repo_name stringlengths 9 55 | path stringlengths 7 120 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 169k | license stringclasses 12 values |
|---|---|---|---|---|---|
joshbohde/scikit-learn | examples/plot_permutation_test_for_classification.py | 2 | 2049 | """
=================================================================
Test with permutations the significance of a classification score
=================================================================
In order to test if a classification score is significative a technique
in repeating the classification procedure after randomizing, permuting,
the labels. The p-value is then given by the percentage of runs for
which the score obtained is greater than the classification score
obtained in the first place.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD
print __doc__
import numpy as np
import pylab as pl
from sklearn.svm import SVC
from sklearn.cross_validation import StratifiedKFold, permutation_test_score
from sklearn import datasets
from sklearn.metrics import zero_one_score
##############################################################################
# Loading a dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target
n_classes = np.unique(y).size
# Some noisy data not correlated
random = np.random.RandomState(seed=0)
E = random.normal(size=(len(X), 2200))
# Add noisy data to the informative features for make the task harder
X = np.c_[X, E]
svm = SVC(kernel='linear')
cv = StratifiedKFold(y, 2)
score, permutation_scores, pvalue = permutation_test_score(svm, X, y,
zero_one_score, cv=cv,
n_permutations=100, n_jobs=1)
print "Classification score %s (pvalue : %s)" % (score, pvalue)
###############################################################################
# View histogram of permutation scores
pl.hist(permutation_scores, label='Permutation scores')
ylim = pl.ylim()
pl.vlines(score, ylim[0], ylim[1], linestyle='--',
color='g', linewidth=3, label='Classification Score'
' (pvalue %s)' % pvalue)
pl.vlines(1.0 / n_classes, ylim[0], ylim[1], linestyle='--',
color='k', linewidth=3, label='Luck')
pl.ylim(ylim)
pl.legend()
pl.xlabel('Score')
pl.show()
| bsd-3-clause |
CG-F16-24-Rutgers/steersuite-rutgers | steerstats/tools/plotting/plotMultiObjectiveData.py | 8 | 1340 |
import csv
import matplotlib.pyplot as plt
import sys
import numpy as np
# filename = '../../data/optimization/sf/multiObjective/SteerStatsOpt2.csv'
filename = sys.argv[1]
xs = []
ys = []
if len(sys.argv) == 2:
csvfile = open(filename, 'r')
spamreader = csv.reader(csvfile, delimiter=',')
xs = []
ys = []
for row in spamreader:
xs.append(float(row[0]))
ys.append(float(row[1]))
elif len(sys.argv) == 3:
for i in range(1, int(sys.argv[2])):
tmp_filename = filename + str(i) + ".log"
csvfile = open(tmp_filename, 'r')
spamreader = csv.reader(csvfile, delimiter=',')
for row in spamreader:
xs.append(float(row[0]))
ys.append(float(row[1]))
else:
print "Wrong"
sys.exit()
print "xs = " + str(xs)
print "ys = " + str(ys)
x_min = np.amin(xs)
x_max = np.amax(xs)
y_min = np.amin(ys)
y_max = np.amax(ys)
new_xs = (xs - x_min) / (x_max - x_min)
new_ys = (ys - y_min) / (y_max - y_min)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(new_xs, new_ys, c="b")
# ax.set_xlim([np.amin(xs), np.amax(xs)])
# ax.set_ylim([np.amin(ys), np.amax(ys)])
ax.set_xlim([0.0, 1.0])
ax.set_ylim([0.0, 1.0])
ax.set_xlabel('efficency metric')
ax.set_ylabel('PLE metric')
ax.set_title("multi-objective optimization")
# plt.axis("tight")
plt.show()
| gpl-3.0 |
maxlikely/scikit-learn | sklearn/pipeline.py | 1 | 13051 | """
The :mod:`sklearn.pipeline` module implements utilites to build a composite
estimator, as a chain of transforms and estimators.
"""
# Author: Edouard Duchesnay
# Gael Varoquaux
# Virgile Fritsch
# Alexandre Gramfort
# Licence: BSD
import numpy as np
from scipy import sparse
from .base import BaseEstimator, TransformerMixin
from .externals.joblib import Parallel, delayed
from .externals import six
__all__ = ['Pipeline', 'FeatureUnion']
# One round of beers on me if someone finds out why the backslash
# is needed in the Attributes section so as not to upset sphinx.
class Pipeline(BaseEstimator):
"""Pipeline of transforms with a final estimator.
Sequentially apply a list of transforms and a final estimator.
Intermediate steps of the pipeline must be 'transforms', that is, they
must implements fit and transform methods.
The final estimator needs only implements fit.
The purpose of the pipeline is to assemble several steps that can be
cross-validated together while setting different parameters.
For this, it enables setting parameters of the various steps using their
names and the parameter name separated by a '__', as in the example below.
Parameters
----------
steps: list
List of (name, transform) tuples (implementing fit/transform) that are
chained, in the order in which they are chained, with the last object
an estimator.
Examples
--------
>>> from sklearn import svm
>>> from sklearn.datasets import samples_generator
>>> from sklearn.feature_selection import SelectKBest
>>> from sklearn.feature_selection import f_regression
>>> from sklearn.pipeline import Pipeline
>>> # generate some data to play with
>>> X, y = samples_generator.make_classification(
... n_informative=5, n_redundant=0, random_state=42)
>>> # ANOVA SVM-C
>>> anova_filter = SelectKBest(f_regression, k=5)
>>> clf = svm.SVC(kernel='linear')
>>> anova_svm = Pipeline([('anova', anova_filter), ('svc', clf)])
>>> # You can set the parameters using the names issued
>>> # For instance, fit using a k of 10 in the SelectKBest
>>> # and a parameter 'C' of the svn
>>> anova_svm.set_params(anova__k=10, svc__C=.1).fit(X, y)
... # doctest: +ELLIPSIS
Pipeline(steps=[...])
>>> prediction = anova_svm.predict(X)
>>> anova_svm.score(X, y)
0.75
"""
# BaseEstimator interface
def __init__(self, steps):
self.named_steps = dict(steps)
names, estimators = zip(*steps)
if len(self.named_steps) != len(steps):
raise ValueError("Names provided are not unique: %s" % names)
self.steps = zip(names, estimators) # shallow copy of steps
transforms = estimators[:-1]
estimator = estimators[-1]
for t in transforms:
if (not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not
hasattr(t, "transform")):
raise TypeError("All intermediate steps a the chain should "
"be transforms and implement fit and transform"
"'%s' (type %s) doesn't)" % (t, type(t)))
if not hasattr(estimator, "fit"):
raise TypeError("Last step of chain should implement fit "
"'%s' (type %s) doesn't)"
% (estimator, type(estimator)))
def get_params(self, deep=True):
if not deep:
return super(Pipeline, self).get_params(deep=False)
else:
out = self.named_steps.copy()
for name, step in six.iteritems(self.named_steps):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
# Estimator interface
def _pre_transform(self, X, y=None, **fit_params):
fit_params_steps = dict((step, {}) for step, _ in self.steps)
for pname, pval in six.iteritems(fit_params):
step, param = pname.split('__', 1)
fit_params_steps[step][param] = pval
Xt = X
for name, transform in self.steps[:-1]:
if hasattr(transform, "fit_transform"):
Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
else:
Xt = transform.fit(Xt, y, **fit_params_steps[name]) \
.transform(Xt)
return Xt, fit_params_steps[self.steps[-1][0]]
def fit(self, X, y=None, **fit_params):
"""Fit all the transforms one after the other and transform the
data, then fit the transformed data using the final estimator.
"""
Xt, fit_params = self._pre_transform(X, y, **fit_params)
self.steps[-1][-1].fit(Xt, y, **fit_params)
return self
def fit_transform(self, X, y=None, **fit_params):
"""Fit all the transforms one after the other and transform the
data, then use fit_transform on transformed data using the final
estimator."""
Xt, fit_params = self._pre_transform(X, y, **fit_params)
if hasattr(self.steps[-1][-1], 'fit_transform'):
return self.steps[-1][-1].fit_transform(Xt, y, **fit_params)
else:
return self.steps[-1][-1].fit(Xt, y, **fit_params).transform(Xt)
def predict(self, X):
"""Applies transforms to the data, and the predict method of the
final estimator. Valid only if the final estimator implements
predict."""
Xt = X
for name, transform in self.steps[:-1]:
Xt = transform.transform(Xt)
return self.steps[-1][-1].predict(Xt)
def predict_proba(self, X):
"""Applies transforms to the data, and the predict_proba method of the
final estimator. Valid only if the final estimator implements
predict_proba."""
Xt = X
for name, transform in self.steps[:-1]:
Xt = transform.transform(Xt)
return self.steps[-1][-1].predict_proba(Xt)
def decision_function(self, X):
"""Applies transforms to the data, and the decision_function method of
the final estimator. Valid only if the final estimator implements
decision_function."""
Xt = X
for name, transform in self.steps[:-1]:
Xt = transform.transform(Xt)
return self.steps[-1][-1].decision_function(Xt)
def predict_log_proba(self, X):
Xt = X
for name, transform in self.steps[:-1]:
Xt = transform.transform(Xt)
return self.steps[-1][-1].predict_log_proba(Xt)
def transform(self, X):
"""Applies transforms to the data, and the transform method of the
final estimator. Valid only if the final estimator implements
transform."""
Xt = X
for name, transform in self.steps:
Xt = transform.transform(Xt)
return Xt
def inverse_transform(self, X):
if X.ndim == 1:
X = X[None, :]
Xt = X
for name, step in self.steps[::-1]:
Xt = step.inverse_transform(Xt)
return Xt
def score(self, X, y=None):
"""Applies transforms to the data, and the score method of the
final estimator. Valid only if the final estimator implements
score."""
Xt = X
for name, transform in self.steps[:-1]:
Xt = transform.transform(Xt)
return self.steps[-1][-1].score(Xt, y)
@property
def _pairwise(self):
# check if first estimator expects pairwise input
return getattr(self.steps[0][1], '_pairwise', False)
def _fit_one_transformer(transformer, X, y):
transformer.fit(X, y)
def _transform_one(transformer, name, X, transformer_weights):
if transformer_weights is not None and name in transformer_weights:
# if we have a weight for this transformer, muliply output
return transformer.transform(X) * transformer_weights[name]
return transformer.transform(X)
def _fit_transform_one(transformer, name, X, y, transformer_weights,
**fit_params):
if transformer_weights is not None and name in transformer_weights:
# if we have a weight for this transformer, muliply output
if hasattr(transformer, 'fit_transform'):
return (transformer.fit_transform(X, y, **fit_params)
* transformer_weights[name])
else:
return (transformer.fit(X, y, **fit_params).transform(X)
* transformer_weights[name])
if hasattr(transformer, 'fit_transform'):
return transformer.fit_transform(X, y, **fit_params)
else:
return transformer.fit(X, y, **fit_params).transform(X)
class FeatureUnion(BaseEstimator, TransformerMixin):
"""Concatenates results of multiple transformer objects.
This estimator applies a list of transformer objects in parallel to the
input data, then concatenates the results. This is useful to combine
several feature extraction mechanisms into a single transformer.
Parameters
----------
transformers: list of (name, transformer)
List of transformer objects to be applied to the data.
n_jobs: int, optional
Number of jobs to run in parallel (default 1).
transformer_weights: dict, optional
Multiplicative weights for features per transformer.
Keys are transformer names, values the weights.
"""
def __init__(self, transformer_list, n_jobs=1, transformer_weights=None):
self.transformer_list = transformer_list
self.n_jobs = n_jobs
self.transformer_weights = transformer_weights
def get_feature_names(self):
"""Get feature names from all transformers.
Returns
-------
feature_names : list of strings
Names of the features produced by transform.
"""
feature_names = []
for name, trans in self.transformer_list:
if not hasattr(trans, 'get_feature_names'):
raise AttributeError("Transformer %s does not provide"
" get_feature_names." % str(name))
feature_names.extend([name + "__" + f for f in
trans.get_feature_names()])
return feature_names
def fit(self, X, y=None):
"""Fit all transformers using X.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
Input data, used to fit transformers.
"""
Parallel(n_jobs=self.n_jobs)(
delayed(_fit_one_transformer)(trans, X, y)
for name, trans in self.transformer_list)
return self
def fit_transform(self, X, y=None, **fit_params):
"""Fit all tranformers using X, transform the data and concatenate
results.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
Input data to be transformed.
Returns
-------
X_t : array-like or sparse matrix, shape (n_samples, sum_n_components)
hstack of results of transformers. sum_n_components is the
sum of n_components (output dimension) over transformers.
"""
Xs = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_transform_one)(trans, name, X, y,
self.transformer_weights, **fit_params)
for name, trans in self.transformer_list)
if any(sparse.issparse(f) for f in Xs):
Xs = sparse.hstack(Xs).tocsr()
else:
Xs = np.hstack(Xs)
return Xs
def transform(self, X):
"""Transform X separately by each transformer, concatenate results.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
Input data to be transformed.
Returns
-------
X_t : array-like or sparse matrix, shape (n_samples, sum_n_components)
hstack of results of transformers. sum_n_components is the
sum of n_components (output dimension) over transformers.
"""
Xs = Parallel(n_jobs=self.n_jobs)(
delayed(_transform_one)(trans, name, X, self.transformer_weights)
for name, trans in self.transformer_list)
if any(sparse.issparse(f) for f in Xs):
Xs = sparse.hstack(Xs).tocsr()
else:
Xs = np.hstack(Xs)
return Xs
def get_params(self, deep=True):
if not deep:
return super(FeatureUnion, self).get_params(deep=False)
else:
out = dict(self.transformer_list)
for name, trans in self.transformer_list:
for key, value in trans.get_params(deep=True).iteritems():
out['%s__%s' % (name, key)] = value
return out
| bsd-3-clause |
martinggww/lucasenlights | MachineLearning/python_tutorial/KNearestNeighborhood.py | 1 | 1274 | '''
Classification algorithm
Create a model that seperate a dataset
proximity probability nearest neighbors
What the hack is K?
if K=2, find the closet 2 points
We want K = odd numbers, K=3, 5, 7...
'''
'''
- - +, 66.7% confidence, confidence, accuracy
Euclid distance, euclid distance middle point
Dataset and the relavent data features
class and label
id, clump_thickness, unif_cell_size, unif_cell_shape
'''
import numpy as np
from sklearn import preprocessing, cross_validation, neighbors
import pandas as pd
df = pd.read_csv('./dataset/breastcancer.csv')
print df.head(2)
#Most algorithm will recognize it as outliers
df.replace('?',-99999, inplace=True)
#drop useless column
df.drop(['id'], 1, inplace=True)
#Convert X to np.array
X = np.array(df.drop(['class'], 1))
y = np.array(df['class'] )
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2)
#Why we didn't set K???
clf = neighbors.KNeighborsClassifier()
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
print accuracy
#it's also a ndarry
example_measures = np.array([[4,2,1,1,1,2,3,2,1],[4,2,1,1,1,2,3,2,1],[4,2,1,1,1,2,3,2,1]])
example_measures = example_measures.reshape(len(example_measures), -1)
score = clf.predict(example_measures)
print score
| cc0-1.0 |
treycausey/scikit-learn | sklearn/feature_selection/__init__.py | 244 | 1088 | """
The :mod:`sklearn.feature_selection` module implements feature selection
algorithms. It currently includes univariate filter selection methods and the
recursive feature elimination algorithm.
