File Content: mocking.py import numpy as np from ..base import BaseEstimator, ClassifierMixin from .testing import assert_true from .validation import _num_samples, check_array class ArraySlicingWrapper(object): def __init__(self, array): self.array = array def __getitem__(self, aslice): return MockDataFrame(self.array[aslice]) class MockDataFrame(object): # have shape an length but don't support indexing. def __init__(self, array): self.array = array self.values = array self.shape = array.shape self.ndim = array.ndim # ugly hack to make iloc work. self.iloc = ArraySlicingWrapper(array) def __len__(self): return len(self.array) def __array__(self, dtype=None): # Pandas data frames also are array-like: we want to make sure that # input validation in cross-validation does not try to call that # method. return self.array def __eq__(self, other): return MockDataFrame(self.array == other.array) class CheckingClassifier(BaseEstimator, ClassifierMixin): """Dummy classifier to test pipelining and meta-estimators. Checks some property of X and y in fit / predict. This allows testing whether pipelines / cross-validation or metaestimators changed the input. """ def __init__(self, check_y=None, check_X=None, foo_param=0): self.check_y = check_y self.check_X = check_X self.foo_param = foo_param def fit(self, X, y): assert_true(len(X) == len(y)) if self.check_X is not None: assert_true(self.check_X(X)) if self.check_y is not None: assert_true(self.check_y(y)) self.classes_ = np.unique(check_array(y, ensure_2d=False, allow_nd=True)) return self def predict(self, T): if self.check_X is not None: assert_true(self.check_X(T)) return self.classes_[np.zeros(_num_samples(T), dtype=np.int)] def score(self, X=None, Y=None): if self.foo_param > 1: score = 1. else: score = 0. return score Submit