhat wrong.
:type normalize: bool
.. note:: Calls during which the current function name changes are not
accounted for. They are typically a sign of functions that did not
properly return, for example functions triggering a context switch
and returning to userspace.
"""
graph = self._get_callgraph(
tag_df=tag_df,
thread_root_functions=thread_root_functions,
)
metrics = _CallGraphNode._METRICS
def get_metric(node, metric):
val = node[metric]
if normalize:
return (node.cpu_capacity / PELT_SCALE) * val
else:
return val
return pd.DataFrame.from_records(
(
(
node.entry_time, node.cpu, node.func_name, FrozenDict(node.tags), node.tagged_name,
*(
get_metric(node, metric)
for metric in metrics
)
)
for node in graph.all_nodes
),
columns=['Time', 'cpu', 'function', 'tags', 'tagged_name'] + metrics,
index='Time',
)
@df_calls.used_events
def compare_with_traces(self, others, normalize=True, **kwargs):
"""
Compare the :class:`~lisa.trace.Trace` it's called on with the other
traces passed as ``others``. The reference is the trace it's called on.
:returns: a :class:`lisa.stats.Stats` object just like
:meth:`profile_stats`.
:param others: List of traces to compare against.
:type others: list(lisa.trace.Trace)
:Variable keyword arguments: Forwarded to :meth:`profile_stats`.
"""
ref = self.trace
traces = [ref] + list(others)
paths = [
trace.trace_path
for trace in traces
]
common_prefix_len = len(os.path.commonprefix(paths))
common_suffix_len = len(os.path.commonprefix(list(map(lambda x: str(reversed(x)), paths))))
def get_name(trace):
name = trace.trace_path[common_prefix_len:common_suffix_len]
if not name:
if trace is ref:
name = 'ref'
else:
name = str(traces.index(trace))
return name
def get_df(trace):
df = trace.analysis.functions.df_calls(normalize=normalize)
df = df.copy(deep=False)
df['trace'] = get_name(trace)
return df
df = df_merge(map(get_df, traces))
ref_group = {
'trace': get_name(ref)
}
return self._profile_stats_from_df(df, ref_group=ref_group, **kwargs)
@df_calls.used_events
def profile_stats(self, tag_df=None, normalize=True, ref_function=None, ref_tags=None, **kwargs):
"""
Create a :class:`lisa.stats.Stats` out of profiling information of the
trace.
:param tag_df: Dataframe of tags, forwarded to :meth:`df_calls`
:type tag_df: pandas.DataFrame or None
:param normalize: Normalize execution time according to CPU capacity,
forwarded to to :meth:`df_calls`
:type normalize: bool
:param metric: Name of the metric to use for statistics. Can be one of:
* ``self_time``: Time spent in the function, not accounting for
time spent in children
* ``cum_time``: Total time spent in the function, including the
time spent in children.
Defaults to ``self_time``.
:type metric: str
:param functions: Restrict the statistics to the given list of
function.
:type functions: list(str) or None
:param ref_function: Function to compare to.
:type ref_function: str or None
:param ref_tags: Function tags to compare to. Ignored if ``ref_function
is None``.
:type ref_tags: dict(str, set(object)) or None
:param cpus: List of CPUs where the functions were called to take into
account. If left to ``None``, all CPUs are considered.
:type cpus: list(int) or None
:param per_cpu: If ``True``, the per-function statistics are separated
for each CPU they ran on. This is useful if the frequency was fixed and
the only variation in speed was coming from the CPU it ran on.
:type per_cpu: bool or None
:param tags: Restrict the statistics to the function tagged with the
given tag values. If a function has multiple values for a given tag
and one of the value is in ``tags``, the function is selected.
:type tags: dict(str, object)
:Variable keyword arguments: Forwarded to :class:`lisa.stats.Stats`.
.. note:: Recursive calls are treated as if they were inlined in their
callers. This means that the count of calls will be counting the
toplevel calls only, and that the ``self_time`` for a recursive
function is directly linked to how much time each level consumes
multiplied by the number of levels. ``cum_time`` will also be
tracked on the top-level call only to provide a more accurate
result.
"""
df = self.df_calls(tag_df=tag_df, normalize=normalize)
if ref_function:
ref_tags = ref_tags or {}
ref_group = {
'f': _CallGraphNode.format_name(ref_function, ref_tags)
}
else:
ref_group = None
return self._profile_stats_from_df(df, ref_group=ref_group, **kwargs)
@staticmethod
def _profile_stats_from_df(df, metric='self_time', functions=None, per_cpu=True, cpus=None, tags=None, **kwargs):
metrics = _CallGraphNode._METRICS
# Get rid of the other value columns to avoid treating them as
# tags
other_metrics = set(metrics) - {metric}
if functions:
df = df[df['function'].isin(functions)]
if cpus is not None:
df = df[df['cpu'].isin(cpus)]
if tags:
# Select all rows that are a subset of the given tags
def select_tag(row_tags):
return all(
val in row_tags.get(tag, [])
for tag, val in tags.items()
)
df = df[df['tags'].apply(select_tag)]
df = df.copy(deep=False)
# Use tagged_name for display
df['f'] = df['tagged_name']
to_drop = list(other_metrics) + ['tags', 'function', 'tagged_name']
# Calls are already uniquely identified by their timestamp, so grouping
# per CPU is optional
if not per_cpu:
to_drop.append('cpu')
df = df.drop(columns=to_drop)
df['unit'] = 's'
index_name = df.index.name
df = df.reset_index()
return Stats(
df,
agg_cols=[index_name],
value_col=metric,
**kwargs,
)
class _CallGraph:
class _EVENT(IntEnum):
"""
To be used as events for the dataframe passed to
:meth:`from_df`.
"""
ENTRY = 1
"""Enter the given function"""
EXIT = 2
"""Exit the given function"""
SET_TAG = 3
"""
Tag the current call graph path (parents and
children) with the given value
"""
SET_CAPACITY = 4
"""
Set the capacity of the current CPU. Values are between 0 and
:attr:`lisa.pelt.PELT_SCALE`.
"""
def __init__(self, cpu_nodes):
self.cpu_nodes = cpu_nodes
@property
def all_nodes(self):
return chain.from_iterable(
node.indirect_children
for node in self.cpu_nodes.values()
)
@classmethod
def from_df(cls, df, thread_root_functions=None, ts_cols=('calltime', 'rettime')):
"""
Build a :class:`_CallGraph` from a :class:`pandas.DataFrame` with the
following columns:
* ``event``: One of :class:`_CallGraph._EVENT` enumeration.
* ``func_name``: Name of the function for ``entry`` and ``exit``
events.
* ``tags``: ``dict(str, object)`` of tags for ``tag`` event.
:param thread_root_functions: Functions that are considered to be a
root of threads. When they appear in the callgraph, the profiler
will consider the current function to be preempted and will not
register the call as a child of it and will avoid to count it in
the cumulative time.
:type thread_root_functions: list(str) or None
:param ts_cols: Name of the columns for the
:attr:`_CallGraph._EVENT.EXIT` rows that contain timestamps for
entry and exit. If they are provided, they will be used instead of
the index.
:type ts_cols: tuple(str) or None
"""
thread_root_functions = set(thread_root_functions) if thread_root_functions else set()
def make_visitor():
_max_thread = -1
def make_thread():
nonlocal _max_thread
_max_thread += 1
return _max_thread
root_node = _CallGraphNode(
func_name=None,
parent=None,
cpu=None,
cpu_capacity=None,
logical_thread=make_thread(),
)
curr_node = root_node
# This is expected to be overriden right away by a SET_CAPACITY
# event
curr_capacity = PELT_SCALE
event_enum = cls._EVENT
def visit(row):
nonlocal curr_node, curr_capacity
curr_event = row['event']
if curr_event == event_enum.ENTRY:
func_name = row['func_name']
cpu = row['__cpu']
# If we got preempted by a function that is considered to
# be part of different logical thread (e.g. the toplevel
# function of an ISR), create a new ID
if func_name in thread_root_functions:
logical_thread = make_thread()
# Otherwise, just inherit it from the parent
else:
logical_thread = curr_node.logical_thread
child = _CallGraphNode(
func_name=func_name,
cpu=cpu,
parent=curr_node,
cpu_capacity=curr_capacity,
entry_time=row.name,
logical_thread=logical_thread,
)
curr_node._children.append(child)
curr_node = child
elif curr_event == event_enum.EXIT:
# We are trying to exit the root, which is probably the sign of
# a missing entry event (could have been cropped out of the
# trace). We therefore just ignore it.
if curr_node is not root_node:
# That node is unusable for stats, since the function
# used to enter the call is not the same one as for the
# exit. This usually means that the kernel returned to
# userspace in between.
if row['func_name'] != curr_node.func_name:
curr_node.valid_metrics = False
if ts_cols is None:
curr_node.exit_time = row.name
else:
entry_ts, exit_ts = ts_cols
curr_node.entry_time = row[entry_ts] * 1e-9
curr_node.exit_time = row[exit_ts] * 1e-9
curr_node = curr_node.parent
elif curr_event == event_enum.SET_TAG:
tags = row['tags']
curr_node.set_tags(tags)
elif curr_event == event_enum.SET_CAPACITY:
curr_capacity = row['capacity']
else:
raise ValueError(f'Unknown event "{curr_event}"')
def finalize(df):
# Fixup the exit time if there were missing exit events
if curr_node is not root_node:
last_time = df.index[-1]
for node in chain([curr_node], curr_node.parents):
node.exit_time = last_time
node.valid_metrics = False
root_children = root_node.children
if root_children:
root_node.entry_time = min(map(attrgetter('entry_time'), root_children))
root_node.exit_time = max(map(attrgetter('exit_time'), root_children))
else:
root_node.entry_time = 0
root_node.exit_time = 0
return (root_node, visit, finalize)
def build_graph(subdf):
root_node, visitor, finalizer = make_visitor()
subdf.apply(visitor, axis=1)
finalizer(subdf)
return root_node
return cls(
cpu_nodes = {
cpu: build_graph(subdf)
for cpu, subdf in df.groupby('__cpu', observed=True)
}
)
class _CallGraphNode(Mapping):
"""
Represent a function call extracted from some profiling information.
"""
__slots__ = [
'func_name',
'cpu',
'cpu_capacity',
'_tags',
'_children',
'parent',
'logical_thread',
'entry_time',
'exit_time',
'valid_metrics',
'__weakref__',
]
_METRICS = sorted((
'cum_time',
'self_time',
))
def __init__(self, func_name, parent, logical_thread, cpu, cpu_capacity, entry_time=None, exit_time=None, valid_metrics=True):
self.func_name = func_name
self.cpu = cpu
self.cpu_capacity = cpu_capacity
self.parent = parent
self.logical_thread = logical_thread
self._children = []
self._tags = {}
self.entry_time = entry_time
self.exit_time = exit_time
self.valid_metrics = valid_metrics
def __hash__(self):
return id(self)
def __eq__(self, other):
return self is other
@property
@memoized
def _expanded_children(self):
def visit(node):
children = node._children
children_visit = map(visit, children)
is_recursive, children_expansion = unzip_into(2, children_visit)
# Check if we are part of any recursive chain
is_recursive = any(is_recursive) or node.func_name == self.func_name
# If we are part of a recursion chain, expand all of our children
# so that they are reparented into our caller
if is_recursive:
expansion = list(chain.from_iterable(children_expansion))
else:
expansion = [node]
return (is_recursive, expansion)
return visit(self)[1]
@property
@memoized
def children(self):
return [
child
for child in self._expanded_children
if not self._is_preempted_by(child)
]
@property
def _preempting_children(self):
return {
child
for child in self._expanded_children
if self._is_preempted_by(child)
}
def _is_preempted_by(self, node):
return self.logical_thread != node.logical_thread
def _str(self, idt):
idt_str = idt * ' '
if self.children:
children = ':\n' + '\n'.join(child._str(idt + 1) for child in self.children)
else:
children = ''
return f'{idt_str}{self.func_name}, self={self["self_time"]}s cum={self["cum_time"]}s tags={self.tags}{children}'
def __str__(self):
return self._str(0)
@property
def tagged_name(self):
return self.format_name(self.func_name, self.tags)
@staticmethod
def format_name(func_name, tags):
tags = tags or {}
tags = ', '.join(
f'{tag}={"|".join(map(str, vals))}'
for tag, vals in sorted(tags.items())
)
tags = f' ({tags})' if tags else ''
return f'{func_name}{tags}'
@memoized
def __getitem__(self, key):
if not self.valid_metrics:
return np.NaN
delta = self.exit_time - self.entry_time
if key == 'self_time':
return delta - sum(
node.exit_time - node.entry_time
# Substract the time spent in all the children, including the
# ones that preempted us
for node in self._expanded_children
)
elif key == 'cum_time':
# Define cum_time in terms of self_time, so that preempting
# children are properly accounted for recurisvely
return self['self_time'] + sum(
node['cum_time']
for node in self.children
)
else:
raise KeyError(f'Unknown metric "{key}"')
def __iter__(self):
return iter(self._METRICS)
def __len__(self):
return len(self._METRICS)
@property
def _inherited_tags(self):
def merge_tags(tags1, tags2):
common_keys = tags1.keys() & tags2.keys()
new = {
tag: tags1[tag] | tags2[tag]
for tag in common_keys
}
for tags in (tags1, tags2):
new.update({
tag: tags[tag]
for tag in tags.keys() - common_keys
})
return new
# Since merge_tags() is commutative (merge_tags(a, b) == merge_tags(b,
# a)), we don't need any specific ordering on the parents
nodes = chain(self.parents, self.indirect_children)
tags = reduce(merge_tags, map(attrgetter('_tags'), nodes), {})
return tags
@property
@memoized
def tags(self):
return dict(
(key, frozenset(vals))
for key, vals in {
**self._inherited_tags,
**self._tags,
}.items()
)
@property
def parents(self):
parent = self.parent
if parent is not None:
yield parent
yield from parent.parents
@property
def indirect_children(self):
for child in self.children:
yield child
yield from child.indirect_children
def set_tags(self, tags):
for tag, val in tags.items():
self._tags.setdefault(tag, set()).add(val)
class JSONStatsFunctionsAnalysis(AnalysisHelpers):
"""
Support for kernel functions profiling and analysis
:param stats_path: Path to JSON function stats as returned by devlib
:meth:`devlib.collector.ftrace.FtraceCollector.get_stats`
:type stats_path: str
"""
name = 'functions_json'
def __init__(self, stats_path):
self.stats_path = stats_path
# Opening functions profiling JSON data file
with open(self.stats_path) as f:
stats = json.load(f)
# Build DataFrame of function stats
frames = {}
for cpu, data in stats.items():
frames[int(cpu)] = pd.DataFrame.from_dict(data, orient='index')
# Build and keep track of the DataFrame
self._df = pd.concat(list(frames.values()),
keys=list(frames.keys()))
def get_default_plot_path(self, **kwargs):
return super().get_default_plot_path(
default_dir=os.path.dirname(self.stats_path),
**kwargs,
)
def df_functions_stats(self, functions=None):
"""
Get a DataFrame of specified kernel functions profile data
For each profiled function a DataFrame is returned which reports stats
on kernel functions execution time. The reported stats are per-CPU and
includes: number of times the function has been executed (hits),
average execution time (avg), overall execution time (time) and samples
variance (s_2).
By default returns a DataFrame of all the functions profiled.
:param functions: the name of the function or a list of function names
to report
:type functions: list(str)
"""
df = self._df
if functions:
return df.loc[df.index.get_level_values(1).isin(functions)]
else:
return df
@AnalysisHelpers.plot_method()
def plot_profiling_stats(self, functions: str=None, axis=None, local_fig=None, metrics: str='avg'):
"""
Plot functions profiling metrics for the specified kernel functions.
For each speficied metric a barplot is generated which report the value
of the metric when the kernel function has been executed on each CPU.
By default all the kernel functions are plotted.
:param functions: the name of list of name of kernel functions to plot
:type functions: str or list(str)
:param metrics: the metrics to plot
avg - average execution time
time - total execution time
:type metrics: list(str)
"""
df = self.df_functions_stats(functions)
# Check that all the required metrics are acutally availabe
available_metrics = df.columns.tolist()
if not set(metrics).issubset(set(available_metrics)):
msg = f'Metrics {(set(metrics) - set(available_metrics))} not supported, available metrics are {available_metrics}'
raise ValueError(msg)
for metric in metrics:
if metric.upper() == 'AVG':
title = 'Average Completion Time per CPUs'
ylabel = 'Completion Time [us]'
if metric.upper() == 'TIME':
title = 'Total Execution Time per CPUs'
ylabel = 'Execution Time [us]'
data = df[metric.casefold()].unstack()
data.plot(kind='bar',
ax=axis, figsize=(16, 8), legend=True,
title=title, table=True)
axis.set_ylabel(ylabel)
axis.get_xaxis().set_visible(False)
# vim :set tabstop=4 shiftwidth=4 expandtab textwidth=80
| apache-2.0 |
joernhees/scikit-learn | sklearn/decomposition/tests/test_fastica.py | 70 | 7808 | """
Test the fastica algorithm.
"""
import itertools
import warnings
import numpy as np
from scipy import stats
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_raises
from sklearn.decomposition import FastICA, fastica, PCA
from sklearn.decomposition.fastica_ import _gs_decorrelation
from sklearn.externals.six import moves
def center_and_norm(x, axis=-1):
""" Centers and norms x **in place**
Parameters
-----------
x: ndarray
Array with an axis of observations (statistical units) measured on
random variables.
axis: int, optional
Axis along which the mean and variance are calculated.
"""
x = np.rollaxis(x, axis)
x -= x.mean(axis=0)
x /= x.std(axis=0)
def test_gs():
# Test gram schmidt orthonormalization
# generate a random orthogonal matrix
rng = np.random.RandomState(0)
W, _, _ = np.linalg.svd(rng.randn(10, 10))
w = rng.randn(10)
_gs_decorrelation(w, W, 10)
assert_less((w ** 2).sum(), 1.e-10)
w = rng.randn(10)
u = _gs_decorrelation(w, W, 5)
tmp = np.dot(u, W.T)
assert_less((tmp[:5] ** 2).sum(), 1.e-10)
def test_fastica_simple(add_noise=False):
# Test the FastICA algorithm on very simple data.
rng = np.random.RandomState(0)
# scipy.stats uses the global RNG:
np.random.seed(0)
n_samples = 1000
# Generate two sources:
s1 = (2 * np.sin(np.linspace(0, 100, n_samples)) > 0) - 1
s2 = stats.t.rvs(1, size=n_samples)
s = np.c_[s1, s2].T
center_and_norm(s)
s1, s2 = s
# Mixing angle
phi = 0.6
mixing = np.array([[np.cos(phi), np.sin(phi)],
[np.sin(phi), -np.cos(phi)]])
m = np.dot(mixing, s)
if add_noise:
m += 0.1 * rng.randn(2, 1000)
center_and_norm(m)
# function as fun arg
def g_test(x):
return x ** 3, (3 * x ** 2).mean(axis=-1)
algos = ['parallel', 'deflation']
nls = ['logcosh', 'exp', 'cube', g_test]
whitening = [True, False]
for algo, nl, whiten in itertools.product(algos, nls, whitening):
if whiten:
k_, mixing_, s_ = fastica(m.T, fun=nl, algorithm=algo)
assert_raises(ValueError, fastica, m.T, fun=np.tanh,
algorithm=algo)
else:
X = PCA(n_components=2, whiten=True).fit_transform(m.T)
k_, mixing_, s_ = fastica(X, fun=nl, algorithm=algo, whiten=False)
assert_raises(ValueError, fastica, X, fun=np.tanh,
algorithm=algo)
s_ = s_.T
# Check that the mixing model described in the docstring holds:
if whiten:
assert_almost_equal(s_, np.dot(np.dot(mixing_, k_), m))
center_and_norm(s_)
s1_, s2_ = s_
# Check to see if the sources have been estimated
# in the wrong order
if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
s2_, s1_ = s_
s1_ *= np.sign(np.dot(s1_, s1))
s2_ *= np.sign(np.dot(s2_, s2))
# Check that we have estimated the original sources
if not add_noise:
assert_almost_equal(np.dot(s1_, s1) / n_samples, 1, decimal=2)
assert_almost_equal(np.dot(s2_, s2) / n_samples, 1, decimal=2)
else:
assert_almost_equal(np.dot(s1_, s1) / n_samples, 1, decimal=1)
assert_almost_equal(np.dot(s2_, s2) / n_samples, 1, decimal=1)
# Test FastICA class
_, _, sources_fun = fastica(m.T, fun=nl, algorithm=algo, random_state=0)
ica = FastICA(fun=nl, algorithm=algo, random_state=0)
sources = ica.fit_transform(m.T)
assert_equal(ica.components_.shape, (2, 2))
assert_equal(sources.shape, (1000, 2))
assert_array_almost_equal(sources_fun, sources)
assert_array_almost_equal(sources, ica.transform(m.T))
assert_equal(ica.mixing_.shape, (2, 2))
for fn in [np.tanh, "exp(-.5(x^2))"]:
ica = FastICA(fun=fn, algorithm=algo, random_state=0)
assert_raises(ValueError, ica.fit, m.T)
assert_raises(TypeError, FastICA(fun=moves.xrange(10)).fit, m.T)
def test_fastica_nowhiten():
m = [[0, 1], [1, 0]]
# test for issue #697
ica = FastICA(n_components=1, whiten=False, random_state=0)
assert_warns(UserWarning, ica.fit, m)
assert_true(hasattr(ica, 'mixing_'))
def test_non_square_fastica(add_noise=False):
# Test the FastICA algorithm on very simple data.
rng = np.random.RandomState(0)
n_samples = 1000
# Generate two sources:
t = np.linspace(0, 100, n_samples)
s1 = np.sin(t)
s2 = np.ceil(np.sin(np.pi * t))
s = np.c_[s1, s2].T
center_and_norm(s)
s1, s2 = s
# Mixing matrix
mixing = rng.randn(6, 2)
m = np.dot(mixing, s)
if add_noise:
m += 0.1 * rng.randn(6, n_samples)
center_and_norm(m)
k_, mixing_, s_ = fastica(m.T, n_components=2, random_state=rng)
s_ = s_.T
# Check that the mixing model described in the docstring holds:
assert_almost_equal(s_, np.dot(np.dot(mixing_, k_), m))
center_and_norm(s_)
s1_, s2_ = s_
# Check to see if the sources have been estimated
# in the wrong order
if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
s2_, s1_ = s_
s1_ *= np.sign(np.dot(s1_, s1))
s2_ *= np.sign(np.dot(s2_, s2))
# Check that we have estimated the original sources
if not add_noise:
assert_almost_equal(np.dot(s1_, s1) / n_samples, 1, decimal=3)
assert_almost_equal(np.dot(s2_, s2) / n_samples, 1, decimal=3)
def test_fit_transform():
# Test FastICA.fit_transform
rng = np.random.RandomState(0)
X = rng.random_sample((100, 10))
for whiten, n_components in [[True, 5], [False, None]]:
n_components_ = (n_components if n_components is not None else
X.shape[1])
ica = FastICA(n_components=n_components, whiten=whiten, random_state=0)
Xt = ica.fit_transform(X)
assert_equal(ica.components_.shape, (n_components_, 10))
assert_equal(Xt.shape, (100, n_components_))
ica = FastICA(n_components=n_components, whiten=whiten, random_state=0)
ica.fit(X)
assert_equal(ica.components_.shape, (n_components_, 10))
Xt2 = ica.transform(X)
assert_array_almost_equal(Xt, Xt2)
def test_inverse_transform():
# Test FastICA.inverse_transform
n_features = 10
n_samples = 100
n1, n2 = 5, 10
rng = np.random.RandomState(0)
X = rng.random_sample((n_samples, n_features))
expected = {(True, n1): (n_features, n1),
(True, n2): (n_features, n2),
(False, n1): (n_features, n2),
(False, n2): (n_features, n2)}
for whiten in [True, False]:
for n_components in [n1, n2]:
n_components_ = (n_components if n_components is not None else
X.shape[1])
ica = FastICA(n_components=n_components, random_state=rng,
whiten=whiten)
with warnings.catch_warnings(record=True):
# catch "n_components ignored" warning
Xt = ica.fit_transform(X)
expected_shape = expected[(whiten, n_components_)]
assert_equal(ica.mixing_.shape, expected_shape)
X2 = ica.inverse_transform(Xt)
assert_equal(X.shape, X2.shape)
# reversibility test in non-reduction case
if n_components == X.shape[1]:
assert_array_almost_equal(X, X2)
| bsd-3-clause |
zhushun0008/sms-tools | software/transformations_interface/sineTransformations_function.py | 2 | 5029 | # function call to the transformation functions of relevance for the sineModel
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import get_window
import sys, os
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../models/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../transformations/'))
import sineModel as SM
import sineTransformations as ST
import utilFunctions as UF
def analysis(inputFile='../../sounds/mridangam.wav', window='hamming', M=801, N=2048, t=-90,
minSineDur=0.01, maxnSines=150, freqDevOffset=20, freqDevSlope=0.02):
"""
Analyze a sound with the sine model
inputFile: input sound file (monophonic with sampling rate of 44100)
window: analysis window type (rectangular, hanning, hamming, blackman, blackmanharris)
M: analysis window size; N: fft size (power of two, bigger or equal than M)
t: magnitude threshold of spectral peaks; minSineDur: minimum duration of sinusoidal tracks
maxnSines: maximum number of parallel sinusoids
freqDevOffset: frequency deviation allowed in the sinusoids from frame to frame at frequency 0
freqDevSlope: slope of the frequency deviation, higher frequencies have bigger deviation
returns inputFile: input file name; fs: sampling rate of input file,
tfreq, tmag: sinusoidal frequencies and magnitudes
"""
# size of fft used in synthesis
Ns = 512
# hop size (has to be 1/4 of Ns)
H = 128
# read input sound
(fs, x) = UF.wavread(inputFile)
# compute analysis window
w = get_window(window, M)
# compute the sine model of the whole sound
tfreq, tmag, tphase = SM.sineModelAnal(x, fs, w, N, H, t, maxnSines, minSineDur, freqDevOffset, freqDevSlope)
# synthesize the sines without original phases
y = SM.sineModelSynth(tfreq, tmag, np.array([]), Ns, H, fs)
# output sound file (monophonic with sampling rate of 44100)
outputFile = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_sineModel.wav'
# write the sound resulting from the inverse stft
UF.wavwrite(y, fs, outputFile)
# create figure to show plots
plt.figure(figsize=(12, 9))
# frequency range to plot
maxplotfreq = 5000.0
# plot the input sound
plt.subplot(3,1,1)
plt.plot(np.arange(x.size)/float(fs), x)
plt.axis([0, x.size/float(fs), min(x), max(x)])
plt.ylabel('amplitude')
plt.xlabel('time (sec)')
plt.title('input sound: x')
# plot the sinusoidal frequencies
if (tfreq.shape[1] > 0):
plt.subplot(3,1,2)
tracks = np.copy(tfreq)
tracks = tracks*np.less(tracks, maxplotfreq)
tracks[tracks<=0] = np.nan
numFrames = int(tracks[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)
plt.plot(frmTime, tracks)
plt.axis([0, x.size/float(fs), 0, maxplotfreq])
plt.title('frequencies of sinusoidal tracks')
# plot the output sound
plt.subplot(3,1,3)
plt.plot(np.arange(y.size)/float(fs), y)
plt.axis([0, y.size/float(fs), min(y), max(y)])
plt.ylabel('amplitude')
plt.xlabel('time (sec)')
plt.title('output sound: y')
plt.tight_layout()
plt.show(block=False)
return inputFile, fs, tfreq, tmag
def transformation_synthesis(inputFile, fs, tfreq, tmag, freqScaling = np.array([0, 2.0, 1, .3]),
timeScaling = np.array([0, .0, .671, .671, 1.978, 1.978+1.0])):
"""
Transform the analysis values returned by the analysis function and synthesize the sound
inputFile: name of input file; fs: sampling rate of input file
tfreq, tmag: sinusoidal frequencies and magnitudes
freqScaling: frequency scaling factors, in time-value pairs
timeScaling: time scaling factors, in time-value pairs
"""
# size of fft used in synthesis
Ns = 512
# hop size (has to be 1/4 of Ns)
H = 128
# frequency scaling of the sinusoidal tracks
ytfreq = ST.sineFreqScaling(tfreq, freqScaling)
# time scale the sinusoidal tracks
ytfreq, ytmag = ST.sineTimeScaling(ytfreq, tmag, timeScaling)
# synthesis
y = SM.sineModelSynth(ytfreq, ytmag, np.array([]), Ns, H, fs)
# write output sound
outputFile = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_sineModelTransformation.wav'
UF.wavwrite(y,fs, outputFile)
# create figure to plot
plt.figure(figsize=(12, 6))
# frequency range to plot
maxplotfreq = 15000.0
# plot the transformed sinusoidal frequencies
if (ytfreq.shape[1] > 0):
plt.subplot(2,1,1)
tracks = np.copy(ytfreq)
tracks = tracks*np.less(tracks, maxplotfreq)
tracks[tracks<=0] = np.nan
numFrames = int(tracks[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)
plt.plot(frmTime, tracks)
plt.title('transformed sinusoidal tracks')
plt.autoscale(tight=True)
# plot the output sound
plt.subplot(2,1,2)
plt.plot(np.arange(y.size)/float(fs), y)
plt.axis([0, y.size/float(fs), min(y), max(y)])
plt.ylabel('amplitude')
plt.xlabel('time (sec)')
plt.title('output sound: y')
plt.tight_layout()
plt.show(block=False)
if __name__ == "__main__":
# analysis
inputFile, fs, tfreq, tmag = analysis()
# transformation and synthesis
transformation_synthesis (inputFile, fs, tfreq, tmag)
plt.show()
| agpl-3.0 |
UNR-AERIAL/scikit-learn | examples/ensemble/plot_random_forest_embedding.py | 286 | 3531 | """
=========================================================
Hashing feature transformation using Totally Random Trees
=========================================================
RandomTreesEmbedding provides a way to map data to a
very high-dimensional, sparse representation, which might
be beneficial for classification.
The mapping is completely unsupervised and very efficient.
This example visualizes the partitions given by several
trees and shows how the transformation can also be used for
non-linear dimensionality reduction or non-linear classification.
Points that are neighboring often share the same leaf of a tree and therefore
share large parts of their hashed representation. This allows to
separate two concentric circles simply based on the principal components of the
transformed data.
In high-dimensional spaces, linear classifiers often achieve
excellent accuracy. For sparse binary data, BernoulliNB
is particularly well-suited. The bottom row compares the
decision boundary obtained by BernoulliNB in the transformed
space with an ExtraTreesClassifier forests learned on the
original data.
"""
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_circles
from sklearn.ensemble import RandomTreesEmbedding, ExtraTreesClassifier
from sklearn.decomposition import TruncatedSVD
from sklearn.naive_bayes import BernoulliNB
# make a synthetic dataset
X, y = make_circles(factor=0.5, random_state=0, noise=0.05)
# use RandomTreesEmbedding to transform data
hasher = RandomTreesEmbedding(n_estimators=10, random_state=0, max_depth=3)
X_transformed = hasher.fit_transform(X)
# Visualize result using PCA
pca = TruncatedSVD(n_components=2)
X_reduced = pca.fit_transform(X_transformed)
# Learn a Naive Bayes classifier on the transformed data
nb = BernoulliNB()
nb.fit(X_transformed, y)
# Learn an ExtraTreesClassifier for comparison
trees = ExtraTreesClassifier(max_depth=3, n_estimators=10, random_state=0)
trees.fit(X, y)
# scatter plot of original and reduced data
fig = plt.figure(figsize=(9, 8))
ax = plt.subplot(221)
ax.scatter(X[:, 0], X[:, 1], c=y, s=50)
ax.set_title("Original Data (2d)")
ax.set_xticks(())
ax.set_yticks(())
ax = plt.subplot(222)
ax.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y, s=50)
ax.set_title("PCA reduction (2d) of transformed data (%dd)" %
X_transformed.shape[1])
ax.set_xticks(())
ax.set_yticks(())
# Plot the decision in original space. For that, we will assign a color to each
# point in the mesh [x_min, m_max] x [y_min, y_max].
h = .01
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# transform grid using RandomTreesEmbedding
transformed_grid = hasher.transform(np.c_[xx.ravel(), yy.ravel()])
y_grid_pred = nb.predict_proba(transformed_grid)[:, 1]
ax = plt.subplot(223)
ax.set_title("Naive Bayes on Transformed data")
ax.pcolormesh(xx, yy, y_grid_pred.reshape(xx.shape))
ax.scatter(X[:, 0], X[:, 1], c=y, s=50)
ax.set_ylim(-1.4, 1.4)
ax.set_xlim(-1.4, 1.4)
ax.set_xticks(())
ax.set_yticks(())
# transform grid using ExtraTreesClassifier
y_grid_pred = trees.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
ax = plt.subplot(224)
ax.set_title("ExtraTrees predictions")
ax.pcolormesh(xx, yy, y_grid_pred.reshape(xx.shape))
ax.scatter(X[:, 0], X[:, 1], c=y, s=50)
ax.set_ylim(-1.4, 1.4)
ax.set_xlim(-1.4, 1.4)
ax.set_xticks(())
ax.set_yticks(())
plt.tight_layout()
plt.show()
| bsd-3-clause |
joshbohde/scikit-learn | sklearn/utils/extmath.py | 1 | 6785 | """
Extended math utilities.
"""
# Authors: G. Varoquaux, A. Gramfort, A. Passos, O. Grisel
# License: BSD
import math
from . import check_random_state
import numpy as np
from scipy import linalg
def norm(v):
v = np.asarray(v)
__nrm2, = linalg.get_blas_funcs(['nrm2'], [v])
return __nrm2(v)
def _fast_logdet(A):
"""
Compute log(det(A)) for A symmetric
Equivalent to : np.log(np.linalg.det(A))
but more robust
It returns -Inf if det(A) is non positive or is not defined.
"""
# XXX: Should be implemented as in numpy, using ATLAS
# http://projects.scipy.org/numpy/browser/trunk/numpy/linalg/linalg.py#L1559
ld = np.sum(np.log(np.diag(A)))
a = np.exp(ld / A.shape[0])
d = np.linalg.det(A / a)
ld += np.log(d)
if not np.isfinite(ld):
return -np.inf
return ld
def _fast_logdet_numpy(A):
"""
Compute log(det(A)) for A symmetric
Equivalent to : np.log(nl.det(A))
but more robust
It returns -Inf if det(A) is non positive or is not defined.
"""
sign, ld = np.linalg.slogdet(A)
if not sign > 0:
return -np.inf
return ld
# Numpy >= 1.5 provides a fast logdet
if hasattr(np.linalg, 'slogdet'):
fast_logdet = _fast_logdet_numpy
else:
fast_logdet = _fast_logdet
try:
factorial = math.factorial
except AttributeError:
# math.factorial is only available in Python >= 2.6
import operator
def factorial(x):
# don't use reduce operator or 2to3 will fail.
# ripped from http://www.joelbdalley.com/page.pl?38
# Ensure that n is a Natural number
n = abs(int(n))
if n < 1: n = 1
# Store n! in variable x
x = 1
# Compute n!
for i in range(1, n + 1):
x = i * x
# Return n!
return x
try:
import itertools
combinations = itertools.combinations
except AttributeError:
def combinations(seq, r=None):
"""Generator returning combinations of items from sequence <seq>
taken <r> at a time. Order is not significant. If <r> is not given,
the entire sequence is returned.
"""
if r == None:
r = len(seq)
if r <= 0:
yield []
else:
for i in xrange(len(seq)):
for cc in combinations(seq[i+1:], r-1):
yield [seq[i]]+cc
def density(w, **kwargs):
"""Compute density of a sparse vector
Return a value between 0 and 1
"""
if hasattr(w, "tocsr"):
d = float(w.data.size) / w.size
else:
d = 0 if w is None else float((w != 0).sum()) / w.size
return d
def safe_sparse_dot(a, b, dense_output=False):
"""Dot product that handle the sparse matrix case correctly"""
from scipy import sparse
if sparse.issparse(a) or sparse.issparse(b):
ret = a * b
if dense_output and hasattr(ret, "toarray"):
ret = ret.toarray()
return ret
else:
return np.dot(a,b)
def fast_svd(M, k, p=None, q=0, transpose='auto', random_state=0):
"""Computes the k-truncated randomized SVD
Parameters
===========
M: ndarray or sparse matrix
Matrix to decompose
k: int
Number of singular values and vectors to extract.
p: int (default is k)
Additional number of samples of the range of M to ensure proper
conditioning. See the notes below.
q: int (default is 0)
Number of power iterations (can be used to deal with very noisy
problems).
transpose: True, False or 'auto' (default)
Whether the algorithm should be applied to M.T instead of M. The
result should approximately be the same. The 'auto' mode will
trigger the transposition if M.shape[1] > M.shape[0] since this
implementation of randomized SVD tend to be a little faster in that
case).
random_state: RandomState or an int seed (0 by default)
A random number generator instance to make behavior
Notes
=====
This algorithm finds the exact truncated singular values decomposition
using randomization to speed up the computations. It is particularly
fast on large matrices on which you whish to extract only a small
number of components.
(k + p) should be strictly higher than the rank of M. This can be
checked by ensuring that the lowest extracted singular value is on
the order of the machine precision of floating points.
References
==========
Finding structure with randomness: Stochastic algorithms for constructing
approximate matrix decompositions
Halko, et al., 2009 (arXiv:909)
A randomized algorithm for the decomposition of matrices
Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert
"""
if p == None:
p = k
random_state = check_random_state(random_state)
n_samples, n_features = M.shape
if transpose == 'auto' and n_samples > n_features:
transpose = True
if transpose:
# this implementation is a bit faster with smaller shape[1]
M = M.T
# generating random gaussian vectors r with shape: (M.shape[1], k + p)
r = random_state.normal(size=(M.shape[1], k + p))
# sampling the range of M using by linear projection of r
Y = safe_sparse_dot(M, r)
del r
# apply q power iterations on Y to make to further 'imprint' the top
# singular values of M in Y
for i in xrange(q):
Y = safe_sparse_dot(M, safe_sparse_dot(M.T, Y))
# extracting an orthonormal basis of the M range samples
from .fixes import qr_economic
Q, R = qr_economic(Y)
del R
# project M to the (k + p) dimensional space using the basis vectors
B = safe_sparse_dot(Q.T, M)
# compute the SVD on the thin matrix: (k + p) wide
from scipy import linalg
Uhat, s, V = linalg.svd(B, full_matrices=False)
del B
U = np.dot(Q, Uhat)
if transpose:
# transpose back the results according to the input convention
return V[:k, :].T, s[:k], U[:, :k].T
else:
return U[:, :k], s[:k], V[:k, :]
def logsum(arr, axis=0):
""" Computes the sum of arr assuming arr is in the log domain.
Returns log(sum(exp(arr))) while minimizing the possibility of
over/underflow.
Examples
========
>>> import numpy as np
>>> from sklearn.utils.extmath import logsum
>>> a = np.arange(10)
>>> np.log(np.sum(np.exp(a)))
9.4586297444267107
>>> logsum(a)
9.4586297444267107
"""
arr = np.rollaxis(arr, axis)
# Use the max to normalize, as with the log this is what accumulates
# the less errors
vmax = arr.max(axis=0)
out = np.log(np.sum(np.exp(arr - vmax), axis=0))
out += vmax
return out
| bsd-3-clause |
kartikkumar/pagmo | PyGMO/algorithm/_cross_entropy.py | 7 | 6923 | from PyGMO.algorithm import base
class py_cross_entropy(base):
"""
Cross-Entropy algorithm (Python)
"""
def __init__(
self,
gen=500,
elite=0.5,
scale=0.3,
variant=1,
screen_output=False):
"""
Constructs a Cross-Entropy Algorithm (Python)
USAGE: algorithm.py_cross_entropy(gen = 1, elite = 0.5, scale = 0.2, variant=1, screen_output = False))
NOTE: A multivariate normal distribution is used.
The first sample is centered around the population champion.
Covariance matrix and mean is evaluated using ind.best_x
* gen: number of generations
* elite: fraction of the population considered as elite (in (0,1])
* scale: scaling factor for the estimated covariance matrix
* variant: algoritmic variant to use (one of [1,2])
1. 'Canonical' - Covariance Matrix is evaluated as sum (x_(i+1)-mu_i)^T (x_(i+1)-mu_i)
2. 'Dario's' - Covariance Matrix is evaluated as sum (x_(i+1)-mu_i^T)^T (x_(i+1)-mu_i^T)
* screen_output: activates screen_output (output at each generation)
"""
try:
import numpy as np
except ImportError:
raise ImportError(
"This algorithm needs numpy to run. Is numpy installed?")
base.__init__(self)
self.__gen = gen
self.__elite = elite
self.__scale = scale
self.__screen_output = screen_output
self.__weights = []
self.__variant = variant
np.random.seed()
def evolve(self, pop):
from numpy import matrix, array, log, diag
from numpy.random import multivariate_normal, random, normal
from numpy.linalg import norm, cholesky, LinAlgError, eig
import matplotlib.pyplot as pl
# Let's rename some variables
prob = pop.problem
lb = prob.lb
ub = prob.ub
dim, cont_dim, int_dim, c_dim = prob.dimension, prob.dimension - \
prob.i_dimension, prob.i_dimension, prob.c_dimension
# And perform checks on the problem type
if cont_dim == 0:
raise ValueError(
"There is no continuous dimension for cross_entropy to optimise!!")
if c_dim > 0:
raise ValueError(
"This version of cross_entropy is not suitable for constrained optimisation")
if int_dim > 0:
raise ValueError(
"The chromosome has an integer part .... this version of cross_entropy is not able to deal with it")
# We then check that the elite is not empty
n_elite = int(len(pop) * self.__elite)
if n_elite == 0:
raise ValueError(
"Elite contains no individuals ..... maybe increase the elite parameter?")
# If the incoming population is empty ... do nothing
np = len(pop)
if np == 0:
return population
# Let's start the algorithm
mu = matrix(pop.champion.x)
C = matrix([[0] * n_elite] * n_elite)
variation = array([[0.0] * dim] * np)
newpop = array([[0.0] * dim] * np)
self.__weights = [log(n_elite + 0.5) - log(i + 1)
for i in range(n_elite)] # recombination weights
# normalize recombination weights array
self.__weights = [w / sum(self.__weights) for w in self.__weights]
for gen in range(self.__gen):
# 1 - We extract the elite from this generation (NOTE: we use
# best_f to rank)
elite = [matrix(pop[idx].best_x)
for idx in pop.get_best_idx(n_elite)]
pl.plot(0, 0.1, 'og')
for ind in elite:
pl.plot(ind[0, 0], ind[0, 1], 'or')
pl.show()
input()
# 2 - We evaluate the Covariance Matrix
if self.__variant == 1:
# as least square estimator of the elite (with mean mu)
C = (elite[0] - mu).T * (elite[0] - mu) * self.__weights[0]
for i in range(1, n_elite):
C = C + (elite[i] - mu).T * \
(elite[i] - mu) * self.__weights[i]
1 / 0
if self.__variant == 2:
# using Dario's method
mu = mu.T
C = (elite[0] - mu).T * (elite[0] - mu) * self.__weights[0]
for i in range(1, n_elite):
C = C + (elite[i] - mu).T * \
(elite[i] - mu) * self.__weights[i]
# C = C / n_elite
# 3 - We compute the new elite mean
mu = elite[0] * self.__weights[0]
for i in range(1, n_elite):
mu = mu + elite[i] * self.__weights[i]
pl.plot(mu[0, 0], mu[0, 1], 'ob')
input()
# 4 - We generate the new sample
variation = multivariate_normal([0] * dim, C, [np])
# eigen decomposition, B==normalized eigenvectors, O(N**3)
D, B = eig(C)
D = [d ** 0.5 for d in D] # D contains standard deviations now
variation = [B * diag(D) * normal(0, 1, [dim, 1])
for i in range(np)]
variation = [[j[0, 0] for j in matr] for matr in variation]
for i, d_mu in enumerate(variation):
newpop[i] = mu + d_mu * self.__scale
pl.plot(newpop[i][0], newpop[i][1], 'ok')
pl.show()
input()
# 5 - We fix it within the bounds
for row in range(newpop.shape[0]):
for col in range(newpop.shape[1]):
if newpop[row, col] > ub[col]:
newpop[row, col] = lb[col] + \
random() * (ub[col] - lb[col])
elif newpop[row, col] < lb[col]:
newpop[row, col] = lb[col] + \
random() * (ub[col] - lb[col])
# 6 - And perform reinsertion
for i in range(np):
pop.set_x(i, newpop[i])
# 7 - We print to screen if necessary
if self.__screen_output:
if not(gen % 20):
print("\nGen.\tChampion\tHighest\t\tLowest\t\tVariation")
print(
"%d\t%e\t%e\t%e\t%e" %
(gen, pop.champion.f[0], max(
[ind.cur_f[0] for ind in pop]), min(
[ind.cur_f[0] for ind in pop]), norm(d_mu)))
return pop
def get_name(self):
return "Cross Entropy (Python)"
def human_readable_extra(self):
return "gen=" + str(self.__gen) + " elite fraction=" + str(self.__elite) + \
" covariance scaling=" + str(self.__scale) + " variant=" + str(self.__variant)
| gpl-3.0 |
BasuruK/sGlass | Outdoor_Object_Recognition_Engine/train_CNN.py | 1 | 7200 | from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense, Dropout
from keras.callbacks import History, ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
from keras.models import load_model
from keras.preprocessing import image as img
import datetime
import os.path
import matplotlib.pyplot as plt
import numpy as np
# Initialize the CNN
classifier = Sequential()
# Step 1 - Convolution
classifier.add(Conv2D(32, (5, 5), input_shape = (64, 64, 3), activation = 'relu', padding='same'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
# Step 2 - Add more Convolution Layers making it Deep followed by a Pooling Layer
classifier.add(Conv2D(32, (5, 5), activation = 'relu', padding='same'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Dropout(0.25))
classifier.add(Conv2D(64, (5, 5), activation = 'relu', padding='same'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Conv2D(128, (5, 5), activation = 'relu', padding='same'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Dropout(0.25))
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Fully Connected Neural Network
# Hidden Layer 1 - Activation Function RELU
classifier.add(Dense(units = 512, activation = 'relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(units = 3, activation = 'softmax'))
# Compile the CNN
# Categorical Crossentropy - to classify between multiple classes of images
classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
# Image Augmentation and Training Section
# Image Augmentation to prevent Overfitting (Applying random transformation on images to train set.ie.
# scaling, rotating and stretching)
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
vertical_flip=True,
rotation_range=0.3,
width_shift_range=0.3,
height_shift_range=0.3
)
test_datagen = ImageDataGenerator(rescale=1./255)
# Load the training dataset folder
training_set = train_datagen.flow_from_directory(
'dataset/training_set',
target_size=(64, 64),
batch_size=105,
class_mode='categorical')
# Load the test data set folder
test_set = test_datagen.flow_from_directory(
'dataset/test_set',
target_size=(64, 64),
batch_size=105,
class_mode='categorical')
# Get the accuracy and loss data to plot the graph
history = History()
# checkpoint = ModelCheckpoint(filepath='models_backups/' + str(str(datetime.datetime.now().hour)), monitor='val_loss',
# verbose=0, mode='auto', period=1)
print(classifier.summary())
# Fit the clasifier on the CNN data
if os.path.isfile('my_model.h5') == False:
classifier.fit_generator(
training_set,
steps_per_epoch=3000,
epochs=5,
validation_data=test_set,
validation_steps=3000,
callbacks=[history]
)
# Save the generated model to my_model.h5
classifier.save('my_model.h5')
else:
classifier = load_model('my_model.h5')
# Returns the labels for the classes according to the folder structre of clases
def get_labels_for_clases():
# return ['car', 'cat', 'dog', 'shoe']
return ['car', 'cat', 'dog']
# Run prediction for a single image
def predict_for_single_image(image):
# label the images according the folder structure
lables = get_labels_for_clases()
out = classifier.predict_classes(image, verbose=0)
return lables[out[0]]
# Run Prediction for image and give the output as percentages for each class similarities
def predict_probabilities_for_classes(classifier, image):
labels = get_labels_for_clases()
probabilities = classifier.predict(image)
print(probabilities)
# Expand two arrays to relevant class structure
probabilities = [(format(x * 100, '.2f') + "%") for x in probabilities[0]]
print(list(zip(labels, probabilities)))
# Plot the graphs
def plot_graphs_on_data(history):
# Plot Accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epocs')
plt.legend(['Train Data', 'Test Data'], loc='upper left')
plt.show()
# Plot Loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epocs')
plt.legend(['Train Data', 'Test Data'], loc='upper left')
plt.show()
# Preparedness image
def preprocess_image(folder_name, file_name):
image = img.load_img(folder_name + '/' + file_name, target_size=(64, 64))
test_image = img.img_to_array(image)
test_image = np.expand_dims(test_image, axis=0)
return test_image
# Run custom set testing
def custom_set_accuracy_test(input_folder_name):
files_in_directory = os.listdir(input_folder_name + '/')
match_count = 0
fail_count = 0
match_and_fail_count_list = []
columns = 6
i = 0
plt.figure(figsize=(15, 15))
# for each image in the directory run prediction and display that with the image
for file_name in files_in_directory:
test_image = preprocess_image(input_folder_name, file_name)
prediction_for_image = predict_for_single_image(test_image)
# Plot the images on a graph
plt.subplot(len(files_in_directory) / columns + 1, columns, i + 1)
if file_name.split(".")[0] == prediction_for_image:
match_and_fail_count_list.append(file_name + " =======>" + " Match")
match_count += 1
# Plot Positive Images on the graph
plt.title(file_name)
plt.xlabel(prediction_for_image)
plt.imshow(plt.imread(input_folder_name + '/' + file_name))
else:
match_and_fail_count_list.append(
file_name + " =======>" + " Fail. " + "Predicted => " + prediction_for_image)
fail_count += 1
# Plot Positive Images on the graph
plt.title(file_name)
plt.xlabel(prediction_for_image)
plt.imshow(plt.imread(input_folder_name + '/' + file_name))
i += 1
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.show()
[print(x) for x in match_and_fail_count_list] # Print each item in list
custom_set_accuracy = (match_count / len(files_in_directory)) * 100
print('Total Images : ', len(files_in_directory))
print('Successes : ', match_count)
print('Failures : ', fail_count)
print("Custom Set accuracy = ", custom_set_accuracy)
# Draw the Graph for the predicted Results
# use this only after training.
# plot_graphs_on_data(history)
# image = img.load_img('custom_test/dog.1.jpg', target_size=(64, 64))
# test_image = img.img_to_array(image)
# test_image = np.expand_dims(test_image, axis=0)
# print(training_set.class_indices)
# predict_probabilities_for_classes(classifier, test_image)
custom_set_accuracy_test('custom_test')
| gpl-3.0 |
LIAMF-USP/word2vec-TF | src/basic_experiment/experiments_eval.py | 1 | 2351 | """
Evaluate all the embeddings produce by the experiments
Before run this program you shuld run
bash experiments_script.sh
"""
import os
import pickle
import sys
import inspect
import matplotlib
matplotlib.use('Agg')
almost_current = os.path.abspath(inspect.getfile(inspect.currentframe()))
currentdir = os.path.dirname(almost_current)
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)
from eval.ModelJudge import ModelJudge
pt_analogy_path = os.path.join(parentdir,
'analogies',
"questions-words-ptbr.txt")
en_analogy_path = os.path.join(parentdir,
'analogies',
"questions-words.txt")
def judge_experiments(file_name, analogy_path, experiment_name):
"""
Given a pickle file called "file_name" with a list of models
and a list of pickles, this fuctions takes one analogy text
to evaluate all models.
:type file_name: str
:type analogy_path: str
:type experiment_name: str
"""
with open(file_name, "rb") as pkl_file:
d = pickle.load(pkl_file)
pass
names = d['names']
pickles = d['pickles']
judge = ModelJudge(names,
pickles,
analogy_path,
verbose=True,
experiment_name=experiment_name)
judge.compare()
file_name1 = os.path.join("pickles", "experiment1.p")
file_name2 = os.path.join("pickles", "experiment2.p")
file_name3 = os.path.join("pickles", "experiment3.p")
file_name4 = os.path.join("pickles", "experiment4.p")
file_name5 = os.path.join("pickles", "experiment5.p")
file_name6 = os.path.join("pickles", "experiment6.p")
file_name7 = os.path.join("pickles", "experiment7.p")
file_name8 = os.path.join("pickles", "experiment8.p")
judge_experiments(file_name1, pt_analogy_path, "experiment1")
judge_experiments(file_name2, en_analogy_path, "experiment2")
judge_experiments(file_name3, pt_analogy_path, "experiment3")
judge_experiments(file_name4, en_analogy_path, "experiment4")
judge_experiments(file_name5, pt_analogy_path, "experiment5")
judge_experiments(file_name6, en_analogy_path, "experiment6")
judge_experiments(file_name7, pt_analogy_path, "experiment7")
judge_experiments(file_name8, en_analogy_path, "experiment8")
| mit |
pv/scikit-learn | sklearn/preprocessing/data.py | 113 | 56747 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Eric Martin <eric@ericmart.in>
# License: BSD 3 clause
from itertools import chain, combinations
import numbers
import warnings
import numpy as np
from scipy import sparse
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..utils import check_array
from ..utils.extmath import row_norms
from ..utils.fixes import combinations_with_replacement as combinations_w_r
from ..utils.sparsefuncs_fast import (inplace_csr_row_normalize_l1,
inplace_csr_row_normalize_l2)
from ..utils.sparsefuncs import (inplace_column_scale, mean_variance_axis,
min_max_axis, inplace_row_scale)
from ..utils.validation import check_is_fitted, FLOAT_DTYPES
zip = six.moves.zip
map = six.moves.map
range = six.moves.range
__all__ = [
'Binarizer',
'KernelCenterer',
'MinMaxScaler',
'MaxAbsScaler',
'Normalizer',
'OneHotEncoder',
'RobustScaler',
'StandardScaler',
'add_dummy_feature',
'binarize',
'normalize',
'scale',
'robust_scale',
'maxabs_scale',
'minmax_scale',
]
def _mean_and_std(X, axis=0, with_mean=True, with_std=True):
"""Compute mean and std deviation for centering, scaling.
Zero valued std components are reset to 1.0 to avoid NaNs when scaling.
"""
X = np.asarray(X)
Xr = np.rollaxis(X, axis)
if with_mean:
mean_ = Xr.mean(axis=0)
else:
mean_ = None
if with_std:
std_ = Xr.std(axis=0)
std_ = _handle_zeros_in_scale(std_)
else:
std_ = None
return mean_, std_
def _handle_zeros_in_scale(scale):
''' Makes sure that whenever scale is zero, we handle it correctly.
This happens in most scalers when we have constant features.'''
# if we are fitting on 1D arrays, scale might be a scalar
if np.isscalar(scale):
if scale == 0:
scale = 1.
elif isinstance(scale, np.ndarray):
scale[scale == 0.0] = 1.0
scale[~np.isfinite(scale)] = 1.0
return scale
def scale(X, axis=0, with_mean=True, with_std=True, copy=True):
"""Standardize a dataset along any axis
Center to the mean and component wise scale to unit variance.
Read more in the :ref:`User Guide <preprocessing_scaler>`.
Parameters
----------
X : array-like or CSR matrix.
The data to center and scale.
axis : int (0 by default)
axis used to compute the means and standard deviations along. If 0,
independently standardize each feature, otherwise (if 1) standardize
each sample.
with_mean : boolean, True by default
If True, center the data before scaling.
with_std : boolean, True by default
If True, scale the data to unit variance (or equivalently,
unit standard deviation).
copy : boolean, optional, default True
set to False to perform inplace row normalization and avoid a
copy (if the input is already a numpy array or a scipy.sparse
CSR matrix and if axis is 1).
Notes
-----
This implementation will refuse to center scipy.sparse matrices
since it would make them non-sparse and would potentially crash the
program with memory exhaustion problems.
Instead the caller is expected to either set explicitly
`with_mean=False` (in that case, only variance scaling will be
performed on the features of the CSR matrix) or to call `X.toarray()`
if he/she expects the materialized dense array to fit in memory.
To avoid memory copy the caller should pass a CSR matrix.
See also
--------
:class:`sklearn.preprocessing.StandardScaler` to perform centering and
scaling using the ``Transformer`` API (e.g. as part of a preprocessing
:class:`sklearn.pipeline.Pipeline`)
"""
X = check_array(X, accept_sparse='csr', copy=copy, ensure_2d=False,
warn_on_dtype=True, estimator='the scale function',
dtype=FLOAT_DTYPES)
if sparse.issparse(X):
if with_mean:
raise ValueError(
"Cannot center sparse matrices: pass `with_mean=False` instead"
" See docstring for motivation and alternatives.")
if axis != 0:
raise ValueError("Can only scale sparse matrix on axis=0, "
" got axis=%d" % axis)
if not sparse.isspmatrix_csr(X):
X = X.tocsr()
copy = False
if copy:
X = X.copy()
_, var = mean_variance_axis(X, axis=0)
var = _handle_zeros_in_scale(var)
inplace_column_scale(X, 1 / np.sqrt(var))
else:
X = np.asarray(X)
mean_, std_ = _mean_and_std(
X, axis, with_mean=with_mean, with_std=with_std)
if copy:
X = X.copy()
# Xr is a view on the original array that enables easy use of
# broadcasting on the axis in which we are interested in
Xr = np.rollaxis(X, axis)
if with_mean:
Xr -= mean_
mean_1 = Xr.mean(axis=0)
# Verify that mean_1 is 'close to zero'. If X contains very
# large values, mean_1 can also be very large, due to a lack of
# precision of mean_. In this case, a pre-scaling of the
# concerned feature is efficient, for instance by its mean or
# maximum.
if not np.allclose(mean_1, 0):
warnings.warn("Numerical issues were encountered "
"when centering the data "
"and might not be solved. Dataset may "
"contain too large values. You may need "
"to prescale your features.")
Xr -= mean_1
if with_std:
Xr /= std_
if with_mean:
mean_2 = Xr.mean(axis=0)
# If mean_2 is not 'close to zero', it comes from the fact that
# std_ is very small so that mean_2 = mean_1/std_ > 0, even if
# mean_1 was close to zero. The problem is thus essentially due
# to the lack of precision of mean_. A solution is then to
# substract the mean again:
if not np.allclose(mean_2, 0):
warnings.warn("Numerical issues were encountered "
"when scaling the data "
"and might not be solved. The standard "
"deviation of the data is probably "
"very close to 0. ")
Xr -= mean_2
return X
class MinMaxScaler(BaseEstimator, TransformerMixin):
"""Transforms features by scaling each feature to a given range.
This estimator scales and translates each feature individually such
that it is in the given range on the training set, i.e. between
zero and one.
The transformation is given by::
X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_scaled = X_std * (max - min) + min
where min, max = feature_range.
This transformation is often used as an alternative to zero mean,
unit variance scaling.
Read more in the :ref:`User Guide <preprocessing_scaler>`.
Parameters
----------
feature_range: tuple (min, max), default=(0, 1)
Desired range of transformed data.
copy : boolean, optional, default True
Set to False to perform inplace row normalization and avoid a
copy (if the input is already a numpy array).
Attributes
----------
min_ : ndarray, shape (n_features,)
Per feature adjustment for minimum.
scale_ : ndarray, shape (n_features,)
Per feature relative scaling of the data.
"""
def __init__(self, feature_range=(0, 1), copy=True):
self.feature_range = feature_range
self.copy = copy
def fit(self, X, y=None):
"""Compute the minimum and maximum to be used for later scaling.
Parameters
----------
X : array-like, shape [n_samples, n_features]
The data used to compute the per-feature minimum and maximum
used for later scaling along the features axis.
