"""Base class and original SMOTE methods for over-sampling""" # Authors: Guillaume Lemaitre # Fernando Nogueira # Christos Aridas # Dzianis Dudnik # License: MIT import math import numbers import warnings import numpy as np from scipy import sparse from scipy.stats import mode from sklearn.base import clone from sklearn.exceptions import DataConversionWarning from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder from sklearn.utils import ( _safe_indexing, check_array, check_random_state, ) from sklearn.utils._param_validation import HasMethods, Interval, StrOptions from sklearn.utils.sparsefuncs_fast import ( csr_mean_variance_axis0, ) from sklearn.utils.validation import _num_features from ...metrics.pairwise import ValueDifferenceMetric from ...utils import Substitution, check_neighbors_object, check_target_type from ...utils._docstring import _random_state_docstring from ...utils._sklearn_compat import _get_column_indices, _is_pandas_df, validate_data from ...utils._validation import _check_X from ..base import BaseOverSampler class BaseSMOTE(BaseOverSampler): """Base class for the different SMOTE algorithms.""" _parameter_constraints: dict = { **BaseOverSampler._parameter_constraints, "k_neighbors": [ Interval(numbers.Integral, 1, None, closed="left"), HasMethods(["kneighbors", "kneighbors_graph"]), ], } def __init__( self, sampling_strategy="auto", random_state=None, k_neighbors=5, ): super().__init__(sampling_strategy=sampling_strategy) self.random_state = random_state self.k_neighbors = k_neighbors def _validate_estimator(self): """Check the NN estimators shared across the different SMOTE algorithms. """ self.nn_k_ = check_neighbors_object( "k_neighbors", self.k_neighbors, additional_neighbor=1 ) def _make_samples( self, X, y_dtype, y_type, nn_data, nn_num, n_samples, step_size=1.0, y=None ): """A support function that returns artificial samples constructed along the line connecting nearest neighbours. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Points from which the points will be created. y_dtype : dtype The data type of the targets. y_type : str or int The minority target value, just so the function can return the target values for the synthetic variables with correct length in a clear format. nn_data : ndarray of shape (n_samples_all, n_features) Data set carrying all the neighbours to be used nn_num : ndarray of shape (n_samples_all, k_nearest_neighbours) The nearest neighbours of each sample in `nn_data`. n_samples : int The number of samples to generate. step_size : float, default=1.0 The step size to create samples. y : ndarray of shape (n_samples_all,), default=None The true target associated with `nn_data`. Used by Borderline SMOTE-2 to weight the distances in the sample generation process. Returns ------- X_new : {ndarray, sparse matrix} of shape (n_samples_new, n_features) Synthetically generated samples. y_new : ndarray of shape (n_samples_new,) Target values for synthetic samples. """ random_state = check_random_state(self.random_state) samples_indices = random_state.randint(low=0, high=nn_num.size, size=n_samples) # np.newaxis for backwards compatability with random_state steps = step_size * random_state.uniform(size=n_samples)[:, np.newaxis] rows = np.floor_divide(samples_indices, nn_num.shape[1]) cols = np.mod(samples_indices, nn_num.shape[1]) X_new = self._generate_samples(X, nn_data, nn_num, rows, cols, steps, y_type, y) y_new = np.full(n_samples, fill_value=y_type, dtype=y_dtype) return X_new, y_new def _generate_samples( self, X, nn_data, nn_num, rows, cols, steps, y_type=None, y=None ): r"""Generate a synthetic sample. The rule for the generation is: .. math:: \mathbf{s_{s}} = \mathbf{s_{i}} + \mathcal{u}(0, 1) \times (\mathbf{s_{i}} - \mathbf{s_{nn}}) \, where \mathbf{s_{s}} is the new synthetic samples, \mathbf{s_{i}} is the current sample, \mathbf{s_{nn}} is a randomly selected neighbors of \mathbf{s_{i}} and \mathcal{u}(0, 1) is a random number between [0, 1). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Points from which the points will be created. nn_data : ndarray of shape (n_samples_all, n_features) Data set carrying all the neighbours to be used. nn_num : ndarray of shape (n_samples_all, k_nearest_neighbours) The nearest neighbours of each sample in `nn_data`. rows : ndarray of shape (n_samples,), dtype=int Indices pointing at feature vector in X which will be used as a base for creating new samples. cols : ndarray of shape (n_samples,), dtype=int Indices pointing at which nearest neighbor of base feature vector will be used when creating new samples. steps : ndarray of shape (n_samples,), dtype=float Step sizes for new samples. y_type : str, int or None, default=None Class label of the current target classes for which we want to generate samples. y : ndarray of shape (n_samples_all,), default=None The true target associated with `nn_data`. Used by Borderline SMOTE-2 to weight the distances in the sample generation process. Returns ------- X_new : {ndarray, sparse matrix} of shape (n_samples, n_features) Synthetically generated samples. """ diffs = nn_data[nn_num[rows, cols]] - X[rows] if y is not None: # only entering for BorderlineSMOTE-2 random_state = check_random_state(self.random_state) mask_pair_samples = y[nn_num[rows, cols]] != y_type diffs[mask_pair_samples] *= random_state.uniform( low=0.0, high=0.5, size=(mask_pair_samples.sum(), 1) ) if sparse.issparse(X): sparse_func = type(X).__name__ steps = getattr(sparse, sparse_func)(steps) X_new = X[rows] + steps.multiply(diffs) else: X_new = X[rows] + steps * diffs return X_new.astype(X.dtype) def _in_danger_noise(self, nn_estimator, samples, target_class, y, kind="danger"): """Estimate if a set of sample are in danger or noise. Used by BorderlineSMOTE and SVMSMOTE. Parameters ---------- nn_estimator : estimator object An estimator that inherits from :class:`~sklearn.neighbors.base.KNeighborsMixin` use to determine if a sample is in danger/noise. samples : {array-like, sparse matrix} of shape (n_samples, n_features) The samples to check if either they are in danger or not. target_class : int or str The target corresponding class being over-sampled. y : array-like of shape (n_samples,) The true label in order to check the neighbour labels. kind : {'danger', 'noise'}, default='danger' The type of classification to use. Can be either: - If 'danger', check if samples are in danger, - If 'noise', check if samples are noise. Returns ------- output : ndarray of shape (n_samples,) A boolean array where True refer to samples in danger or noise. """ x = nn_estimator.kneighbors(samples, return_distance=False)[:, 1:] nn_label = (y[x] != target_class).astype(int) n_maj = np.sum(nn_label, axis=1) if kind == "danger": # Samples are in danger for m/2 <= m' < m return np.bitwise_and( n_maj >= (nn_estimator.n_neighbors - 1) / 2, n_maj < nn_estimator.n_neighbors - 1, ) else: # kind == "noise": # Samples are noise for m = m' return n_maj == nn_estimator.n_neighbors - 1 @Substitution( sampling_strategy=BaseOverSampler._sampling_strategy_docstring, random_state=_random_state_docstring, ) class SMOTE(BaseSMOTE): """Class to perform over-sampling using SMOTE. This object is an implementation of SMOTE - Synthetic Minority Over-sampling Technique as presented in [1]_. Read more in the :ref:`User Guide `. Parameters ---------- {sampling_strategy} {random_state} k_neighbors : int or object, default=5 The nearest neighbors used to define the neighborhood of samples to use to generate the synthetic samples. You can pass: - an `int` corresponding to the number of neighbors to use. A `~sklearn.neighbors.NearestNeighbors` instance will be fitted in this case. - an instance of a compatible nearest neighbors algorithm that should implement both methods `kneighbors` and `kneighbors_graph`. For instance, it could correspond to a :class:`~sklearn.neighbors.NearestNeighbors` but could be extended to any compatible class. 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. nn_k_ : estimator object Validated k-nearest neighbours created from the `k_neighbors` parameter. 