code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
from abc import abstractmethod
from math import pi
from .base import dot, transpose, safe_log, safe_exp
from .utils import check_array, check_types, check_version
__all__ = ["GaussianNBPure", "MultinomialNBPure", "ComplementNBPure"]
class _BaseNBPure:
"""Base class for naive Bayes classifiers"""
@abstractm... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/naive_bayes.py | 0.931952 | 0.583856 | naive_bayes.py | pypi |
from operator import add
from ..utils import check_array, ndim, shape, check_types
from ..base import dot, expit, ravel
class LinearClassifierMixinPure:
"""Mixin for linear classifiers"""
def __init__(self, estimator):
self.coef_ = estimator.coef_.tolist()
self.classes_ = estimator.classes_.... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/linear_model/_base.py | 0.772702 | 0.315413 | _base.py | pypi |
import re
import unicodedata
from functools import partial
from math import isnan
import warnings
from ._hash import _FeatureHasherPure
from ..map import convert_estimator
from ..preprocessing import normalize_pure
from ..utils import (
convert_type,
sparse_list,
shape,
check_array,
check_types,
... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/feature_extraction/text.py | 0.586641 | 0.316316 | text.py | pypi |
import numbers
from ..utils import check_types, sparse_list
MAX_INT = 2147483647
def _xrange(a, b, c):
return range(a, b, c)
def _xencode(x):
if isinstance(x, (bytes, bytearray)):
return x
else:
return x.encode()
def _iteritems(d):
"""Like d.iteritems, but accepts any collections.... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/feature_extraction/_hash.py | 0.605566 | 0.343645 | _hash.py | pypi |
from ._label import _encode, _encode_check_unknown
from ..base import accumu, apply_2d
from ..utils import (
check_types,
check_array,
shape,
sparse_list,
convert_type,
check_version,
)
class _BaseEncoderPure:
"""
Base class for encoders that includes the code to categorize and
tra... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/preprocessing/_encoders.py | 0.827793 | 0.416915 | _encoders.py | pypi |
from math import sqrt
from copy import copy as cp
from ..utils import sparse_list, issparse, check_array, check_types, check_version
from ..base import transpose, apply_2d, apply_axis_2d, matmult_same_dim
def _handle_zeros_in_scale(scale, copy=True):
"""Makes sure that whenever scale is zero, we handle it correc... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/preprocessing/_data.py | 0.847747 | 0.490907 | _data.py | pypi |
from ..base import sfmax, expit
from ..tree import DecisionTreeRegressorPure
from ..utils import check_types, check_array
MIN_VERSION = "0.82"
SUPPORTED_OBJ = ["binary:logistic", "multi:softprob"]
SUPPORTED_BOOSTER = ["gbtree"]
class XGBClassifierPure:
"""
Pure python implementation of `XGBClassifier`. Only ... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/xgboost/_classes.py | 0.685002 | 0.384392 | _classes.py | pypi |
import warnings
from math import isnan
from ..base import safe_log
from ..utils import check_array, check_types, check_version
class _DecisionTreeBase:
"""Decision tree base class"""
def __init__(self, estimator):
if isinstance(estimator, dict):
# sourced from xgboost booster object tre... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/tree/_classes.py | 0.795142 | 0.333313 | _classes.py | pypi |
from ..base import transpose, apply_axis_2d, apply_2d, safe_exp, safe_log, ravel, expit
from ..utils import check_types, shape
EPS = 1.1920929e-07
def _clip(a, a_min, a_max):
if a < a_min:
return a_min
elif a > a_max:
return a_max
else:
return a
class _MultinomialDeviancePure:
... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/ensemble/_gb_losses.py | 0.618665 | 0.504089 | _gb_losses.py | pypi |
from operator import add
from ._gb_losses import (
_MultinomialDeviancePure,
_BinomialDeviancePure,
_ExponentialLossPure,
)
from ..base import transpose, apply_2d, safe_log, operate_2d
from ..utils import check_version, check_types, check_array, shape
from ..map import convert_estimator
class GradientBo... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/ensemble/_gb.py | 0.865991 | 0.385519 | _gb.py | pypi |
from math import isnan
from ..utils import shape, check_array, check_types, check_version
from ..base import apply_2d, apply_axis_2d
def _to_impute(val, missing_values):
if isnan(missing_values):
return isnan(val)
else:
return val == missing_values
class MissingIndicatorPure:
"""
Pu... | /scikit-endpoint-0.0.3.tar.gz/scikit-endpoint-0.0.3/scikit_endpoint/impute/_base.py | 0.829492 | 0.471223 | _base.py | pypi |
<p>
<img src="https://github.com/monte-flora/scikit-explain/blob/master/images/mintpy_logo.png?raw=true" align="right" width="400" height="400" />
</p>
[![codecov](https://codecov.io/gh/monte-flora/s... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/README.md | 0.838845 | 0.944331 | README.md | pypi |
import numpy as np
import sklearn
from multiprocessing.pool import Pool
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier, _tree
from distutils.version import LooseVersion