"""
X = check_array(X, copy=self.copy, ensure_2d=False, warn_on_dtype=True,
estimator=self, dtype=FLOAT_DTYPES)
feature_range = self.feature_range
if feature_range[0] >= feature_range[1]:
raise ValueError("Minimum of desired feature range must be smaller"
" than maximum. Got %s." % str(feature_range))
data_min = np.min(X, axis=0)
data_range = np.max(X, axis=0) - data_min
data_range = _handle_zeros_in_scale(data_range)
self.scale_ = (feature_range[1] - feature_range[0]) / data_range
self.min_ = feature_range[0] - data_min * self.scale_
self.data_range = data_range
self.data_min = data_min
return self
def transform(self, X):
"""Scaling features of X according to feature_range.
Parameters
----------
X : array-like with shape [n_samples, n_features]
Input data that will be transformed.
"""
check_is_fitted(self, 'scale_')
X = check_array(X, copy=self.copy, ensure_2d=False)
X *= self.scale_
X += self.min_
return X
def inverse_transform(self, X):
"""Undo the scaling of X according to feature_range.
Parameters
----------
X : array-like with shape [n_samples, n_features]
Input data that will be transformed.
"""
check_is_fitted(self, 'scale_')
X = check_array(X, copy=self.copy, ensure_2d=False)
X -= self.min_
X /= self.scale_
return X
def minmax_scale(X, feature_range=(0, 1), axis=0, copy=True):
"""Transforms features by scaling each feature to a given range.
This estimator scales and translates each feature individually such
that it is in the given range on the training set, i.e. between
zero and one.
The transformation is given by::
X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_scaled = X_std * (max - min) + min
where min, max = feature_range.
This transformation is often used as an alternative to zero mean,
unit variance scaling.
Read more in the :ref:`User Guide <preprocessing_scaler>`.
Parameters
----------
feature_range: tuple (min, max), default=(0, 1)
Desired range of transformed data.
axis : int (0 by default)
axis used to scale along. If 0, independently scale each feature,
otherwise (if 1) scale each sample.
copy : boolean, optional, default is True
Set to False to perform inplace scaling and avoid a copy (if the input
is already a numpy array).
"""
s = MinMaxScaler(feature_range=feature_range, copy=copy)
if axis == 0:
return s.fit_transform(X)
else:
return s.fit_transform(X.T).T
class StandardScaler(BaseEstimator, TransformerMixin):
"""Standardize features by removing the mean and scaling to unit variance
Centering and scaling happen independently on each feature by computing
the relevant statistics on the samples in the training set. Mean and
standard deviation are then stored to be used on later data using the
`transform` method.
Standardization of a dataset is a common requirement for many
machine learning estimators: they might behave badly if the
individual feature do not more or less look like standard normally
distributed data (e.g. Gaussian with 0 mean and unit variance).
For instance many elements used in the objective function of
a learning algorithm (such as the RBF kernel of Support Vector
Machines or the L1 and L2 regularizers of linear models) assume that
all features are centered around 0 and have variance in the same
order. If a feature has a variance that is orders of magnitude larger
that others, it might dominate the objective function and make the
estimator unable to learn from other features correctly as expected.
Read more in the :ref:`User Guide <preprocessing_scaler>`.
Parameters
----------
with_mean : boolean, True by default
If True, center the data before scaling.
This does not work (and will raise an exception) when attempted on
sparse matrices, because centering them entails building a dense
matrix which in common use cases is likely to be too large to fit in
memory.
with_std : boolean, True by default
If True, scale the data to unit variance (or equivalently,
unit standard deviation).
copy : boolean, optional, default True
If False, try to avoid a copy and do inplace scaling instead.
This is not guaranteed to always work inplace; e.g. if the data is
not a NumPy array or scipy.sparse CSR matrix, a copy may still be
returned.
Attributes
----------
mean_ : array of floats with shape [n_features]
The mean value for each feature in the training set.
std_ : array of floats with shape [n_features]
The standard deviation for each feature in the training set.
Set to one if the standard deviation is zero for a given feature.
See also
--------
:func:`sklearn.preprocessing.scale` to perform centering and
scaling without using the ``Transformer`` object oriented API
:class:`sklearn.decomposition.RandomizedPCA` with `whiten=True`
to further remove the linear correlation across features.
"""
def __init__(self, copy=True, with_mean=True, with_std=True):
self.with_mean = with_mean
self.with_std = with_std
self.copy = copy
def fit(self, X, y=None):
"""Compute the mean and std to be used for later scaling.
Parameters
----------
X : array-like or CSR matrix with shape [n_samples, n_features]
The data used to compute the mean and standard deviation
used for later scaling along the features axis.
"""
X = check_array(X, accept_sparse='csr', copy=self.copy,
ensure_2d=False, warn_on_dtype=True,
estimator=self, dtype=FLOAT_DTYPES)
if sparse.issparse(X):
if self.with_mean:
raise ValueError(
"Cannot center sparse matrices: pass `with_mean=False` "
"instead. See docstring for motivation and alternatives.")
self.mean_ = None
if self.with_std:
var = mean_variance_axis(X, axis=0)[1]
self.std_ = np.sqrt(var)
self.std_ = _handle_zeros_in_scale(self.std_)
else:
self.std_ = None
return self
else:
self.mean_, self.std_ = _mean_and_std(
X, axis=0, with_mean=self.with_mean, with_std=self.with_std)
return self
def transform(self, X, y=None, copy=None):
"""Perform standardization by centering and scaling
Parameters
----------
X : array-like with shape [n_samples, n_features]
The data used to scale along the features axis.
"""
check_is_fitted(self, 'std_')
copy = copy if copy is not None else self.copy
X = check_array(X, accept_sparse='csr', copy=copy,
ensure_2d=False, warn_on_dtype=True,
estimator=self, dtype=FLOAT_DTYPES)
if sparse.issparse(X):
if self.with_mean:
raise ValueError(
"Cannot center sparse matrices: pass `with_mean=False` "
"instead. See docstring for motivation and alternatives.")
if self.std_ is not None:
inplace_column_scale(X, 1 / self.std_)
else:
if self.with_mean:
X -= self.mean_
if self.with_std:
X /= self.std_
return X
def inverse_transform(self, X, copy=None):
"""Scale back the data to the original representation
Parameters
----------
X : array-like with shape [n_samples, n_features]
The data used to scale along the features axis.
"""
check_is_fitted(self, 'std_')
copy = copy if copy is not None else self.copy
if sparse.issparse(X):
if self.with_mean:
raise ValueError(
"Cannot uncenter sparse matrices: pass `with_mean=False` "
"instead See docstring for motivation and alternatives.")
if not sparse.isspmatrix_csr(X):
X = X.tocsr()
copy = False
if copy:
X = X.copy()
if self.std_ is not None:
inplace_column_scale(X, self.std_)
else:
X = np.asarray(X)
if copy:
X = X.copy()
if self.with_std:
X *= self.std_
if self.with_mean:
X += self.mean_
return X
class MaxAbsScaler(BaseEstimator, TransformerMixin):
"""Scale each feature by its maximum absolute value.
This estimator scales and translates each feature individually such
that the maximal absolute value of each feature in the
training set will be 1.0. It does not shift/center the data, and
thus does not destroy any sparsity.
This scaler can also be applied to sparse CSR or CSC matrices.
Parameters
----------
copy : boolean, optional, default is True
Set to False to perform inplace scaling and avoid a copy (if the input
is already a numpy array).
Attributes
----------
scale_ : ndarray, shape (n_features,)
Per feature relative scaling of the data.
"""
def __init__(self, copy=True):
self.copy = copy
def fit(self, X, y=None):
"""Compute the minimum and maximum to be used for later scaling.
Parameters
----------
X : array-like, shape [n_samples, n_features]
The data used to compute the per-feature minimum and maximum
used for later scaling along the features axis.
"""
X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
ensure_2d=False, estimator=self, dtype=FLOAT_DTYPES)
if sparse.issparse(X):
mins, maxs = min_max_axis(X, axis=0)
scales = np.maximum(np.abs(mins), np.abs(maxs))
else:
scales = np.abs(X).max(axis=0)
scales = np.array(scales)
scales = scales.reshape(-1)
self.scale_ = _handle_zeros_in_scale(scales)
return self
def transform(self, X, y=None):
"""Scale the data
Parameters
----------
X : array-like or CSR matrix.
The data that should be scaled.
"""
check_is_fitted(self, 'scale_')
X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
ensure_2d=False, estimator=self, dtype=FLOAT_DTYPES)
if sparse.issparse(X):
if X.shape[0] == 1:
inplace_row_scale(X, 1.0 / self.scale_)
else:
inplace_column_scale(X, 1.0 / self.scale_)
else:
X /= self.scale_
return X
def inverse_transform(self, X):
"""Scale back the data to the original representation
Parameters
----------
X : array-like or CSR matrix.
The data that should be transformed back.
"""
check_is_fitted(self, 'scale_')
X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
ensure_2d=False, estimator=self, dtype=FLOAT_DTYPES)
if sparse.issparse(X):
if X.shape[0] == 1:
inplace_row_scale(X, self.scale_)
else:
inplace_column_scale(X, self.scale_)
else:
X *= self.scale_
return X
def maxabs_scale(X, axis=0, copy=True):
"""Scale each feature to the [-1, 1] range without breaking the sparsity.
This estimator scales each feature individually such
that the maximal absolute value of each feature in the
training set will be 1.0.
This scaler can also be applied to sparse CSR or CSC matrices.
Parameters
----------
axis : int (0 by default)
axis used to scale along. If 0, independently scale each feature,
otherwise (if 1) scale each sample.
copy : boolean, optional, default is True
Set to False to perform inplace scaling and avoid a copy (if the input
is already a numpy array).
"""
s = MaxAbsScaler(copy=copy)
if axis == 0:
return s.fit_transform(X)
else:
return s.fit_transform(X.T).T
class RobustScaler(BaseEstimator, TransformerMixin):
"""Scale features using statistics that are robust to outliers.
This Scaler removes the median and scales the data according to
the Interquartile Range (IQR). The IQR is the range between the 1st
quartile (25th quantile) and the 3rd quartile (75th quantile).
Centering and scaling happen independently on each feature (or each
sample, depending on the `axis` argument) by computing the relevant
statistics on the samples in the training set. Median and interquartile
range are then stored to be used on later data using the `transform`
method.
Standardization of a dataset is a common requirement for many
machine learning estimators. Typically this is done by removing the mean
and scaling to unit variance. However, outliers can often influence the
sample mean / variance in a negative way. In such cases, the median and
the interquartile range often give better results.
Read more in the :ref:`User Guide <preprocessing_scaler>`.
Parameters
----------
with_centering : boolean, True by default
If True, center the data before scaling.
This does not work (and will raise an exception) when attempted on
sparse matrices, because centering them entails building a dense
matrix which in common use cases is likely to be too large to fit in
memory.
with_scaling : boolean, True by default
If True, scale the data to interquartile range.
copy : boolean, optional, default is True
If False, try to avoid a copy and do inplace scaling instead.
This is not guaranteed to always work inplace; e.g. if the data is
not a NumPy array or scipy.sparse CSR matrix, a copy may still be
returned.
Attributes
----------
center_ : array of floats
The median value for each feature in the training set.
scale_ : array of floats
The (scaled) interquartile range for each feature in the training set.
See also
--------
:class:`sklearn.preprocessing.StandardScaler` to perform centering
and scaling using mean and variance.
:class:`sklearn.decomposition.RandomizedPCA` with `whiten=True`
to further remove the linear correlation across features.
Notes
-----
See examples/preprocessing/plot_robust_scaling.py for an example.
http://en.wikipedia.org/wiki/Median_(statistics)
http://en.wikipedia.org/wiki/Interquartile_range
"""
def __init__(self, with_centering=True, with_scaling=True, copy=True):
self.with_centering = with_centering
self.with_scaling = with_scaling
self.copy = copy
def _check_array(self, X, copy):
"""Makes sure centering is not enabled for sparse matrices."""
X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
ensure_2d=False, estimator=self, dtype=FLOAT_DTYPES)
if sparse.issparse(X):
if self.with_centering:
raise ValueError(
"Cannot center sparse matrices: use `with_centering=False`"
" instead. See docstring for motivation and alternatives.")
return X
def fit(self, X, y=None):
"""Compute the median and quantiles to be used for scaling.
Parameters
----------
X : array-like with shape [n_samples, n_features]
The data used to compute the median and quantiles
used for later scaling along the features axis.
"""
if sparse.issparse(X):
raise TypeError("RobustScaler cannot be fitted on sparse inputs")
X = self._check_array(X, self.copy)
if self.with_centering:
self.center_ = np.median(X, axis=0)
if self.with_scaling:
q = np.percentile(X, (25, 75), axis=0)
self.scale_ = (q[1] - q[0])
self.scale_ = _handle_zeros_in_scale(self.scale_)
return self
def transform(self, X, y=None):
"""Center and scale the data
Parameters
----------
X : array-like or CSR matrix.
The data used to scale along the specified axis.
"""
if self.with_centering:
check_is_fitted(self, 'center_')
if self.with_scaling:
check_is_fitted(self, 'scale_')
X = self._check_array(X, self.copy)
if sparse.issparse(X):
if self.with_scaling:
if X.shape[0] == 1:
inplace_row_scale(X, 1.0 / self.scale_)
elif self.axis == 0:
inplace_column_scale(X, 1.0 / self.scale_)
else:
if self.with_centering:
X -= self.center_
if self.with_scaling:
X /= self.scale_
return X
def inverse_transform(self, X):
"""Scale back the data to the original representation
Parameters
----------
X : array-like or CSR matrix.
The data used to scale along the specified axis.
"""
if self.with_centering:
check_is_fitted(self, 'center_')
if self.with_scaling:
check_is_fitted(self, 'scale_')
X = self._check_array(X, self.copy)
if sparse.issparse(X):
if self.with_scaling:
if X.shape[0] == 1:
inplace_row_scale(X, self.scale_)
else:
inplace_column_scale(X, self.scale_)
else:
if self.with_scaling:
X *= self.scale_
if self.with_centering:
X += self.center_
return X
def robust_scale(X, axis=0, with_centering=True, with_scaling=True, copy=True):
"""Standardize a dataset along any axis
Center to the median and component wise scale
according to the interquartile range.
Read more in the :ref:`User Guide <preprocessing_scaler>`.
Parameters
----------
X : array-like.
The data to center and scale.
axis : int (0 by default)
axis used to compute the medians and IQR along. If 0,
independently scale each feature, otherwise (if 1) scale
each sample.
with_centering : boolean, True by default
If True, center the data before scaling.
with_scaling : boolean, True by default
If True, scale the data to unit variance (or equivalently,
unit standard deviation).
copy : boolean, optional, default is True
set to False to perform inplace row normalization and avoid a
copy (if the input is already a numpy array or a scipy.sparse
CSR matrix and if axis is 1).
Notes
-----
This implementation will refuse to center scipy.sparse matrices
since it would make them non-sparse and would potentially crash the
program with memory exhaustion problems.
Instead the caller is expected to either set explicitly
`with_centering=False` (in that case, only variance scaling will be
performed on the features of the CSR matrix) or to call `X.toarray()`
if he/she expects the materialized dense array to fit in memory.
To avoid memory copy the caller should pass a CSR matrix.
See also
--------
:class:`sklearn.preprocessing.RobustScaler` to perform centering and
scaling using the ``Transformer`` API (e.g. as part of a preprocessing
:class:`sklearn.pipeline.Pipeline`)
"""
s = RobustScaler(with_centering=with_centering, with_scaling=with_scaling,
copy=copy)
if axis == 0:
return s.fit_transform(X)
else:
return s.fit_transform(X.T).T
class PolynomialFeatures(BaseEstimator, TransformerMixin):
"""Generate polynomial and interaction features.
Generate a new feature matrix consisting of all polynomial combinations
of the features with degree less than or equal to the specified degree.
For example, if an input sample is two dimensional and of the form
[a, b], the degree-2 polynomial features are [1, a, b, a^2, ab, b^2].
Parameters
----------
degree : integer
The degree of the polynomial features. Default = 2.
interaction_only : boolean, default = False
If true, only interaction features are produced: features that are
products of at most ``degree`` *distinct* input features (so not
``x[1] ** 2``, ``x[0] * x[2] ** 3``, etc.).
include_bias : boolean
If True (default), then include a bias column, the feature in which
all polynomial powers are zero (i.e. a column of ones - acts as an
intercept term in a linear model).
Examples
--------
>>> X = np.arange(6).reshape(3, 2)
>>> X
array([[0, 1],
[2, 3],
[4, 5]])
>>> poly = PolynomialFeatures(2)
>>> poly.fit_transform(X)
array([[ 1, 0, 1, 0, 0, 1],
[ 1, 2, 3, 4, 6, 9],
[ 1, 4, 5, 16, 20, 25]])
>>> poly = PolynomialFeatures(interaction_only=True)
>>> poly.fit_transform(X)
array([[ 1, 0, 1, 0],
[ 1, 2, 3, 6],
[ 1, 4, 5, 20]])
Attributes
----------
powers_ : array, shape (n_input_features, n_output_features)
powers_[i, j] is the exponent of the jth input in the ith output.
n_input_features_ : int
The total number of input features.
n_output_features_ : int
The total number of polynomial output features. The number of output
features is computed by iterating over all suitably sized combinations
of input features.
Notes
-----
Be aware that the number of features in the output array scales
polynomially in the number of features of the input array, and
exponentially in the degree. High degrees can cause overfitting.
See :ref:`examples/linear_model/plot_polynomial_interpolation.py
<example_linear_model_plot_polynomial_interpolation.py>`
"""
def __init__(self, degree=2, interaction_only=False, include_bias=True):
self.degree = degree
self.interaction_only = interaction_only
self.include_bias = include_bias
@staticmethod
def _combinations(n_features, degree, interaction_only, include_bias):
comb = (combinations if interaction_only else combinations_w_r)
start = int(not include_bias)
return chain.from_iterable(comb(range(n_features), i)
for i in range(start, degree + 1))
@property
def powers_(self):
check_is_fitted(self, 'n_input_features_')
combinations = self._combinations(self.n_input_features_, self.degree,
self.interaction_only,
self.include_bias)
return np.vstack(np.bincount(c, minlength=self.n_input_features_)
for c in combinations)
def fit(self, X, y=None):
"""
Compute number of output features.
"""
n_samples, n_features = check_array(X).shape
combinations = self._combinations(n_features, self.degree,
self.interaction_only,
self.include_bias)
self.n_input_features_ = n_features
self.n_output_features_ = sum(1 for _ in combinations)
return self
def transform(self, X, y=None):
"""Transform data to polynomial features
Parameters
----------
X : array with shape [n_samples, n_features]
The data to transform, row by row.
Returns
-------
XP : np.ndarray shape [n_samples, NP]
The matrix of features, where NP is the number of polynomial
features generated from the combination of inputs.
"""
check_is_fitted(self, ['n_input_features_', 'n_output_features_'])
X = check_array(X)
n_samples, n_features = X.shape
if n_features != self.n_input_features_:
raise ValueError("X shape does not match training shape")
# allocate output data
XP = np.empty((n_samples, self.n_output_features_), dtype=X.dtype)
combinations = self._combinations(n_features, self.degree,
self.interaction_only,
self.include_bias)
for i, c in enumerate(combinations):
XP[:, i] = X[:, c].prod(1)
return XP
def normalize(X, norm='l2', axis=1, copy=True):
"""Scale input vectors individually to unit norm (vector length).
Read more in the :ref:`User Guide <preprocessing_normalization>`.
Parameters
----------
X : array or scipy.sparse matrix with shape [n_samples, n_features]
The data to normalize, element by element.
scipy.sparse matrices should be in CSR format to avoid an
un-necessary copy.
norm : 'l1', 'l2', or 'max', optional ('l2' by default)
The norm to use to normalize each non zero sample (or each non-zero
feature if axis is 0).
axis : 0 or 1, optional (1 by default)
axis used to normalize the data along. If 1, independently normalize
each sample, otherwise (if 0) normalize each feature.
copy : boolean, optional, default True
set to False to perform inplace row normalization and avoid a
copy (if the input is already a numpy array or a scipy.sparse
CSR matrix and if axis is 1).
See also
--------
:class:`sklearn.preprocessing.Normalizer` to perform normalization
using the ``Transformer`` API (e.g. as part of a preprocessing
:class:`sklearn.pipeline.Pipeline`)
"""
if norm not in ('l1', 'l2', 'max'):
raise ValueError("'%s' is not a supported norm" % norm)
if axis == 0:
sparse_format = 'csc'
elif axis == 1:
sparse_format = 'csr'
else:
raise ValueError("'%d' is not a supported axis" % axis)
X = check_array(X, sparse_format, copy=copy, warn_on_dtype=True,
estimator='the normalize function', dtype=FLOAT_DTYPES)
if axis == 0:
X = X.T
if sparse.issparse(X):
if norm == 'l1':
inplace_csr_row_normalize_l1(X)
elif norm == 'l2':
inplace_csr_row_normalize_l2(X)
elif norm == 'max':
_, norms = min_max_axis(X, 1)
norms = norms.repeat(np.diff(X.indptr))
mask = norms != 0
X.data[mask] /= norms[mask]
else:
if norm == 'l1':
norms = np.abs(X).sum(axis=1)
elif norm == 'l2':
norms = row_norms(X)
elif norm == 'max':
norms = np.max(X, axis=1)
norms = _handle_zeros_in_scale(norms)
X /= norms[:, np.newaxis]
if axis == 0:
X = X.T
return X
class Normalizer(BaseEstimator, TransformerMixin):
"""Normalize samples individually to unit norm.
Each sample (i.e. each row of the data matrix) with at least one
non zero component is rescaled independently of other samples so
that its norm (l1 or l2) equals one.
This transformer is able to work both with dense numpy arrays and
scipy.sparse matrix (use CSR format if you want to avoid the burden of
a copy / conversion).
Scaling inputs to unit norms is a common operation for text
classification or clustering for instance. For instance the dot
product of two l2-normalized TF-IDF vectors is the cosine similarity
of the vectors and is the base similarity metric for the Vector
Space Model commonly used by the Information Retrieval community.
Read more in the :ref:`User Guide <preprocessing_normalization>`.
Parameters
----------
norm : 'l1', 'l2', or 'max', optional ('l2' by default)
The norm to use to normalize each non zero sample.
copy : boolean, optional, default True
set to False to perform inplace row normalization and avoid a
copy (if the input is already a numpy array or a scipy.sparse
CSR matrix).
Notes
-----
This estimator is stateless (besides constructor parameters), the
fit method does nothing but is useful when used in a pipeline.
See also
--------
:func:`sklearn.preprocessing.normalize` equivalent function
without the object oriented API
"""
def __init__(self, norm='l2', copy=True):
self.norm = norm
self.copy = copy
def fit(self, X, y=None):
"""Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence
work in pipelines.
"""
X = check_array(X, accept_sparse='csr')
return self
def transform(self, X, y=None, copy=None):
"""Scale each non zero row of X to unit norm
Parameters
----------
X : array or scipy.sparse matrix with shape [n_samples, n_features]
The data to normalize, row by row. scipy.sparse matrices should be
in CSR format to avoid an un-necessary copy.
"""
copy = copy if copy is not None else self.copy
X = check_array(X, accept_sparse='csr')
return normalize(X, norm=self.norm, axis=1, copy=copy)
def binarize(X, threshold=0.0, copy=True):
"""Boolean thresholding of array-like or scipy.sparse matrix
Read more in the :ref:`User Guide <preprocessing_binarization>`.
Parameters
----------
X : array or scipy.sparse matrix with shape [n_samples, n_features]
The data to binarize, element by element.
scipy.sparse matrices should be in CSR or CSC format to avoid an
un-necessary copy.
threshold : float, optional (0.0 by default)
Feature values below or equal to this are replaced by 0, above it by 1.
Threshold may not be less than 0 for operations on sparse matrices.
copy : boolean, optional, default True
set to False to perform inplace binarization and avoid a copy
(if the input is already a numpy array or a scipy.sparse CSR / CSC
matrix and if axis is 1).
See also
--------
:class:`sklearn.preprocessing.Binarizer` to perform binarization
using the ``Transformer`` API (e.g. as part of a preprocessing
:class:`sklearn.pipeline.Pipeline`)
"""
X = check_array(X, accept_sparse=['csr', 'csc'], copy=copy)
if sparse.issparse(X):
if threshold < 0:
raise ValueError('Cannot binarize a sparse matrix with threshold '
'< 0')
cond = X.data > threshold
not_cond = np.logical_not(cond)
X.data[cond] = 1
X.data[not_cond] = 0
X.eliminate_zeros()
else:
cond = X > threshold
not_cond = np.logical_not(cond)
X[cond] = 1
X[not_cond] = 0
return X
class Binarizer(BaseEstimator, TransformerMixin):
"""Binarize data (set feature values to 0 or 1) according to a threshold
Values greater than the threshold map to 1, while values less than
or equal to the threshold map to 0. With the default threshold of 0,
only positive values map to 1.
Binarization is a common operation on text count data where the
analyst can decide to only consider the presence or absence of a
feature rather than a quantified number of occurrences for instance.
It can also be used as a pre-processing step for estimators that
consider boolean random variables (e.g. modelled using the Bernoulli
distribution in a Bayesian setting).
Read more in the :ref:`User Guide <preprocessing_binarization>`.
Parameters
----------
threshold : float, optional (0.0 by default)
Feature values below or equal to this are replaced by 0, above it by 1.
Threshold may not be less than 0 for operations on sparse matrices.
copy : boolean, optional, default True
set to False to perform inplace binarization and avoid a copy (if
the input is already a numpy array or a scipy.sparse CSR matrix).
Notes
-----
If the input is a sparse matrix, only the non-zero values are subject
to update by the Binarizer class.
This estimator is stateless (besides constructor parameters), the
fit method does nothing but is useful when used in a pipeline.
"""
def __init__(self, threshold=0.0, copy=True):
self.threshold = threshold
self.copy = copy
def fit(self, X, y=None):
"""Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence
work in pipelines.
"""
check_array(X, accept_sparse='csr')
return self
def transform(self, X, y=None, copy=None):
"""Binarize each element of X
Parameters
----------
X : array or scipy.sparse matrix with shape [n_samples, n_features]
The data to binarize, element by element.
scipy.sparse matrices should be in CSR format to avoid an
un-necessary copy.
"""
copy = copy if copy is not None else self.copy
return binarize(X, threshold=self.threshold, copy=copy)
class KernelCenterer(BaseEstimator, TransformerMixin):
"""Center a kernel matrix
Let K(x, z) be a kernel defined by phi(x)^T phi(z), where phi is a
function mapping x to a Hilbert space. KernelCenterer centers (i.e.,
normalize to have zero mean) the data without explicitly computing phi(x).
It is equivalent to centering phi(x) with
sklearn.preprocessing.StandardScaler(with_std=False).
Read more in the :ref:`User Guide <kernel_centering>`.
"""
def fit(self, K, y=None):
"""Fit KernelCenterer
Parameters
----------
K : numpy array of shape [n_samples, n_samples]
Kernel matrix.
Returns
-------
self : returns an instance of self.
"""
K = check_array(K)
n_samples = K.shape[0]
self.K_fit_rows_ = np.sum(K, axis=0) / n_samples
self.K_fit_all_ = self.K_fit_rows_.sum() / n_samples
return self
def transform(self, K, y=None, copy=True):
"""Center kernel matrix.
Parameters
----------
K : numpy array of shape [n_samples1, n_samples2]
Kernel matrix.
copy : boolean, optional, default True
Set to False to perform inplace computation.
Returns
-------
K_new : numpy array of shape [n_samples1, n_samples2]
"""
check_is_fitted(self, 'K_fit_all_')
K = check_array(K)
if copy:
K = K.copy()
K_pred_cols = (np.sum(K, axis=1) /
self.K_fit_rows_.shape[0])[:, np.newaxis]
K -= self.K_fit_rows_
K -= K_pred_cols
K += self.K_fit_all_
return K
def add_dummy_feature(X, value=1.0):
"""Augment dataset with an additional dummy feature.
This is useful for fitting an intercept term with implementations which
cannot otherwise fit it directly.
Parameters
----------
X : array or scipy.sparse matrix with shape [n_samples, n_features]
Data.
value : float
Value to use for the dummy feature.
Returns
-------
X : array or scipy.sparse matrix with shape [n_samples, n_features + 1]
Same data with dummy feature added as first column.
Examples
--------
>>> from sklearn.preprocessing import add_dummy_feature
>>> add_dummy_feature([[0, 1], [1, 0]])
array([[ 1., 0., 1.],
[ 1., 1., 0.]])
"""
X = check_array(X, accept_sparse=['csc', 'csr', 'coo'])
n_samples, n_features = X.shape
shape = (n_samples, n_features + 1)
if sparse.issparse(X):
if sparse.isspmatrix_coo(X):
# Shift columns to the right.
col = X.col + 1
# Column indices of dummy feature are 0 everywhere.
col = np.concatenate((np.zeros(n_samples), col))
# Row indices of dummy feature are 0, ..., n_samples-1.
row = np.concatenate((np.arange(n_samples), X.row))
# Prepend the dummy feature n_samples times.
data = np.concatenate((np.ones(n_samples) * value, X.data))
return sparse.coo_matrix((data, (row, col)), shape)
elif sparse.isspmatrix_csc(X):
# Shift index pointers since we need to add n_samples elements.
indptr = X.indptr + n_samples
# indptr[0] must be 0.
indptr = np.concatenate((np.array([0]), indptr))
# Row indices of dummy feature are 0, ..., n_samples-1.
indices = np.concatenate((np.arange(n_samples), X.indices))
# Prepend the dummy feature n_samples times.
data = np.concatenate((np.ones(n_samples) * value, X.data))
return sparse.csc_matrix((data, indices, indptr), shape)
else:
klass = X.__class__
return klass(add_dummy_feature(X.tocoo(), value))
else:
return np.hstack((np.ones((n_samples, 1)) * value, X))
def _transform_selected(X, transform, selected="all", copy=True):
"""Apply a transform function to portion of selected features
Parameters
----------
X : array-like or sparse matrix, shape=(n_samples, n_features)
Dense array or sparse matrix.
transform : callable
A callable transform(X) -> X_transformed
copy : boolean, optional
Copy X even if it could be avoided.
selected: "all" or array of indices or mask
Specify which features to apply the transform to.
Returns
-------
X : array or sparse matrix, shape=(n_samples, n_features_new)
"""
if selected == "all":
return transform(X)
X = check_array(X, accept_sparse='csc', copy=copy)
if len(selected) == 0:
return X
n_features = X.shape[1]
ind = np.arange(n_features)
sel = np.zeros(n_features, dtype=bool)
sel[np.asarray(selected)] = True
not_sel = np.logical_not(sel)
n_selected = np.sum(sel)
if n_selected == 0:
# No features selected.
return X
elif n_selected == n_features:
# All features selected.
return transform(X)
else:
X_sel = transform(X[:, ind[sel]])
X_not_sel = X[:, ind[not_sel]]
if sparse.issparse(X_sel) or sparse.issparse(X_not_sel):
return sparse.hstack((X_sel, X_not_sel))
else:
return np.hstack((X_sel, X_not_sel))
class OneHotEncoder(BaseEstimator, TransformerMixin):
"""Encode categorical integer features using a one-hot aka one-of-K scheme.
The input to this transformer should be a matrix of integers, denoting
the values taken on by categorical (discrete) features. The output will be
a sparse matrix where each column corresponds to one possible value of one
feature. It is assumed that input features take on values in the range
[0, n_values).
This encoding is needed for feeding categorical data to many scikit-learn
estimators, notably linear models and SVMs with the standard kernels.
Read more in the :ref:`User Guide <preprocessing_categorical_features>`.
Parameters
----------
n_values : 'auto', int or array of ints
Number of values per feature.
- 'auto' : determine value range from training data.
- int : maximum value for all features.
- array : maximum value per feature.
categorical_features: "all" or array of indices or mask
Specify what features are treated as categorical.
- 'all' (default): All features are treated as categorical.
- array of indices: Array of categorical feature indices.
- mask: Array of length n_features and with dtype=bool.
Non-categorical features are always stacked to the right of the matrix.
dtype : number type, default=np.float
Desired dtype of output.
sparse : boolean, default=True
Will return sparse matrix if set True else will return an array.
handle_unknown : str, 'error' or 'ignore'
Whether to raise an error or ignore if a unknown categorical feature is
present during transform.
Attributes
----------
active_features_ : array
Indices for active features, meaning values that actually occur
in the training set. Only available when n_values is ``'auto'``.
feature_indices_ : array of shape (n_features,)
Indices to feature ranges.
Feature ``i`` in the original data is mapped to features
from ``feature_indices_[i]`` to ``feature_indices_[i+1]``
(and then potentially masked by `active_features_` afterwards)
n_values_ : array of shape (n_features,)
Maximum number of values per feature.
Examples
--------
Given a dataset with three features and two samples, we let the encoder
find the maximum value per feature and transform the data to a binary
one-hot encoding.
>>> from sklearn.preprocessing import OneHotEncoder
>>> enc = OneHotEncoder()
>>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], \
[1, 0, 2]]) # doctest: +ELLIPSIS
OneHotEncoder(categorical_features='all', dtype=<... 'float'>,
handle_unknown='error', n_values='auto', sparse=True)
>>> enc.n_values_
array([2, 3, 4])
>>> enc.feature_indices_
array([0, 2, 5, 9])
>>> enc.transform([[0, 1, 1]]).toarray()
array([[ 1., 0., 0., 1., 0., 0., 1., 0., 0.]])
See also
--------
sklearn.feature_extraction.DictVectorizer : performs a one-hot encoding of
dictionary items (also handles string-valued features).
sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot
encoding of dictionary items or strings.
"""
def __init__(self, n_values="auto", categorical_features="all",
dtype=np.float, sparse=True, handle_unknown='error'):
self.n_values = n_values
self.categorical_features = categorical_features
self.dtype = dtype
self.sparse = sparse
self.handle_unknown = handle_unknown
def fit(self, X, y=None):
"""Fit OneHotEncoder to X.
Parameters
----------
X : array-like, shape=(n_samples, n_feature)
Input array of type int.
Returns
-------
self
"""
self.fit_transform(X)
return self
def _fit_transform(self, X):
"""Assumes X contains only categorical features."""
X = check_array(X, dtype=np.int)
if np.any(X < 0):
raise ValueError("X needs to contain only non-negative integers.")
n_samples, n_features = X.shape
if self.n_values == 'auto':
n_values = np.max(X, axis=0) + 1
elif isinstance(self.n_values, numbers.Integral):
if (np.max(X, axis=0) >= self.n_values).any():
raise ValueError("Feature out of bounds for n_values=%d"
% self.n_values)
n_values = np.empty(n_features, dtype=np.int)
n_values.fill(self.n_values)
else:
try:
n_values = np.asarray(self.n_values, dtype=int)
except (ValueError, TypeError):
raise TypeError("Wrong type for parameter `n_values`. Expected"
" 'auto', int or array of ints, got %r"
% type(X))
if n_values.ndim < 1 or n_values.shape[0] != X.shape[1]:
raise ValueError("Shape mismatch: if n_values is an array,"
" it has to be of shape (n_features,).")
self.n_values_ = n_values
n_values = np.hstack([[0], n_values])
indices = np.cumsum(n_values)
self.feature_indices_ = indices
column_indices = (X + indices[:-1]).ravel()
row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),
n_features)
data = np.ones(n_samples * n_features)
out = sparse.coo_matrix((data, (row_indices, column_indices)),
shape=(n_samples, indices[-1]),
dtype=self.dtype).tocsr()
if self.n_values == 'auto':
mask = np.array(out.sum(axis=0)).ravel() != 0
active_features = np.where(mask)[0]
out = out[:, active_features]
self.active_features_ = active_features
return out if self.sparse else out.toarray()
def fit_transform(self, X, y=None):
"""Fit OneHotEncoder to X, then transform X.
Equivalent to self.fit(X).transform(X), but more convenient and more
efficient. See fit for the parameters, transform for the return value.
"""
return _transform_selected(X, self._fit_transform,
self.categorical_features, copy=True)
def _transform(self, X):
"""Assumes X contains only categorical features."""
X = check_array(X, dtype=np.int)
if np.any(X < 0):
raise ValueError("X needs to contain only non-negative integers.")
n_samples, n_features = X.shape
indices = self.feature_indices_
if n_features != indices.shape[0] - 1:
raise ValueError("X has different shape than during fitting."
" Expected %d, got %d."
% (indices.shape[0] - 1, n_features))
# We use only those catgorical features of X that are known using fit.
# i.e lesser than n_values_ using mask.
# This means, if self.handle_unknown is "ignore", the row_indices and
# col_indices corresponding to the unknown categorical feature are
# ignored.
mask = (X < self.n_values_).ravel()
if np.any(~mask):
if self.handle_unknown not in ['error', 'ignore']:
raise ValueError("handle_unknown should be either error or "
"unknown got %s" % self.handle_unknown)
if self.handle_unknown == 'error':
raise ValueError("unknown categorical feature present %s "
"during transform." % X[~mask])
column_indices = (X + indices[:-1]).ravel()[mask]
row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),
n_features)[mask]
data = np.ones(np.sum(mask))
out = sparse.coo_matrix((data, (row_indices, column_indices)),
shape=(n_samples, indices[-1]),
dtype=self.dtype).tocsr()
if self.n_values == 'auto':
out = out[:, self.active_features_]
return out if self.sparse else out.toarray()
def transform(self, X):
"""Transform X using one-hot encoding.
Parameters
----------
X : array-like, shape=(n_samples, n_features)
Input array of type int.
Returns
-------
X_out : sparse matrix if sparse=True else a 2-d array, dtype=int
Transformed input.
"""
return _transform_selected(X, self._transform,
self.categorical_features, copy=True)
| bsd-3-clause |
shyamalschandra/scikit-learn | examples/calibration/plot_calibration.py | 33 | 4794 | """
======================================
Probability calibration of classifiers
======================================
When performing classification you often want to predict not only
the class label, but also the associated probability. This probability
gives you some kind of confidence on the prediction. However, not all
classifiers provide well-calibrated probabilities, some being over-confident
while others being under-confident. Thus, a separate calibration of predicted
probabilities is often desirable as a postprocessing. This example illustrates
two different methods for this calibration and evaluates the quality of the
returned probabilities using Brier's score
(see http://en.wikipedia.org/wiki/Brier_score).
Compared are the estimated probability using a Gaussian naive Bayes classifier
without calibration, with a sigmoid calibration, and with a non-parametric
isotonic calibration. One can observe that only the non-parametric model is able
to provide a probability calibration that returns probabilities close to the
expected 0.5 for most of the samples belonging to the middle cluster with
heterogeneous labels. This results in a significantly improved Brier score.
"""
print(__doc__)
# Author: Mathieu Blondel <mathieu@mblondel.org>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Balazs Kegl <balazs.kegl@gmail.com>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD Style.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from sklearn.datasets import make_blobs
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import brier_score_loss
from sklearn.calibration import CalibratedClassifierCV
from sklearn.model_selection import train_test_split
n_samples = 50000
n_bins = 3 # use 3 bins for calibration_curve as we have 3 clusters here
# Generate 3 blobs with 2 classes where the second blob contains
# half positive samples and half negative samples. Probability in this
# blob is therefore 0.5.
centers = [(-5, -5), (0, 0), (5, 5)]
X, y = make_blobs(n_samples=n_samples, n_features=2, cluster_std=1.0,
centers=centers, shuffle=False, random_state=42)
y[:n_samples // 2] = 0
y[n_samples // 2:] = 1
sample_weight = np.random.RandomState(42).rand(y.shape[0])
# split train, test for calibration
X_train, X_test, y_train, y_test, sw_train, sw_test = \
train_test_split(X, y, sample_weight, test_size=0.9, random_state=42)
# Gaussian Naive-Bayes with no calibration
clf = GaussianNB()
clf.fit(X_train, y_train) # GaussianNB itself does not support sample-weights
prob_pos_clf = clf.predict_proba(X_test)[:, 1]
# Gaussian Naive-Bayes with isotonic calibration
clf_isotonic = CalibratedClassifierCV(clf, cv=2, method='isotonic')
clf_isotonic.fit(X_train, y_train, sw_train)
prob_pos_isotonic = clf_isotonic.predict_proba(X_test)[:, 1]
# Gaussian Naive-Bayes with sigmoid calibration
clf_sigmoid = CalibratedClassifierCV(clf, cv=2, method='sigmoid')
clf_sigmoid.fit(X_train, y_train, sw_train)
prob_pos_sigmoid = clf_sigmoid.predict_proba(X_test)[:, 1]
print("Brier scores: (the smaller the better)")
clf_score = brier_score_loss(y_test, prob_pos_clf, sw_test)
print("No calibration: %1.3f" % clf_score)
clf_isotonic_score = brier_score_loss(y_test, prob_pos_isotonic, sw_test)
print("With isotonic calibration: %1.3f" % clf_isotonic_score)
clf_sigmoid_score = brier_score_loss(y_test, prob_pos_sigmoid, sw_test)
print("With sigmoid calibration: %1.3f" % clf_sigmoid_score)
###############################################################################
# Plot the data and the predicted probabilities
plt.figure()
y_unique = np.unique(y)
colors = cm.rainbow(np.linspace(0.0, 1.0, y_unique.size))
for this_y, color in zip(y_unique, colors):
this_X = X_train[y_train == this_y]
this_sw = sw_train[y_train == this_y]
plt.scatter(this_X[:, 0], this_X[:, 1], s=this_sw * 50, c=color, alpha=0.5,
label="Class %s" % this_y)
plt.legend(loc="best")
plt.title("Data")
plt.figure()
order = np.lexsort((prob_pos_clf, ))
plt.plot(prob_pos_clf[order], 'r', label='No calibration (%1.3f)' % clf_score)
plt.plot(prob_pos_isotonic[order], 'g', linewidth=3,
label='Isotonic calibration (%1.3f)' % clf_isotonic_score)
plt.plot(prob_pos_sigmoid[order], 'b', linewidth=3,
label='Sigmoid calibration (%1.3f)' % clf_sigmoid_score)
plt.plot(np.linspace(0, y_test.size, 51)[1::2],
y_test[order].reshape(25, -1).mean(1),
'k', linewidth=3, label=r'Empirical')
plt.ylim([-0.05, 1.05])
plt.xlabel("Instances sorted according to predicted probability "
"(uncalibrated GNB)")
plt.ylabel("P(y=1)")
plt.legend(loc="upper left")
plt.title("Gaussian naive Bayes probabilities")
plt.show()
| bsd-3-clause |
yugangzhang/chxanalys | chxanalys/Stitching.py | 1 | 19112 | import sys, os, re, PIL
import numpy as np
from scipy.signal import savgol_filter as sf
import matplotlib.pyplot as plt
from chxanalys.chx_generic_functions import show_img, plot1D
from chxanalys.DataGonio import convert_Qmap
def get_base_all_filenames( inDir, base_filename_cut_length = -7 ):
'''YG Sep 26, 2017 @SMI
Get base filenames and their related all filenames
Input:
inDir, str, input data dir
base_filename_cut_length: to which length the base name is unique
Output:
dict: keys, base filename
vales, all realted filename
'''
from os import listdir
from os.path import isfile, join
tifs = np.array( [f for f in listdir(inDir) if isfile(join(inDir, f))] )
tifsc = list(tifs.copy())
utifs = np.sort( np.unique( np.array([ f[:base_filename_cut_length] for f in tifs] ) ) )[::-1]
files = {}
for uf in utifs:
files[uf] = []
i = 0
reName = []
for i in range(len(tifsc)):
if uf in tifsc[i]:
files[uf].append( inDir + tifsc[i] )
reName.append(tifsc[i])
for fn in reName:
tifsc.remove(fn)
return files
def check_stitch_two_imgs( img1, img2, overlap_width, badpixel_width =10 ):
"""YG Check the stitched two imgs
For SMI, overlap_width = 85 #for calibration data--Kevin gives one, 4 degree
overlap_width = 58 #for TWD data, 5 degree step
For SMI:
d0 = np.array( PIL.Image.open(infiles[0]).convert('I') ).T
d1 = np.array( PIL.Image.open(infiles[1]).convert('I') ).T
Example:
img1 = np.array( PIL.Image.open(infiles[0]).convert('I') ).T
img2 = np.array( PIL.Image.open(infiles[1]).convert('I') ).T
img12 = check_stitch_two_imgs( img1, img2, overlap_width=58, badpixel_width =10 )
show_img(img12[200:800, 120:250], logs = False, cmap = cmap_vge_hdr, vmin=0.8*img12.min(),
vmax= 1.2*img12.max(), aspect=1,image_name = 'Check_Overlap')
"""
w = overlap_width
ow = badpixel_width
M,N = img1.shape[0], img1.shape[1]
d0 =img1
d1 = img2
d01 = np.zeros( [ M, N*2 - w*( 2 -1) ] )
d01[:, :N ] = d0
i=1
a1,a2, b1, b2 = N*i - w*(i-1) - ow, N*(i+1) - w*i, w - ow, N
d01[:,a1:a2] = d1[:, b1:b2 ]
return d01
def Correct_Overlap_Images_Intensities( infiles, window_length=101, polyorder=5,
overlap_width=58, badpixel_width =10 ):
"""YG Correct WAXS Images intensities by using overlap area intensity
Image intensity keep same for the first image
Other image intensity is scaled by a pixel-width intensity array, which is averaged in the overlap area and then smoothed by
scipy.signal import savgol_filter with parameters as window_length=101, polyorder=5,
from scipy.signal import savgol_filter as sf
Return: data: array, stitched image with corrected intensity
dataM: dict, each value is the image with correted intensity
scale: scale for each image, the first scale=1 by defination
scale_smooth: smoothed scale
Exampe:
data, dataM, scale,scale_smooth = Correct_Overlap_Images_Intensities( infiles, window_length=101, polyorder=5,
overlap_width=58, badpixel_width =10 )
show_img(data, logs = True, vmin=0.8* data.min(), vmax= 1.2*data.max()
cmap = cmap_vge_hdr, aspect=1, image_name = 'Merged_Sca_Img')
fig = plt.figure()# figsize=[2,8])
for i in range(len(infiles)):
#print(i)
ax = fig.add_subplot(1,8, i+1)
d = process.load( infiles[i] )
show_img( dataM[i], logs = True, show_colorbar= False,show_ticks =False,
ax= [fig, ax], image_name= '%02d'%(i+1), cmap = cmap_vge_hdr, vmin=100, vmax=2e3,
aspect=1, save=True, path=ResDir)
"""
w = overlap_width
ow = badpixel_width
Nf = len(infiles)
dataM = {}
for i in range(len(infiles)):
d = np.array( PIL.Image.open(infiles[i]).convert('I') ).T/1.0
if i ==0:
M,N = d.shape[0], d.shape[1]
data = np.zeros( [ M, N*Nf - w*( Nf -1) ] )
data[:, :N ] = d
scale=np.zeros( [len(infiles), M] )
scale_smooth=np.zeros( [len(infiles), M] )
overlap_int = np.zeros( [ 2* len(infiles) - 1, M] )
overlap_int[0] = np.average( d[:, N - w: N-ow ], axis=1)
scale[0] = 1
scale_smooth[0] = 1
dataM[0] = d
else:
a1,a2, b1, b2 = N*i - w*(i-1) - ow, N*(i+1) - w*i, w - ow, N
overlap_int[2*i-1] = np.average( d[:, 0: w - ow ], axis=1 )
overlap_int[2*i] = np.average( d[:, N - w: N-ow ] , axis=1 )
scale[i] = overlap_int[2*i-2]/overlap_int[2*i-1] *scale[i-1]
scale_smooth[i] = sf( scale[i], window_length=window_length, polyorder= polyorder, deriv=0, delta=1.0, axis=-1,
mode='mirror', cval=0.0)
data[:,a1:a2] = d[:, b1:b2 ] * np.repeat(scale_smooth[i], b2-b1, axis=0).reshape([M,b2-b1])
dataM[i] = np.zeros_like( dataM[i-1])
dataM[i][:,0:w-ow] =dataM[i-1][:,N-w:N-ow]
dataM[i][:,w-ow:] = data[:,a1:a2]
return data, dataM, scale,scale_smooth
def check_overlap_scaling_factor( scale,scale_smooth, i=1, filename=None, save=False ):
"""check_overlap_scaling_factor( scale,scale_smooth, i=1 )"""
fig,ax=plt.subplots()
plot1D( scale[i], m='o', c='k',ax=ax, title='Scale_averaged_line_intensity_%s'%i,
ls='', legend = 'Data')
plot1D( scale_smooth[i], ax=ax, title='Scale_averaged_line_intensity_%s'%i, m='',c='r',
ls='-', legend='Smoothed')
if save:
fig.savefig( filename)
def stitch_WAXS_in_Qspace( dataM, phis, calibration,
dx= 0, dy = 22, dz = 0, dq=0.015, mask = None ):
"""YG Octo 11, 2017 stitch waxs scattering images in qspace
dataM: the data (with corrected intensity), dict format (todolist, make array also avialable)
phis: for SMI, the rotation angle around z-aixs
For SMI
dx= 0 #in pixel unit
dy = 22 #in pixel unit
dz = 0
calibration: class, for calibration
Return: Intensity_map, qxs, qzs
Example:
phis = np.array( [get_phi(infile,
phi_offset=4.649, phi_start=1.0, phi_spacing=5.0,) for infile in infiles] ) # For TWD data
calibration = CalibrationGonio(wavelength_A=0.619920987) # 20.0 keV
#calibration.set_image_size( data.shape[1], data.shape[0] )
calibration.set_image_size(195, height=1475) # Pilatus300kW vertical
calibration.set_pixel_size(pixel_size_um=172.0)
calibration.set_beam_position(97.0, 1314.0)
calibration.set_distance(0.275)
Intensity_map, qxs, qzs = stitch_WAXS_in_Qspace( dataM, phis, calibration)
#Get center of the qmap
bx,by = np.argmin( np.abs(qxs) ), np.argmin( np.abs(qzs) )
print( bx, by )
"""
q_range = get_qmap_range( calibration, phis[0], phis[-1] )
if q_range[0] >0:
q_range[0]= 0
if q_range[2]>0:
q_range[2]= 0
qx_min=q_range[0]
qx_max=q_range[1]
qxs = np.arange(q_range[0], q_range[1], dq)
qzs = np.arange(q_range[2], q_range[3], dq)
QXs, QZs = np.meshgrid(qxs, qzs)
num_qx=len(qxs)
qz_min=q_range[2]
qz_max=q_range[3]
num_qz=len(qzs)
phi = phis[0]
Intensity_map = np.zeros( (len(qzs), len(qxs)) )
count_map = np.zeros( (len(qzs), len(qxs)) )
#Intensity_mapN = np.zeros( (8, len(qzs), len(qxs)) )
for i in range( len( phis ) ):
dM = np.rot90( dataM[i].T )
D = dM.ravel()
phi = phis[i]
calibration.set_angles(det_phi_g=phi, det_theta_g=0.,
offset_x = dx, offset_y = dy, offset_z= dz)
calibration.clear_maps()
QZ = calibration.qz_map().ravel() #[pixel_list]
QX = calibration.qx_map().ravel() #[pixel_list]
bins = [num_qz, num_qx]
rangeq = [ [qz_min,qz_max], [qx_min,qx_max] ]
#Nov 7,2017 using new func to qmap
remesh_data, zbins, xbins = convert_Qmap(dM, QZ, QX, bins=bins, range=rangeq, mask=mask)
# Normalize by the binning
num_per_bin, zbins, xbins = convert_Qmap(np.ones_like(dM), QZ, QX, bins=bins, range=rangeq, mask=mask)
#remesh_data, zbins, xbins = np.histogram2d(QZ, QX, bins=bins, range=rangeq, normed=False, weights=D)
# Normalize by the binning
#num_per_bin, zbins, xbins = np.histogram2d(QZ, QX, bins=bins, range=rangeq, normed=False, weights=None)
Intensity_map += remesh_data
count_map += num_per_bin
#Intensity_mapN[i] = np.nan_to_num( remesh_data/num_per_bin )
Intensity_map = np.nan_to_num( Intensity_map/count_map )
return Intensity_map, qxs, qzs
def plot_qmap_in_folder( inDir ):
'''YG. Sep 27@SMI
Plot Qmap data from inDir, which contains qmap data and extent data
'''
from chxanalys.chx_generic_functions import show_img
from chxanalys.chx_libs import cmap_vge_hdr, plt
import pickle as cpl
fp = get_base_all_filenames(inDir,base_filename_cut_length=-10)
print('There will %s samples and totally %s files to be analyzed.'%( len(fp.keys()), len( np.concatenate( list(fp.values()) ))))
for k in list(fp.keys()):
for s in fp[k]:
if 'npy' in s:
d = np.load(s) #* qmask
if 'pkl' in s:
xs, zs = cpl.load( open( s, 'rb'))
show_img( d, logs= False, show_colorbar= True, show_ticks= True,
xlabel='$q_x \, (\AA^{-1})$', ylabel='$q_z \, (\AA^{-1})$',
cmap=cmap_vge_hdr, #vmin= np.min(d), vmax = np.max(d),
aspect=1, vmin= -1, vmax = np.max(d) * 0.5,
extent=[xs[0], xs[-1], zs[0],zs[-1]], image_name = k[:-1],
path= inDir, save=True)
plt.close('all')
def get_qmap_range( calibration, phi_min, phi_max ):
'''YG Sep 27@SMI
Get q_range, [ qx_start, qx_end, qz_start, qz_end ] for SMI WAXS qmap
(only rotate around z-axis, so det_theta_g=0.,actually being the y-axis for beamline conventional defination)
based on calibration on Sep 22, offset_x= 0, offset_y= 22
Input:
calibration: class, See SciAnalysis.XSAnalysis.DataGonio.CalibrationGonio
phi_min: min of phis
phi_max: max of phis
Output:
qrange: np.array([ qx_start, qx_end, qz_start, qz_end ])
'''
calibration.set_angles(det_phi_g= phi_max, det_theta_g=0., offset_x= 0, offset_y= 22 )
calibration._generate_qxyz_maps()
qx_end = np.max(calibration.qx_map_data)
qz_start = np.min(calibration.qz_map_data)
qz_end = np.max(calibration.qz_map_data)
calibration.set_angles(det_phi_g= phi_min, det_theta_g=0., offset_x= 0, offset_y= 22 )
calibration._generate_qxyz_maps()
qx_start = np.min(calibration.qx_map_data)
return np.array([ qx_start, qx_end, qz_start, qz_end ])
def get_phi(filename, phi_offset= 0, phi_start= 4.5, phi_spacing= 4.0, polarity=-1,ext='_WAXS.tif'):
pattern_re='^.+\/?([a-zA-Z0-9_]+_)(\d\d\d\d\d\d)(\%s)$'%ext
#print( pattern_re )
#pattern_re='^.+\/?([a-zA-Z0-9_]+_)(\d\d\d)(\.tif)$'
phi_re = re.compile(pattern_re)
phi_offset = phi_offset
m = phi_re.match(filename)
if m:
idx = float(m.groups()[1])
#print(idx)
#phi_c = polarity*( phi_offset + phi_start + (idx-1)*phi_spacing )
phi_c = polarity*( phi_offset + phi_start + (idx-0)*phi_spacing )
else:
print("ERROR: File {} doesn't match phi_re".format(filename))
phi_c = 0.0
return phi_c
############For CHX beamline
def get_qmap_qxyz_range( calibration, det_theta_g_lim, det_phi_g_lim,
sam_phi_lim, sam_theta_lim, sam_chi_lim,
offset_x= 0, offset_y= 0, offset_z= 0
):
'''YG Nov 8, 2017@CHX
Get q_range, [ qx_start, qx_end, qz_start, qz_end ] for SMI WAXS qmap
(only rotate around z-axis, so det_theta_g=0.,actually being the y-axis for beamline conventional defination)
based on calibration on Sep 22, offset_x= 0, offset_y= 22
Input:
calibration: class, See SciAnalysis.XSAnalysis.DataGonio.CalibrationGonio
phi_min: min of phis
phi_max: max of phis
Output:
qrange: np.array([ qx_start, qx_end, qz_start, qz_end ])
'''
i=0
calibration.set_angles( det_theta_g= det_theta_g_lim[i], det_phi_g= det_phi_g_lim[i],
sam_phi= sam_phi_lim[i], sam_theta = sam_theta_lim[i], sam_chi= sam_chi_lim[i],
offset_x= offset_x, offset_y= offset_y, offset_z= offset_z
)
calibration._generate_qxyz_maps()
qx_start = np.min(calibration.qx_map_data)
qy_start = np.min(calibration.qy_map_data)
qz_start = np.min(calibration.qz_map_data)
i= 1
calibration.set_angles( det_theta_g= det_theta_g_lim[i], det_phi_g= det_phi_g_lim[i],
sam_phi= sam_phi_lim[i], sam_theta = sam_theta_lim[i], sam_chi= sam_chi_lim[i],
offset_x= offset_x, offset_y= offset_y, offset_z= offset_z
)
calibration._generate_qxyz_maps()
qx_end = np.min(calibration.qx_map_data)
qy_end = np.min(calibration.qy_map_data)
qz_end = np.min(calibration.qz_map_data)
return np.array([ qx_start, qx_end]),np.array([ qy_start, qy_end]),np.array([ qz_start, qz_end ])
def stitch_WAXS_in_Qspace_CHX( data, angle_dict, calibration, vary_angle='phi',
qxlim=None, qylim=None, qzlim=None,
det_theta_g= 0, det_phi_g= 0.,
sam_phi= 0, sam_theta= 0, sam_chi=0,
dx= 0, dy = 0, dz = 0, dq=0.0008 ):
"""YG Octo 11, 2017 stitch waxs scattering images in qspace
dataM: the data (with corrected intensity), dict format (todolist, make array also avialable)
phis: for SMI, the rotation angle around z-aixs
For SMI
dx= 0 #in pixel unit
dy = 22 #in pixel unit
dz = 0
calibration: class, for calibration
Return: Intensity_map, qxs, qzs
Example:
phis = np.array( [get_phi(infile,
phi_offset=4.649, phi_start=1.0, phi_spacing=5.0,) for infile in infiles] ) # For TWD data
calibration = CalibrationGonio(wavelength_A=0.619920987) # 20.0 keV
#calibration.set_image_size( data.shape[1], data.shape[0] )
calibration.set_image_size(195, height=1475) # Pilatus300kW vertical
calibration.set_pixel_size(pixel_size_um=172.0)
calibration.set_beam_position(97.0, 1314.0)
calibration.set_distance(0.275)
Intensity_map, qxs, qzs = stitch_WAXS_in_Qspace( dataM, phis, calibration)
#Get center of the qmap
bx,by = np.argmin( np.abs(qxs) ), np.argmin( np.abs(qzs) )
print( bx, by )
"""
qx_min, qx_max = qxlim[0], qxlim[1]
qy_min, qy_max = qylim[0], qylim[1]
qz_min, qz_max = qzlim[0], qzlim[1]
qxs = np.arange(qxlim[0], qxlim[1], dq)
qys = np.arange(qylim[0], qylim[1], dq)
qzs = np.arange(qzlim[0], qzlim[1], dq)
QXs, QYs = np.meshgrid(qxs, qys)
QZs, QYs = np.meshgrid(qzs, qys)
QZs, QXs = np.meshgrid(qzs, qxs)
num_qx=len(qxs)
num_qy=len(qys)
num_qz=len(qzs)
Intensity_map_XY = np.zeros( (len(qxs), len(qys)) )
count_map_XY = np.zeros( (len(qxs), len(qys)) )
Intensity_map_ZY = np.zeros( (len(qzs), len(qys)) )
count_map_ZY = np.zeros( (len(qzs), len(qys)) )
Intensity_map_ZX = np.zeros( (len(qzs), len(qxs)) )
count_map_ZX = np.zeros( (len(qzs), len(qxs)) )
N = len(data)
N = len( angle_dict[vary_angle] )
print(N)
#Intensity_mapN = np.zeros( (8, len(qzs), len(qxs)) )
for i in range( N ):
dM = data[i]
D = dM.ravel()
sam_phi = angle_dict[vary_angle][i]
print(i, sam_phi )
calibration.set_angles(
det_theta_g= det_theta_g, det_phi_g= det_phi_g,
sam_phi= sam_phi, sam_theta = sam_theta, sam_chi= sam_chi,
offset_x = dx, offset_y = dy, offset_z= dz)
calibration.clear_maps()
calibration._generate_qxyz_maps()
QZ = calibration.qz_map_lab_data.ravel() #[pixel_list]
QX = calibration.qx_map_lab_data.ravel() #[pixel_list]
QY = calibration.qy_map_lab_data.ravel() #[pixel_list]
bins_xy = [num_qx, num_qy]
bins_zy = [num_qz, num_qy]
bins_zx = [num_qz, num_qx]
rangeq_xy = [ [qx_min,qx_max], [qy_min,qy_max] ]
rangeq_zy = [ [qz_min,qz_max], [qy_min,qy_max] ]
rangeq_zx = [ [qz_min,qz_max], [qx_min,qx_max] ]
print( rangeq_xy, rangeq_zy, rangeq_zx )
remesh_dataxy, xbins, ybins = np.histogram2d(QX, QY, bins=bins_xy, range=rangeq_xy, normed=False, weights=D)
# Normalize by the binning
num_per_binxy, xbins, ybins = np.histogram2d(QX, QY, bins=bins_xy, range=rangeq_xy, normed=False, weights=None)
Intensity_map_XY += remesh_dataxy
count_map_XY += num_per_binxy
remesh_datazy, zbins, ybins = np.histogram2d(QZ, QY, bins=bins_zy, range=rangeq_zy, normed=False, weights=D)
# Normalize by the binning
num_per_binzy, zbins, ybins = np.histogram2d(QZ, QY, bins=bins_zy, range=rangeq_zy, normed=False, weights=None)
Intensity_map_ZY += remesh_datazy
count_map_ZY += num_per_binzy
remesh_datazx, zbins, xbins = np.histogram2d(QZ, QX, bins=bins_zx, range=rangeq_zx, normed=False, weights=D)
# Normalize by the binning
num_per_binzx, zbins, xbins = np.histogram2d(QZ, QX, bins=bins_zx, range=rangeq_zx, normed=False, weights=None)
Intensity_map_ZX += remesh_datazx
count_map_ZX += num_per_binzx
#Intensity_mapN[i] = np.nan_to_num( remesh_data/num_per_bin )
Intensity_map_XY = np.nan_to_num( Intensity_map_XY/count_map_XY )
Intensity_map_ZY = np.nan_to_num( Intensity_map_ZY/count_map_ZY )
Intensity_map_ZX = np.nan_to_num( Intensity_map_ZX/count_map_ZX )
return Intensity_map_XY,Intensity_map_ZY,Intensity_map_ZX, qxs, qys, qzs
| bsd-3-clause |
thientu/scikit-learn | sklearn/utils/graph.py | 289 | 6239 | """
Graph utilities and algorithms
Graphs are represented with their adjacency matrices, preferably using
sparse matrices.