"""
from .univariate_selection import chi2
from .univariate_selection import f_classif
from .univariate_selection import f_oneway
from .univariate_selection import f_regression
from .univariate_selection import SelectPercentile
from .univariate_selection import SelectKBest
from .univariate_selection import SelectFpr
from .univariate_selection import SelectFdr
from .univariate_selection import SelectFwe
from .univariate_selection import GenericUnivariateSelect
from .variance_threshold import VarianceThreshold
from .rfe import RFE
from .rfe import RFECV
__all__ = ['GenericUnivariateSelect',
'RFE',
'RFECV',
'SelectFdr',
'SelectFpr',
'SelectFwe',
'SelectKBest',
'SelectPercentile',
'VarianceThreshold',
'chi2',
'f_classif',
'f_oneway',
'f_regression']
| bsd-3-clause |
chenyyx/scikit-learn-doc-zh | examples/zh/cluster/plot_dict_face_patches.py | 9 | 2747 | """
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 = []
index = 1
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()
| gpl-3.0 |
jrcapriles/gameSimulator | gameSimulator.py | 1 | 6837 | # -*- coding: utf-8 -*-
"""
Created on Thu Sep 18 19:27:57 2014
@author: joser
"""
import pygame, ode, random, Buttons
from math import atan2, acos, asin, sin, cos
import matplotlib.pyplot as plt
from pygame.locals import *
from numpy import *
from Point import *
from Buttons import *
class gameSimulator( object ):
def __init__( self, *args, **kwargs):
# Initialize pygame
pygame.init()
self.width = kwargs.get('width',600)
self.height = kwargs.get('height',400)
self.length = kwargs.get('length',200)
self.fps = kwargs.get('fps',50)
self.G = kwargs.get('gravity',-9.81)
self.world = ode.World()
self.world.setGravity((0,self.G,0))
self.createScreen()
self.createButtons()
#Variables of this game
self.FIRE = False
self.ANGLE = 0.25
self.POWER = 6.5
self.dt = 1.0/self.fps
self.buffer = []
self.bufferHead = 0
self.bufferTail = 0
self.bufferIsEmpty = True
self.bufferSize = 100
self.correctX = 0
self.correctY = 0
self.initBuffer()
def initBuffer(self):
for i in range(0,self.bufferSize):
self.buffer.append((0,0))
def createScreen(self):
# Open a display
self.srf = pygame.display.set_mode((self.width,self.height))
pygame.display.set_caption("Game Simulator")
#Parameters
self.dt = 1.0/self.fps
self.loopFlag = True
def createButtons(self):
#Buttons
self.goal_button = Buttons.Button(self.srf, color = (200,0,0), x = 10, y = 10, length = 50, height = 25, width = 0, text = "Button_1", text_color = (255,255,255), font_size = 20, fade_on = False)
self.switch_button = Buttons.Button(self.srf, color = (200,0,0), x = 60, y = 10, length = 50, height = 25, width = 0, text = "Button_2", text_color = (255,255,255), font_size = 20, fade_on = False)
self.follow_button = Buttons.Button(self.srf, color = (200,0,0), x = 110, y = 10, length = 50, height = 25, width = 0, text = "Button_3", text_color = (255,255,255), font_size = 20, fade_on = False)
self.noise_button = Buttons.Button(self.srf, color = (200,0,0), x = 160, y = 10, length = 50, height = 25, width = 0, text = "Button_4", text_color = (255,255,255), font_size = 20, fade_on = False)
#Button Dictionary
self.buttons = {0 : self.goal_button,
1 : self.switch_button,
2 : self.follow_button,
3 : self.noise_button}
def loadBackground(self,filename):
self.backgroundImage = pygame.image.load(filename).convert()
self.backgroundRect = self.backgroundImage.get_rect()
def loadImage(self, filename):
image = pygame.image.load(filename)
return image
def world2screen(self, x, y):
return int(self.width/2 + 128*x), int(self.length/2 - 128*y)
def screen2world(self, x, y):
return (float(x - self.width/2)/128), (float(-y + self.length/2)/128)
def checkEvents(self):
events = pygame.event.get()
for e in events:
if e.type==QUIT:
pygame.quit()
elif e.type==KEYDOWN:
if e.key == K_f:
print "FIRE!!!"
self.FIRE = True
self.Vox = self.POWER * cos(self.ANGLE)
self.Voy = self.POWER * sin(self.ANGLE)
elif e.key == K_UP:
self.ANGLE = self.ANGLE + 0.1
print self.POWER, self.ANGLE
elif e.key == K_DOWN:
self.ANGLE = self.ANGLE - 0.1
print self.POWER, self.ANGLE
elif e.key == K_LEFT:
self.POWER = self.POWER - 0.1
print self.POWER, self.ANGLE
elif e.key == K_RIGHT:
self.POWER = self.POWER + 0.1
print self.POWER, self.ANGLE
else:
pygame.quit()
def updateBackground(self, color = None):
if color is not None:
self.srf.fill(color)
else:
self.srf.blit(self.backgroundImage,self.backgroundRect)
def updateImage(self,image, position):
self.srf.blit(image,position)
self.addBuffer(position)
def getBuffer(self):
return zip(*self.buffer)
def addBuffer(self,newValue):
self.buffer[self.bufferHead] = newValue
if self.bufferHead == self.bufferSize-1:
self.bufferHead = 0
self.bufferTail = 0
else:
if self.bufferHead == self.bufferTail and not self.bufferIsEmpty:
self.bufferHead = self.bufferHead +1
self.bufferTail = self.bufferHead
else:
self.bufferHead = self.bufferHead +1
self.bufferIsEmpty = False
def updateTrace(self,x,y,color):
for i in range(0,self.bufferSize):
self.srf.set_at((x[i]+self.correctX,y[i]+self.correctY),color)
def run(self):
# Simulation loop.
self.clk = pygame.time.Clock()
self.loadBackground("images/screen.png")
gun = self.loadImage("images/gun.jpg")
gunPos = [50,320]
bullet = self.loadImage("images/bullet.jpg")
self.correctX, self.correctY = bullet.get_rect().size
self.correctX = self.correctX/2
self.correctY = self.correctY/2
x,y = self.screen2world(230,320)
while True:
# Check for events
self.checkEvents()
self.updateBackground()
self.updateImage(gun,gunPos)
if self.FIRE:
self.Voy = self.Voy + self.G* self.dt
x = x + self.Vox*self.dt
y = y + self.Voy*self.dt + 0.5*self.G*self.dt**2
self.updateImage(bullet, self.world2screen(x,y))
else:
if self.bufferIsEmpty is False:
plotx, ploty = self.getBuffer()
self.updateTrace(plotx, ploty,(255,255,255))
if self.FIRE and (y < -2.5 or y >2.5 or x >2.5 or x<-2.5):
self.FIRE = False
x,y = self.screen2world(230,320)
plotx, ploty = self.getBuffer()
pygame.display.flip()
# Next simulation step
self.world.step(self.dt)
# Try to keep the specified framerate
self.clk.tick(self.fps)
| mit |
stevenzhang18/Indeed-Flask | lib/pandas/tests/test_expressions.py | 9 | 16557 | # -*- coding: utf-8 -*-
from __future__ import print_function
# pylint: disable-msg=W0612,E1101
import nose
import re
from numpy.random import randn
import operator
import numpy as np
from pandas.core.api import DataFrame, Panel
from pandas.computation import expressions as expr
from pandas import compat
from pandas.util.testing import (assert_almost_equal, assert_series_equal,
assert_frame_equal, assert_panel_equal,
assert_panel4d_equal)
import pandas.util.testing as tm
from numpy.testing.decorators import slow
if not expr._USE_NUMEXPR:
try:
import numexpr
except ImportError:
msg = "don't have"
else:
msg = "not using"
raise nose.SkipTest("{0} numexpr".format(msg))
_frame = DataFrame(randn(10000, 4), columns=list('ABCD'), dtype='float64')
_frame2 = DataFrame(randn(100, 4), columns = list('ABCD'), dtype='float64')
_mixed = DataFrame({ 'A' : _frame['A'].copy(), 'B' : _frame['B'].astype('float32'), 'C' : _frame['C'].astype('int64'), 'D' : _frame['D'].astype('int32') })
_mixed2 = DataFrame({ 'A' : _frame2['A'].copy(), 'B' : _frame2['B'].astype('float32'), 'C' : _frame2['C'].astype('int64'), 'D' : _frame2['D'].astype('int32') })
_integer = DataFrame(np.random.randint(1, 100, size=(10001, 4)), columns = list('ABCD'), dtype='int64')
_integer2 = DataFrame(np.random.randint(1, 100, size=(101, 4)),
columns=list('ABCD'), dtype='int64')
_frame_panel = Panel(dict(ItemA=_frame.copy(), ItemB=(_frame.copy() + 3), ItemC=_frame.copy(), ItemD=_frame.copy()))
_frame2_panel = Panel(dict(ItemA=_frame2.copy(), ItemB=(_frame2.copy() + 3),
ItemC=_frame2.copy(), ItemD=_frame2.copy()))
_integer_panel = Panel(dict(ItemA=_integer,
ItemB=(_integer + 34).astype('int64')))
_integer2_panel = Panel(dict(ItemA=_integer2,
ItemB=(_integer2 + 34).astype('int64')))
_mixed_panel = Panel(dict(ItemA=_mixed, ItemB=(_mixed + 3)))
_mixed2_panel = Panel(dict(ItemA=_mixed2, ItemB=(_mixed2 + 3)))
class TestExpressions(tm.TestCase):
_multiprocess_can_split_ = False
def setUp(self):
self.frame = _frame.copy()
self.frame2 = _frame2.copy()
self.mixed = _mixed.copy()
self.mixed2 = _mixed2.copy()
self.integer = _integer.copy()
self._MIN_ELEMENTS = expr._MIN_ELEMENTS
def tearDown(self):
expr._MIN_ELEMENTS = self._MIN_ELEMENTS
@nose.tools.nottest
def run_arithmetic_test(self, df, other, assert_func, check_dtype=False,
test_flex=True):
expr._MIN_ELEMENTS = 0
operations = ['add', 'sub', 'mul', 'mod', 'truediv', 'floordiv', 'pow']
if not compat.PY3:
operations.append('div')
for arith in operations:
operator_name = arith
if arith == 'div':
operator_name = 'truediv'
if test_flex:
op = lambda x, y: getattr(df, arith)(y)
op.__name__ = arith
else:
op = getattr(operator, operator_name)
expr.set_use_numexpr(False)
expected = op(df, other)
expr.set_use_numexpr(True)
result = op(df, other)
try:
if check_dtype:
if arith == 'truediv':
assert expected.dtype.kind == 'f'
assert_func(expected, result)
except Exception:
com.pprint_thing("Failed test with operator %r" % op.__name__)
raise
def test_integer_arithmetic(self):
self.run_arithmetic_test(self.integer, self.integer,
assert_frame_equal)
self.run_arithmetic_test(self.integer.iloc[:,0], self.integer.iloc[:, 0],
assert_series_equal, check_dtype=True)
@nose.tools.nottest
def run_binary_test(self, df, other, assert_func,
test_flex=False, numexpr_ops=set(['gt', 'lt', 'ge',
'le', 'eq', 'ne'])):
"""
tests solely that the result is the same whether or not numexpr is
enabled. Need to test whether the function does the correct thing
elsewhere.
"""
expr._MIN_ELEMENTS = 0
expr.set_test_mode(True)
operations = ['gt', 'lt', 'ge', 'le', 'eq', 'ne']
for arith in operations:
if test_flex:
op = lambda x, y: getattr(df, arith)(y)
op.__name__ = arith
else:
op = getattr(operator, arith)
expr.set_use_numexpr(False)
expected = op(df, other)
expr.set_use_numexpr(True)
expr.get_test_result()
result = op(df, other)
used_numexpr = expr.get_test_result()
try:
if arith in numexpr_ops:
assert used_numexpr, "Did not use numexpr as expected."
else:
assert not used_numexpr, "Used numexpr unexpectedly."
assert_func(expected, result)
except Exception:
com.pprint_thing("Failed test with operation %r" % arith)
com.pprint_thing("test_flex was %r" % test_flex)
raise
def run_frame(self, df, other, binary_comp=None, run_binary=True,
**kwargs):
self.run_arithmetic_test(df, other, assert_frame_equal,
test_flex=False, **kwargs)
self.run_arithmetic_test(df, other, assert_frame_equal, test_flex=True,
**kwargs)
if run_binary:
if binary_comp is None:
expr.set_use_numexpr(False)
binary_comp = other + 1
expr.set_use_numexpr(True)
self.run_binary_test(df, binary_comp, assert_frame_equal,
test_flex=False, **kwargs)
self.run_binary_test(df, binary_comp, assert_frame_equal,
test_flex=True, **kwargs)
def run_series(self, ser, other, binary_comp=None, **kwargs):
self.run_arithmetic_test(ser, other, assert_series_equal,
test_flex=False, **kwargs)
self.run_arithmetic_test(ser, other, assert_almost_equal,
test_flex=True, **kwargs)
# series doesn't uses vec_compare instead of numexpr...
# if binary_comp is None:
# binary_comp = other + 1
# self.run_binary_test(ser, binary_comp, assert_frame_equal, test_flex=False,
# **kwargs)
# self.run_binary_test(ser, binary_comp, assert_frame_equal, test_flex=True,
# **kwargs)
def run_panel(self, panel, other, binary_comp=None, run_binary=True,
assert_func=assert_panel_equal, **kwargs):
self.run_arithmetic_test(panel, other, assert_func, test_flex=False,
**kwargs)
self.run_arithmetic_test(panel, other, assert_func, test_flex=True,
**kwargs)
if run_binary:
if binary_comp is None:
binary_comp = other + 1
self.run_binary_test(panel, binary_comp, assert_func,
test_flex=False, **kwargs)
self.run_binary_test(panel, binary_comp, assert_func,
test_flex=True, **kwargs)
def test_integer_arithmetic_frame(self):
self.run_frame(self.integer, self.integer)
def test_integer_arithmetic_series(self):
self.run_series(self.integer.iloc[:, 0], self.integer.iloc[:, 0])
@slow
def test_integer_panel(self):
self.run_panel(_integer2_panel, np.random.randint(1, 100))
def test_float_arithemtic_frame(self):
self.run_frame(self.frame2, self.frame2)
def test_float_arithmetic_series(self):
self.run_series(self.frame2.iloc[:, 0], self.frame2.iloc[:, 0])
@slow
def test_float_panel(self):
self.run_panel(_frame2_panel, np.random.randn() + 0.1, binary_comp=0.8)
@slow
def test_panel4d(self):
self.run_panel(tm.makePanel4D(), np.random.randn() + 0.5,
assert_func=assert_panel4d_equal, binary_comp=3)
def test_mixed_arithmetic_frame(self):