from tqdm import tqdm
if LooseVersion(sklearn.__v... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/main/tree_interpreter.py | 0.817793 | 0.402216 | tree_interpreter.py | pypi |
import numpy as np
from .error_handling import InvalidStrategyException
__all__ = [
"verify_scoring_strategy",
"VALID_SCORING_STRATEGIES",
"argmin_of_mean",
"argmax_of_mean",
"indexer_of_converter",
]
def verify_scoring_strategy(scoring_strategy):
"""Asserts that the scoring strategy is vali... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/main/PermutationImportance/scoring_strategies.py | 0.827793 | 0.480418 | scoring_strategies.py | pypi |
from .abstract_runner import abstract_variable_importance
from .selection_strategies import (
SequentialForwardSelectionStrategy,
SequentialBackwardSelectionStrategy,
)
from .sklearn_api import (
score_untrained_sklearn_model,
score_untrained_sklearn_model_with_probabilities,
)
__all__ = [
"sequent... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/main/PermutationImportance/sequential_selection.py | 0.913141 | 0.528229 | sequential_selection.py | pypi |
from multiprocessing import Process, Queue, cpu_count
try:
from Queue import Full as QueueFull
from Queue import Empty as QueueEmpty
except ImportError: # python3
from queue import Full as QueueFull
from queue import Empty as QueueEmpty
__all__ = ["pool_imap_unordered"]
def worker(func, recvq, send... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/main/PermutationImportance/multiprocessing_utils.py | 0.494629 | 0.225843 | multiprocessing_utils.py | pypi |
import numpy as np
import pandas as pd
from .utils import get_data_subset, make_data_from_columns, conditional_permutations
__all__ = [
"SequentialForwardSelectionStrategy",
"SequentialBackwardSelectionStrategy",
"PermutationImportanceSelectionStrategy",
"SelectionStrategy",
]
class SelectionStrateg... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/main/PermutationImportance/selection_strategies.py | 0.842053 | 0.553928 | selection_strategies.py | pypi |
import numpy as np
import pandas as pd
import numbers
from .error_handling import InvalidDataException
__all__ = ["add_ranks_to_dict", "get_data_subset", "make_data_from_columns"]
def add_ranks_to_dict(result, variable_names, scoring_strategy):
"""Takes a list of (var, score) and converts to a dictionary of
... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/main/PermutationImportance/utils.py | 0.79909 | 0.591045 | utils.py | pypi |
class InvalidStrategyException(Exception):
"""Thrown when a scoring strategy is invalid"""
def __init__(self, strategy, msg=None, options=None):
if msg is None:
msg = (
"%s is not a valid strategy for determining the optimal variable. "
% strategy
... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/main/PermutationImportance/error_handling.py | 0.790611 | 0.24243 | error_handling.py | pypi |
import numpy as np
import pandas as pd
from .error_handling import InvalidDataException, InvalidInputException
try:
basestring
except NameError: # Python3
basestring = str
__all__ = ["verify_data", "determine_variable_names"]
def verify_data(data):
"""Verifies that the data tuple is of the right forma... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/main/PermutationImportance/data_verification.py | 0.619241 | 0.616936 | data_verification.py | pypi |
import numpy as np
from sklearn.base import clone
from .utils import get_data_subset, bootstrap_generator
from joblib import Parallel, delayed
__all__ = [
"model_scorer",
"score_untrained_sklearn_model",
"score_untrained_sklearn_model_with_probabilities",
"score_trained_sklearn_model",
"score_tra... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/main/PermutationImportance/sklearn_api.py | 0.903559 | 0.66888 | sklearn_api.py | pypi |
import warnings
try:
from itertools import izip as zip
except ImportError: # python3
pass
from .error_handling import FullImportanceResultWarning
class ImportanceResult(object):
"""Houses the result of any importance method, which consists of a
sequence of contexts and results. An individual result... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/main/PermutationImportance/result.py | 0.715821 | 0.439146 | result.py | pypi |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator, FormatStrFormatter, AutoMinorLocator
import matplotlib.ticker as mticker
from matplotlib import rcParams
from matplotlib.colors import ListedColormap
from matplotlib.gridspec import GridSpec
import matplotlib
import seaborn... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/plot/base_plotting.py | 0.727395 | 0.518973 | base_plotting.py | pypi |
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import ListedColormap
from scipy.stats import gaussian_kde
from scipy.ndimage import gaussian_filter
import scipy
import itertools
import numpy as np
import matplotlib as mpl
from scipy.ndimage import gaussian_filter
from .base_plotting import... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/plot/_kde_2d.py | 0.665084 | 0.351589 | _kde_2d.py | pypi |
import numpy as np
import collections
from ..common.importance_utils import find_correlated_pairs_among_top_features