"""
# Authors: Aric Hagberg <hagberg@lanl.gov>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Jake Vanderplas <vanderplas@astro.washington.edu>
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from .validation import check_array
from .graph_shortest_path import graph_shortest_path
###############################################################################
# Path and connected component analysis.
# Code adapted from networkx
def single_source_shortest_path_length(graph, source, cutoff=None):
"""Return the shortest path length from source to all reachable nodes.
Returns a dictionary of shortest path lengths keyed by target.
Parameters
----------
graph: sparse matrix or 2D array (preferably LIL matrix)
Adjacency matrix of the graph
source : node label
Starting node for path
cutoff : integer, optional
Depth to stop the search - only
paths of length <= cutoff are returned.
Examples
--------
>>> from sklearn.utils.graph import single_source_shortest_path_length
>>> import numpy as np
>>> graph = np.array([[ 0, 1, 0, 0],
... [ 1, 0, 1, 0],
... [ 0, 1, 0, 1],
... [ 0, 0, 1, 0]])
>>> single_source_shortest_path_length(graph, 0)
{0: 0, 1: 1, 2: 2, 3: 3}
>>> single_source_shortest_path_length(np.ones((6, 6)), 2)
{0: 1, 1: 1, 2: 0, 3: 1, 4: 1, 5: 1}
"""
if sparse.isspmatrix(graph):
graph = graph.tolil()
else:
graph = sparse.lil_matrix(graph)
seen = {} # level (number of hops) when seen in BFS
level = 0 # the current level
next_level = [source] # dict of nodes to check at next level
while next_level:
this_level = next_level # advance to next level
next_level = set() # and start a new list (fringe)
for v in this_level:
if v not in seen:
seen[v] = level # set the level of vertex v
next_level.update(graph.rows[v])
if cutoff is not None and cutoff <= level:
break
level += 1
return seen # return all path lengths as dictionary
if hasattr(sparse, 'connected_components'):
connected_components = sparse.connected_components
else:
from .sparsetools import connected_components
###############################################################################
# Graph laplacian
def graph_laplacian(csgraph, normed=False, return_diag=False):
""" Return the Laplacian matrix of a directed graph.
For non-symmetric graphs the out-degree is used in the computation.
Parameters
----------
csgraph : array_like or sparse matrix, 2 dimensions
compressed-sparse graph, with shape (N, N).
normed : bool, optional
If True, then compute normalized Laplacian.
return_diag : bool, optional
If True, then return diagonal as well as laplacian.
Returns
-------
lap : ndarray
The N x N laplacian matrix of graph.
diag : ndarray
The length-N diagonal of the laplacian matrix.
diag is returned only if return_diag is True.
Notes
-----
The Laplacian matrix of a graph is sometimes referred to as the
"Kirchoff matrix" or the "admittance matrix", and is useful in many
parts of spectral graph theory. In particular, the eigen-decomposition
of the laplacian matrix can give insight into many properties of the graph.
For non-symmetric directed graphs, the laplacian is computed using the
out-degree of each node.
"""
if csgraph.ndim != 2 or csgraph.shape[0] != csgraph.shape[1]:
raise ValueError('csgraph must be a square matrix or array')
if normed and (np.issubdtype(csgraph.dtype, np.int)
or np.issubdtype(csgraph.dtype, np.uint)):
csgraph = check_array(csgraph, dtype=np.float64, accept_sparse=True)
if sparse.isspmatrix(csgraph):
return _laplacian_sparse(csgraph, normed=normed,
return_diag=return_diag)
else:
return _laplacian_dense(csgraph, normed=normed,
return_diag=return_diag)
def _laplacian_sparse(graph, normed=False, return_diag=False):
n_nodes = graph.shape[0]
if not graph.format == 'coo':
lap = (-graph).tocoo()
else:
lap = -graph.copy()
diag_mask = (lap.row == lap.col)
if not diag_mask.sum() == n_nodes:
# The sparsity pattern of the matrix has holes on the diagonal,
# we need to fix that
diag_idx = lap.row[diag_mask]
diagonal_holes = list(set(range(n_nodes)).difference(diag_idx))
new_data = np.concatenate([lap.data, np.ones(len(diagonal_holes))])
new_row = np.concatenate([lap.row, diagonal_holes])
new_col = np.concatenate([lap.col, diagonal_holes])
lap = sparse.coo_matrix((new_data, (new_row, new_col)),
shape=lap.shape)
diag_mask = (lap.row == lap.col)
lap.data[diag_mask] = 0
w = -np.asarray(lap.sum(axis=1)).squeeze()
if normed:
w = np.sqrt(w)
w_zeros = (w == 0)
w[w_zeros] = 1
lap.data /= w[lap.row]
lap.data /= w[lap.col]
lap.data[diag_mask] = (1 - w_zeros[lap.row[diag_mask]]).astype(
lap.data.dtype)
else:
lap.data[diag_mask] = w[lap.row[diag_mask]]
if return_diag:
return lap, w
return lap
def _laplacian_dense(graph, normed=False, return_diag=False):
n_nodes = graph.shape[0]
lap = -np.asarray(graph) # minus sign leads to a copy
# set diagonal to zero
lap.flat[::n_nodes + 1] = 0
w = -lap.sum(axis=0)
if normed:
w = np.sqrt(w)
w_zeros = (w == 0)
w[w_zeros] = 1
lap /= w
lap /= w[:, np.newaxis]
lap.flat[::n_nodes + 1] = (1 - w_zeros).astype(lap.dtype)
else:
lap.flat[::n_nodes + 1] = w.astype(lap.dtype)
if return_diag:
return lap, w
return lap
| bsd-3-clause |
shl198/Projects | Modules/f05_IDConvert.py | 2 | 23093 | """
This file includes functions of all kinds of ID converts, such as gene symbols to gene ids, protein ids to gene ids...
"""
import pandas as pd
import os
import subprocess
from Bio import Entrez
Entrez.email = "shl198@eng.ucsd.edu"
#===============================================================================
# This part is for htseq-count results. Convert geneSymbols to Entrez genes in the given files
#===============================================================================
def geneSymbol2EntrezID(DictFile,output_path,inputpath,sym2ID='yes'):
"""
This fuction converts gene symbol in htseq-count file to entriz_gene_ID, and ID to symbol
* Dict: Convert file. 1st column is ID, 2nd column is symbol. eg: /data/shangzhong/Database/cho/gff_chok1_ID_symbol.txt
* output_path: a folder stores all the files end with sort.Count.txt
* inputpath: a folder stores all the htseq count files.
* sym2ID: if yes: symbol to ID
if no : ID to symbol
"""
allFiles = os.listdir(inputpath)
filelist = [f for f in allFiles if f.endswith('sort.txt')]
if filelist == []:
assert False, 'no file in the input folder'
#=========== build dictionary ============
dic = {}
result = open(DictFile,'r')
if sym2ID =='yes':
for line in result:
item = line.split('\t')
dic[item[1][:-1]] = item[0]
else:
for line in result:
item = line.split('\t')
dic[item[0]] = item[1][:-1]
for filename in filelist:
outputName = filename[:-3] + 'Count.txt'
result = open(inputpath + '/' + filename,'r')
output = open(output_path + '/' + outputName,'w')
for line in result:
item = line.split('\t')
try:
item[0] = dic[item[0]]
output.write('\t'.join(item))
except:
output.write('\t'.join(item))
print item[0],'does not have the mapping ids'
result.close()
output.close()
os.remove(inputpath + '/' + filename)
# DictFile = '/data/shangzhong/Database/cho/gff_chok1_ID_symbol.txt'
# output_path = inputpath = '/data/shangzhong/RibosomeProfiling/TotalRNA_align/htseq'
# geneSymbol2EntrezID(DictFile,output_path,inputpath,sym2ID='yes')
#===============================================================================
# This part is for cufflinks
#===============================================================================
def addEntrezGeneID2CufflinkResultWithEnsemblAnnotation(ConvertFile,geneFpkmFile):
"""
This function insert entrez gene ids as the 1st column in the convertfile.
The cufflink results are got based on ensemble annotation file
* ConvertFile: filename. has 2 columsn, 1st is entrez gene id, 2nd is ensembl gene id.
* geneFpkmFile: the result file genes.fpkm_tracking returned by cufflinks
"""
mappedIDs = []
mapData = pd.read_csv(ConvertFile,sep='\t',names =['GeneID','EnsemblID'],header=0)
cuffData = pd.read_csv(geneFpkmFile,sep='\t')
ensemblGenes = mapData['EnsemblID'].tolist()
geneIDs = mapData['GeneID'].tolist()
for gene in cuffData['gene_id']:
index = gene.index('.')
ensembl_gene = gene[:index]
try:
index = ensemblGenes.index(ensembl_gene)
geneID = geneIDs[index]
mappedIDs.append(geneID)
except:
mappedIDs.append('-')
cuffData.insert(0, 'EntrezID', pd.Series(mappedIDs) )
outputFile = geneFpkmFile + '.txt'
cuffData.to_csv(outputFile,sep='\t')
return outputFile
# ConvertFile = '/data/shangzhong/Database/human/20150522gene2ensembl_gene_ensemble.human.txt'
# geneFpkmFile = '/data/shangzhong/DE/sjoerd/trim_KBM7_1_cufflinks/genes.fpkm_tracking'
# new = addEntrezGeneID2CufflinkResultWithEnsemblAnnotation(ConvertFile,geneFpkmFile)
# df = pd.read_csv(new,sep='\t',header=0)
# df = df[['EntrezID','FPKM']].groupby(['EntrezID']).sum()
# df.to_csv(new,sep='\t')
def addEntrezID2CufflinkResultWithNCBIAnnotation(ConvertFile,geneFpkmFile):
"""
This function converts the gene symbols to gene id in cufflinks results
and then return a file with two columns:1st Entrez geneID, 2nd FPKM
* ConvertFile:str. Filename of the id file, first 3 columns should be 'GeneID','GeneSymbol,'Acession'.
Chromosome Accession is necessary because some gene ids map to many chromosomes.
* geneFpkmFile:str. Filename of cufflink genefpkmfile.
return the new filename with id added, it's in the same folder with geneFpkmFile.
"""
cuffData = pd.read_csv(geneFpkmFile,sep='\t',header=0)
mapData = pd.read_csv(ConvertFile,sep='\t',names =['GeneID','GeneSymbol','Accession'],header=0)
geneIDs = []
outputFile = geneFpkmFile + '.txt'
mapData['SymbolAc'] = mapData['GeneSymbol'].map(str) + mapData['Accession']
dic = mapData.set_index('SymbolAc')['GeneID'].to_dict()
for symbol,accession_locus in zip(cuffData['gene_short_name'],cuffData['locus']):
# first, get the accession number
accession_number = accession_locus.split(':')[0]
try:
#geneID = mapData[(mapData['GeneSymbol']==symbol) & (mapData['Accession']== accession_number)].GeneID.values[0]
geneID = dic[symbol+accession_number]
geneIDs.append(geneID)
except:
geneIDs.append(symbol)
cuffData.insert(0,'Entrez_ID',pd.Series(geneIDs))
cuffData.to_csv(outputFile,sep='\t',index=False)
return outputFile
# pathway = '/data/shangzhong/DE/fpkm/01_result'
# os.chdir(pathway)
# folders = [f for f in os.listdir(pathway) if os.path.isdir(os.path.join(pathway, f))]
# folders.sort()
# for folder in folders:
# geneFpkmFile = folder + '/genes.fpkm_tracking'
# new_res_file = addEntrezID2CufflinkResultWithNCBIAnnotation('/data/shangzhong/Database/cho/gff_chok1_ID_symbol_accession.txt',
# geneFpkmFile)
# df = pd.read_csv(new_res_file,sep='\t',header=0)
# df = df[['Entrez_ID','FPKM']].groupby(['Entrez_ID']).sum()
# df.to_csv(new_res_file,sep='\t')
def addProduct2CufflinkResultWithNCBIAnnotation(ConvertFile,geneFpkmFile):
"""
This function insert the product name of gene into the 1st column of cufflinks results
* ConvertFile: str. Filename of gene_info from ncbi ftp for interested organism
* geneFpkmFile: str. Filename of gene_tracking file
return filename with product at 4th column. filename.product.csv
"""
# build dictionary
mapdf = pd.read_csv(ConvertFile,sep='\t',header=None,skiprows=[0],
usecols=[1,2,8],names=['GeneID','GeneSymbol','Product'])
dic = mapdf.set_index('GeneSymbol')['Product'].to_dict()
# add column
df = pd.read_csv(geneFpkmFile,header=0,sep='\t')
values = []
key_col = 'gene_short_name'
for symbol in df[key_col]:
if symbol in dic:
values.append(dic[symbol])
else:
values.append('')
df.insert(0,'Product',pd.Series(values))
outFile = geneFpkmFile[:-4]+'.product.csv'
df.to_csv(outFile,index=False,sep='\t')
return outFile
#===============================================================================
# This part is for DESeq
#===============================================================================
def addGeneIDorNameForDESeqResult(InFile,MapFile,addType='gene_id',IDVersion='no'):
"""
this function insert gene name or gene id in the first column of DESeqResult
* InFile: str. Filename of the DESeq result. eg: filename.csv
* MapFile: str. Filename for storing the mapping information. 1st column is gene id, 2nd is gene symbol.
eg: /data/shangzhong/Database/cho/gff_chok1_ID_symbol.txt
* addType: str. If 'gene_id', then add gene id, if 'gene_symbol' then add name
* IDVersion: str. If gene id has version information (eg:ESN00001.1 indicats version 1)
return csv file. eg: filename.name.csv
"""
if InFile.endswith('xlsx'):
df = pd.read_excel(InFile,header=0)
else:
df = pd.read_csv(InFile,header=0,sep='\t')
mapdf = pd.read_csv(MapFile,header=0,usecols=[0,1],names=['GeneID','GeneSymbol'],sep='\t')
if IDVersion == 'yes':
mapdf['GeneID'] = mapdf['GeneID'].apply(lambda x: str(x[:x.index('.')]))
mapdf = mapdf.drop_duplicates().astype(str)
# build dictionary
if addType == 'gene_id':
dic = mapdf.set_index('GeneSymbol')['GeneID'].to_dict()
else:
dic = mapdf.set_index('GeneID')['GeneSymbol'].to_dict()
values = []
for key in df[df.columns[0]]:
try:
values.append(dic[str(key)])
except:
print key,'not in the mapping file'
values.append('-')
if addType == 'gene_id':
added = 'Gene_ID'
else:
added = 'gene_short_name'
df.insert(0,added,pd.Series(values))
output = InFile[:-4] + '.name.csv'
df.to_csv(InFile[:-4] + '.name.csv',index=False,sep='\t')
return output
#===============================================================================
# This part is more useful for blast results
#===============================================================================
def extractIDFromBlastName(name):
"""
This function deals with names in the form: gi|625204682|ref|XP_007643745.1|,
will return only the id, 625204682
* name: name described above.
"""
index = name.replace('|','x',1).index('|')
ids = name[3:index]
return ids
def extract_from_gene2ref(filename,taxID,organism,columnNum=[]):
"""
This file extracts rows by taxonomy id, and columns from the ncbi gene2refseq file.
* filename: 'gene2ref.gz' or 'gene2ref'
* taxID: integer indicates taxonomy id of interested organism.
* organism: name of organism to be extracted
* columnNums: a list of columns to extract
"""
taxID = str(taxID)
columnNum = map(str,columnNum)
# gzipped file
if filename.endswith('.gz'):
outputfile = filename[:-2] + organism + '.txt'
if columnNum == []:
cmd = ('gunzip -c {input} | awk -F {sep} {parse} {printout} | '
'uniq > {output}').format(sep='\"\\t\"',parse='\'$1 == '+taxID+' || $1 ~ /^#/',
printout='{print $0}\'',input=filename,
output=outputfile)
else:
printout = 'print '
for index in columnNum:
printout = printout + '$' + index + ' ' + '\"\\t\"' + ' '
printout = printout[:-6]
cmd = ('gunzip -c {input} | awk -F {sep} {parse} {printcmd} '
'| uniq > {output}').format(sep='\"\\t\"',parse = '\'$1 == '+taxID+' || $1 ~ /^#/',
printcmd='{' + printout + '}\'',
input=filename,output=outputfile)
# unzipped file
else:
outputfile = filename + organism + '.txt'
if columnNum == []:
cmd = ('awk -F {sep} {parse} {printout} {input} | '
'uniq > {output}').format(sep='\"\\t\"',parse='\'$1 == '+taxID+' || $1 ~ /^#/',
printout='{print $0}\'',input=filename,
output=outputfile)
else:
printout = 'print '
for index in columnNum:
printout = printout + '$' + index + ' ' + '\"\\t\"' + ' '
printout = printout[:-6]
cmd = ('awk -F {sep} {parse} {printcmd} {inputfile} | uniq > {output}').format(sep='\"\\t\"',
printcmd='{' + printout + '}\'',inputfile=filename,output=outputfile,
parse = '\'$1 == '+taxID+' || $1 ~ /^#/')
subprocess.call(cmd,shell=True)
return outputfile
def mRNA_prID2geneIDRemote(prID,IDtype='protein',target='gene'):
"""
This function searches latest gene ID using given RNA or protein ID
through biopython. Or search rna given protein id.
It connects to romote ncbi latest database.
Remote search is slow,so you should only use this if you cannot map gene IDs
using local gene2refseq file.
return correspond geneID
* prID: protein or mRNA entrez id.
* IDtype: defalut is protein.
alternative is nucleotide.
"""
handle = Entrez.efetch(db=IDtype,id=str(prID),rettype='gb')
record = handle.read()
if target == 'gene':
pattern = 'GeneID:'
end_sig = '\"'
if target =='rna':
pattern = 'coded_by=\"'
end_sig = ':\"'
try:
geneIndex = record.rindex(pattern) # get the last index
endIndex = geneIndex + len(pattern)
while record[endIndex] not in end_sig:
endIndex = endIndex + 1
geneId = record[geneIndex + len(pattern):endIndex]
except:
geneId = '-'
print prID,'is not traceble online'
return geneId
def prID2mRNAIDRemote(prID,IDtype='protein'):
"""
This function searches latest rna ID using given prtein id.
* prID: str. Protein id
"""
handle = Entrez.efetch(db=IDtype,id=str(prID),rettype='gb')
record = handle.read()
try:
rnaIndex = record.rindex('coded_by=')
endIndex = rnaIndex + 9
while record[endIndex] !=':':
endIndex = endIndex + 1
rnaID = record[rnaIndex + 9:endIndex]
except:
geneID = 'NA'
print prID,'is not traceble online'
def mRNA_prIDMap2geneIDMap(prMapFile,gene2refseq1,gene2refseq2,switch='False',IDtype = 'protein'):
"""
This function change protein id mapping between two species into
gene id mappings, after changing to gene ID, extract the unique
mapping.
* prMapFile: file name. The first two columns of this file must be protein ID, each is
protein id for respective organism. The other columns can be any string
* gene2refseq1: filename of organism corresponds to the 1st column in
prMapFile. This file should have three columns, 1st is gene
id, 2nd is protein accession id, 3rd is protein id.
* gene2refseq2: filename of organism corresponds to the 2nd column in
prMapFile. This file should have three columns, 1st is gene
id, 2nd is protein accession id, 3rd is protien id.
* switch: if False, get the uniqueline id mapping sorted by 1st column.
if True, get the uniqueline id mapping sorted by 2nd column.
* IDtype: default is IDtype, alternative is nucleotide.
return filename.topn.gene.uniqline.txt
"""
# build library for each gene2refseq
protein_gene_lib = {}
accession_gene_lib = {}
genePr1 = open(gene2refseq1,'r')
genePr2 = open(gene2refseq2,'r')
# * library for the 1st organism
for line1 in genePr1:
line1 = line1[:-1]
item1 = line1.split('\t')
if item1[1] == '-' or item1[2] == '-':
continue
protein_gene_lib[item1[2]] = item1[0]
accession_gene_lib[item1[1][:-2]] = item1[0]
# * library for the 2nd organism
for line2 in genePr2:
line2 = line2[:-1]
item2 = line2.split('\t')
if item2[1] == '-' or item2[2] == '-':
continue
protein_gene_lib[item2[2]] = item2[0]
accession_gene_lib[item2[1][:-2]] = item2[0]
# define output file
outputfile = prMapFile[:-3] + 'gene.txt' # stores the files with gene id mapping
# change protein id to gene id
prMap = open(prMapFile,'r')
geneMap = open(outputfile,'w')
for line in prMap:
line = line[:-1]
item = line.split('\t')
# test whether there is outdated ids
try:
item[0] = protein_gene_lib[item[0]]
except:
# get updated ids.
item[0] = mRNA_prID2geneIDRemote(item[0],IDtype)
try:
item[1] = protein_gene_lib[item[1]]
except:
# get updated ids.
item[1] = mRNA_prID2geneIDRemote(item[1],IDtype)
geneMap.write('\t'.join(item) + '\n')
geneMap.close()
prMap.close()
return outputfile
def uniq1stGene(prMappingFile):
"""
This file list unique gene ids in the first column, the second column would list
all genes mapping to the gene id in the 1st column and can be repeated.
* prMappingFile: a file with two columns. each column is gene id.
"""
# # # get unique gene ids in the first column
uniq1stGene = prMappingFile[:-3] + 'uniq1stgene.txt'
inputFile = open(prMappingFile,'r')
# create library for uniqline.txt
library = {}
for line in inputFile:
item = line[:-1].split('\t')
if item[0] in library:
library[item[0]].append(item[1])
else:
library[item[0]] = [item[1]]
inputFile.close()
# write to file
output = open(uniq1stGene,'w')
for line in library:
output.write(line + '\t' + ','.join(library[line]) + '\n')
output.close()
return uniq1stGene
def indexUniqline(uniqFile,switch='False'):
"""
This function index the id mapping file which has uniqlines. For one id in org1 that has many ids in org2 mapping to, the
ids in org2 are indexed by blast order.
* uniqFile: string. filename that have uniquelines of id mapping.
* switch: if False, it means index the 2nd column,
if True, it means index the 1st column.
"""
res = open(uniqFile,'r')
outputfile = uniqFile[:-3] + 'index.txt'
output = open(outputfile,'w')
i = 0
before = ''
for line in res:
item = line[:-1].split('\t')
if switch == 'False': # sort by 1st column
if item[0] == before:
i = i + 1
else:
before = item[0]
i = 1
else: # switch is true
if item[1] == before:
i = i + 1
else:
before = item[1]
i = 1
output.write('\t'.join(item) + '\t' + str(i) + '\n')
res.close()
output.close()
return outputfile
def uniqFirst2Col(index_sort,indexColumn):
"""
This function extracts the unique first two columns. The file has at least three columsns, the first two are
gene ids, the last one is index. The file should be sorted by 1st then 2nd columns.
* index_sort: filename
* indexColumn: the index of the indexColumn
"""
res = open(index_sort,'r') # cho2human.top250.gene.uniq.index.sort.txt
outputfile = index_sort[:-3] + 'uniqline.txt'
output = open(outputfile,'w')
before = res.readline()[:-1].split('\t')
next(res)
for line in res:
item = line[:-1].split('\t')
if item[0] == before[0] and item[1] == before[1]:
before[indexColumn] = str(min(int(item[indexColumn]),int(before[indexColumn])))
else:
output.write('\t'.join(before) + '\n')
before = item
output.write('\t'.join(before) + '\n')
res.close()
output.close()
return outputfile
def nonMappedID(mergeFile,annoGeneID,gene_info):
"""
This function extracts non mapped IDs in annotation file by extracting
the non overlapping genes, which means gene Ids in annoGeneID, but not in
mergeFile (mapping) file
* mergeFile: file with geneIDs in the 1st column
* annoGeneID: file with geneIDs in the 1st column
* gene_info: file with geneIDs in 1st column, gene name in second column.
"""
# 1. bulid library
Info = open(gene_info,'r')
IDname = {}
for line in Info:
item = line[:-1].split('\t')
IDname[item[0]] = item[1]
Info.close()
# 2. detect nonOverlap
file1 = open(mergeFile,'r')
gene1 = []
for line in file1:
item = line[:-1].split('\t')
gene1.append(item[0])
file1.close()
file2 = open(annoGeneID,'r')
nonMapFile = annoGeneID[:-3] + 'nonMap.txt'
output = open(nonMapFile,'w')
for line in file2:
item = line[:-1].split('\t')
if item[0] not in gene1:
output.write(item[0]+ '\t'+ IDname[item[0]] + '\n')
# sort by name
sortNonMapFile = nonMapFile[:-3] + 'sortbyname.txt'
file2.close()
output.close()
cmd = ('sort -k2 {input} > {output}').format(input=nonMapFile,output=sortNonMapFile)
subprocess.call(cmd,shell=True)
return sortNonMapFile
# file1 = uniq1stGene('/data/shangzhong/CHO2Human/2wayBlastPresult/human2cho.top1.gene.uniqline.txt')
# file2 = uniq1stGene('/data/shangzhong/CHO2Human/2wayBlastPresult/cho2human.top1.gene.uniqline.txt')
# mergeFile = '/data/shangzhong/CHO2Human/MergeMapping.txt'
# annoGeneID = '/data/shangzhong/CHO2Human/CHO_geneID.txt'
# gene_info = '/data/shangzhong/CHO2Human/namemapping/141028gene_info.cho.txt'
# res = nonMappedID(mergeFile,annoGeneID,gene_info)
def ConvertIDBetweenOrganisms(org1ID,mapping,output):
"""
This functions convert IDs in org1 into IDs in org2 based on Homology. mapping file is ID mapping files,
1st column is IDs in org1, 2nd column is IDs in org2. Mapping means they have the same function.
* org1ID: file containing the id file of org1.
* mapping: file containing ids mapping from org1 to org2.
* output: file containing ids of org2
"""
# build dictionary
dic = {}
res = open(mapping,'r')
for line in res:
item = line[:-1].split('\t')
if ';' in item[1]:
dic[item[0]] = item[1].split(';')
else:
dic[item[0]] = item[1].split(',')
res.close()
# convert
outputfile = open(output,'w')
inputfile = open(org1ID,'r')
humanID = []
for line in inputfile:
gene1 = line[:-1]
try:
gene2 = dic[gene1]
humanID.extend(gene2)
except:
print gene1, 'has no mapping'
continue
humanID = list(set(humanID))
outputfile.write('\n'.join(humanID) + '\n')
outputfile.close()
def geneID2proteinID(geneIDFile,gene2refseq):
"""
This function gets all protein ids corresponds to the gene id.
* geneIDFile: string. filename which stores gene ids each line.
* gene2refseq: string. filename which stores the gene ids mapping to protein ids.
"""
# 1. build dictionary
dic = {}
res = open(gene2refseq,'r')
for line in res:
item = line[:-1].split('\t')
if item[0] in dic:
if item[2] != '-':
dic[item[0]].append(item[2])
else:
dic[item[0]] = [item[2]]
res.close()
# 2. output transferred ids
outfile = geneIDFile[:-3] + 'prID.txt'
with open(outfile,'w') as output:
with open(geneIDFile,'r') as inputfile:
for line in inputfile:
item = line[:-1]
output.write('\n'.join(dic[item]) + '\n')
return outfile | mit |
marianotepper/nmu_rfit | rnmu/test/test_climate.py | 1 | 5970 | from __future__ import absolute_import, print_function
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import numpy as np
import os
import scipy.io
import scipy.sparse.linalg as sp_linalg
import seaborn.apionly as sns
import sys
import timeit
import rnmu.nmu as nmu
import rnmu.test.utils as test_utils
def plot_approximation(dir_name, filename, n_factors=1):
f = scipy.io.loadmat('../data/' + filename + '.mat')
mat = f['A']
mat /= mat.max()
if filename.find('mon') != -1:
years_step = 10
x_labels_pos = np.arange(0, mat.shape[1], 12 * years_step)
x_labels_names = ['{}/{}'.format(1 + i % 12, 1948 + i / 12)
for i in x_labels_pos]
elif filename.find('day') != -1:
years_step = 10
years = np.cumsum(np.insert(f['years'], 0, 0))
x_labels_pos = years[::years_step]
x_labels_names = ['01/01/{}'.format(1948 + i / 12)
for i in range(len(x_labels_pos))]
else:
x_labels_pos = None
t = timeit.default_timer()
factors = nmu.recursive_nmu(mat, r=n_factors, tol=1e-5)
t = timeit.default_timer() - t
print('time {:.2f}'.format(t))
approx = 0
for i, (lf, rf) in enumerate(factors):
approx += lf.dot(rf)
err = np.linalg.norm(approx - mat) ** 2 / np.linalg.norm(mat) ** 2
print('ncols {}, error {:.4f}, energy {:.4f}'.format(i + 1, err,
1 - err))
test_name = dir_name + filename
for k in range(n_factors):
shape = (73, 144)
img = np.squeeze(factors[k][0]).reshape(shape)
img = np.roll(np.flipud(img), shape[1] // 2, axis=1)
plt.figure()
ax = plt.axes(projection=ccrs.Robinson())
ax.imshow(img, vmin=0, vmax=1, cmap='RdYlBu_r',
transform=ccrs.PlateCarree())
ax.set_global()
ax.coastlines()
sm = plt.cm.ScalarMappable(cmap='RdYlBu_r')
sm._A = []
cb = plt.colorbar(sm, shrink=0.5)
cb.set_ticks([0, 1])
plt.savefig(test_name + '_comp{}_left.png'.format(k+1),
dpi=150, bbox_inches='tight')
plt.close()
with sns.axes_style("whitegrid"):
plt.figure()
sns.regplot(np.arange(mat.shape[1]), np.squeeze(factors[k][1]),
scatter_kws={'s': 10, 'facecolor': '#1f78b4'},
line_kws={'color': '#e41a1c'},
order=1, ci=None, truncate=True)
if x_labels_pos is not None:
plt.xticks(x_labels_pos, x_labels_names, rotation=45,
size='x-large')
for item in plt.yticks()[1]:
item.set_fontsize('x-large')
# plt.title('Component {}'.format(k + 1))
plt.savefig(test_name + '_comp{}_right.pdf'.format(k + 1),
dpi=150, bbox_inches='tight')
plt.close()
def plot_comparison(dir_name, filename):
f = scipy.io.loadmat('../data/' + filename + '.mat')
mat = f['A']
mat /= mat.max()
u_svd, s, v_svd = sp_linalg.svds(mat, k=1)
u_lag, v_lag = nmu.nmu(mat, max_iter=5e2, tol=1e-5)
u_adm, v_adm = nmu.nmu_admm(mat, max_iter=5e2, tol=1e-5)
def plot_hist(diff, title):
print(diff.min(), diff.max())
plt.hist(diff.flatten(), bins=100, normed=True,
histtype='stepfilled', color='#a6cee3', edgecolor='#1f78b4')
bbox = plt.ylim()
plt.plot([0, 0], [bbox[0], bbox[1]], color='#e41a1c', linewidth=2,
linestyle='--')
plt.ylim(bbox)
bbox = plt.xlim()
locs = np.round([0, bbox[0], bbox[-1]]).astype(np.int)
plt.xticks(locs, ['{}'.format(x) for x in locs])
plt.yticks([])
plt.title(title)
diff_svd = mat - s[0] * u_svd.dot(v_svd)
diff_lag = mat - u_lag.dot(v_lag)
diff_adm = mat - u_adm.dot(v_adm)
with sns.axes_style("white"):
fig = plt.figure(figsize=(7, 3))
plt.subplot(131)
plot_hist(diff_svd, 'SVD/NMF')
plt.subplot(132)
plot_hist(diff_lag, 'NMU - LR')
plt.subplot(133)
plot_hist(diff_adm, 'NMU - ADMM')
fig.set_tight_layout(True)
fig.savefig(dir_name + filename + '_comp_hist.pdf',
dpi=150, bbox_inches='tight')
plt.close()
def plot_errors(dir_name, filename, n_factors=4):
f = scipy.io.loadmat('../data/' + filename + '.mat')
mat = f['A']
mat /= mat.max()
err_R = {}
for i in range(n_factors):
out = nmu.nmu_admm(mat, max_iter=5e2, tol=1e-20, ret_errors=True)
u, v, _, _, err_R[i] = out
mat -= u.dot(v)
mat = np.maximum(mat, 0)
with sns.axes_style("whitegrid"):
plt.figure()
palette = sns.color_palette('Set1', n_colors=n_factors)
for i in range(n_factors):
plt.plot(err_R[i], linewidth=2, color=palette[i], linestyle='-',
label='Remainder {}'.format(i + 1))
for item in plt.xticks()[1]:
item.set_fontsize('x-large')
for item in plt.yticks()[1]:
item.set_fontsize('x-large')
plt.xlabel('Iterations', size='x-large')
plt.ylabel('Relative errror', size='x-large')
plt.legend(loc='lower center', ncol=2, prop={'size': 'x-large'})
plt.ylim([0, 1])
plt.tight_layout()
plt.savefig(dir_name + filename + '_convergence.pdf',
dpi=150, bbox_inches='tight')
if __name__ == '__main__':
dir_name = '../results/climate/'
if not os.path.exists(dir_name):
os.mkdir(dir_name)
logger = test_utils.Logger(dir_name + 'test.txt')
sys.stdout = logger
plot_approximation(dir_name, 'air_mon', n_factors=4)
plot_comparison(dir_name, 'air_mon')
plot_errors(dir_name, 'air_mon')
sys.stdout = logger.stdout
logger.close()
plt.show()
| bsd-3-clause |
hong-chen/dotfiles | setup.py | 1 | 4636 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import sys
import shutil
import re
class SETUP(object):
def __init__(self):
print(sys.platform)
if 'darwin' in sys.platform:
self.system = 'macOS'
print('Message [SETUP]: Setting up environment on macOS...')
if 'linux' in sys.platform:
self.system = 'linux'
print('Message [SETUP]: Setting up environment on Linux...')
exit()
self.fdir_cur = os.getcwd()
self.VIM()
self.GIT()
self.TMUX()
self.MATPLOTLIB()
def VIM(self):
if shutil.which('vim'):
print('Message [SETUP]: Setting up Vim...')
os.system('rm -rf ~/.vim*')
os.system('ln -sf %s/vimrc ~/.vimrc' % self.fdir_cur)
os.system('mkdir -p ~/.vim/after')
os.system('ln -sf %s/data/my_snippets ~/.vim/after/my_snippets' % self.fdir_cur)
print('Message [SETUP]: Vim setup is complete.')
else:
print('Message [SETUP]: Vim does not exist, aborting setup for Vim...')
def GIT(self):
if shutil.which('git'):
print('Message [SETUP]: Setting up Git...')
os.system('ln -sf %s/gitconfig ~/.gitconfig' % self.fdir_cur)
print('Message [SETUP]: Git setup is complete.')
else:
print('Message [SETUP]: Git does not exist, aborting setup for Git...')
def TMUX(self):
if shutil.which('tmux'):
print('Message [SETUP]: Setting up tmux...')
os.system('ln -sf %s/tmux.conf ~/.tmux.conf' % self.fdir_cur)
print('Message [SETUP]: tmux setup is complete.')
else:
print('Message [SETUP]: tmux does not exist, aborting setup for tmux...')
def MATPLOTLIB(self):
try:
import matplotlib as mpl
fdir_mpl_data = mpl.get_data_path()
fdir_mpl_cfg = mpl.get_configdir()
fdir_mpl_cache = mpl.get_cachedir()
# check if matplotlibrc exists under configure directory
fname_cfg = '%s/matplotlibrc' % fdir_mpl_cfg
fname_cfg_backup = '%s/matplotlibrc_backup' % fdir_mpl_cfg
if not os.path.isfile(fname_cfg):
os.system('cp %s/matplotlibrc %s' % (fdir_mpl_data, fname_cfg_backup))
else:
os.system('mv %s %s' % (fname_cfg, fname_cfg_backup))
exit()
my_rcParams = {
'lines.linewidth' : '1.5',
'patch.edgecolor' : 'none',
'font.size' : '16.0',
'font.serif' : 'Times New Roman',
'font.sans-serif' : 'Times New Roman',
'mathtext.fontset': 'stix',
'axes.prop_cycle' : 'cycler(''color'', ''krbgcmy'')',
'xtick.major.size' : '4.0',
'xtick.minor.size' : '2.0',
'xtick.major.width' : '1.0',
'xtick.minor.width' : '1.0',
'xtick.direction' : 'in',
'ytick.major.size' : '4.0',
'ytick.minor.size' : '2.0',
'ytick.major.width' : '1.0',
'ytick.minor.width' : '1.0',
'ytick.direction' : 'in',
'grid.color' : 'grey',
'grid.linewidth' : '0.5',
'figure.figsize' : '6.0, 4.0',
'figure.dpi' : '100',
'figure.max_open_warning': '1000',
'savefig.dpi' : '220',
'savefig.format' : 'svg',
'savefig.bbox' : 'tight',
'savefig.transparent' : 'True'
}
pattern = re.compile('.*\..*')
f_backup = open(fname_cfg_backup, 'r')
for line in f_backup:
if ':' in line:
vname0 = line.split(':')[0].replace('#', '').replace(' ', '')
if pattern.match(vname0) and vname0 in my_rcParams.keys():
print(line, line[1:].find('#'))
else:
newline = line
else:
newline = line
f_backup.close()
os.system('rm -rf %s' % fdir_mpl_cache)
print(fdir_mpl_data)
except ImportError:
exit('Warning [SETUP.MATPLOTLIB]: matplotlib is not available.')
if __name__ == "__main__":
init = SETUP()
exit()
| mit |
malcolmw/seismic-python | seispy/core/defaults.py | 3 | 2062 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 27 14:00:58 2018
@author: malcolcw
This provides access to default arguments.
"""
import matplotlib.pyplot as plt
DEFAULT_RECTANGLE_KWARGS = {"origin": (33.5731, -116.633, 0),
"strike": 125,
"length": 40,
"width": 25}
DEFAULT_BASEMAP_BASEKWARGS = {"llcrnrlat": 32.5,
"llcrnrlon": -117.5,
"urcrnrlat": 34.5,
"urcrnrlon": -115.5,
"resolution": "c",
"projection": "cea",
"lat_ts": 33.5}
DEFAULT_BASEMAP_KWARGS = {"fill_color": "aqua",
"continent_color": "coral",
"lake_color": "aqua",
"bgstyle": "basic",
"meridians": dict(stride=1,
labels=[False, False, False, True]),
"parallels": dict(stride=1,
labels=[True, False, False, False]),
"fault_color": "k",
"fault_linewidth": 1}
DEFAULT_SECTION_KWARGS = {
"general": {"ax": None,
"fig_width": 10,
"origin": (33.5731, -116.633, 0),
"strike": 125,
"length": 40,
"width": 15,
"ymin": 0,
"ymax": 25,
"xlabel": "Horizontal offset [$km$]",
"ylabel": "Depth [$km$]",
"special": None,
"colorbar_label": "",
"invert_colorbar": False
},
"scatter_kwargs": {"s": 1,
"cmap": plt.get_cmap("hot"),
"zorder": 2
},
"colorbar_kwargs": {"shrink": 0.75}
}
DEFAULT_BEACHBALL_KWARGS = dict(
linewidth=0.5,
width=25,
zorder=2
) | gpl-3.0 |
platinhom/ManualHom | Coding/Python/scipy-html-0.16.1/generated/scipy-stats-halfgennorm-1.py | 1 | 1149 | from scipy.stats import halfgennorm
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
# Calculate a few first moments:
beta = 0.675
mean, var, skew, kurt = halfgennorm.stats(beta, moments='mvsk')
# Display the probability density function (``pdf``):
x = np.linspace(halfgennorm.ppf(0.01, beta),
halfgennorm.ppf(0.99, beta), 100)
ax.plot(x, halfgennorm.pdf(x, beta),
'r-', lw=5, alpha=0.6, label='halfgennorm pdf')
# Alternatively, the distribution object can be called (as a function)
# to fix the shape, location and scale parameters. This returns a "frozen"
# RV object holding the given parameters fixed.
# Freeze the distribution and display the frozen ``pdf``:
rv = halfgennorm(beta)
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
# Check accuracy of ``cdf`` and ``ppf``:
vals = halfgennorm.ppf([0.001, 0.5, 0.999], beta)
np.allclose([0.001, 0.5, 0.999], halfgennorm.cdf(vals, beta))
# True
# Generate random numbers:
r = halfgennorm.rvs(beta, size=1000)
# And compare the histogram:
ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2)
ax.legend(loc='best', frameon=False)
plt.show()
| gpl-2.0 |
KaliLab/optimizer | doc/conf.py | 2 | 8614 | # -*- coding: utf-8 -*-
#
# optimizer documentation build configuration file, created by
# sphinx-quickstart on Thu Sep 12 01:59:02 2013.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys, os
from mock import Mock as MagicMock
sys.path.insert(0, os.path.abspath('../optimizer'))
sys.path.insert(0, os.path.abspath('..'))
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#sys.path.insert(0, os.path.abspath('.'))
#following line is from Peter's computer, should probably be removed
sys.path.insert(0,os.path.abspath('/home/osboxes/Optimizer_sasaray/optimizer/'))
# -- General configuration -----------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = ['sphinx.ext.autodoc']
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'optimizer'
copyright = u'2013, Szabolcs Káli, Péter Friedrich, Mike Vella'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '0.0.1'
# The full version, including alpha/beta/rc tags.
release = '0.0.1'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = []
# The reST default role (used for this markup: `text`) to use for all documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# -- Options for HTML output ---------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'default'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Output file base name for HTML help builder.
htmlhelp_basename = 'optimizerdoc'
# -- Options for LaTeX output --------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
('index', 'optimizer.tex', u'optimizer Documentation',
u'Szabolcs Káli, Péter Friedrich, Mike Vella', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Options for manual page output --------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
('index', 'optimizer', u'optimizer Documentation',
[u'Szabolcs Káli, Péter Friedrich, Mike Vella'], 1)
]
# If true, show URL addresses after external links.
#man_show_urls = False
# -- Options for Texinfo output ------------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
('index', 'optimizer', u'optimizer Documentation',
u'Szabolcs Káli, Péter Friedrich, Mike Vella', 'optimizer', 'One line description of project.',
'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
#texinfo_appendices = []
# If false, no module index is generated.
#texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
class Mock(MagicMock):
@classmethod
def __getattr__(cls, name):
return Mock()
MOCK_MODULES = ['inspyred','wx','ec','inspyred.ec','pyelectro','scipy','numpy','analysis',
'pyelectro.analysis','interpolate','scipy.interpolate','matplotlib',
'matplotlib.backends.backend_wxagg','backends.backend_wxagg','backend_wxagg','gtk']
#sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
| lgpl-2.1 |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/pandas/tests/indexes/common.py | 3 | 35517 | # -*- coding: utf-8 -*-
import pytest
from pandas import compat
from pandas.compat import PY3
import numpy as np
from pandas import (Series, Index, Float64Index, Int64Index, UInt64Index,
RangeIndex, MultiIndex, CategoricalIndex, DatetimeIndex,
TimedeltaIndex, PeriodIndex, IntervalIndex,
notnull, isnull)
from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
from pandas.core.dtypes.common import needs_i8_conversion
from pandas._libs.tslib import iNaT
import pandas.util.testing as tm
import pandas as pd
class Base(object):
""" base class for index sub-class tests """
_holder = None
_compat_props = ['shape', 'ndim', 'size', 'itemsize', 'nbytes']
def setup_indices(self):
for name, idx in self.indices.items():
setattr(self, name, idx)
def verify_pickle(self, index):
unpickled = tm.round_trip_pickle(index)
assert index.equals(unpickled)
def test_pickle_compat_construction(self):
# this is testing for pickle compat
if self._holder is None:
return
# need an object to create with
pytest.raises(TypeError, self._holder)
def test_to_series(self):
# assert that we are creating a copy of the index
idx = self.create_index()
s = idx.to_series()
assert s.values is not idx.values
assert s.index is not idx
assert s.name == idx.name
def test_shift(self):
# GH8083 test the base class for shift
idx = self.create_index()
pytest.raises(NotImplementedError, idx.shift, 1)
pytest.raises(NotImplementedError, idx.shift, 1, 2)
def test_create_index_existing_name(self):
# GH11193, when an existing index is passed, and a new name is not
# specified, the new index should inherit the previous object name
expected = self.create_index()
if not isinstance(expected, MultiIndex):
expected.name = 'foo'
result = pd.Index(expected)
tm.assert_index_equal(result, expected)
result = pd.Index(expected, name='bar')
expected.name = 'bar'
tm.assert_index_equal(result, expected)
else:
expected.names = ['foo', 'bar']
result = pd.Index(expected)
tm.assert_index_equal(
result, Index(Index([('foo', 'one'), ('foo', 'two'),
('bar', 'one'), ('baz', 'two'),
('qux', 'one'), ('qux', 'two')],
dtype='object'),
names=['foo', 'bar']))
result = pd.Index(expected, names=['A', 'B'])
tm.assert_index_equal(
result,
Index(Index([('foo', 'one'), ('foo', 'two'), ('bar', 'one'),
('baz', 'two'), ('qux', 'one'), ('qux', 'two')],
dtype='object'), names=['A', 'B']))
def test_numeric_compat(self):
idx = self.create_index()
tm.assert_raises_regex(TypeError, "cannot perform __mul__",
lambda: idx * 1)
tm.assert_raises_regex(TypeError, "cannot perform __mul__",
lambda: 1 * idx)
div_err = "cannot perform __truediv__" if PY3 \
else "cannot perform __div__"
tm.assert_raises_regex(TypeError, div_err, lambda: idx / 1)
tm.assert_raises_regex(TypeError, div_err, lambda: 1 / idx)
tm.assert_raises_regex(TypeError, "cannot perform __floordiv__",
lambda: idx // 1)
tm.assert_raises_regex(TypeError, "cannot perform __floordiv__",
lambda: 1 // idx)
def test_logical_compat(self):
idx = self.create_index()
tm.assert_raises_regex(TypeError, 'cannot perform all',
lambda: idx.all())
tm.assert_raises_regex(TypeError, 'cannot perform any',
lambda: idx.any())
def test_boolean_context_compat(self):
# boolean context compat
idx = self.create_index()
def f():
if idx:
pass
tm.assert_raises_regex(ValueError, 'The truth value of a', f)
def test_reindex_base(self):
idx = self.create_index()
expected = np.arange(idx.size, dtype=np.intp)
actual = idx.get_indexer(idx)
tm.assert_numpy_array_equal(expected, actual)
with tm.assert_raises_regex(ValueError, 'Invalid fill method'):
idx.get_indexer(idx, method='invalid')
def test_ndarray_compat_properties(self):
idx = self.create_index()
assert idx.T.equals(idx)
assert idx.transpose().equals(idx)
values = idx.values
for prop in self._compat_props:
assert getattr(idx, prop) == getattr(values, prop)
# test for validity
idx.nbytes
idx.values.nbytes
def test_repr_roundtrip(self):
idx = self.create_index()
tm.assert_index_equal(eval(repr(idx)), idx)
def test_str(self):
# test the string repr
idx = self.create_index()
idx.name = 'foo'
assert "'foo'" in str(idx)
assert idx.__class__.__name__ in str(idx)
def test_dtype_str(self):
for idx in self.indices.values():
dtype = idx.dtype_str
assert isinstance(dtype, compat.string_types)
assert dtype == str(idx.dtype)
def test_repr_max_seq_item_setting(self):
# GH10182
idx = self.create_index()
idx = idx.repeat(50)
with pd.option_context("display.max_seq_items", None):
repr(idx)
assert '...' not in str(idx)
def test_wrong_number_names(self):
def testit(ind):
ind.names = ["apple", "banana", "carrot"]
for ind in self.indices.values():
tm.assert_raises_regex(ValueError, "^Length", testit, ind)
def test_set_name_methods(self):
new_name = "This is the new name for this index"
for ind in self.indices.values():
# don't tests a MultiIndex here (as its tested separated)
if isinstance(ind, MultiIndex):
continue
original_name = ind.name
new_ind = ind.set_names([new_name])
assert new_ind.name == new_name
assert ind.name == original_name
res = ind.rename(new_name, inplace=True)
# should return None
assert res is None
assert ind.name == new_name
assert ind.names == [new_name]
# with tm.assert_raises_regex(TypeError, "list-like"):
# # should still fail even if it would be the right length
# ind.set_names("a")
with tm.assert_raises_regex(ValueError, "Level must be None"):
ind.set_names("a", level=0)
# rename in place just leaves tuples and other containers alone
name = ('A', 'B')
ind.rename(name, inplace=True)
assert ind.name == name
assert ind.names == [name]
def test_hash_error(self):
for ind in self.indices.values():
with tm.assert_raises_regex(TypeError, "unhashable type: %r" %
type(ind).__name__):
hash(ind)
def test_copy_name(self):
# gh-12309: Check that the "name" argument
# passed at initialization is honored.
for name, index in compat.iteritems(self.indices):
if isinstance(index, MultiIndex):
continue
first = index.__class__(index, copy=True, name='mario')
second = first.__class__(first, copy=False)
# Even though "copy=False", we want a new object.
assert first is not second
# Not using tm.assert_index_equal() since names differ.
assert index.equals(first)
assert first.name == 'mario'
assert second.name == 'mario'
s1 = Series(2, index=first)
s2 = Series(3, index=second[:-1])
if not isinstance(index, CategoricalIndex):
# See gh-13365
s3 = s1 * s2
assert s3.index.name == 'mario'
def test_ensure_copied_data(self):
# Check the "copy" argument of each Index.__new__ is honoured
# GH12309
for name, index in compat.iteritems(self.indices):
init_kwargs = {}
if isinstance(index, PeriodIndex):
# Needs "freq" specification:
init_kwargs['freq'] = index.freq
elif isinstance(index, (RangeIndex, MultiIndex, CategoricalIndex)):
# RangeIndex cannot be initialized from data
# MultiIndex and CategoricalIndex are tested separately
continue
index_type = index.__class__
result = index_type(index.values, copy=True, **init_kwargs)
tm.assert_index_equal(index, result)
tm.assert_numpy_array_equal(index.values, result.values,
check_same='copy')
if isinstance(index, PeriodIndex):
# .values an object array of Period, thus copied
result = index_type(ordinal=index.asi8, copy=False,
**init_kwargs)
tm.assert_numpy_array_equal(index._values, result._values,
check_same='same')
elif isinstance(index, IntervalIndex):
# checked in test_interval.py
pass
else:
result = index_type(index.values, copy=False, **init_kwargs)
tm.assert_numpy_array_equal(index.values, result.values,
check_same='same')
tm.assert_numpy_array_equal(index._values, result._values,
check_same='same')
def test_copy_and_deepcopy(self):
from copy import copy, deepcopy
for ind in self.indices.values():
# don't tests a MultiIndex here (as its tested separated)
if isinstance(ind, MultiIndex):
continue
for func in (copy, deepcopy):
idx_copy = func(ind)
assert idx_copy is not ind
assert idx_copy.equals(ind)
new_copy = ind.copy(deep=True, name="banana")
assert new_copy.name == "banana"
def test_duplicates(self):
for ind in self.indices.values():
if not len(ind):
continue
if isinstance(ind, MultiIndex):
continue
idx = self._holder([ind[0]] * 5)
assert not idx.is_unique
assert idx.has_duplicates
# GH 10115
# preserve names
idx.name = 'foo'
result = idx.drop_duplicates()
assert result.name == 'foo'
tm.assert_index_equal(result, Index([ind[0]], name='foo'))
def test_get_unique_index(self):
for ind in self.indices.values():
# MultiIndex tested separately
if not len(ind) or isinstance(ind, MultiIndex):
continue
idx = ind[[0] * 5]
idx_unique = ind[[0]]
