Search is not available for this dataset
repo stringlengths 2 152 ⌀ | file stringlengths 15 239 | code stringlengths 0 58.4M | file_length int64 0 58.4M | avg_line_length float64 0 1.81M | max_line_length int64 0 12.7M | extension_type stringclasses 364
values |
|---|---|---|---|---|---|---|
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/__init__.py | from typing import Optional, Type
import os
from collections import OrderedDict
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from ..base import (
AutoSklearnChoice,
AutoSkle... | 5,180 | 33.085526 | 87 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/densifier.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
class Densif... | 1,374 | 28.255319 | 82 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
Constant,
UniformFloatHyperparameter,
UniformIntegerHyperparameter,
UnParametrizedHyperparameter,
)
from autosklearn.askl_typing import FE... | 6,035 | 32.910112 | 82 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py | from typing import Optional
import numpy as np
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
Constant,
UniformFloatHyperparameter,
UniformIntegerHyperparameter,
UnParametrizedHyperparameter,
)
from autosklearn.as... | 5,839 | 32.563218 | 82 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/fast_ica.py | from typing import Optional
import warnings
from ConfigSpace.conditions import EqualsCondition
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformIntegerHyperparameter,
)
from autosklearn.askl_typing import FEAT_TYPE_TYPE... | 3,377 | 33.469388 | 85 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/feature_agglomeration.py | from typing import Optional
import numpy as np
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.forbidden import (
ForbiddenAndConjunction,
ForbiddenEqualsClause,
ForbiddenInClause,
)
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformIntegerH... | 3,144 | 33.184783 | 87 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py | from typing import Optional
import warnings
import numpy as np
from ConfigSpace.conditions import EqualsCondition, InCondition
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformFloatHyperparameter,
UniformIntegerHyper... | 4,046 | 34.5 | 86 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py | from typing import Optional, Union
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
UniformFloatHyperparameter,
UniformIntegerHyperparameter,
)
from numpy.random import RandomState
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from autosklearn.pipel... | 2,777 | 31.682353 | 87 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
Constant,
UniformFloatHyperparameter,
)
from autosklearn.askl_typ... | 4,173 | 32.126984 | 88 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/no_preprocessing.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
class NoPrep... | 1,395 | 30.022222 | 82 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py | from typing import Optional
from ConfigSpace.conditions import EqualsCondition, InCondition
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformFloatHyperparameter,
UniformIntegerHyperparameter,
)
from autosklearn.askl_... | 4,578 | 32.918519 | 85 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/pca.py | from typing import Optional
import numpy as np
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformFloatHyperparameter,
)
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from autosklearn.pipeline.components.base import A... | 2,404 | 31.945205 | 82 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/polynomial.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformIntegerHyperparameter,
)
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from autosklearn.pipeline.components.base import AutoSklearnPreproc... | 2,590 | 33.092105 | 82 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
Constant,
UniformIntegerHyperparameter,
UnParametrizedHyperparameter,
)
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from autosklearn.pi... | 4,586 | 33.488722 | 84 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/select_percentile.py | class SelectPercentileBase(object):
def fit(self, X, y):
import sklearn.feature_selection
self.preprocessor = sklearn.feature_selection.SelectPercentile(
score_func=self.score_func, percentile=self.percentile
)
self.preprocessor.fit(X, y)
return self
def tr... | 590 | 30.105263 | 82 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/select_percentile_classification.py | from typing import Optional
from functools import partial
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
Constant,
UniformFloatHyperparameter,
)
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from autosklearn.pipelin... | 4,591 | 32.518248 | 85 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py | from typing import Optional
from functools import partial
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformFloatHyperparameter,
)
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from autosklearn.pipeline.components.b... | 2,527 | 33.630137 | 85 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/select_rates_classification.py | from typing import Optional
from functools import partial
from ConfigSpace import NotEqualsCondition
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformFloatHyperparameter,
)
from autosklearn.askl_typing import FEAT_TYPE_... | 5,098 | 32.768212 | 86 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/select_rates_regression.py | from typing import Optional
from functools import partial