# TODO: FIGURE OUT HOW TO GET IT TO WORK...
# can't do arithmetic because comparison methods try to do *entire*
# frame instead of by-column
self.run_frame(self.mixed2, self.mixed2, run_binary=False)
def test_mixed_arithmetic_series(self):
for col in self.mixed2.columns:
self.run_series(self.mixed2[col], self.mixed2[col], binary_comp=4)
@slow
def test_mixed_panel(self):
self.run_panel(_mixed2_panel, np.random.randint(1, 100),
binary_comp=-2)
def test_float_arithemtic(self):
self.run_arithmetic_test(self.frame, self.frame, assert_frame_equal)
self.run_arithmetic_test(self.frame.iloc[:, 0], self.frame.iloc[:, 0],
assert_series_equal, check_dtype=True)
def test_mixed_arithmetic(self):
self.run_arithmetic_test(self.mixed, self.mixed, assert_frame_equal)
for col in self.mixed.columns:
self.run_arithmetic_test(self.mixed[col], self.mixed[col],
assert_series_equal)
def test_integer_with_zeros(self):
self.integer *= np.random.randint(0, 2, size=np.shape(self.integer))
self.run_arithmetic_test(self.integer, self.integer, assert_frame_equal)
self.run_arithmetic_test(self.integer.iloc[:, 0], self.integer.iloc[:, 0],
assert_series_equal)
def test_invalid(self):
# no op
result = expr._can_use_numexpr(operator.add, None, self.frame, self.frame, 'evaluate')
self.assertFalse(result)
# mixed
result = expr._can_use_numexpr(operator.add, '+', self.mixed, self.frame, 'evaluate')
self.assertFalse(result)
# min elements
result = expr._can_use_numexpr(operator.add, '+', self.frame2, self.frame2, 'evaluate')
self.assertFalse(result)
# ok, we only check on first part of expression
result = expr._can_use_numexpr(operator.add, '+', self.frame, self.frame2, 'evaluate')
self.assertTrue(result)
def test_binary_ops(self):
def testit():
for f, f2 in [ (self.frame, self.frame2), (self.mixed, self.mixed2) ]:
for op, op_str in [('add','+'),('sub','-'),('mul','*'),('div','/'),('pow','**')]:
if op == 'div':
op = getattr(operator, 'truediv', None)
else:
op = getattr(operator, op, None)
if op is not None:
result = expr._can_use_numexpr(op, op_str, f, f, 'evaluate')
self.assertNotEqual(result, f._is_mixed_type)
result = expr.evaluate(op, op_str, f, f, use_numexpr=True)
expected = expr.evaluate(op, op_str, f, f, use_numexpr=False)
tm.assert_numpy_array_equal(result,expected.values)
result = expr._can_use_numexpr(op, op_str, f2, f2, 'evaluate')
self.assertFalse(result)
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def test_boolean_ops(self):
def testit():
for f, f2 in [ (self.frame, self.frame2), (self.mixed, self.mixed2) ]:
f11 = f
f12 = f + 1
f21 = f2
f22 = f2 + 1
for op, op_str in [('gt','>'),('lt','<'),('ge','>='),('le','<='),('eq','=='),('ne','!=')]:
op = getattr(operator,op)
result = expr._can_use_numexpr(op, op_str, f11, f12, 'evaluate')
self.assertNotEqual(result, f11._is_mixed_type)
result = expr.evaluate(op, op_str, f11, f12, use_numexpr=True)
expected = expr.evaluate(op, op_str, f11, f12, use_numexpr=False)
tm.assert_numpy_array_equal(result,expected.values)
result = expr._can_use_numexpr(op, op_str, f21, f22, 'evaluate')
self.assertFalse(result)
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def test_where(self):
def testit():
for f in [ self.frame, self.frame2, self.mixed, self.mixed2 ]:
for cond in [ True, False ]:
c = np.empty(f.shape,dtype=np.bool_)
c.fill(cond)
result = expr.where(c, f.values, f.values+1)
expected = np.where(c, f.values, f.values+1)
tm.assert_numpy_array_equal(result,expected)
expr.set_use_numexpr(False)
testit()
expr.set_use_numexpr(True)
expr.set_numexpr_threads(1)
testit()
expr.set_numexpr_threads()
testit()
def test_bool_ops_raise_on_arithmetic(self):
df = DataFrame({'a': np.random.rand(10) > 0.5,
'b': np.random.rand(10) > 0.5})
names = 'div', 'truediv', 'floordiv', 'pow'
ops = '/', '/', '//', '**'
msg = 'operator %r not implemented for bool dtypes'
for op, name in zip(ops, names):
if not compat.PY3 or name != 'div':
f = getattr(operator, name)
err_msg = re.escape(msg % op)
with tm.assertRaisesRegexp(NotImplementedError, err_msg):
f(df, df)
with tm.assertRaisesRegexp(NotImplementedError, err_msg):
f(df.a, df.b)
with tm.assertRaisesRegexp(NotImplementedError, err_msg):
f(df.a, True)
with tm.assertRaisesRegexp(NotImplementedError, err_msg):
f(False, df.a)
with tm.assertRaisesRegexp(TypeError, err_msg):
f(False, df)
with tm.assertRaisesRegexp(TypeError, err_msg):
f(df, True)
def test_bool_ops_warn_on_arithmetic(self):
n = 10
df = DataFrame({'a': np.random.rand(n) > 0.5,
'b': np.random.rand(n) > 0.5})
names = 'add', 'mul', 'sub'
ops = '+', '*', '-'
subs = {'+': '|', '*': '&', '-': '^'}
sub_funcs = {'|': 'or_', '&': 'and_', '^': 'xor'}
for op, name in zip(ops, names):
f = getattr(operator, name)
fe = getattr(operator, sub_funcs[subs[op]])
with tm.use_numexpr(True, min_elements=5):
with tm.assert_produces_warning(check_stacklevel=False):
r = f(df, df)
e = fe(df, df)
tm.assert_frame_equal(r, e)
with tm.assert_produces_warning(check_stacklevel=False):
r = f(df.a, df.b)
e = fe(df.a, df.b)
tm.assert_series_equal(r, e)
with tm.assert_produces_warning(check_stacklevel=False):
r = f(df.a, True)
e = fe(df.a, True)
tm.assert_series_equal(r, e)
with tm.assert_produces_warning(check_stacklevel=False):
r = f(False, df.a)
e = fe(False, df.a)
tm.assert_series_equal(r, e)
with tm.assert_produces_warning(check_stacklevel=False):
r = f(False, df)
e = fe(False, df)
tm.assert_frame_equal(r, e)
with tm.assert_produces_warning(check_stacklevel=False):
r = f(df, True)
e = fe(df, True)
tm.assert_frame_equal(r, e)
if __name__ == '__main__':
import nose
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
| apache-2.0 |
drusk/pml | pml/unsupervised/clustering.py | 1 | 11112 | # Copyright (C) 2012 David Rusk
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
"""
Clustering algorithms for unsupervised learning tasks.
@author: drusk
"""
import itertools
import random
import pandas as pd
from pml.data import model
from pml.utils.errors import UnlabelledDataSetError
from pml.utils.distance_utils import euclidean
from pml.utils.pandas_util import are_dataframes_equal
class ClusteredDataSet(model.DataSet):
"""
A collection of data which has been analysed by a clustering algorithm.
It contains both the original DataSet and the results of the clustering.
It provides methods for analysing these clustering results.
"""
def __init__(self, dataset, cluster_assignments):
"""
Creates a new ClusteredDataSet.
Args:
dataset: model.DataSet
A dataset which does not have cluster assignments.
cluster_assignments: pandas.Series
A Series with the cluster assignment for each sample in the
dataset.
"""
super(ClusteredDataSet, self).__init__(dataset.get_data_frame(),
dataset.get_labels())
self.cluster_assignments = cluster_assignments
def get_cluster_assignments(self):
"""
Retrieves the cluster assignments produced for this dataset by a
clustering algorithm.
Returns:
A pandas Series. It contains the index of the original dataset
with a numerical value representing the cluster it is a part of.
"""
return self.cluster_assignments
def calculate_purity(self):
"""
Calculate the purity, a measurement of quality for the clustering
results.
Each cluster is assigned to the class which is most frequent in the
cluster. Using these classes, the percent accuracy is then calculated.
Returns:
A number between 0 and 1. Poor clusterings have a purity close to 0
while a perfect clustering has a purity of 1.
Raises:
UnlabelledDataSetError if the dataset is not labelled.
"""
if not self.is_labelled():
raise UnlabelledDataSetError()
# get the set of unique cluster ids
clusters = set(self.cluster_assignments.values)
# find out what class is most frequent in each cluster
cluster_classes = {}
for cluster in clusters:
# get the indices of rows in this cluster
indices = self.cluster_assignments[self.cluster_assignments ==
cluster].index
# filter the labels series down to those in this cluster
cluster_labels = self.labels[indices]
# assign the most common label to be the label for this cluster
cluster_classes[cluster] = cluster_labels.value_counts().idxmax()
def get_label(cluster):
"""
Get the label for a sample based on its cluster.
"""
return cluster_classes[cluster]
# get the list of labels as determined by each cluster's most frequent
# label
labels_by_clustering = self.cluster_assignments.map(get_label)
# See how the clustering labels compare with the actual labels.
# Return the percentage of indices in agreement.
num_agreed = 0
for ind in labels_by_clustering.index:
if labels_by_clustering[ind] == self.labels[ind]:
num_agreed += 1
return float(num_agreed) / labels_by_clustering.size
def calculate_rand_index(self):
"""
Calculate the Rand index, a measurement of quality for the clustering
results. It is essentially the percent accuracy of the clustering.
The clustering is viewed as a series of decisions. There are
N*(N-1)/2 pairs of samples in the dataset to be considered. The
decision is considered correct if the pairs have the same label and
are in the same cluster, or have different labels and are in different
clusters. The number of correct decisions divided by the total number
of decisions gives the Rand index, or accuracy.
Returns:
The accuracy, a number between 0 and 1. The closer to 1, the better
the clustering.
Raises:
UnlabelledDataSetError if the dataset is not labelled.
"""
if not self.is_labelled():
raise UnlabelledDataSetError()
correct = 0
total = 0
for index_combo in itertools.combinations(self.get_sample_ids(), 2):
index1 = index_combo[0]
index2 = index_combo[1]
same_class = (self.labels[index1] == self.labels[index2])
same_cluster = (self.cluster_assignments[index1]
== self.cluster_assignments[index2])
if same_class and same_cluster:
correct += 1
elif not same_class and not same_cluster:
correct += 1
total += 1
return float(correct) / total
def create_random_centroids(dataset, k):
"""
Initializes centroids at random positions.
The random value chosen for each feature will always be limited to the
range of values found in the dataset. For example, if a certain feature
has a minimum value of 0 in the dataset, and maximum value of 9, the
value chosen will be between 0 and 9.
Args:
dataset: DataSet
The DataSet to create the random centroids for.
k: int
The number of centroids to create.
Returns:
A list of centroids. Each centroid is a pandas Series with the same
labels as the dataset's headers.
"""
min_maxs = zip(dataset.reduce_features(min).values,
dataset.reduce_features(max).values)
def rand_range(range_tuple):
"""
Generates a random floating point number in the range specified by
the tuple.
"""
return random.uniform(range_tuple[0], range_tuple[1])
return [pd.Series(map(rand_range, min_maxs),
index=dataset.feature_list(),
name=i)
for i in range(k)]
def kmeans(dataset, k=2, distance=euclidean, centroids=None):
"""
K-means clustering algorithm.
This algorithm partitions a dataset into k clusters in which each
observation (sample) belongs to the cluster with the nearest mean.
Args:
dataset: model.DataSet
The DataSet to perform the clustering on.
k: int
The number of clusters to partition the dataset into.
centroids: list of pandas Series
The initial centroids for the clusters. Defaults to None in which
case they are selected randomly.
Returns:
A ClusteredDataSet which contains the cluster assignments as well as the
original data. In the cluster assignments, each sample index is
assigned a numerical value representing the cluster it is part of.
"""
# If dataset is not already a model.DataSet object, make it one.
dataset = model.as_dataset(dataset)
# Initialize k centroids
if centroids is None:
centroids = create_random_centroids(dataset, k)
# Iteratively compute best clusters until they stabilize
assignments = None
clusters_changed = True
while clusters_changed:
centroids, new_assignments = _compute_iteration(dataset, centroids,
distance)
if are_dataframes_equal(new_assignments, assignments):
clusters_changed = False
assignments = new_assignments
return ClusteredDataSet(dataset, assignments)
def _get_distances_to_centroids(dataset, centroids, distance_measure):
"""
Calculates the calc_distance from each data point to each centroid.
Args:
dataset: model.DataSet
The DataSet whose samples are being
centroids: list of pandas Series
The centroids to compare each data point with.
Returns:
A pandas DataFrame with a row for each sample in dataset and a column
for the distance to each centroid.
"""
distances = {}
for i, centroid in enumerate(centroids):
def calc_distance(sample):
return distance_measure(sample, centroid)
distances[i] = dataset.reduce_rows(calc_distance)
# each dictionary entry is interpreted as a column
return pd.DataFrame(distances)
def _compute_iteration(dataset, centroids, distance_measure):
"""
Computes an iteration of the k-means algorithm.
Args:
dataset: model.DataSet
The dataset being clustered.
centroids: list of pandas Series
The current centroids at the start of the iteration.
Returns:
new_centroids: list of pandas Series
The updated centroids.
cluster_assignments: pandas Series
The current cluster assignments for each sample.