from ..common.utils import is_list, is_correlated
from .base_plotting import PlotStructure
import random
class PlotImportance(PlotStructure):
"""
PlotImportance handles plotting feature rankin... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/plot/plot_permutation_importance.py | 0.730578 | 0.371479 | plot_permutation_importance.py | pypi |
import matplotlib.pyplot as plt
import seaborn as sns
def rounding(v):
"""Rounding for pretty plots"""
if v > 100:
return int(round(v))
elif v > 0 and v < 100:
return round(v, 1)
elif v >= 0.1 and v < 1:
return round(v, 1)
elif v >= 0 and v < 0.1:
return round(v, 3)... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/plot/_box_and_whisker.py | 0.754192 | 0.526404 | _box_and_whisker.py | pypi |
from functools import partial
from sklearn.metrics._base import _average_binary_score
from sklearn.utils.multiclass import type_of_target
from sklearn.metrics import (
brier_score_loss,
average_precision_score,
precision_recall_curve,
)
import numpy as np
def brier_skill_score(y_values, forecast_probabili... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/common/metrics.py | 0.920567 | 0.582996 | metrics.py | pypi |
import xarray as xr
import numpy as np
from skexplain.common.utils import compute_bootstrap_indices
import pandas as pd
def method_average_ranking(data, features, methods, estimator_names, n_features=12):
"""
Compute the median ranking across the results of different ranking methods.
Also, include the 25-7... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/common/importance_utils.py | 0.885749 | 0.579311 | importance_utils.py | pypi |
import numpy as np
import xarray as xr
import pandas as pd
from collections import ChainMap
from statsmodels.distributions.empirical_distribution import ECDF
from scipy.stats import t
from sklearn.linear_model import Ridge
class MissingFeaturesError(Exception):
""" Raised when features are missing.
E.g.... | /scikit-explain-0.1.3.tar.gz/scikit-explain-0.1.3/skexplain/common/utils.py | 0.792304 | 0.412767 | utils.py | pypi |
import numpy as np
import time
import _pickle as cPickle
from sklearn.metrics.scorer import _BaseScorer
class TimeScorer(_BaseScorer):
def _score(self, method_caller, estimator, X, y_true=None, n_iter=1, unit=True, scoring=None, tradeoff=None, sample_weight=None):
"""
Evaluate prediction latency.
... | /scikit-ext-0.1.16.tar.gz/scikit-ext-0.1.16/scikit_ext/scorers.py | 0.910438 | 0.401219 | scorers.py | pypi |
import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix
from scipy.stats import rankdata
from sklearn.model_selection import GridSearchCV
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import normalize
from sklearn.base import (
BaseEstimator, ClassifierMixin,
... | /scikit-ext-0.1.16.tar.gz/scikit-ext-0.1.16/scikit_ext/estimators.py | 0.886131 | 0.543833 | estimators.py | pypi |
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted
from skfair.common import as_list
def scalar_projection(vec, unto):
return vec.dot(unto) / unto.dot(unto)
def vector_project... | /scikit-fairness-0.0.1.tar.gz/scikit-fairness-0.0.1/skfair/preprocessing/informationfilter.py | 0.883104 | 0.645888 | informationfilter.py | pypi |
.. image:: https://raw.githubusercontent.com/GAA-UAM/scikit-fda/develop/docs/logos/title_logo/title_logo.png
:alt: scikit-fda: Functional Data Analysis in Python
scikit-fda: Functional Data Analysis in Python
===================================================
|python|_ |build-status| |docs| |Codecov|_ |PyPIBadge|_ ... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/README.rst | 0.904158 | 0.76947 | README.rst | pypi |
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Any, Generic, TypeVar, overload
import sklearn.base
if TYPE_CHECKING:
from ..typing._numpy import NDArrayFloat, NDArrayInt
SelfType = TypeVar("SelfType")
TransformerNoTarget = TypeVar(
"TransformerNoTarg... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/_utils/_sklearn_adapter.py | 0.875814 | 0.271692 | _sklearn_adapter.py | pypi |
from __future__ import annotations
from typing import TYPE_CHECKING, Optional
import numpy as np
from scipy.interpolate import PchipInterpolator
from ..typing._base import DomainRangeLike
from ..typing._numpy import ArrayLike, NDArrayFloat
if TYPE_CHECKING:
from ..representation import FDataGrid
def invert_wa... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/_utils/_warping.py | 0.970099 | 0.665635 | _warping.py | pypi |
from __future__ import annotations
import functools
import numbers
from functools import singledispatch
from typing import (
TYPE_CHECKING,
Any,
Callable,
Iterable,
List,
Optional,
Sequence,
Sized,
Tuple,
Type,
TypeVar,
Union,
cast,
overload,
)
import numpy as ... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/_utils/_utils.py | 0.922067 | 0.426919 | _utils.py | pypi |
from __future__ import annotations
import abc
import math
from typing import TypeVar
import numpy as np
import scipy.stats
import sklearn
from scipy.special import comb
from typing_extensions import Literal
from ..._utils._sklearn_adapter import BaseEstimator, InductiveTransformerMixin