from ConfigSpace import NotEqualsCondition
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformFloatHyperparameter,
)
from autosklearn.askl_typing import FEAT_TYPE_... | 4,039 | 32.94958 | 86 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/feature_preprocessing/truncatedSVD.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformIntegerHyperparameter
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
from autosklearn.pipelin... | 2,229 | 34.396825 | 102 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/regression/__init__.py | from typing import Type
import os
from collections import OrderedDict
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from ..base import (
AutoSklearnChoice,
AutoSklearnRegress... | 5,353 | 32.049383 | 83 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/regression/adaboost.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformFloatHyperparameter,
UniformIntegerHyperparameter,
)
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from autosklearn.pipeline.component... | 3,133 | 33.43956 | 88 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/regression/ard_regression.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
UniformFloatHyperparameter,
UnParametrizedHyperparameter,
)
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from autosklearn.pipeline.components.base import AutoSklearnRegres... | 4,454 | 28.7 | 79 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/regression/decision_tree.py | from typing import Optional
import numpy as np
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
Constant,
UniformFloatHyperparameter,
UniformIntegerHyperparameter,
UnParametrizedHyperparameter,
)
from autosklearn.as... | 4,904 | 34.035714 | 84 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/regression/extra_trees.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformFloatHyperparameter,
UniformIntegerHyperparameter,
UnParametrizedHyperparameter,
)
from autosklearn.askl_typing import FEAT_TYPE_TYPE
f... | 6,625 | 31.965174 | 84 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/regression/gaussian_process.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm
from autosklearn.pipeline.con... | 2,850 | 31.770115 | 84 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/regression/gradient_boosting.py | from typing import Optional
import numpy as np
from ConfigSpace.conditions import EqualsCondition, InCondition
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
Constant,
UniformFloatHyperparameter,
UniformIntegerHyperpar... | 8,105 | 33.202532 | 85 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/regression/k_nearest_neighbors.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformIntegerHyperparameter,
)
from autosklearn.askl_typing import FEAT_TYPE_TYPE
from autosklearn.pipeline.components.base import AutoSklearnRegress... | 2,378 | 31.148649 | 84 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/regression/liblinear_svr.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
Constant,
UniformFloatHyperparameter,
)
from autosklearn.askl_typ... | 3,916 | 31.106557 | 88 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/regression/libsvm_svr.py | from typing import Optional
import resource
import sys
from ConfigSpace.conditions import EqualsCondition, InCondition
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformFloatHyperparameter,
UniformIntegerHyperparamete... | 6,771 | 31.401914 | 86 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/regression/mlp.py | from typing import Optional
import numpy as np
from ConfigSpace.conditions import InCondition
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
Constant,
UniformFloatHyperparameter,
UniformIntegerHyperparameter,
UnPar... | 11,159 | 34.205047 | 87 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/regression/random_forest.py | from typing import Optional
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformFloatHyperparameter,
UniformIntegerHyperparameter,
UnParametrizedHyperparameter,
)
from autosklearn.askl_typing import FEAT_TYPE_TYPE
f... | 6,151 | 31.723404 | 86 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/components/regression/sgd.py | from typing import Optional
from ConfigSpace.conditions import EqualsCondition, InCondition
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformFloatHyperparameter,
UnParametrizedHyperparameter,
)
from autosklearn.askl_... | 8,916 | 32.025926 | 87 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/implementations/CategoryShift.py | import numpy as np
from scipy import sparse
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import check_array
class CategoryShift(BaseEstimator, TransformerMixin):
"""Add 3 to every category."""
def __init__(self, random_state=None):
self.random_state = random_state
... | 1,516 | 36 | 86 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/implementations/MinorityCoalescer.py | import numpy as np
from scipy import sparse
from sklearn.base import BaseEstimator, TransformerMixin
class MinorityCoalescer(BaseEstimator, TransformerMixin):
"""Group together categories which occurence is less than a specified
minimum fraction. Coalesced categories get index of one.
"""
def __init_... | 2,861 | 34.775 | 82 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/implementations/SparseOneHotEncoder.py | import numpy as np
from scipy import sparse
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import check_array
class SparseOneHotEncoder(BaseEstimator, TransformerMixin):
"""Encode categorical integer features using a one-hot aka one-of-K scheme.