# We test against `idx_unique`, so first we make sure it's unique
# and doesn't contain nans.
assert idx_unique.is_unique
try:
assert not idx_unique.hasnans
except NotImplementedError:
pass
for dropna in [False, True]:
result = idx._get_unique_index(dropna=dropna)
tm.assert_index_equal(result, idx_unique)
# nans:
if not ind._can_hold_na:
continue
if needs_i8_conversion(ind):
vals = ind.asi8[[0] * 5]
vals[0] = iNaT
else:
vals = ind.values[[0] * 5]
vals[0] = np.nan
vals_unique = vals[:2]
idx_nan = ind._shallow_copy(vals)
idx_unique_nan = ind._shallow_copy(vals_unique)
assert idx_unique_nan.is_unique
assert idx_nan.dtype == ind.dtype
assert idx_unique_nan.dtype == ind.dtype
for dropna, expected in zip([False, True],
[idx_unique_nan, idx_unique]):
for i in [idx_nan, idx_unique_nan]:
result = i._get_unique_index(dropna=dropna)
tm.assert_index_equal(result, expected)
def test_sort(self):
for ind in self.indices.values():
pytest.raises(TypeError, ind.sort)
def test_mutability(self):
for ind in self.indices.values():
if not len(ind):
continue
pytest.raises(TypeError, ind.__setitem__, 0, ind[0])
def test_view(self):
for ind in self.indices.values():
i_view = ind.view()
assert i_view.name == ind.name
def test_compat(self):
for ind in self.indices.values():
assert ind.tolist() == list(ind)
def test_memory_usage(self):
for name, index in compat.iteritems(self.indices):
result = index.memory_usage()
if len(index):
index.get_loc(index[0])
result2 = index.memory_usage()
result3 = index.memory_usage(deep=True)
# RangeIndex, IntervalIndex
# don't have engines
if not isinstance(index, (RangeIndex, IntervalIndex)):
assert result2 > result
if index.inferred_type == 'object':
assert result3 > result2
else:
# we report 0 for no-length
assert result == 0
def test_argsort(self):
for k, ind in self.indices.items():
# separately tested
if k in ['catIndex']:
continue
result = ind.argsort()
expected = np.array(ind).argsort()
tm.assert_numpy_array_equal(result, expected, check_dtype=False)
def test_numpy_argsort(self):
for k, ind in self.indices.items():
result = np.argsort(ind)
expected = ind.argsort()
tm.assert_numpy_array_equal(result, expected)
# these are the only two types that perform
# pandas compatibility input validation - the
# rest already perform separate (or no) such
# validation via their 'values' attribute as
# defined in pandas.core.indexes/base.py - they
# cannot be changed at the moment due to
# backwards compatibility concerns
if isinstance(type(ind), (CategoricalIndex, RangeIndex)):
msg = "the 'axis' parameter is not supported"
tm.assert_raises_regex(ValueError, msg,
np.argsort, ind, axis=1)
msg = "the 'kind' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.argsort,
ind, kind='mergesort')
msg = "the 'order' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.argsort,
ind, order=('a', 'b'))
def test_pickle(self):
for ind in self.indices.values():
self.verify_pickle(ind)
ind.name = 'foo'
self.verify_pickle(ind)
def test_take(self):
indexer = [4, 3, 0, 2]
for k, ind in self.indices.items():
# separate
if k in ['boolIndex', 'tuples', 'empty']:
continue
result = ind.take(indexer)
expected = ind[indexer]
assert result.equals(expected)
if not isinstance(ind,
(DatetimeIndex, PeriodIndex, TimedeltaIndex)):
# GH 10791
with pytest.raises(AttributeError):
ind.freq
def test_take_invalid_kwargs(self):
idx = self.create_index()
indices = [1, 2]
msg = r"take\(\) got an unexpected keyword argument 'foo'"
tm.assert_raises_regex(TypeError, msg, idx.take,
indices, foo=2)
msg = "the 'out' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, idx.take,
indices, out=indices)
msg = "the 'mode' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, idx.take,
indices, mode='clip')
def test_repeat(self):
rep = 2
i = self.create_index()
expected = pd.Index(i.values.repeat(rep), name=i.name)
tm.assert_index_equal(i.repeat(rep), expected)
i = self.create_index()
rep = np.arange(len(i))
expected = pd.Index(i.values.repeat(rep), name=i.name)
tm.assert_index_equal(i.repeat(rep), expected)
def test_numpy_repeat(self):
rep = 2
i = self.create_index()
expected = i.repeat(rep)
tm.assert_index_equal(np.repeat(i, rep), expected)
msg = "the 'axis' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.repeat,
i, rep, axis=0)
def test_where(self):
i = self.create_index()
result = i.where(notnull(i))
expected = i
tm.assert_index_equal(result, expected)
_nan = i._na_value
cond = [False] + [True] * len(i[1:])
expected = pd.Index([_nan] + i[1:].tolist(), dtype=i.dtype)
result = i.where(cond)
tm.assert_index_equal(result, expected)
def test_where_array_like(self):
i = self.create_index()
_nan = i._na_value
cond = [False] + [True] * (len(i) - 1)
klasses = [list, tuple, np.array, pd.Series]
expected = pd.Index([_nan] + i[1:].tolist(), dtype=i.dtype)
for klass in klasses:
result = i.where(klass(cond))
tm.assert_index_equal(result, expected)
def test_setops_errorcases(self):
for name, idx in compat.iteritems(self.indices):
# # non-iterable input
cases = [0.5, 'xxx']
methods = [idx.intersection, idx.union, idx.difference,
idx.symmetric_difference]
for method in methods:
for case in cases:
tm.assert_raises_regex(TypeError,
"Input must be Index "
"or array-like",
method, case)
def test_intersection_base(self):
for name, idx in compat.iteritems(self.indices):
first = idx[:5]
second = idx[:3]
intersect = first.intersection(second)
if isinstance(idx, CategoricalIndex):
pass
else:
assert tm.equalContents(intersect, second)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, PeriodIndex):
msg = "can only call with other PeriodIndex-ed objects"
with tm.assert_raises_regex(ValueError, msg):
result = first.intersection(case)
elif isinstance(idx, CategoricalIndex):
pass
else:
result = first.intersection(case)
assert tm.equalContents(result, second)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with tm.assert_raises_regex(TypeError, msg):
result = first.intersection([1, 2, 3])
def test_union_base(self):
for name, idx in compat.iteritems(self.indices):
first = idx[3:]
second = idx[:5]
everything = idx
union = first.union(second)
assert tm.equalContents(union, everything)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, PeriodIndex):
msg = "can only call with other PeriodIndex-ed objects"
with tm.assert_raises_regex(ValueError, msg):
result = first.union(case)
elif isinstance(idx, CategoricalIndex):
pass
else:
result = first.union(case)
assert tm.equalContents(result, everything)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with tm.assert_raises_regex(TypeError, msg):
result = first.union([1, 2, 3])
def test_difference_base(self):
for name, idx in compat.iteritems(self.indices):
first = idx[2:]
second = idx[:4]
answer = idx[4:]
result = first.difference(second)
if isinstance(idx, CategoricalIndex):
pass
else:
assert tm.equalContents(result, answer)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, PeriodIndex):
msg = "can only call with other PeriodIndex-ed objects"
with tm.assert_raises_regex(ValueError, msg):
result = first.difference(case)
elif isinstance(idx, CategoricalIndex):
pass
elif isinstance(idx, (DatetimeIndex, TimedeltaIndex)):
assert result.__class__ == answer.__class__
tm.assert_numpy_array_equal(result.asi8, answer.asi8)
else:
result = first.difference(case)
assert tm.equalContents(result, answer)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with tm.assert_raises_regex(TypeError, msg):
result = first.difference([1, 2, 3])
def test_symmetric_difference(self):
for name, idx in compat.iteritems(self.indices):
first = idx[1:]
second = idx[:-1]
if isinstance(idx, CategoricalIndex):
pass
else:
answer = idx[[0, -1]]
result = first.symmetric_difference(second)
assert tm.equalContents(result, answer)
# GH 10149
cases = [klass(second.values)
for klass in [np.array, Series, list]]
for case in cases:
if isinstance(idx, PeriodIndex):
msg = "can only call with other PeriodIndex-ed objects"
with tm.assert_raises_regex(ValueError, msg):
result = first.symmetric_difference(case)
elif isinstance(idx, CategoricalIndex):
pass
else:
result = first.symmetric_difference(case)
assert tm.equalContents(result, answer)
if isinstance(idx, MultiIndex):
msg = "other must be a MultiIndex or a list of tuples"
with tm.assert_raises_regex(TypeError, msg):
result = first.symmetric_difference([1, 2, 3])
# 12591 deprecated
with tm.assert_produces_warning(FutureWarning):
first.sym_diff(second)
def test_insert_base(self):
for name, idx in compat.iteritems(self.indices):
result = idx[1:4]
if not len(idx):
continue
# test 0th element
assert idx[0:4].equals(result.insert(0, idx[0]))
def test_delete_base(self):
for name, idx in compat.iteritems(self.indices):
if not len(idx):
continue
if isinstance(idx, RangeIndex):
# tested in class
continue
expected = idx[1:]
result = idx.delete(0)
assert result.equals(expected)
assert result.name == expected.name
expected = idx[:-1]
result = idx.delete(-1)
assert result.equals(expected)
assert result.name == expected.name
with pytest.raises((IndexError, ValueError)):
# either depending on numpy version
result = idx.delete(len(idx))
def test_equals(self):
for name, idx in compat.iteritems(self.indices):
assert idx.equals(idx)
assert idx.equals(idx.copy())
assert idx.equals(idx.astype(object))
assert not idx.equals(list(idx))
assert not idx.equals(np.array(idx))
# Cannot pass in non-int64 dtype to RangeIndex
if not isinstance(idx, RangeIndex):
same_values = Index(idx, dtype=object)
assert idx.equals(same_values)
assert same_values.equals(idx)
if idx.nlevels == 1:
# do not test MultiIndex
assert not idx.equals(pd.Series(idx))
def test_equals_op(self):
# GH9947, GH10637
index_a = self.create_index()
if isinstance(index_a, PeriodIndex):
return
n = len(index_a)
index_b = index_a[0:-1]
index_c = index_a[0:-1].append(index_a[-2:-1])
index_d = index_a[0:1]
with tm.assert_raises_regex(ValueError, "Lengths must match"):
index_a == index_b
expected1 = np.array([True] * n)
expected2 = np.array([True] * (n - 1) + [False])
tm.assert_numpy_array_equal(index_a == index_a, expected1)
tm.assert_numpy_array_equal(index_a == index_c, expected2)
# test comparisons with numpy arrays
array_a = np.array(index_a)
array_b = np.array(index_a[0:-1])
array_c = np.array(index_a[0:-1].append(index_a[-2:-1]))
array_d = np.array(index_a[0:1])
with tm.assert_raises_regex(ValueError, "Lengths must match"):
index_a == array_b
tm.assert_numpy_array_equal(index_a == array_a, expected1)
tm.assert_numpy_array_equal(index_a == array_c, expected2)
# test comparisons with Series
series_a = Series(array_a)
series_b = Series(array_b)
series_c = Series(array_c)
series_d = Series(array_d)
with tm.assert_raises_regex(ValueError, "Lengths must match"):
index_a == series_b
tm.assert_numpy_array_equal(index_a == series_a, expected1)
tm.assert_numpy_array_equal(index_a == series_c, expected2)
# cases where length is 1 for one of them
with tm.assert_raises_regex(ValueError, "Lengths must match"):
index_a == index_d
with tm.assert_raises_regex(ValueError, "Lengths must match"):
index_a == series_d
with tm.assert_raises_regex(ValueError, "Lengths must match"):
index_a == array_d
msg = "Can only compare identically-labeled Series objects"
with tm.assert_raises_regex(ValueError, msg):
series_a == series_d
with tm.assert_raises_regex(ValueError, "Lengths must match"):
series_a == array_d
# comparing with a scalar should broadcast; note that we are excluding
# MultiIndex because in this case each item in the index is a tuple of
# length 2, and therefore is considered an array of length 2 in the
# comparison instead of a scalar
if not isinstance(index_a, MultiIndex):
expected3 = np.array([False] * (len(index_a) - 2) + [True, False])
# assuming the 2nd to last item is unique in the data
item = index_a[-2]
tm.assert_numpy_array_equal(index_a == item, expected3)
tm.assert_series_equal(series_a == item, Series(expected3))
def test_numpy_ufuncs(self):
# test ufuncs of numpy 1.9.2. see:
# http://docs.scipy.org/doc/numpy/reference/ufuncs.html
# some functions are skipped because it may return different result
# for unicode input depending on numpy version
for name, idx in compat.iteritems(self.indices):
for func in [np.exp, np.exp2, np.expm1, np.log, np.log2, np.log10,
np.log1p, np.sqrt, np.sin, np.cos, np.tan, np.arcsin,
np.arccos, np.arctan, np.sinh, np.cosh, np.tanh,
np.arcsinh, np.arccosh, np.arctanh, np.deg2rad,
np.rad2deg]:
if isinstance(idx, DatetimeIndexOpsMixin):
# raise TypeError or ValueError (PeriodIndex)
# PeriodIndex behavior should be changed in future version
with pytest.raises(Exception):
with np.errstate(all='ignore'):
func(idx)
elif isinstance(idx, (Float64Index, Int64Index, UInt64Index)):
# coerces to float (e.g. np.sin)
with np.errstate(all='ignore'):
result = func(idx)
exp = Index(func(idx.values), name=idx.name)
tm.assert_index_equal(result, exp)
assert isinstance(result, pd.Float64Index)
else:
# raise AttributeError or TypeError
if len(idx) == 0:
continue
else:
with pytest.raises(Exception):
with np.errstate(all='ignore'):
func(idx)
for func in [np.isfinite, np.isinf, np.isnan, np.signbit]:
if isinstance(idx, DatetimeIndexOpsMixin):
# raise TypeError or ValueError (PeriodIndex)
with pytest.raises(Exception):
func(idx)
elif isinstance(idx, (Float64Index, Int64Index, UInt64Index)):
# Results in bool array
result = func(idx)
assert isinstance(result, np.ndarray)
assert not isinstance(result, Index)
else:
if len(idx) == 0:
continue
else:
with pytest.raises(Exception):
func(idx)
def test_hasnans_isnans(self):
# GH 11343, added tests for hasnans / isnans
for name, index in self.indices.items():
if isinstance(index, MultiIndex):
pass
else:
idx = index.copy()
# cases in indices doesn't include NaN
expected = np.array([False] * len(idx), dtype=bool)
tm.assert_numpy_array_equal(idx._isnan, expected)
assert not idx.hasnans
idx = index.copy()
values = idx.values
if len(index) == 0:
continue
elif isinstance(index, DatetimeIndexOpsMixin):
values[1] = iNaT
elif isinstance(index, (Int64Index, UInt64Index)):
continue
else:
values[1] = np.nan
if isinstance(index, PeriodIndex):
idx = index.__class__(values, freq=index.freq)
else:
idx = index.__class__(values)
expected = np.array([False] * len(idx), dtype=bool)
expected[1] = True
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans
def test_fillna(self):
# GH 11343
for name, index in self.indices.items():
if len(index) == 0:
pass
elif isinstance(index, MultiIndex):
idx = index.copy()
msg = "isnull is not defined for MultiIndex"
with tm.assert_raises_regex(NotImplementedError, msg):
idx.fillna(idx[0])
else:
idx = index.copy()
result = idx.fillna(idx[0])
tm.assert_index_equal(result, idx)
assert result is not idx
msg = "'value' must be a scalar, passed: "
with tm.assert_raises_regex(TypeError, msg):
idx.fillna([idx[0]])
idx = index.copy()
values = idx.values
if isinstance(index, DatetimeIndexOpsMixin):
values[1] = iNaT
elif isinstance(index, (Int64Index, UInt64Index)):
continue
else:
values[1] = np.nan
if isinstance(index, PeriodIndex):
idx = index.__class__(values, freq=index.freq)
else:
idx = index.__class__(values)
expected = np.array([False] * len(idx), dtype=bool)
expected[1] = True
tm.assert_numpy_array_equal(idx._isnan, expected)
assert idx.hasnans
def test_nulls(self):
# this is really a smoke test for the methods
# as these are adequately tested for function elsewhere
for name, index in self.indices.items():
if len(index) == 0:
tm.assert_numpy_array_equal(
index.isnull(), np.array([], dtype=bool))
elif isinstance(index, MultiIndex):
idx = index.copy()
msg = "isnull is not defined for MultiIndex"
with tm.assert_raises_regex(NotImplementedError, msg):
idx.isnull()
else:
if not index.hasnans:
tm.assert_numpy_array_equal(
index.isnull(), np.zeros(len(index), dtype=bool))
tm.assert_numpy_array_equal(
index.notnull(), np.ones(len(index), dtype=bool))
else:
result = isnull(index)
tm.assert_numpy_array_equal(index.isnull(), result)
tm.assert_numpy_array_equal(index.notnull(), ~result)
def test_empty(self):
# GH 15270
index = self.create_index()
assert not index.empty
assert index[:0].empty
@pytest.mark.parametrize('how', ['outer', 'inner', 'left', 'right'])
def test_join_self_unique(self, how):
index = self.create_index()
if index.is_unique:
joined = index.join(index, how=how)
assert (index == joined).all()
| mit |
kdebrab/pandas | pandas/tests/generic/test_panel.py | 3 | 1831 | # -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from warnings import catch_warnings
from pandas import Panel
from pandas.util.testing import (assert_panel_equal,
assert_almost_equal)
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from .test_generic import Generic
class TestPanel(Generic):
_typ = Panel
_comparator = lambda self, x, y: assert_panel_equal(x, y, by_blocks=True)
@td.skip_if_no('xarray', min_version='0.7.0')
def test_to_xarray(self):
from xarray import DataArray
with catch_warnings(record=True):
p = tm.makePanel()
result = p.to_xarray()
assert isinstance(result, DataArray)
assert len(result.coords) == 3
assert_almost_equal(list(result.coords.keys()),
['items', 'major_axis', 'minor_axis'])
assert len(result.dims) == 3
# idempotency
assert_panel_equal(result.to_pandas(), p)
# run all the tests, but wrap each in a warning catcher
for t in ['test_rename', 'test_get_numeric_data',
'test_get_default', 'test_nonzero',
'test_downcast', 'test_constructor_compound_dtypes',
'test_head_tail',
'test_size_compat', 'test_split_compat',
'test_unexpected_keyword',
'test_stat_unexpected_keyword', 'test_api_compat',
'test_stat_non_defaults_args',
'test_truncate_out_of_bounds',
'test_metadata_propagation', 'test_copy_and_deepcopy',
'test_pct_change', 'test_sample']:
def f():
def tester(self):
f = getattr(super(TestPanel, self), t)
with catch_warnings(record=True):
f()
return tester
setattr(TestPanel, t, f())
| bsd-3-clause |
mshakya/PyPiReT | piret/Runs/GAGE.py | 2 | 2713 | # #! /usr/bin/env python
# """Check design."""
# import luigi
# from luigi import LocalTarget
# from piret import Summ
# from luigi.util import inherits, requires
# import pandas as pd
# from plumbum.cmd import Rscript
# @requires(DGE.edgeR)
# class GAGE(luigi.Task):
# """Perform GAGE analysis."""
# exp_design = luigi.Parameter()
# p_value = luigi.FloatParameter()
# bindir = luigi.Parameter()
# def output(self):
# """ Expected output of GAGE analysis."""
# def run(self):
# # if se
# edger_dir = os.path.join(self.workdir, "edgeR", self.kingdom)
# def output(self):
# """Expected output of DGE using edgeR."""
# fcount_dir = os.path.join(self.workdir, "featureCounts", self.kingdom)
# edger_dir = os.path.join(self.workdir, "edgeR", self.kingdom)
# for root, dirs, files in os.walk(fcount_dir):
# for file in files:
# if file.endswith("csv"):
# out_filename = file.split(".tsv")[0] + "_RPKM.csv"
# out_filepath = os.path.join(edger_dir, out_filename)
# return LocalTarget(out_filepath)
# def run(self):
# """Run edgeR."""
# fcount_dir = os.path.join(self.workdir, "featureCounts", self.kingdom)
# edger_dir = os.path.join(self.workdir, "edgeR", self.kingdom)
# edger_location = os.path.join(self.bindir, "../scripts/edgeR.R")
# if not os.path.exists(edger_dir):
# os.makedirs(edger_dir)
# for root, dirs, files in os.walk(fcount_dir):
# for file in files:
# if file.endswith("tsv"):
# name = file.split("_")[-2]
# edger_list = [edger_location,
# "-r", os.path.join(root, file),
# "-e", self.exp_design,
# "-p", self.p_value,
# "-n", name,
# "-o", edger_dir]
# edger_cmd = Rscript[edger_list]
# edger_cmd()
# self.summ_summ()
# def summ_summ(self):
# """Summarize the summary table to be displayed in edge"""
# edger_dir = self.workdir + "/edgeR/" + self.kingdom
# all_files = os.listdir(edger_dir)
# if all_files:
# out_file = os.path.join(edger_dir, "summary_updown.csv")
# summ_files = [pd.read_csv(os.path.join(edger_dir, file),
# index_col=0) for file in all_files if "summary.csv" in file ]
# summ_df = pd.concat(summ_files)
# summ_df.to_csv(out_file) | bsd-3-clause |
mitdrc/pronto | motion_estimate/scripts/republish_multisense_state.py | 2 | 1128 | #!/usr/bin/python
# MIT uses hokuyo_joint
# other teams use motor_joint, rename here
import os,sys
import lcm
import time
from lcm import LCM
from math import *
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
from threading import Thread
import threading
home_dir =os.getenv("HOME")
#print home_dir
sys.path.append(home_dir + "/drc/software/build/lib/python2.7/site-packages")
sys.path.append(home_dir + "/drc/software/build/lib/python2.7/dist-packages")
from pronto.multisense_state_t import multisense_state_t
########################################################################################
def timestamp_now (): return int (time.time () * 1000000)
def on_multisense_state(channel, data):
m = multisense_state_t.decode(data)
m.joint_name[0] = 'hokuyo_joint'
lc.publish("MULTISENSE_STATE", m.encode())
####################################################################
lc = lcm.LCM()
print "started"
sub1 = lc.subscribe("MULTISENSE_STATE_LOGGED", on_multisense_state)
while True:
## Handle LCM if new messages have arrived.
lc.handle()
lc.unsubscribe(sub1)
| lgpl-2.1 |
CETodd/4501project | main.py | 1 | 13695 | from scipy.misc import imread, imresize, imsave, fromimage, toimage
from scipy.optimize import fmin_l_bfgs_b
import scipy.interpolate
import scipy.ndimage
import numpy as np
import time
import argparse
import warnings
from sklearn.feature_extraction.image import reconstruct_from_patches_2d, extract_patches_2d
from keras.models import Model
from keras.layers import Input
from keras.layers.convolutional import Convolution2D, AveragePooling2D, MaxPooling2D
from keras import backend as K
from keras.utils.data_utils import get_file
from keras.utils.layer_utils import convert_all_kernels_in_model
TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
parser = argparse.ArgumentParser(description='Neural style transfer with Keras.')
parser.add_argument('base_image_path', metavar='base', type=str,
help='Path to the image to transform.')
parser.add_argument('style_image_paths', metavar='ref', nargs='+', type=str,
help='Path to the style reference image.')
parser.add_argument('result_prefix', metavar='res_prefix', type=str,
help='Prefix for the saved results.')
parser.add_argument("--image_size", dest="img_size", default=400, type=int,
help='Minimum image size')
parser.add_argument("--content_weight", dest="content_weight", default=0.025, type=float,
help="Weight of content")
parser.add_argument("--style_weight", dest="style_weight", nargs='+', default=[1], type=float,
help="Weight of style, can be multiple for multiple styles")
parser.add_argument("--total_variation_weight", dest="tv_weight", default=8.5e-5, type=float,
help="Total Variation weight")
parser.add_argument("--style_scale", dest="style_scale", default=1.0, type=float,
help="Scale the weighing of the style")
parser.add_argument("--num_iter", dest="num_iter", default=10, type=int,
help="Number of iterations")
parser.add_argument("--content_loss_type", default=0, type=int,
help='Can be one of 0, 1 or 2. Readme contains the required information of each mode.')
parser.add_argument("--content_layer", dest="content_layer", default="conv5_2", type=str,
help="Content layer used for content loss.")
parser.add_argument("--init_image", dest="init_image", default="content", type=str,
help="Initial image used to generate the final image. Options are 'content', 'noise', or 'gray'")
args = parser.parse_args()
base_image_path = args.base_image_path
style_reference_image_paths = args.style_image_paths
style_image_paths = [path for path in args.style_image_paths]
result_prefix = args.result_prefix
content_weight = args.content_weight
total_variation_weight = args.tv_weight
img_width = img_height = 0
img_WIDTH = img_HEIGHT = 0
aspect_ratio = 0
read_mode = "color"
style_weights = []
if len(style_image_paths) != len(args.style_weight):
weight_sum = sum(args.style_weight) * args.style_scale
count = len(style_image_paths)
for i in range(len(style_image_paths)):
style_weights.append(weight_sum / count)
else:
style_weights = [weight*args.style_scale for weight in args.style_weight]
def pooling_func(x):
# return AveragePooling2D((2, 2), strides=(2, 2))(x)
return MaxPooling2D((2, 2), strides=(2, 2))(x)
#start proc_img
def preprocess_image(image_path, load_dims=False):
global img_width, img_height, img_WIDTH, img_HEIGHT, aspect_ratio
mode = "RGB"
# mode = "RGB" if read_mode == "color" else "L"
img = imread(image_path, mode=mode) # Prevents crashes due to PNG images (ARGB)
if load_dims:
img_WIDTH = img.shape[0]
img_HEIGHT = img.shape[1]
aspect_ratio = float(img_HEIGHT) / img_WIDTH
img_width = args.img_size
img_height = int(img_width * aspect_ratio)
img = imresize(img, (img_width, img_height)).astype('float32')
# RGB -> BGR
img = img[:, :, ::-1]
img[:, :, 0] -= 103.939
img[:, :, 1] -= 116.779
img[:, :, 2] -= 123.68
img = np.expand_dims(img, axis=0)
return img
# util function to convert a tensor into a valid image
def deprocess_image(x):
x = x.reshape((img_width, img_height, 3))
x[:, :, 0] += 103.939
x[:, :, 1] += 116.779
x[:, :, 2] += 123.68
# BGR -> RGB
x = x[:, :, ::-1]
x = np.clip(x, 0, 255).astype('uint8')
return x
base_image = K.variable(preprocess_image(base_image_path, True))
style_reference_images = [K.variable(preprocess_image(path)) for path in style_image_paths]
# this will contain our generated image
combination_image = K.placeholder((1, img_width, img_height, 3)) # tensorflow
image_tensors = [base_image]
for style_image_tensor in style_reference_images:
image_tensors.append(style_image_tensor)
image_tensors.append(combination_image)
nb_tensors = len(image_tensors)
nb_style_images = nb_tensors - 2 # Content and Output image not considered
# combine the various images into a single Keras tensor
input_tensor = K.concatenate(image_tensors, axis=0)
shape = (nb_tensors, img_width, img_height, 3) #tensorflow
#build the model
model_input = Input(tensor=input_tensor, shape=shape)
# build the VGG16 network with our 3 images as input
x = Convolution2D(64, 3, 3, activation='relu', name='conv1_1', border_mode='same')(model_input)
x = Convolution2D(64, 3, 3, activation='relu', name='conv1_2', border_mode='same')(x)
x = pooling_func(x)
x = Convolution2D(128, 3, 3, activation='relu', name='conv2_1', border_mode='same')(x)
x = Convolution2D(128, 3, 3, activation='relu', name='conv2_2', border_mode='same')(x)
x = pooling_func(x)
x = Convolution2D(256, 3, 3, activation='relu', name='conv3_1', border_mode='same')(x)
x = Convolution2D(256, 3, 3, activation='relu', name='conv3_2', border_mode='same')(x)
x = Convolution2D(256, 3, 3, activation='relu', name='conv3_3', border_mode='same')(x)
x = pooling_func(x)
x = Convolution2D(512, 3, 3, activation='relu', name='conv4_1', border_mode='same')(x)
x = Convolution2D(512, 3, 3, activation='relu', name='conv4_2', border_mode='same')(x)
x = Convolution2D(512, 3, 3, activation='relu', name='conv4_3', border_mode='same')(x)
x = pooling_func(x)
x = Convolution2D(512, 3, 3, activation='relu', name='conv5_1', border_mode='same')(x)
x = Convolution2D(512, 3, 3, activation='relu', name='conv5_2', border_mode='same')(x)
x = Convolution2D(512, 3, 3, activation='relu', name='conv5_3', border_mode='same')(x)
x = pooling_func(x)
model = Model(model_input, x)
weights = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', TF_WEIGHTS_PATH_NO_TOP, cache_subdir='models')
print("Weights Path: ", weights)
model.load_weights(weights)
print('Model loaded.')
# get the symbolic outputs of each "key" layer (we gave them unique names).
outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])
shape_dict = dict([(layer.name, layer.output_shape) for layer in model.layers])
def make_patches(x, patch_size, patch_stride):
'''Break image `x` up into a bunch of patches.'''
from theano.tensor.nnet.neighbours import images2neibs
x = K.expand_dims(x, 0)
patches = images2neibs(x,
(patch_size, patch_size), (patch_stride, patch_stride),
mode='valid')
# neibs are sorted per-channel
patches = K.reshape(patches, (K.shape(x)[1], K.shape(patches)[0] // K.shape(x)[1], patch_size, patch_size))
patches = K.permute_dimensions(patches, (1, 0, 2, 3))
patches_norm = K.sqrt(K.sum(K.square(patches), axis=(1,2,3), keepdims=True))
return patches, patches_norm
def find_patch_matches(a, a_norm, b):
'''For each patch in A, find the best matching patch in B'''
# we want cross-correlation here so flip the kernels
convs = K.conv2d(a, b[:, :, ::-1, ::-1], border_mode='valid')
argmax = K.argmax(convs / a_norm, axis=1)
return argmax
# compute the neural style loss
# first we need to define 4 util functions
# the gram matrix of an image tensor (feature-wise outer product)
def gram_matrix(x):
features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
gram = K.dot(features, K.transpose(features))
return gram
# the "style loss" is designed to maintain
# the style of the reference image in the generated image.
# It is based on the gram matrices (which capture style) of
# feature maps from the style reference image
# and from the generated image
def style_loss(style, combination):
style_gram = gram_matrix(style)
combo_gram = gram_matrix(combination)
channels = 3
size = img_width * img_height
return K.sum(K.square(style_gram - combo_gram)) / (4. * (channels ** 2) * (size ** 2))
# an auxiliary loss function
# designed to maintain the "content" of the
# base image in the generated image
def content_loss(base, combination):
channels = K.shape(base)[-1]
size = img_width * img_height
if args.content_loss_type == 1:
multiplier = 1 / (2. * channels ** 0.5 * size ** 0.5)
elif args.content_loss_type == 2:
multiplier = 1 / (channels * size)
else:
multiplier = 1.
return multiplier * K.sum(K.square(combination - base))
# the 3rd loss function, total variation loss,
# designed to keep the generated image locally coherent
def total_variation_loss(x):
assert K.ndim(x) == 4
a = K.square(x[:, :img_width - 1, :img_height - 1, :] - x[:, 1:, :img_height - 1, :])
b = K.square(x[:, :img_width - 1, :img_height - 1, :] - x[:, :img_width - 1, 1:, :])
return K.sum(K.pow(a + b, 1.25))
# combine these loss functions into a single scalar
loss = K.variable(0.)
layer_features = outputs_dict[args.content_layer] # 'conv5_2' or 'conv4_2'
base_image_features = layer_features[0, :, :, :]
combination_features = layer_features[nb_tensors - 1, :, :, :]
loss += content_weight * content_loss(base_image_features,
combination_features)
channel_index = -1
#Style Loss calculation
feature_layers = ['conv1_1', 'conv2_1', 'conv3_1', 'conv4_1', 'conv5_1']
for layer_name in feature_layers:
output_features = outputs_dict[layer_name]
shape = shape_dict[layer_name]
combination_features = output_features[nb_tensors - 1, :, :, :]
style_features = output_features[1:nb_tensors - 1, :, :, :]
sl = []
for j in range(nb_style_images):
sl.append(style_loss(style_features[j], combination_features))
for j in range(nb_style_images):
loss += (style_weights[j] / len(feature_layers)) * sl[j]
loss += total_variation_weight * total_variation_loss(combination_image)
# get the gradients of the generated image wrt the loss
grads = K.gradients(loss, combination_image)
outputs = [loss]
if type(grads) in {list, tuple}:
outputs += grads
else:
outputs.append(grads)
f_outputs = K.function([combination_image], outputs)
def eval_loss_and_grads(x):
x = x.reshape((1, img_width, img_height, 3))
outs = f_outputs([x])
loss_value = outs[0]
if len(outs[1:]) == 1:
grad_values = outs[1].flatten().astype('float64')
else:
grad_values = np.array(outs[1:]).flatten().astype('float64')
return loss_value, grad_values
# # this Evaluator class makes it possible
# # to compute loss and gradients in one pass
# # while retrieving them via two separate functions,
# # "loss" and "grads". This is done because scipy.optimize
# # requires separate functions for loss and gradients,
# # but computing them separately would be inefficient.
class Evaluator(object):
def __init__(self):
self.loss_value = None
self.grads_values = None
def loss(self, x):
assert self.loss_value is None
loss_value, grad_values = eval_loss_and_grads(x)
self.loss_value = loss_value
self.grad_values = grad_values
return self.loss_value
def grads(self, x):
assert self.loss_value is not None
grad_values = np.copy(self.grad_values)
self.loss_value = None
self.grad_values = None
return grad_values
evaluator = Evaluator()
# run scipy-based optimization (L-BFGS) over the pixels of the generated image
# so as to minimize the neural style loss
if "content" in args.init_image or "gray" in args.init_image:
x = preprocess_image(base_image_path, True)
elif "noise" in args.init_image:
x = np.random.uniform(0, 255, (1, img_width, img_height, 3)) - 128.
if K.image_dim_ordering() == "th":
x = x.transpose((0, 3, 1, 2))
else:
print("Using initial image : ", args.init_image)
x = preprocess_image(args.init_image)
num_iter = args.num_iter
prev_min_val = -1
for i in range(num_iter):
print("Starting iteration %d of %d" % ((i + 1), num_iter))
start_time = time.time()
x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(), fprime=evaluator.grads, maxfun=20)
if prev_min_val == -1:
prev_min_val = min_val
improvement = (prev_min_val - min_val) / prev_min_val * 100
print('Current loss value:', min_val, " Improvement : %0.3f" % improvement, "%")
prev_min_val = min_val
# save current generated image
img = deprocess_image(x.copy())
img_ht = int(img_width * aspect_ratio)
print("Rescaling Image to (%d, %d)" % (img_width, img_ht))
img = imresize(img, (img_width, img_ht), interp="bilinear")
fname = result_prefix + '_at_iteration_%d.png' % (i + 1)
imsave(fname, img)
end_time = time.time()
print('Image saved as', fname)
print('Iteration %d completed in %ds' % (i + 1, end_time - start_time))
| mit |
trungnt13/scikit-learn | examples/model_selection/plot_train_error_vs_test_error.py | 349 | 2577 | """
=========================
Train error vs Test error
=========================
Illustration of how the performance of an estimator on unseen data (test data)
is not the same as the performance on training data. As the regularization
increases the performance on train decreases while the performance on test
is optimal within a range of values of the regularization parameter.
The example with an Elastic-Net regression model and the performance is
measured using the explained variance a.k.a. R^2.
"""
print(__doc__)
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import numpy as np
from sklearn import linear_model
###############################################################################
# Generate sample data
n_samples_train, n_samples_test, n_features = 75, 150, 500
np.random.seed(0)
coef = np.random.randn(n_features)
coef[50:] = 0.0 # only the top 10 features are impacting the model
X = np.random.randn(n_samples_train + n_samples_test, n_features)
y = np.dot(X, coef)
# Split train and test data
X_train, X_test = X[:n_samples_train], X[n_samples_train:]
y_train, y_test = y[:n_samples_train], y[n_samples_train:]
###############################################################################
# Compute train and test errors
alphas = np.logspace(-5, 1, 60)
enet = linear_model.ElasticNet(l1_ratio=0.7)
train_errors = list()
test_errors = list()
for alpha in alphas:
enet.set_params(alpha=alpha)
enet.fit(X_train, y_train)
train_errors.append(enet.score(X_train, y_train))
test_errors.append(enet.score(X_test, y_test))
i_alpha_optim = np.argmax(test_errors)
alpha_optim = alphas[i_alpha_optim]
print("Optimal regularization parameter : %s" % alpha_optim)
# Estimate the coef_ on full data with optimal regularization parameter
enet.set_params(alpha=alpha_optim)
coef_ = enet.fit(X, y).coef_
###############################################################################
# Plot results functions
import matplotlib.pyplot as plt
plt.subplot(2, 1, 1)
plt.semilogx(alphas, train_errors, label='Train')
plt.semilogx(alphas, test_errors, label='Test')
plt.vlines(alpha_optim, plt.ylim()[0], np.max(test_errors), color='k',
linewidth=3, label='Optimum on test')
plt.legend(loc='lower left')
plt.ylim([0, 1.2])
plt.xlabel('Regularization parameter')
plt.ylabel('Performance')
# Show estimated coef_ vs true coef
plt.subplot(2, 1, 2)
plt.plot(coef, label='True coef')
plt.plot(coef_, label='Estimated coef')
plt.legend()
plt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.26)
plt.show()
| bsd-3-clause |
PrashntS/scikit-learn | examples/covariance/plot_robust_vs_empirical_covariance.py | 248 | 6359 | r"""
=======================================
Robust vs Empirical covariance estimate
=======================================
The usual covariance maximum likelihood estimate is very sensitive to the
presence of outliers in the data set. In such a case, it would be better to
use a robust estimator of covariance to guarantee that the estimation is
resistant to "erroneous" observations in the data set.
Minimum Covariance Determinant Estimator
----------------------------------------
The Minimum Covariance Determinant estimator is a robust, high-breakdown point
(i.e. it can be used to estimate the covariance matrix of highly contaminated
datasets, up to
:math:`\frac{n_\text{samples} - n_\text{features}-1}{2}` outliers) estimator of
covariance. The idea is to find
:math:`\frac{n_\text{samples} + n_\text{features}+1}{2}`
observations whose empirical covariance has the smallest determinant, yielding
a "pure" subset of observations from which to compute standards estimates of
location and covariance. After a correction step aiming at compensating the
fact that the estimates were learned from only a portion of the initial data,
we end up with robust estimates of the data set location and covariance.
The Minimum Covariance Determinant estimator (MCD) has been introduced by
P.J.Rousseuw in [1]_.
Evaluation
----------
In this example, we compare the estimation errors that are made when using
various types of location and covariance estimates on contaminated Gaussian
distributed data sets:
- The mean and the empirical covariance of the full dataset, which break
down as soon as there are outliers in the data set
- The robust MCD, that has a low error provided
:math:`n_\text{samples} > 5n_\text{features}`
- The mean and the empirical covariance of the observations that are known
to be good ones. This can be considered as a "perfect" MCD estimation,
so one can trust our implementation by comparing to this case.
References
----------
.. [1] P. J. Rousseeuw. Least median of squares regression. J. Am
Stat Ass, 79:871, 1984.
.. [2] Johanna Hardin, David M Rocke. Journal of Computational and
Graphical Statistics. December 1, 2005, 14(4): 928-946.
.. [3] Zoubir A., Koivunen V., Chakhchoukh Y. and Muma M. (2012). Robust
estimation in signal processing: A tutorial-style treatment of
fundamental concepts. IEEE Signal Processing Magazine 29(4), 61-80.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.font_manager
from sklearn.covariance import EmpiricalCovariance, MinCovDet
# example settings
n_samples = 80
n_features = 5
repeat = 10
range_n_outliers = np.concatenate(
(np.linspace(0, n_samples / 8, 5),
np.linspace(n_samples / 8, n_samples / 2, 5)[1:-1]))
# definition of arrays to store results
err_loc_mcd = np.zeros((range_n_outliers.size, repeat))
err_cov_mcd = np.zeros((range_n_outliers.size, repeat))
err_loc_emp_full = np.zeros((range_n_outliers.size, repeat))
err_cov_emp_full = np.zeros((range_n_outliers.size, repeat))
err_loc_emp_pure = np.zeros((range_n_outliers.size, repeat))
err_cov_emp_pure = np.zeros((range_n_outliers.size, repeat))
# computation
for i, n_outliers in enumerate(range_n_outliers):
for j in range(repeat):
rng = np.random.RandomState(i * j)
# generate data
X = rng.randn(n_samples, n_features)
# add some outliers
outliers_index = rng.permutation(n_samples)[:n_outliers]
outliers_offset = 10. * \
(np.random.randint(2, size=(n_outliers, n_features)) - 0.5)
X[outliers_index] += outliers_offset
inliers_mask = np.ones(n_samples).astype(bool)
inliers_mask[outliers_index] = False
# fit a Minimum Covariance Determinant (MCD) robust estimator to data
mcd = MinCovDet().fit(X)
# compare raw robust estimates with the true location and covariance
err_loc_mcd[i, j] = np.sum(mcd.location_ ** 2)
err_cov_mcd[i, j] = mcd.error_norm(np.eye(n_features))
# compare estimators learned from the full data set with true
# parameters
err_loc_emp_full[i, j] = np.sum(X.mean(0) ** 2)
err_cov_emp_full[i, j] = EmpiricalCovariance().fit(X).error_norm(
np.eye(n_features))
# compare with an empirical covariance learned from a pure data set
# (i.e. "perfect" mcd)
pure_X = X[inliers_mask]
pure_location = pure_X.mean(0)
pure_emp_cov = EmpiricalCovariance().fit(pure_X)
err_loc_emp_pure[i, j] = np.sum(pure_location ** 2)
err_cov_emp_pure[i, j] = pure_emp_cov.error_norm(np.eye(n_features))
# Display results
font_prop = matplotlib.font_manager.FontProperties(size=11)
plt.subplot(2, 1, 1)
plt.errorbar(range_n_outliers, err_loc_mcd.mean(1),
yerr=err_loc_mcd.std(1) / np.sqrt(repeat),
label="Robust location", color='m')
plt.errorbar(range_n_outliers, err_loc_emp_full.mean(1),
yerr=err_loc_emp_full.std(1) / np.sqrt(repeat),
label="Full data set mean", color='green')
plt.errorbar(range_n_outliers, err_loc_emp_pure.mean(1),
yerr=err_loc_emp_pure.std(1) / np.sqrt(repeat),
label="Pure data set mean", color='black')
plt.title("Influence of outliers on the location estimation")
plt.ylabel(r"Error ($||\mu - \hat{\mu}||_2^2$)")
plt.legend(loc="upper left", prop=font_prop)
plt.subplot(2, 1, 2)
x_size = range_n_outliers.size
plt.errorbar(range_n_outliers, err_cov_mcd.mean(1),
yerr=err_cov_mcd.std(1),
label="Robust covariance (mcd)", color='m')
plt.errorbar(range_n_outliers[:(x_size / 5 + 1)],
err_cov_emp_full.mean(1)[:(x_size / 5 + 1)],
yerr=err_cov_emp_full.std(1)[:(x_size / 5 + 1)],
label="Full data set empirical covariance", color='green')
plt.plot(range_n_outliers[(x_size / 5):(x_size / 2 - 1)],
err_cov_emp_full.mean(1)[(x_size / 5):(x_size / 2 - 1)], color='green',
ls='--')
plt.errorbar(range_n_outliers, err_cov_emp_pure.mean(1),
yerr=err_cov_emp_pure.std(1),
label="Pure data set empirical covariance", color='black')
plt.title("Influence of outliers on the covariance estimation")
plt.xlabel("Amount of contamination (%)")
plt.ylabel("RMSE")
plt.legend(loc="upper center", prop=font_prop)
plt.show()
| bsd-3-clause |
nick-thompson/neuro | plots/clipping.py | 1 | 1221 | #!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-np.pi, np.pi, 201)
y = np.sin(x)
plt.figure()
plt.subplot(121)
# Color in the axes
plt.axvline(linewidth=1, color='#bbbbbb')
plt.axhline(linewidth=1, color='#bbbbbb')
x_one_one = np.linspace(-1, 1, 201)
# Soft Saturation
plt.plot(x_one_one, np.arctan(x_one_one), color='m')
# Soft saturation approximation
plt.plot(x_one_one, np.arctan(x_one_one) - (np.arctan(x_one_one) ** 3) / 3, color='c')
# Hard clipping
plt.plot(x_one_one, 0.5 * (abs(x_one_one + 0.85) - abs(x_one_one - 0.85)), color='b')
# S-Curve
plt.plot(x_one_one, (1 + 4) * x_one_one / (1 + 4 * abs(x_one_one)), color='y')
# Identity
plt.plot(x_one_one, x_one_one, color='k')
plt.axis('tight')
plt.subplot(122)
# Color in the axes
plt.axvline(linewidth=1, color='#bbbbbb')
plt.axhline(linewidth=1, color='#bbbbbb')
# Soft saturation
plt.plot(x, np.arctan(y), color='m')
# Soft saturation approximation
plt.plot(x, y - (y ** 3) / 3, color='c')
# Hard clipping
plt.plot(x, 0.5 * (abs(y + 0.85) - abs(y - 0.85)), color='b')
# S-Curve
plt.plot(x, (1 + 4) * y / (1 + 4 * abs(y)), color='y')
# Original
plt.plot(x, y, color='k')
plt.axis('tight')
plt.show()
| mit |
ericdill/bokeh | examples/plotting/file/unemployment.py | 46 | 1846 | from collections import OrderedDict
import numpy as np
from bokeh.plotting import ColumnDataSource, figure, show, output_file
from bokeh.models import HoverTool
from bokeh.sampledata.unemployment1948 import data
# Read in the data with pandas. Convert the year column to string
data['Year'] = [str(x) for x in data['Year']]
years = list(data['Year'])
months = ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]
data = data.set_index('Year')
# this is the colormap from the original plot
colors = [
"#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2", "#dfccce",
"#ddb7b1", "#cc7878", "#933b41", "#550b1d"
]
# Set up the data for plotting. We will need to have values for every
# pair of year/month names. Map the rate to a color.
month = []
year = []
color = []
rate = []
for y in years:
for m in months:
month.append(m)
year.append(y)
monthly_rate = data[m][y]
rate.append(monthly_rate)
color.append(colors[min(int(monthly_rate)-2, 8)])
source = ColumnDataSource(
data=dict(month=month, year=year, color=color, rate=rate)
)
output_file('unemployment.html')
TOOLS = "resize,hover,save,pan,box_zoom,wheel_zoom"
p = figure(title="US Unemployment (1948 - 2013)",
x_range=years, y_range=list(reversed(months)),
x_axis_location="above", plot_width=900, plot_height=400,
toolbar_location="left", tools=TOOLS)
p.rect("year", "month", 1, 1, source=source,
color="color", line_color=None)
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "5pt"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = np.pi/3
hover = p.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
('date', '@month @year'),
('rate', '@rate'),
])
show(p) # show the plot
| bsd-3-clause |
mayblue9/scikit-learn | examples/linear_model/plot_omp.py | 385 | 2263 | """
===========================
Orthogonal Matching Pursuit
===========================
Using orthogonal matching pursuit for recovering a sparse signal from a noisy
measurement encoded with a dictionary
"""
print(__doc__)
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import OrthogonalMatchingPursuit
from sklearn.linear_model import OrthogonalMatchingPursuitCV
from sklearn.datasets import make_sparse_coded_signal
n_components, n_features = 512, 100
n_nonzero_coefs = 17
# generate the data
###################
# y = Xw
# |x|_0 = n_nonzero_coefs
y, X, w = make_sparse_coded_signal(n_samples=1,
n_components=n_components,
n_features=n_features,
n_nonzero_coefs=n_nonzero_coefs,
random_state=0)
idx, = w.nonzero()
# distort the clean signal
##########################
y_noisy = y + 0.05 * np.random.randn(len(y))
# plot the sparse signal
########################
plt.figure(figsize=(7, 7))
plt.subplot(4, 1, 1)
plt.xlim(0, 512)
plt.title("Sparse signal")
plt.stem(idx, w[idx])
# plot the noise-free reconstruction
####################################
omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)
omp.fit(X, y)
coef = omp.coef_
idx_r, = coef.nonzero()
plt.subplot(4, 1, 2)
plt.xlim(0, 512)
plt.title("Recovered signal from noise-free measurements")
plt.stem(idx_r, coef[idx_r])
# plot the noisy reconstruction
###############################
omp.fit(X, y_noisy)
coef = omp.coef_
idx_r, = coef.nonzero()
plt.subplot(4, 1, 3)
plt.xlim(0, 512)
plt.title("Recovered signal from noisy measurements")
plt.stem(idx_r, coef[idx_r])
# plot the noisy reconstruction with number of non-zeros set by CV
##################################################################
omp_cv = OrthogonalMatchingPursuitCV()
omp_cv.fit(X, y_noisy)
coef = omp_cv.coef_
idx_r, = coef.nonzero()
plt.subplot(4, 1, 4)
plt.xlim(0, 512)
plt.title("Recovered signal from noisy measurements with CV")
plt.stem(idx_r, coef[idx_r])
plt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)
plt.suptitle('Sparse signal recovery with Orthogonal Matching Pursuit',
fontsize=16)
plt.show()
| bsd-3-clause |
Sentient07/scikit-learn | benchmarks/bench_plot_nmf.py | 28 | 15630 | """
Benchmarks of Non-Negative Matrix Factorization
"""
# Authors: Tom Dupre la Tour (benchmark)
# Chih-Jen Linn (original projected gradient NMF implementation)
# Anthony Di Franco (projected gradient, Python and NumPy port)
# License: BSD 3 clause
from __future__ import print_function
from time import time
import sys
import warnings
import numbers
import numpy as np
import matplotlib.pyplot as plt
import pandas
from sklearn.utils.testing import ignore_warnings
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition.nmf import NMF
from sklearn.decomposition.nmf import _initialize_nmf
from sklearn.decomposition.nmf import _beta_divergence
from sklearn.decomposition.nmf import INTEGER_TYPES, _check_init
from sklearn.externals.joblib import Memory
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils.extmath import fast_dot, safe_sparse_dot, squared_norm
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted, check_non_negative
mem = Memory(cachedir='.', verbose=0)
###################
# Start of _PGNMF #
###################
# This class implements a projected gradient solver for the NMF.
# The projected gradient solver was removed from scikit-learn in version 0.19,
# and a simplified copy is used here for comparison purpose only.
# It is not tested, and it may change or disappear without notice.
def _norm(x):
"""Dot product-based Euclidean norm implementation
See: http://fseoane.net/blog/2011/computing-the-vector-norm/
"""
return np.sqrt(squared_norm(x))
def _nls_subproblem(X, W, H, tol, max_iter, alpha=0., l1_ratio=0.,
sigma=0.01, beta=0.1):
"""Non-negative least square solver
Solves a non-negative least squares subproblem using the projected
gradient descent algorithm.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Constant matrix.
W : array-like, shape (n_samples, n_components)
Constant matrix.
H : array-like, shape (n_components, n_features)
Initial guess for the solution.
tol : float
Tolerance of the stopping condition.
max_iter : int
Maximum number of iterations before timing out.
alpha : double, default: 0.
Constant that multiplies the regularization terms. Set it to zero to
have no regularization.
l1_ratio : double, default: 0.
The regularization mixing parameter, with 0 <= l1_ratio <= 1.
For l1_ratio = 0 the penalty is an L2 penalty.
For l1_ratio = 1 it is an L1 penalty.
For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
sigma : float
Constant used in the sufficient decrease condition checked by the line
search. Smaller values lead to a looser sufficient decrease condition,
thus reducing the time taken by the line search, but potentially
increasing the number of iterations of the projected gradient
procedure. 0.01 is a commonly used value in the optimization
literature.
beta : float
Factor by which the step size is decreased (resp. increased) until
(resp. as long as) the sufficient decrease condition is satisfied.
Larger values allow to find a better step size but lead to longer line
search. 0.1 is a commonly used value in the optimization literature.
Returns
-------
H : array-like, shape (n_components, n_features)
Solution to the non-negative least squares problem.
grad : array-like, shape (n_components, n_features)
The gradient.
n_iter : int
The number of iterations done by the algorithm.
References
----------
C.-J. Lin. Projected gradient methods for non-negative matrix
factorization. Neural Computation, 19(2007), 2756-2779.
http://www.csie.ntu.edu.tw/~cjlin/nmf/
"""
WtX = safe_sparse_dot(W.T, X)
WtW = fast_dot(W.T, W)
# values justified in the paper (alpha is renamed gamma)
gamma = 1
for n_iter in range(1, max_iter + 1):
grad = np.dot(WtW, H) - WtX
if alpha > 0 and l1_ratio == 1.:
grad += alpha
elif alpha > 0:
grad += alpha * (l1_ratio + (1 - l1_ratio) * H)
# The following multiplication with a boolean array is more than twice
# as fast as indexing into grad.
if _norm(grad * np.logical_or(grad < 0, H > 0)) < tol:
break
Hp = H
for inner_iter in range(20):
# Gradient step.
Hn = H - gamma * grad
# Projection step.
Hn *= Hn > 0
d = Hn - H
gradd = np.dot(grad.ravel(), d.ravel())
dQd = np.dot(np.dot(WtW, d).ravel(), d.ravel())
suff_decr = (1 - sigma) * gradd + 0.5 * dQd < 0
if inner_iter == 0:
decr_gamma = not suff_decr
if decr_gamma:
if suff_decr:
H = Hn
break
else:
gamma *= beta
elif not suff_decr or (Hp == Hn).all():
H = Hp
break
else:
gamma /= beta
Hp = Hn
if n_iter == max_iter:
warnings.warn("Iteration limit reached in nls subproblem.",
ConvergenceWarning)
return H, grad, n_iter
def _fit_projected_gradient(X, W, H, tol, max_iter, nls_max_iter, alpha,
l1_ratio):
gradW = (np.dot(W, np.dot(H, H.T)) -
safe_sparse_dot(X, H.T, dense_output=True))
gradH = (np.dot(np.dot(W.T, W), H) -
safe_sparse_dot(W.T, X, dense_output=True))
init_grad = squared_norm(gradW) + squared_norm(gradH.T)
# max(0.001, tol) to force alternating minimizations of W and H
tolW = max(0.001, tol) * np.sqrt(init_grad)
tolH = tolW
for n_iter in range(1, max_iter + 1):
# stopping condition as discussed in paper
proj_grad_W = squared_norm(gradW * np.logical_or(gradW < 0, W > 0))
proj_grad_H = squared_norm(gradH * np.logical_or(gradH < 0, H > 0))
if (proj_grad_W + proj_grad_H) / init_grad < tol ** 2:
break
# update W
Wt, gradWt, iterW = _nls_subproblem(X.T, H.T, W.T, tolW, nls_max_iter,
alpha=alpha, l1_ratio=l1_ratio)
W, gradW = Wt.T, gradWt.T
if iterW == 1:
tolW = 0.1 * tolW
# update H
H, gradH, iterH = _nls_subproblem(X, W, H, tolH, nls_max_iter,
alpha=alpha, l1_ratio=l1_ratio)
if iterH == 1:
tolH = 0.1 * tolH
H[H == 0] = 0 # fix up negative zeros
if n_iter == max_iter:
Wt, _, _ = _nls_subproblem(X.T, H.T, W.T, tolW, nls_max_iter,
alpha=alpha, l1_ratio=l1_ratio)
W = Wt.T
return W, H, n_iter
class _PGNMF(NMF):
"""Non-Negative Matrix Factorization (NMF) with projected gradient solver.
This class is private and for comparison purpose only.
It may change or disappear without notice.