"""
# Calculate calc_distance from each data point to each centroid
distances = _get_distances_to_centroids(dataset, centroids,
distance_measure)
# Find each datapoint's nearest centroid
cluster_assignments = distances.idxmin(axis=1)
def nearest_centroid(sample_index):
return cluster_assignments[sample_index]
# Calculate mean position of datapoints in each centroid's cluster
new_centroids = dataset.get_data_frame().groupby(nearest_centroid).mean()
# XXX turning each row in dataframe into a series... refactor!
list_of_series = [new_centroids.ix[ind] for ind in new_centroids.index]
return list_of_series, cluster_assignments
| mit |
r39132/airflow | tests/contrib/operators/test_hive_to_dynamodb_operator.py | 7 | 5053 | # -*- coding: utf-8 -*-
#
# 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.
#
import json
import unittest
import datetime
import mock
import pandas as pd
from airflow import configuration, DAG
from airflow.contrib.hooks.aws_dynamodb_hook import AwsDynamoDBHook
import airflow.contrib.operators.hive_to_dynamodb
configuration.load_test_config()
DEFAULT_DATE = datetime.datetime(2015, 1, 1)
DEFAULT_DATE_ISO = DEFAULT_DATE.isoformat()
DEFAULT_DATE_DS = DEFAULT_DATE_ISO[:10]
try:
from moto import mock_dynamodb2
except ImportError:
mock_dynamodb2 = None
class HiveToDynamoDBTransferOperatorTest(unittest.TestCase):
def setUp(self):
configuration.load_test_config()
args = {'owner': 'airflow', 'start_date': DEFAULT_DATE}
dag = DAG('test_dag_id', default_args=args)
self.dag = dag
self.sql = 'SELECT 1'
self.hook = AwsDynamoDBHook(
aws_conn_id='aws_default', region_name='us-east-1')
@staticmethod
def process_data(data, *args, **kwargs):
return json.loads(data.to_json(orient='records'))
@unittest.skipIf(mock_dynamodb2 is None, 'mock_dynamodb2 package not present')
@mock_dynamodb2
def test_get_conn_returns_a_boto3_connection(self):
hook = AwsDynamoDBHook(aws_conn_id='aws_default')
self.assertIsNotNone(hook.get_conn())
@mock.patch('airflow.hooks.hive_hooks.HiveServer2Hook.get_pandas_df',
return_value=pd.DataFrame(data=[('1', 'sid')], columns=['id', 'name']))
@unittest.skipIf(mock_dynamodb2 is None, 'mock_dynamodb2 package not present')
@mock_dynamodb2
def test_get_records_with_schema(self, get_results_mock):
# this table needs to be created in production
self.hook.get_conn().create_table(
TableName='test_airflow',
KeySchema=[
{
'AttributeName': 'id',
'KeyType': 'HASH'
},
],
AttributeDefinitions=[
{
'AttributeName': 'name',
'AttributeType': 'S'
}
],
ProvisionedThroughput={
'ReadCapacityUnits': 10,
'WriteCapacityUnits': 10
}
)
operator = airflow.contrib.operators.hive_to_dynamodb.HiveToDynamoDBTransferOperator(
sql=self.sql,
table_name="test_airflow",
task_id='hive_to_dynamodb_check',
table_keys=['id'],
dag=self.dag)
operator.execute(None)
table = self.hook.get_conn().Table('test_airflow')
table.meta.client.get_waiter(
'table_exists').wait(TableName='test_airflow')
self.assertEqual(table.item_count, 1)
@mock.patch('airflow.hooks.hive_hooks.HiveServer2Hook.get_pandas_df',
return_value=pd.DataFrame(data=[('1', 'sid'), ('1', 'gupta')], columns=['id', 'name']))
@unittest.skipIf(mock_dynamodb2 is None, 'mock_dynamodb2 package not present')
@mock_dynamodb2
def test_pre_process_records_with_schema(self, get_results_mock):
# this table needs to be created in production
self.hook.get_conn().create_table(
TableName='test_airflow',
KeySchema=[
{
'AttributeName': 'id',
'KeyType': 'HASH'
},
],
AttributeDefinitions=[
{
'AttributeName': 'name',
'AttributeType': 'S'
}
],
ProvisionedThroughput={
'ReadCapacityUnits': 10,
'WriteCapacityUnits': 10
}
)
operator = airflow.contrib.operators.hive_to_dynamodb.HiveToDynamoDBTransferOperator(
sql=self.sql,
table_name='test_airflow',
task_id='hive_to_dynamodb_check',
table_keys=['id'],
pre_process=self.process_data,
dag=self.dag)
operator.execute(None)
table = self.hook.get_conn().Table('test_airflow')
table.meta.client.get_waiter('table_exists').wait(TableName='test_airflow')
self.assertEqual(table.item_count, 1)
if __name__ == '__main__':
unittest.main()
| apache-2.0 |
jiangzhonglian/MachineLearning | src/py2.x/ml/6.SVM/svm-complete_Non-Kernel.py | 1 | 13440 | #!/usr/bin/python
# coding:utf8
"""
Created on Nov 4, 2010
Update on 2017-05-18
Chapter 5 source file for Machine Learing in Action
Author: Peter/geekidentity/片刻
GitHub: https://github.com/apachecn/AiLearning
"""
from __future__ import print_function
from numpy import *
import matplotlib.pyplot as plt
class optStruct:
def __init__(self, dataMatIn, classLabels, C, toler): # Initialize the structure with the parameters
self.X = dataMatIn
self.labelMat = classLabels
self.C = C
self.tol = toler
self.m = shape(dataMatIn)[0]
self.alphas = mat(zeros((self.m, 1)))
self.b = 0
self.eCache = mat(zeros((self.m, 2))) # first column is valid flag
def loadDataSet(fileName):
"""loadDataSet（对文件进行逐行解析，从而得到第行的类标签和整个数据矩阵）
Args:
fileName 文件名
Returns:
dataMat 数据矩阵
labelMat 类标签
"""
dataMat = []
labelMat = []
fr = open(fileName)
for line in fr.readlines():
lineArr = line.strip().split('\t')
dataMat.append([float(lineArr[0]), float(lineArr[1])])
labelMat.append(float(lineArr[2]))
return dataMat, labelMat
def selectJrand(i, m):
"""
随机选择一个整数
Args:
i 第一个alpha的下标
m 所有alpha的数目
Returns:
j 返回一个不为i的随机数，在0~m之间的整数值
"""
j = i
while j == i:
j = int(random.uniform(0, m))
return j
def clipAlpha(aj, H, L):
"""clipAlpha(调整aj的值，使aj处于 L<=aj<=H)
Args:
aj 目标值
H 最大值
L 最小值
Returns:
aj 目标值
"""
if aj > H:
aj = H
if L > aj:
aj = L
return aj
def calcEk(oS, k):
"""calcEk（求 Ek误差：预测值-真实值的差）
该过程在完整版的SMO算法中陪出现次数较多，因此将其单独作为一个方法
Args:
oS optStruct对象
k 具体的某一行
Returns:
Ek 预测结果与真实结果比对，计算误差Ek
"""
fXk = float(multiply(oS.alphas, oS.labelMat).T * (oS.X * oS.X[k, :].T)) + oS.b
Ek = fXk - float(oS.labelMat[k])
return Ek
def selectJ(i, oS, Ei): # this is the second choice -heurstic, and calcs Ej
"""selectJ（返回最优的j和Ej）
内循环的启发式方法。
选择第二个(内循环)alpha的alpha值
这里的目标是选择合适的第二个alpha值以保证每次优化中采用最大步长。
该函数的误差与第一个alpha值Ei和下标i有关。
Args:
i 具体的第i一行
oS optStruct对象
Ei 预测结果与真实结果比对，计算误差Ei
Returns:
j 随机选出的第j一行
Ej 预测结果与真实结果比对，计算误差Ej
"""
maxK = -1
maxDeltaE = 0
Ej = 0
# 首先将输入值Ei在缓存中设置成为有效的。这里的有效意味着它已经计算好了。
oS.eCache[i] = [1, Ei]
# print 'oS.eCache[%s]=%s' % (i, oS.eCache[i])
# print 'oS.eCache[:, 0].A=%s' % oS.eCache[:, 0].A.T
# """
# # 返回非0的：行列值
# nonzero(oS.eCache[:, 0].A)= (
# 行： array([ 0, 2, 4, 5, 8, 10, 17, 18, 20, 21, 23, 25, 26, 29, 30, 39, 46,52, 54, 55, 62, 69, 70, 76, 79, 82, 94, 97]),
# 列： array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0])
# )
# """
# print 'nonzero(oS.eCache[:, 0].A)=', nonzero(oS.eCache[:, 0].A)
# # 取行的list
# print 'nonzero(oS.eCache[:, 0].A)[0]=', nonzero(oS.eCache[:, 0].A)[0]
# 非零E值的行的list列表，所对应的alpha值
validEcacheList = nonzero(oS.eCache[:, 0].A)[0]
if (len(validEcacheList)) > 1:
for k in validEcacheList: # 在所有的值上进行循环，并选择其中使得改变最大的那个值
if k == i:
continue # don't calc for i, waste of time
# 求 Ek误差：预测值-真实值的差
Ek = calcEk(oS, k)
deltaE = abs(Ei - Ek)
if (deltaE > maxDeltaE):
maxK = k
maxDeltaE = deltaE
Ej = Ek
return maxK, Ej
else: # 如果是第一次循环，则随机选择一个alpha值
j = selectJrand(i, oS.m)
# 求 Ek误差：预测值-真实值的差
Ej = calcEk(oS, j)
return j, Ej
def updateEk(oS, k): # after any alpha has changed update the new value in the cache
"""updateEk（计算误差值并存入缓存中。）
在对alpha值进行优化之后会用到这个值。
Args:
oS optStruct对象
k 某一列的行号
"""
# 求 误差：预测值-真实值的差
Ek = calcEk(oS, k)
oS.eCache[k] = [1, Ek]
def innerL(i, oS):
"""innerL
内循环代码
Args:
i 具体的某一行
oS optStruct对象
Returns:
0 找不到最优的值
1 找到了最优的值，并且oS.Cache到缓存中
"""
# 求 Ek误差：预测值-真实值的差
Ei = calcEk(oS, i)
# 约束条件 (KKT条件是解决最优化问题的时用到的一种方法。我们这里提到的最优化问题通常是指对于给定的某一函数，求其在指定作用域上的全局最小值)
# 0<=alphas[i]<=C，但由于0和C是边界值，我们无法进行优化，因为需要增加一个alphas和降低一个alphas。
# 表示发生错误的概率：labelMat[i]*Ei 如果超出了 toler， 才需要优化。至于正负号，我们考虑绝对值就对了。
'''
# 检验训练样本(xi, yi)是否满足KKT条件
yi*f(i) >= 1 and alpha = 0 (outside the boundary)
yi*f(i) == 1 and 0<alpha< C (on the boundary)
yi*f(i) <= 1 and alpha = C (between the boundary)
'''
if ((oS.labelMat[i] * Ei < -oS.tol) and (oS.alphas[i] < oS.C)) or ((oS.labelMat[i] * Ei > oS.tol) and (oS.alphas[i] > 0)):
# 选择最大的误差对应的j进行优化。效果更明显
j, Ej = selectJ(i, oS, Ei)
alphaIold = oS.alphas[i].copy()
alphaJold = oS.alphas[j].copy()
# L和H用于将alphas[j]调整到0-C之间。如果L==H，就不做任何改变，直接return 0
if (oS.labelMat[i] != oS.labelMat[j]):
L = max(0, oS.alphas[j] - oS.alphas[i])
H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])
else:
L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)
H = min(oS.C, oS.alphas[j] + oS.alphas[i])
if L == H:
print("L==H")
return 0
# eta是alphas[j]的最优修改量，如果eta==0，需要退出for循环的当前迭代过程
# 参考《统计学习方法》李航-P125~P128<序列最小最优化算法>
eta = 2.0 * oS.X[i, :] * oS.X[j, :].T - oS.X[i, :] * oS.X[i, :].T - oS.X[j, :] * oS.X[j, :].T
if eta >= 0:
print("eta>=0")
return 0
# 计算出一个新的alphas[j]值
oS.alphas[j] -= oS.labelMat[j] * (Ei - Ej) / eta
# 并使用辅助函数，以及L和H对其进行调整
oS.alphas[j] = clipAlpha(oS.alphas[j], H, L)
# 更新误差缓存
updateEk(oS, j)
# 检查alpha[j]是否只是轻微的改变，如果是的话，就退出for循环。
if (abs(oS.alphas[j] - alphaJold) < 0.00001):
print("j not moving enough")
return 0
# 然后alphas[i]和alphas[j]同样进行改变，虽然改变的大小一样，但是改变的方向正好相反
oS.alphas[i] += oS.labelMat[j] * oS.labelMat[i] * (alphaJold - oS.alphas[j])
# 更新误差缓存
updateEk(oS, i)
# 在对alpha[i], alpha[j] 进行优化之后，给这两个alpha值设置一个常数b。
# w= Σ[1~n] ai*yi*xi => b = yj Σ[1~n] ai*yi(xi*xj)
# 所以： b1 - b = (y1-y) - Σ[1~n] yi*(a1-a)*(xi*x1)
# 为什么减2遍？ 因为是 减去Σ[1~n]，正好2个变量i和j，所以减2遍
b1 = oS.b - Ei - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.X[i, :] * oS.X[i, :].T - oS.labelMat[j] * (oS.alphas[j] - alphaJold) * oS.X[i, :] * oS.X[j, :].T
b2 = oS.b - Ej - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.X[i, :] * oS.X[j, :].T - oS.labelMat[j] * (oS.alphas[j] - alphaJold) * oS.X[j, :] * oS.X[j, :].T
if (0 < oS.alphas[i]) and (oS.C > oS.alphas[i]):
oS.b = b1
elif (0 < oS.alphas[j]) and (oS.C > oS.alphas[j]):
oS.b = b2
else:
oS.b = (b1 + b2) / 2.0
return 1
else:
return 0
def smoP(dataMatIn, classLabels, C, toler, maxIter):
"""
完整SMO算法外循环，与smoSimple有些类似，但这里的循环退出条件更多一些
Args:
dataMatIn 数据集
classLabels 类别标签
C 松弛变量(常量值)，允许有些数据点可以处于分隔面的错误一侧。
控制最大化间隔和保证大部分的函数间隔小于1.0这两个目标的权重。
可以通过调节该参数达到不同的结果。
toler 容错率
maxIter 退出前最大的循环次数
Returns:
b 模型的常量值
alphas 拉格朗日乘子
"""
# 创建一个 optStruct 对象
oS = optStruct(mat(dataMatIn), mat(classLabels).transpose(), C, toler)
iter = 0
entireSet = True
alphaPairsChanged = 0
# 循环遍历：循环maxIter次 并且 （alphaPairsChanged存在可以改变 or 所有行遍历一遍）
# 循环迭代结束 或者 循环遍历所有alpha后，alphaPairs还是没变化
while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):
alphaPairsChanged = 0
# 当entireSet=true or 非边界alpha对没有了；就开始寻找 alpha对，然后决定是否要进行else。
if entireSet:
# 在数据集上遍历所有可能的alpha
for i in range(oS.m):
# 是否存在alpha对，存在就+1
alphaPairsChanged += innerL(i, oS)
print("fullSet, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
iter += 1
# 对已存在 alpha对，选出非边界的alpha值，进行优化。
else:
# 遍历所有的非边界alpha值，也就是不在边界0或C上的值。
nonBoundIs = nonzero((oS.alphas.A > 0) * (oS.alphas.A < C))[0]
for i in nonBoundIs:
alphaPairsChanged += innerL(i, oS)
print("non-bound, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
iter += 1
# 如果找到alpha对，就优化非边界alpha值，否则，就重新进行寻找，如果寻找一遍 遍历所有的行还是没找到，就退出循环。
if entireSet:
entireSet = False # toggle entire set loop
elif (alphaPairsChanged == 0):
entireSet = True
print("iteration number: %d" % iter)
return oS.b, oS.alphas
def calcWs(alphas, dataArr, classLabels):
"""
基于alpha计算w值
Args:
alphas 拉格朗日乘子
dataArr feature数据集
classLabels 目标变量数据集
Returns:
wc 回归系数
"""
X = mat(dataArr)
labelMat = mat(classLabels).transpose()
m, n = shape(X)
w = zeros((n, 1))
for i in range(m):
w += multiply(alphas[i] * labelMat[i], X[i, :].T)
return w
def plotfig_SVM(xArr, yArr, ws, b, alphas):
"""
参考地址：
http://blog.csdn.net/maoersong/article/details/24315633
http://www.cnblogs.com/JustForCS/p/5283489.html
http://blog.csdn.net/kkxgx/article/details/6951959
"""
xMat = mat(xArr)
yMat = mat(yArr)
# b原来是矩阵，先转为数组类型后其数组大小为（1,1），所以后面加[0]，变为(1,)
b = array(b)[0]
fig = plt.figure()
ax = fig.add_subplot(111)
# 注意flatten的用法
ax.scatter(xMat[:, 0].flatten().A[0], xMat[:, 1].flatten().A[0])
# x最大值，最小值根据原数据集dataArr[:, 0]的大小而定
x = arange(-1.0, 10.0, 0.1)
# 根据x.w + b = 0 得到，其式子展开为w0.x1 + w1.x2 + b = 0, x2就是y值
y = (-b-ws[0, 0]*x)/ws[1, 0]
ax.plot(x, y)
for i in range(shape(yMat[0, :])[1]):
if yMat[0, i] > 0:
ax.plot(xMat[i, 0], xMat[i, 1], 'cx')
else:
ax.plot(xMat[i, 0], xMat[i, 1], 'kp')
# 找到支持向量，并在图中标红
for i in range(100):
if alphas[i] > 0.0:
ax.plot(xMat[i, 0], xMat[i, 1], 'ro')
plt.show()
if __name__ == "__main__":
# 获取特征和目标变量
dataArr, labelArr = loadDataSet('data/6.SVM/testSet.txt')
# print labelArr
# b是常量值， alphas是拉格朗日乘子
b, alphas = smoP(dataArr, labelArr, 0.6, 0.001, 40)
print('/n/n/n')
print('b=', b)
print('alphas[alphas>0]=', alphas[alphas > 0])
print('shape(alphas[alphas > 0])=', shape(alphas[alphas > 0]))
for i in range(100):
if alphas[i] > 0:
print(dataArr[i], labelArr[i])
# 画图
ws = calcWs(alphas, dataArr, labelArr)
plotfig_SVM(dataArr, labelArr, ws, b, alphas)
| gpl-3.0 |
Titan-C/sympy | sympy/physics/quantum/circuitplot.py | 6 | 12937 | """Matplotlib based plotting of quantum circuits.