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 -------- SMOTENC : Over-sample using SMOTE for continuous and categorical features. SMOTEN : Over-sample using the SMOTE variant specifically for categorical features only. BorderlineSMOTE : Over-sample using the borderline-SMOTE variant. SVMSMOTE : Over-sample using the SVM-SMOTE variant. ADASYN : Over-sample using ADASYN. KMeansSMOTE : Over-sample applying a clustering before to oversample using SMOTE. Notes ----- See the original papers: [1]_ for more details. Supports multi-class resampling. A one-vs.-rest scheme is used as originally proposed in [1]_. References ---------- .. [1] N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of artificial intelligence research, 321-357, 2002. Examples -------- >>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from imblearn.over_sampling import SMOTE >>> 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) >>> print('Original dataset shape %s' % Counter(y)) Original dataset shape Counter({{1: 900, 0: 100}}) >>> sm = SMOTE(random_state=42) >>> X_res, y_res = sm.fit_resample(X, y) >>> print('Resampled dataset shape %s' % Counter(y_res)) Resampled dataset shape Counter({{0: 900, 1: 900}}) """ def __init__( self, *, sampling_strategy="auto", random_state=None, k_neighbors=5, ): super().__init__( sampling_strategy=sampling_strategy, random_state=random_state, k_neighbors=k_neighbors, ) def _fit_resample(self, X, y): self._validate_estimator() X_resampled = [X.copy()] y_resampled = [y.copy()] for class_sample, n_samples in self.sampling_strategy_.items(): if n_samples == 0: continue target_class_indices = np.flatnonzero(y == class_sample) X_class = _safe_indexing(X, target_class_indices) self.nn_k_.fit(X_class) nns = self.nn_k_.kneighbors(X_class, return_distance=False)[:, 1:] X_new, y_new = self._make_samples( X_class, y.dtype, class_sample, X_class, nns, n_samples, 1.0 ) X_resampled.append(X_new) y_resampled.append(y_new) if sparse.issparse(X): X_resampled = sparse.vstack(X_resampled, format=X.format) else: X_resampled = np.vstack(X_resampled) y_resampled = np.hstack(y_resampled) return X_resampled, y_resampled @Substitution( sampling_strategy=BaseOverSampler._sampling_strategy_docstring, random_state=_random_state_docstring, ) class SMOTENC(SMOTE): """Synthetic Minority Over-sampling Technique for Nominal and Continuous. Unlike :class:`SMOTE`, SMOTE-NC for dataset containing numerical and categorical features. However, it is not designed to work with only categorical features. Read more in the :ref:`User Guide `. .. versionadded:: 0.4 Parameters ---------- categorical_features : "infer" or array-like of shape (n_cat_features,) or \ (n_features,), dtype={{bool, int, str}} Specified which features are categorical. Can either be: - "auto" (default) to automatically detect categorical features. Only supported when `X` is a :class:`pandas.DataFrame` and it corresponds to columns that have a :class:`pandas.CategoricalDtype`; - array of `int` corresponding to the indices specifying the categorical features; - array of `str` corresponding to the feature names. `X` should be a pandas :class:`pandas.DataFrame` in this case. - mask array of shape (n_features, ) and ``bool`` dtype for which ``True`` indicates the categorical features. categorical_encoder : estimator, default=None One-hot encoder used to encode the categorical features. If `None`, a :class:`~sklearn.preprocessing.OneHotEncoder` is used with default parameters apart from `handle_unknown` which is set to 'ignore'. {sampling_strategy} {random_state} k_neighbors : int or object, default=5 The nearest neighbors used to define the neighborhood of samples to use to generate the synthetic samples. You can pass: - an `int` corresponding to the number of neighbors to use. A `~sklearn.neighbors.NearestNeighbors` instance will be fitted in this case. - an instance of a compatible nearest neighbors algorithm that should implement both methods `kneighbors` and `kneighbors_graph`. For instance, it could correspond to a :class:`~sklearn.neighbors.NearestNeighbors` but could be extended to any compatible class. 