"""
def __init__(self, n_components=None, solver='pg', init=None,
tol=1e-4, max_iter=200, random_state=None,
alpha=0., l1_ratio=0., nls_max_iter=10):
self.nls_max_iter = nls_max_iter
self.n_components = n_components
self.init = init
self.solver = solver
self.tol = tol
self.max_iter = max_iter
self.random_state = random_state
self.alpha = alpha
self.l1_ratio = l1_ratio
def fit(self, X, y=None, **params):
self.fit_transform(X, **params)
return self
def transform(self, X):
check_is_fitted(self, 'components_')
H = self.components_
W, _, self.n_iter_ = self._fit_transform(X, H=H, update_H=False)
return W
def inverse_transform(self, W):
check_is_fitted(self, 'components_')
return np.dot(W, self.components_)
def fit_transform(self, X, y=None, W=None, H=None):
W, H, self.n_iter = self._fit_transform(X, W=W, H=H, update_H=True)
self.components_ = H
return W
def _fit_transform(self, X, y=None, W=None, H=None, update_H=True):
X = check_array(X, accept_sparse=('csr', 'csc'))
check_non_negative(X, "NMF (input X)")
n_samples, n_features = X.shape
n_components = self.n_components
if n_components is None:
n_components = n_features
if (not isinstance(n_components, INTEGER_TYPES) or
n_components <= 0):
raise ValueError("Number of components must be a positive integer;"
" got (n_components=%r)" % n_components)
if not isinstance(self.max_iter, INTEGER_TYPES) or self.max_iter < 0:
raise ValueError("Maximum number of iterations must be a positive "
"integer; got (max_iter=%r)" % self.max_iter)
if not isinstance(self.tol, numbers.Number) or self.tol < 0:
raise ValueError("Tolerance for stopping criteria must be "
"positive; got (tol=%r)" % self.tol)
# check W and H, or initialize them
if self.init == 'custom' and update_H:
_check_init(H, (n_components, n_features), "NMF (input H)")
_check_init(W, (n_samples, n_components), "NMF (input W)")
elif not update_H:
_check_init(H, (n_components, n_features), "NMF (input H)")
W = np.zeros((n_samples, n_components))
else:
W, H = _initialize_nmf(X, n_components, init=self.init,
random_state=self.random_state)
if update_H: # fit_transform
W, H, n_iter = _fit_projected_gradient(
X, W, H, self.tol, self.max_iter, self.nls_max_iter,
self.alpha, self.l1_ratio)
else: # transform
Wt, _, n_iter = _nls_subproblem(X.T, H.T, W.T, self.tol,
self.nls_max_iter,
alpha=self.alpha,
l1_ratio=self.l1_ratio)
W = Wt.T
if n_iter == self.max_iter and self.tol > 0:
warnings.warn("Maximum number of iteration %d reached. Increase it"
" to improve convergence." % self.max_iter,
ConvergenceWarning)
return W, H, n_iter
#################
# End of _PGNMF #
#################
def plot_results(results_df, plot_name):
if results_df is None:
return None
plt.figure(figsize=(16, 6))
colors = 'bgr'
markers = 'ovs'
ax = plt.subplot(1, 3, 1)
for i, init in enumerate(np.unique(results_df['init'])):
plt.subplot(1, 3, i + 1, sharex=ax, sharey=ax)
for j, method in enumerate(np.unique(results_df['method'])):
mask = np.logical_and(results_df['init'] == init,
results_df['method'] == method)
selected_items = results_df[mask]
plt.plot(selected_items['time'], selected_items['loss'],
color=colors[j % len(colors)], ls='-',
marker=markers[j % len(markers)],
label=method)
plt.legend(loc=0, fontsize='x-small')
plt.xlabel("Time (s)")
plt.ylabel("loss")
plt.title("%s" % init)
plt.suptitle(plot_name, fontsize=16)
@ignore_warnings(category=ConvergenceWarning)
# use joblib to cache the results.
# X_shape is specified in arguments for avoiding hashing X
@mem.cache(ignore=['X', 'W0', 'H0'])
def bench_one(name, X, W0, H0, X_shape, clf_type, clf_params, init,
n_components, random_state):
W = W0.copy()
H = H0.copy()
clf = clf_type(**clf_params)
st = time()
W = clf.fit_transform(X, W=W, H=H)
end = time()
H = clf.components_
this_loss = _beta_divergence(X, W, H, 2.0, True)
duration = end - st
return this_loss, duration
def run_bench(X, clfs, plot_name, n_components, tol, alpha, l1_ratio):
start = time()
results = []
for name, clf_type, iter_range, clf_params in clfs:
print("Training %s:" % name)
for rs, init in enumerate(('nndsvd', 'nndsvdar', 'random')):
print(" %s %s: " % (init, " " * (8 - len(init))), end="")
W, H = _initialize_nmf(X, n_components, init, 1e-6, rs)
for max_iter in iter_range:
clf_params['alpha'] = alpha
clf_params['l1_ratio'] = l1_ratio
clf_params['max_iter'] = max_iter
clf_params['tol'] = tol
clf_params['random_state'] = rs
clf_params['init'] = 'custom'
clf_params['n_components'] = n_components
this_loss, duration = bench_one(name, X, W, H, X.shape,
clf_type, clf_params,
init, n_components, rs)
init_name = "init='%s'" % init
results.append((name, this_loss, duration, init_name))
# print("loss: %.6f, time: %.3f sec" % (this_loss, duration))
print(".", end="")
sys.stdout.flush()
print(" ")
# Use a panda dataframe to organize the results
results_df = pandas.DataFrame(results,
columns="method loss time init".split())
print("Total time = %0.3f sec\n" % (time() - start))
# plot the results
plot_results(results_df, plot_name)
return results_df
def load_20news():
print("Loading 20 newsgroups dataset")
print("-----------------------------")
from sklearn.datasets import fetch_20newsgroups
dataset = fetch_20newsgroups(shuffle=True, random_state=1,
remove=('headers', 'footers', 'quotes'))
vectorizer = TfidfVectorizer(max_df=0.95, min_df=2, stop_words='english')
tfidf = vectorizer.fit_transform(dataset.data)
return tfidf
def load_faces():
print("Loading Olivetti face dataset")
print("-----------------------------")
from sklearn.datasets import fetch_olivetti_faces
faces = fetch_olivetti_faces(shuffle=True)
return faces.data
def build_clfs(cd_iters, pg_iters, mu_iters):
clfs = [("Coordinate Descent", NMF, cd_iters, {'solver': 'cd'}),
("Projected Gradient", _PGNMF, pg_iters, {'solver': 'pg'}),
("Multiplicative Update", NMF, mu_iters, {'solver': 'mu'}),
]
return clfs
if __name__ == '__main__':
alpha = 0.
l1_ratio = 0.5
n_components = 10
tol = 1e-15
# first benchmark on 20 newsgroup dataset: sparse, shape(11314, 39116)
plot_name = "20 Newsgroups sparse dataset"
cd_iters = np.arange(1, 30)
pg_iters = np.arange(1, 6)
mu_iters = np.arange(1, 30)
clfs = build_clfs(cd_iters, pg_iters, mu_iters)
X_20news = load_20news()
run_bench(X_20news, clfs, plot_name, n_components, tol, alpha, l1_ratio)
# second benchmark on Olivetti faces dataset: dense, shape(400, 4096)
plot_name = "Olivetti Faces dense dataset"
cd_iters = np.arange(1, 30)
pg_iters = np.arange(1, 12)
mu_iters = np.arange(1, 30)
clfs = build_clfs(cd_iters, pg_iters, mu_iters)
X_faces = load_faces()
run_bench(X_faces, clfs, plot_name, n_components, tol, alpha, l1_ratio,)
plt.show()
| bsd-3-clause |
krez13/scikit-learn | sklearn/linear_model/tests/test_sparse_coordinate_descent.py | 244 | 9986 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import ignore_warnings
from sklearn.linear_model.coordinate_descent import (Lasso, ElasticNet,
LassoCV, ElasticNetCV)
def test_sparse_coef():
# Check that the sparse_coef propery works
clf = ElasticNet()
clf.coef_ = [1, 2, 3]
assert_true(sp.isspmatrix(clf.sparse_coef_))
assert_equal(clf.sparse_coef_.toarray().tolist()[0], clf.coef_)
def test_normalize_option():
# Check that the normalize option in enet works
X = sp.csc_matrix([[-1], [0], [1]])
y = [-1, 0, 1]
clf_dense = ElasticNet(fit_intercept=True, normalize=True)
clf_sparse = ElasticNet(fit_intercept=True, normalize=True)
clf_dense.fit(X, y)
X = sp.csc_matrix(X)
clf_sparse.fit(X, y)
assert_almost_equal(clf_dense.dual_gap_, 0)
assert_array_almost_equal(clf_dense.coef_, clf_sparse.coef_)
def test_lasso_zero():
# Check that the sparse lasso can handle zero data without crashing
X = sp.csc_matrix((3, 1))
y = [0, 0, 0]
T = np.array([[1], [2], [3]])
clf = Lasso().fit(X, y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0])
assert_array_almost_equal(pred, [0, 0, 0])
assert_almost_equal(clf.dual_gap_, 0)
def test_enet_toy_list_input():
# Test ElasticNet for various values of alpha and l1_ratio with list X
X = np.array([[-1], [0], [1]])
X = sp.csc_matrix(X)
Y = [-1, 0, 1] # just a straight line
T = np.array([[2], [3], [4]]) # test sample
# this should be the same as unregularized least squares
clf = ElasticNet(alpha=0, l1_ratio=1.0)
# catch warning about alpha=0.
# this is discouraged but should work.
ignore_warnings(clf.fit)(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [1])
assert_array_almost_equal(pred, [2, 3, 4])
assert_almost_equal(clf.dual_gap_, 0)
clf = ElasticNet(alpha=0.5, l1_ratio=0.3, max_iter=1000)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3)
assert_almost_equal(clf.dual_gap_, 0)
clf = ElasticNet(alpha=0.5, l1_ratio=0.5)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.45454], 3)
assert_array_almost_equal(pred, [0.9090, 1.3636, 1.8181], 3)
assert_almost_equal(clf.dual_gap_, 0)
def test_enet_toy_explicit_sparse_input():
# Test ElasticNet for various values of alpha and l1_ratio with sparse X
f = ignore_warnings
# training samples
X = sp.lil_matrix((3, 1))
X[0, 0] = -1
# X[1, 0] = 0
X[2, 0] = 1
Y = [-1, 0, 1] # just a straight line (the identity function)
# test samples
T = sp.lil_matrix((3, 1))
T[0, 0] = 2
T[1, 0] = 3
T[2, 0] = 4
# this should be the same as lasso
clf = ElasticNet(alpha=0, l1_ratio=1.0)
f(clf.fit)(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [1])
assert_array_almost_equal(pred, [2, 3, 4])
assert_almost_equal(clf.dual_gap_, 0)
clf = ElasticNet(alpha=0.5, l1_ratio=0.3, max_iter=1000)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.50819], decimal=3)
assert_array_almost_equal(pred, [1.0163, 1.5245, 2.0327], decimal=3)
assert_almost_equal(clf.dual_gap_, 0)
clf = ElasticNet(alpha=0.5, l1_ratio=0.5)
clf.fit(X, Y)
pred = clf.predict(T)
assert_array_almost_equal(clf.coef_, [0.45454], 3)
assert_array_almost_equal(pred, [0.9090, 1.3636, 1.8181], 3)
assert_almost_equal(clf.dual_gap_, 0)
def make_sparse_data(n_samples=100, n_features=100, n_informative=10, seed=42,
positive=False, n_targets=1):
random_state = np.random.RandomState(seed)
# build an ill-posed linear regression problem with many noisy features and
# comparatively few samples
# generate a ground truth model
w = random_state.randn(n_features, n_targets)
w[n_informative:] = 0.0 # only the top features are impacting the model
if positive:
w = np.abs(w)
X = random_state.randn(n_samples, n_features)
rnd = random_state.uniform(size=(n_samples, n_features))
X[rnd > 0.5] = 0.0 # 50% of zeros in input signal
# generate training ground truth labels
y = np.dot(X, w)
X = sp.csc_matrix(X)
if n_targets == 1:
y = np.ravel(y)
return X, y
def _test_sparse_enet_not_as_toy_dataset(alpha, fit_intercept, positive):
n_samples, n_features, max_iter = 100, 100, 1000
n_informative = 10
X, y = make_sparse_data(n_samples, n_features, n_informative,
positive=positive)
X_train, X_test = X[n_samples // 2:], X[:n_samples // 2]
y_train, y_test = y[n_samples // 2:], y[:n_samples // 2]
s_clf = ElasticNet(alpha=alpha, l1_ratio=0.8, fit_intercept=fit_intercept,
max_iter=max_iter, tol=1e-7, positive=positive,
warm_start=True)
s_clf.fit(X_train, y_train)
assert_almost_equal(s_clf.dual_gap_, 0, 4)
assert_greater(s_clf.score(X_test, y_test), 0.85)
# check the convergence is the same as the dense version
d_clf = ElasticNet(alpha=alpha, l1_ratio=0.8, fit_intercept=fit_intercept,
max_iter=max_iter, tol=1e-7, positive=positive,
warm_start=True)
d_clf.fit(X_train.toarray(), y_train)
assert_almost_equal(d_clf.dual_gap_, 0, 4)
assert_greater(d_clf.score(X_test, y_test), 0.85)
assert_almost_equal(s_clf.coef_, d_clf.coef_, 5)
assert_almost_equal(s_clf.intercept_, d_clf.intercept_, 5)
# check that the coefs are sparse
assert_less(np.sum(s_clf.coef_ != 0.0), 2 * n_informative)
def test_sparse_enet_not_as_toy_dataset():
_test_sparse_enet_not_as_toy_dataset(alpha=0.1, fit_intercept=False,
positive=False)
_test_sparse_enet_not_as_toy_dataset(alpha=0.1, fit_intercept=True,
positive=False)
_test_sparse_enet_not_as_toy_dataset(alpha=1e-3, fit_intercept=False,
positive=True)
_test_sparse_enet_not_as_toy_dataset(alpha=1e-3, fit_intercept=True,
positive=True)
def test_sparse_lasso_not_as_toy_dataset():
n_samples = 100
max_iter = 1000
n_informative = 10
X, y = make_sparse_data(n_samples=n_samples, n_informative=n_informative)
X_train, X_test = X[n_samples // 2:], X[:n_samples // 2]
y_train, y_test = y[n_samples // 2:], y[:n_samples // 2]
s_clf = Lasso(alpha=0.1, fit_intercept=False, max_iter=max_iter, tol=1e-7)
s_clf.fit(X_train, y_train)
assert_almost_equal(s_clf.dual_gap_, 0, 4)
assert_greater(s_clf.score(X_test, y_test), 0.85)
# check the convergence is the same as the dense version
d_clf = Lasso(alpha=0.1, fit_intercept=False, max_iter=max_iter, tol=1e-7)
d_clf.fit(X_train.toarray(), y_train)
assert_almost_equal(d_clf.dual_gap_, 0, 4)
assert_greater(d_clf.score(X_test, y_test), 0.85)
# check that the coefs are sparse
assert_equal(np.sum(s_clf.coef_ != 0.0), n_informative)
def test_enet_multitarget():
n_targets = 3
X, y = make_sparse_data(n_targets=n_targets)
estimator = ElasticNet(alpha=0.01, fit_intercept=True, precompute=None)
# XXX: There is a bug when precompute is not None!
estimator.fit(X, y)
coef, intercept, dual_gap = (estimator.coef_,
estimator.intercept_,
estimator.dual_gap_)
for k in range(n_targets):
estimator.fit(X, y[:, k])
assert_array_almost_equal(coef[k, :], estimator.coef_)
assert_array_almost_equal(intercept[k], estimator.intercept_)
assert_array_almost_equal(dual_gap[k], estimator.dual_gap_)
def test_path_parameters():
X, y = make_sparse_data()
max_iter = 50
n_alphas = 10
clf = ElasticNetCV(n_alphas=n_alphas, eps=1e-3, max_iter=max_iter,
l1_ratio=0.5, fit_intercept=False)
ignore_warnings(clf.fit)(X, y) # new params
assert_almost_equal(0.5, clf.l1_ratio)
assert_equal(n_alphas, clf.n_alphas)
assert_equal(n_alphas, len(clf.alphas_))
sparse_mse_path = clf.mse_path_
ignore_warnings(clf.fit)(X.toarray(), y) # compare with dense data
assert_almost_equal(clf.mse_path_, sparse_mse_path)
def test_same_output_sparse_dense_lasso_and_enet_cv():
X, y = make_sparse_data(n_samples=40, n_features=10)
for normalize in [True, False]:
clfs = ElasticNetCV(max_iter=100, cv=5, normalize=normalize)
ignore_warnings(clfs.fit)(X, y)
clfd = ElasticNetCV(max_iter=100, cv=5, normalize=normalize)
ignore_warnings(clfd.fit)(X.toarray(), y)
assert_almost_equal(clfs.alpha_, clfd.alpha_, 7)
assert_almost_equal(clfs.intercept_, clfd.intercept_, 7)
assert_array_almost_equal(clfs.mse_path_, clfd.mse_path_)
assert_array_almost_equal(clfs.alphas_, clfd.alphas_)
clfs = LassoCV(max_iter=100, cv=4, normalize=normalize)
ignore_warnings(clfs.fit)(X, y)
clfd = LassoCV(max_iter=100, cv=4, normalize=normalize)
ignore_warnings(clfd.fit)(X.toarray(), y)
assert_almost_equal(clfs.alpha_, clfd.alpha_, 7)
assert_almost_equal(clfs.intercept_, clfd.intercept_, 7)
assert_array_almost_equal(clfs.mse_path_, clfd.mse_path_)
assert_array_almost_equal(clfs.alphas_, clfd.alphas_)
| bsd-3-clause |
datapythonista/pandas | pandas/tests/indexes/datetimes/test_ops.py | 3 | 4838 | from datetime import datetime
from dateutil.tz import tzlocal
import pytest
from pandas.compat import IS64
from pandas import (
DateOffset,
DatetimeIndex,
Index,
Series,
bdate_range,
date_range,
)
import pandas._testing as tm
from pandas.tseries.offsets import (
BDay,
Day,
Hour,
)
START, END = datetime(2009, 1, 1), datetime(2010, 1, 1)
class TestDatetimeIndexOps:
def test_ops_properties_basic(self, datetime_series):
# sanity check that the behavior didn't change
# GH#7206
for op in ["year", "day", "second", "weekday"]:
msg = f"'Series' object has no attribute '{op}'"
with pytest.raises(AttributeError, match=msg):
getattr(datetime_series, op)
# attribute access should still work!
s = Series({"year": 2000, "month": 1, "day": 10})
assert s.year == 2000
assert s.month == 1
assert s.day == 10
msg = "'Series' object has no attribute 'weekday'"
with pytest.raises(AttributeError, match=msg):
s.weekday
@pytest.mark.parametrize(
"freq,expected",
[
("A", "day"),
("Q", "day"),
("M", "day"),
("D", "day"),
("H", "hour"),
("T", "minute"),
("S", "second"),
("L", "millisecond"),
("U", "microsecond"),
],
)
def test_resolution(self, request, tz_naive_fixture, freq, expected):
tz = tz_naive_fixture
if freq == "A" and not IS64 and isinstance(tz, tzlocal):
request.node.add_marker(
pytest.mark.xfail(reason="OverflowError inside tzlocal past 2038")
)
idx = date_range(start="2013-04-01", periods=30, freq=freq, tz=tz)
assert idx.resolution == expected
def test_infer_freq(self, freq_sample):
# GH 11018
idx = date_range("2011-01-01 09:00:00", freq=freq_sample, periods=10)
result = DatetimeIndex(idx.asi8, freq="infer")
tm.assert_index_equal(idx, result)
assert result.freq == freq_sample
@pytest.mark.parametrize("values", [["20180101", "20180103", "20180105"], []])
@pytest.mark.parametrize("freq", ["2D", Day(2), "2B", BDay(2), "48H", Hour(48)])
@pytest.mark.parametrize("tz", [None, "US/Eastern"])
def test_freq_setter(self, values, freq, tz):
# GH 20678
idx = DatetimeIndex(values, tz=tz)
# can set to an offset, converting from string if necessary
idx._data.freq = freq
assert idx.freq == freq
assert isinstance(idx.freq, DateOffset)
# can reset to None
idx._data.freq = None
assert idx.freq is None
def test_freq_setter_errors(self):
# GH 20678
idx = DatetimeIndex(["20180101", "20180103", "20180105"])
# setting with an incompatible freq
msg = (
"Inferred frequency 2D from passed values does not conform to "
"passed frequency 5D"
)
with pytest.raises(ValueError, match=msg):
idx._data.freq = "5D"
# setting with non-freq string
with pytest.raises(ValueError, match="Invalid frequency"):
idx._data.freq = "foo"
def test_freq_view_safe(self):
# Setting the freq for one DatetimeIndex shouldn't alter the freq
# for another that views the same data
dti = date_range("2016-01-01", periods=5)
dta = dti._data
dti2 = DatetimeIndex(dta)._with_freq(None)
assert dti2.freq is None
# Original was not altered
assert dti.freq == "D"
assert dta.freq == "D"
class TestBusinessDatetimeIndex:
def setup_method(self, method):
self.rng = bdate_range(START, END)
def test_comparison(self):
d = self.rng[10]
comp = self.rng > d
assert comp[11]
assert not comp[9]
def test_copy(self):
cp = self.rng.copy()
repr(cp)
tm.assert_index_equal(cp, self.rng)
def test_identical(self):
t1 = self.rng.copy()
t2 = self.rng.copy()
assert t1.identical(t2)
# name
t1 = t1.rename("foo")
assert t1.equals(t2)
assert not t1.identical(t2)
t2 = t2.rename("foo")
assert t1.identical(t2)
# freq
t2v = Index(t2.values)
assert t1.equals(t2v)
assert not t1.identical(t2v)
class TestCustomDatetimeIndex:
def setup_method(self, method):
self.rng = bdate_range(START, END, freq="C")
def test_comparison(self):
d = self.rng[10]
comp = self.rng > d
assert comp[11]
assert not comp[9]
def test_copy(self):
cp = self.rng.copy()
repr(cp)
tm.assert_index_equal(cp, self.rng)
| bsd-3-clause |
glouppe/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | 176 | 2169 | from nose.tools import assert_equal
import numpy as np
from sklearn.preprocessing import FunctionTransformer
def _make_func(args_store, kwargs_store, func=lambda X, *a, **k: X):
def _func(X, *args, **kwargs):
args_store.append(X)
args_store.extend(args)
kwargs_store.update(kwargs)
return func(X)
return _func
def test_delegate_to_func():
# (args|kwargs)_store will hold the positional and keyword arguments
# passed to the function inside the FunctionTransformer.
args_store = []
kwargs_store = {}
X = np.arange(10).reshape((5, 2))
np.testing.assert_array_equal(
FunctionTransformer(_make_func(args_store, kwargs_store)).transform(X),
X,
'transform should have returned X unchanged',
)
# The function should only have recieved X.
assert_equal(
args_store,
[X],
'Incorrect positional arguments passed to func: {args}'.format(
args=args_store,
),
)
assert_equal(
kwargs_store,
{},
'Unexpected keyword arguments passed to func: {args}'.format(
args=kwargs_store,
),
)
# reset the argument stores.
args_store[:] = [] # python2 compatible inplace list clear.
kwargs_store.clear()
y = object()
np.testing.assert_array_equal(
FunctionTransformer(
_make_func(args_store, kwargs_store),
pass_y=True,
).transform(X, y),
X,
'transform should have returned X unchanged',
)
# The function should have recieved X and y.
assert_equal(
args_store,
[X, y],
'Incorrect positional arguments passed to func: {args}'.format(
args=args_store,
),
)
assert_equal(
kwargs_store,
{},
'Unexpected keyword arguments passed to func: {args}'.format(
args=kwargs_store,
),
)
def test_np_log():
X = np.arange(10).reshape((5, 2))
# Test that the numpy.log example still works.
np.testing.assert_array_equal(
FunctionTransformer(np.log1p).transform(X),
np.log1p(X),
)
| bsd-3-clause |
SkyRocketToys/ardupilot | libraries/AP_Math/tools/geodesic_grid/plot.py | 110 | 2876 | # Copyright (C) 2016 Intel Corporation. All rights reserved.
#
# This file is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This file is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program. If not, see <http://www.gnu.org/licenses/>.
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import icosahedron as ico
import grid
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_xlim3d(-2, 2)
ax.set_ylim3d(-2, 2)
ax.set_zlim3d(-2, 2)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.invert_zaxis()
ax.invert_xaxis()
ax.set_aspect('equal')
added_polygons = set()
added_sections = set()
def polygons(polygons):
for p in polygons:
polygon(p)
def polygon(polygon):
added_polygons.add(polygon)
def section(s):
added_sections.add(s)
def sections(sections):
for s in sections:
section(s)
def show(subtriangles=False):
polygons = []
facecolors = []
triangles_indexes = set()
subtriangle_facecolors = (
'#CCCCCC',
'#CCE5FF',
'#E5FFCC',
'#FFCCCC',
)
if added_sections:
subtriangles = True
for p in added_polygons:
try:
i = ico.triangles.index(p)
except ValueError:
polygons.append(p)
continue
if subtriangles:
sections(range(i * 4, i * 4 + 4))
else:
triangles_indexes.add(i)
polygons.append(p)
facecolors.append('#DDDDDD')
for s in added_sections:
triangles_indexes.add(int(s / 4))
subtriangle_index = s % 4
polygons.append(grid.section_triangle(s))
facecolors.append(subtriangle_facecolors[subtriangle_index])
ax.add_collection3d(Poly3DCollection(
polygons,
facecolors=facecolors,
edgecolors="#777777",
))
for i in triangles_indexes:
t = ico.triangles[i]
mx = my = mz = 0
for x, y, z in t:
mx += x
my += y
mz += z
ax.text(mx / 2.6, my / 2.6, mz / 2.6, i, color='#444444')
if subtriangles:
ax.legend(
handles=tuple(
mpatches.Patch(color=c, label='Sub-triangle #%d' % i)
for i, c in enumerate(subtriangle_facecolors)
),
)
plt.show()
| gpl-3.0 |
SidWatch/pyPSD | src/PowerSpectrumDensityProcessor.py | 1 | 2916 | __author__ = 'Administrator'
import yaml
import io
import h5py
import datetime as dt
import math
import numpy as np
import sys, getopt
from matplotlib.mlab import psd
class FrequencyUtility:
def __init__(self):
"""
Constructor
"""
def load_data(self, inputFileName):
with open(inputFileName, "r") as ins:
array = []
for line in ins:
d = float(line)
array.append(d)
values = np.zeros(len(array), np.float32)
i = 0
for item in array:
values[i] = item
i += 1
return array
def write_data(self, outputFileName, pxx, frequencies):
with open(outputFileName, "w") as outs:
i = 0
size = pxx.size
for x in range(0, size):
outputLine = str(frequencies[x]) + '|' + str(pxx[x]) + '\r\n'
outs.write(outputLine)
return
def process_psd(self, data, nfft=1024, audio_sampling_rate=96000):
return psd(data, nfft, audio_sampling_rate)
def array_from_bytes(self, data_chunk, sample_width, data_type):
data_length = len(data_chunk)
remainder = data_length % sample_width
if remainder == 0:
reading_count = data_length // sample_width
channel1 = np.zeros(reading_count, dtype=data_type)
current_position = 0
for x in range(0, reading_count):
byte_array = bytearray(sample_width)
bytearray.zfill(byte_array, sample_width)
for y in range(0, sample_width):
byte_array[y] = data_chunk[current_position]
current_position += 1
if data_type == np.int16 or data_type == np.int32:
channel1[x] = int.from_bytes(byte_array, byteorder='little', signed=True)
else:
channel1[x] = float.from_bytes(byte_array, byteorder='little', signed=True)
return {'Channel1': channel1 }
else:
return None
def main(argv):
inputfile = ''
outputfile = ''
try:
opts, args = getopt.getopt(argv,"hi:o:",["ifile=","ofile="])
except getopt.GetoptError:
print('PowerSpectrumDensityProcessor.py -i <inputfile> -o <outputfile>')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print('test.py -i <inputfile> -o <outputfile>')
sys.exit()
elif opt in ("-i", "--ifile"):
inputfile = arg
elif opt in ("-o", "--ofile"):
outputfile = arg
freqUtil = FrequencyUtility()
inputData = freqUtil.load_data(inputfile)
pxx, frequencies = freqUtil.process_psd(inputData, 1024, len(inputData))
freqUtil.write_data(outputfile, pxx, frequencies)
if __name__ == "__main__":
main(sys.argv[1:]) | mit |
jaantollander/Pointwise-Convergence | src_legacy/analysis/plotting.py | 8 | 3846 | # coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.widgets import Button
from src_legacy.analysis.convergence import max_slope
from src_legacy.io.load.load import LoadCsv
from src_legacy.other.settings import timeit
class ConvergencePlot:
"""
Interactive plot for results.
http://bastibe.de/2013-05-30-speeding-up-matplotlib.html
http://stackoverflow.com/questions/29277080/efficient-matplotlib-redrawing
"""
def __init__(self, filename, function):
self.initial = True # Flag
loads = LoadCsv(filename, function)
self.function = function
self.index = loads.errors.index.values
self.errors = loads.errors
self.a = loads.inputs[0]
self.x = loads.inputs[1]
self.fig, self.ax = plt.subplots(figsize=(10, 8))
self.fig.subplots_adjust(bottom=0.2)
self.ax.set(ylim=(10 ** -6, np.max(self.errors.values) + 0.1),
xlim=(self.index.min(), self.index.max()),
xlabel=r'$ p $',
ylabel=r'$ \varepsilon $')
self.line, = self.ax.loglog([], [], lw=1, marker='*')
self.cline, = self.ax.loglog([], [], lw=2, marker='*')
self.background = self.fig.canvas.copy_from_bbox(self.ax.bbox)
# rect = [left, bottom, width, height] in normalized (0, 1) units
xprev = plt.axes([0.44, 0.05, 0.1, 0.075])
xnext = plt.axes([0.56, 0.05, 0.1, 0.075])
bnext = Button(xnext, r'$ x \Rightarrow $')
bprev = Button(xprev, r'$ \Leftarrow x $')
bnext.on_clicked(self.xnext)
bprev.on_clicked(self.xprev)
aprev = plt.axes([0.91, 0.45, 0.08, 0.075])
anext = plt.axes([0.91, 0.55, 0.08, 0.075])
cnext = Button(anext, r'$ a \Rightarrow $')
cprev = Button(aprev, r'$ \Leftarrow a $')
cnext.on_clicked(self.anext)
cprev.on_clicked(self.aprev)
self.index_x = 0
self.index_a = 0
self.line.set_xdata(self.index)
self.draw()
@timeit
def draw(self):
"""
Redraw the axis
"""
a_ = self.a[self.index_a]
x_ = self.x[self.index_x]
data = self.errors[str(a_)]
data = data[str(x_)]
mask = max_slope(data)
convergence = data.iloc[mask]
self.line.set_ydata(data.values)
self.cline.set_data(convergence.index.values, convergence.values)
self.ax.set_title(r'{function}: '.format(function=self.function) +
r'$ a: {}\approx {:.4f} $, '.format(a_, float(a_)) +
r'$ x: {}\approx {:.4f} $ '.format(x_, float(x_)))
if self.initial:
self.fig.canvas.draw()
self.initial = False
plt.show()
else:
self.fig.canvas.restore_region(self.background)
self.ax.draw_artist(self.ax.patch)
self.ax.draw_artist(self.line)
self.ax.draw_artist(self.cline)
# self.fig.canvas.update()
# self.fig.canvas.flush_events()
self.fig.canvas.blit(self.ax.bbox)
def xnext(self, event):
if self.index_x < len(self.x) - 1:
self.index_x += 1
self.draw()
def xprev(self, event):
if self.index_x > -len(self.x):
self.index_x -= 1
self.draw()
def anext(self, event):
if self.index_a < len(self.a) - 1:
self.index_a += 1
self.draw()
def aprev(self, event):
if self.index_a > 0:
self.index_a -= 1
self.draw()
sns.set()
ConvergencePlot('100000_391_1', 'step_function')
| mit |
Orcuslc/ECSGCC-Data | scripts/max_load_predict/modify_csv.py | 1 | 1192 | import numpy as np
import pandas as pd
import datetime as dt
path = '../../modified-data/dailyDATA_clean.csv'
save_path = '../../modified-data/simp_daily_data_2.csv'
data = pd.read_csv(path, encoding='gbk')
def dump_data(data):
new_data = pd.DataFrame()
new_data['date'] = data['date']
new_data['max_load'] = data['max_load']
new_data['temperature'] = data['average_c']
weekday_list = [1, 2, 3, 4, 5, 6, 7]
weekdays = []
for i in range(len(data['weekday'])):
day = (i+6)%7
if day == 0:
day = 7
weekdays.append(day)
weekdays = pd.Series(weekdays, name = 'weekday')
new_data['weekday'] = weekdays
####################################################################
## We Choose 0 for weekday, 1 for weekend, -1 for long holidays ##
####################################################################
holiday_list = [0, -1, 1]
hol = data['hol']
holidays = []
for i in hol:
if i == '小长假':
holidays.append(-1)
elif i == '周末':
holidays.append(1)
else:
holidays.append(0)
holidays = pd.Series(holidays, name = 'holiday')
new_data['holiday'] = holidays
new_data.to_csv(save_path, index = False)
if __name__ == '__main__':
dump_data(data) | gpl-3.0 |
kernc/scikit-learn | sklearn/linear_model/randomized_l1.py | 9 | 24350 | """
Randomized Lasso/Logistic: feature selection based on Lasso and
sparse Logistic Regression
"""
# Author: Gael Varoquaux, Alexandre Gramfort
#
# License: BSD 3 clause
import itertools
from abc import ABCMeta, abstractmethod
import warnings
import numpy as np
from scipy.sparse import issparse
from scipy import sparse
from scipy.interpolate import interp1d
from .base import _preprocess_data
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..externals.joblib import Memory, Parallel, delayed
from ..utils import (as_float_array, check_random_state, check_X_y,
check_array, safe_mask)
from ..utils.validation import check_is_fitted
from .least_angle import lars_path, LassoLarsIC
from .logistic import LogisticRegression
from ..exceptions import ConvergenceWarning
###############################################################################
# Randomized linear model: feature selection
def _resample_model(estimator_func, X, y, scaling=.5, n_resampling=200,
n_jobs=1, verbose=False, pre_dispatch='3*n_jobs',
random_state=None, sample_fraction=.75, **params):
random_state = check_random_state(random_state)
# We are generating 1 - weights, and not weights
n_samples, n_features = X.shape
if not (0 < scaling < 1):
raise ValueError(
"'scaling' should be between 0 and 1. Got %r instead." % scaling)
scaling = 1. - scaling
scores_ = 0.0
for active_set in Parallel(n_jobs=n_jobs, verbose=verbose,
pre_dispatch=pre_dispatch)(
delayed(estimator_func)(
X, y, weights=scaling * random_state.random_integers(
0, 1, size=(n_features,)),
mask=(random_state.rand(n_samples) < sample_fraction),
verbose=max(0, verbose - 1),
**params)
for _ in range(n_resampling)):
scores_ += active_set
scores_ /= n_resampling
return scores_
class BaseRandomizedLinearModel(six.with_metaclass(ABCMeta, BaseEstimator,
TransformerMixin)):
"""Base class to implement randomized linear models for feature selection
This implements the strategy by Meinshausen and Buhlman:
stability selection with randomized sampling, and random re-weighting of
the penalty.
"""
@abstractmethod
def __init__(self):
pass
_preprocess_data = staticmethod(_preprocess_data)
def fit(self, X, y):
"""Fit the model using X, y as training data.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training data.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : object
Returns an instance of self.
"""
X, y = check_X_y(X, y, ['csr', 'csc'], y_numeric=True,
ensure_min_samples=2, estimator=self)
X = as_float_array(X, copy=False)
n_samples, n_features = X.shape
X, y, X_offset, y_offset, X_scale = \
self._preprocess_data(X, y, self.fit_intercept, self.normalize)
estimator_func, params = self._make_estimator_and_params(X, y)
memory = self.memory
if isinstance(memory, six.string_types):
memory = Memory(cachedir=memory)
scores_ = memory.cache(
_resample_model, ignore=['verbose', 'n_jobs', 'pre_dispatch']
)(
estimator_func, X, y,
scaling=self.scaling, n_resampling=self.n_resampling,
n_jobs=self.n_jobs, verbose=self.verbose,
pre_dispatch=self.pre_dispatch, random_state=self.random_state,
sample_fraction=self.sample_fraction, **params)
if scores_.ndim == 1:
scores_ = scores_[:, np.newaxis]
self.all_scores_ = scores_
self.scores_ = np.max(self.all_scores_, axis=1)
return self
def _make_estimator_and_params(self, X, y):
"""Return the parameters passed to the estimator"""
raise NotImplementedError
def get_support(self, indices=False):
"""Return a mask, or list, of the features/indices selected."""
check_is_fitted(self, 'scores_')
mask = self.scores_ > self.selection_threshold
return mask if not indices else np.where(mask)[0]
# XXX: the two function below are copy/pasted from feature_selection,
# Should we add an intermediate base class?
def transform(self, X):
"""Transform a new matrix using the selected features"""
mask = self.get_support()
X = check_array(X)
if len(mask) != X.shape[1]:
raise ValueError("X has a different shape than during fitting.")
return check_array(X)[:, safe_mask(X, mask)]
def inverse_transform(self, X):
"""Transform a new matrix using the selected features"""
support = self.get_support()
if X.ndim == 1:
X = X[None, :]
Xt = np.zeros((X.shape[0], support.size))
Xt[:, support] = X
return Xt
###############################################################################
# Randomized lasso: regression settings
def _randomized_lasso(X, y, weights, mask, alpha=1., verbose=False,
precompute=False, eps=np.finfo(np.float).eps,
max_iter=500):
X = X[safe_mask(X, mask)]
y = y[mask]
# Center X and y to avoid fit the intercept
X -= X.mean(axis=0)
y -= y.mean()
alpha = np.atleast_1d(np.asarray(alpha, dtype=np.float64))
X = (1 - weights) * X
with warnings.catch_warnings():
warnings.simplefilter('ignore', ConvergenceWarning)
alphas_, _, coef_ = lars_path(X, y,
Gram=precompute, copy_X=False,
copy_Gram=False, alpha_min=np.min(alpha),
method='lasso', verbose=verbose,
max_iter=max_iter, eps=eps)
if len(alpha) > 1:
if len(alphas_) > 1: # np.min(alpha) < alpha_min
interpolator = interp1d(alphas_[::-1], coef_[:, ::-1],
bounds_error=False, fill_value=0.)
scores = (interpolator(alpha) != 0.0)
else:
scores = np.zeros((X.shape[1], len(alpha)), dtype=np.bool)
else:
scores = coef_[:, -1] != 0.0
return scores
class RandomizedLasso(BaseRandomizedLinearModel):
"""Randomized Lasso.
Randomized Lasso works by resampling the train data and computing
a Lasso on each resampling. In short, the features selected more
often are good features. It is also known as stability selection.
Read more in the :ref:`User Guide <randomized_l1>`.
Parameters
----------
alpha : float, 'aic', or 'bic', optional
The regularization parameter alpha parameter in the Lasso.
Warning: this is not the alpha parameter in the stability selection
article which is scaling.
scaling : float, optional
The alpha parameter in the stability selection article used to
randomly scale the features. Should be between 0 and 1.
sample_fraction : float, optional
The fraction of samples to be used in each randomized design.
Should be between 0 and 1. If 1, all samples are used.
n_resampling : int, optional
Number of randomized models.
selection_threshold: float, optional
The score above which features should be selected.
fit_intercept : boolean, optional
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
verbose : boolean or integer, optional
Sets the verbosity amount
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
This parameter is ignored when `fit_intercept` is set to False.
When the regressors are normalized, note that this makes the
hyperparameters learnt more robust and almost independent of the number
of samples. The same property is not valid for standardized data.
However, if you wish to standardize, please use
`preprocessing.StandardScaler` before calling `fit` on an estimator
with `normalize=False`.
precompute : True | False | 'auto'
Whether to use a precomputed Gram matrix to speed up
calculations. If set to 'auto' let us decide. The Gram
matrix can also be passed as argument.
max_iter : integer, optional
Maximum number of iterations to perform in the Lars algorithm.
eps : float, optional
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems. Unlike the 'tol' parameter in some iterative
optimization-based algorithms, this parameter does not control
the tolerance of the optimization.
n_jobs : integer, optional
Number of CPUs to use during the resampling. If '-1', use
all the CPUs
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel
execution. Reducing this number can be useful to avoid an
explosion of memory consumption when more jobs get dispatched
than CPUs can process. This parameter can be:
- None, in which case all the jobs are immediately
created and spawned. Use this for lightweight and
fast-running jobs, to avoid delays due to on-demand
spawning of the jobs
- An int, giving the exact number of total jobs that are
spawned
- A string, giving an expression as a function of n_jobs,
as in '2*n_jobs'
memory : Instance of joblib.Memory or string
Used for internal caching. By default, no caching is done.
If a string is given, it is the path to the caching directory.
Attributes
----------
scores_ : array, shape = [n_features]
Feature scores between 0 and 1.
all_scores_ : array, shape = [n_features, n_reg_parameter]
Feature scores between 0 and 1 for all values of the regularization \
parameter. The reference article suggests ``scores_`` is the max of \
``all_scores_``.
Examples
--------
>>> from sklearn.linear_model import RandomizedLasso
>>> randomized_lasso = RandomizedLasso()
Notes
-----
See examples/linear_model/plot_sparse_recovery.py for an example.
References
----------
Stability selection
Nicolai Meinshausen, Peter Buhlmann
Journal of the Royal Statistical Society: Series B
Volume 72, Issue 4, pages 417-473, September 2010
DOI: 10.1111/j.1467-9868.2010.00740.x
See also
--------
RandomizedLogisticRegression, LogisticRegression
"""
def __init__(self, alpha='aic', scaling=.5, sample_fraction=.75,
n_resampling=200, selection_threshold=.25,
fit_intercept=True, verbose=False,
normalize=True, precompute='auto',
max_iter=500,
eps=np.finfo(np.float).eps, random_state=None,
n_jobs=1, pre_dispatch='3*n_jobs',
memory=Memory(cachedir=None, verbose=0)):
self.alpha = alpha
self.scaling = scaling
self.sample_fraction = sample_fraction
self.n_resampling = n_resampling
self.fit_intercept = fit_intercept
self.max_iter = max_iter
self.verbose = verbose
self.normalize = normalize
self.precompute = precompute
self.eps = eps
self.random_state = random_state
self.n_jobs = n_jobs
self.selection_threshold = selection_threshold
self.pre_dispatch = pre_dispatch
self.memory = memory
def _make_estimator_and_params(self, X, y):
assert self.precompute in (True, False, None, 'auto')
alpha = self.alpha
if isinstance(alpha, six.string_types) and alpha in ('aic', 'bic'):
model = LassoLarsIC(precompute=self.precompute,
criterion=self.alpha,
max_iter=self.max_iter,
eps=self.eps)
model.fit(X, y)
self.alpha_ = alpha = model.alpha_
return _randomized_lasso, dict(alpha=alpha, max_iter=self.max_iter,
eps=self.eps,
precompute=self.precompute)
###############################################################################
# Randomized logistic: classification settings
def _randomized_logistic(X, y, weights, mask, C=1., verbose=False,
fit_intercept=True, tol=1e-3):
X = X[safe_mask(X, mask)]
y = y[mask]
if issparse(X):
size = len(weights)
weight_dia = sparse.dia_matrix((1 - weights, 0), (size, size))
X = X * weight_dia
else:
X *= (1 - weights)
C = np.atleast_1d(np.asarray(C, dtype=np.float64))
scores = np.zeros((X.shape[1], len(C)), dtype=np.bool)
for this_C, this_scores in zip(C, scores.T):
# XXX : would be great to do it with a warm_start ...
clf = LogisticRegression(C=this_C, tol=tol, penalty='l1', dual=False,
fit_intercept=fit_intercept)
clf.fit(X, y)
this_scores[:] = np.any(
np.abs(clf.coef_) > 10 * np.finfo(np.float).eps, axis=0)
return scores
class RandomizedLogisticRegression(BaseRandomizedLinearModel):
"""Randomized Logistic Regression
Randomized Regression works by resampling the train data and computing
a LogisticRegression on each resampling. In short, the features selected
more often are good features. It is also known as stability selection.
Read more in the :ref:`User Guide <randomized_l1>`.
Parameters
----------
C : float, optional, default=1
The regularization parameter C in the LogisticRegression.
scaling : float, optional, default=0.5
The alpha parameter in the stability selection article used to
randomly scale the features. Should be between 0 and 1.
sample_fraction : float, optional, default=0.75
The fraction of samples to be used in each randomized design.
Should be between 0 and 1. If 1, all samples are used.
n_resampling : int, optional, default=200
Number of randomized models.
selection_threshold : float, optional, default=0.25
The score above which features should be selected.
fit_intercept : boolean, optional, default=True
whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).
verbose : boolean or integer, optional
Sets the verbosity amount
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
This parameter is ignored when `fit_intercept` is set to False.
When the regressors are normalized, note that this makes the
hyperparameters learnt more robust and almost independent of the number
of samples. The same property is not valid for standardized data.
However, if you wish to standardize, please use
`preprocessing.StandardScaler` before calling `fit` on an estimator
with `normalize=False`.
tol : float, optional, default=1e-3
tolerance for stopping criteria of LogisticRegression
n_jobs : integer, optional
Number of CPUs to use during the resampling. If '-1', use
all the CPUs
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel
execution. Reducing this number can be useful to avoid an
explosion of memory consumption when more jobs get dispatched
than CPUs can process. This parameter can be:
- None, in which case all the jobs are immediately
created and spawned. Use this for lightweight and
fast-running jobs, to avoid delays due to on-demand
spawning of the jobs
- An int, giving the exact number of total jobs that are
spawned
- A string, giving an expression as a function of n_jobs,
as in '2*n_jobs'
memory : Instance of joblib.Memory or string
Used for internal caching. By default, no caching is done.
If a string is given, it is the path to the caching directory.
Attributes
----------
scores_ : array, shape = [n_features]
Feature scores between 0 and 1.
all_scores_ : array, shape = [n_features, n_reg_parameter]
Feature scores between 0 and 1 for all values of the regularization \
parameter. The reference article suggests ``scores_`` is the max \
of ``all_scores_``.
Examples
--------
>>> from sklearn.linear_model import RandomizedLogisticRegression
>>> randomized_logistic = RandomizedLogisticRegression()
Notes
-----
See examples/linear_model/plot_sparse_recovery.py for an example.
References
----------
Stability selection
Nicolai Meinshausen, Peter Buhlmann
Journal of the Royal Statistical Society: Series B
Volume 72, Issue 4, pages 417-473, September 2010
DOI: 10.1111/j.1467-9868.2010.00740.x
See also
--------
RandomizedLasso, Lasso, ElasticNet
"""
def __init__(self, C=1, scaling=.5, sample_fraction=.75,
n_resampling=200,
selection_threshold=.25, tol=1e-3,
fit_intercept=True, verbose=False,
normalize=True,
random_state=None,
n_jobs=1, pre_dispatch='3*n_jobs',
memory=Memory(cachedir=None, verbose=0)):
self.C = C
self.scaling = scaling
self.sample_fraction = sample_fraction
self.n_resampling = n_resampling
self.fit_intercept = fit_intercept
self.verbose = verbose
self.normalize = normalize
self.tol = tol
self.random_state = random_state
self.n_jobs = n_jobs
self.selection_threshold = selection_threshold
self.pre_dispatch = pre_dispatch
self.memory = memory
def _make_estimator_and_params(self, X, y):
params = dict(C=self.C, tol=self.tol,
fit_intercept=self.fit_intercept)
return _randomized_logistic, params
def _preprocess_data(self, X, y, fit_intercept, normalize=False):
"""Center the data in X but not in y"""
X, _, X_offset, _, X_scale = _preprocess_data(X, y, fit_intercept,
normalize=normalize)
return X, y, X_offset, y, X_scale
###############################################################################
# Stability paths
def _lasso_stability_path(X, y, mask, weights, eps):
"Inner loop of lasso_stability_path"
X = X * weights[np.newaxis, :]
X = X[safe_mask(X, mask), :]
y = y[mask]
alpha_max = np.max(np.abs(np.dot(X.T, y))) / X.shape[0]
alpha_min = eps * alpha_max # set for early stopping in path
with warnings.catch_warnings():
warnings.simplefilter('ignore', ConvergenceWarning)
alphas, _, coefs = lars_path(X, y, method='lasso', verbose=False,
alpha_min=alpha_min)
# Scale alpha by alpha_max
alphas /= alphas[0]
# Sort alphas in assending order
alphas = alphas[::-1]
coefs = coefs[:, ::-1]
# Get rid of the alphas that are too small
mask = alphas >= eps
# We also want to keep the first one: it should be close to the OLS
# solution
mask[0] = True
alphas = alphas[mask]
coefs = coefs[:, mask]
return alphas, coefs
def lasso_stability_path(X, y, scaling=0.5, random_state=None,
n_resampling=200, n_grid=100,
sample_fraction=0.75,
eps=4 * np.finfo(np.float).eps, n_jobs=1,
verbose=False):
"""Stabiliy path based on randomized Lasso estimates
Read more in the :ref:`User Guide <randomized_l1>`.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
training data.
y : array-like, shape = [n_samples]
target values.
scaling : float, optional, default=0.5
The alpha parameter in the stability selection article used to
randomly scale the features. Should be between 0 and 1.
random_state : integer or numpy.random.RandomState, optional
The generator used to randomize the design.
n_resampling : int, optional, default=200
Number of randomized models.
n_grid : int, optional, default=100
Number of grid points. The path is linearly reinterpolated
on a grid between 0 and 1 before computing the scores.
sample_fraction : float, optional, default=0.75
The fraction of samples to be used in each randomized design.
Should be between 0 and 1. If 1, all samples are used.
eps : float, optional
Smallest value of alpha / alpha_max considered
n_jobs : integer, optional
Number of CPUs to use during the resampling. If '-1', use
all the CPUs
verbose : boolean or integer, optional
Sets the verbosity amount
Returns
-------
alphas_grid : array, shape ~ [n_grid]
The grid points between 0 and 1: alpha/alpha_max
scores_path : array, shape = [n_features, n_grid]
The scores for each feature along the path.
Notes
-----
See examples/linear_model/plot_sparse_recovery.py for an example.
"""
rng = check_random_state(random_state)
if not (0 < scaling < 1):
raise ValueError("Parameter 'scaling' should be between 0 and 1."
" Got %r instead." % scaling)
n_samples, n_features = X.shape
paths = Parallel(n_jobs=n_jobs, verbose=verbose)(
delayed(_lasso_stability_path)(
X, y, mask=rng.rand(n_samples) < sample_fraction,
weights=1. - scaling * rng.random_integers(0, 1,
size=(n_features,)),
eps=eps)
for k in range(n_resampling))
all_alphas = sorted(list(set(itertools.chain(*[p[0] for p in paths]))))
# Take approximately n_grid values
stride = int(max(1, int(len(all_alphas) / float(n_grid))))
all_alphas = all_alphas[::stride]
if not all_alphas[-1] == 1:
all_alphas.append(1.)
all_alphas = np.array(all_alphas)
scores_path = np.zeros((n_features, len(all_alphas)))
for alphas, coefs in paths:
if alphas[0] != 0:
alphas = np.r_[0, alphas]
coefs = np.c_[np.ones((n_features, 1)), coefs]
if alphas[-1] != all_alphas[-1]:
alphas = np.r_[alphas, all_alphas[-1]]
coefs = np.c_[coefs, np.zeros((n_features, 1))]
scores_path += (interp1d(alphas, coefs,
kind='nearest', bounds_error=False,
fill_value=0, axis=-1)(all_alphas) != 0)
scores_path /= n_resampling
return all_alphas, scores_path
| bsd-3-clause |
sarahgrogan/scikit-learn | sklearn/metrics/cluster/__init__.py | 312 | 1322 | """
The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
cluster analysis results. There are two forms of evaluation:
- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the 'quality' of the model itself.
"""
from .supervised import adjusted_mutual_info_score
from .supervised import normalized_mutual_info_score
from .supervised import adjusted_rand_score
from .supervised import completeness_score
from .supervised import contingency_matrix
from .supervised import expected_mutual_information
from .supervised import homogeneity_completeness_v_measure
from .supervised import homogeneity_score
from .supervised import mutual_info_score
from .supervised import v_measure_score
from .supervised import entropy
from .unsupervised import silhouette_samples
from .unsupervised import silhouette_score
from .bicluster import consensus_score
__all__ = ["adjusted_mutual_info_score", "normalized_mutual_info_score",
"adjusted_rand_score", "completeness_score", "contingency_matrix",
"expected_mutual_information", "homogeneity_completeness_v_measure",
"homogeneity_score", "mutual_info_score", "v_measure_score",
"entropy", "silhouette_samples", "silhouette_score",
"consensus_score"]
| bsd-3-clause |
MTgeophysics/mtpy | mtpy/modeling/modem/plot_rms_maps.py | 1 | 34831 | """
==================
ModEM
==================
# Generate files for ModEM
# revised by JP 2017
# revised by AK 2017 to bring across functionality from ak branch
Revision History:
brenainn.moushall@ga.gov.au 31-03-2020 13:38:10 AEDT:
- Add ability to plot on background geotiff
- Add ability to write RMS map as shapefile
- Add 'plot_elements' attribute for selecting whether to plot
impedance, tippers or both
- Allow selection of period by providing period in seconds
"""
import os
import logging
import numpy as np
import geopandas as gpd
from shapely.geometry import Point, Polygon
from matplotlib import colors as colors, pyplot as plt, colorbar as mcb, cm
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
from mtpy.utils import basemap_tools
from mtpy.utils.plot_geotiff_imshow import plot_geotiff_on_axes
from mtpy.utils.mtpylog import MtPyLog
from mtpy.utils.gis_tools import epsg_project
from mtpy.modeling.modem import Data, Residual
__all__ = ['PlotRMSMaps']
_logger = MtPyLog.get_mtpy_logger(__name__)
class PlotRMSMaps(object):
"""
plots the RMS as (data-model)/(error) in map view for all components
of the data file. Gets this infomration from the .res file output
by ModEM.
Arguments:
------------------
**residual_fn** : string
full path to .res file
=================== =======================================================
Attributes Description
=================== =======================================================
fig matplotlib.figure instance for a single plot
fig_dpi dots-per-inch resolution of figure *default* is 200
fig_num number of fig instance *default* is 1
fig_size size of figure in inches [width, height]
*default* is [7,6]
font_size font size of tick labels, axis labels are +2
*default* is 8
marker marker style for station rms,
see matplotlib.line for options,
*default* is 's' --> square
marker_size size of marker in points. *default* is 10
pad_x padding in map units from edge of the axis to stations
at the extremeties in longitude.
*default* is 1/2 tick_locator
pad_y padding in map units from edge of the axis to stations
at the extremeties in latitude.
*default* is 1/2 tick_locator
period_index index of the period you want to plot according to
self.residual.period_list. *default* is 1
plot_yn [ 'y' | 'n' ] default is 'y' to plot on instantiation
plot_z_list internal variable for plotting
residual modem.Data instance that holds all the information
from the residual_fn given
residual_fn full path to .res file
rms_cmap matplotlib.cm object for coloring the markers
rms_cmap_dict dictionary of color values for rms_cmap
rms_max maximum rms to plot. *default* is 5.0
rms_min minimum rms to plot. *default* is 1.0
save_path path to save figures to. *default* is directory of
residual_fn
subplot_bottom spacing from axis to bottom of figure canvas.
*default* is .1
subplot_hspace horizontal spacing between subplots.
*default* is .1
subplot_left spacing from axis to left of figure canvas.
*default* is .1
subplot_right spacing from axis to right of figure canvas.
*default* is .9
subplot_top spacing from axis to top of figure canvas.
*default* is .95
subplot_vspace vertical spacing between subplots.
*default* is .01
tick_locator increment for x and y major ticks. *default* is
limits/5
bimg path to a geotiff to display as background of
plotted maps
bimg_band band of bimg to plot. *default* is None, which
will plot all available bands
bimg_cmap cmap for bimg. *default* is 'viridis'. Ignored
if bimg is RBG/A
=================== =======================================================
=================== =======================================================
Methods Description
=================== =======================================================
plot plot rms maps for a single period
plot_loop loop over all frequencies and save figures to save_path
read_residual_fn read in residual_fn
redraw_plot after updating attributes call redraw_plot to
well redraw the plot
save_figure save the figure to a file
=================== =======================================================
:Example: ::
>>> import mtpy.modeling.modem as modem
>>> rms_plot = PlotRMSMaps(r"/home/ModEM/Inv1/mb_NLCG_030.res")
>>> # change some attributes
>>> rms_plot.fig_size = [6, 4]
>>> rms_plot.rms_max = 3
>>> rms_plot.redraw_plot()
>>> # happy with the look now loop over all periods
>>> rms_plot.plot_loop()
"""
def __init__(self, residual_fn, **kwargs):
self._residual_fn = None
self.residual = None
self.residual_fn = residual_fn
self.model_epsg = kwargs.pop('model_epsg', None)
self.read_residual_fn()
self.save_path = kwargs.pop('save_path', os.path.dirname(self.residual_fn))
self.period = kwargs.pop('period', None)
if self.period is not None:
# Get period index closest to provided period
index = np.argmin(np.fabs(self.residual.period_list - self.period))
_logger.info("Plotting nearest available period ({}s) for selected period ({}s)"
.format(self.residual.period_list[index], self.period))
self.period_index = index
else:
self.period_index = kwargs.pop('period_index', 0)
self.plot_elements = kwargs.pop('plot_elements', 'both')
self.subplot_left = kwargs.pop('subplot_left', .1)
self.subplot_right = kwargs.pop('subplot_right', .9)
self.subplot_top = kwargs.pop('subplot_top', .95)
self.subplot_bottom = kwargs.pop('subplot_bottom', .1)
self.subplot_hspace = kwargs.pop('subplot_hspace', .1)
self.subplot_vspace = kwargs.pop('subplot_vspace', .01)
self.font_size = kwargs.pop('font_size', 8)
self.fig = None
self.fig_size = kwargs.pop('fig_size', [7.75, 6.75])
self.fig_dpi = kwargs.pop('fig_dpi', 200)
self.fig_num = kwargs.pop('fig_num', 1)
self.font_dict = {'size': self.font_size + 2, 'weight': 'bold'}
self.marker = kwargs.pop('marker', 's')
self.marker_size = kwargs.pop('marker_size', 10)
self.rms_max = kwargs.pop('rms_max', 5)
self.rms_min = kwargs.pop('rms_min', 0)
self.tick_locator = kwargs.pop('tick_locator', None)
self.pad_x = kwargs.pop('pad_x', None)
self.pad_y = kwargs.pop('pad_y', None)
self.plot_yn = kwargs.pop('plot_yn', 'y')
self.bimg = kwargs.pop('bimg', None)
if self.bimg and self.model_epsg is None:
_logger.warning("You have provided a geotiff as a background image but model_epsg is "
"not set. It's assumed that the CRS of the model and the CRS of the "
"geotiff are the same. If this is not the case, please provide "
"model_epsg to PlotRMSMaps.")
self.bimg_band = kwargs.pop('bimg_band', None)
self.bimg_cmap = kwargs.pop('bimg_cmap', 'viridis')
# colormap for rms, goes white to black from 0 to rms max and
# red below 1 to show where the data is being over fit
self.rms_cmap_dict = {'red': ((0.0, 1.0, 1.0),
(0.2, 1.0, 1.0),
(1.0, 0.0, 0.0)),
'green': ((0.0, 0.0, 0.0),
(0.2, 1.0, 1.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 0.0),
(0.2, 1.0, 1.0),
(1.0, 0.0, 0.0))}
self.rms_cmap = None
if 'rms_cmap' in list(kwargs.keys()):
# check if it is a valid matplotlib color stretch
if kwargs['rms_cmap'] in dir(cm):
self.rms_cmap = cm.get_cmap(kwargs['rms_cmap'])
else:
print("provided rms_cmap invalid, using default colormap")
if self.rms_cmap is None:
self.rms_cmap = colors.LinearSegmentedColormap('rms_cmap',
self.rms_cmap_dict,
256)
if self.plot_elements == 'both':
self.plot_z_list = [{'label': r'$Z_{xx}$', 'index': (0, 0), 'plot_num': 1},
{'label': r'$Z_{xy}$', 'index': (0, 1), 'plot_num': 2},
{'label': r'$Z_{yx}$', 'index': (1, 0), 'plot_num': 3},
{'label': r'$Z_{yy}$', 'index': (1, 1), 'plot_num': 4},
{'label': r'$T_{x}$', 'index': (0, 0), 'plot_num': 5},
{'label': r'$T_{y}$', 'index': (0, 1), 'plot_num': 6}]
elif self.plot_elements == 'impedance':
self.plot_z_list = [{'label': r'$Z_{xx}$', 'index': (0, 0), 'plot_num': 1},
{'label': r'$Z_{xy}$', 'index': (0, 1), 'plot_num': 2},
{'label': r'$Z_{yx}$', 'index': (1, 0), 'plot_num': 3},
{'label': r'$Z_{yy}$', 'index': (1, 1), 'plot_num': 4}]
elif self.plot_elements == 'tippers':
self.plot_z_list = [{'label': r'$T_{x}$', 'index': (0, 0), 'plot_num': 1},
{'label': r'$T_{y}$', 'index': (0, 1), 'plot_num': 2}]
else:
raise ValueError("'plot_elements' value '{}' is not recognised. Please set "
"'plot_elements' to 'impedance', 'tippers' or 'both'.")
if self.plot_yn == 'y':
self.plot()
def _fig_title(self, font_size, font_weight):
if self.period_index == 'all':
title = 'All periods'
else:
title = 'period = {0:.5g} (s)'.format(self.residual.period_list[self.period_index])
self.fig.suptitle(title, fontdict={'size': font_size, 'weight': font_weight})
def _calculate_rms(self, plot_dict):
ii = plot_dict['index'][0]
jj = plot_dict['index'][1]
rms = np.zeros(self.residual.residual_array.shape[0])
self.residual.get_rms()
if plot_dict['label'].startswith('$Z'):
if self.period_index == 'all':
rms = self.residual.rms_array['rms_z_component'][:, ii, jj]
else:
rms = self.residual.rms_array['rms_z_component_period'][:, self.period_index, ii, jj]
elif plot_dict['label'].startswith('$T'):
if self.period_index == 'all':
rms = self.residual.rms_array['rms_tip_component'][:, ii, jj]
else:
rms = self.residual.rms_array['rms_tip_component_period'][:, self.period_index, ii, jj]
filt = np.nan_to_num(rms).astype(bool)
# for ridx in range(len(self.residual.residual_array)):
# if self.period_index == 'all':
# r_arr = self.residual.rms_array[ridx]
# if plot_dict['label'].startswith('$Z'):
# rms[ridx] = r_arr['rms_z']
# else:
# rms[ridx] = r_arr['rms_tip']
# else:
# r_arr = self.residual.residual_array[ridx]
# # calulate the rms self.residual/error
# if plot_dict['label'].startswith('$Z'):
# rms[ridx] = r_arr['z'][self.period_index, ii, jj].__abs__() / \
# r_arr['z_err'][self.period_index, ii, jj].real
# else:
# rms[ridx] = r_arr['tip'][self.period_index, ii, jj].__abs__() / \
# r_arr['tip_err'][self.period_index, ii, jj].real
filt = np.nan_to_num(rms).astype(bool)
if len(rms[filt]) == 0:
_logger.warning("No RMS available for component {}"
.format(self._normalize_label(plot_dict['label'])))
return rms, filt
@staticmethod
def _normalize_label(label):
return label.replace('$', '').replace('{', '').replace('}', '').replace('_', '')
def read_residual_fn(self):
if self.residual is None:
self.residual = Residual(residual_fn=self.residual_fn,
model_epsg=self.model_epsg)
self.residual.read_residual_file()
self.residual.get_rms()
else:
pass
def create_shapefiles(self, dst_epsg, save_path=None):
"""
Creates RMS map elements as shapefiles which can displayed in a
GIS viewer. Intended to be called as part of the 'plot'
function.
The points to plot defined by `lons` and `lats` are the centre
of the rectangular markers.
If `model_epsg` hasn't been set on class, then 4326 is assumed.
Parameters
----------
dst_epsg : int
EPSG code of the CRS that Shapefiles will be projected to.
Make this the same as the CRS of the geotiff you intend to
display on.
marker_width : float
Radius of the circular markers. Units are defined by
`model_epsg`.