Todo:
* Optimize printing of large circuits.
* Get this to work with single gates.
* Do a better job checking the form of circuits to make sure it is a Mul of
Gates.
* Get multi-target gates plotting.
* Get initial and final states to plot.
* Get measurements to plot. Might need to rethink measurement as a gate
issue.
* Get scale and figsize to be handled in a better way.
* Write some tests/examples!
"""
from __future__ import print_function, division
from sympy import Mul
from sympy.core.compatibility import u, range
from sympy.external import import_module
from sympy.physics.quantum.gate import Gate, OneQubitGate, CGate, CGateS
from sympy.core.core import BasicMeta
from sympy.core.assumptions import ManagedProperties
__all__ = [
'CircuitPlot',
'circuit_plot',
'labeller',
'Mz',
'Mx',
'CreateOneQubitGate',
'CreateCGate',
]
np = import_module('numpy')
matplotlib = import_module(
'matplotlib', __import__kwargs={'fromlist': ['pyplot']},
catch=(RuntimeError,)) # This is raised in environments that have no display.
if not np or not matplotlib:
class CircuitPlot(object):
def __init__(*args, **kwargs):
raise ImportError('numpy or matplotlib not available.')
def circuit_plot(*args, **kwargs):
raise ImportError('numpy or matplotlib not available.')
else:
pyplot = matplotlib.pyplot
Line2D = matplotlib.lines.Line2D
Circle = matplotlib.patches.Circle
#from matplotlib import rc
#rc('text',usetex=True)
class CircuitPlot(object):
"""A class for managing a circuit plot."""
scale = 1.0
fontsize = 20.0
linewidth = 1.0
control_radius = 0.05
not_radius = 0.15
swap_delta = 0.05
labels = []
inits = {}
label_buffer = 0.5
def __init__(self, c, nqubits, **kwargs):
self.circuit = c
self.ngates = len(self.circuit.args)
self.nqubits = nqubits
self.update(kwargs)
self._create_grid()
self._create_figure()
self._plot_wires()
self._plot_gates()
self._finish()
def update(self, kwargs):
"""Load the kwargs into the instance dict."""
self.__dict__.update(kwargs)
def _create_grid(self):
"""Create the grid of wires."""
scale = self.scale
wire_grid = np.arange(0.0, self.nqubits*scale, scale, dtype=float)
gate_grid = np.arange(0.0, self.ngates*scale, scale, dtype=float)
self._wire_grid = wire_grid
self._gate_grid = gate_grid
def _create_figure(self):
"""Create the main matplotlib figure."""
self._figure = pyplot.figure(
figsize=(self.ngates*self.scale, self.nqubits*self.scale),
facecolor='w',
edgecolor='w'
)
ax = self._figure.add_subplot(
1, 1, 1,
frameon=True
)
ax.set_axis_off()
offset = 0.5*self.scale
ax.set_xlim(self._gate_grid[0] - offset, self._gate_grid[-1] + offset)
ax.set_ylim(self._wire_grid[0] - offset, self._wire_grid[-1] + offset)
ax.set_aspect('equal')
self._axes = ax
def _plot_wires(self):
"""Plot the wires of the circuit diagram."""
xstart = self._gate_grid[0]
xstop = self._gate_grid[-1]
xdata = (xstart - self.scale, xstop + self.scale)
for i in range(self.nqubits):
ydata = (self._wire_grid[i], self._wire_grid[i])
line = Line2D(
xdata, ydata,
color='k',
lw=self.linewidth
)
self._axes.add_line(line)
if self.labels:
init_label_buffer = 0
if self.inits.get(self.labels[i]): init_label_buffer = 0.25
self._axes.text(
xdata[0]-self.label_buffer-init_label_buffer,ydata[0],
render_label(self.labels[i],self.inits),
size=self.fontsize,
color='k',ha='center',va='center')
self._plot_measured_wires()
def _plot_measured_wires(self):
ismeasured = self._measurements()
xstop = self._gate_grid[-1]
dy = 0.04 # amount to shift wires when doubled
# Plot doubled wires after they are measured
for im in ismeasured:
xdata = (self._gate_grid[ismeasured[im]],xstop+self.scale)
ydata = (self._wire_grid[im]+dy,self._wire_grid[im]+dy)
line = Line2D(
xdata, ydata,
color='k',
lw=self.linewidth
)
self._axes.add_line(line)
# Also double any controlled lines off these wires
for i,g in enumerate(self._gates()):
if isinstance(g, CGate) or isinstance(g, CGateS):
wires = g.controls + g.targets
for wire in wires:
if wire in ismeasured and \
self._gate_grid[i] > self._gate_grid[ismeasured[wire]]:
ydata = min(wires), max(wires)
xdata = self._gate_grid[i]-dy, self._gate_grid[i]-dy
line = Line2D(
xdata, ydata,
color='k',
lw=self.linewidth
)
self._axes.add_line(line)
def _gates(self):
"""Create a list of all gates in the circuit plot."""
gates = []
if isinstance(self.circuit, Mul):
for g in reversed(self.circuit.args):
if isinstance(g, Gate):
gates.append(g)
elif isinstance(self.circuit, Gate):
gates.append(self.circuit)
return gates
def _plot_gates(self):
"""Iterate through the gates and plot each of them."""
for i, gate in enumerate(self._gates()):
gate.plot_gate(self, i)
def _measurements(self):
"""Return a dict {i:j} where i is the index of the wire that has
been measured, and j is the gate where the wire is measured.
"""
ismeasured = {}
for i,g in enumerate(self._gates()):
if getattr(g,'measurement',False):
for target in g.targets:
if target in ismeasured:
if ismeasured[target] > i:
ismeasured[target] = i
else:
ismeasured[target] = i
return ismeasured
def _finish(self):
# Disable clipping to make panning work well for large circuits.
for o in self._figure.findobj():
o.set_clip_on(False)
def one_qubit_box(self, t, gate_idx, wire_idx):
"""Draw a box for a single qubit gate."""
x = self._gate_grid[gate_idx]
y = self._wire_grid[wire_idx]
self._axes.text(
x, y, t,
color='k',
ha='center',
va='center',
bbox=dict(ec='k', fc='w', fill=True, lw=self.linewidth),
size=self.fontsize
)
def two_qubit_box(self, t, gate_idx, wire_idx):
"""Draw a box for a two qubit gate. Doesn't work yet.
"""
x = self._gate_grid[gate_idx]
y = self._wire_grid[wire_idx]+0.5
print(self._gate_grid)
print(self._wire_grid)
obj = self._axes.text(
x, y, t,
color='k',
ha='center',
va='center',
bbox=dict(ec='k', fc='w', fill=True, lw=self.linewidth),
size=self.fontsize
)
def control_line(self, gate_idx, min_wire, max_wire):
"""Draw a vertical control line."""
xdata = (self._gate_grid[gate_idx], self._gate_grid[gate_idx])
ydata = (self._wire_grid[min_wire], self._wire_grid[max_wire])
line = Line2D(
xdata, ydata,
color='k',
lw=self.linewidth
)
self._axes.add_line(line)
def control_point(self, gate_idx, wire_idx):
"""Draw a control point."""
x = self._gate_grid[gate_idx]
y = self._wire_grid[wire_idx]
radius = self.control_radius
c = Circle(
(x, y),
radius*self.scale,
ec='k',
fc='k',
fill=True,
lw=self.linewidth
)
self._axes.add_patch(c)
def not_point(self, gate_idx, wire_idx):
"""Draw a NOT gates as the circle with plus in the middle."""
x = self._gate_grid[gate_idx]
y = self._wire_grid[wire_idx]
radius = self.not_radius
c = Circle(
(x, y),
radius,
ec='k',
fc='w',
fill=False,
lw=self.linewidth
)
self._axes.add_patch(c)
l = Line2D(
(x, x), (y - radius, y + radius),
color='k',
lw=self.linewidth
)
self._axes.add_line(l)
def swap_point(self, gate_idx, wire_idx):
"""Draw a swap point as a cross."""
x = self._gate_grid[gate_idx]
y = self._wire_grid[wire_idx]
d = self.swap_delta
l1 = Line2D(
(x - d, x + d),
(y - d, y + d),
color='k',
lw=self.linewidth
)
l2 = Line2D(
(x - d, x + d),
(y + d, y - d),
color='k',
lw=self.linewidth
)
self._axes.add_line(l1)
self._axes.add_line(l2)
def circuit_plot(c, nqubits, **kwargs):
"""Draw the circuit diagram for the circuit with nqubits.
Parameters
==========
c : circuit
The circuit to plot. Should be a product of Gate instances.
nqubits : int
The number of qubits to include in the circuit. Must be at least
as big as the largest `min_qubits`` of the gates.
"""
return CircuitPlot(c, nqubits, **kwargs)
def render_label(label, inits={}):
"""Slightly more flexible way to render labels.
>>> from sympy.physics.quantum.circuitplot import render_label
>>> render_label('q0')
'$|q0\\\\rangle$'
>>> render_label('q0', {'q0':'0'})
'$|q0\\\\rangle=|0\\\\rangle$'
"""
init = inits.get(label)
if init:
return r'$|%s\rangle=|%s\rangle$' % (label, init)
return r'$|%s\rangle$' % label
def labeller(n, symbol='q'):
"""Autogenerate labels for wires of quantum circuits.
Parameters
==========
n : int
number of qubits in the circuit
symbol : string
A character string to precede all gate labels. E.g. 'q_0', 'q_1', etc.
>>> from sympy.physics.quantum.circuitplot import labeller
>>> labeller(2)
['q_1', 'q_0']
>>> labeller(3,'j')
['j_2', 'j_1', 'j_0']
"""
return ['%s_%d' % (symbol,n-i-1) for i in range(n)]
class Mz(OneQubitGate):
"""Mock-up of a z measurement gate.
This is in circuitplot rather than gate.py because it's not a real
gate, it just draws one.
"""
measurement = True
gate_name='Mz'
gate_name_latex=u'M_z'
class Mx(OneQubitGate):
"""Mock-up of an x measurement gate.
This is in circuitplot rather than gate.py because it's not a real
gate, it just draws one.
"""
measurement = True
gate_name='Mx'
gate_name_latex=u'M_x'
class CreateOneQubitGate(ManagedProperties):
def __new__(mcl, name, latexname=None):
if not latexname:
latexname = name
return BasicMeta.__new__(mcl, name + "Gate", (OneQubitGate,),
{'gate_name': name, 'gate_name_latex': latexname})
def CreateCGate(name, latexname=None):
"""Use a lexical closure to make a controlled gate.
"""
if not latexname:
latexname = name
onequbitgate = CreateOneQubitGate(name, latexname)
def ControlledGate(ctrls,target):
return CGate(tuple(ctrls),onequbitgate(target))
return ControlledGate
| bsd-3-clause |
LiaoPan/scikit-learn | examples/svm/plot_iris.py | 225 | 3252 | """
==================================================
Plot different SVM classifiers in the iris dataset
==================================================
Comparison of different linear SVM classifiers on a 2D projection of the iris
dataset. We only consider the first 2 features of this dataset:
- Sepal length
- Sepal width
This example shows how to plot the decision surface for four SVM classifiers
with different kernels.
The linear models ``LinearSVC()`` and ``SVC(kernel='linear')`` yield slightly
different decision boundaries. This can be a consequence of the following
differences:
- ``LinearSVC`` minimizes the squared hinge loss while ``SVC`` minimizes the
regular hinge loss.
- ``LinearSVC`` uses the One-vs-All (also known as One-vs-Rest) multiclass
reduction while ``SVC`` uses the One-vs-One multiclass reduction.