The input to this transformer shou... | 4,492 | 34.944 | 87 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/implementations/__init__.py | __author__ = "feurerm"
| 23 | 11 | 22 | py |
auto-sklearn | auto-sklearn-master/autosklearn/pipeline/implementations/util.py | import numpy as np
def softmax(df):
if len(df.shape) == 1:
df[df > 20] = 20
df[df < -20] = -20
ppositive = 1 / (1 + np.exp(-df))
ppositive[ppositive > 0.999999] = 1
ppositive[ppositive < 0.0000001] = 0
return np.transpose(np.array((1 - ppositive, ppositive)))
el... | 2,697 | 34.973333 | 82 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/__init__.py | # -*- encoding: utf-8 -*-
import re
SUBPATTERN = r"((?P<operation%d>==|>=|>|<)(?P<version%d>(\d+)?(\.[a-zA-Z0-9]+)?(\.[a-zA-Z0-9]+)?))" # noqa: E501
RE_PATTERN = re.compile(
r"^(?P<name>[\w\-]+)%s?(,%s)?$" % (SUBPATTERN % (1, 1), SUBPATTERN % (2, 2))
)
| 259 | 31.5 | 113 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/common.py | # -*- encoding: utf-8 -*-
import os
import warnings
import numpy as np
__all__ = ["check_pid", "warn_if_not_float"]
def warn_if_not_float(X: np.ndarray, estimator: str = "This algorithm") -> bool:
"""Warning utility function to check that data type is floating point.
Returns True if a warning was raised (i... | 1,407 | 21.709677 | 80 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/dask.py | """ Provides simplified 2 use cases of dask that we consider
1. A UserDask is when a user supplies a dask client, in which case
we don't close this down and leave it up to the user to control its lifetime.
2. A LocalDask is one we use when no user dask is supplied. In this case
we make sure to spin up and close down ... | 3,972 | 26.783217 | 81 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/data.py | from typing import (
Any,
Dict,
Iterator,
List,
Mapping,
Optional,
Sequence,
Tuple,
Type,
Union,
cast,
)
import warnings
import numpy as np
import pandas as pd
from scipy.sparse import spmatrix
from sklearn.model_selection import train_test_split
from autosklearn.evaluatio... | 16,587 | 32.176 | 88 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/dependencies.py | from typing import List, Optional, Union, no_type_check
import importlib
from distutils.version import LooseVersion
import pkg_resources
from autosklearn.util import RE_PATTERN
def verify_packages(packages: Optional[Union[str, List[str]]]) -> None:
if not packages:
return
if isinstance(packages, st... | 3,271 | 29.867925 | 81 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/disk.py | from __future__ import annotations
from typing import Any
import math
import shutil
import tempfile
import uuid
from pathlib import Path
sizes = {
"B": 0,
"KB": 1,
"MB": 2,
"GB": 3,
"TB": 4,
}
def sizeof(path: Path | str, unit: str = "B") -> float:
"""Get the size of some path object
P... | 2,506 | 23.105769 | 78 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/functional.py | from __future__ import annotations
from typing import Callable, Hashable, Iterable, Iterator, TypeVar
from functools import reduce
from itertools import chain, cycle, islice, tee
T = TypeVar("T")
def intersection(*items: Iterable[T]) -> set[T]:
"""Does an intersection over all collection of items
..code::... | 5,970 | 22.143411 | 88 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/logging.yaml | ---
version: 1
disable_existing_loggers: False
formatters:
simple:
format: '[%(levelname)s] [%(asctime)s:%(name)s] %(message)s'
handlers:
console:
class: logging.StreamHandler
level: WARNING
formatter: simple
stream: ext://sys.stdout
file_handler:
class: logging.FileHandler
level: DE... | 1,182 | 18.716667 | 64 | yaml |
auto-sklearn | auto-sklearn-master/autosklearn/util/logging_.py | """
For accessing a logger, please default to using
`get_named_client_logger(name, host, port)`
"""
from typing import Any, Dict, Iterator, Optional, TextIO, Type, cast
import logging
import logging.config
import logging.handlers
import multiprocessing
import os
import pickle
import random
import select
import sockets... | 13,236 | 32.767857 | 115 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/multiobjective.py | import operator