"""
if save_path is None:
save_path = self.save_path
lon = self.residual.residual_array['lon']
lat = self.residual.residual_array['lat']
if self.model_epsg is None:
_logger.warning("model_epsg has not been provided. Model EPSG is assumed to be 4326. "
"If this is not correct, please provide model_epsg to PlotRMSMaps. "
"Otherwise, shapefiles may have projection errors.")
src_epsg = 4326
else:
src_epsg = self.model_epsg
src_epsg = {'init': 'epsg:{}'.format(src_epsg)}
for p_dict in self.plot_z_list:
rms, _ = self._calculate_rms(p_dict)
markers = []
for x, y in zip(lon, lat):
markers.append(Point(x, y))
df = gpd.GeoDataFrame({'lon': lon, 'lat': lat, 'rms': rms},
crs=src_epsg, geometry=markers)
df.to_crs(epsg=dst_epsg, inplace=True)
if self.period_index == 'all':
period = 'all'
else:
period = self.residual.period_list[self.period_index]
filename = '{}_EPSG_{}_Period_{}.shp'.format(self._normalize_label(p_dict['label']),
dst_epsg, period)
directory = os.path.join(self.save_path, 'shapefiles_for_period_{}s'.format(period))
if not os.path.exists(directory):
os.mkdir(directory)
outpath = os.path.join(directory, filename)
df.to_file(outpath, driver='ESRI Shapefile')
print("Saved shapefiles to %s", outpath)
def plot(self):
"""
plot rms in map view
"""
if self.tick_locator is None:
x_locator = np.round((self.residual.residual_array['lon'].max() -
self.residual.residual_array['lon'].min()) / 5, 2)
y_locator = np.round((self.residual.residual_array['lat'].max() -
self.residual.residual_array['lat'].min()) / 5, 2)
if x_locator > y_locator:
self.tick_locator = x_locator
elif x_locator <= y_locator:
self.tick_locator = y_locator
if self.pad_x is None:
self.pad_x = self.tick_locator / 2
if self.pad_y is None:
self.pad_y = self.tick_locator / 2
# Get number of rows based on what is being plotted.
sp_rows, sp_cols = len(self.plot_z_list) / 2, 2
# Adjust dimensions based on number of rows.
# Hardcoded - having issues getting the right spacing between
# labels and subplots.
if sp_rows == 1:
self.fig_size[1] = 3.
elif sp_rows == 2:
self.fig_size[1] = 5.6
plt.rcParams['font.size'] = self.font_size
plt.rcParams['figure.subplot.left'] = self.subplot_left
plt.rcParams['figure.subplot.right'] = self.subplot_right
plt.rcParams['figure.subplot.bottom'] = self.subplot_bottom
plt.rcParams['figure.subplot.top'] = self.subplot_top
plt.rcParams['figure.subplot.wspace'] = self.subplot_hspace
plt.rcParams['figure.subplot.hspace'] = self.subplot_vspace
self.fig = plt.figure(self.fig_num, self.fig_size, dpi=self.fig_dpi)
lon = self.residual.residual_array['lon']
lat = self.residual.residual_array['lat']
for p_dict in self.plot_z_list:
rms, filt = self._calculate_rms(p_dict)
ax = self.fig.add_subplot(sp_rows, sp_cols,
p_dict['plot_num'],
aspect='equal')
plt.scatter(lon[filt],
lat[filt],
c=rms[filt],
marker=self.marker,
edgecolors=(0, 0, 0),
cmap=self.rms_cmap,
norm=colors.Normalize(vmin=self.rms_min,
vmax=self.rms_max),
)
if not np.all(filt):
filt2 = (1 - filt).astype(bool)
plt.plot(lon[filt2],
lat[filt2],
'.',
ms=0.1,
mec=(0, 0, 0),
mfc=(1, 1, 1)
)
# Hide y-ticks on subplots in column 2.
if p_dict['plot_num'] in (2, 4, 6):
plt.setp(ax.get_yticklabels(), visible=False)
else:
ax.set_ylabel('Latitude (deg)', fontdict=self.font_dict)
# Only show x-ticks in final row.
if p_dict['plot_num'] in (sp_rows * 2 - 1, sp_rows * 2):
ax.set_xlabel('Longitude (deg)', fontdict=self.font_dict)
else:
plt.setp(ax.get_xticklabels(), visible=False)
ax.text(self.residual.residual_array['lon'].min() + .005 - self.pad_x,
self.residual.residual_array['lat'].max() - .005 + self.pad_y,
p_dict['label'],
verticalalignment='top',
horizontalalignment='left',
bbox={'facecolor': 'white'},
zorder=3)
ax.tick_params(direction='out')
ax.grid(zorder=0, color=(.75, .75, .75))
ax.set_xlim(self.residual.residual_array['lon'].min() - self.pad_x,
self.residual.residual_array['lon'].max() + self.pad_x)
ax.set_ylim(self.residual.residual_array['lat'].min() - self.pad_y,
self.residual.residual_array['lat'].max() + self.pad_y)
if self.bimg:
plot_geotiff_on_axes(self.bimg, ax, epsg_code=self.model_epsg,
band_number=self.bimg_band, cmap=self.bimg_cmap)
ax.xaxis.set_major_locator(MultipleLocator(self.tick_locator))
ax.yaxis.set_major_locator(MultipleLocator(self.tick_locator))
ax.xaxis.set_major_formatter(FormatStrFormatter('%2.2f'))
ax.yaxis.set_major_formatter(FormatStrFormatter('%2.2f'))
cb_ax = self.fig.add_axes([self.subplot_right + .02, .225, .02, .45])
color_bar = mcb.ColorbarBase(cb_ax,
cmap=self.rms_cmap,
norm=colors.Normalize(vmin=self.rms_min,
vmax=self.rms_max),
orientation='vertical')
color_bar.set_label('RMS', fontdict=self.font_dict)
self._fig_title(font_size=self.font_size + 3, font_weight='bold')
self.fig.show()
# BM: Is this still in use? `Residual` has no attribute `data_array`
# which breaks this function.
def plot_map(self):
"""
plot the misfit as a map instead of points
"""
rms_1 = 1. / self.rms_max
if self.tick_locator is None:
x_locator = np.round((self.residual.data_array['lon'].max() -
self.residual.data_array['lon'].min()) / 5, 2)
y_locator = np.round((self.residual.data_array['lat'].max() -
self.residual.data_array['lat'].min()) / 5, 2)
if x_locator > y_locator:
self.tick_locator = x_locator
elif x_locator < y_locator:
self.tick_locator = y_locator
if self.pad_x is None:
self.pad_x = self.tick_locator / 2
if self.pad_y is None:
self.pad_y = self.tick_locator / 2
plt.rcParams['font.size'] = self.font_size
plt.rcParams['figure.subplot.left'] = self.subplot_left
plt.rcParams['figure.subplot.right'] = self.subplot_right
plt.rcParams['figure.subplot.bottom'] = self.subplot_bottom
plt.rcParams['figure.subplot.top'] = self.subplot_top
plt.rcParams['figure.subplot.wspace'] = self.subplot_hspace
plt.rcParams['figure.subplot.hspace'] = self.subplot_vspace
self.fig = plt.figure(self.fig_num, self.fig_size, dpi=self.fig_dpi)
lat_arr = self.residual.data_array['lat']
lon_arr = self.residual.data_array['lon']
data_points = np.array([lon_arr, lat_arr])
interp_lat = np.linspace(lat_arr.min(),
lat_arr.max(),
3 * self.residual.data_array.size)
interp_lon = np.linspace(lon_arr.min(),
lon_arr.max(),
3 * self.residual.data_array.size)
grid_x, grid_y = np.meshgrid(interp_lon, interp_lat)
# calculate rms
z_err = self.residual.data_array['z_err'].copy()
z_err[np.where(z_err == 0.0)] = 1.0
z_rms = np.abs(self.residual.data_array['z']) / z_err.real
t_err = self.residual.data_array['tip_err'].copy()
t_err[np.where(t_err == 0.0)] = 1.0
t_rms = np.abs(self.residual.data_array['tip']) / t_err.real
# --> plot maps
for p_dict in self.plot_z_list:
ax = self.fig.add_subplot(3, 2, p_dict['plot_num'], aspect='equal')
if p_dict['plot_num'] == 1 or p_dict['plot_num'] == 3:
ax.set_ylabel('Latitude (deg)', fontdict=self.font_dict)
plt.setp(ax.get_xticklabels(), visible=False)
elif p_dict['plot_num'] == 2 or p_dict['plot_num'] == 4:
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
elif p_dict['plot_num'] == 6:
plt.setp(ax.get_yticklabels(), visible=False)
ax.set_xlabel('Longitude (deg)', fontdict=self.font_dict)
else:
ax.set_xlabel('Longitude (deg)', fontdict=self.font_dict)
ax.set_ylabel('Latitude (deg)', fontdict=self.font_dict)
ax.text(self.residual.data_array['lon'].min() + .005 - self.pad_x,
self.residual.data_array['lat'].max() - .005 + self.pad_y,
p_dict['label'],
verticalalignment='top',
horizontalalignment='left',
bbox={'facecolor': 'white'},
zorder=3)
ax.tick_params(direction='out')
ax.grid(zorder=0, color=(.75, .75, .75), lw=.75)
# [line.set_zorder(3) for line in ax.lines]
ax.set_xlim(self.residual.data_array['lon'].min() - self.pad_x,
self.residual.data_array['lon'].max() + self.pad_x)
ax.set_ylim(self.residual.data_array['lat'].min() - self.pad_y,
self.residual.data_array['lat'].max() + self.pad_y)
ax.xaxis.set_major_locator(MultipleLocator(self.tick_locator))
ax.yaxis.set_major_locator(MultipleLocator(self.tick_locator))
ax.xaxis.set_major_formatter(FormatStrFormatter('%2.2f'))
ax.yaxis.set_major_formatter(FormatStrFormatter('%2.2f'))
# -----------------------------
ii = p_dict['index'][0]
jj = p_dict['index'][1]
# calulate the rms self.residual/error
if p_dict['plot_num'] < 5:
rms = z_rms[:, self.period_index, ii, jj]
else:
rms = t_rms[:, self.period_index, ii, jj]
# check for non zeros
nz = np.nonzero(rms)
data_points = np.array([lon_arr[nz], lat_arr[nz]])
rms = rms[nz]
if len(rms) < 5:
continue
# interpolate onto a grid
rms_map = interpolate.griddata(data_points.T,
rms,
(grid_x, grid_y),
method='cubic')
# plot the grid
im = ax.pcolormesh(grid_x,
grid_y,
rms_map,
cmap=self.rms_map_cmap,
vmin=self.rms_min,
vmax=self.rms_max,
zorder=3)
ax.grid(zorder=0, color=(.75, .75, .75), lw=.75)
# cb_ax = mcb.make_axes(ax, orientation='vertical', fraction=.1)
cb_ax = self.fig.add_axes([self.subplot_right + .02, .225, .02, .45])
color_bar = mcb.ColorbarBase(cb_ax,
cmap=self.rms_map_cmap,
norm=colors.Normalize(vmin=self.rms_min,
vmax=self.rms_max),
orientation='vertical')
color_bar.set_label('RMS', fontdict=self.font_dict)
self._fig_title(font_size=self.font_size + 3, font_weight='bold')
self.fig.show()
def basemap_plot(self, datatype='all', tick_interval=None, save=False,
savepath=None, new_figure=True, mesh_rotation_angle=0.,
show_topography=False, **basemap_kwargs):
"""
plot RMS misfit on a basemap using basemap modules in matplotlib
:param datatype: type of data to plot misfit for, either 'z', 'tip', or
'all' to plot overall RMS
:param tick_interval: tick interval on map in degrees, if None it is
calculated from the data extent
:param save: True/False, whether or not to save and close figure
:param savepath: full path of file to save to, if None, saves to
self.save_path
:new_figure: True/False, whether or not to initiate a new figure for
the plot
:param mesh_rotation_angle: rotation angle of mesh, in degrees
clockwise from north
:param show_topography: True/False, option to show the topograpy in the
background
:param **basemap_kwargs: provide any valid arguments to Basemap
instance (e.g. projection etc - see
https://basemaptutorial.readthedocs.io/en/latest/basemap.html)
and these will be passed to the map.
"""
if self.model_epsg is None:
print("No projection information provided, please provide the model epsg code relevant to your model")
return
if new_figure:
self.fig = plt.figure()
# rotate stations
if mesh_rotation_angle != 0:
if hasattr(self, 'mesh_rotation_angle'):
angle_to_rotate = self.mesh_rotation_angle - mesh_rotation_angle
else:
angle_to_rotate = -mesh_rotation_angle
self.mesh_rotation_angle = mesh_rotation_angle
self.residual.station_locations.rotate_stations(angle_to_rotate)
# get relative locations
seast, snorth = self.residual.station_locations.rel_east + self.residual.station_locations.center_point['east'],\
self.residual.station_locations.rel_north + self.residual.station_locations.center_point['north']
# project station location eastings and northings to lat/long
slon, slat = epsg_project(seast, snorth, self.model_epsg, 4326)
self.residual.station_locations.station_locations['lon'] = slon
self.residual.station_locations.station_locations['lat'] = slat
# initialise a basemap with extents, projection etc calculated from data
# if not provided in basemap_kwargs # BM: todo?
self.bm = basemap_tools.initialise_basemap(self.residual.station_locations, **basemap_kwargs)
basemap_tools.add_basemap_frame(self.bm, tick_interval=tick_interval)
# project to basemap coordinates
sx, sy = self.bm(slon, slat)
# make scatter plot
if datatype == 'all':
if self.period_index == 'all':
rms = self.residual.rms_array['rms']
else:
rms = self.residual.rms_array['rms_period'][:, self.period_index]
elif datatype in ['z', 'tip']:
if self.period_index == 'all':
rms = self.residual.rms_array['rms_{}'.format(datatype)]
else:
rms = self.residual.rms_array['rms_{}_period'.format(datatype)][:, self.period_index]
filt = np.nan_to_num(rms).astype(bool)
self.bm.scatter(sx[filt], sy[filt],
c=rms[filt],
marker=self.marker,
edgecolors=(0, 0, 0),
cmap=self.rms_cmap,
norm=colors.Normalize(vmin=self.rms_min,
vmax=self.rms_max)
)
if not np.all(filt):
filt2 = (1 - filt).astype(bool)
self.bm.plot(sx[filt2], sy[filt2], 'k.')
color_bar = plt.colorbar(cmap=self.rms_cmap,
shrink=0.6,
norm=colors.Normalize(vmin=self.rms_min,
vmax=self.rms_max),
orientation='vertical')
color_bar.set_label('RMS')
title_dict = {'all': 'Z + Tipper', 'z': 'Z', 'tip': 'Tipper'}
if self.period_index == 'all':
plt.title('RMS misfit over all periods for ' + title_dict[datatype])
else:
plt.title('RMS misfit for period = {0:.5g} (s)'.format(self.residual.period_list[self.period_index]))
def redraw_plot(self):
plt.close(self.fig)
self.plot()
def save_figure(self, save_path=None, save_fn_basename=None,
save_fig_dpi=None, fig_format='png', fig_close=True):
"""
save figure in the desired format
"""
if save_path is not None:
self.save_path = save_path
if save_fn_basename is not None:
pass
else:
if self.period_index == 'all':
save_fn_basename = 'RMS_AllPeriods.{}'.format(fig_format)
else:
save_fn_basename = '{0:02}_RMS_{1:.5g}_s.{2}'.format(self.period_index,
self.residual.period_list[self.period_index],
fig_format)
save_fn = os.path.join(self.save_path, save_fn_basename)
if save_fig_dpi is not None:
self.fig_dpi = save_fig_dpi
self.fig.savefig(save_fn, dpi=self.fig_dpi)
print('saved file to {0}'.format(save_fn))
if fig_close:
plt.close(self.fig)
def plot_loop(self, fig_format='png', style='point'):
"""
loop over all periods and save figures accordingly
:param: style [ 'point' | 'map' ]
"""
for f_index in range(self.residual.period_list.size):
self.period_index = f_index
if style == 'point':
self.plot()
self.save_figure(fig_format=fig_format)
elif style == 'map':
self.plot_map()
self.save_figure(fig_format=fig_format)
# ==================================================================================
# FZ: add example usage code
# Justdo> python mtpy/modeling/modem/plot_rms_maps.py
# ==================================================================================
if __name__ == "__main__":
from mtpy.mtpy_globals import *
# directory where files are located
wd = os.path.join(SAMPLE_DIR, 'ModEM')
# file stem for inversion result
filestem = 'Modular_MPI_NLCG_004'
# directory to save to
save_path = NEW_TEMP_DIR
# period index to plot (0 plots the first (shortest) period, 1 for the second, etc)
period_index = 0
# plot map
rmsmap = PlotRMSMaps(residual_fn=os.path.join(wd, filestem + '.res'), period_index=period_index,
xminorticks=50000, yminorticks=50000, save_plots='y', plot_yn='n')
rmsmap.plot()
rmsmap.save_figure(save_path, fig_close=False) # this will save a file to
| gpl-3.0 |
CopyChat/Plotting | Python/TestCode/sankey_demo_rankine.py | 7 | 3810 | """Demonstrate the Sankey class with a practicle example of a Rankine power cycle.
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.sankey import Sankey
fig = plt.figure(figsize=(8, 9))
ax = fig.add_subplot(1, 1, 1, xticks=[], yticks=[],
title="Rankine Power Cycle: Example 8.6 from Moran and Shapiro\n"
+ "\x22Fundamentals of Engineering Thermodynamics\x22, 6th ed., 2008")
Hdot = [260.431, 35.078, 180.794, 221.115, 22.700,
142.361, 10.193, 10.210, 43.670, 44.312,
68.631, 10.758, 10.758, 0.017, 0.642,
232.121, 44.559, 100.613, 132.168] # MW
sankey = Sankey(ax=ax, format='%.3G', unit=' MW', gap=0.5, scale=1.0/Hdot[0])
sankey.add(patchlabel='\n\nPump 1', rotation=90, facecolor='#37c959',
flows=[Hdot[13], Hdot[6], -Hdot[7]],
labels=['Shaft power', '', None],
pathlengths=[0.4, 0.883, 0.25],
orientations=[1, -1, 0])
sankey.add(patchlabel='\n\nOpen\nheater', facecolor='#37c959',
flows=[Hdot[11], Hdot[7], Hdot[4], -Hdot[8]],
labels=[None, '', None, None],
pathlengths=[0.25, 0.25, 1.93, 0.25],
orientations=[1, 0, -1, 0], prior=0, connect=(2, 1))
sankey.add(patchlabel='\n\nPump 2', facecolor='#37c959',
flows=[Hdot[14], Hdot[8], -Hdot[9]],
labels=['Shaft power', '', None],
pathlengths=[0.4, 0.25, 0.25],
orientations=[1, 0, 0], prior=1, connect=(3, 1))
sankey.add(patchlabel='Closed\nheater', trunklength=2.914, fc='#37c959',
flows=[Hdot[9], Hdot[1], -Hdot[11], -Hdot[10]],
pathlengths=[0.25, 1.543, 0.25, 0.25],
labels=['', '', None, None],
orientations=[0, -1, 1, -1], prior=2, connect=(2, 0))
sankey.add(patchlabel='Trap', facecolor='#37c959', trunklength=5.102,
flows=[Hdot[11], -Hdot[12]],
labels=['\n', None],
pathlengths=[1.0, 1.01],
orientations=[1, 1], prior=3, connect=(2, 0))
sankey.add(patchlabel='Steam\ngenerator', facecolor='#ff5555',
flows=[Hdot[15], Hdot[10], Hdot[2], -Hdot[3], -Hdot[0]],
labels=['Heat rate', '', '', None, None],
pathlengths=0.25,
orientations=[1, 0, -1, -1, -1], prior=3, connect=(3, 1))
sankey.add(patchlabel='\n\n\nTurbine 1', facecolor='#37c959',
flows=[Hdot[0], -Hdot[16], -Hdot[1], -Hdot[2]],
labels=['', None, None, None],
pathlengths=[0.25, 0.153, 1.543, 0.25],
orientations=[0, 1, -1, -1], prior=5, connect=(4, 0))
sankey.add(patchlabel='\n\n\nReheat', facecolor='#37c959',
flows=[Hdot[2], -Hdot[2]],
labels=[None, None],
pathlengths=[0.725, 0.25],
orientations=[-1, 0], prior=6, connect=(3, 0))
sankey.add(patchlabel='Turbine 2', trunklength=3.212, facecolor='#37c959',
flows=[Hdot[3], Hdot[16], -Hdot[5], -Hdot[4], -Hdot[17]],
labels=[None, 'Shaft power', None, '', 'Shaft power'],
pathlengths=[0.751, 0.15, 0.25, 1.93, 0.25],
orientations=[0, -1, 0, -1, 1], prior=6, connect=(1, 1))
sankey.add(patchlabel='Condenser', facecolor='#58b1fa', trunklength=1.764,
flows=[Hdot[5], -Hdot[18], -Hdot[6]],
labels=['', 'Heat rate', None],
pathlengths=[0.45, 0.25, 0.883],
orientations=[-1, 1, 0], prior=8, connect=(2, 0))
diagrams = sankey.finish()
for diagram in diagrams:
diagram.text.set_fontweight('bold')
diagram.text.set_fontsize('10')
for text in diagram.texts:
text.set_fontsize('10')
# Notice that the explicit connections are handled automatically, but the
# implicit ones currently are not. The lengths of the paths and the trunks
# must be adjusted manually, and that is a bit tricky.
plt.show()
| gpl-3.0 |
kcavagnolo/astroML | book_figures/chapter1/fig_moving_objects.py | 3 | 1911 | """
SDSS Moving Object Data
-----------------------
Figure 1.8.
The orbital semimajor axis vs. the orbital inclination angle diagram for the
first 10,000 catalog entries from the SDSS Moving Object Catalog (after
applying several quality cuts). The gaps at approximately 2.5, 2.8, and 3.3 AU
are called the Kirkwood gaps and are due to orbital resonances with Jupiter.
The several distinct clumps are called asteroid families and represent remnants
from collisions of larger asteroids.
"""
# Author: Jake VanderPlas
# License: BSD
# The figure produced by this code is published in the textbook
# "Statistics, Data Mining, and Machine Learning in Astronomy" (2013)
# For more information, see http://astroML.github.com
# To report a bug or issue, use the following forum:
# https://groups.google.com/forum/#!forum/astroml-general
from matplotlib import pyplot as plt
from astroML.datasets import fetch_moving_objects
#----------------------------------------------------------------------
# This function adjusts matplotlib settings for a uniform feel in the textbook.
# Note that with usetex=True, fonts are rendered with LaTeX. This may
# result in an error if LaTeX is not installed on your system. In that case,
# you can set usetex to False.
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=True)
#------------------------------------------------------------
# Fetch the moving object data
data = fetch_moving_objects(Parker2008_cuts=True)
# Use only the first 10000 points
data = data[:10000]
a = data['aprime']
sini = data['sin_iprime']
#------------------------------------------------------------
# Plot the results
fig, ax = plt.subplots(figsize=(5, 3.75))
ax.plot(a, sini, '.', markersize=2, color='black')
ax.set_xlim(2.0, 3.6)
ax.set_ylim(-0.01, 0.31)
ax.set_xlabel('Semimajor Axis (AU)')
ax.set_ylabel('Sine of Inclination Angle')
plt.show()
| bsd-2-clause |
teonlamont/mne-python | examples/decoding/plot_linear_model_patterns.py | 4 | 4361 | # -*- coding: utf-8 -*-
"""
===============================================================
Linear classifier on sensor data with plot patterns and filters
===============================================================
Here decoding, a.k.a MVPA or supervised machine learning, is applied to M/EEG
data in sensor space. Fit a linear classifier with the LinearModel object
providing topographical patterns which are more neurophysiologically
interpretable [1]_ than the classifier filters (weight vectors).
The patterns explain how the MEG and EEG data were generated from the
discriminant neural sources which are extracted by the filters.
Note patterns/filters in MEG data are more similar than EEG data
because the noise is less spatially correlated in MEG than EEG.
References
----------
.. [1] Haufe, S., Meinecke, F., Görgen, K., Dähne, S., Haynes, J.-D.,
Blankertz, B., & Bießmann, F. (2014). On the interpretation of
weight vectors of linear models in multivariate neuroimaging.
NeuroImage, 87, 96–110. doi:10.1016/j.neuroimage.2013.10.067
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Romain Trachel <trachelr@gmail.com>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne import io, EvokedArray
from mne.datasets import sample
from mne.decoding import Vectorizer, get_coef
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
# import a linear classifier from mne.decoding
from mne.decoding import LinearModel
print(__doc__)
data_path = sample.data_path()
###############################################################################
# Set parameters
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
tmin, tmax = -0.1, 0.4
event_id = dict(aud_l=1, vis_l=3)
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname, preload=True)
raw.filter(.5, 25, fir_design='firwin')
events = mne.read_events(event_fname)
# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
decim=2, baseline=None, preload=True)
labels = epochs.events[:, -1]
# get MEG and EEG data
meg_epochs = epochs.copy().pick_types(meg=True, eeg=False)
meg_data = meg_epochs.get_data().reshape(len(labels), -1)
###############################################################################
# Decoding in sensor space using a LogisticRegression classifier
clf = LogisticRegression()
scaler = StandardScaler()
# create a linear model with LogisticRegression
model = LinearModel(clf)
# fit the classifier on MEG data
X = scaler.fit_transform(meg_data)
model.fit(X, labels)
# Extract and plot spatial filters and spatial patterns
for name, coef in (('patterns', model.patterns_), ('filters', model.filters_)):
# We fitted the linear model onto Z-scored data. To make the filters
# interpretable, we must reverse this normalization step
coef = scaler.inverse_transform([coef])[0]
# The data was vectorized to fit a single model across all time points and
# all channels. We thus reshape it:
coef = coef.reshape(len(meg_epochs.ch_names), -1)
# Plot
evoked = EvokedArray(coef, meg_epochs.info, tmin=epochs.tmin)
evoked.plot_topomap(title='MEG %s' % name, time_unit='s')
###############################################################################
# Let's do the same on EEG data using a scikit-learn pipeline
X = epochs.pick_types(meg=False, eeg=True)
y = epochs.events[:, 2]
# Define a unique pipeline to sequentially:
clf = make_pipeline(
Vectorizer(), # 1) vectorize across time and channels
StandardScaler(), # 2) normalize features across trials
LinearModel(LogisticRegression())) # 3) fits a logistic regression
clf.fit(X, y)
# Extract and plot patterns and filters
for name in ('patterns_', 'filters_'):
# The `inverse_transform` parameter will call this method on any estimator
# contained in the pipeline, in reverse order.
coef = get_coef(clf, name, inverse_transform=True)
evoked = EvokedArray(coef, epochs.info, tmin=epochs.tmin)
evoked.plot_topomap(title='EEG %s' % name[:-1], time_unit='s')
| bsd-3-clause |
ibmsoe/tensorflow | tensorflow/contrib/learn/python/learn/tests/dataframe/feeding_functions_test.py | 62 | 9268 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests feeding functions using arrays and `DataFrames`."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
from tensorflow.contrib.learn.python.learn.dataframe.queues import feeding_functions as ff
from tensorflow.python.platform import test
# pylint: disable=g-import-not-at-top
try:
import pandas as pd
HAS_PANDAS = True
except ImportError:
HAS_PANDAS = False
def vals_to_list(a):
return {
key: val.tolist() if isinstance(val, np.ndarray) else val
for key, val in a.items()
}
class _FeedingFunctionsTestCase(test.TestCase):
"""Tests for feeding functions."""
def testArrayFeedFnBatchOne(self):
array = np.arange(32).reshape([16, 2])
placeholders = ["index_placeholder", "value_placeholder"]
aff = ff._ArrayFeedFn(placeholders, array, 1)
# cycle around a couple times
for x in range(0, 100):
i = x % 16
expected = {
"index_placeholder": [i],
"value_placeholder": [[2 * i, 2 * i + 1]]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
def testArrayFeedFnBatchFive(self):
array = np.arange(32).reshape([16, 2])
placeholders = ["index_placeholder", "value_placeholder"]
aff = ff._ArrayFeedFn(placeholders, array, 5)
# cycle around a couple times
for _ in range(0, 101, 2):
aff()
expected = {
"index_placeholder": [15, 0, 1, 2, 3],
"value_placeholder": [[30, 31], [0, 1], [2, 3], [4, 5], [6, 7]]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
def testArrayFeedFnBatchTwoWithOneEpoch(self):
array = np.arange(5) + 10
placeholders = ["index_placeholder", "value_placeholder"]
aff = ff._ArrayFeedFn(placeholders, array, batch_size=2, num_epochs=1)
expected = {
"index_placeholder": [0, 1],
"value_placeholder": [10, 11]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
expected = {
"index_placeholder": [2, 3],
"value_placeholder": [12, 13]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
expected = {
"index_placeholder": [4],
"value_placeholder": [14]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
def testArrayFeedFnBatchOneHundred(self):
array = np.arange(32).reshape([16, 2])
placeholders = ["index_placeholder", "value_placeholder"]
aff = ff._ArrayFeedFn(placeholders, array, 100)
expected = {
"index_placeholder":
list(range(0, 16)) * 6 + list(range(0, 4)),
"value_placeholder":
np.arange(32).reshape([16, 2]).tolist() * 6 +
[[0, 1], [2, 3], [4, 5], [6, 7]]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
def testArrayFeedFnBatchOneHundredWithSmallerArrayAndMultipleEpochs(self):
array = np.arange(2) + 10
placeholders = ["index_placeholder", "value_placeholder"]
aff = ff._ArrayFeedFn(placeholders, array, batch_size=100, num_epochs=2)
expected = {
"index_placeholder": [0, 1, 0, 1],
"value_placeholder": [10, 11, 10, 11],
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
def testPandasFeedFnBatchOne(self):
if not HAS_PANDAS:
return
array1 = np.arange(32, 64)
array2 = np.arange(64, 96)
df = pd.DataFrame({"a": array1, "b": array2}, index=np.arange(96, 128))
placeholders = ["index_placeholder", "a_placeholder", "b_placeholder"]
aff = ff._PandasFeedFn(placeholders, df, 1)
# cycle around a couple times
for x in range(0, 100):
i = x % 32
expected = {
"index_placeholder": [i + 96],
"a_placeholder": [32 + i],
"b_placeholder": [64 + i]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
def testPandasFeedFnBatchFive(self):
if not HAS_PANDAS:
return
array1 = np.arange(32, 64)
array2 = np.arange(64, 96)
df = pd.DataFrame({"a": array1, "b": array2}, index=np.arange(96, 128))
placeholders = ["index_placeholder", "a_placeholder", "b_placeholder"]
aff = ff._PandasFeedFn(placeholders, df, 5)
# cycle around a couple times
for _ in range(0, 101, 2):
aff()
expected = {
"index_placeholder": [127, 96, 97, 98, 99],
"a_placeholder": [63, 32, 33, 34, 35],
"b_placeholder": [95, 64, 65, 66, 67]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
def testPandasFeedFnBatchTwoWithOneEpoch(self):
if not HAS_PANDAS:
return
array1 = np.arange(32, 37)
array2 = np.arange(64, 69)
df = pd.DataFrame({"a": array1, "b": array2}, index=np.arange(96, 101))
placeholders = ["index_placeholder", "a_placeholder", "b_placeholder"]
aff = ff._PandasFeedFn(placeholders, df, batch_size=2, num_epochs=1)
expected = {
"index_placeholder": [96, 97],
"a_placeholder": [32, 33],
"b_placeholder": [64, 65]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
expected = {
"index_placeholder": [98, 99],
"a_placeholder": [34, 35],
"b_placeholder": [66, 67]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
expected = {
"index_placeholder": [100],
"a_placeholder": [36],
"b_placeholder": [68]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
def testPandasFeedFnBatchOneHundred(self):
if not HAS_PANDAS:
return
array1 = np.arange(32, 64)
array2 = np.arange(64, 96)
df = pd.DataFrame({"a": array1, "b": array2}, index=np.arange(96, 128))
placeholders = ["index_placeholder", "a_placeholder", "b_placeholder"]
aff = ff._PandasFeedFn(placeholders, df, 100)
expected = {
"index_placeholder": list(range(96, 128)) * 3 + list(range(96, 100)),
"a_placeholder": list(range(32, 64)) * 3 + list(range(32, 36)),
"b_placeholder": list(range(64, 96)) * 3 + list(range(64, 68))
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
def testPandasFeedFnBatchOneHundredWithSmallDataArrayAndMultipleEpochs(self):
if not HAS_PANDAS:
return
array1 = np.arange(32, 34)
array2 = np.arange(64, 66)
df = pd.DataFrame({"a": array1, "b": array2}, index=np.arange(96, 98))
placeholders = ["index_placeholder", "a_placeholder", "b_placeholder"]
aff = ff._PandasFeedFn(placeholders, df, batch_size=100, num_epochs=2)
expected = {
"index_placeholder": [96, 97, 96, 97],
"a_placeholder": [32, 33, 32, 33],
"b_placeholder": [64, 65, 64, 65]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
def testOrderedDictNumpyFeedFnBatchTwoWithOneEpoch(self):
a = np.arange(32, 37)
b = np.arange(64, 69)
x = {"a": a, "b": b}
ordered_dict_x = collections.OrderedDict(
sorted(x.items(), key=lambda t: t[0]))
placeholders = ["index_placeholder", "a_placeholder", "b_placeholder"]
aff = ff._OrderedDictNumpyFeedFn(
placeholders, ordered_dict_x, batch_size=2, num_epochs=1)
expected = {
"index_placeholder": [0, 1],
"a_placeholder": [32, 33],
"b_placeholder": [64, 65]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
expected = {
"index_placeholder": [2, 3],
"a_placeholder": [34, 35],
"b_placeholder": [66, 67]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
expected = {
"index_placeholder": [4],
"a_placeholder": [36],
"b_placeholder": [68]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
def testOrderedDictNumpyFeedFnLargeBatchWithSmallArrayAndMultipleEpochs(self):
a = np.arange(32, 34)
b = np.arange(64, 66)
x = {"a": a, "b": b}
ordered_dict_x = collections.OrderedDict(
sorted(x.items(), key=lambda t: t[0]))
placeholders = ["index_placeholder", "a_placeholder", "b_placeholder"]
aff = ff._OrderedDictNumpyFeedFn(
placeholders, ordered_dict_x, batch_size=100, num_epochs=2)
expected = {
"index_placeholder": [0, 1, 0, 1],
"a_placeholder": [32, 33, 32, 33],
"b_placeholder": [64, 65, 64, 65]
}
actual = aff()
self.assertEqual(expected, vals_to_list(actual))
if __name__ == "__main__":
test.main()
| apache-2.0 |
asurve/arvind-sysml2 | src/main/python/tests/test_mllearn_df.py | 4 | 5381 | #!/usr/bin/python
#-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
#-------------------------------------------------------------
# To run:
# - Python 2: `PYSPARK_PYTHON=python2 spark-submit --master local[*] --driver-class-path SystemML.jar test_mllearn_df.py`
# - Python 3: `PYSPARK_PYTHON=python3 spark-submit --master local[*] --driver-class-path SystemML.jar test_mllearn_df.py`
# Make the `systemml` package importable
import os
import sys
path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "../")
sys.path.insert(0, path)
import unittest
import numpy as np
from pyspark.context import SparkContext
from pyspark.ml import Pipeline
from pyspark.ml.feature import HashingTF, Tokenizer
from pyspark.sql import SparkSession
from sklearn import datasets, metrics, neighbors
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import linear_model
from sklearn.metrics import accuracy_score, r2_score
from systemml.mllearn import LinearRegression, LogisticRegression, NaiveBayes, SVM
sc = SparkContext()
sparkSession = SparkSession.builder.getOrCreate()
# Currently not integrated with JUnit test
# ~/spark-1.6.1-scala-2.11/bin/spark-submit --master local[*] --driver-class-path SystemML.jar test.py
class TestMLLearn(unittest.TestCase):
def test_logistic_sk2(self):
digits = datasets.load_digits()
X_digits = digits.data
y_digits = digits.target
n_samples = len(X_digits)
X_train = X_digits[:int(.9 * n_samples)]
y_train = y_digits[:int(.9 * n_samples)]
X_test = X_digits[int(.9 * n_samples):]
y_test = y_digits[int(.9 * n_samples):]
# Convert to DataFrame for i/o: current way to transfer data
logistic = LogisticRegression(sparkSession, transferUsingDF=True)
logistic.fit(X_train, y_train)
mllearn_predicted = logistic.predict(X_test)
sklearn_logistic = linear_model.LogisticRegression()
sklearn_logistic.fit(X_train, y_train)
self.failUnless(accuracy_score(sklearn_logistic.predict(X_test), mllearn_predicted) > 0.95) # We are comparable to a similar algorithm in scikit learn
def test_linear_regression(self):
diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]
regr = LinearRegression(sparkSession, solver='direct-solve', transferUsingDF=True)
regr.fit(diabetes_X_train, diabetes_y_train)
mllearn_predicted = regr.predict(diabetes_X_test)
sklearn_regr = linear_model.LinearRegression()
sklearn_regr.fit(diabetes_X_train, diabetes_y_train)
self.failUnless(r2_score(sklearn_regr.predict(diabetes_X_test), mllearn_predicted) > 0.95) # We are comparable to a similar algorithm in scikit learn
def test_linear_regression_cg(self):
diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]
regr = LinearRegression(sparkSession, solver='newton-cg', transferUsingDF=True)
regr.fit(diabetes_X_train, diabetes_y_train)
mllearn_predicted = regr.predict(diabetes_X_test)
sklearn_regr = linear_model.LinearRegression()
sklearn_regr.fit(diabetes_X_train, diabetes_y_train)
self.failUnless(r2_score(sklearn_regr.predict(diabetes_X_test), mllearn_predicted) > 0.95) # We are comparable to a similar algorithm in scikit learn
def test_svm_sk2(self):
digits = datasets.load_digits()
X_digits = digits.data
y_digits = digits.target
n_samples = len(X_digits)
X_train = X_digits[:int(.9 * n_samples)]
y_train = y_digits[:int(.9 * n_samples)]
X_test = X_digits[int(.9 * n_samples):]
y_test = y_digits[int(.9 * n_samples):]
svm = SVM(sparkSession, is_multi_class=True, transferUsingDF=True)
mllearn_predicted = svm.fit(X_train, y_train).predict(X_test)
from sklearn import linear_model, svm
clf = svm.LinearSVC()
sklearn_predicted = clf.fit(X_train, y_train).predict(X_test)
self.failUnless(accuracy_score(sklearn_predicted, mllearn_predicted) > 0.95 )
if __name__ == '__main__':
unittest.main()
| apache-2.0 |
narendrameena/featuerSelectionAssignment | example.py | 1 | 2524 | print(__doc__)
# Author: Andreas Mueller <amueller@ais.uni-bonn.de>
# Jaques Grobler <jaques.grobler@inria.fr>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import LinearSVC
from sklearn.cross_validation import ShuffleSplit
from sklearn.grid_search import GridSearchCV
from sklearn.utils import check_random_state
from sklearn import datasets
rnd = check_random_state(1)
# set up dataset
n_samples = 100
n_features = 300
# l1 data (only 5 informative features)
X_1, y_1 = datasets.make_classification(n_samples=n_samples,
n_features=n_features, n_informative=5,
random_state=1)
print(len(X_1))
print(X_1)
'''
# l2 data: non sparse, but less features
y_2 = np.sign(.5 - rnd.rand(n_samples))
X_2 = rnd.randn(n_samples, n_features / 5) + y_2[:, np.newaxis]
X_2 += 5 * rnd.randn(n_samples, n_features / 5)
clf_sets = [(LinearSVC(penalty='l1', loss='squared_hinge', dual=False,
tol=1e-3),
np.logspace(-2.3, -1.3, 10), X_1, y_1),
(LinearSVC(penalty='l2', loss='squared_hinge', dual=True,
tol=1e-4),
np.logspace(-4.5, -2, 10), X_2, y_2)]
colors = ['b', 'g', 'r', 'c']
for fignum, (clf, cs, X, y) in enumerate(clf_sets):
# set up the plot for each regressor
plt.figure(fignum, figsize=(9, 10))
for k, train_size in enumerate(np.linspace(0.3, 0.7, 3)[::-1]):
param_grid = dict(C=cs)
# To get nice curve, we need a large number of iterations to
# reduce the variance
grid = GridSearchCV(clf, refit=False, param_grid=param_grid,
cv=ShuffleSplit(n=n_samples, train_size=train_size,
n_iter=250, random_state=1))
grid.fit(X, y)
scores = [x[1] for x in grid.grid_scores_]
scales = [(1, 'No scaling'),
((n_samples * train_size), '1/n_samples'),
]
for subplotnum, (scaler, name) in enumerate(scales):
plt.subplot(2, 1, subplotnum + 1)
plt.xlabel('C')
plt.ylabel('CV Score')
grid_cs = cs * float(scaler) # scale the C's
plt.semilogx(grid_cs, scores, label="fraction %.2f" %
train_size)
plt.title('scaling=%s, penalty=%s, loss=%s' %
(name, clf.penalty, clf.loss))
plt.legend(loc="best")
plt.show()
''' | cc0-1.0 |
liyinwei/pandas | quickstart/03_selection.py | 1 | 10410 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: liyinwei
@E-mail: coridc@foxmail.com
@Time: 2016/11/21 11:33
@Description:
1.数据选择
2.尽管Python/Numpy已自带选择和赋值，为出于性能上的考虑，官方推荐在生产环境采用pandas中经过优化的方法，包括：
a).at
b).iat
c).loc
d).iloc
e).ix
3.包括以下几个部分
a)获取(Getting)
b)通过标签选择(Selection by Label) @see detail: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-label
c)通过位置选择(Selection by Position) @see detail: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-integer
d)通过布尔索引选择(Boolean Indexing)
e)赋值(Setting)
4.详细介绍：
a)Indexing and Selecting Data: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing
b)MultiIndex / Advanced Indexing: http://pandas.pydata.org/pandas-docs/stable/advanced.html#advanced
"""
import pandas as pd
import numpy as np
"""
ID: 01_02
Desc: 根据numpy的Array，结合一个日期索引和标签列创建一个DataFrame对象
Output1:
DatetimeIndex(['2016-11-21', '2016-11-22', '2016-11-23', '2016-11-24',
'2016-11-25', '2016-11-26'],
dtype='datetime64[ns]', freq='D')
Output2:
a b c d
2016-11-21 -0.073623 1.407345 0.875421 -0.720421
2016-11-22 1.133543 0.375845 -0.670956 0.932758
2016-11-23 0.752048 -1.439229 -0.994142 0.226627
2016-11-24 1.152852 -1.084070 -1.660017 1.755162
2016-11-25 0.781526 -0.240901 -0.390660 0.156266
2016-11-26 1.069615 -0.112030 -1.482118 -0.421262
"""
dates = pd.date_range("20161121", periods=6)
print(dates)
df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('abcd'))
print(df)
"""
ID: 03_01
Type: Getting
Desc: 以Series类型返回单独一列，等同于df.a
Output:
2016-11-21 -0.073623
2016-11-22 1.133543
2016-11-23 0.752048
2016-11-24 1.152852
2016-11-25 0.781526
2016-11-26 1.069615
Freq: D, Name: a, dtype: float64
"""
print(df['a'])
print(df.a)
"""
ID: 03_02
Type: Getting
Desc: 通过[]对行进行切片（slices the rows）
Output1:
a b c d
2016-11-21 -0.073623 1.407345 0.875421 -0.720421
2016-11-22 1.133543 0.375845 -0.670956 0.932758
2016-11-23 0.752048 -1.439229 -0.994142 0.226627
Output2:
a b c d
2016-11-22 1.133543 0.375845 -0.670956 0.932758
2016-11-23 0.752048 -1.439229 -0.994142 0.226627
2016-11-24 1.152852 -1.084070 -1.660017 1.755162
"""
print(df[0:3])
print(df['20161122':'20161124'])
"""
ID: 03_03
Type: Selection by Label
Desc: 通过标签获取一个交叉区域
Output:
a -0.073623
b 1.407345
c 0.875421
d -0.720421
Name: 2016-11-21 00:00:00, dtype: float64
"""
print(df.loc[dates[0]])
"""
ID: 03_04
Type: Selection by Label
Desc: 在多个轴上进行标签进行选择
Output:
a b
2016-11-21 -0.073623 1.407345
2016-11-22 1.133543 0.375845
2016-11-23 0.752048 -1.439229
2016-11-24 1.152852 -1.084070
2016-11-25 0.781526 -0.240901
2016-11-26 1.069615 -0.112030
"""
print(df.loc[:, ['a', 'b']])
"""
ID: 03_05
Type: Selection by Label
Desc: 标签切片（label slicing）
Output:
a b
2016-11-21 -0.073623 1.407345
2016-11-22 1.133543 0.375845
2016-11-23 0.752048 -1.439229
2016-11-24 1.152852 -1.084070
"""
print(df.loc['20161121':'20161124', ['a', 'b']])
"""
ID: 03_06
Type: Selection by Label
Desc: 对返回的结果进行维度缩减（Reduction in the dimensions）
Output:
a -0.073623
b 1.407345
Name: 2016-11-21 00:00:00, dtype: float64
"""
print(df.loc['20161121', ['a', 'b']])
"""
ID: 03_07
Type: Selection by Label
Desc: 访问一个标量（scalar value）
Output:
-0.0736228935906
"""
print(df.loc[dates[0], 'a'])
"""
ID: 03_08
Type: Selection by Label
Desc: 快速访问一个标量（scalar value），与.loc等价
Output:
-0.0736228935906
"""
print(df.loc[dates[0], 'a'])
"""
ID: 03_09
Type: Selection by Position
Desc: 根据下标访问（行下标）
Output:
a 1.152852
b -1.084070
c -1.660017
d 1.755162
Name: 2016-11-24 00:00:00, dtype: float64
"""
print(df.iloc[3])
"""
ID: 03_10
Type: Selection by Position
Desc: 通过下标切片，与python/numpy类似
Output:
a b
2016-11-24 1.152852 -1.084070
2016-11-25 0.781526 -0.240901
"""
print(df.iloc[3:5, 0:2])
"""
ID: 03_11
Type: Selection by Position
Desc: 通过下标切片，与python/numpy类似
Output:
a c
2016-11-22 1.133543 -0.670956
2016-11-23 0.752048 -0.994142
2016-11-25 0.781526 -0.390660
"""
print(df.iloc[[1, 2, 4], [0, 2]])
"""
ID: 03_12
Type: Selection by Position
Desc: 对行进行切片
Output:
a b c d
2016-11-22 1.133543 0.375845 -0.670956 0.932758
2016-11-23 0.752048 -1.439229 -0.994142 0.226627
"""
print(df.iloc[1:3, :])
"""
ID: 03_13
Type: Selection by Position
Desc: 对列进行切片
Output:
b c
2016-11-21 1.407345 0.875421
2016-11-22 0.375845 -0.670956
2016-11-23 -1.439229 -0.994142
2016-11-24 -1.084070 -1.660017
2016-11-25 -0.240901 -0.390660
2016-11-26 -0.112030 -1.482118
"""
print(df.iloc[:, 1:3])
"""
ID: 03_14
Type: Selection by Position
Desc: 直接获取一个值
Output:
0.375845191797
"""
print(df.iloc[1, 1])
"""
ID: 03_15
Type: Selection by Position
Desc: 快速直接获取一个值
Output:
0.375845191797
"""
print(df.iat[1, 1])
"""
ID: 03_16
Type: Boolean Indexing
Desc: 通过单独一列的值进行索引
Output:
a b c d
2016-11-22 1.133543 0.375845 -0.670956 0.932758
2016-11-23 0.752048 -1.439229 -0.994142 0.226627
2016-11-24 1.152852 -1.084070 -1.660017 1.755162
2016-11-25 0.781526 -0.240901 -0.390660 0.156266
2016-11-26 1.069615 -0.112030 -1.482118 -0.421262
"""
print(df[df.a > 0])
"""
ID: 03_17
Type: Boolean Indexing
Desc: 通过where操作选择数据
Output:
a b c d
2016-11-21 NaN 1.407345 0.875421 NaN
2016-11-22 1.133543 0.375845 NaN 0.932758
2016-11-23 0.752048 NaN NaN 0.226627
2016-11-24 1.152852 NaN NaN 1.755162
2016-11-25 0.781526 NaN NaN 0.156266
2016-11-26 1.069615 NaN NaN NaN
"""
print(df[df > 0])
"""
ID: 03_18
Type: Boolean Indexing
Desc: 通过isin()方法来过滤
Output1:
a b c d e
2016-11-21 -0.073623 1.407345 0.875421 -0.720421 one
2016-11-22 1.133543 0.375845 -0.670956 0.932758 one
2016-11-23 0.752048 -1.439229 -0.994142 0.226627 two
2016-11-24 1.152852 -1.084070 -1.660017 1.755162 three
2016-11-25 0.781526 -0.240901 -0.390660 0.156266 four
2016-11-26 1.069615 -0.112030 -1.482118 -0.421262 three
Output2:
a b c d e
2016-11-23 0.752048 -1.439229 -0.994142 0.226627 two
2016-11-25 0.781526 -0.240901 -0.390660 0.156266 four
"""
df2 = df.copy()
df2['e'] = ['one', 'one', 'two', 'three', 'four', 'three']
print(df2)
print(df2[df2['e'].isin(['two', 'four'])])
"""
ID: 03_19
Type: Setting
Desc: 初始化一个变量
Output:
2016-11-21 1
2016-11-22 2
2016-11-23 3
2016-11-24 4
2016-11-25 5
2016-11-26 6
Freq: D, dtype: int64
"""
s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range('20161121', periods=6))
print(s1)
"""
ID: 03_20
Type: Setting
Desc: 三种赋值方式
Output:
a b c d
2016-11-21 0.000000 0.000000 0.875421 5
2016-11-22 1.133543 0.375845 -0.670956 5
2016-11-23 0.752048 -1.439229 -0.994142 5
2016-11-24 1.152852 -1.084070 -1.660017 5
2016-11-25 0.781526 -0.240901 -0.390660 5
2016-11-26 1.069615 -0.112030 -1.482118 5
"""
# 通过标签赋值
df.at[dates[0], 'a'] = 0
# 通过下标赋值
df.iat[0, 1] = 0
# 赋一个numpy数组
df.loc[:, 'd'] = np.array([5] * len(df))
print(df)
"""
ID: 03_21
Type: Setting
Desc: 通过where操作赋值
Output:
a b c d
2016-11-21 0.000000 0.000000 -0.875421 -5
2016-11-22 -1.133543 -0.375845 -0.670956 -5
2016-11-23 -0.752048 -1.439229 -0.994142 -5
2016-11-24 -1.152852 -1.084070 -1.660017 -5
2016-11-25 -0.781526 -0.240901 -0.390660 -5
2016-11-26 -1.069615 -0.112030 -1.482118 -5
"""
df2 = df.copy()
df2[df2 > 0] = -df2
print(df2)
if __name__ == '__main__':
pass
| gpl-3.0 |
AZMAG/urbansim | urbansim/utils/tests/test_misc.py | 4 | 5198 | import os
import shutil
import numpy as np
import pandas as pd
import pytest
from .. import misc
class _FakeTable(object):
def __init__(self, name, columns):
self.name = name
self.columns = columns
@pytest.fixture
def fta():
return _FakeTable('a', ['aa', 'ab', 'ac'])
@pytest.fixture
def ftb():
return _FakeTable('b', ['bx', 'by', 'bz'])
@pytest.fixture
def clean_fake_data_home(request):
def fin():
if os.path.isdir('fake_data_home'):
shutil.rmtree('fake_data_home')
request.addfinalizer(fin)
def test_column_map_raises(fta, ftb):
with pytest.raises(RuntimeError):
misc.column_map([fta, ftb], ['aa', 'by', 'bz', 'cw'])
def test_column_map_none(fta, ftb):
assert misc.column_map([fta, ftb], None) == {'a': None, 'b': None}
def test_column_map(fta, ftb):
result = misc.column_map([fta, ftb], ['aa', 'by', 'bz'])
# misc.column_map() does not guarantee order, so sort for testing
result_sorted = {k: sorted(v) for k, v in result.items()}
assert result_sorted == {'a': ['aa'], 'b': ['by', 'bz']}
result = misc.column_map([fta, ftb], ['by', 'bz'])
result_sorted = {k: sorted(v) for k, v in result.items()}
assert result_sorted == {'a': [], 'b': ['by', 'bz']}
def test_dirs(clean_fake_data_home):
misc._mkifnotexists("fake_data_home")
os.environ["DATA_HOME"] = "fake_data_home"
misc.get_run_number()
misc.get_run_number()
misc.data_dir()
misc.configs_dir()
misc.models_dir()
misc.charts_dir()
misc.maps_dir()
misc.simulations_dir()
misc.reports_dir()
misc.runs_dir()
misc.config("test")
@pytest.fixture
def range_df():
df = pd.DataFrame({'to_zone_id': [2, 3, 4],
'from_zone_id': [1, 1, 1],
'distance': [.1, .2, .9]})
df = df.set_index(['from_zone_id', 'to_zone_id'])
return df
@pytest.fixture
def range_series():
return pd.Series([10, 150, 75, 275], index=[1, 2, 3, 4])
def test_compute_range(range_df, range_series):
assert misc.compute_range(range_df, range_series, "distance", .5).loc[1] == 225
def test_reindex():
s = pd.Series([.5, 1.0, 1.5], index=[2, 1, 3])
s2 = pd.Series([1, 2, 3], index=['a', 'b', 'c'])
assert list(misc.reindex(s, s2).values) == [1.0, .5, 1.5]
def test_naics():
assert misc.naicsname(54) == "Professional"
def test_signif():
assert misc.signif(4.0) == '***'
assert misc.signif(3.0) == '**'
assert misc.signif(2.0) == '*'
assert misc.signif(1.5) == '.'
assert misc.signif(1.0) == ''
@pytest.fixture
def simple_dev_inputs():
return pd.DataFrame(
{'residential': [40, 40, 40],
'office': [15, 18, 15],
'retail': [12, 10, 10],
'industrial': [12, 12, 12],
'land_cost': [1000000, 2000000, 3000000],
'parcel_size': [10000, 20000, 30000],
'max_far': [2.0, 3.0, 4.0],
'names': ['a', 'b', 'c'],
'max_height': [40, 60, 80]},
index=['a', 'b', 'c'])
def test_misc_dffunctions(simple_dev_inputs):
misc.df64bitto32bit(simple_dev_inputs)
misc.pandasdfsummarytojson(simple_dev_inputs[['land_cost', 'parcel_size']])
misc.numpymat2df(np.array([[1, 2], [3, 4]]))
def test_column_list(fta, ftb):
result = misc.column_list([fta, ftb], ['aa', 'by', 'bz', 'c'])
assert sorted(result) == ['aa', 'by', 'bz']
######################
# FK REINDEX TESTS
######################
@pytest.fixture()
def left_df():
return pd.DataFrame({
'some_val': [10, 9, 8, 7, 6],
'fk': ['z', 'g', 'g', 'b', 't'],
'grp': ['r', 'g', 'r', 'g', 'r']
})
@pytest.fixture()
def right_df():
return pd.DataFrame(
{
'col1': [100, 200, 50],
'col2': [1, 2, 3]
},
index=pd.Index(['g', 'b', 'z'])
)
@pytest.fixture()
def right_df2(right_df):
df = pd.concat([right_df, right_df * -1])
df['fk'] = df.index
df['grp'] = ['r', 'r', 'r', 'g', 'g', 'g']
df.set_index(['fk', 'grp'], inplace=True)
return df
def test_fidx_right_not_unique(right_df, left_df):
with pytest.raises(ValueError):
s = right_df.col1
misc.fidx(s.append(s), left_df.fk)
def test_series_fidx(right_df, left_df):
b = misc.fidx(right_df.col1, left_df.fk).fillna(-1)
assert (b.values == [50, 100, 100, 200, -1]).all()
def assert_df_fidx(b):
assert (b.col1.values == [50, 100, 100, 200, -1]).all()
assert (b.col2.values == [3, 1, 1, 2, -1]).all()
def test_df_fidx(right_df, left_df):
b = misc.fidx(right_df, left_df.fk).fillna(-1)
assert_df_fidx(b)
def test_fk_reindex_with_fk_col(right_df, left_df):
b = misc.fidx(right_df, left_df, 'fk').fillna(-1)
assert_df_fidx(b)
def test_series_multi_col_fidx(right_df2, left_df):
b = misc.fidx(right_df2.col1, left_df, ['fk', 'grp']).fillna(-9999)
assert (b.values == [50, -100, 100, -200, -9999]).all()
def test_df_multi_col_fidx(right_df2, left_df):
b = misc.fidx(right_df2, left_df, ['fk', 'grp']).fillna(-9999)
assert (b.col1.values == [50, -100, 100, -200, -9999]).all()
assert (b.col2.values == [3, -1, 1, -2, -9999]).all()
| bsd-3-clause |
kmike/scikit-learn | sklearn/decomposition/tests/test_nmf.py | 3 | 5597 | import numpy as np
from scipy import linalg
from sklearn.decomposition import nmf
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import raises
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_greater
random_state = np.random.mtrand.RandomState(0)
@raises(ValueError)
def test_initialize_nn_input():
"""Test NNDSVD behaviour on negative input"""
nmf._initialize_nmf(-np.ones((2, 2)), 2)
def test_initialize_nn_output():
"""Test that NNDSVD does not return negative values"""
data = np.abs(random_state.randn(10, 10))
for var in (None, 'a', 'ar'):
W, H = nmf._initialize_nmf(data, 10)
assert_false((W < 0).any() or (H < 0).any())
def test_initialize_close():
"""Test NNDSVD error
Test that _initialize_nmf error is less than the standard deviation of the
entries in the matrix.
"""
A = np.abs(random_state.randn(10, 10))
W, H = nmf._initialize_nmf(A, 10)
error = linalg.norm(np.dot(W, H) - A)
sdev = linalg.norm(A - A.mean())
assert_true(error <= sdev)
def test_initialize_variants():
"""Test NNDSVD variants correctness
Test that the variants 'a' and 'ar' differ from basic NNDSVD only where
the basic version has zeros.
"""
data = np.abs(random_state.randn(10, 10))
W0, H0 = nmf._initialize_nmf(data, 10, variant=None)
Wa, Ha = nmf._initialize_nmf(data, 10, variant='a')
War, Har = nmf._initialize_nmf(data, 10, variant='ar')
for ref, evl in ((W0, Wa), (W0, War), (H0, Ha), (H0, Har)):
assert_true(np.allclose(evl[ref != 0], ref[ref != 0]))
@raises(ValueError)
def test_projgrad_nmf_fit_nn_input():
"""Test model fit behaviour on negative input"""
A = -np.ones((2, 2))
m = nmf.ProjectedGradientNMF(n_components=2, init=None)
m.fit(A)
def test_projgrad_nmf_fit_nn_output():
"""Test that the decomposition does not contain negative values"""
A = np.c_[5 * np.ones(5) - np.arange(1, 6),
5 * np.ones(5) + np.arange(1, 6)]
for init in (None, 'nndsvd', 'nndsvda', 'nndsvdar'):
model = nmf.ProjectedGradientNMF(n_components=2, init=init)
transf = model.fit_transform(A)
assert_false((model.components_ < 0).any() or
(transf < 0).any())
def test_projgrad_nmf_fit_close():
"""Test that the fit is not too far away"""
assert_true(nmf.ProjectedGradientNMF(5, init='nndsvda').fit(np.abs(
random_state.randn(6, 5))).reconstruction_err_ < 0.05)
@raises(ValueError)
def test_nls_nn_input():
"""Test NLS solver's behaviour on negative input"""
A = np.ones((2, 2))
nmf._nls_subproblem(A, A, -A, 0.001, 20)
def test_nls_nn_output():
"""Test that NLS solver doesn't return negative values"""
A = np.arange(1, 5).reshape(1, -1)
Ap, _, _ = nmf._nls_subproblem(np.dot(A.T, -A), A.T, A, 0.001, 100)
assert_false((Ap < 0).any())
def test_nls_close():
"""Test that the NLS results should be close"""
A = np.arange(1, 5).reshape(1, -1)
Ap, _, _ = nmf._nls_subproblem(np.dot(A.T, A), A.T, np.zeros_like(A),
0.001, 100)
assert_true((np.abs(Ap - A) < 0.01).all())
def test_projgrad_nmf_transform():
"""Test that NMF.transform returns close values
(transform uses scipy.optimize.nnls for now)
"""
A = np.abs(random_state.randn(6, 5))
m = nmf.ProjectedGradientNMF(n_components=5, init='nndsvd')
transf = m.fit_transform(A)
assert_true(np.allclose(transf, m.transform(A), atol=1e-2, rtol=0))
def test_n_components_greater_n_features():
"""Smoke test for the case of more components than features."""
A = np.abs(random_state.randn(30, 10))
nmf.ProjectedGradientNMF(n_components=15, sparseness='data').fit(A)
def test_projgrad_nmf_sparseness():
"""Test sparseness
Test that sparsity contraints actually increase sparseness in the
part where they are applied.
"""
A = np.abs(random_state.randn(10, 10))
m = nmf.ProjectedGradientNMF(n_components=5).fit(A)
data_sp = nmf.ProjectedGradientNMF(
n_components=5, sparseness='data').fit(A).data_sparseness_
comp_sp = nmf.ProjectedGradientNMF(
n_components=5, sparseness='components').fit(A).comp_sparseness_
assert_greater(data_sp, m.data_sparseness_)
assert_greater(comp_sp, m.comp_sparseness_)
def test_sparse_input():
"""Test that sparse matrices are accepted as input"""
from scipy.sparse import csr_matrix
A = np.abs(random_state.randn(10, 10))
A[:, 2 * np.arange(5)] = 0
T1 = nmf.ProjectedGradientNMF(n_components=5, init='random',
random_state=999).fit_transform(A)
A_sparse = csr_matrix(A)
pg_nmf = nmf.ProjectedGradientNMF(n_components=5, init='random',
random_state=999)
T2 = pg_nmf.fit_transform(A_sparse)
assert_array_almost_equal(pg_nmf.reconstruction_err_,
linalg.norm(A - np.dot(T2, pg_nmf.components_),
'fro'))
assert_array_almost_equal(T1, T2)
# same with sparseness
T2 = nmf.ProjectedGradientNMF(
n_components=5, init='random', sparseness='data',
random_state=999).fit_transform(A_sparse)
T1 = nmf.ProjectedGradientNMF(
n_components=5, init='random', sparseness='data',
random_state=999).fit_transform(A)
if __name__ == '__main__':
import nose
nose.run(argv=['', __file__])
| bsd-3-clause |
nmmarquez/pymc | pymc3/examples/stochastic_volatility.py | 2 | 4664 | # -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <codecell>
from matplotlib.pylab import *
import numpy as np
from pymc import *
from pymc.distributions.timeseries import *
from scipy.sparse import csc_matrix
from scipy import optimize
# <markdowncell>
# Asset prices have time-varying volatility (variance of day over day `returns`). In some periods, returns are highly variable, while in others very stable. Stochastic volatility models model this with a latent volatility variable, modeled as a stochastic process. The following model is similar to the one described in the No-U-Turn Sampler paper, Hoffman (2011) p21.