Both linear models have linear decision boundaries (intersecting hyperplanes)
while the non-linear kernel models (polynomial or Gaussian RBF) have more
flexible non-linear decision boundaries with shapes that depend on the kind of
kernel and its parameters.
.. NOTE:: while plotting the decision function of classifiers for toy 2D
datasets can help get an intuitive understanding of their respective
expressive power, be aware that those intuitions don't always generalize to
more realistic high-dimensional problems.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
y = iris.target
h = .02 # step size in the mesh
# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
C = 1.0 # SVM regularization parameter
svc = svm.SVC(kernel='linear', C=C).fit(X, y)
rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, y)
poly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X, y)
lin_svc = svm.LinearSVC(C=C).fit(X, y)
# create a mesh to plot in
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
# title for the plots
titles = ['SVC with linear kernel',
'LinearSVC (linear kernel)',
'SVC with RBF kernel',
'SVC with polynomial (degree 3) kernel']
for i, clf in enumerate((svc, lin_svc, rbf_svc, poly_svc)):
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
plt.subplot(2, 2, i + 1)
plt.subplots_adjust(wspace=0.4, hspace=0.4)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.title(titles[i])
plt.show()
| bsd-3-clause |
joernhees/scikit-learn | sklearn/ensemble/weight_boosting.py | 29 | 41090 | """Weight Boosting
This module contains weight boosting estimators for both classification and
regression.
The module structure is the following:
- The ``BaseWeightBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regression and classification
only differ from each other in the loss function that is optimized.
- ``AdaBoostClassifier`` implements adaptive boosting (AdaBoost-SAMME) for
classification problems.
- ``AdaBoostRegressor`` implements adaptive boosting (AdaBoost.R2) for
regression problems.
"""
# Authors: Noel Dawe <noel@dawe.me>
# Gilles Louppe <g.louppe@gmail.com>
# Hamzeh Alsalhi <ha258@cornell.edu>
# Arnaud Joly <arnaud.v.joly@gmail.com>
#
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
import numpy as np
from numpy.core.umath_tests import inner1d
from .base import BaseEnsemble
from ..base import ClassifierMixin, RegressorMixin, is_regressor
from ..externals import six
from ..externals.six.moves import zip
from ..externals.six.moves import xrange as range
from .forest import BaseForest
from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
from ..tree.tree import BaseDecisionTree
from ..tree._tree import DTYPE
from ..utils import check_array, check_X_y, check_random_state
from ..utils.extmath import stable_cumsum
from ..metrics import accuracy_score, r2_score
from sklearn.utils.validation import has_fit_parameter, check_is_fitted
__all__ = [
'AdaBoostClassifier',
'AdaBoostRegressor',
]
class BaseWeightBoosting(six.with_metaclass(ABCMeta, BaseEnsemble)):
"""Base class for AdaBoost estimators.
Warning: This class should not be used directly. Use derived classes
instead.
"""
@abstractmethod
def __init__(self,
base_estimator=None,
n_estimators=50,
estimator_params=tuple(),
learning_rate=1.,
random_state=None):
super(BaseWeightBoosting, self).__init__(
base_estimator=base_estimator,
n_estimators=n_estimators,
estimator_params=estimator_params)
self.learning_rate = learning_rate
self.random_state = random_state
def fit(self, X, y, sample_weight=None):
"""Build a boosted classifier/regressor from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR. The dtype is
forced to DTYPE from tree._tree if the base classifier of this
ensemble weighted boosting classifier is a tree or forest.
y : array-like of shape = [n_samples]
The target values (class labels in classification, real numbers in
regression).
sample_weight : array-like of shape = [n_samples], optional
Sample weights. If None, the sample weights are initialized to
1 / n_samples.
Returns
-------
self : object
Returns self.
"""
# Check parameters
if self.learning_rate <= 0:
raise ValueError("learning_rate must be greater than zero")
if (self.base_estimator is None or
isinstance(self.base_estimator, (BaseDecisionTree,
BaseForest))):
dtype = DTYPE
accept_sparse = 'csc'
else:
dtype = None
accept_sparse = ['csr', 'csc']
X, y = check_X_y(X, y, accept_sparse=accept_sparse, dtype=dtype,
y_numeric=is_regressor(self))
if sample_weight is None:
# Initialize weights to 1 / n_samples
sample_weight = np.empty(X.shape[0], dtype=np.float64)
sample_weight[:] = 1. / X.shape[0]
else:
sample_weight = check_array(sample_weight, ensure_2d=False)
# Normalize existing weights
sample_weight = sample_weight / sample_weight.sum(dtype=np.float64)
# Check that the sample weights sum is positive
if sample_weight.sum() <= 0:
raise ValueError(
"Attempting to fit with a non-positive "
"weighted number of samples.")
# Check parameters
self._validate_estimator()
# Clear any previous fit results
self.estimators_ = []
self.estimator_weights_ = np.zeros(self.n_estimators, dtype=np.float64)
self.estimator_errors_ = np.ones(self.n_estimators, dtype=np.float64)
random_state = check_random_state(self.random_state)
for iboost in range(self.n_estimators):
# Boosting step
sample_weight, estimator_weight, estimator_error = self._boost(
iboost,
X, y,
sample_weight,
random_state)
# Early termination
if sample_weight is None:
break
self.estimator_weights_[iboost] = estimator_weight
self.estimator_errors_[iboost] = estimator_error
# Stop if error is zero
if estimator_error == 0:
break
sample_weight_sum = np.sum(sample_weight)
# Stop if the sum of sample weights has become non-positive
if sample_weight_sum <= 0:
break
if iboost < self.n_estimators - 1:
# Normalize
sample_weight /= sample_weight_sum
return self
@abstractmethod
def _boost(self, iboost, X, y, sample_weight, random_state):
"""Implement a single boost.
Warning: This method needs to be overridden by subclasses.
Parameters
----------
iboost : int
The index of the current boost iteration.
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. COO, DOK, and LIL are converted to CSR.
y : array-like of shape = [n_samples]
The target values (class labels).
sample_weight : array-like of shape = [n_samples]
The current sample weights.
random_state : numpy.RandomState
The current random number generator
Returns
-------
sample_weight : array-like of shape = [n_samples] or None
The reweighted sample weights.
If None then boosting has terminated early.
estimator_weight : float
The weight for the current boost.
If None then boosting has terminated early.
error : float
The classification error for the current boost.
If None then boosting has terminated early.
"""
pass
def staged_score(self, X, y, sample_weight=None):
"""Return staged scores for X, y.
This generator method yields the ensemble score after each iteration of
boosting and therefore allows monitoring, such as to determine the
score on a test set after each boost.
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. DOK and LIL are converted to CSR.
y : array-like, shape = [n_samples]
Labels for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
Returns
-------
z : float
"""
for y_pred in self.staged_predict(X):
if isinstance(self, ClassifierMixin):
yield accuracy_score(y, y_pred, sample_weight=sample_weight)
else:
yield r2_score(y, y_pred, sample_weight=sample_weight)
@property
def feature_importances_(self):
"""Return the feature importances (the higher, the more important the
feature).
Returns
-------
feature_importances_ : array, shape = [n_features]
"""
if self.estimators_ is None or len(self.estimators_) == 0:
raise ValueError("Estimator not fitted, "
"call `fit` before `feature_importances_`.")
try:
norm = self.estimator_weights_.sum()
return (sum(weight * clf.feature_importances_ for weight, clf
in zip(self.estimator_weights_, self.estimators_))
/ norm)
except AttributeError:
raise AttributeError(
"Unable to compute feature importances "
"since base_estimator does not have a "
"feature_importances_ attribute")
def _validate_X_predict(self, X):
"""Ensure that X is in the proper format"""
if (self.base_estimator is None or
isinstance(self.base_estimator,
(BaseDecisionTree, BaseForest))):
X = check_array(X, accept_sparse='csr', dtype=DTYPE)
else:
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
return X
def _samme_proba(estimator, n_classes, X):
"""Calculate algorithm 4, step 2, equation c) of Zhu et al [1].
References
----------
.. [1] J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost", 2009.
"""
proba = estimator.predict_proba(X)
# Displace zero probabilities so the log is defined.
# Also fix negative elements which may occur with
# negative sample weights.
proba[proba < np.finfo(proba.dtype).eps] = np.finfo(proba.dtype).eps
log_proba = np.log(proba)
return (n_classes - 1) * (log_proba - (1. / n_classes)
* log_proba.sum(axis=1)[:, np.newaxis])
class AdaBoostClassifier(BaseWeightBoosting, ClassifierMixin):
"""An AdaBoost classifier.
An AdaBoost [1] classifier is a meta-estimator that begins by fitting a
classifier on the original dataset and then fits additional copies of the
classifier on the same dataset but where the weights of incorrectly
classified instances are adjusted such that subsequent classifiers focus
more on difficult cases.
This class implements the algorithm known as AdaBoost-SAMME [2].
Read more in the :ref:`User Guide <adaboost>`.
Parameters
----------
base_estimator : object, optional (default=DecisionTreeClassifier)
The base estimator from which the boosted ensemble is built.
Support for sample weighting is required, as well as proper `classes_`
and `n_classes_` attributes.
n_estimators : integer, optional (default=50)
The maximum number of estimators at which boosting is terminated.
In case of perfect fit, the learning procedure is stopped early.
learning_rate : float, optional (default=1.)
Learning rate shrinks the contribution of each classifier by
``learning_rate``. There is a trade-off between ``learning_rate`` and
``n_estimators``.
algorithm : {'SAMME', 'SAMME.R'}, optional (default='SAMME.R')
If 'SAMME.R' then use the SAMME.R real boosting algorithm.
``base_estimator`` must support calculation of class probabilities.
If 'SAMME' then use the SAMME discrete boosting algorithm.
The SAMME.R algorithm typically converges faster than SAMME,
achieving a lower test error with fewer boosting iterations.
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`.
Attributes
----------
estimators_ : list of classifiers
The collection of fitted sub-estimators.
classes_ : array of shape = [n_classes]
The classes labels.
n_classes_ : int
The number of classes.
estimator_weights_ : array of floats
Weights for each estimator in the boosted ensemble.
estimator_errors_ : array of floats
Classification error for each estimator in the boosted
ensemble.
feature_importances_ : array of shape = [n_features]
The feature importances if supported by the ``base_estimator``.
See also
--------
AdaBoostRegressor, GradientBoostingClassifier, DecisionTreeClassifier
References
----------
.. [1] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of
on-Line Learning and an Application to Boosting", 1995.
.. [2] J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class AdaBoost", 2009.
"""
def __init__(self,
base_estimator=None,
n_estimators=50,
learning_rate=1.,
algorithm='SAMME.R',
random_state=None):
super(AdaBoostClassifier, self).__init__(
base_estimator=base_estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state)
self.algorithm = algorithm
def fit(self, X, y, sample_weight=None):
"""Build a boosted classifier from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. DOK and LIL are converted to CSR.
y : array-like of shape = [n_samples]
The target values (class labels).
sample_weight : array-like of shape = [n_samples], optional
Sample weights. If None, the sample weights are initialized to
``1 / n_samples``.
Returns
-------
self : object
Returns self.
"""
# Check that algorithm is supported
if self.algorithm not in ('SAMME', 'SAMME.R'):
raise ValueError("algorithm %s is not supported" % self.algorithm)
# Fit
return super(AdaBoostClassifier, self).fit(X, y, sample_weight)
def _validate_estimator(self):
"""Check the estimator and set the base_estimator_ attribute."""
super(AdaBoostClassifier, self)._validate_estimator(
default=DecisionTreeClassifier(max_depth=1))
# SAMME-R requires predict_proba-enabled base estimators
if self.algorithm == 'SAMME.R':
if not hasattr(self.base_estimator_, 'predict_proba'):
raise TypeError(
"AdaBoostClassifier with algorithm='SAMME.R' requires "
"that the weak learner supports the calculation of class "
"probabilities with a predict_proba method.\n"
"Please change the base estimator or set "
"algorithm='SAMME' instead.")
if not has_fit_parameter(self.base_estimator_, "sample_weight"):
raise ValueError("%s doesn't support sample_weight."
% self.base_estimator_.__class__.__name__)
def _boost(self, iboost, X, y, sample_weight, random_state):
"""Implement a single boost.
Perform a single boost according to the real multi-class SAMME.R
algorithm or to the discrete SAMME algorithm and return the updated
sample weights.
Parameters
----------
iboost : int
The index of the current boost iteration.
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. DOK and LIL are converted to CSR.
y : array-like of shape = [n_samples]
The target values (class labels).
sample_weight : array-like of shape = [n_samples]
The current sample weights.
random_state : numpy.RandomState
The current random number generator
Returns
-------
sample_weight : array-like of shape = [n_samples] or None
The reweighted sample weights.
If None then boosting has terminated early.
estimator_weight : float
The weight for the current boost.
If None then boosting has terminated early.
estimator_error : float
The classification error for the current boost.
If None then boosting has terminated early.
"""
if self.algorithm == 'SAMME.R':
return self._boost_real(iboost, X, y, sample_weight, random_state)
else: # elif self.algorithm == "SAMME":
return self._boost_discrete(iboost, X, y, sample_weight,
random_state)
def _boost_real(self, iboost, X, y, sample_weight, random_state):
"""Implement a single boost using the SAMME.R real algorithm."""
estimator = self._make_estimator(random_state=random_state)
estimator.fit(X, y, sample_weight=sample_weight)
y_predict_proba = estimator.predict_proba(X)
if iboost == 0:
self.classes_ = getattr(estimator, 'classes_', None)
self.n_classes_ = len(self.classes_)
y_predict = self.classes_.take(np.argmax(y_predict_proba, axis=1),
axis=0)
# Instances incorrectly classified
incorrect = y_predict != y
# Error fraction
estimator_error = np.mean(
np.average(incorrect, weights=sample_weight, axis=0))
# Stop if classification is perfect
if estimator_error <= 0:
return sample_weight, 1., 0.
