87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
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import numpy as np
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from ..base import BaseEstimator, ClassifierMixin
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from .testing import assert_true
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from .validation import _num_samples, check_array
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class ArraySlicingWrapper(object):
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def __init__(self, array):
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self.array = array
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def __getitem__(self, aslice):
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return MockDataFrame(self.array[aslice])
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class MockDataFrame(object):
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# have shape an length but don't support indexing.
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def __init__(self, array):
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self.array = array
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self.values = array
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self.shape = array.shape
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self.ndim = array.ndim
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# ugly hack to make iloc work.
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self.iloc = ArraySlicingWrapper(array)
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def __len__(self):
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return len(self.array)
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def __array__(self, dtype=None):
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# Pandas data frames also are array-like: we want to make sure that
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# input validation in cross-validation does not try to call that
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# method.
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return self.array
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def __eq__(self, other):
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return MockDataFrame(self.array == other.array)
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def __ne__(self, other):
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return not self == other
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class CheckingClassifier(BaseEstimator, ClassifierMixin):
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"""Dummy classifier to test pipelining and meta-estimators.
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Checks some property of X and y in fit / predict.
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This allows testing whether pipelines / cross-validation or metaestimators
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changed the input.
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"""
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def __init__(self, check_y=None, check_X=None, foo_param=0,
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expected_fit_params=None):
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self.check_y = check_y
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self.check_X = check_X
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self.foo_param = foo_param
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self.expected_fit_params = expected_fit_params
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def fit(self, X, y, **fit_params):
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assert_true(len(X) == len(y))
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if self.check_X is not None:
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assert_true(self.check_X(X))
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if self.check_y is not None:
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assert_true(self.check_y(y))
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self.classes_ = np.unique(check_array(y, ensure_2d=False,
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allow_nd=True))
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if self.expected_fit_params:
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missing = set(self.expected_fit_params) - set(fit_params)
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assert_true(len(missing) == 0, 'Expected fit parameter(s) %s not '
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'seen.' % list(missing))
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for key, value in fit_params.items():
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assert_true(len(value) == len(X),
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'Fit parameter %s has length %d; '
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'expected %d.' % (key, len(value), len(X)))
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return self
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def predict(self, T):
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if self.check_X is not None:
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assert_true(self.check_X(T))
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return self.classes_[np.zeros(_num_samples(T), dtype=np.int)]
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def score(self, X=None, Y=None):
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if self.foo_param > 1:
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score = 1.
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else:
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score = 0.
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return score
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