from ...typing._numpy import ... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/exploratory/depth/multivariate.py | 0.946312 | 0.6372 | multivariate.py | pypi |
from __future__ import annotations
import itertools
from typing import TypeVar
import numpy as np
import scipy.integrate
from ..._utils._sklearn_adapter import BaseEstimator
from ...misc.metrics import l2_distance
from ...misc.metrics._utils import _fit_metric
from ...representation import FData, FDataGrid
from ...t... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/exploratory/depth/_depth.py | 0.933081 | 0.494629 | _depth.py | pypi |
from __future__ import annotations
from builtins import isinstance
from typing import TypeVar, Union
import numpy as np
from scipy import integrate
from scipy.stats import rankdata
from ...misc.metrics._lp_distances import l2_distance
from ...representation import FData, FDataGrid
from ...typing._metric import Metri... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/exploratory/stats/_stats.py | 0.980186 | 0.679205 | _stats.py | pypi |
from __future__ import annotations
import copy
import itertools
from functools import partial
from typing import Generator, List, Sequence, Tuple, Type, cast
import numpy as np
from matplotlib.artist import Artist
from matplotlib.axes import Axes
from matplotlib.backend_bases import Event
from matplotlib.figure impor... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/exploratory/visualization/_multiple_display.py | 0.948858 | 0.437042 | _multiple_display.py | pypi |
from __future__ import annotations
from typing import Any, Sequence
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.artist import Artist
from matplotlib.axes import Axes
from matplotlib.colors import Colormap
from matplotlib.figure import Figure
from matplotlib.patches import Elli... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/exploratory/visualization/_magnitude_shape_plot.py | 0.959317 | 0.741545 | _magnitude_shape_plot.py | pypi |
from __future__ import annotations
from typing import Sequence, Tuple
import matplotlib
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.artist import Artist
from matplotlib.axes import Axes
from matplotlib.collections import PatchCollection
from matplotlib.fig... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/exploratory/visualization/clustering.py | 0.967302 | 0.519765 | clustering.py | pypi |
from __future__ import annotations
import numpy as np
from matplotlib.artist import Artist
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from ...representation import FDataGrid
from ..outliers import OutliergramOutlierDetector
from ._baseplot import BasePlot
class Outliergram(BasePlot):
... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/exploratory/visualization/_outliergram.py | 0.952364 | 0.799403 | _outliergram.py | pypi |
from __future__ import annotations
from typing import Any, Dict, Sequence, Sized, Tuple, TypeVar
import matplotlib.cm
import matplotlib.patches
import numpy as np
from matplotlib.artist import Artist
from matplotlib.axes import Axes
from matplotlib.colors import Colormap
from matplotlib.figure import Figure
from typi... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/exploratory/visualization/representation.py | 0.961198 | 0.46563 | representation.py | pypi |
from __future__ import annotations
import warnings
from typing import Sequence
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from skfda.exploratory.visualization.representation import GraphPlot
from skfda.representation import FData
from ._baseplot import BasePlot
class FPCAPlot(BasePlot):... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/exploratory/visualization/fpca.py | 0.963343 | 0.501587 | fpca.py | pypi |
from __future__ import annotations
from typing import Dict, Sequence, TypeVar
import numpy as np
from matplotlib.artist import Artist
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from ...representation import FData
from ._baseplot import BasePlot
from ._utils import ColorLike
from .represent... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/exploratory/visualization/_parametric_plot.py | 0.940216 | 0.526708 | _parametric_plot.py | pypi |
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Sequence, Tuple
import matplotlib.pyplot as plt
from matplotlib.artist import Artist
from matplotlib.axes import Axes
from matplotlib.backend_bases import LocationEvent, MouseEvent
from matplotlib.collections import PathCollecti... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/exploratory/visualization/_baseplot.py | 0.952142 | 0.551574 | _baseplot.py | pypi |
from __future__ import annotations
import io
import math
import re
from itertools import repeat
from typing import Sequence, Tuple, TypeVar, Union
import matplotlib.backends.backend_svg
import matplotlib.pyplot as plt
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from typing_extensions import ... | /scikit-fda-sim-0.7.1.tar.gz/scikit-fda-sim-0.7.1/skfda/exploratory/visualization/_utils.py | 0.925331 | 0.407333 | _utils.py | pypi |
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