"""Base class for sampling""" # Authors: Guillaume Lemaitre # Christos Aridas # License: MIT from abc import ABCMeta, abstractmethod import numpy as np from sklearn.base import BaseEstimator try: # scikit-learn >= 1.2 from sklearn.base import OneToOneFeatureMixin except ImportError: from sklearn.base import _OneToOneFeatureMixin as OneToOneFeatureMixin from sklearn.preprocessing import label_binarize from sklearn.utils.multiclass import check_classification_targets from .utils import check_sampling_strategy, check_target_type from .utils._param_validation import validate_parameter_constraints from .utils._validation import ArraysTransformer class SamplerMixin(BaseEstimator, metaclass=ABCMeta): """Mixin class for samplers with abstract method. Warning: This class should not be used directly. Use the derive classes instead. """ _estimator_type = "sampler" def fit(self, X, y): """Check inputs and statistics of the sampler. You should use ``fit_resample`` in all cases. Parameters ---------- X : {array-like, dataframe, sparse matrix} of shape \ (n_samples, n_features) Data array. y : array-like of shape (n_samples,) Target array. Returns ------- self : object Return the instance itself. """ X, y, _ = self._check_X_y(X, y) self.sampling_strategy_ = check_sampling_strategy( self.sampling_strategy, y, self._sampling_type ) return self def fit_resample(self, X, y): """Resample the dataset. Parameters ---------- X : {array-like, dataframe, sparse matrix} of shape \ (n_samples, n_features) Matrix containing the data which have to be sampled. y : array-like of shape (n_samples,) Corresponding label for each sample in X. Returns ------- X_resampled : {array-like, dataframe, sparse matrix} of shape \ (n_samples_new, n_features) The array containing the resampled data. y_resampled : array-like of shape (n_samples_new,) The corresponding label of `X_resampled`. """ check_classification_targets(y) arrays_transformer = ArraysTransformer(X, y) X, y, binarize_y = self._check_X_y(X, y) self.sampling_strategy_ = check_sampling_strategy( self.sampling_strategy, y, self._sampling_type ) output = self._fit_resample(X, y) y_ = ( label_binarize(output[1], classes=np.unique(y)) if binarize_y else output[1] ) X_, y_ = arrays_transformer.transform(output[0], y_) return (X_, y_) if len(output) == 2 else (X_, y_, output[2]) @abstractmethod def _fit_resample(self, X, y): """Base method defined in each sampler to defined the sampling strategy. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Matrix containing the data which have to be sampled. y : array-like of shape (n_samples,) Corresponding label for each sample in X. Returns ------- X_resampled : {ndarray, sparse matrix} of shape \ (n_samples_new, n_features) The array containing the resampled data. y_resampled : ndarray of shape (n_samples_new,) The corresponding label of `X_resampled`. """ pass class _ParamsValidationMixin: """Mixin class to validate parameters.""" def _validate_params(self): """Validate types and values of constructor parameters. The expected type and values must be defined in the `_parameter_constraints` class attribute, which is a dictionary `param_name: list of constraints`. See the docstring of `validate_parameter_constraints` for a description of the accepted constraints. """ if hasattr(self, "_parameter_constraints"): validate_parameter_constraints( self._parameter_constraints, self.get_params(deep=False), caller_name=self.__class__.__name__, ) class BaseSampler(SamplerMixin, OneToOneFeatureMixin, _ParamsValidationMixin): """Base class for sampling algorithms. Warning: This class should not be used directly. Use the derive classes instead. """ def __init__(self, sampling_strategy="auto"): self.sampling_strategy = sampling_strategy def _check_X_y(self, X, y, accept_sparse=None): if accept_sparse is None: accept_sparse = ["csr", "csc"] y, binarize_y = check_target_type(y, indicate_one_vs_all=True) X, y = self._validate_data(X, y, reset=True, accept_sparse=accept_sparse) return X, y, binarize_y def fit(self, X, y): """Check inputs and statistics of the sampler. You should use ``fit_resample`` in all cases. Parameters ---------- X : {array-like, dataframe, sparse matrix} of shape \ (n_samples, n_features) Data array. y : array-like of shape (n_samples,) Target array. Returns ------- self : object Return the instance itself. """ self._validate_params() return super().fit(X, y) def fit_resample(self, X, y): """Resample the dataset. Parameters ---------- X : {array-like, dataframe, sparse matrix} of shape \ (n_samples, n_features) Matrix containing the data which have to be sampled. y : array-like of shape (n_samples,) Corresponding label for each sample in X. Returns ------- X_resampled : {array-like, dataframe, sparse matrix} of shape \ (n_samples_new, n_features) The array containing the resampled data. y_resampled : array-like of shape (n_samples_new,) The corresponding label of `X_resampled`. """ self._validate_params() return super().fit_resample(X, y) def _more_tags(self): return {"X_types": ["2darray", "sparse", "dataframe"]} def _identity(X, y): return X, y def is_sampler(estimator): """Return True if the given estimator is a sampler, False otherwise. Parameters ---------- estimator : object Estimator to test. Returns ------- is_sampler : bool True if estimator is a sampler, otherwise False. """ if estimator._estimator_type == "sampler": return True return False class FunctionSampler(BaseSampler): """Construct a sampler from calling an arbitrary callable. Read more in the :ref:`User Guide `. Parameters ---------- func : callable, default=None The callable to use for the transformation. This will be passed the same arguments as transform, with args and kwargs forwarded. If func is None, then func will be the identity function. accept_sparse : bool, default=True Whether sparse input are supported. By default, sparse inputs are supported. kw_args : dict, default=None The keyword argument expected by ``func``. validate : bool, default=True Whether or not to bypass the validation of ``X`` and ``y``. Turning-off validation allows to use the ``FunctionSampler`` with any type of data. .. versionadded:: 0.6 Attributes ---------- sampling_strategy_ : dict Dictionary containing the information to sample the dataset. The keys corresponds to the class labels from which to sample and the values are the number of samples to sample. n_features_in_ : int Number of features in the input dataset. .. versionadded:: 0.9 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during `fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 0.10 See Also -------- sklearn.preprocessing.FunctionTransfomer : Stateless transformer. Notes ----- See :ref:`sphx_glr_auto_examples_applications_plot_outlier_rejections.py` Examples -------- >>> import numpy as np >>> from sklearn.datasets import make_classification >>> from imblearn import FunctionSampler >>> X, y = make_classification(n_classes=2, class_sep=2, ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10) We can create to select only the first ten samples for instance. >>> def func(X, y): ... return X[:10], y[:10] >>> sampler = FunctionSampler(func=func) >>> X_res, y_res = sampler.fit_resample(X, y) >>> np.all(X_res == X[:10]) True >>> np.all(y_res == y[:10]) True We can also create a specific function which take some arguments. >>> from collections import Counter >>> from imblearn.under_sampling import RandomUnderSampler >>> def func(X, y, sampling_strategy, random_state): ... return RandomUnderSampler( ... sampling_strategy=sampling_strategy, ... random_state=random_state).fit_resample(X, y) >>> sampler = FunctionSampler(func=func, ... kw_args={'sampling_strategy': 'auto', ... 'random_state': 0}) >>> X_res, y_res = sampler.fit_resample(X, y) >>> print(f'Resampled dataset shape {sorted(Counter(y_res).items())}') Resampled dataset shape [(0, 100), (1, 100)] """ _sampling_type = "bypass" _parameter_constraints: dict = { "func": [callable, None], "accept_sparse": ["boolean"], "kw_args": [dict, None], "validate": ["boolean"], } def __init__(self, *, func=None, accept_sparse=True, kw_args=None, validate=True): super().__init__() self.func = func self.accept_sparse = accept_sparse self.kw_args = kw_args self.validate = validate def fit(self, X, y): """Check inputs and statistics of the sampler. You should use ``fit_resample`` in all cases. Parameters ---------- X : {array-like, dataframe, sparse matrix} of shape \ (n_samples, n_features) Data array. y : array-like of shape (n_samples,) Target array. Returns ------- self : object Return the instance itself. """ self._validate_params() # we need to overwrite SamplerMixin.fit to bypass the validation if self.validate: check_classification_targets(y) X, y, _ = self._check_X_y(X, y, accept_sparse=self.accept_sparse) self.sampling_strategy_ = check_sampling_strategy( self.sampling_strategy, y, self._sampling_type ) return self def fit_resample(self, X, y): """Resample the dataset. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Matrix containing the data which have to be sampled. y : array-like of shape (n_samples,) Corresponding label for each sample in X. Returns ------- X_resampled : {array-like, sparse matrix} of shape \ (n_samples_new, n_features) The array containing the resampled data. y_resampled : array-like of shape (n_samples_new,) The corresponding label of `X_resampled`. """ self._validate_params() arrays_transformer = ArraysTransformer(X, y) if self.validate: check_classification_targets(y) X, y, binarize_y = self._check_X_y(X, y, accept_sparse=self.accept_sparse) self.sampling_strategy_ = check_sampling_strategy( self.sampling_strategy, y, self._sampling_type ) output = self._fit_resample(X, y) if self.validate: y_ = ( label_binarize(output[1], classes=np.unique(y)) if binarize_y else output[1] ) X_, y_ = arrays_transformer.transform(output[0], y_) return (X_, y_) if len(output) == 2 else (X_, y_, output[2]) return output def _fit_resample(self, X, y): func = _identity if self.func is None else self.func output = func(X, y, **(self.kw_args if self.kw_args else {})) return output