import numpy as np
def pareto_front(values: np.ndarray, *, is_loss: bool = True) -> np.ndarray:
"""Calculate the pareto front
source from: https://stackoverflow.com/a/40239615
Note
----
Works on the assumption that every value is either something to minimize or
something to ... | 1,061 | 24.902439 | 84 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/parallel.py | import multiprocessing
import sys
def preload_modules(context: multiprocessing.context.BaseContext) -> None:
all_loaded_modules = sys.modules.keys()
preload = [
loaded_module
for loaded_module in all_loaded_modules
if loaded_module.split(".")[0]
in (
"smac",
... | 581 | 23.25 | 74 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/pipeline.py | # -*- encoding: utf-8 -*-
from typing import Dict, List, Optional, Union
import numpy as np
from ConfigSpace.configuration_space import ConfigurationSpace
from autosklearn.constants import (
BINARY_CLASSIFICATION,
MULTICLASS_CLASSIFICATION,
MULTILABEL_CLASSIFICATION,
MULTIOUTPUT_REGRESSION,
REGRES... | 5,289 | 31.654321 | 87 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/single_thread_client.py | import typing
from typing import Any
from pathlib import Path
import dask.distributed
class DummyFuture(dask.distributed.Future):
"""
A class that mimics a distributed Future, the outcome of
performing submit on a distributed client.
"""
def __init__(self, result: typing.Any) -> None:
s... | 2,925 | 24.008547 | 76 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/smac_wrap.py | from __future__ import annotations
from typing import Callable, Union
from smac.callbacks import IncorporateRunResultCallback
from smac.optimizer.smbo import SMBO
from smac.runhistory.runhistory import RunInfo, RunValue
SMACCallback = Callable[[SMBO, RunInfo, RunValue, float], Union[bool, None]]
class SmacRunCallb... | 1,078 | 23.522727 | 76 | py |
auto-sklearn | auto-sklearn-master/autosklearn/util/stopwatch.py | from __future__ import annotations
from typing import Iterator, Mapping, Optional, Tuple
import sys
import time
from contextlib import contextmanager
from itertools import repeat
from typing_extensions import Literal
class TimingTask:
"""A task to start"""
def __init__(self, name: str):
"""
... | 6,853 | 27.558333 | 86 | py |
auto-sklearn | auto-sklearn-master/doc/conf.py | # -*- coding: utf-8 -*-
#
# AutoSklearn documentation build configuration file, created by
# sphinx-quickstart on Thu May 21 13:40:42 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#... | 14,434 | 32.726636 | 124 | py |
auto-sklearn | auto-sklearn-master/doc/_templates/layout.html | {% extends "!layout.html" %}
{# Custom CSS overrides #}
{# set bootswatch_css_custom = ['_static/my-styles.css'] #}
{# Add github banner (from: https://github.com/blog/273-github-ribbons). #}
{% block header %}
{{ super() }}
<a href="https://github.com/automl/auto-sklearn"
class="visible-desktop hidden-xs"><... | 720 | 29.041667 | 77 | html |
auto-sklearn | auto-sklearn-master/examples/20_basic/example_classification.py | # -*- encoding: utf-8 -*-
"""
==============
Classification
==============
The following example shows how to fit a simple classification model with
*auto-sklearn*.
"""
from pprint import pprint
import sklearn.datasets
import sklearn.metrics
import autosklearn.classification
#######################################... | 1,616 | 27.875 | 77 | py |
auto-sklearn | auto-sklearn-master/examples/20_basic/example_multilabel_classification.py | """
==========================
Multi-label Classification
==========================
This examples shows how to format the targets for a multilabel classification
problem. Details on multilabel classification can be found
`here <https://scikit-learn.org/stable/modules/multiclass.html>`_.
"""
import numpy as np
from pp... | 2,866 | 32.729412 | 84 | py |
auto-sklearn | auto-sklearn-master/examples/20_basic/example_multioutput_regression.py | # -*- encoding: utf-8 -*-
"""
=======================
Multi-output Regression
=======================
The following example shows how to fit a multioutput regression model with
*auto-sklearn*.