# Construct y coding as described in Zhu et al [2]:
#
# y_k = 1 if c == k else -1 / (K - 1)
#
# where K == n_classes_ and c, k in [0, K) are indices along the second
# axis of the y coding with c being the index corresponding to the true
# class label.
n_classes = self.n_classes_
classes = self.classes_
y_codes = np.array([-1. / (n_classes - 1), 1.])
y_coding = y_codes.take(classes == y[:, np.newaxis])
# Displace zero probabilities so the log is defined.
# Also fix negative elements which may occur with
# negative sample weights.
proba = y_predict_proba # alias for readability
proba[proba < np.finfo(proba.dtype).eps] = np.finfo(proba.dtype).eps
# Boost weight using multi-class AdaBoost SAMME.R alg
estimator_weight = (-1. * self.learning_rate
* (((n_classes - 1.) / n_classes) *
inner1d(y_coding, np.log(y_predict_proba))))
# Only boost the weights if it will fit again
if not iboost == self.n_estimators - 1:
# Only boost positive weights
sample_weight *= np.exp(estimator_weight *
((sample_weight > 0) |
(estimator_weight < 0)))
return sample_weight, 1., estimator_error
def _boost_discrete(self, iboost, X, y, sample_weight, random_state):
"""Implement a single boost using the SAMME discrete algorithm."""
estimator = self._make_estimator(random_state=random_state)
estimator.fit(X, y, sample_weight=sample_weight)
y_predict = estimator.predict(X)
if iboost == 0:
self.classes_ = getattr(estimator, 'classes_', None)
self.n_classes_ = len(self.classes_)
# Instances incorrectly classified
incorrect = y_predict != y
# Error fraction
estimator_error = np.mean(
np.average(incorrect, weights=sample_weight, axis=0))
# Stop if classification is perfect
if estimator_error <= 0:
return sample_weight, 1., 0.
n_classes = self.n_classes_
# Stop if the error is at least as bad as random guessing
if estimator_error >= 1. - (1. / n_classes):
self.estimators_.pop(-1)
if len(self.estimators_) == 0:
raise ValueError('BaseClassifier in AdaBoostClassifier '
'ensemble is worse than random, ensemble '
'can not be fit.')
return None, None, None
# Boost weight using multi-class AdaBoost SAMME alg
estimator_weight = self.learning_rate * (
np.log((1. - estimator_error) / estimator_error) +
np.log(n_classes - 1.))
# Only boost the weights if I will fit again
if not iboost == self.n_estimators - 1:
# Only boost positive weights
sample_weight *= np.exp(estimator_weight * incorrect *
((sample_weight > 0) |
(estimator_weight < 0)))
return sample_weight, estimator_weight, estimator_error
def predict(self, X):
"""Predict classes for X.
The predicted class of an input sample is computed as the weighted mean
prediction of the classifiers in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. DOK and LIL are converted to CSR.
Returns
-------
y : array of shape = [n_samples]
The predicted classes.
"""
pred = self.decision_function(X)
if self.n_classes_ == 2:
return self.classes_.take(pred > 0, axis=0)
return self.classes_.take(np.argmax(pred, axis=1), axis=0)
def staged_predict(self, X):
"""Return staged predictions for X.
The predicted class of an input sample is computed as the weighted mean
prediction of the classifiers in the ensemble.
This generator method yields the ensemble prediction after each
iteration of boosting and therefore allows monitoring, such as to
determine the prediction on a test set after each boost.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
Returns
-------
y : generator of array, shape = [n_samples]
The predicted classes.
"""
n_classes = self.n_classes_
classes = self.classes_
if n_classes == 2:
for pred in self.staged_decision_function(X):
yield np.array(classes.take(pred > 0, axis=0))
else:
for pred in self.staged_decision_function(X):
yield np.array(classes.take(
np.argmax(pred, axis=1), axis=0))
def decision_function(self, X):
"""Compute the decision function of ``X``.
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. DOK and LIL are converted to CSR.
Returns
-------
score : array, shape = [n_samples, k]
The decision function of the input samples. The order of
outputs is the same of that of the `classes_` attribute.
Binary classification is a special cases with ``k == 1``,
otherwise ``k==n_classes``. For binary classification,
values closer to -1 or 1 mean more like the first or second
class in ``classes_``, respectively.
"""
check_is_fitted(self, "n_classes_")
X = self._validate_X_predict(X)
n_classes = self.n_classes_
classes = self.classes_[:, np.newaxis]
pred = None
if self.algorithm == 'SAMME.R':
# The weights are all 1. for SAMME.R
pred = sum(_samme_proba(estimator, n_classes, X)
for estimator in self.estimators_)
else: # self.algorithm == "SAMME"
pred = sum((estimator.predict(X) == classes).T * w
for estimator, w in zip(self.estimators_,
self.estimator_weights_))
pred /= self.estimator_weights_.sum()
if n_classes == 2:
pred[:, 0] *= -1
return pred.sum(axis=1)
return pred
def staged_decision_function(self, X):
"""Compute decision function of ``X`` for each boosting iteration.
This method allows monitoring (i.e. determine error on testing set)
after each boosting iteration.
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. DOK and LIL are converted to CSR.
Returns
-------
score : generator of array, shape = [n_samples, k]
The decision function of the input samples. The order of
outputs is the same of that of the `classes_` attribute.
Binary classification is a special cases with ``k == 1``,
otherwise ``k==n_classes``. For binary classification,
values closer to -1 or 1 mean more like the first or second
class in ``classes_``, respectively.
"""
check_is_fitted(self, "n_classes_")
X = self._validate_X_predict(X)
n_classes = self.n_classes_
classes = self.classes_[:, np.newaxis]
pred = None
norm = 0.
for weight, estimator in zip(self.estimator_weights_,
self.estimators_):
norm += weight
if self.algorithm == 'SAMME.R':
# The weights are all 1. for SAMME.R
current_pred = _samme_proba(estimator, n_classes, X)
else: # elif self.algorithm == "SAMME":
current_pred = estimator.predict(X)
current_pred = (current_pred == classes).T * weight
if pred is None:
pred = current_pred
else:
pred += current_pred
if n_classes == 2:
tmp_pred = np.copy(pred)
tmp_pred[:, 0] *= -1
yield (tmp_pred / norm).sum(axis=1)
else:
yield pred / norm
def predict_proba(self, X):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the weighted mean predicted class probabilities of the classifiers
in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. DOK and LIL are converted to CSR.
Returns
-------
p : array of shape = [n_samples]
The class probabilities of the input samples. The order of
outputs is the same of that of the `classes_` attribute.
"""
check_is_fitted(self, "n_classes_")
n_classes = self.n_classes_
X = self._validate_X_predict(X)
if n_classes == 1:
return np.ones((X.shape[0], 1))
if self.algorithm == 'SAMME.R':
# The weights are all 1. for SAMME.R
proba = sum(_samme_proba(estimator, n_classes, X)
for estimator in self.estimators_)
else: # self.algorithm == "SAMME"
proba = sum(estimator.predict_proba(X) * w
for estimator, w in zip(self.estimators_,
self.estimator_weights_))
proba /= self.estimator_weights_.sum()
proba = np.exp((1. / (n_classes - 1)) * proba)
normalizer = proba.sum(axis=1)[:, np.newaxis]
normalizer[normalizer == 0.0] = 1.0
proba /= normalizer
return proba
def staged_predict_proba(self, X):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the weighted mean predicted class probabilities of the classifiers
in the ensemble.
This generator method yields the ensemble predicted class probabilities
after each iteration of boosting and therefore allows monitoring, such
as to determine the predicted class probabilities on a test set after
each boost.
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. DOK and LIL are converted to CSR.
Returns
-------
p : generator of array, shape = [n_samples]
The class probabilities of the input samples. The order of
outputs is the same of that of the `classes_` attribute.
"""
X = self._validate_X_predict(X)
n_classes = self.n_classes_
proba = None
norm = 0.
for weight, estimator in zip(self.estimator_weights_,
self.estimators_):
norm += weight
if self.algorithm == 'SAMME.R':
# The weights are all 1. for SAMME.R
current_proba = _samme_proba(estimator, n_classes, X)
else: # elif self.algorithm == "SAMME":
current_proba = estimator.predict_proba(X) * weight
if proba is None:
proba = current_proba
else:
proba += current_proba
real_proba = np.exp((1. / (n_classes - 1)) * (proba / norm))
normalizer = real_proba.sum(axis=1)[:, np.newaxis]
normalizer[normalizer == 0.0] = 1.0
real_proba /= normalizer
yield real_proba
def predict_log_proba(self, X):
"""Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the weighted mean predicted class log-probabilities of the classifiers
in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. DOK and LIL are converted to CSR.
Returns
-------
p : array of shape = [n_samples]
The class probabilities of the input samples. The order of
outputs is the same of that of the `classes_` attribute.
"""
return np.log(self.predict_proba(X))
class AdaBoostRegressor(BaseWeightBoosting, RegressorMixin):
"""An AdaBoost regressor.
An AdaBoost [1] regressor is a meta-estimator that begins by fitting a
regressor on the original dataset and then fits additional copies of the
regressor on the same dataset but where the weights of instances are
adjusted according to the error of the current prediction. As such,
subsequent regressors focus more on difficult cases.
This class implements the algorithm known as AdaBoost.R2 [2].
Read more in the :ref:`User Guide <adaboost>`.
Parameters
----------
base_estimator : object, optional (default=DecisionTreeRegressor)
The base estimator from which the boosted ensemble is built.
Support for sample weighting is required.
n_estimators : integer, optional (default=50)
The maximum number of estimators at which boosting is terminated.
In case of perfect fit, the learning procedure is stopped early.
learning_rate : float, optional (default=1.)
Learning rate shrinks the contribution of each regressor by
``learning_rate``. There is a trade-off between ``learning_rate`` and
``n_estimators``.
loss : {'linear', 'square', 'exponential'}, optional (default='linear')
The loss function to use when updating the weights after each
boosting iteration.
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`.
Attributes
----------
estimators_ : list of classifiers
The collection of fitted sub-estimators.
estimator_weights_ : array of floats
Weights for each estimator in the boosted ensemble.
estimator_errors_ : array of floats
Regression error for each estimator in the boosted ensemble.
feature_importances_ : array of shape = [n_features]
The feature importances if supported by the ``base_estimator``.
See also
--------
AdaBoostClassifier, GradientBoostingRegressor, DecisionTreeRegressor
References
----------
.. [1] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of
on-Line Learning and an Application to Boosting", 1995.
.. [2] H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
"""
def __init__(self,
base_estimator=None,
n_estimators=50,
learning_rate=1.,
loss='linear',
random_state=None):
super(AdaBoostRegressor, self).__init__(
base_estimator=base_estimator,
n_estimators=n_estimators,
learning_rate=learning_rate,
random_state=random_state)
self.loss = loss
self.random_state = random_state
def fit(self, X, y, sample_weight=None):
"""Build a boosted regressor from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. DOK and LIL are converted to CSR.
y : array-like of shape = [n_samples]
The target values (real numbers).
sample_weight : array-like of shape = [n_samples], optional
Sample weights. If None, the sample weights are initialized to
1 / n_samples.
Returns
-------
self : object
Returns self.
"""
# Check loss
if self.loss not in ('linear', 'square', 'exponential'):
raise ValueError(
"loss must be 'linear', 'square', or 'exponential'")
# Fit
return super(AdaBoostRegressor, self).fit(X, y, sample_weight)
def _validate_estimator(self):
"""Check the estimator and set the base_estimator_ attribute."""
super(AdaBoostRegressor, self)._validate_estimator(
default=DecisionTreeRegressor(max_depth=3))
def _boost(self, iboost, X, y, sample_weight, random_state):
"""Implement a single boost for regression
Perform a single boost according to the AdaBoost.R2 algorithm and
return the updated sample weights.
Parameters
----------
iboost : int
The index of the current boost iteration.
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. DOK and LIL are converted to CSR.
y : array-like of shape = [n_samples]
The target values (class labels in classification, real numbers in
regression).
sample_weight : array-like of shape = [n_samples]
The current sample weights.
random_state : numpy.RandomState
The current random number generator
Returns
-------
sample_weight : array-like of shape = [n_samples] or None
The reweighted sample weights.
If None then boosting has terminated early.
estimator_weight : float
The weight for the current boost.
If None then boosting has terminated early.
estimator_error : float
The regression error for the current boost.
If None then boosting has terminated early.
"""
estimator = self._make_estimator(random_state=random_state)
# Weighted sampling of the training set with replacement
# For NumPy >= 1.7.0 use np.random.choice
cdf = stable_cumsum(sample_weight)
cdf /= cdf[-1]
uniform_samples = random_state.random_sample(X.shape[0])
bootstrap_idx = cdf.searchsorted(uniform_samples, side='right')
# searchsorted returns a scalar
bootstrap_idx = np.array(bootstrap_idx, copy=False)
# Fit on the bootstrapped sample and obtain a prediction
# for all samples in the training set
estimator.fit(X[bootstrap_idx], y[bootstrap_idx])
y_predict = estimator.predict(X)
error_vect = np.abs(y_predict - y)
error_max = error_vect.max()
if error_max != 0.:
error_vect /= error_max
if self.loss == 'square':
error_vect **= 2
elif self.loss == 'exponential':
error_vect = 1. - np.exp(- error_vect)
# Calculate the average loss
estimator_error = (sample_weight * error_vect).sum()
if estimator_error <= 0:
# Stop if fit is perfect
return sample_weight, 1., 0.
elif estimator_error >= 0.5:
# Discard current estimator only if it isn't the only one
if len(self.estimators_) > 1:
self.estimators_.pop(-1)
return None, None, None
beta = estimator_error / (1. - estimator_error)
# Boost weight using AdaBoost.R2 alg
estimator_weight = self.learning_rate * np.log(1. / beta)
if not iboost == self.n_estimators - 1:
sample_weight *= np.power(
beta,
(1. - error_vect) * self.learning_rate)
return sample_weight, estimator_weight, estimator_error
def _get_median_predict(self, X, limit):
# Evaluate predictions of all estimators
predictions = np.array([
est.predict(X) for est in self.estimators_[:limit]]).T
# Sort the predictions
sorted_idx = np.argsort(predictions, axis=1)
# Find index of median prediction for each sample
weight_cdf = stable_cumsum(self.estimator_weights_[sorted_idx], axis=1)
median_or_above = weight_cdf >= 0.5 * weight_cdf[:, -1][:, np.newaxis]
median_idx = median_or_above.argmax(axis=1)
median_estimators = sorted_idx[np.arange(X.shape[0]), median_idx]
# Return median predictions
return predictions[np.arange(X.shape[0]), median_estimators]
def predict(self, X):
"""Predict regression value for X.
The predicted regression value of an input sample is computed
as the weighted median prediction of the classifiers in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. DOK and LIL are converted to CSR.
Returns
-------
y : array of shape = [n_samples]
The predicted regression values.
"""
check_is_fitted(self, "estimator_weights_")
X = self._validate_X_predict(X)
return self._get_median_predict(X, len(self.estimators_))
def staged_predict(self, X):
"""Return staged predictions for X.
The predicted regression value of an input sample is computed
as the weighted median prediction of the classifiers in the ensemble.
This generator method yields the ensemble prediction after each
iteration of boosting and therefore allows monitoring, such as to
determine the prediction on a test set after each boost.
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrix can be CSC, CSR, COO,
DOK, or LIL. DOK and LIL are converted to CSR.
Returns
-------
y : generator of array, shape = [n_samples]
The predicted regression values.