#
# $$ \sigma \sim Exponential(50) $$
#
# $$ \nu \sim Exponential(.1) $$
#
# $$ s_i \sim Normal(s_{i-1}, \sigma^{-2}) $$
#
# $$ log(\frac{y_i}{y_{i-1}}) \sim t(\nu, 0, exp(-2 s_i)) $$
#
# Here, $y$ is the daily return series and $s$ is the latent log
# volatility process.
# <markdowncell>
# ## Build Model
# <markdowncell>
# First we load some daily returns of the S&P 500.
# <codecell>
n = 400
returns = np.genfromtxt(get_data_file('pymc.examples', "data/SP500.csv"))[-n:]
returns[:5]
# <markdowncell>
# Specifying the model in pymc mirrors its statistical specification.
#
# However, it is easier to sample the scale of the log volatility process innovations, $\sigma$, on a log scale, so we create it using `TransformedVar` and use `logtransform`. `TransformedVar` creates one variable in the transformed space and one in the normal space. The one in the transformed space (here $\text{log}(\sigma) $) is the one over which sampling will occur, and the one in the normal space is the one to use throughout the rest of the model.
#
# It takes a variable name, a distribution and a transformation to use.
# <codecell>
model = Model()
with model:
sigma, log_sigma = model.TransformedVar(
'sigma', Exponential.dist(1. / .02, testval=.1),
logtransform)
nu = Exponential('nu', 1. / 10)
s = GaussianRandomWalk('s', sigma ** -2, shape=n)
r = T('r', nu, lam=exp(-2 * s), observed=returns)
# <markdowncell>
# ## Fit Model
#
# To get a decent scaling matrix for the Hamiltonian sampler, we find the Hessian at a point. The method `Model.d2logpc` gives us a `Theano` compiled function that returns the matrix of 2nd derivatives.
#
# However, the 2nd derivatives for the degrees of freedom parameter, `nu`, are negative and thus not very informative and make the matrix non-positive definite, so we replace that entry with a reasonable guess at the scale. The interactions between `log_sigma`/`nu` and `s` are also not very useful, so we set them to zero.
#
# The Hessian matrix is also sparse, so we can get faster sampling by
# using a sparse scaling matrix. If you have `scikits.sparse` installed,
# convert the Hessian to a csc matrixs by uncommenting the appropriate
# line below.
# <codecell>
H = model.fastd2logp()
def hessian(point, nusd):
h = H(Point(point))
h[1, 1] = nusd ** -2
h[:2, 2:] = h[2:, :2] = 0
# h = csc_matrix(h)
return h
# <markdowncell>
# For this model, the full maximum a posteriori (MAP) point is degenerate and has infinite density. However, if we fix `log_sigma` and `nu` it is no longer degenerate, so we find the MAP with respect to the volatility process, 's', keeping `log_sigma` and `nu` constant at their default values.
#
# We use L-BFGS because it is more efficient for high dimensional
# functions (`s` has n elements).
# <codecell>
with model:
start = find_MAP(vars=[s], fmin=optimize.fmin_l_bfgs_b)
# <markdowncell>
# We do a short initial run to get near the right area, then start again
# using a new Hessian at the new starting point to get faster sampling due
# to better scaling. We do a short run since this is an interactive
# example.
# <codecell>
with model:
step = NUTS(model.vars, hessian(start, 6))
def run(n=2000):
if n == "short":
n = 50
with model:
trace = sample(5, step, start, trace=model.vars + [sigma])
# Start next run at the last sampled position.
start2 = trace.point(-1)
step2 = HamiltonianMC(model.vars, hessian(start2, 6), path_length=4.)
trace = sample(n, step2, trace=trace)
# <codecell>
# figsize(12,6)
title(str(s))
plot(trace[s][::10].T, 'b', alpha=.03)
xlabel('time')
ylabel('log volatility')
# figsize(12,6)
traceplot(trace, model.vars[:-1])
if __name__ == '__main__':
run()
# <markdowncell>
# ## References
#
# 1. Hoffman & Gelman. (2011). [The No-U-Turn Sampler: Adaptively Setting
# Path Lengths in Hamiltonian Monte
# Carlo](http://arxiv.org/abs/1111.4246).
| apache-2.0 |
gertingold/scipy | scipy/signal/spectral.py | 4 | 73530 | """Tools for spectral analysis.
"""
from __future__ import division, print_function, absolute_import
import numpy as np
from scipy import fft as sp_fft
from . import signaltools
from .windows import get_window
from ._spectral import _lombscargle
from ._arraytools import const_ext, even_ext, odd_ext, zero_ext
import warnings
from scipy._lib.six import string_types
__all__ = ['periodogram', 'welch', 'lombscargle', 'csd', 'coherence',
'spectrogram', 'stft', 'istft', 'check_COLA', 'check_NOLA']
def lombscargle(x,
y,
freqs,
precenter=False,
normalize=False):
"""
lombscargle(x, y, freqs)
Computes the Lomb-Scargle periodogram.
The Lomb-Scargle periodogram was developed by Lomb [1]_ and further
extended by Scargle [2]_ to find, and test the significance of weak
periodic signals with uneven temporal sampling.
When *normalize* is False (default) the computed periodogram
is unnormalized, it takes the value ``(A**2) * N/4`` for a harmonic
signal with amplitude A for sufficiently large N.
When *normalize* is True the computed periodogram is normalized by
the residuals of the data around a constant reference model (at zero).
Input arrays should be one-dimensional and will be cast to float64.
Parameters
----------
x : array_like
Sample times.
y : array_like
Measurement values.
freqs : array_like
Angular frequencies for output periodogram.
precenter : bool, optional
Pre-center amplitudes by subtracting the mean.
normalize : bool, optional
Compute normalized periodogram.
Returns
-------
pgram : array_like
Lomb-Scargle periodogram.
Raises
------
ValueError
If the input arrays `x` and `y` do not have the same shape.
Notes
-----
This subroutine calculates the periodogram using a slightly
modified algorithm due to Townsend [3]_ which allows the
periodogram to be calculated using only a single pass through
the input arrays for each frequency.
The algorithm running time scales roughly as O(x * freqs) or O(N^2)
for a large number of samples and frequencies.
References
----------
.. [1] N.R. Lomb "Least-squares frequency analysis of unequally spaced
data", Astrophysics and Space Science, vol 39, pp. 447-462, 1976
.. [2] J.D. Scargle "Studies in astronomical time series analysis. II -
Statistical aspects of spectral analysis of unevenly spaced data",
The Astrophysical Journal, vol 263, pp. 835-853, 1982
.. [3] R.H.D. Townsend, "Fast calculation of the Lomb-Scargle
periodogram using graphics processing units.", The Astrophysical
Journal Supplement Series, vol 191, pp. 247-253, 2010
See Also
--------
istft: Inverse Short Time Fourier Transform
check_COLA: Check whether the Constant OverLap Add (COLA) constraint is met
welch: Power spectral density by Welch's method
spectrogram: Spectrogram by Welch's method
csd: Cross spectral density by Welch's method
Examples
--------
>>> import matplotlib.pyplot as plt
First define some input parameters for the signal:
>>> A = 2.
>>> w = 1.
>>> phi = 0.5 * np.pi
>>> nin = 1000
>>> nout = 100000
>>> frac_points = 0.9 # Fraction of points to select
Randomly select a fraction of an array with timesteps:
>>> r = np.random.rand(nin)
>>> x = np.linspace(0.01, 10*np.pi, nin)
>>> x = x[r >= frac_points]
Plot a sine wave for the selected times:
>>> y = A * np.sin(w*x+phi)
Define the array of frequencies for which to compute the periodogram:
>>> f = np.linspace(0.01, 10, nout)
Calculate Lomb-Scargle periodogram:
>>> import scipy.signal as signal
>>> pgram = signal.lombscargle(x, y, f, normalize=True)
Now make a plot of the input data:
>>> plt.subplot(2, 1, 1)
>>> plt.plot(x, y, 'b+')
Then plot the normalized periodogram:
>>> plt.subplot(2, 1, 2)
>>> plt.plot(f, pgram)
>>> plt.show()
"""
x = np.asarray(x, dtype=np.float64)
y = np.asarray(y, dtype=np.float64)
freqs = np.asarray(freqs, dtype=np.float64)
assert x.ndim == 1
assert y.ndim == 1
assert freqs.ndim == 1
if precenter:
pgram = _lombscargle(x, y - y.mean(), freqs)
else:
pgram = _lombscargle(x, y, freqs)
if normalize:
pgram *= 2 / np.dot(y, y)
return pgram
def periodogram(x, fs=1.0, window='boxcar', nfft=None, detrend='constant',
return_onesided=True, scaling='density', axis=-1):
"""
Estimate power spectral density using a periodogram.
Parameters
----------
x : array_like
Time series of measurement values
fs : float, optional
Sampling frequency of the `x` time series. Defaults to 1.0.
window : str or tuple or array_like, optional
Desired window to use. If `window` is a string or tuple, it is
passed to `get_window` to generate the window values, which are
DFT-even by default. See `get_window` for a list of windows and
required parameters. If `window` is array_like it will be used
directly as the window and its length must be nperseg. Defaults
to 'boxcar'.
nfft : int, optional
Length of the FFT used. If `None` the length of `x` will be
used.
detrend : str or function or `False`, optional
Specifies how to detrend each segment. If `detrend` is a
string, it is passed as the `type` argument to the `detrend`
function. If it is a function, it takes a segment and returns a
detrended segment. If `detrend` is `False`, no detrending is
done. Defaults to 'constant'.
return_onesided : bool, optional
If `True`, return a one-sided spectrum for real data. If
`False` return a two-sided spectrum. Defaults to `True`, but for
complex data, a two-sided spectrum is always returned.
scaling : { 'density', 'spectrum' }, optional
Selects between computing the power spectral density ('density')
where `Pxx` has units of V**2/Hz and computing the power
spectrum ('spectrum') where `Pxx` has units of V**2, if `x`
is measured in V and `fs` is measured in Hz. Defaults to
'density'
axis : int, optional
Axis along which the periodogram is computed; the default is
over the last axis (i.e. ``axis=-1``).
Returns
-------
f : ndarray
Array of sample frequencies.
Pxx : ndarray
Power spectral density or power spectrum of `x`.
Notes
-----
.. versionadded:: 0.12.0
See Also
--------
welch: Estimate power spectral density using Welch's method
lombscargle: Lomb-Scargle periodogram for unevenly sampled data
Examples
--------
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> np.random.seed(1234)
Generate a test signal, a 2 Vrms sine wave at 1234 Hz, corrupted by
0.001 V**2/Hz of white noise sampled at 10 kHz.
>>> fs = 10e3
>>> N = 1e5
>>> amp = 2*np.sqrt(2)
>>> freq = 1234.0
>>> noise_power = 0.001 * fs / 2
>>> time = np.arange(N) / fs
>>> x = amp*np.sin(2*np.pi*freq*time)
>>> x += np.random.normal(scale=np.sqrt(noise_power), size=time.shape)
Compute and plot the power spectral density.
>>> f, Pxx_den = signal.periodogram(x, fs)
>>> plt.semilogy(f, Pxx_den)
>>> plt.ylim([1e-7, 1e2])
>>> plt.xlabel('frequency [Hz]')
>>> plt.ylabel('PSD [V**2/Hz]')
>>> plt.show()
If we average the last half of the spectral density, to exclude the
peak, we can recover the noise power on the signal.
>>> np.mean(Pxx_den[25000:])
0.00099728892368242854
Now compute and plot the power spectrum.
>>> f, Pxx_spec = signal.periodogram(x, fs, 'flattop', scaling='spectrum')
>>> plt.figure()
>>> plt.semilogy(f, np.sqrt(Pxx_spec))
>>> plt.ylim([1e-4, 1e1])
>>> plt.xlabel('frequency [Hz]')
>>> plt.ylabel('Linear spectrum [V RMS]')
>>> plt.show()
The peak height in the power spectrum is an estimate of the RMS
amplitude.
>>> np.sqrt(Pxx_spec.max())
2.0077340678640727
"""
x = np.asarray(x)
if x.size == 0:
return np.empty(x.shape), np.empty(x.shape)
if window is None:
window = 'boxcar'
if nfft is None:
nperseg = x.shape[axis]
elif nfft == x.shape[axis]:
nperseg = nfft
elif nfft > x.shape[axis]:
nperseg = x.shape[axis]
elif nfft < x.shape[axis]:
s = [np.s_[:]]*len(x.shape)
s[axis] = np.s_[:nfft]
x = x[tuple(s)]
nperseg = nfft
nfft = None
return welch(x, fs=fs, window=window, nperseg=nperseg, noverlap=0,
nfft=nfft, detrend=detrend, return_onesided=return_onesided,
scaling=scaling, axis=axis)
def welch(x, fs=1.0, window='hann', nperseg=None, noverlap=None, nfft=None,
detrend='constant', return_onesided=True, scaling='density',
axis=-1, average='mean'):
r"""
Estimate power spectral density using Welch's method.
Welch's method [1]_ computes an estimate of the power spectral
density by dividing the data into overlapping segments, computing a
modified periodogram for each segment and averaging the
periodograms.
Parameters
----------
x : array_like
Time series of measurement values
fs : float, optional
Sampling frequency of the `x` time series. Defaults to 1.0.
window : str or tuple or array_like, optional
Desired window to use. If `window` is a string or tuple, it is
passed to `get_window` to generate the window values, which are
DFT-even by default. See `get_window` for a list of windows and
required parameters. If `window` is array_like it will be used
directly as the window and its length must be nperseg. Defaults
to a Hann window.
nperseg : int, optional
Length of each segment. Defaults to None, but if window is str or
tuple, is set to 256, and if window is array_like, is set to the
length of the window.
noverlap : int, optional
Number of points to overlap between segments. If `None`,
``noverlap = nperseg // 2``. Defaults to `None`.
nfft : int, optional
Length of the FFT used, if a zero padded FFT is desired. If
`None`, the FFT length is `nperseg`. Defaults to `None`.
detrend : str or function or `False`, optional
Specifies how to detrend each segment. If `detrend` is a
string, it is passed as the `type` argument to the `detrend`
function. If it is a function, it takes a segment and returns a
detrended segment. If `detrend` is `False`, no detrending is
done. Defaults to 'constant'.
return_onesided : bool, optional
If `True`, return a one-sided spectrum for real data. If
`False` return a two-sided spectrum. Defaults to `True`, but for
complex data, a two-sided spectrum is always returned.
scaling : { 'density', 'spectrum' }, optional
Selects between computing the power spectral density ('density')
where `Pxx` has units of V**2/Hz and computing the power
spectrum ('spectrum') where `Pxx` has units of V**2, if `x`
is measured in V and `fs` is measured in Hz. Defaults to
'density'
axis : int, optional
Axis along which the periodogram is computed; the default is
over the last axis (i.e. ``axis=-1``).
average : { 'mean', 'median' }, optional
Method to use when averaging periodograms. Defaults to 'mean'.
.. versionadded:: 1.2.0
Returns
-------
f : ndarray
Array of sample frequencies.
Pxx : ndarray
Power spectral density or power spectrum of x.
See Also
--------
periodogram: Simple, optionally modified periodogram
lombscargle: Lomb-Scargle periodogram for unevenly sampled data
Notes
-----
An appropriate amount of overlap will depend on the choice of window
and on your requirements. For the default Hann window an overlap of
50% is a reasonable trade off between accurately estimating the
signal power, while not over counting any of the data. Narrower
windows may require a larger overlap.
If `noverlap` is 0, this method is equivalent to Bartlett's method
[2]_.
.. versionadded:: 0.12.0
References
----------
.. [1] P. Welch, "The use of the fast Fourier transform for the
estimation of power spectra: A method based on time averaging
over short, modified periodograms", IEEE Trans. Audio
Electroacoust. vol. 15, pp. 70-73, 1967.
.. [2] M.S. Bartlett, "Periodogram Analysis and Continuous Spectra",
Biometrika, vol. 37, pp. 1-16, 1950.
Examples
--------
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> np.random.seed(1234)
Generate a test signal, a 2 Vrms sine wave at 1234 Hz, corrupted by
0.001 V**2/Hz of white noise sampled at 10 kHz.
>>> fs = 10e3
>>> N = 1e5
>>> amp = 2*np.sqrt(2)
>>> freq = 1234.0
>>> noise_power = 0.001 * fs / 2
>>> time = np.arange(N) / fs
>>> x = amp*np.sin(2*np.pi*freq*time)
>>> x += np.random.normal(scale=np.sqrt(noise_power), size=time.shape)
Compute and plot the power spectral density.
>>> f, Pxx_den = signal.welch(x, fs, nperseg=1024)
>>> plt.semilogy(f, Pxx_den)
>>> plt.ylim([0.5e-3, 1])
>>> plt.xlabel('frequency [Hz]')
>>> plt.ylabel('PSD [V**2/Hz]')
>>> plt.show()
If we average the last half of the spectral density, to exclude the
peak, we can recover the noise power on the signal.
>>> np.mean(Pxx_den[256:])
0.0009924865443739191
Now compute and plot the power spectrum.
>>> f, Pxx_spec = signal.welch(x, fs, 'flattop', 1024, scaling='spectrum')
>>> plt.figure()
>>> plt.semilogy(f, np.sqrt(Pxx_spec))
>>> plt.xlabel('frequency [Hz]')
>>> plt.ylabel('Linear spectrum [V RMS]')
>>> plt.show()
The peak height in the power spectrum is an estimate of the RMS
amplitude.
>>> np.sqrt(Pxx_spec.max())
2.0077340678640727
If we now introduce a discontinuity in the signal, by increasing the
amplitude of a small portion of the signal by 50, we can see the
corruption of the mean average power spectral density, but using a
median average better estimates the normal behaviour.
>>> x[int(N//2):int(N//2)+10] *= 50.
>>> f, Pxx_den = signal.welch(x, fs, nperseg=1024)
>>> f_med, Pxx_den_med = signal.welch(x, fs, nperseg=1024, average='median')
>>> plt.semilogy(f, Pxx_den, label='mean')
>>> plt.semilogy(f_med, Pxx_den_med, label='median')
>>> plt.ylim([0.5e-3, 1])
>>> plt.xlabel('frequency [Hz]')
>>> plt.ylabel('PSD [V**2/Hz]')
>>> plt.legend()
>>> plt.show()
"""
freqs, Pxx = csd(x, x, fs=fs, window=window, nperseg=nperseg,
noverlap=noverlap, nfft=nfft, detrend=detrend,
return_onesided=return_onesided, scaling=scaling,
axis=axis, average=average)
return freqs, Pxx.real
def csd(x, y, fs=1.0, window='hann', nperseg=None, noverlap=None, nfft=None,
detrend='constant', return_onesided=True, scaling='density',
axis=-1, average='mean'):
r"""
Estimate the cross power spectral density, Pxy, using Welch's
method.
Parameters
----------
x : array_like
Time series of measurement values
y : array_like
Time series of measurement values
fs : float, optional
Sampling frequency of the `x` and `y` time series. Defaults
to 1.0.
window : str or tuple or array_like, optional
Desired window to use. If `window` is a string or tuple, it is
passed to `get_window` to generate the window values, which are
DFT-even by default. See `get_window` for a list of windows and
required parameters. If `window` is array_like it will be used
directly as the window and its length must be nperseg. Defaults
to a Hann window.
nperseg : int, optional
Length of each segment. Defaults to None, but if window is str or
tuple, is set to 256, and if window is array_like, is set to the
length of the window.
noverlap: int, optional
Number of points to overlap between segments. If `None`,
``noverlap = nperseg // 2``. Defaults to `None`.
nfft : int, optional
Length of the FFT used, if a zero padded FFT is desired. If
`None`, the FFT length is `nperseg`. Defaults to `None`.
detrend : str or function or `False`, optional
Specifies how to detrend each segment. If `detrend` is a
string, it is passed as the `type` argument to the `detrend`
function. If it is a function, it takes a segment and returns a
detrended segment. If `detrend` is `False`, no detrending is
done. Defaults to 'constant'.
return_onesided : bool, optional
If `True`, return a one-sided spectrum for real data. If
`False` return a two-sided spectrum. Defaults to `True`, but for
complex data, a two-sided spectrum is always returned.
scaling : { 'density', 'spectrum' }, optional
Selects between computing the cross spectral density ('density')
where `Pxy` has units of V**2/Hz and computing the cross spectrum
('spectrum') where `Pxy` has units of V**2, if `x` and `y` are
measured in V and `fs` is measured in Hz. Defaults to 'density'
axis : int, optional
Axis along which the CSD is computed for both inputs; the
default is over the last axis (i.e. ``axis=-1``).
average : { 'mean', 'median' }, optional
Method to use when averaging periodograms. Defaults to 'mean'.
.. versionadded:: 1.2.0
Returns
-------
f : ndarray
Array of sample frequencies.
Pxy : ndarray
Cross spectral density or cross power spectrum of x,y.
See Also
--------
periodogram: Simple, optionally modified periodogram
lombscargle: Lomb-Scargle periodogram for unevenly sampled data
welch: Power spectral density by Welch's method. [Equivalent to
csd(x,x)]
coherence: Magnitude squared coherence by Welch's method.
Notes
--------
By convention, Pxy is computed with the conjugate FFT of X
multiplied by the FFT of Y.
If the input series differ in length, the shorter series will be
zero-padded to match.
An appropriate amount of overlap will depend on the choice of window
and on your requirements. For the default Hann window an overlap of
50% is a reasonable trade off between accurately estimating the
signal power, while not over counting any of the data. Narrower
windows may require a larger overlap.
.. versionadded:: 0.16.0
References
----------
.. [1] P. Welch, "The use of the fast Fourier transform for the
estimation of power spectra: A method based on time averaging
over short, modified periodograms", IEEE Trans. Audio
Electroacoust. vol. 15, pp. 70-73, 1967.
.. [2] Rabiner, Lawrence R., and B. Gold. "Theory and Application of
Digital Signal Processing" Prentice-Hall, pp. 414-419, 1975
Examples
--------
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
Generate two test signals with some common features.
>>> fs = 10e3
>>> N = 1e5
>>> amp = 20
>>> freq = 1234.0
>>> noise_power = 0.001 * fs / 2
>>> time = np.arange(N) / fs
>>> b, a = signal.butter(2, 0.25, 'low')
>>> x = np.random.normal(scale=np.sqrt(noise_power), size=time.shape)
>>> y = signal.lfilter(b, a, x)
>>> x += amp*np.sin(2*np.pi*freq*time)
>>> y += np.random.normal(scale=0.1*np.sqrt(noise_power), size=time.shape)
Compute and plot the magnitude of the cross spectral density.
>>> f, Pxy = signal.csd(x, y, fs, nperseg=1024)
>>> plt.semilogy(f, np.abs(Pxy))
>>> plt.xlabel('frequency [Hz]')
>>> plt.ylabel('CSD [V**2/Hz]')
>>> plt.show()
"""
freqs, _, Pxy = _spectral_helper(x, y, fs, window, nperseg, noverlap, nfft,
detrend, return_onesided, scaling, axis,
mode='psd')
# Average over windows.
if len(Pxy.shape) >= 2 and Pxy.size > 0:
if Pxy.shape[-1] > 1:
if average == 'median':
Pxy = np.median(Pxy, axis=-1) / _median_bias(Pxy.shape[-1])
elif average == 'mean':
Pxy = Pxy.mean(axis=-1)
else:
raise ValueError('average must be "median" or "mean", got %s'
% (average,))
else:
Pxy = np.reshape(Pxy, Pxy.shape[:-1])
return freqs, Pxy
def spectrogram(x, fs=1.0, window=('tukey', .25), nperseg=None, noverlap=None,
nfft=None, detrend='constant', return_onesided=True,
scaling='density', axis=-1, mode='psd'):
"""
Compute a spectrogram with consecutive Fourier transforms.
Spectrograms can be used as a way of visualizing the change of a
nonstationary signal's frequency content over time.
Parameters
----------
x : array_like
Time series of measurement values
fs : float, optional
Sampling frequency of the `x` time series. Defaults to 1.0.
window : str or tuple or array_like, optional
Desired window to use. If `window` is a string or tuple, it is
passed to `get_window` to generate the window values, which are
DFT-even by default. See `get_window` for a list of windows and
required parameters. If `window` is array_like it will be used
directly as the window and its length must be nperseg.
Defaults to a Tukey window with shape parameter of 0.25.
nperseg : int, optional
Length of each segment. Defaults to None, but if window is str or
tuple, is set to 256, and if window is array_like, is set to the
length of the window.
noverlap : int, optional
Number of points to overlap between segments. If `None`,
``noverlap = nperseg // 8``. Defaults to `None`.
nfft : int, optional
Length of the FFT used, if a zero padded FFT is desired. If
`None`, the FFT length is `nperseg`. Defaults to `None`.
detrend : str or function or `False`, optional
Specifies how to detrend each segment. If `detrend` is a
string, it is passed as the `type` argument to the `detrend`
function. If it is a function, it takes a segment and returns a
detrended segment. If `detrend` is `False`, no detrending is
done. Defaults to 'constant'.
return_onesided : bool, optional
If `True`, return a one-sided spectrum for real data. If
`False` return a two-sided spectrum. Defaults to `True`, but for
complex data, a two-sided spectrum is always returned.
scaling : { 'density', 'spectrum' }, optional
Selects between computing the power spectral density ('density')
where `Sxx` has units of V**2/Hz and computing the power
spectrum ('spectrum') where `Sxx` has units of V**2, if `x`
is measured in V and `fs` is measured in Hz. Defaults to
'density'.
axis : int, optional
Axis along which the spectrogram is computed; the default is over
the last axis (i.e. ``axis=-1``).
mode : str, optional
Defines what kind of return values are expected. Options are
['psd', 'complex', 'magnitude', 'angle', 'phase']. 'complex' is
equivalent to the output of `stft` with no padding or boundary
extension. 'magnitude' returns the absolute magnitude of the
STFT. 'angle' and 'phase' return the complex angle of the STFT,
with and without unwrapping, respectively.
Returns
-------
f : ndarray
Array of sample frequencies.
t : ndarray
Array of segment times.
Sxx : ndarray
Spectrogram of x. By default, the last axis of Sxx corresponds
to the segment times.
See Also
--------
periodogram: Simple, optionally modified periodogram
lombscargle: Lomb-Scargle periodogram for unevenly sampled data
welch: Power spectral density by Welch's method.
csd: Cross spectral density by Welch's method.
Notes
-----
An appropriate amount of overlap will depend on the choice of window
and on your requirements. In contrast to welch's method, where the
entire data stream is averaged over, one may wish to use a smaller
overlap (or perhaps none at all) when computing a spectrogram, to
maintain some statistical independence between individual segments.
It is for this reason that the default window is a Tukey window with
1/8th of a window's length overlap at each end.
.. versionadded:: 0.16.0
References
----------
.. [1] Oppenheim, Alan V., Ronald W. Schafer, John R. Buck
"Discrete-Time Signal Processing", Prentice Hall, 1999.
Examples
--------
>>> from scipy import signal
>>> from scipy.fft import fftshift
>>> import matplotlib.pyplot as plt
Generate a test signal, a 2 Vrms sine wave whose frequency is slowly
modulated around 3kHz, corrupted by white noise of exponentially
decreasing magnitude sampled at 10 kHz.
>>> fs = 10e3
>>> N = 1e5
>>> amp = 2 * np.sqrt(2)
>>> noise_power = 0.01 * fs / 2
>>> time = np.arange(N) / float(fs)
>>> mod = 500*np.cos(2*np.pi*0.25*time)
>>> carrier = amp * np.sin(2*np.pi*3e3*time + mod)
>>> noise = np.random.normal(scale=np.sqrt(noise_power), size=time.shape)
>>> noise *= np.exp(-time/5)
>>> x = carrier + noise
Compute and plot the spectrogram.
>>> f, t, Sxx = signal.spectrogram(x, fs)
>>> plt.pcolormesh(t, f, Sxx)
>>> plt.ylabel('Frequency [Hz]')
>>> plt.xlabel('Time [sec]')
>>> plt.show()
Note, if using output that is not one sided, then use the following:
>>> f, t, Sxx = signal.spectrogram(x, fs, return_onesided=False)
>>> plt.pcolormesh(t, fftshift(f), fftshift(Sxx, axes=0))
>>> plt.ylabel('Frequency [Hz]')
>>> plt.xlabel('Time [sec]')
>>> plt.show()
"""
modelist = ['psd', 'complex', 'magnitude', 'angle', 'phase']
if mode not in modelist:
raise ValueError('unknown value for mode {}, must be one of {}'
.format(mode, modelist))
# need to set default for nperseg before setting default for noverlap below
window, nperseg = _triage_segments(window, nperseg,
input_length=x.shape[axis])
# Less overlap than welch, so samples are more statisically independent
if noverlap is None:
noverlap = nperseg // 8
if mode == 'psd':
freqs, time, Sxx = _spectral_helper(x, x, fs, window, nperseg,
noverlap, nfft, detrend,
return_onesided, scaling, axis,
mode='psd')
else:
freqs, time, Sxx = _spectral_helper(x, x, fs, window, nperseg,
noverlap, nfft, detrend,
return_onesided, scaling, axis,
mode='stft')
if mode == 'magnitude':
Sxx = np.abs(Sxx)
elif mode in ['angle', 'phase']:
Sxx = np.angle(Sxx)
if mode == 'phase':
# Sxx has one additional dimension for time strides
if axis < 0:
axis -= 1
Sxx = np.unwrap(Sxx, axis=axis)
# mode =='complex' is same as `stft`, doesn't need modification
return freqs, time, Sxx
def check_COLA(window, nperseg, noverlap, tol=1e-10):
r"""
Check whether the Constant OverLap Add (COLA) constraint is met
Parameters
----------
window : str or tuple or array_like
Desired window to use. If `window` is a string or tuple, it is
passed to `get_window` to generate the window values, which are
DFT-even by default. See `get_window` for a list of windows and
required parameters. If `window` is array_like it will be used
directly as the window and its length must be nperseg.
nperseg : int
Length of each segment.
noverlap : int
Number of points to overlap between segments.
tol : float, optional
The allowed variance of a bin's weighted sum from the median bin
sum.
Returns
-------
verdict : bool
`True` if chosen combination satisfies COLA within `tol`,
`False` otherwise
See Also
--------
check_NOLA: Check whether the Nonzero Overlap Add (NOLA) constraint is met
stft: Short Time Fourier Transform
istft: Inverse Short Time Fourier Transform
Notes
-----
In order to enable inversion of an STFT via the inverse STFT in
`istft`, it is sufficient that the signal windowing obeys the constraint of
"Constant OverLap Add" (COLA). This ensures that every point in the input
data is equally weighted, thereby avoiding aliasing and allowing full
reconstruction.
Some examples of windows that satisfy COLA:
- Rectangular window at overlap of 0, 1/2, 2/3, 3/4, ...
- Bartlett window at overlap of 1/2, 3/4, 5/6, ...
- Hann window at 1/2, 2/3, 3/4, ...
- Any Blackman family window at 2/3 overlap
- Any window with ``noverlap = nperseg-1``
A very comprehensive list of other windows may be found in [2]_,
wherein the COLA condition is satisfied when the "Amplitude
Flatness" is unity.
.. versionadded:: 0.19.0
References
----------
.. [1] Julius O. Smith III, "Spectral Audio Signal Processing", W3K
Publishing, 2011,ISBN 978-0-9745607-3-1.
.. [2] G. Heinzel, A. Ruediger and R. Schilling, "Spectrum and
spectral density estimation by the Discrete Fourier transform
(DFT), including a comprehensive list of window functions and
some new at-top windows", 2002,
http://hdl.handle.net/11858/00-001M-0000-0013-557A-5
Examples
--------
>>> from scipy import signal
Confirm COLA condition for rectangular window of 75% (3/4) overlap:
>>> signal.check_COLA(signal.boxcar(100), 100, 75)
True
COLA is not true for 25% (1/4) overlap, though:
>>> signal.check_COLA(signal.boxcar(100), 100, 25)
False
"Symmetrical" Hann window (for filter design) is not COLA:
>>> signal.check_COLA(signal.hann(120, sym=True), 120, 60)
False
"Periodic" or "DFT-even" Hann window (for FFT analysis) is COLA for
overlap of 1/2, 2/3, 3/4, etc.:
>>> signal.check_COLA(signal.hann(120, sym=False), 120, 60)
True
>>> signal.check_COLA(signal.hann(120, sym=False), 120, 80)
True
>>> signal.check_COLA(signal.hann(120, sym=False), 120, 90)
True
"""
nperseg = int(nperseg)
if nperseg < 1:
raise ValueError('nperseg must be a positive integer')
if noverlap >= nperseg:
raise ValueError('noverlap must be less than nperseg.')
noverlap = int(noverlap)
if isinstance(window, string_types) or type(window) is tuple:
win = get_window(window, nperseg)
else:
win = np.asarray(window)
if len(win.shape) != 1:
raise ValueError('window must be 1-D')
if win.shape[0] != nperseg:
raise ValueError('window must have length of nperseg')
step = nperseg - noverlap
binsums = sum(win[ii*step:(ii+1)*step] for ii in range(nperseg//step))
if nperseg % step != 0:
binsums[:nperseg % step] += win[-(nperseg % step):]
deviation = binsums - np.median(binsums)
return np.max(np.abs(deviation)) < tol
def check_NOLA(window, nperseg, noverlap, tol=1e-10):
r"""
Check whether the Nonzero Overlap Add (NOLA) constraint is met
Parameters
----------
window : str or tuple or array_like
Desired window to use. If `window` is a string or tuple, it is
passed to `get_window` to generate the window values, which are
DFT-even by default. See `get_window` for a list of windows and
required parameters. If `window` is array_like it will be used
directly as the window and its length must be nperseg.
nperseg : int
Length of each segment.
noverlap : int
Number of points to overlap between segments.
tol : float, optional
The allowed variance of a bin's weighted sum from the median bin
sum.
Returns
-------
verdict : bool
`True` if chosen combination satisfies the NOLA constraint within
`tol`, `False` otherwise
See Also
--------
check_COLA: Check whether the Constant OverLap Add (COLA) constraint is met
stft: Short Time Fourier Transform
istft: Inverse Short Time Fourier Transform
Notes
-----
In order to enable inversion of an STFT via the inverse STFT in
`istft`, the signal windowing must obey the constraint of "nonzero
overlap add" (NOLA):
.. math:: \sum_{t}w^{2}[n-tH] \ne 0
for all :math:`n`, where :math:`w` is the window function, :math:`t` is the
frame index, and :math:`H` is the hop size (:math:`H` = `nperseg` -
`noverlap`).
This ensures that the normalization factors in the denominator of the
overlap-add inversion equation are not zero. Only very pathological windows
will fail the NOLA constraint.
.. versionadded:: 1.2.0
References
----------
.. [1] Julius O. Smith III, "Spectral Audio Signal Processing", W3K
Publishing, 2011,ISBN 978-0-9745607-3-1.
.. [2] G. Heinzel, A. Ruediger and R. Schilling, "Spectrum and
spectral density estimation by the Discrete Fourier transform
(DFT), including a comprehensive list of window functions and
some new at-top windows", 2002,
http://hdl.handle.net/11858/00-001M-0000-0013-557A-5
Examples
--------
>>> from scipy import signal
Confirm NOLA condition for rectangular window of 75% (3/4) overlap:
>>> signal.check_NOLA(signal.boxcar(100), 100, 75)
True
NOLA is also true for 25% (1/4) overlap:
>>> signal.check_NOLA(signal.boxcar(100), 100, 25)
True
"Symmetrical" Hann window (for filter design) is also NOLA:
>>> signal.check_NOLA(signal.hann(120, sym=True), 120, 60)
True
As long as there is overlap, it takes quite a pathological window to fail
NOLA:
>>> w = np.ones(64, dtype="float")
>>> w[::2] = 0
>>> signal.check_NOLA(w, 64, 32)
False
If there is not enough overlap, a window with zeros at the ends will not
work:
>>> signal.check_NOLA(signal.hann(64), 64, 0)
False
>>> signal.check_NOLA(signal.hann(64), 64, 1)
False
>>> signal.check_NOLA(signal.hann(64), 64, 2)
True
"""
nperseg = int(nperseg)
if nperseg < 1:
raise ValueError('nperseg must be a positive integer')
if noverlap >= nperseg:
raise ValueError('noverlap must be less than nperseg')
if noverlap < 0:
raise ValueError('noverlap must be a nonnegative integer')
noverlap = int(noverlap)
if isinstance(window, string_types) or type(window) is tuple:
win = get_window(window, nperseg)
else:
win = np.asarray(window)
if len(win.shape) != 1:
raise ValueError('window must be 1-D')
if win.shape[0] != nperseg:
raise ValueError('window must have length of nperseg')
step = nperseg - noverlap
binsums = sum(win[ii*step:(ii+1)*step]**2 for ii in range(nperseg//step))
if nperseg % step != 0:
binsums[:nperseg % step] += win[-(nperseg % step):]**2
return np.min(binsums) > tol
def stft(x, fs=1.0, window='hann', nperseg=256, noverlap=None, nfft=None,
detrend=False, return_onesided=True, boundary='zeros', padded=True,
axis=-1):
r"""
Compute the Short Time Fourier Transform (STFT).
STFTs can be used as a way of quantifying the change of a
nonstationary signal's frequency and phase content over time.
Parameters
----------
x : array_like
Time series of measurement values
fs : float, optional
Sampling frequency of the `x` time series. Defaults to 1.0.
window : str or tuple or array_like, optional
Desired window to use. If `window` is a string or tuple, it is
passed to `get_window` to generate the window values, which are
DFT-even by default. See `get_window` for a list of windows and
required parameters. If `window` is array_like it will be used
directly as the window and its length must be nperseg. Defaults
to a Hann window.
nperseg : int, optional
Length of each segment. Defaults to 256.
noverlap : int, optional
Number of points to overlap between segments. If `None`,
``noverlap = nperseg // 2``. Defaults to `None`. When
specified, the COLA constraint must be met (see Notes below).
nfft : int, optional
Length of the FFT used, if a zero padded FFT is desired. If
`None`, the FFT length is `nperseg`. Defaults to `None`.
detrend : str or function or `False`, optional
Specifies how to detrend each segment. If `detrend` is a
string, it is passed as the `type` argument to the `detrend`
function. If it is a function, it takes a segment and returns a
detrended segment. If `detrend` is `False`, no detrending is
done. Defaults to `False`.
return_onesided : bool, optional
If `True`, return a one-sided spectrum for real data. If
`False` return a two-sided spectrum. Defaults to `True`, but for
complex data, a two-sided spectrum is always returned.
boundary : str or None, optional
Specifies whether the input signal is extended at both ends, and
how to generate the new values, in order to center the first
windowed segment on the first input point. This has the benefit
of enabling reconstruction of the first input point when the
employed window function starts at zero. Valid options are
``['even', 'odd', 'constant', 'zeros', None]``. Defaults to
'zeros', for zero padding extension. I.e. ``[1, 2, 3, 4]`` is
extended to ``[0, 1, 2, 3, 4, 0]`` for ``nperseg=3``.
padded : bool, optional
Specifies whether the input signal is zero-padded at the end to
make the signal fit exactly into an integer number of window
segments, so that all of the signal is included in the output.
Defaults to `True`. Padding occurs after boundary extension, if
`boundary` is not `None`, and `padded` is `True`, as is the
default.
axis : int, optional
Axis along which the STFT is computed; the default is over the
last axis (i.e. ``axis=-1``).
Returns
-------
f : ndarray
Array of sample frequencies.
t : ndarray
Array of segment times.
Zxx : ndarray
STFT of `x`. By default, the last axis of `Zxx` corresponds
to the segment times.
See Also
--------
istft: Inverse Short Time Fourier Transform
check_COLA: Check whether the Constant OverLap Add (COLA) constraint
is met
check_NOLA: Check whether the Nonzero Overlap Add (NOLA) constraint is met
welch: Power spectral density by Welch's method.
spectrogram: Spectrogram by Welch's method.
csd: Cross spectral density by Welch's method.
lombscargle: Lomb-Scargle periodogram for unevenly sampled data
Notes
-----
In order to enable inversion of an STFT via the inverse STFT in
`istft`, the signal windowing must obey the constraint of "Nonzero
OverLap Add" (NOLA), and the input signal must have complete
windowing coverage (i.e. ``(x.shape[axis] - nperseg) %
(nperseg-noverlap) == 0``). The `padded` argument may be used to
accomplish this.
Given a time-domain signal :math:`x[n]`, a window :math:`w[n]`, and a hop
size :math:`H` = `nperseg - noverlap`, the windowed frame at time index
:math:`t` is given by
.. math:: x_{t}[n]=x[n]w[n-tH]
The overlap-add (OLA) reconstruction equation is given by
.. math:: x[n]=\frac{\sum_{t}x_{t}[n]w[n-tH]}{\sum_{t}w^{2}[n-tH]}
The NOLA constraint ensures that every normalization term that appears
in the denomimator of the OLA reconstruction equation is nonzero. Whether a
choice of `window`, `nperseg`, and `noverlap` satisfy this constraint can
be tested with `check_NOLA`.
.. versionadded:: 0.19.0
References
----------
.. [1] Oppenheim, Alan V., Ronald W. Schafer, John R. Buck
"Discrete-Time Signal Processing", Prentice Hall, 1999.
.. [2] Daniel W. Griffin, Jae S. Lim "Signal Estimation from
Modified Short-Time Fourier Transform", IEEE 1984,
10.1109/TASSP.1984.1164317
Examples
--------
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
Generate a test signal, a 2 Vrms sine wave whose frequency is slowly
modulated around 3kHz, corrupted by white noise of exponentially
decreasing magnitude sampled at 10 kHz.
>>> fs = 10e3
>>> N = 1e5
>>> amp = 2 * np.sqrt(2)
>>> noise_power = 0.01 * fs / 2
>>> time = np.arange(N) / float(fs)
>>> mod = 500*np.cos(2*np.pi*0.25*time)
>>> carrier = amp * np.sin(2*np.pi*3e3*time + mod)
>>> noise = np.random.normal(scale=np.sqrt(noise_power),
... size=time.shape)
>>> noise *= np.exp(-time/5)
>>> x = carrier + noise
Compute and plot the STFT's magnitude.
>>> f, t, Zxx = signal.stft(x, fs, nperseg=1000)
>>> plt.pcolormesh(t, f, np.abs(Zxx), vmin=0, vmax=amp)
>>> plt.title('STFT Magnitude')
>>> plt.ylabel('Frequency [Hz]')
>>> plt.xlabel('Time [sec]')
>>> plt.show()
"""
freqs, time, Zxx = _spectral_helper(x, x, fs, window, nperseg, noverlap,
nfft, detrend, return_onesided,
scaling='spectrum', axis=axis,
mode='stft', boundary=boundary,
padded=padded)
return freqs, time, Zxx
def istft(Zxx, fs=1.0, window='hann', nperseg=None, noverlap=None, nfft=None,
input_onesided=True, boundary=True, time_axis=-1, freq_axis=-2):
r"""
Perform the inverse Short Time Fourier transform (iSTFT).
Parameters
----------
Zxx : array_like
STFT of the signal to be reconstructed. If a purely real array
is passed, it will be cast to a complex data type.
fs : float, optional
Sampling frequency of the time series. Defaults to 1.0.
window : str or tuple or array_like, optional
Desired window to use. If `window` is a string or tuple, it is
passed to `get_window` to generate the window values, which are
DFT-even by default. See `get_window` for a list of windows and
required parameters. If `window` is array_like it will be used
directly as the window and its length must be nperseg. Defaults
to a Hann window. Must match the window used to generate the
STFT for faithful inversion.
nperseg : int, optional
Number of data points corresponding to each STFT segment. This
parameter must be specified if the number of data points per
segment is odd, or if the STFT was padded via ``nfft >
nperseg``. If `None`, the value depends on the shape of
`Zxx` and `input_onesided`. If `input_onesided` is `True`,
``nperseg=2*(Zxx.shape[freq_axis] - 1)``. Otherwise,
``nperseg=Zxx.shape[freq_axis]``. Defaults to `None`.
noverlap : int, optional
Number of points to overlap between segments. If `None`, half
of the segment length. Defaults to `None`. When specified, the
COLA constraint must be met (see Notes below), and should match
the parameter used to generate the STFT. Defaults to `None`.
nfft : int, optional
Number of FFT points corresponding to each STFT segment. This
parameter must be specified if the STFT was padded via ``nfft >
nperseg``. If `None`, the default values are the same as for
`nperseg`, detailed above, with one exception: if
`input_onesided` is True and
``nperseg==2*Zxx.shape[freq_axis] - 1``, `nfft` also takes on
that value. This case allows the proper inversion of an
odd-length unpadded STFT using ``nfft=None``. Defaults to
`None`.
input_onesided : bool, optional
If `True`, interpret the input array as one-sided FFTs, such
as is returned by `stft` with ``return_onesided=True`` and
`numpy.fft.rfft`. If `False`, interpret the input as a a
two-sided FFT. Defaults to `True`.
boundary : bool, optional
Specifies whether the input signal was extended at its
boundaries by supplying a non-`None` ``boundary`` argument to
`stft`. Defaults to `True`.
time_axis : int, optional
Where the time segments of the STFT is located; the default is
the last axis (i.e. ``axis=-1``).
freq_axis : int, optional
Where the frequency axis of the STFT is located; the default is
the penultimate axis (i.e. ``axis=-2``).
Returns
-------
t : ndarray
Array of output data times.
x : ndarray
iSTFT of `Zxx`.
See Also
--------
stft: Short Time Fourier Transform
check_COLA: Check whether the Constant OverLap Add (COLA) constraint
is met
check_NOLA: Check whether the Nonzero Overlap Add (NOLA) constraint is met
Notes
-----
In order to enable inversion of an STFT via the inverse STFT with
`istft`, the signal windowing must obey the constraint of "nonzero
overlap add" (NOLA):
.. math:: \sum_{t}w^{2}[n-tH] \ne 0
This ensures that the normalization factors that appear in the denominator
of the overlap-add reconstruction equation
.. math:: x[n]=\frac{\sum_{t}x_{t}[n]w[n-tH]}{\sum_{t}w^{2}[n-tH]}
are not zero. The NOLA constraint can be checked with the `check_NOLA`
function.
An STFT which has been modified (via masking or otherwise) is not
guaranteed to correspond to a exactly realizible signal. This
function implements the iSTFT via the least-squares estimation
algorithm detailed in [2]_, which produces a signal that minimizes
the mean squared error between the STFT of the returned signal and
the modified STFT.
.. versionadded:: 0.19.0
References
----------
.. [1] Oppenheim, Alan V., Ronald W. Schafer, John R. Buck
"Discrete-Time Signal Processing", Prentice Hall, 1999.
.. [2] Daniel W. Griffin, Jae S. Lim "Signal Estimation from
Modified Short-Time Fourier Transform", IEEE 1984,
10.1109/TASSP.1984.1164317
Examples
--------
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
Generate a test signal, a 2 Vrms sine wave at 50Hz corrupted by
0.001 V**2/Hz of white noise sampled at 1024 Hz.
>>> fs = 1024
>>> N = 10*fs
>>> nperseg = 512
>>> amp = 2 * np.sqrt(2)
>>> noise_power = 0.001 * fs / 2
>>> time = np.arange(N) / float(fs)
>>> carrier = amp * np.sin(2*np.pi*50*time)
>>> noise = np.random.normal(scale=np.sqrt(noise_power),
... size=time.shape)
>>> x = carrier + noise
Compute the STFT, and plot its magnitude
>>> f, t, Zxx = signal.stft(x, fs=fs, nperseg=nperseg)
>>> plt.figure()
>>> plt.pcolormesh(t, f, np.abs(Zxx), vmin=0, vmax=amp)
>>> plt.ylim([f[1], f[-1]])
>>> plt.title('STFT Magnitude')
>>> plt.ylabel('Frequency [Hz]')
>>> plt.xlabel('Time [sec]')
>>> plt.yscale('log')
>>> plt.show()
Zero the components that are 10% or less of the carrier magnitude,
then convert back to a time series via inverse STFT
>>> Zxx = np.where(np.abs(Zxx) >= amp/10, Zxx, 0)
>>> _, xrec = signal.istft(Zxx, fs)
Compare the cleaned signal with the original and true carrier signals.
>>> plt.figure()
>>> plt.plot(time, x, time, xrec, time, carrier)
>>> plt.xlim([2, 2.1])
>>> plt.xlabel('Time [sec]')
>>> plt.ylabel('Signal')
>>> plt.legend(['Carrier + Noise', 'Filtered via STFT', 'True Carrier'])
>>> plt.show()
Note that the cleaned signal does not start as abruptly as the original,
since some of the coefficients of the transient were also removed:
>>> plt.figure()
>>> plt.plot(time, x, time, xrec, time, carrier)
>>> plt.xlim([0, 0.1])
>>> plt.xlabel('Time [sec]')
>>> plt.ylabel('Signal')
>>> plt.legend(['Carrier + Noise', 'Filtered via STFT', 'True Carrier'])
>>> plt.show()
"""
# Make sure input is an ndarray of appropriate complex dtype
Zxx = np.asarray(Zxx) + 0j
freq_axis = int(freq_axis)
time_axis = int(time_axis)
if Zxx.ndim < 2:
raise ValueError('Input stft must be at least 2d!')
if freq_axis == time_axis:
raise ValueError('Must specify differing time and frequency axes!')
nseg = Zxx.shape[time_axis]
if input_onesided:
# Assume even segment length
n_default = 2*(Zxx.shape[freq_axis] - 1)
else:
n_default = Zxx.shape[freq_axis]
# Check windowing parameters
if nperseg is None:
nperseg = n_default
else:
nperseg = int(nperseg)
if nperseg < 1:
raise ValueError('nperseg must be a positive integer')
if nfft is None:
if (input_onesided) and (nperseg == n_default + 1):
# Odd nperseg, no FFT padding
nfft = nperseg
else:
nfft = n_default
elif nfft < nperseg:
raise ValueError('nfft must be greater than or equal to nperseg.')
else:
nfft = int(nfft)
if noverlap is None:
noverlap = nperseg//2
else:
noverlap = int(noverlap)
if noverlap >= nperseg:
raise ValueError('noverlap must be less than nperseg.')
nstep = nperseg - noverlap
# Rearrange axes if necessary
if time_axis != Zxx.ndim-1 or freq_axis != Zxx.ndim-2:
# Turn negative indices to positive for the call to transpose
if freq_axis < 0:
freq_axis = Zxx.ndim + freq_axis
if time_axis < 0:
time_axis = Zxx.ndim + time_axis
zouter = list(range(Zxx.ndim))
for ax in sorted([time_axis, freq_axis], reverse=True):
zouter.pop(ax)
Zxx = np.transpose(Zxx, zouter+[freq_axis, time_axis])
# Get window as array
if isinstance(window, string_types) or type(window) is tuple:
win = get_window(window, nperseg)
else:
win = np.asarray(window)
if len(win.shape) != 1:
raise ValueError('window must be 1-D')
if win.shape[0] != nperseg:
raise ValueError('window must have length of {0}'.format(nperseg))
ifunc = sp_fft.irfft if input_onesided else sp_fft.ifft
xsubs = ifunc(Zxx, axis=-2, n=nfft)[..., :nperseg, :]
# Initialize output and normalization arrays
outputlength = nperseg + (nseg-1)*nstep
x = np.zeros(list(Zxx.shape[:-2])+[outputlength], dtype=xsubs.dtype)
norm = np.zeros(outputlength, dtype=xsubs.dtype)
if np.result_type(win, xsubs) != xsubs.dtype:
win = win.astype(xsubs.dtype)
xsubs *= win.sum() # This takes care of the 'spectrum' scaling
# Construct the output from the ifft segments
# This loop could perhaps be vectorized/strided somehow...
for ii in range(nseg):
# Window the ifft
x[..., ii*nstep:ii*nstep+nperseg] += xsubs[..., ii] * win
norm[..., ii*nstep:ii*nstep+nperseg] += win**2
# Remove extension points
if boundary:
x = x[..., nperseg//2:-(nperseg//2)]
norm = norm[..., nperseg//2:-(nperseg//2)]
# Divide out normalization where non-tiny
if np.sum(norm > 1e-10) != len(norm):
warnings.warn("NOLA condition failed, STFT may not be invertible")
x /= np.where(norm > 1e-10, norm, 1.0)
if input_onesided:
x = x.real
# Put axes back
if x.ndim > 1:
if time_axis != Zxx.ndim-1:
if freq_axis < time_axis:
time_axis -= 1
x = np.rollaxis(x, -1, time_axis)
time = np.arange(x.shape[0])/float(fs)
return time, x
def coherence(x, y, fs=1.0, window='hann', nperseg=None, noverlap=None,
nfft=None, detrend='constant', axis=-1):
r"""
Estimate the magnitude squared coherence estimate, Cxy, of
discrete-time signals X and Y using Welch's method.
``Cxy = abs(Pxy)**2/(Pxx*Pyy)``, where `Pxx` and `Pyy` are power
spectral density estimates of X and Y, and `Pxy` is the cross
spectral density estimate of X and Y.
Parameters
----------
x : array_like
Time series of measurement values
y : array_like
Time series of measurement values
fs : float, optional
Sampling frequency of the `x` and `y` time series. Defaults
to 1.0.
window : str or tuple or array_like, optional
Desired window to use. If `window` is a string or tuple, it is
passed to `get_window` to generate the window values, which are
DFT-even by default. See `get_window` for a list of windows and
required parameters. If `window` is array_like it will be used
directly as the window and its length must be nperseg. Defaults
to a Hann window.
nperseg : int, optional
Length of each segment. Defaults to None, but if window is str or
tuple, is set to 256, and if window is array_like, is set to the
length of the window.
noverlap: int, optional
Number of points to overlap between segments. If `None`,
``noverlap = nperseg // 2``. Defaults to `None`.
nfft : int, optional
Length of the FFT used, if a zero padded FFT is desired. If
`None`, the FFT length is `nperseg`. Defaults to `None`.
detrend : str or function or `False`, optional
Specifies how to detrend each segment. If `detrend` is a
string, it is passed as the `type` argument to the `detrend`
function. If it is a function, it takes a segment and returns a
detrended segment. If `detrend` is `False`, no detrending is
done. Defaults to 'constant'.
axis : int, optional
Axis along which the coherence is computed for both inputs; the
default is over the last axis (i.e. ``axis=-1``).
Returns
-------
f : ndarray
Array of sample frequencies.
Cxy : ndarray
Magnitude squared coherence of x and y.
See Also
--------
periodogram: Simple, optionally modified periodogram
lombscargle: Lomb-Scargle periodogram for unevenly sampled data
welch: Power spectral density by Welch's method.
csd: Cross spectral density by Welch's method.
Notes
--------
An appropriate amount of overlap will depend on the choice of window
and on your requirements. For the default Hann window an overlap of
50% is a reasonable trade off between accurately estimating the
signal power, while not over counting any of the data. Narrower
windows may require a larger overlap.
.. versionadded:: 0.16.0
References
----------
.. [1] P. Welch, "The use of the fast Fourier transform for the
estimation of power spectra: A method based on time averaging
over short, modified periodograms", IEEE Trans. Audio
Electroacoust. vol. 15, pp. 70-73, 1967.
.. [2] Stoica, Petre, and Randolph Moses, "Spectral Analysis of
Signals" Prentice Hall, 2005
Examples
--------
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
Generate two test signals with some common features.
>>> fs = 10e3
>>> N = 1e5
>>> amp = 20
>>> freq = 1234.0
>>> noise_power = 0.001 * fs / 2
>>> time = np.arange(N) / fs
>>> b, a = signal.butter(2, 0.25, 'low')
>>> x = np.random.normal(scale=np.sqrt(noise_power), size=time.shape)
>>> y = signal.lfilter(b, a, x)
>>> x += amp*np.sin(2*np.pi*freq*time)
>>> y += np.random.normal(scale=0.1*np.sqrt(noise_power), size=time.shape)
Compute and plot the coherence.
>>> f, Cxy = signal.coherence(x, y, fs, nperseg=1024)
>>> plt.semilogy(f, Cxy)
>>> plt.xlabel('frequency [Hz]')
>>> plt.ylabel('Coherence')
>>> plt.show()
"""
freqs, Pxx = welch(x, fs=fs, window=window, nperseg=nperseg,
noverlap=noverlap, nfft=nfft, detrend=detrend,
axis=axis)
_, Pyy = welch(y, fs=fs, window=window, nperseg=nperseg, noverlap=noverlap,
nfft=nfft, detrend=detrend, axis=axis)
_, Pxy = csd(x, y, fs=fs, window=window, nperseg=nperseg,
noverlap=noverlap, nfft=nfft, detrend=detrend, axis=axis)
Cxy = np.abs(Pxy)**2 / Pxx / Pyy
return freqs, Cxy
def _spectral_helper(x, y, fs=1.0, window='hann', nperseg=None, noverlap=None,
nfft=None, detrend='constant', return_onesided=True,
scaling='density', axis=-1, mode='psd', boundary=None,
padded=False):
"""
Calculate various forms of windowed FFTs for PSD, CSD, etc.
This is a helper function that implements the commonality between
the stft, psd, csd, and spectrogram functions. It is not designed to
be called externally. The windows are not averaged over; the result
from each window is returned.
Parameters
---------
x : array_like
Array or sequence containing the data to be analyzed.
y : array_like
Array or sequence containing the data to be analyzed. If this is
the same object in memory as `x` (i.e. ``_spectral_helper(x,
x, ...)``), the extra computations are spared.
fs : float, optional
Sampling frequency of the time series. Defaults to 1.0.
window : str or tuple or array_like, optional
Desired window to use. If `window` is a string or tuple, it is
passed to `get_window` to generate the window values, which are
DFT-even by default. See `get_window` for a list of windows and
required parameters. If `window` is array_like it will be used
directly as the window and its length must be nperseg. Defaults
to a Hann window.
nperseg : int, optional
Length of each segment. Defaults to None, but if window is str or
tuple, is set to 256, and if window is array_like, is set to the
length of the window.
noverlap : int, optional
Number of points to overlap between segments. If `None`,
``noverlap = nperseg // 2``. Defaults to `None`.
nfft : int, optional
Length of the FFT used, if a zero padded FFT is desired. If
`None`, the FFT length is `nperseg`. Defaults to `None`.
detrend : str or function or `False`, optional
Specifies how to detrend each segment. If `detrend` is a
string, it is passed as the `type` argument to the `detrend`
function. If it is a function, it takes a segment and returns a
detrended segment. If `detrend` is `False`, no detrending is
done. Defaults to 'constant'.
return_onesided : bool, optional
If `True`, return a one-sided spectrum for real data. If
`False` return a two-sided spectrum. Defaults to `True`, but for
complex data, a two-sided spectrum is always returned.
scaling : { 'density', 'spectrum' }, optional
Selects between computing the cross spectral density ('density')
where `Pxy` has units of V**2/Hz and computing the cross
spectrum ('spectrum') where `Pxy` has units of V**2, if `x`
and `y` are measured in V and `fs` is measured in Hz.
Defaults to 'density'
axis : int, optional
Axis along which the FFTs are computed; the default is over the
last axis (i.e. ``axis=-1``).
mode: str {'psd', 'stft'}, optional
Defines what kind of return values are expected. Defaults to
'psd'.
boundary : str or None, optional
Specifies whether the input signal is extended at both ends, and
how to generate the new values, in order to center the first
windowed segment on the first input point. This has the benefit
of enabling reconstruction of the first input point when the
employed window function starts at zero. Valid options are
``['even', 'odd', 'constant', 'zeros', None]``. Defaults to
`None`.
padded : bool, optional
Specifies whether the input signal is zero-padded at the end to
make the signal fit exactly into an integer number of window
segments, so that all of the signal is included in the output.