"""
import numpy as numpy
from pprint import pprint
from sklearn.datasets import make_regression
from sklearn.metrics import ... | 1,963 | 29.215385 | 83 | py |
auto-sklearn | auto-sklearn-master/examples/20_basic/example_regression.py | # -*- encoding: utf-8 -*-
"""
==========
Regression
==========
The following example shows how to fit a simple regression model with
*auto-sklearn*.
"""
from pprint import pprint
import sklearn.datasets
import sklearn.metrics
import autosklearn.regression
import matplotlib.pyplot as plt
############################... | 2,721 | 31.795181 | 100 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/__init__.py | 0 | 0 | 0 | py | |
auto-sklearn | auto-sklearn-master/examples/40_advanced/custom_metrics.py | """Custom metrics to be used by example_metrics.py
They reside in a different file so they can be used by Auto-sklearn."""
import numpy as np
############################################################################
# Custom metrics definition
# =========================
def accuracy(solution, prediction):
... | 895 | 26.151515 | 76 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/example_calc_multiple_metrics.py | # -*- encoding: utf-8 -*-
"""
=======
Metrics
=======
In *Auto-sklearn*, model is optimized over a metric, either built-in or
custom metric. Moreover, it is also possible to calculate multiple metrics
per run. The following examples show how to calculate metrics built-in
and self-defined metrics for a classification p... | 2,178 | 29.263889 | 81 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/example_debug_logging.py | # -*- encoding: utf-8 -*-
"""
=====================
Logging and debugging
=====================
This example shows how to provide a custom logging configuration to *auto-sklearn*.
We will be fitting 2 pipelines and showing any INFO-level msg on console.
Even if you do not provide a logging_configuration, autosklearn c... | 3,537 | 32.695238 | 102 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/example_early_stopping_and_callbacks.py | """
============================
Early stopping and Callbacks
============================
The example below shows how we can use the ``get_trials_callback`` parameter of
auto-sklearn to implement an early-stopping mechanism through a callback.
These callbacks give access to the result of each model + hyperparameter ... | 2,818 | 33.378049 | 88 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/example_feature_types.py | # -*- encoding: utf-8 -*-
"""
=============
Feature Types
=============
In *auto-sklearn* it is possible to specify the feature types of a dataset when calling
the method :meth:`fit() <autosklearn.classification.AutoSklearnClassifier.fit>` by
specifying the argument ``feat_type``.
The following example demonstrates a ... | 2,362 | 35.353846 | 88 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/example_get_pipeline_components.py | # -*- encoding: utf-8 -*-
"""
======================
Obtain run information
======================
The following example shows how to obtain information from a finished
Auto-sklearn run. In particular, it shows:
* how to query which models were evaluated by Auto-sklearn
* how to query the models in the final ensemble
... | 7,965 | 39.232323 | 129 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/example_inspect_predictions.py | # -*- encoding: utf-8 -*-
"""
=================
Model Explanation
=================
The following example shows how to fit a simple classification model with
*auto-sklearn* and use the `inspect module <https://scikit-learn.org/stable/inspection.html>`_ from
scikit-learn to understand what affects the predictions.
"""
... | 4,767 | 37.451613 | 103 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/example_interpretable_models.py | # -*- encoding: utf-8 -*-
"""
====================
Interpretable models
====================
The following example shows how to inspect the models which *auto-sklearn*
optimizes over and how to restrict them to an interpretable subset.
"""
from pprint import pprint
import autosklearn.classification
import sklearn.dat... | 2,894 | 32.662791 | 77 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/example_metrics.py | # -*- encoding: utf-8 -*-
"""
=======
Metrics
=======
*Auto-sklearn* supports various built-in metrics, which can be found in the
:ref:`metrics section in the API <api:Built-in Metrics>`. However, it is also
possible to define your own metric and use it to fit and evaluate your model.