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. nn_k_ : estimator object Validated k-nearest neighbours created from the `k_neighbors` parameter. categorical_encoder_ : estimator The encoder used to encode the categorical features. categorical_features_ : ndarray of shape (n_cat_features,), dtype=np.int64 Indices of the categorical features. continuous_features_ : ndarray of shape (n_cont_features,), dtype=np.int64 Indices of the continuous features. median_std_ : dict of int -> float Median of the standard deviation of the continuous features for each class to be over-sampled. n_features_ : int Number of features observed at `fit`. 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 -------- SMOTE : Over-sample using SMOTE. SMOTEN : Over-sample using the SMOTE variant specifically for categorical features only. SVMSMOTE : Over-sample using SVM-SMOTE variant. BorderlineSMOTE : Over-sample using Borderline-SMOTE variant. ADASYN : Over-sample using ADASYN. KMeansSMOTE : Over-sample applying a clustering before to oversample using SMOTE. Notes ----- See the original paper [1]_ for more details. Supports multi-class resampling. A one-vs.-rest scheme is used as originally proposed in [1]_. See :ref:`sphx_glr_auto_examples_over-sampling_plot_comparison_over_sampling.py`, and :ref:`sphx_glr_auto_examples_over-sampling_plot_illustration_generation_sample.py`. References ---------- .. [1] N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of artificial intelligence research, 321-357, 2002. Examples -------- >>> from collections import Counter >>> from numpy.random import RandomState >>> from sklearn.datasets import make_classification >>> from imblearn.over_sampling import SMOTENC >>> 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) >>> print(f'Original dataset shape {{X.shape}}') Original dataset shape (1000, 20) >>> print(f'Original dataset samples per class {{Counter(y)}}') Original dataset samples per class Counter({{1: 900, 0: 100}}) >>> # simulate the 2 last columns to be categorical features >>> X[:, -2:] = RandomState(10).randint(0, 4, size=(1000, 2)) >>> sm = SMOTENC(random_state=42, categorical_features=[18, 19]) >>> X_res, y_res = sm.fit_resample(X, y) >>> print(f'Resampled dataset samples per class {{Counter(y_res)}}') Resampled dataset samples per class Counter({{0: 900, 1: 900}}) """ _required_parameters = ["categorical_features"] _parameter_constraints: dict = { **SMOTE._parameter_constraints, "categorical_features": ["array-like", StrOptions({"auto"})], "categorical_encoder": [ HasMethods(["fit_transform", "inverse_transform"]), None, ], } def __init__( self, categorical_features, *, categorical_encoder=None, sampling_strategy="auto", random_state=None, k_neighbors=5, ): super().__init__( sampling_strategy=sampling_strategy, random_state=random_state, k_neighbors=k_neighbors, ) self.categorical_features = categorical_features self.categorical_encoder = categorical_encoder def _check_X_y(self, X, y): """Overwrite the checking to let pass some string for categorical features. """ y, binarize_y = check_target_type(y, indicate_one_vs_all=True) X = _check_X(X) validate_data(self, X=X, y=y, reset=True, skip_check_array=True) return X, y, binarize_y def _validate_column_types(self, X): """Compute the indices of the categorical and continuous features.""" if self.categorical_features == "auto": if not _is_pandas_df(X): raise ValueError( "When `categorical_features='auto'`, the input data " f"should be a pandas.DataFrame. Got {type(X)} instead." ) import pandas as pd # safely import pandas now are_columns_categorical = np.array( [isinstance(col_dtype, pd.CategoricalDtype) for col_dtype in X.dtypes] ) self.categorical_features_ = np.flatnonzero(are_columns_categorical) self.continuous_features_ = np.flatnonzero(~are_columns_categorical) else: self.categorical_features_ = np.array( _get_column_indices(X, self.categorical_features) ) self.continuous_features_ = np.setdiff1d( np.arange(self.n_features_), self.categorical_features_ ) def _validate_estimator(self): super()._validate_estimator() if self.categorical_features_.size == self.n_features_in_: raise ValueError( "SMOTE-NC is not designed to work only with categorical " "features. It requires some numerical features." ) elif self.categorical_features_.size == 0: raise ValueError( "SMOTE-NC is not designed to work only with numerical " "features. It requires some categorical features." ) def _fit_resample(self, X, y): self.n_features_ = _num_features(X) self._validate_column_types(X) self._validate_estimator() X_continuous = _safe_indexing(X, self.continuous_features_, axis=1) X_continuous = check_array(X_continuous, accept_sparse=["csr", "csc"]) X_categorical = _safe_indexing(X, self.categorical_features_, axis=1) if X_continuous.dtype.name != "object": dtype_ohe = X_continuous.dtype else: dtype_ohe = np.float64 if self.categorical_encoder is None: self.categorical_encoder_ = OneHotEncoder( handle_unknown="ignore", dtype=dtype_ohe ) else: self.categorical_encoder_ = clone(self.categorical_encoder) # the input of the OneHotEncoder needs to be dense X_ohe = self.categorical_encoder_.fit_transform( X_categorical.toarray() if sparse.issparse(X_categorical) else X_categorical ) if not sparse.issparse(X_ohe): X_ohe = sparse.csr_matrix(X_ohe, dtype=dtype_ohe) X_encoded = sparse.hstack((X_continuous, X_ohe), format="csr", dtype=dtype_ohe) X_resampled = [X_encoded.copy()] y_resampled = [y.copy()] # SMOTE resampling starts here self.median_std_ = {} for class_sample, n_samples in self.sampling_strategy_.items(): if n_samples == 0: continue target_class_indices = np.flatnonzero(y == class_sample) X_class = _safe_indexing(X_encoded, target_class_indices) _, var = csr_mean_variance_axis0( X_class[:, : self.continuous_features_.size] ) self.median_std_[class_sample] = np.median(np.sqrt(var)) # In the edge case where the median of the std is equal to 0, the 1s # entries will be also nullified. In this case, we store the original # categorical encoding which will be later used for inverting the OHE if math.isclose(self.median_std_[class_sample], 0): # This variable will be used when generating data self._X_categorical_minority_encoded = X_class[ :, self.continuous_features_.size : ].toarray() # we can replace the 1 entries of the categorical features with the # median of the standard deviation. It will ensure that whenever # distance is computed between 2 samples, the difference will be equal # to the median of the standard deviation as in the original paper. X_class_categorical = X_class[:, self.continuous_features_.size :] # With one-hot encoding, the median will be repeated twice. We need # to divide by sqrt(2) such that we only have one median value # contributing to the Euclidean distance X_class_categorical.data[:] = self.median_std_[class_sample] / np.sqrt(2) X_class[:, self.continuous_features_.size :] = X_class_categorical self.nn_k_.fit(X_class) nns = self.nn_k_.kneighbors(X_class, return_distance=False)[:, 1:] X_new, y_new = self._make_samples( X_class, y.dtype, class_sample, X_class, nns, n_samples, 1.0 ) X_resampled.append(X_new) y_resampled.append(y_new) X_resampled = sparse.vstack(X_resampled, format=X_encoded.format) y_resampled = np.hstack(y_resampled) # SMOTE resampling ends here # reverse the encoding of the categorical features X_res_cat = X_resampled[:, self.continuous_features_.size :] X_res_cat.data = np.ones_like(X_res_cat.data) X_res_cat_dec = self.categorical_encoder_.inverse_transform(X_res_cat) if sparse.issparse(X): X_resampled = sparse.hstack( ( X_resampled[:, : self.continuous_features_.size], X_res_cat_dec, ), format="csr", ) else: X_resampled = np.hstack( ( X_resampled[:, : self.continuous_features_.size].toarray(), X_res_cat_dec, ) ) indices_reordered = np.argsort( np.hstack((self.continuous_features_, self.categorical_features_)) ) if sparse.issparse(X_resampled): # the matrix is supposed to be in the CSR format after the