"""
check_is_fitted(self, "estimator_weights_")
X = self._validate_X_predict(X)
for i, _ in enumerate(self.estimators_, 1):
yield self._get_median_predict(X, limit=i)
| bsd-3-clause |
NolanBecker/aima-python | grading/neuralNet-submissions.py | 4 | 2217 | import importlib
import traceback
from grading.util import roster, print_table
# from logic import FolKB
# from utils import expr
import os
from sklearn.neural_network import MLPClassifier
mlpc = MLPClassifier()
def indent(howMuch = 1):
space = ' '
for i in range(1, howMuch):
space += ' '
return space
def tryOne(label, fAndP):
frame = fAndP['frame']
if 'mlpc' in fAndP.keys():
clf = fAndP['mlpc']
else:
clf = mlpc
try:
fit = clf.fit(frame.data, frame.target)
except:
pass
print('')
# print_table(fit.theta_,
# header=[frame.feature_names],
# topLeft=[label],
# leftColumn=frame.target_names,
# numfmt='%6.3f',
# njust='center',
# tjust='rjust',
# )
y_pred = fit.predict(frame.data)
print("Number of mislabeled points out of a total %d points : %d"
% (len(frame.data), (frame.target != y_pred).sum()))
def tryExamples(examples):
for label in examples:
example = examples[label]
main = getattr(example, 'main', None)
if main != None:
example.main()
else:
tryOne(label, example)
submissions = {}
scores = {}
message1 = 'Submissions that compile:'
root = os.getcwd()
for student in roster:
try:
os.chdir(root + '/submissions/' + student)
# http://stackoverflow.com/a/17136796/2619926
mod = importlib.import_module('submissions.' + student + '.myNN')
submissions[student] = mod.Examples
message1 += ' ' + student
except ImportError:
pass
except:
traceback.print_exc()
os.chdir(root)
print(message1)
print('----------------------------------------')
for student in roster:
if not student in submissions.keys():
continue
scores[student] = []
try:
examples = submissions[student]
print('Bayesian Networks from:', student)
tryExamples(examples)
except:
traceback.print_exc()
print(student + ' scores ' + str(scores[student]) + ' = ' + str(sum(scores[student])))
print('----------------------------------------') | mit |
guillemborrell/gtable | tests/test_table_creation.py | 1 | 4044 | from gtable import Table
import numpy as np
import pandas as pd
def test_empty_table():
t = Table()
assert t.data == []
def test_simple_table():
t = Table({'a': [1, 2, 3], 'b': np.array([4, 5, 6])})
assert t.to_dict()['a'][2] == 3
assert np.all(t.index == np.ones((2, 3), dtype=np.uint8))
def test_hcat_table():
t = Table({'a': [1, 2, 3], 'b': np.array([4, 5, 6])})
t.add_column('c', [7, 8, 9])
assert np.all(t.index == np.ones((3, 3), dtype=np.uint8))
assert np.all(t.c.values == np.array([7, 8, 9]))
t.add_column('d', [0, 1, 2, 3, 4])
assert np.all(t.index == np.array([[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0],
[1, 1, 1, 1, 1]], dtype=np.uint8))
def test_vcat_table():
t = Table({'a': [1, 2, 3], 'b': np.array([4, 5, 6])})
t1 = Table({'a': [1, 2, 3], 'd': np.array([4, 5, 6])})
t.stack(t1)
assert np.all(t.index == np.array([[1, 1, 1, 1, 1, 1],
[1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1]], dtype=np.uint8))
def test_compute_column():
t = Table({'a': [1, 2, 3], 'b': np.array([4, 5, 6])})
t1 = Table({'a': [1, 2, 3], 'd': np.array([4, 5, 6])})
t.stack(t1)
t.c = t.a + t.a/2
records = [r for r in t.records()]
assert records == [
{'a': 1, 'b': 4, 'c': 1.5},
{'a': 2, 'b': 5, 'c': 3.0},
{'a': 3, 'b': 6, 'c': 4.5},
{'a': 1, 'd': 4, 'c': 1.5},
{'a': 2, 'd': 5, 'c': 3.0},
{'a': 3, 'd': 6, 'c': 4.5}]
def test_compute_wrong_size():
t = Table({'a': [1, 2, 3], 'b': np.array([4, 5, 6])})
t1 = Table({'a': [1, 2, 3], 'd': np.array([4, 5, 6])})
t.stack(t1)
t.c = t.a + t.b/2
assert np.all(t.c.values == np.array([3, 4.5, 6]))
assert np.all(t.c.index == np.array([1, 1, 1, 0, 0, 0]))
def test_add_array():
t = Table()
t.a = np.arange(10)
assert t.__repr__()[:13] == "<Table[ a[10]"
def test_add_one():
tb = Table({'a': pd.date_range('2000-01-01', freq='M', periods=10),
'b': np.random.randn(10)})
tb.add_column('schedule', np.array(['first']))
assert np.all(tb.index == np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0]]))
def test_vcat_heterogeneous():
tb = Table({'a': pd.date_range('2000-01-01', freq='M', periods=3),
'b': np.random.randn(3)})
tb.add_column('schedule', np.array(['first']))
tb1 = tb.copy()
tb1.schedule.values[0] = 'second'
tb.stack(tb1)
assert np.all(tb.index == np.array([[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 0, 0, 1, 0, 0]], dtype=np.uint8))
assert np.all(tb.schedule.values == np.array(['first', 'secon']))
def test_from_pandas():
df = pd.DataFrame(
{'a': [1, 2, 3, 4],
'b': np.arange(4, dtype=np.float64),
'c': pd.date_range('2002-01-01', periods=4, freq='M')}
)
t = Table.from_pandas(df)
assert np.all(t.a.values == df.a.values)
assert np.all(t.b.values == df.b.values)
assert np.all(t.c.values == df.c.values)
assert np.all(t.idx.values == df.index.values)
def test_from_pandas_sparse():
df = pd.DataFrame(
{'a': [1, 2, 3, np.nan],
'b': np.arange(4, dtype=np.float64),
'c': pd.date_range('2002-01-01', periods=4, freq='M')}
)
t = Table.from_pandas(df)
assert np.all(t.index == np.array(
[[1, 1, 1, 1],
[1, 1, 1, 0],
[1, 1, 1, 1],
[1, 1, 1, 1]], dtype=np.uint8))
assert np.all(t.a.values == np.array([1,2,3], dtype=np.float64))
def test_simple_rename():
t = Table({'a': [1, 2, 3], 'b': np.array([4, 5, 6])})
t.rename_column('a', 'c')
assert t.keys == ['c', 'b']
| bsd-3-clause |
sysid/nbs | ml_old/Prognose/Evaluator.py | 1 | 3134 | from twBase import * # NOQA
from pandas import DataFrame
from pandas import read_csv
from pandas import datetime
from sklearn.preprocessing import MinMaxScaler
# date-time parsing function for loading the dataset
def parser(x):
return datetime.strptime('190'+x, '%Y-%m')
def get_time():
return time.strftime('%Y-%m-%d %H:%M:%S', time.gmtime())
class DataHarness(object):
def __init__(self, fn, isSample=False):
if isinstance(fn, str):
self.data = self.load(fn, isSample)
else:
self.data = fn
self.differenced = self.diff(n=1, axis=0)
self.supervised = self.series_to_supervised(n_in=3, n_out=2)
self.train, self.test = self.split(0.8)
# fit scaler on train data
self.scaler = MinMaxScaler(feature_range=(-1, 1))
self.scaler = self.scaler.fit(self.train)
log.info("Rescaling data...")
self.train_scaled = self.scaler.transform(self.train)
self.test_scaled = self.scaler.transform(self.test)
def load(self, fn, isSample=False):
'''
Loads csv data from fn
Return: numpy array
'''
# load dataset
log.info("Loading dataset", fn=fn)
if isSample:
log.info("Loading only sample.")
nrows = 100
else:
nrows = None
df = read_csv(fn, sep=';', decimal=',', nrows=nrows)
cols = ['Ist [MWh]']
data = df[cols]
return data.values
def diff(self, **kwargs):
'''
The n-th differences. The shape of the output is the same as a except along axis where the dimension is smaller by n
Paramters analog np.diff
'''
log.info("Differencing...")
return np.diff(self.data, **kwargs)
def series_to_supervised(self, **kwargs):
log.info("Transforming for supervised learning...")
supervised = series_to_supervised(self.differenced, **kwargs)
return supervised.values
def split(self, split):
log.info("Splitting data")
splitIndex = int(split * len(self.data))
return self.supervised[:splitIndex], self.supervised[splitIndex:]
def invert_scale(self, data):
log.info("Inverting scaling..")
assert self.scaler is not None, "scaler scaler instantiated"
return self.scaler.inverse_transform(data)
def invert_diff(self, data, start_value):
data = np.insert(data, 0, start_value, axis=0)
result = np.cumsum(data)
return result[1:]
def main(argv=None):
logging.basicConfig(format="", stream=sys.stderr, level=logging.DEBUG)
logcfg(sys.stderr, logging.DEBUG, RenderEnum.console)
log = structlog.get_logger(__name__)
twStart()
DATA_FN = './data/Lastprognose/2015.csv'
evaluator = DataHarness(DATA_FN)
data = evaluator.invert_scale(evaluator.train)
diffdata = evaluator.invert_diff(evaluator.train[0], evaluator.data[0])
assert np.array_equal(diffdata, np.squeeze(evaluator.data[1:6].reshape((1,5))))
twEnd()
return 0 # success
if __name__ == '__main__':
sys.exit(main(sys.argv[1:]))
| mit |
jjo31/ATHAM-Fluidity | tests/gls-Kato_Phillips-mixed_layer_depth/mixed_layer_depth_all.py | 4 | 4600 | #!/usr/bin/env python
from numpy import arange,concatenate,array,argsort
import os
import sys
import vtktools
import math
from pylab import *
from matplotlib.ticker import MaxNLocator
import re
from scipy.interpolate import UnivariateSpline
import glob
#### taken from http://www.codinghorror.com/blog/archives/001018.html #######
def sort_nicely( l ):
""" Sort the given list in the way that humans expect.
"""
convert = lambda text: int(text) if text.isdigit() else text
alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ]
l.sort( key=alphanum_key )
##############################################################################
# compute the mixed layer depth over time
def MLD(filelist):
x0 = 0.
tke0 = 1.0e-5
last_mld = 0
times = []
depths = []
for file in filelist:
try:
os.stat(file)
except:
print "No such file: %s" % file
sys.exit(1)
u=vtktools.vtu(file)
time = u.GetScalarField('Time')
tt = time[0]
kk = u.GetScalarField('GLSTurbulentKineticEnergy')
pos = u.GetLocations()
if (tt < 100):
continue
xyzkk = []
for i in range(0,len(kk)):
if( abs(pos[i,0] - x0) < 0.1 ):
xyzkk.append((pos[i,0],-pos[i,1],pos[i,2],(kk[i])))
xyzkkarr = vtktools.arr(xyzkk)
III = argsort(xyzkkarr[:,1])
xyzkkarrsort = xyzkkarr[III,:]
# march down the column, grabbing the last value above tk0 and the first
# one less than tke0. Interpolate between to get the MLD
kea = 1000
keb = 0
zza = 0
zzb = 0
for values in xyzkkarrsort:
if (values[3] > tke0):
kea = values[3]
zza = -values[1]
if (values[3] < tke0):
keb = values[3]
zzb = -values[1]
break
mld = zza
if (last_mld == mld):
continue
times.append(tt/3600)
depths.append(-1.0*mld)
last_mld = mld
return times, depths
path = sys.argv[1]
x0 = 0.
tke0 = 1.0e-5
files_to_look_through_ke = [
"Kato_Phillips-mld-k_e-CA",
"Kato_Phillips-mld-k_e-CB",
"Kato_Phillips-mld-k_e-GL",
"Kato_Phillips-mld-k_e-KC"
]
files_to_look_through_gen = [
"Kato_Phillips-mld-gen-CA",
"Kato_Phillips-mld-gen-CB",
"Kato_Phillips-mld-gen-GL",
"Kato_Phillips-mld-gen-KC"
]
files_to_look_through_kw = [
"Kato_Phillips-mld-k_w-CA",
"Kato_Phillips-mld-k_w-CB",
"Kato_Phillips-mld-k_w-GL",
"Kato_Phillips-mld-k_w-KC"
]
files_to_look_through_kkl = [
"Kato_Phillips-mld-k_kl-CA",
"Kato_Phillips-mld-k_kl-KC"
]
colours = ['r','g','b','#8000FF']
times2 = arange(0, 10, 0.1)
Dm = 1.05*1.0e-2*(1.0/sqrt(0.01))*sqrt((times2*60*60));
figke = figure(figsize=(9.172,4.5),dpi=90)
ax = figke.add_subplot(111)
i = 0
for simulation in files_to_look_through_ke:
filelist = glob.glob(path+simulation+"*.vtu")
sort_nicely(filelist)
times, depths = MLD(filelist)
ax.plot(times,depths,colours[i],label=simulation)
i = i+1
ax.plot(times2,Dm,'k-',label='Analytical')
ax.set_ylim(ax.get_ylim()[::-1])
xlabel('Time (hours)')
ylabel('ML Depth (m)')
legend(loc=0)
savefig(path + '/ke.png', dpi=90,format='png')
figkw = figure(figsize=(9.172,4.5),dpi=90)
ax = figkw.add_subplot(111)
i = 0
for simulation in files_to_look_through_kw:
filelist = glob.glob(path+simulation+"*.vtu")
sort_nicely(filelist)
times, depths = MLD(filelist)
ax.plot(times,depths,colours[i],label=simulation)
i = i+1
ax.plot(times2,Dm,'k-',label='Analytical')
ax.set_ylim(ax.get_ylim()[::-1])
xlabel('Time (hours)')
ylabel('ML Depth (m)')
legend(loc=0)
savefig(path + '/kw.png', dpi=90,format='png')
figgen = figure(figsize=(9.172,4.5),dpi=90)
ax = figgen.add_subplot(111)
i = 0
for simulation in files_to_look_through_gen:
filelist = glob.glob(path+simulation+"*.vtu")
sort_nicely(filelist)
times, depths = MLD(filelist)
ax.plot(times,depths,colours[i],label=simulation)
i = i+1
ax.plot(times2,Dm,'k-',label='Analytical')
ax.set_ylim(ax.get_ylim()[::-1])
xlabel('Time (hours)')
ylabel('ML Depth (m)')
legend(loc=0)
savefig(path + '/gen.png', dpi=90,format='png')
figkkl = figure(figsize=(9.172,4.5),dpi=90)
ax = figkkl.add_subplot(111)
i = 0
for simulation in files_to_look_through_kkl:
filelist = glob.glob(path+simulation+"*.vtu")
sort_nicely(filelist)
times, depths = MLD(filelist)
ax.plot(times,depths,colours[i],label=simulation)
i = i+1
ax.plot(times2,Dm,'k-',label='Analytical')
ax.set_ylim(ax.get_ylim()[::-1])
xlabel('Time (hours)')
ylabel('ML Depth (m)')
legend(loc=0)
savefig(path + '/kkl.png', dpi=90,format='png')
| lgpl-2.1 |
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