The following examples show how t... | 6,895 | 28.982609 | 88 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/example_multi_objective.py | # -*- encoding: utf-8 -*-
"""
==============
Classification
==============
The following example shows how to fit *auto-sklearn* to optimize for two
competing metrics: `precision` and `recall` (read more on this tradeoff
in the `scikit-learn docs <https://scikit-learn.org/stable/auto_examples/model_selection/plot_prec... | 3,229 | 34.494505 | 121 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/example_pandas_train_test.py | # -*- encoding: utf-8 -*-
"""
==========================
Performance-over-time plot
==========================
This example shows, how to use the *performance_over_time_* attribute to plot the performance
over train time. *performance_over_time_* can contain multiple metrics within a pandas dataframe, namely:
- e... | 4,289 | 37.303571 | 106 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/example_resampling.py | """
=====================
Resampling Strategies
=====================
In *auto-sklearn* it is possible to use different resampling strategies
by specifying the arguments ``resampling_strategy`` and
``resampling_strategy_arguments``. The following example shows common
settings for the ``AutoSklearnClassifier``.
"""
im... | 4,994 | 36.556391 | 97 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/example_single_configuration.py | # -*- encoding: utf-8 -*-
"""
==========================
Fit a single configuration
==========================
*Auto-sklearn* searches for the best combination of machine learning algorithms
and their hyper-parameter configuration for a given task, using Scikit-Learn Pipelines.
To further improve performance, this pip... | 3,441 | 35.231579 | 114 | py |
auto-sklearn | auto-sklearn-master/examples/40_advanced/example_text_preprocessing.py | # -*- encoding: utf-8 -*-
"""
==================
Text preprocessing
==================
The following example shows how to fit a simple NLP problem with
*auto-sklearn*.
For an introduction to text preprocessing you can follow these links:
1. https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text... | 3,241 | 37.595238 | 99 | py |
auto-sklearn | auto-sklearn-master/examples/60_search/example_parallel_manual_spawning_cli.py | # -*- encoding: utf-8 -*-
"""
======================================================
Parallel Usage: Spawning workers from the command line
======================================================
*Auto-sklearn* uses
`dask.distributed <https://distributed.dask.org/en/latest/index.html>`_
for parallel optimization.
This... | 7,869 | 33.217391 | 96 | py |
auto-sklearn | auto-sklearn-master/examples/60_search/example_parallel_manual_spawning_python.py | # -*- encoding: utf-8 -*-
"""
===================================================
Parallel Usage: Spawning workers from within Python
===================================================
**NOTE**: Disabled due to issue https://github.com/dask/distributed/issues/5627
*Auto-sklearn* uses
`dask.distributed <https://distr... | 6,040 | 36.75625 | 93 | py |
auto-sklearn | auto-sklearn-master/examples/60_search/example_parallel_n_jobs.py | # -*- encoding: utf-8 -*-
"""
===================================
Parallel Usage on a single machine
===================================
*Auto-sklearn* uses
`dask.distributed <https://distributed.dask.org/en/latest/index.html`>_
for parallel optimization.
This example shows how to start *Auto-sklearn* to use multipl... | 1,775 | 31.290909 | 76 | py |
auto-sklearn | auto-sklearn-master/examples/60_search/example_random_search.py | """
=============
Random Search
=============
A crucial feature of *auto-sklearn* is automatically optimizing the hyperparameters through SMAC,
introduced `here <https://ml.informatik.uni-freiburg.de/papers/11-LION5-SMAC.pdf>`_.
Additionally, it is possible to use
`random search <https://www.jmlr.org/papers/v13/bergst... | 4,942 | 31.953333 | 121 | py |
auto-sklearn | auto-sklearn-master/examples/60_search/example_sequential.py | """
================
Sequential Usage
================
By default, *auto-sklearn* fits the machine learning models and build their
ensembles in parallel. However, it is also possible to run the two processes
sequentially. The example below shows how to first fit the models and build the
ensembles afterwards.
"""
from ... | 2,195 | 34.419355 | 79 | py |
auto-sklearn | auto-sklearn-master/examples/60_search/example_successive_halving.py | """
==================
Successive Halving
==================
This advanced example illustrates how to interact with
the SMAC callback and get relevant information from the run, like
the number of iterations. Particularly, it exemplifies how to select
the intensification strategy to use in smac, in this case:
`Success... | 10,244 | 36.665441 | 138 | py |
auto-sklearn | auto-sklearn-master/examples/80_extending/example_extending_classification.py | """
====================================================
Extending Auto-Sklearn with Classification Component
====================================================
The following example demonstrates how to create a new classification
component for using in auto-sklearn.