stacking col_indices = X_resampled.indices.copy() for idx, col_idx in enumerate(indices_reordered): mask = X_resampled.indices == col_idx col_indices[mask] = idx X_resampled.indices = col_indices else: X_resampled = X_resampled[:, indices_reordered] return X_resampled, y_resampled def _generate_samples(self, X, nn_data, nn_num, rows, cols, steps, y_type, y=None): """Generate a synthetic sample with an additional steps for the categorical features. Each new sample is generated the same way than in SMOTE. However, the categorical features are mapped to the most frequent nearest neighbors of the majority class. """ rng = check_random_state(self.random_state) X_new = super()._generate_samples(X, nn_data, nn_num, rows, cols, steps) # change in sparsity structure more efficient with LIL than CSR X_new = X_new.tolil() if sparse.issparse(X_new) else X_new # convert to dense array since scipy.sparse doesn't handle 3D nn_data = nn_data.toarray() if sparse.issparse(nn_data) else nn_data # In the case that the median std was equal to zeros, we have to # create non-null entry based on the encoded of OHE if math.isclose(self.median_std_[y_type], 0): nn_data[:, self.continuous_features_.size :] = ( self._X_categorical_minority_encoded ) all_neighbors = nn_data[nn_num[rows]] categories_size = [self.continuous_features_.size] + [ cat.size for cat in self.categorical_encoder_.categories_ ] for start_idx, end_idx in zip( np.cumsum(categories_size)[:-1], np.cumsum(categories_size)[1:] ): col_maxs = all_neighbors[:, :, start_idx:end_idx].sum(axis=1) # tie breaking argmax is_max = np.isclose(col_maxs, col_maxs.max(axis=1, keepdims=True)) max_idxs = rng.permutation(np.argwhere(is_max)) xs, idx_sels = np.unique(max_idxs[:, 0], return_index=True) col_sels = max_idxs[idx_sels, 1] ys = start_idx + col_sels X_new[:, start_idx:end_idx] = 0 X_new[xs, ys] = 1 return X_new def _more_tags(self): return {"X_types": ["2darray", "dataframe", "string"]} def __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.sparse = False tags.input_tags.string = True return tags @Substitution( sampling_strategy=BaseOverSampler._sampling_strategy_docstring, random_state=_random_state_docstring, ) class SMOTEN(SMOTE): """Synthetic Minority Over-sampling Technique for Nominal. This method is referred as SMOTEN in [1]_. It expects that the data to resample are only made of categorical features. Read more in the :ref:`User Guide `. .. versionadded:: 0.8 Parameters ---------- categorical_encoder : estimator, default=None Ordinal encoder used to encode the categorical features. If `None`, a :class:`~sklearn.preprocessing.OrdinalEncoder` is used with default parameters. {sampling_strategy} {random_state} k_neighbors : int or object, default=5 The nearest neighbors used to define the neighborhood of samples to use to generate the synthetic samples. You can pass: - an `int` corresponding to the number of neighbors to use. A `~sklearn.neighbors.NearestNeighbors` instance will be fitted in this case. - an instance of a compatible nearest neighbors algorithm that should implement both methods `kneighbors` and `kneighbors_graph`. For instance, it could correspond to a :class:`~sklearn.neighbors.NearestNeighbors` but could be extended to any compatible class. Attributes ---------- categorical_encoder_ : estimator The encoder used to encode the categorical features. 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. nn_k_ : estimator object Validated k-nearest neighbours created from the `k_neighbors` parameter. 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 -------- SMOTE : Over-sample using SMOTE. SMOTENC : Over-sample using SMOTE for continuous and categorical features. BorderlineSMOTE : Over-sample using the borderline-SMOTE variant. SVMSMOTE : Over-sample using the SVM-SMOTE variant. ADASYN : Over-sample using ADASYN. KMeansSMOTE : Over-sample applying a clustering before to oversample using SMOTE. Notes ----- See the original papers: [1]_ for more details. Supports multi-class resampling. A one-vs.