Defaults to `False`. Padding occurs after boundary extension, if
`boundary` is not `None`, and `padded` is `True`.
Returns
-------
freqs : ndarray
Array of sample frequencies.
t : ndarray
Array of times corresponding to each data segment
result : ndarray
Array of output data, contents dependent on *mode* kwarg.
Notes
-----
Adapted from matplotlib.mlab
.. versionadded:: 0.16.0
"""
if mode not in ['psd', 'stft']:
raise ValueError("Unknown value for mode %s, must be one of: "
"{'psd', 'stft'}" % mode)
boundary_funcs = {'even': even_ext,
'odd': odd_ext,
'constant': const_ext,
'zeros': zero_ext,
None: None}
if boundary not in boundary_funcs:
raise ValueError("Unknown boundary option '{0}', must be one of: {1}"
.format(boundary, list(boundary_funcs.keys())))
# If x and y are the same object we can save ourselves some computation.
same_data = y is x
if not same_data and mode != 'psd':
raise ValueError("x and y must be equal if mode is 'stft'")
axis = int(axis)
# Ensure we have np.arrays, get outdtype
x = np.asarray(x)
if not same_data:
y = np.asarray(y)
outdtype = np.result_type(x, y, np.complex64)
else:
outdtype = np.result_type(x, np.complex64)
if not same_data:
# Check if we can broadcast the outer axes together
xouter = list(x.shape)
youter = list(y.shape)
xouter.pop(axis)
youter.pop(axis)
try:
outershape = np.broadcast(np.empty(xouter), np.empty(youter)).shape
except ValueError:
raise ValueError('x and y cannot be broadcast together.')
if same_data:
if x.size == 0:
return np.empty(x.shape), np.empty(x.shape), np.empty(x.shape)
else:
if x.size == 0 or y.size == 0:
outshape = outershape + (min([x.shape[axis], y.shape[axis]]),)
emptyout = np.rollaxis(np.empty(outshape), -1, axis)
return emptyout, emptyout, emptyout
if x.ndim > 1:
if axis != -1:
x = np.rollaxis(x, axis, len(x.shape))
if not same_data and y.ndim > 1:
y = np.rollaxis(y, axis, len(y.shape))
# Check if x and y are the same length, zero-pad if necessary
if not same_data:
if x.shape[-1] != y.shape[-1]:
if x.shape[-1] < y.shape[-1]:
pad_shape = list(x.shape)
pad_shape[-1] = y.shape[-1] - x.shape[-1]
x = np.concatenate((x, np.zeros(pad_shape)), -1)
else:
pad_shape = list(y.shape)
pad_shape[-1] = x.shape[-1] - y.shape[-1]
y = np.concatenate((y, np.zeros(pad_shape)), -1)
if nperseg is not None: # if specified by user
nperseg = int(nperseg)
if nperseg < 1:
raise ValueError('nperseg must be a positive integer')
# parse window; if array like, then set nperseg = win.shape
win, nperseg = _triage_segments(window, nperseg, input_length=x.shape[-1])
if nfft is None:
nfft = nperseg
elif nfft < nperseg:
raise ValueError('nfft must be greater than or equal to nperseg.')
else:
nfft = int(nfft)
if noverlap is None:
noverlap = nperseg//2
else:
noverlap = int(noverlap)
if noverlap >= nperseg:
raise ValueError('noverlap must be less than nperseg.')
nstep = nperseg - noverlap
# Padding occurs after boundary extension, so that the extended signal ends
# in zeros, instead of introducing an impulse at the end.
# I.e. if x = [..., 3, 2]
# extend then pad -> [..., 3, 2, 2, 3, 0, 0, 0]
# pad then extend -> [..., 3, 2, 0, 0, 0, 2, 3]
if boundary is not None:
ext_func = boundary_funcs[boundary]
x = ext_func(x, nperseg//2, axis=-1)
if not same_data:
y = ext_func(y, nperseg//2, axis=-1)
if padded:
# Pad to integer number of windowed segments
# I.e make x.shape[-1] = nperseg + (nseg-1)*nstep, with integer nseg
nadd = (-(x.shape[-1]-nperseg) % nstep) % nperseg
zeros_shape = list(x.shape[:-1]) + [nadd]
x = np.concatenate((x, np.zeros(zeros_shape)), axis=-1)
if not same_data:
zeros_shape = list(y.shape[:-1]) + [nadd]
y = np.concatenate((y, np.zeros(zeros_shape)), axis=-1)
# Handle detrending and window functions
if not detrend:
def detrend_func(d):
return d
elif not hasattr(detrend, '__call__'):
def detrend_func(d):
return signaltools.detrend(d, type=detrend, axis=-1)
elif axis != -1:
# Wrap this function so that it receives a shape that it could
# reasonably expect to receive.
def detrend_func(d):
d = np.rollaxis(d, -1, axis)
d = detrend(d)
return np.rollaxis(d, axis, len(d.shape))
else:
detrend_func = detrend
if np.result_type(win, np.complex64) != outdtype:
win = win.astype(outdtype)
if scaling == 'density':
scale = 1.0 / (fs * (win*win).sum())
elif scaling == 'spectrum':
scale = 1.0 / win.sum()**2
else:
raise ValueError('Unknown scaling: %r' % scaling)
if mode == 'stft':
scale = np.sqrt(scale)
if return_onesided:
if np.iscomplexobj(x):
sides = 'twosided'
warnings.warn('Input data is complex, switching to '
'return_onesided=False')
else:
sides = 'onesided'
if not same_data:
if np.iscomplexobj(y):
sides = 'twosided'
warnings.warn('Input data is complex, switching to '
'return_onesided=False')
else:
sides = 'twosided'
if sides == 'twosided':
freqs = sp_fft.fftfreq(nfft, 1/fs)
elif sides == 'onesided':
freqs = sp_fft.rfftfreq(nfft, 1/fs)
# Perform the windowed FFTs
result = _fft_helper(x, win, detrend_func, nperseg, noverlap, nfft, sides)
if not same_data:
# All the same operations on the y data
result_y = _fft_helper(y, win, detrend_func, nperseg, noverlap, nfft,
sides)
result = np.conjugate(result) * result_y
elif mode == 'psd':
result = np.conjugate(result) * result
result *= scale
if sides == 'onesided' and mode == 'psd':
if nfft % 2:
result[..., 1:] *= 2
else:
# Last point is unpaired Nyquist freq point, don't double
result[..., 1:-1] *= 2
time = np.arange(nperseg/2, x.shape[-1] - nperseg/2 + 1,
nperseg - noverlap)/float(fs)
if boundary is not None:
time -= (nperseg/2) / fs
result = result.astype(outdtype)
# All imaginary parts are zero anyways
if same_data and mode != 'stft':
result = result.real
# Output is going to have new last axis for time/window index, so a
# negative axis index shifts down one
if axis < 0:
axis -= 1
# Roll frequency axis back to axis where the data came from
result = np.rollaxis(result, -1, axis)
return freqs, time, result
def _fft_helper(x, win, detrend_func, nperseg, noverlap, nfft, sides):
"""
Calculate windowed FFT, for internal use by
scipy.signal._spectral_helper
This is a helper function that does the main FFT calculation for
`_spectral helper`. All input validation is performed there, and the
data axis is assumed to be the last axis of x. It is not designed to
be called externally. The windows are not averaged over; the result
from each window is returned.
Returns
-------
result : ndarray
Array of FFT data
Notes
-----
Adapted from matplotlib.mlab
.. versionadded:: 0.16.0
"""
# Created strided array of data segments
if nperseg == 1 and noverlap == 0:
result = x[..., np.newaxis]
else:
# https://stackoverflow.com/a/5568169
step = nperseg - noverlap
shape = x.shape[:-1]+((x.shape[-1]-noverlap)//step, nperseg)
strides = x.strides[:-1]+(step*x.strides[-1], x.strides[-1])
result = np.lib.stride_tricks.as_strided(x, shape=shape,
strides=strides)
# Detrend each data segment individually
result = detrend_func(result)
# Apply window by multiplication
result = win * result
# Perform the fft. Acts on last axis by default. Zero-pads automatically
if sides == 'twosided':
func = sp_fft.fft
else:
result = result.real
func = sp_fft.rfft
result = func(result, n=nfft)
return result
def _triage_segments(window, nperseg, input_length):
"""
Parses window and nperseg arguments for spectrogram and _spectral_helper.
This is a helper function, not meant to be called externally.
Parameters
----------
window : string, tuple, or ndarray
If window is specified by a string or tuple and nperseg is not
specified, nperseg is set to the default of 256 and returns a window of
that length.
If instead the window is array_like and nperseg is not specified, then
nperseg is set to the length of the window. A ValueError is raised if
the user supplies both an array_like window and a value for nperseg but
nperseg does not equal the length of the window.
nperseg : int
Length of each segment
input_length: int
Length of input signal, i.e. x.shape[-1]. Used to test for errors.
Returns
-------
win : ndarray
window. If function was called with string or tuple than this will hold
the actual array used as a window.
nperseg : int
Length of each segment. If window is str or tuple, nperseg is set to
256. If window is array_like, nperseg is set to the length of the
6
window.
"""
# parse window; if array like, then set nperseg = win.shape
if isinstance(window, string_types) or isinstance(window, tuple):
# if nperseg not specified
if nperseg is None:
nperseg = 256 # then change to default
if nperseg > input_length:
warnings.warn('nperseg = {0:d} is greater than input length '
' = {1:d}, using nperseg = {1:d}'
.format(nperseg, input_length))
nperseg = input_length
win = get_window(window, nperseg)
else:
win = np.asarray(window)
if len(win.shape) != 1:
raise ValueError('window must be 1-D')
if input_length < win.shape[-1]:
raise ValueError('window is longer than input signal')
if nperseg is None:
nperseg = win.shape[0]
elif nperseg is not None:
if nperseg != win.shape[0]:
raise ValueError("value specified for nperseg is different"
" from length of window")
return win, nperseg
def _median_bias(n):
"""
Returns the bias of the median of a set of periodograms relative to
the mean.
See arXiv:gr-qc/0509116 Appendix B for details.
Parameters
----------
n : int
Numbers of periodograms being averaged.
Returns
-------
bias : float
Calculated bias.
"""
ii_2 = 2 * np.arange(1., (n-1) // 2 + 1)
return 1 + np.sum(1. / (ii_2 + 1) - 1. / ii_2)
| bsd-3-clause |
kernc/scikit-learn | sklearn/model_selection/_search.py | 16 | 38824 | """
The :mod:`sklearn.model_selection._search` includes utilities to fine-tune the
parameters of an estimator.
"""
from __future__ import print_function
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
from collections import Mapping, namedtuple, Sized
from functools import partial, reduce
from itertools import product
import operator
import warnings
import numpy as np
from ..base import BaseEstimator, is_classifier, clone
from ..base import MetaEstimatorMixin, ChangedBehaviorWarning
from ._split import check_cv
from ._validation import _fit_and_score
from ..externals.joblib import Parallel, delayed
from ..externals import six
from ..utils import check_random_state
from ..utils.fixes import sp_version
from ..utils.random import sample_without_replacement
from ..utils.validation import _num_samples, indexable
from ..utils.metaestimators import if_delegate_has_method
from ..metrics.scorer import check_scoring
__all__ = ['GridSearchCV', 'ParameterGrid', 'fit_grid_point',
'ParameterSampler', 'RandomizedSearchCV']
class ParameterGrid(object):
"""Grid of parameters with a discrete number of values for each.
Can be used to iterate over parameter value combinations with the
Python built-in function iter.
Read more in the :ref:`User Guide <search>`.
Parameters
----------
param_grid : dict of string to sequence, or sequence of such
The parameter grid to explore, as a dictionary mapping estimator
parameters to sequences of allowed values.
An empty dict signifies default parameters.
A sequence of dicts signifies a sequence of grids to search, and is
useful to avoid exploring parameter combinations that make no sense
or have no effect. See the examples below.
Examples
--------
>>> from sklearn.model_selection import ParameterGrid
>>> param_grid = {'a': [1, 2], 'b': [True, False]}
>>> list(ParameterGrid(param_grid)) == (
... [{'a': 1, 'b': True}, {'a': 1, 'b': False},
... {'a': 2, 'b': True}, {'a': 2, 'b': False}])
True
>>> grid = [{'kernel': ['linear']}, {'kernel': ['rbf'], 'gamma': [1, 10]}]
>>> list(ParameterGrid(grid)) == [{'kernel': 'linear'},
... {'kernel': 'rbf', 'gamma': 1},
... {'kernel': 'rbf', 'gamma': 10}]
True
>>> ParameterGrid(grid)[1] == {'kernel': 'rbf', 'gamma': 1}
True
See also
--------
:class:`GridSearchCV`:
Uses :class:`ParameterGrid` to perform a full parallelized parameter
search.
"""
def __init__(self, param_grid):
if isinstance(param_grid, Mapping):
# wrap dictionary in a singleton list to support either dict
# or list of dicts
param_grid = [param_grid]
self.param_grid = param_grid
def __iter__(self):
"""Iterate over the points in the grid.
Returns
-------
params : iterator over dict of string to any
Yields dictionaries mapping each estimator parameter to one of its
allowed values.
"""
for p in self.param_grid:
# Always sort the keys of a dictionary, for reproducibility
items = sorted(p.items())
if not items:
yield {}
else:
keys, values = zip(*items)
for v in product(*values):
params = dict(zip(keys, v))
yield params
def __len__(self):
"""Number of points on the grid."""
# Product function that can handle iterables (np.product can't).
product = partial(reduce, operator.mul)
return sum(product(len(v) for v in p.values()) if p else 1
for p in self.param_grid)
def __getitem__(self, ind):
"""Get the parameters that would be ``ind``th in iteration
Parameters
----------
ind : int
The iteration index
Returns
-------
params : dict of string to any
Equal to list(self)[ind]
"""
# This is used to make discrete sampling without replacement memory
# efficient.
for sub_grid in self.param_grid:
# XXX: could memoize information used here
if not sub_grid:
if ind == 0:
return {}
else:
ind -= 1
continue
# Reverse so most frequent cycling parameter comes first
keys, values_lists = zip(*sorted(sub_grid.items())[::-1])
sizes = [len(v_list) for v_list in values_lists]
total = np.product(sizes)
if ind >= total:
# Try the next grid
ind -= total
else:
out = {}
for key, v_list, n in zip(keys, values_lists, sizes):
ind, offset = divmod(ind, n)
out[key] = v_list[offset]
return out
raise IndexError('ParameterGrid index out of range')
class ParameterSampler(object):
"""Generator on parameters sampled from given distributions.
Non-deterministic iterable over random candidate combinations for hyper-
parameter search. If all parameters are presented as a list,
sampling without replacement is performed. If at least one parameter
is given as a distribution, sampling with replacement is used.
It is highly recommended to use continuous distributions for continuous
parameters.
Note that before SciPy 0.16, the ``scipy.stats.distributions`` do not
accept a custom RNG instance and always use the singleton RNG from
``numpy.random``. Hence setting ``random_state`` will not guarantee a
deterministic iteration whenever ``scipy.stats`` distributions are used to
define the parameter search space. Deterministic behavior is however
guaranteed from SciPy 0.16 onwards.
Read more in the :ref:`User Guide <search>`.
Parameters
----------
param_distributions : dict
Dictionary where the keys are parameters and values
are distributions from which a parameter is to be sampled.
Distributions either have to provide a ``rvs`` function
to sample from them, or can be given as a list of values,
where a uniform distribution is assumed.
n_iter : integer
Number of parameter settings that are produced.
random_state : int or RandomState
Pseudo random number generator state used for random uniform sampling
from lists of possible values instead of scipy.stats distributions.
Returns
-------
params : dict of string to any
**Yields** dictionaries mapping each estimator parameter to
as sampled value.
Examples
--------
>>> from sklearn.model_selection import ParameterSampler
>>> from scipy.stats.distributions import expon
>>> import numpy as np
>>> np.random.seed(0)
>>> param_grid = {'a':[1, 2], 'b': expon()}
>>> param_list = list(ParameterSampler(param_grid, n_iter=4))
>>> rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items())
... for d in param_list]
>>> rounded_list == [{'b': 0.89856, 'a': 1},
... {'b': 0.923223, 'a': 1},
... {'b': 1.878964, 'a': 2},
... {'b': 1.038159, 'a': 2}]
True
"""
def __init__(self, param_distributions, n_iter, random_state=None):
self.param_distributions = param_distributions
self.n_iter = n_iter
self.random_state = random_state
def __iter__(self):
# check if all distributions are given as lists
# in this case we want to sample without replacement
all_lists = np.all([not hasattr(v, "rvs")
for v in self.param_distributions.values()])
rnd = check_random_state(self.random_state)
if all_lists:
# look up sampled parameter settings in parameter grid
param_grid = ParameterGrid(self.param_distributions)
grid_size = len(param_grid)
if grid_size < self.n_iter:
raise ValueError(
"The total space of parameters %d is smaller "
"than n_iter=%d." % (grid_size, self.n_iter)
+ " For exhaustive searches, use GridSearchCV.")
for i in sample_without_replacement(grid_size, self.n_iter,
random_state=rnd):
yield param_grid[i]
else:
# Always sort the keys of a dictionary, for reproducibility
items = sorted(self.param_distributions.items())
for _ in six.moves.range(self.n_iter):
params = dict()
for k, v in items:
if hasattr(v, "rvs"):
if sp_version < (0, 16):
params[k] = v.rvs()
else:
params[k] = v.rvs(random_state=rnd)
else:
params[k] = v[rnd.randint(len(v))]
yield params
def __len__(self):
"""Number of points that will be sampled."""
return self.n_iter
def fit_grid_point(X, y, estimator, parameters, train, test, scorer,
verbose, error_score='raise', **fit_params):
"""Run fit on one set of parameters.
Parameters
----------
X : array-like, sparse matrix or list
Input data.
y : array-like or None
Targets for input data.
estimator : estimator object
A object of that type is instantiated for each grid point.
This is assumed to implement the scikit-learn estimator interface.
Either estimator needs to provide a ``score`` function,
or ``scoring`` must be passed.
parameters : dict
Parameters to be set on estimator for this grid point.
train : ndarray, dtype int or bool
Boolean mask or indices for training set.
test : ndarray, dtype int or bool
Boolean mask or indices for test set.
scorer : callable or None.
If provided must be a scorer callable object / function with signature
``scorer(estimator, X, y)``.
verbose : int
Verbosity level.
**fit_params : kwargs
Additional parameter passed to the fit function of the estimator.
error_score : 'raise' (default) or numeric
Value to assign to the score if an error occurs in estimator fitting.
If set to 'raise', the error is raised. If a numeric value is given,
FitFailedWarning is raised. This parameter does not affect the refit
step, which will always raise the error.
Returns
-------
score : float
Score of this parameter setting on given training / test split.
parameters : dict
The parameters that have been evaluated.
n_samples_test : int
Number of test samples in this split.
"""
score, n_samples_test, _ = _fit_and_score(estimator, X, y, scorer, train,
test, verbose, parameters,
fit_params, error_score)
return score, parameters, n_samples_test
def _check_param_grid(param_grid):
if hasattr(param_grid, 'items'):
param_grid = [param_grid]
for p in param_grid:
for v in p.values():
if isinstance(v, np.ndarray) and v.ndim > 1:
raise ValueError("Parameter array should be one-dimensional.")
check = [isinstance(v, k) for k in (list, tuple, np.ndarray)]
if True not in check:
raise ValueError("Parameter values should be a list.")
if len(v) == 0:
raise ValueError("Parameter values should be a non-empty "
"list.")
class _CVScoreTuple (namedtuple('_CVScoreTuple',
('parameters',
'mean_validation_score',
'cv_validation_scores'))):
# A raw namedtuple is very memory efficient as it packs the attributes
# in a struct to get rid of the __dict__ of attributes in particular it
# does not copy the string for the keys on each instance.
# By deriving a namedtuple class just to introduce the __repr__ method we
# would also reintroduce the __dict__ on the instance. By telling the
# Python interpreter that this subclass uses static __slots__ instead of
# dynamic attributes. Furthermore we don't need any additional slot in the
# subclass so we set __slots__ to the empty tuple.
__slots__ = ()
def __repr__(self):
"""Simple custom repr to summarize the main info"""
return "mean: {0:.5f}, std: {1:.5f}, params: {2}".format(
self.mean_validation_score,
np.std(self.cv_validation_scores),
self.parameters)
class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator,
MetaEstimatorMixin)):
"""Base class for hyper parameter search with cross-validation."""
@abstractmethod
def __init__(self, estimator, scoring=None,
fit_params=None, n_jobs=1, iid=True,
refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs',
error_score='raise'):
self.scoring = scoring
self.estimator = estimator
self.n_jobs = n_jobs
self.fit_params = fit_params if fit_params is not None else {}
self.iid = iid
self.refit = refit
self.cv = cv
self.verbose = verbose
self.pre_dispatch = pre_dispatch
self.error_score = error_score
@property
def _estimator_type(self):
return self.estimator._estimator_type
def score(self, X, y=None):
"""Returns the score on the given data, if the estimator has been refit.
This uses the score defined by ``scoring`` where provided, and the
``best_estimator_.score`` method otherwise.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Input data, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression;
None for unsupervised learning.
Returns
-------
score : float
Notes
-----
* The long-standing behavior of this method changed in version 0.16.
* It no longer uses the metric provided by ``estimator.score`` if the
``scoring`` parameter was set when fitting.
"""
if self.scorer_ is None:
raise ValueError("No score function explicitly defined, "
"and the estimator doesn't provide one %s"
% self.best_estimator_)
if self.scoring is not None and hasattr(self.best_estimator_, 'score'):
warnings.warn("The long-standing behavior to use the estimator's "
"score function in {0}.score has changed. The "
"scoring parameter is now used."
"".format(self.__class__.__name__),
ChangedBehaviorWarning)
return self.scorer_(self.best_estimator_, X, y)
@if_delegate_has_method(delegate='estimator')
def predict(self, X):
"""Call predict on the estimator with the best found parameters.
Only available if ``refit=True`` and the underlying estimator supports
``predict``.
Parameters
-----------
X : indexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
"""
return self.best_estimator_.predict(X)
@if_delegate_has_method(delegate='estimator')
def predict_proba(self, X):
"""Call predict_proba on the estimator with the best found parameters.
Only available if ``refit=True`` and the underlying estimator supports
``predict_proba``.
Parameters
-----------
X : indexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
"""
return self.best_estimator_.predict_proba(X)
@if_delegate_has_method(delegate='estimator')
def predict_log_proba(self, X):
"""Call predict_log_proba on the estimator with the best found parameters.
Only available if ``refit=True`` and the underlying estimator supports
``predict_log_proba``.
Parameters
-----------
X : indexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
"""
return self.best_estimator_.predict_log_proba(X)
@if_delegate_has_method(delegate='estimator')
def decision_function(self, X):
"""Call decision_function on the estimator with the best found parameters.
Only available if ``refit=True`` and the underlying estimator supports
``decision_function``.
Parameters
-----------
X : indexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
"""
return self.best_estimator_.decision_function(X)
@if_delegate_has_method(delegate='estimator')
def transform(self, X):
"""Call transform on the estimator with the best found parameters.
Only available if the underlying estimator supports ``transform`` and
``refit=True``.
Parameters
-----------
X : indexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
"""
return self.best_estimator_.transform(X)
@if_delegate_has_method(delegate='estimator')
def inverse_transform(self, Xt):
"""Call inverse_transform on the estimator with the best found parameters.
Only available if the underlying estimator implements
``inverse_transform`` and ``refit=True``.
Parameters
-----------
Xt : indexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
"""
return self.best_estimator_.transform(Xt)
def _fit(self, X, y, labels, parameter_iterable):
"""Actual fitting, performing the search over parameters."""
estimator = self.estimator
cv = check_cv(self.cv, y, classifier=is_classifier(estimator))
self.scorer_ = check_scoring(self.estimator, scoring=self.scoring)
n_samples = _num_samples(X)
X, y, labels = indexable(X, y, labels)
if y is not None:
if len(y) != n_samples:
raise ValueError('Target variable (y) has a different number '
'of samples (%i) than data (X: %i samples)'
% (len(y), n_samples))
n_splits = cv.get_n_splits(X, y, labels)
if self.verbose > 0 and isinstance(parameter_iterable, Sized):
n_candidates = len(parameter_iterable)
print("Fitting {0} folds for each of {1} candidates, totalling"
" {2} fits".format(n_splits, n_candidates,
n_candidates * n_splits))
base_estimator = clone(self.estimator)
pre_dispatch = self.pre_dispatch
out = Parallel(
n_jobs=self.n_jobs, verbose=self.verbose,
pre_dispatch=pre_dispatch
)(delayed(_fit_and_score)(clone(base_estimator), X, y, self.scorer_,
train, test, self.verbose, parameters,
self.fit_params, return_parameters=True,
error_score=self.error_score)
for parameters in parameter_iterable
for train, test in cv.split(X, y, labels))
# Out is a list of triplet: score, estimator, n_test_samples
n_fits = len(out)
scores = list()
grid_scores = list()
for grid_start in range(0, n_fits, n_splits):
n_test_samples = 0
score = 0
all_scores = []
for this_score, this_n_test_samples, _, parameters in \
out[grid_start:grid_start + n_splits]:
all_scores.append(this_score)
if self.iid:
this_score *= this_n_test_samples
n_test_samples += this_n_test_samples
score += this_score
if self.iid:
score /= float(n_test_samples)
else:
score /= float(n_splits)
scores.append((score, parameters))
# TODO: shall we also store the test_fold_sizes?
grid_scores.append(_CVScoreTuple(
parameters,
score,
np.array(all_scores)))
# Store the computed scores
self.grid_scores_ = grid_scores
# Find the best parameters by comparing on the mean validation score:
# note that `sorted` is deterministic in the way it breaks ties
best = sorted(grid_scores, key=lambda x: x.mean_validation_score,
reverse=True)[0]
self.best_params_ = best.parameters
self.best_score_ = best.mean_validation_score
if self.refit:
# fit the best estimator using the entire dataset
# clone first to work around broken estimators
best_estimator = clone(base_estimator).set_params(
**best.parameters)
if y is not None:
best_estimator.fit(X, y, **self.fit_params)
else:
best_estimator.fit(X, **self.fit_params)
self.best_estimator_ = best_estimator
return self
class GridSearchCV(BaseSearchCV):
"""Exhaustive search over specified parameter values for an estimator.
Important members are fit, predict.
GridSearchCV implements a "fit" and a "score" method.
It also implements "predict", "predict_proba", "decision_function",
"transform" and "inverse_transform" if they are implemented in the
estimator used.
The parameters of the estimator used to apply these methods are optimized
by cross-validated grid-search over a parameter grid.
Read more in the :ref:`User Guide <grid_search>`.
Parameters
----------
estimator : estimator object.
This is assumed to implement the scikit-learn estimator interface.
Either estimator needs to provide a ``score`` function,
or ``scoring`` must be passed.
param_grid : dict or list of dictionaries
Dictionary with parameters names (string) as keys and lists of
parameter settings to try as values, or a list of such
dictionaries, in which case the grids spanned by each dictionary
in the list are explored. This enables searching over any sequence
of parameter settings.
scoring : string, callable or None, default=None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
If ``None``, the ``score`` method of the estimator is used.
fit_params : dict, optional
Parameters to pass to the fit method.
n_jobs : int, default=1
Number of jobs to run in parallel.
pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel
execution. Reducing this number can be useful to avoid an
explosion of memory consumption when more jobs get dispatched
than CPUs can process. This parameter can be:
- None, in which case all the jobs are immediately
created and spawned. Use this for lightweight and
fast-running jobs, to avoid delays due to on-demand
spawning of the jobs
- An int, giving the exact number of total jobs that are
spawned
- A string, giving an expression as a function of n_jobs,
as in '2*n_jobs'
iid : boolean, default=True
If True, the data is assumed to be identically distributed across
the folds, and the loss minimized is the total loss per sample,
and not the mean loss across the folds.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- An object to be used as a cross-validation generator.
- An iterable yielding train, test splits.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` used. In all
other cases, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
refit : boolean, default=True
Refit the best estimator with the entire dataset.
If "False", it is impossible to make predictions using
this GridSearchCV instance after fitting.
verbose : integer
Controls the verbosity: the higher, the more messages.
error_score : 'raise' (default) or numeric
Value to assign to the score if an error occurs in estimator fitting.
If set to 'raise', the error is raised. If a numeric value is given,
FitFailedWarning is raised. This parameter does not affect the refit
step, which will always raise the error.
Examples
--------
>>> from sklearn import svm, datasets
>>> from sklearn.model_selection import GridSearchCV
>>> iris = datasets.load_iris()
>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
>>> svr = svm.SVC()
>>> clf = GridSearchCV(svr, parameters)
>>> clf.fit(iris.data, iris.target)
... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
GridSearchCV(cv=None, error_score=...,
estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=...,
decision_function_shape=None, degree=..., gamma=...,
kernel='rbf', max_iter=-1, probability=False,
random_state=None, shrinking=True, tol=...,
verbose=False),
fit_params={}, iid=..., n_jobs=1,
param_grid=..., pre_dispatch=..., refit=...,
scoring=..., verbose=...)
Attributes
----------
grid_scores_ : list of named tuples
Contains scores for all parameter combinations in param_grid.
Each entry corresponds to one parameter setting.
Each named tuple has the attributes:
* ``parameters``, a dict of parameter settings
* ``mean_validation_score``, the mean score over the
cross-validation folds
* ``cv_validation_scores``, the list of scores for each fold
best_estimator_ : estimator
Estimator that was chosen by the search, i.e. estimator
which gave highest score (or smallest loss if specified)
on the left out data. Not available if refit=False.
best_score_ : float
Score of best_estimator on the left out data.
best_params_ : dict
Parameter setting that gave the best results on the hold out data.
scorer_ : function
Scorer function used on the held out data to choose the best
parameters for the model.
Notes
------
The parameters selected are those that maximize the score of the left out
data, unless an explicit score is passed in which case it is used instead.
If `n_jobs` was set to a value higher than one, the data is copied for each
point in the grid (and not `n_jobs` times). This is done for efficiency
reasons if individual jobs take very little time, but may raise errors if
the dataset is large and not enough memory is available. A workaround in
this case is to set `pre_dispatch`. Then, the memory is copied only
`pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
n_jobs`.
See Also
---------
:class:`ParameterGrid`:
generates all the combinations of a hyperparameter grid.
:func:`sklearn.model_selection.train_test_split`:
utility function to split the data into a development set usable
for fitting a GridSearchCV instance and an evaluation set for
its final evaluation.
:func:`sklearn.metrics.make_scorer`:
Make a scorer from a performance metric or loss function.
"""
def __init__(self, estimator, param_grid, scoring=None, fit_params=None,
n_jobs=1, iid=True, refit=True, cv=None, verbose=0,
pre_dispatch='2*n_jobs', error_score='raise'):
super(GridSearchCV, self).__init__(
estimator=estimator, scoring=scoring, fit_params=fit_params,
n_jobs=n_jobs, iid=iid, refit=refit, cv=cv, verbose=verbose,
pre_dispatch=pre_dispatch, error_score=error_score)
self.param_grid = param_grid
_check_param_grid(param_grid)
def fit(self, X, y=None, labels=None):
"""Run fit with all sets of parameters.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression;
None for unsupervised learning.
labels : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into
train/test set.
"""
return self._fit(X, y, labels, ParameterGrid(self.param_grid))
class RandomizedSearchCV(BaseSearchCV):
"""Randomized search on hyper parameters.
RandomizedSearchCV implements a "fit" and a "score" method.
It also implements "predict", "predict_proba", "decision_function",
"transform" and "inverse_transform" if they are implemented in the
estimator used.
The parameters of the estimator used to apply these methods are optimized
by cross-validated search over parameter settings.
In contrast to GridSearchCV, not all parameter values are tried out, but
rather a fixed number of parameter settings is sampled from the specified
distributions. The number of parameter settings that are tried is
given by n_iter.
If all parameters are presented as a list,
sampling without replacement is performed. If at least one parameter
is given as a distribution, sampling with replacement is used.
It is highly recommended to use continuous distributions for continuous
parameters.
Read more in the :ref:`User Guide <randomized_parameter_search>`.
Parameters
----------
estimator : estimator object.
A object of that type is instantiated for each grid point.
This is assumed to implement the scikit-learn estimator interface.
Either estimator needs to provide a ``score`` function,
or ``scoring`` must be passed.
param_distributions : dict
Dictionary with parameters names (string) as keys and distributions
or lists of parameters to try. Distributions must provide a ``rvs``
method for sampling (such as those from scipy.stats.distributions).
If a list is given, it is sampled uniformly.
n_iter : int, default=10
Number of parameter settings that are sampled. n_iter trades
off runtime vs quality of the solution.
scoring : string, callable or None, default=None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
If ``None``, the ``score`` method of the estimator is used.
fit_params : dict, optional
Parameters to pass to the fit method.
n_jobs : int, default=1
Number of jobs to run in parallel.
pre_dispatch : int, or string, optional
Controls the number of jobs that get dispatched during parallel
execution. Reducing this number can be useful to avoid an
explosion of memory consumption when more jobs get dispatched
than CPUs can process. This parameter can be:
- None, in which case all the jobs are immediately
created and spawned. Use this for lightweight and
fast-running jobs, to avoid delays due to on-demand
spawning of the jobs
- An int, giving the exact number of total jobs that are
spawned
- A string, giving an expression as a function of n_jobs,
as in '2*n_jobs'
iid : boolean, default=True
If True, the data is assumed to be identically distributed across
the folds, and the loss minimized is the total loss per sample,
and not the mean loss across the folds.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- An object to be used as a cross-validation generator.
- An iterable yielding train, test splits.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` used. In all
other cases, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
refit : boolean, default=True
Refit the best estimator with the entire dataset.
If "False", it is impossible to make predictions using
this RandomizedSearchCV instance after fitting.
verbose : integer
Controls the verbosity: the higher, the more messages.
random_state : int or RandomState
Pseudo random number generator state used for random uniform sampling
from lists of possible values instead of scipy.stats distributions.
error_score : 'raise' (default) or numeric
Value to assign to the score if an error occurs in estimator fitting.
If set to 'raise', the error is raised. If a numeric value is given,
FitFailedWarning is raised. This parameter does not affect the refit
step, which will always raise the error.
Attributes
----------
grid_scores_ : list of named tuples
Contains scores for all parameter combinations in param_grid.
Each entry corresponds to one parameter setting.
Each named tuple has the attributes:
* ``parameters``, a dict of parameter settings
* ``mean_validation_score``, the mean score over the
cross-validation folds
* ``cv_validation_scores``, the list of scores for each fold
best_estimator_ : estimator
Estimator that was chosen by the search, i.e. estimator
which gave highest score (or smallest loss if specified)
on the left out data. Not available if refit=False.
best_score_ : float
Score of best_estimator on the left out data.
best_params_ : dict
Parameter setting that gave the best results on the hold out data.
Notes
-----
The parameters selected are those that maximize the score of the held-out
data, according to the scoring parameter.
If `n_jobs` was set to a value higher than one, the data is copied for each
parameter setting(and not `n_jobs` times). This is done for efficiency
reasons if individual jobs take very little time, but may raise errors if
the dataset is large and not enough memory is available. A workaround in
this case is to set `pre_dispatch`. Then, the memory is copied only
`pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
n_jobs`.
See Also
--------
:class:`GridSearchCV`:
Does exhaustive search over a grid of parameters.
:class:`ParameterSampler`:
A generator over parameter settins, constructed from
param_distributions.
"""
def __init__(self, estimator, param_distributions, n_iter=10, scoring=None,
fit_params=None, n_jobs=1, iid=True, refit=True, cv=None,
verbose=0, pre_dispatch='2*n_jobs', random_state=None,
error_score='raise'):
self.param_distributions = param_distributions
self.n_iter = n_iter
self.random_state = random_state
super(RandomizedSearchCV, self).__init__(
estimator=estimator, scoring=scoring, fit_params=fit_params,
n_jobs=n_jobs, iid=iid, refit=refit, cv=cv, verbose=verbose,
pre_dispatch=pre_dispatch, error_score=error_score)
def fit(self, X, y=None, labels=None):
"""Run fit on the estimator with randomly drawn parameters.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression;
None for unsupervised learning.
labels : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into
train/test set.
"""
sampled_params = ParameterSampler(self.param_distributions,
self.n_iter,
random_state=self.random_state)
return self._fit(X, y, labels, sampled_params)
| bsd-3-clause |
RodenLuo/LSolver | crop_at_candidate.py | 2 | 11554 | # coding: utf-8
import sys
import numpy as np # linear algebra
subset = sys.argv[1]
crop_window_len = np.int(sys.argv[2])
# subset = 'train1'
# crop_window_len = 13
saving_mm_name = str(crop_window_len * 2 +1) + 'mm_POI'
import cv2
from skimage import segmentation
from sklearn.cluster import DBSCAN
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import skimage, os
from skimage.morphology import ball, disk, dilation, binary_erosion, remove_small_objects, erosion, closing, reconstruction, binary_closing
from skimage.measure import label,regionprops, perimeter
from skimage.morphology import binary_dilation, binary_opening
from skimage.filters import roberts, sobel
from skimage import measure, feature
from skimage.segmentation import clear_border
# from skimage import data
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import dicom
import scipy.misc
import numpy as np
from skimage.segmentation import clear_border
from skimage.feature import peak_local_max
#!/usr/bin/env python
#======================================================================
#Program: Diffusion Weighted MRI Reconstruction
#Link: https://code.google.com/archive/p/diffusion-mri
#Module: $RCSfile: mhd_utils.py,v $
#Language: Python
#Author: $Author: bjian $
#Date: $Date: 2008/10/27 05:55:55 $
#Version:
# $Revision: 1.1 by PJackson 2013/06/06 $
# Modification: Adapted to 3D
# Link: https://sites.google.com/site/pjmedphys/tutorials/medical-images-in-python
#
# $Revision: 2 by RodenLuo 2017/03/12 $
# Modication: Adapted to LUNA2016 data set for DSB2017
# Link:
#======================================================================
import os
import numpy
import array
def save_nodule(nodule_crop, name_index, path):
np.save(path + str(name_index) + '.npy', nodule_crop)
# write_mhd_file(path + str(name_index) + '.mhd', nodule_crop, nodule_crop.shape[::-1])
import SimpleITK as sitk
import numpy as np
from glob import glob
import pandas as pd
import scipy.ndimage
## Read annotation data and filter those without images
# Learned from Jonathan Mulholland and Aaron Sander, Booz Allen Hamilton
# https://www.kaggle.com/c/data-science-bowl-2017#tutorial
# Predefine some parameters, this will affect final performance
low_cutoff = -650
subsample_number = 200
# Set input path
luna_path = '/LUNA16/Ori_data/'
df_candidate = pd.read_csv(luna_path+'CSVFILES/candidates_V2_w_file_path.csv')
luna_mhd_path = luna_path + subset +'/'
file_list = glob(luna_mhd_path + "*.mhd")
def crop_nodule(big_img, v_center, crop_len = crop_window_len):
'''
img and v_center is in [Z, Y, X], and in numpy.array type
'''
[max_Z, max_Y, max_X] = big_img.shape
zyx_1 = v_center - crop_len # Attention: Z, Y, X
zyx_2 = v_center + crop_len + 1
img_crop = big_img[ max(zyx_1[0], 0):min(zyx_2[0], max_Z),
max(zyx_1[1], 0):min(zyx_2[1], max_Y),
max(zyx_1[2], 0):min(zyx_2[2], max_X) ]
[crop_Z, crop_Y, crop_X] = img_crop.shape
if min(crop_Z, crop_Y, crop_X) < crop_len*2+1:
crop_block_len = crop_len*2+1
crop_block = np.array(np.ones([crop_block_len, crop_block_len, crop_block_len]))
crop_block *= -1024
crop_block = np.asarray(crop_block, dtype=np.int16)
start_Z = int((crop_block_len - crop_Z)/2)
start_Y = int((crop_block_len - crop_Y)/2)
start_X = int((crop_block_len - crop_X)/2)
crop_block[start_Z:start_Z+crop_Z, start_Y:start_Y+crop_Y, start_X:start_X+crop_X] = img_crop
img_crop = crop_block
return img_crop
# http://stackoverflow.com/questions/10818546/finding-index-of-nearest-point-in-numpy-arrays-of-x-and-y-coordinates
# http://stackoverflow.com/questions/32424604/find-all-nearest-neighbors-within-a-specific-distance
from scipy import spatial
## Collect patients with nodule and crop the nodule
# In this code snippet, the cropped nodule is a [19, 19, 19] volume with [1, 1, 1]mm spacing.
# Learned from Jonathan Mulholland and Aaron Sander, Booz Allen Hamilton
# https://www.kaggle.com/c/data-science-bowl-2017#tutorial
def grids_generaotr(img_shape, grid_size = 30):
grid_shape = img_shape
grids = []
start_ind = [0, np.int(grid_size/2)]
for Z_ in range(len(start_ind)):
for Y_ in range(len(start_ind)):
for X_ in range(len(start_ind)):
# initialize
grid = np.array(np.zeros(grid_shape))
indices=[None] * 3
# get grid indices
indices[0] = np.array(range(grid_shape[0]))[start_ind[Z_]::grid_size]
indices[1] = np.array(range(grid_shape[1]))[start_ind[Y_]::grid_size]
indices[2] = np.array(range(grid_shape[2]))[start_ind[X_]::grid_size]
# create grid from indices
grid[indices[0], :, :] = 1
grid[:, indices[1], :] = 1
grid[:, :, indices[2]] = 1
grid = grid > 0
# add to list
grids.append(grid)
return grids
def seg_one_slice_seg(img, large_label_size = 3, low_cutoff_ = -650):
img_bw = img > low_cutoff_
# img_bw = img_bw * 255
img_bw = np.array(img_bw, dtype=np.uint8)
img_label = label(img_bw)
img_label_props = regionprops(img_label)
large_label = np.array(np.zeros(img.shape), dtype=np.uint8)
for r in img_label_props:
max_x, max_y = 0, 0
min_x, min_y = 1000, 1000
for c in r.coords:
max_y = max(c[0], max_y)
max_x = max(c[1], max_x)
min_y = min(c[0], min_y)
min_x = min(c[1], min_x)
if ( (max_y - min_y) < 3 or (max_x - min_x) < 3 ):
for c in r.coords:
img_bw[ c[0], c[1] ] = 0
# Area threshold
img_label = label(img_bw)
img_label_props = regionprops(img_label)
for r in img_label_props:
if ( r.area > large_label_size ):
for c in r.coords:
large_label[c[0], c[1]] = 255
# Finding sure foreground area for large label
dist_transform = cv2.distanceTransform(large_label, cv2.DIST_L2, 5)
distance_threshold = 1.0
ret, large_label_processed = cv2.threshold(dist_transform, distance_threshold, 255, 0)
return large_label_processed
def break_large_label_by_grid(large_label, grid):
large_label_break = np.array(large_label)
large_label_break[grid] = 0
break_centroid = np.array([r.centroid for r in regionprops(label(large_label_break))])
return break_centroid
def collect_POI_from_lung(lung_image, large_diameter_threshold = 4):
processed_lung = np.array([seg_one_slice_seg(img) for img in lung_image])
processed_lung_label_props = regionprops(label(processed_lung))
small_label = np.array(np.zeros(processed_lung.shape), dtype=np.uint8)
large_label = np.array(np.zeros(processed_lung.shape), dtype=np.uint8)
for r in processed_lung_label_props:
if ( r.equivalent_diameter > large_diameter_threshold ):
for c in r.coords:
large_label[c[0], c[1], c[2]] = 255
else:
for c in r.coords:
small_label[c[0], c[1], c[2]] = 255
all_centroid = np.array([r.centroid for r in regionprops(label(small_label))])
grids = grids_generaotr(large_label.shape, grid_size = 30)
for grid in grids:
break_centroid = break_large_label_by_grid(large_label, grid)
all_centroid = np.append(all_centroid, break_centroid, axis = 0)
return all_centroid
def get_POI(ct_lung):
all_centroid = collect_POI_from_lung(ct_lung)
db = DBSCAN(eps=1, min_samples=1).fit(all_centroid)
all_POI = np.array([np.mean(all_centroid[db.labels_ == ind], axis=0) for ind in np.unique(db.labels_)])
return np.array(np.rint(all_POI), dtype=np.int)
def get_nodule_for_patient(patient):
# Check whether this patient has nodule or not
patient_nodules = df_node[df_node.file == patient]
ct_scan = np.load(patient)
patient_name = str(os.path.split(patient)[1]).replace('.npy', '')
ct_lung = np.load('/DSB2017/LUNA16/new_lung/lung/' + patient_name + '_lung.npy')
all_POI = get_POI(ct_lung)
# load metadata
origin = np.load('/DSB2017/LUNA16/resampled_origin_new_spacing_npy/' + patient_name + '_origin.npy')
new_spacing = np.load('/DSB2017/LUNA16/resampled_origin_new_spacing_npy/' + patient_name + '_new_spacing.npy')
# print('Start save nodule')
for index, nodule in patient_nodules.iterrows():
if nodule.diameter_mm < 4:
print('Patient:' + patient + ', ' + 'Nodule: ' + str(index) + ' is too small, diameter: ' + str(nodule.diameter_mm))
continue
nodule_center = np.array([nodule.coordZ, nodule.coordY, nodule.coordX]) # Attention: Z, Y, X
v_center = np.rint( (nodule_center - origin) / new_spacing ).astype(int)
# find POI closest to nodule center
POI_idx_closest_to_center = spatial.KDTree(all_POI).query(v_center)[1]
center_POI = all_POI[POI_idx_closest_to_center]
dist = numpy.linalg.norm(center_POI - v_center)
if dist > 9:
print('Patient:' + patient + ', ' + 'Nodule: ' + str(index) +
', True_Center: ' + np.array_str(v_center) + ', CenterPOI: ' +
np.array_str(center_POI) + ', Distance: '+ str(dist) +' is too long')
return # do not consider the whole patient
# crop and save nodule
img_crop = crop_nodule(ct_scan, v_center=np.array(center_POI))
saving_path = '/Train_data/' + saving_mm_name + '/'+subset+'/nodule/'
if not os.path.exists(saving_path):
os.makedirs(saving_path)
save_nodule(img_crop, index, path = saving_path)
print('Patient:' + patient + ', ' + 'Nodule: ' + str(index) +
', True_Center: ' + np.array_str(v_center) + ', CenterPOI: ' +
np.array_str(center_POI) + ', Distance: '+ str(dist))
# remove the surrounding POIs
point_tree = spatial.cKDTree(all_POI)
with_in_range_index = point_tree.query_ball_point(center_POI, crop_window_len)
all_POI = np.delete(all_POI, with_in_range_index, 0)
# crop non-nodule boxes
new_POI_img = np.zeros(ct_lung.shape)
new_POI_img[ all_POI[:,0], all_POI[:,1], all_POI[:,2] ] = 1
new_all_POI = np.argwhere(new_POI_img>0)
new_all_POI_subsample = new_all_POI[
np.random.choice(len(new_all_POI), subsample_number, replace = False)]
for coor in new_all_POI_subsample:
img_crop = crop_nodule(ct_scan, v_center=np.array(coor))
save_name = patient_name+'_'+str(coor[0])+'_'+str(coor[1])+'_'+str(coor[2])
saving_path = '/Train_data/' + saving_mm_name + '/'+subset+'/non_nodule_boxes/'
if not os.path.exists(saving_path):
os.makedirs(saving_path)
save_nodule(img_crop, save_name, path = saving_path)
from joblib import Parallel, delayed
import multiprocessing
num_cores = multiprocessing.cpu_count()
Parallel(n_jobs=num_cores)(delayed(get_nodule_for_patient)(patient_name) for patient_name in file_list)
print('Done for all')
| mit |
freedomDR/shiny-robot | homework/project/project0202.py | 1 | 1432 | import cv2 as cv
import matplotlib.pyplot as plt
def show(img_show, position):
plt.subplot(position[0], position[1], position[2])
plt.imshow(img_show, cmap='Greys_r')
plt.xticks([]), plt.yticks([])
plt.title(position)
# plt.tight_layout()
if __name__ == '__main__':
plt.figure(1)
img = cv.imread('../../ImageMaterial/DIP3E_Original_Images_CH02/Fig0221(a)(ctskull-256).tif', cv.IMREAD_GRAYSCALE)
k = 2
img128 = [[img[x][y] - img[x][y] % k for y in range(img.shape[1])] for x in range(img.shape[0])]
k = 4
img64 = [[img[x][y] - img[x][y] % k for y in range(img.shape[1])] for x in range(img.shape[0])]
k = 8
img32 = [[img[x][y] - img[x][y] % k for y in range(img.shape[1])] for x in range(img.shape[0])]
k = 16
img16 = [[img[x][y] - img[x][y] % k for y in range(img.shape[1])] for x in range(img.shape[0])]
k = 32
img8 = [[img[x][y] - img[x][y] % k for y in range(img.shape[1])] for x in range(img.shape[0])]
k = 64
img4 = [[img[x][y] - img[x][y] % k for y in range(img.shape[1])] for x in range(img.shape[0])]
k = 128
img2 = [[img[x][y] - img[x][y] % k for y in range(img.shape[1])] for x in range(img.shape[0])]
show(img, [2, 4, 1])
show(img128, [2, 4, 2])
show(img64, [2, 4, 3])
show(img32, [2, 4, 4])
show(img16, [2, 4, 5])
show(img8, [2, 4, 6])
show(img4, [2, 4, 7])
show(img2, [2, 4, 8])
plt.show()
| gpl-3.0 |
WilliamLanghoff/hemo | scripts/simulations.py | 1 | 4927 |
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
import hemo.sims as sims
import scipy.integrate
import system
import importlib
def get_Wt(G, times, soln, liposomes=False):
"""Compute W(t) for an entire network
Parameters
----------
G
Graph Structure
times : array_like
Array of time values for simulation
soln : array_like
Solution - the output from odeint
Returns
-------
Wt : array_like
Array of W(t) values corresponding to the times passed to the function
"""
n_edges = len(G.edges())
Wt = np.zeros_like(times)
for src, sink in G.edges():
if liposomes:
Wt += 30 * G[src][sink]['volume'] * soln[:, 2*n_edges + G[src][sink]['idx']]
else:
Wt += 30 * G[src][sink]['volume'] * soln[:, n_edges + G[src][sink]['idx']]
return Wt
def run_sim(n, k=0, symmetric=False):
if symmetric:
G = nx.read_gpickle('C:/Users/Bill/Documents/Python/hemo/hemo/data/networks/G_%i_symm.gpickle' % n)
else:
G = nx.read_gpickle('C:/Users/Bill/Documents/Python/hemo/hemo/data/networks/G_%i_%i.gpickle' % (n,k))
sims.create_source(G)
importlib.reload(system)
times = np.linspace(0, 450, 450 + 1)
y0 = np.zeros(2 * len(G.edges()))
soln = scipy.integrate.odeint(system.dydt, y0, times)
#wt = get_Wt(G, times, soln)
return times, soln
if __name__ == '__main__':
# import time
# #integration_time = np.load('integration_time.npy')
# time_lengths = []
# for n in [4,5,6,7,8,9,10,11]:
# k = 0
# start = time.time()
# times, soln = run_sim(n, k)
# #np.save('times.np', times)
# np.save('soln_%i_%i.np' % (n, k), soln)
# end = time.time()
# dt = end-start
# time_lengths.append(dt)
# print('%i - %i: %.01f.' % (n, k, dt))
# np.save('time_len', time_lengths)
#
# plt.plot([3,4,5,6,7,8,9,10], time_lengths)
# plt.show()
# vessels = [3*n*((n+1)**2) for n in [3,4,5,6,7,8,9,10]]
# time = np.load('C:/Users/Bill/Documents/Python/hemo/scripts/integration_time.npy')
# print(time)
mean_radii = []
mean_steady_states = []
all_radii, all_steady_states = [], []
for n in [11,12]:
rr = []
ss = []
for k in [4,5,6,7,8,9,10]:
times = np.linspace(0, 450, 450 + 1)
G = nx.read_gpickle('C:/Users/Bill/Documents/Python/hemo/hemo/data/networks/G_%i_%i.gpickle' %(n,k))
soln = np.load('C:/Users/Bill/Documents/Python/hemo/hemo/data/sims/soln_%i_%i.np.npy' % (n, k))
wt = get_Wt(G, times, soln)
ss.append(wt[-1])
radii = [G[src][sink]['radius'] for src, sink in G.edges()]
rr.append(np.mean(radii))
#plt.plot(times, wt)
mean_radii.append(np.mean(rr))
mean_steady_states.append(np.mean(ss))
all_radii.extend(rr)
all_steady_states.extend(ss)
#
G = nx.read_gpickle('C:/Users/Bill/Documents/Python/hemo/hemo/data/networks/G_%i_%i.gpickle' % (11, 0))
soln = np.load('C:/Users/Bill/Documents/Python/hemo/hemo/data/sims/soln_%i_%i.np.npy' % (11, 0))
mean_steady_states.append(wt[-1])
radii = [G[src][sink]['radius'] for src, sink in G.edges()]
mean_radii.append(np.mean(radii))
fig = plt.figure()
ax = plt.gca()
ax.scatter([10 ** 4 * radius for radius in all_radii], all_steady_states, alpha=0.5, edgecolors='none')
ax.plot([10 ** 4 * radius for radius in mean_radii], mean_steady_states, c='k')
#ax.set_yscale('log')
#ax.set_xscale('log')
ax.set_ylim([0.00, 0.0030])
ax.set_xlim([0, 130])
plt.title('Steady States')
plt.xlabel('Average Radius ($\mu$m)')
plt.ylabel('Drug ($\mu$mol)')
plt.show()
# subdivs, flow = [], []
# for n in [4, 5, 6, 7, 8, 9, 10]:
# for k in [1,2,3,4,5,6,7,8,9,10]:
# inflow = 0
# G = nx.read_gpickle('C:/Users/Bill/Documents/Python/hemo/hemo/data/networks/G_%i_%i.gpickle' %(n, k))
# subdivs.append(n-1)
# for src, sink in G.edges():
# if G.node[src]['ntype'] == 'source':
# inflow += G[src][sink]['inverse_transit_time'] * (np.pi * G[src][sink]['length'] * G[src][sink]['radius']**2)
# flow.append(inflow)
#
# plt.scatter(subdivs, flow, edgecolors='none', alpha=0.5)
# plt.title('Flow rates')
# plt.ylabel('Flow rate (ml/sec)')
# plt.show()
# times = np.linspace(0, 450, 450 + 1)
# for n in [4,5,6,7,8,9,10,11]:
# G = nx.read_gpickle('C:/Users/Bill/Documents/Python/hemo/hemo/data/networks/G_%i_0.gpickle' % n)
# soln = np.load('C:/Users/Bill/Documents/Python/hemo/hemo/data/sims/soln_%i_1.np.npy' % n)
# wt = get_Wt(G, times, soln)
# plt.plot(times, wt)
# plt.legend([4,5,6,7,8,9,10,11])
# plt.show() | gpl-3.0 |
nikste/tensorflow | tensorflow/contrib/learn/python/learn/estimators/_sklearn.py | 153 | 6723 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""sklearn cross-support."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import os
import numpy as np
import six
def _pprint(d):
return ', '.join(['%s=%s' % (key, str(value)) for key, value in d.items()])
class _BaseEstimator(object):
"""This is a cross-import when sklearn is not available.
Adopted from sklearn.BaseEstimator implementation.
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py
"""
def get_params(self, deep=True):
"""Get parameters for this estimator.
Args:
deep: boolean, optional
If `True`, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns:
params : mapping of string to any
Parameter names mapped to their values.
"""
out = dict()
param_names = [name for name in self.__dict__ if not name.startswith('_')]
for key in param_names:
value = getattr(self, key, None)
if isinstance(value, collections.Callable):
continue
# XXX: should we rather test if instance of estimator?
if deep and hasattr(value, 'get_params'):
deep_items = value.get_params().items()
out.update((key + '__' + k, val) for k, val in deep_items)
out[key] = value
return out
def set_params(self, **params):
"""Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The former have parameters of the form
``<component>__<parameter>`` so that it's possible to update each
component of a nested object.
Args:
**params: Parameters.
Returns:
self
Raises:
ValueError: If params contain invalid names.
"""
if not params:
# Simple optimisation to gain speed (inspect is slow)
return self
valid_params = self.get_params(deep=True)
for key, value in six.iteritems(params):
split = key.split('__', 1)
if len(split) > 1:
# nested objects case
name, sub_name = split
if name not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(name, self))
sub_object = valid_params[name]
sub_object.set_params(**{sub_name: value})
else:
# simple objects case
if key not in valid_params:
raise ValueError('Invalid parameter %s for estimator %s. '
'Check the list of available parameters '
'with `estimator.get_params().keys()`.' %
(key, self.__class__.__name__))
setattr(self, key, value)
return self
def __repr__(self):
class_name = self.__class__.__name__
return '%s(%s)' % (class_name,
_pprint(self.get_params(deep=False)),)
# pylint: disable=old-style-class
class _ClassifierMixin():
"""Mixin class for all classifiers."""
pass
class _RegressorMixin():
"""Mixin class for all regression estimators."""
pass
class _TransformerMixin():
"""Mixin class for all transformer estimators."""
class NotFittedError(ValueError, AttributeError):
"""Exception class to raise if estimator is used before fitting.
This class inherits from both ValueError and AttributeError to help with
exception handling and backward compatibility.
Examples:
>>> from sklearn.svm import LinearSVC
>>> from sklearn.exceptions import NotFittedError
>>> try:
... LinearSVC().predict([[1, 2], [2, 3], [3, 4]])
... except NotFittedError as e:
... print(repr(e))
... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
NotFittedError('This LinearSVC instance is not fitted yet',)
Copied from
https://github.com/scikit-learn/scikit-learn/master/sklearn/exceptions.py
"""
# pylint: enable=old-style-class
def _accuracy_score(y_true, y_pred):
score = y_true == y_pred
return np.average(score)
def _mean_squared_error(y_true, y_pred):
if len(y_true.shape) > 1:
y_true = np.squeeze(y_true)
if len(y_pred.shape) > 1:
y_pred = np.squeeze(y_pred)
return np.average((y_true - y_pred)**2)
def _train_test_split(*args, **options):
# pylint: disable=missing-docstring
test_size = options.pop('test_size', None)
train_size = options.pop('train_size', None)
random_state = options.pop('random_state', None)
if test_size is None and train_size is None:
train_size = 0.75
elif train_size is None:
train_size = 1 - test_size
train_size = int(train_size * args[0].shape[0])
np.random.seed(random_state)
indices = np.random.permutation(args[0].shape[0])
train_idx, test_idx = indices[:train_size], indices[train_size:]
result = []
for x in args:
result += [x.take(train_idx, axis=0), x.take(test_idx, axis=0)]
return tuple(result)
# If "TENSORFLOW_SKLEARN" flag is defined then try to import from sklearn.
TRY_IMPORT_SKLEARN = os.environ.get('TENSORFLOW_SKLEARN', False)
if TRY_IMPORT_SKLEARN:
# pylint: disable=g-import-not-at-top,g-multiple-import,unused-import
from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, TransformerMixin
from sklearn.metrics import accuracy_score, log_loss, mean_squared_error
from sklearn.cross_validation import train_test_split
try:
from sklearn.exceptions import NotFittedError
except ImportError:
try:
from sklearn.utils.validation import NotFittedError
except ImportError:
pass
else:
# Naive implementations of sklearn classes and functions.
BaseEstimator = _BaseEstimator
ClassifierMixin = _ClassifierMixin
RegressorMixin = _RegressorMixin
TransformerMixin = _TransformerMixin
accuracy_score = _accuracy_score
log_loss = None
mean_squared_error = _mean_squared_error
train_test_split = _train_test_split
| apache-2.0 |
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