"""
from typing import Optional
from pprint impor... | 5,576 | 31.236994 | 83 | py |
auto-sklearn | auto-sklearn-master/examples/80_extending/example_extending_data_preprocessor.py | """
=======================================================
Extending Auto-Sklearn with Data Preprocessor Component
=======================================================
The following example demonstrates how to turn off data preprocessing step in auto-skearn.
"""
from typing import Optional
from pprint import pprin... | 3,470 | 35.15625 | 90 | py |
auto-sklearn | auto-sklearn-master/examples/80_extending/example_extending_preprocessor.py | """
==================================================
Extending Auto-Sklearn with Preprocessor Component
==================================================
The following example demonstrates how to create a wrapper around the linear
discriminant analysis (LDA) algorithm from sklearn and use it as a preprocessor
in au... | 4,807 | 33.589928 | 85 | py |
auto-sklearn | auto-sklearn-master/examples/80_extending/example_extending_regression.py | """
================================================
Extending Auto-Sklearn with Regression Component
================================================
The following example demonstrates how to create a new regression
component for using in auto-sklearn.
"""
from typing import Optional
from pprint import pprint
from C... | 5,223 | 32.063291 | 79 | py |
auto-sklearn | auto-sklearn-master/examples/80_extending/example_restrict_number_of_hyperparameters.py | """
===================================================================
Restricting the number of hyperparameters for an existing component
===================================================================
The following example demonstrates how to replace an existing
component with a new component, implementing the ... | 5,275 | 35.136986 | 100 | py |
auto-sklearn | auto-sklearn-master/misc/create_hyperparameter_table.py | from argparse import ArgumentParser
from collections import OrderedDict
import os
import shlex
import subprocess
import ConfigSpace.hyperparameters
import autosklearn.pipeline.classification
import autosklearn.pipeline.regression
# Some macros
COND = "conditional"
CAT = "categorical"
CONT = "continuous"
CONST = "cons... | 8,184 | 28.336918 | 110 | py |
auto-sklearn | auto-sklearn-master/misc/create_list_of_potential_models.py | import os
import glob
import inspect
import importlib
import sklearn.base
files = glob.glob(
os.path.join(os.path.dirname(sklearn.__file__), "**/*.py"), recursive=True
)
def find_all(cls):
found = set()
for file in files:
parts = file.split("/")
parts[-1] = parts[-1].replace(".py", "")
... | 942 | 24.486486 | 78 | py |
auto-sklearn | auto-sklearn-master/scripts/01_create_commands.py | import argparse
import itertools
import os
import sys
import openml
sys.path.append(".")
from update_metadata_util import classification_tasks, regression_tasks
parser = argparse.ArgumentParser()
parser.add_argument("--working-directory", type=str, required=True)
parser.add_argument("--test", action="store_true")
a... | 1,815 | 32.62963 | 85 | py |
auto-sklearn | auto-sklearn-master/scripts/02_retrieve_metadata.py | from argparse import ArgumentParser
import csv
import glob
import itertools
import json
import os
import arff
import numpy as np
from ConfigSpace.configuration_space import Configuration
from ConfigSpace.util import deactivate_inactive_hyperparameters
from autosklearn.data.abstract_data_manager import AbstractDataMa... | 9,379 | 32.862816 | 90 | py |
auto-sklearn | auto-sklearn-master/scripts/03_calculate_metafeatures.py | from argparse import ArgumentParser
from collections import defaultdict, OrderedDict
import copy
import logging
import os
import sys
import unittest.mock
import tempfile
import arff
import joblib
import numpy as np
import pandas as pd
from autosklearn.constants import (
BINARY_CLASSIFICATION,
MULTICLASS_CLASS... | 9,709 | 34.567766 | 87 | py |
auto-sklearn | auto-sklearn-master/scripts/04_create_aslib_files.py | from argparse import ArgumentParser
import itertools
import os
import arff
from autosklearn.constants import *
from autosklearn.metrics import CLASSIFICATION_METRICS, REGRESSION_METRICS
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--working-directory", type=str, required=True)
... | 5,742 | 38.606897 | 87 | py |
auto-sklearn | auto-sklearn-master/scripts/readme.md | # How to update metadata
(to be moved to the documentation)
## 1. Create a working directory and set the task type
The working directory will be used to save all temporary and final output.