-rest scheme is used as originally proposed in [1]_. References ---------- .. [1] N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of artificial intelligence research, 321-357, 2002. Examples -------- >>> import numpy as np >>> X = np.array(["A"] * 10 + ["B"] * 20 + ["C"] * 30, dtype=object).reshape(-1, 1) >>> y = np.array([0] * 20 + [1] * 40, dtype=np.int32) >>> from collections import Counter >>> print(f"Original class counts: {{Counter(y)}}") Original class counts: Counter({{1: 40, 0: 20}}) >>> from imblearn.over_sampling import SMOTEN >>> sampler = SMOTEN(random_state=0) >>> X_res, y_res = sampler.fit_resample(X, y) >>> print(f"Class counts after resampling {{Counter(y_res)}}") Class counts after resampling Counter({{0: 40, 1: 40}}) """ _parameter_constraints: dict = { **SMOTE._parameter_constraints, "categorical_encoder": [ HasMethods(["fit_transform", "inverse_transform"]), None, ], } def __init__( self, categorical_encoder=None, *, sampling_strategy="auto", random_state=None, k_neighbors=5, ): super().__init__( sampling_strategy=sampling_strategy, random_state=random_state, k_neighbors=k_neighbors, ) self.categorical_encoder = categorical_encoder def _check_X_y(self, X, y): """Check should accept strings and not sparse matrices.""" y, binarize_y = check_target_type(y, indicate_one_vs_all=True) X, y = validate_data( self, X=X, y=y, reset=True, dtype=None, accept_sparse=["csr", "csc"], ) return X, y, binarize_y def _validate_estimator(self): """Force to use precomputed distance matrix.""" super()._validate_estimator() self.nn_k_.set_params(metric="precomputed") def _make_samples(self, X_class, klass, y_dtype, nn_indices, n_samples): random_state = check_random_state(self.random_state) # generate sample indices that will be used to generate new samples samples_indices = random_state.choice( np.arange(X_class.shape[0]), size=n_samples, replace=True ) # for each drawn samples, select its k-neighbors and generate a sample # where for each feature individually, each category generated is the # most common category X_new = np.squeeze( mode(X_class[nn_indices[samples_indices]], axis=1, keepdims=True).mode, axis=1, ) y_new = np.full(n_samples, fill_value=klass, dtype=y_dtype) return X_new, y_new def _fit_resample(self, X, y): if sparse.issparse(X): X_sparse_format = X.format X = X.toarray() warnings.warn( ( "Passing a sparse matrix to SMOTEN is not really efficient since it" " is converted to a dense array internally." ), DataConversionWarning, ) else: X_sparse_format = None self._validate_estimator() X_resampled = [X.copy()] y_resampled = [y.copy()] if self.categorical_encoder is None: self.categorical_encoder_ = OrdinalEncoder(dtype=np.int32) else: self.categorical_encoder_ = clone(self.categorical_encoder) X_encoded = self.categorical_encoder_.fit_transform(X) vdm = ValueDifferenceMetric( n_categories=[len(cat) for cat in self.categorical_encoder_.categories_] ).fit(X_encoded, y) for class_sample, n_samples in self.sampling_strategy_.items(): if n_samples == 0: continue target_class_indices = np.flatnonzero(y == class_sample) X_class = _safe_indexing(X_encoded, target_class_indices) X_class_dist = vdm.pairwise(X_class) self.nn_k_.fit(X_class_dist) # the kneigbors search will include the sample itself which is # expected from the original algorithm nn_indices = self.nn_k_.kneighbors(X_class_dist, return_distance=False) X_new, y_new = self._make_samples( X_class, class_sample, y.dtype, nn_indices, n_samples ) X_new = self.categorical_encoder_.inverse_transform(X_new) X_resampled.append(X_new) y_resampled.append(y_new) X_resampled = np.vstack(X_resampled) y_resampled = np.hstack(y_resampled) if X_sparse_format == "csr": return sparse.csr_matrix(X_resampled), y_resampled elif X_sparse_format == "csc": return sparse.csc_matrix(X_resampled), y_resampled else: return X_resampled, y_resampled def _more_tags(self): return {"X_types": ["2darray", "dataframe", "string"]} def __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.sparse = False tags.input_tags.string = True return tags