working_directory=~/auto-sklearn-metadata/001
mkdir -p $working_directory
The task type defines whether you want upda... | 1,622 | 30.823529 | 102 | md |
auto-sklearn | auto-sklearn-master/scripts/run_auto-sklearn_for_metadata_generation.py | if __name__ == "__main__":
import argparse
import json
import logging
import os
import subprocess
import sys
import tempfile
from autosklearn.classification import AutoSklearnClassifier
from autosklearn.regression import AutoSklearnRegressor
from autosklearn.evaluation import E... | 8,378 | 32.650602 | 87 | py |
auto-sklearn | auto-sklearn-master/scripts/update_metadata_util.py | import numpy as np
import openml
classification_tasks = [
232,
236,
241,
245,
253,
254,
256,
258,
260,
262,
267,
271,
273,
275,
279,
288,
336,
340,
2119,
2120,
2121,
2122,
2123,
2125,
2356,
3044,
3047,
3048... | 4,638 | 12.10452 | 77 | py |
auto-sklearn | auto-sklearn-master/scripts/2015_nips_paper/Readme.md | ## Reproduce results of Efficient and Robust Automated Machine Learning (Feurer et al.)
This folder contains all necessary scripts in order to reproduce the results shown in
Figure 3 of Efficient and Robust Automated Machine Learning (Feurer et al.). The scripts
can be modified to include different datasets, change the... | 1,111 | 30.771429 | 88 | md |
auto-sklearn | auto-sklearn-master/scripts/2015_nips_paper/plot/plot_ranks.py | #!/usr/bin/env python3
import csv
import sys
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def read_csv(fn, has_header=True, data_type=str):
"""
Function which reads the csv files containing trajectories
of the auto-sklearn runs.
"""
data = list()
header =... | 5,710 | 32.594118 | 89 | py |
auto-sklearn | auto-sklearn-master/scripts/2015_nips_paper/run/remove_dataset_from_metadata.py | import os
import arff
from shutil import copyfile
def remove_dataset_from_aslib_arff(
input_file,
output_file,
id,
):
with open(input_file) as fh:
arff_object = arff.load(fh)
for i in range(len(arff_object["data"]) - 1, -1, -1):
if str(arff_object["data"][i][0]) == str(id):
... | 2,192 | 31.731343 | 79 | py |
auto-sklearn | auto-sklearn-master/scripts/2015_nips_paper/run/run_auto_sklearn.py | #!/usr/bin/env python3
import os
import argparse
import numpy as np
import openml
from autosklearn.classification import AutoSklearnClassifier
from autosklearn.metrics import balanced_accuracy
from remove_dataset_from_metadata import remove_dataset
import score_ensemble
def load_task(task_id):
"""Function used ... | 6,060 | 29.457286 | 88 | py |
auto-sklearn | auto-sklearn-master/scripts/2015_nips_paper/run/run_commands.sh | #!/usr/bin/env bash
# Run all commands in commands.txt. Each command line executes first the model fitting,
# and then creates the trajectory of a single model and the ensemble. Therefore, each
# line can be executed separately and in parallel, for example, on a cluster environment.
cat "../commands.txt" | while read ... | 347 | 57 | 89 | sh |
auto-sklearn | auto-sklearn-master/scripts/2015_nips_paper/run/score_ensemble.py | from argparse import ArgumentParser
import csv
import glob
import os
import time
import joblib
import numpy as np
from autosklearn.ensembles.ensemble_selection import EnsembleSelection
from autosklearn.metrics import balanced_accuracy
from autosklearn.automl_common.common.utils.backend import create
def _load_file... | 8,574 | 32.236434 | 86 | py |