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802caae add basic tests for mlsmote
SimonErm9b2ec7f add mlsmote implementation
SimonErm948da4a format code
SimonErmbef0487 fix refactor error
SimonErm25eb158 compute imbalance_ratio_per_label just once
SimonErme6c847e add sparse matrix support for labels
SimonErm4bb3474 calculate imbalance ratio on every run instead once
SimonErm32a7b55 use list comprehension for better performance
SimonErmf134349 fix vdm and add experimental sparse implementation
SimonErm3361578 remove prints
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2 changes: 2 additions & 0 deletionsimblearn/over_sampling/__init__.py
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281 changes: 281 additions & 0 deletionsimblearn/over_sampling/_mlsmote.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,281 @@ | ||
| import numpy as np | ||
| import itertools | ||
| import collections | ||
| import random | ||
| from scipy import sparse | ||
| class MLSMOTE: | ||
| """Over-sampling using MLSMOTE. | ||
| Parameters | ||
| ---------- | ||
| sampling_strategy: 'ranking','union' or 'intersection' default: 'ranking' | ||
| Strategy to generate labelsets | ||
| k_neighbors : int or object, default=5 | ||
| If ``int``, number of nearest neighbours to used to construct synthetic | ||
| samples. | ||
| categorical_features : ndarray of shape (n_cat_features,) or (n_features,) | ||
| Specified which features are categorical. Can either be: | ||
| - array of indices specifying the categorical features; | ||
| - mask array of shape (n_features, ) and ``bool`` dtype for which | ||
| ``True`` indicates the categorical features. | ||
| Notes | ||
| ----- | ||
| See the original papers: [1]_ for more details. | ||
| References | ||
| ---------- | ||
| .. [1] Charte, F. & Rivera Rivas, Antonio & Del Jesus, María José & Herrera, Francisco. (2015). | ||
| MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation. | ||
| Knowledge-Based Systems. -. 10.1016/j.knosys.2015.07.019. | ||
| """ | ||
| def __init__(self, categorical_features, k_neighbors=5, sampling_strategy='ranking'): | ||
| self.k_neighbors = k_neighbors | ||
| self.sampling_strategy_ = sampling_strategy | ||
| self.categorical_features = categorical_features | ||
| self.continuous_features_ = None | ||
| self.unique_labels = [] | ||
| self.labels = [] | ||
| self.features = [] | ||
| def fit_resample(self, X, y): | ||
| self.n_features_ = X.shape[1] | ||
| self._validate_estimator() | ||
| X_resampled = X.copy() | ||
| y_resampled = y.copy() | ||
| if sparse.issparse(y): | ||
| self.labels = y | ||
| self.unique_labels = range(0, y.shape[1]) | ||
| else: | ||
| self.labels = np.array([np.array(xi) for xi in y]) | ||
| self.unique_labels = self._collect_unique_labels(y) | ||
| self.features = X | ||
| X_synth = [] | ||
| y_synth = [] | ||
| append_X_synth = X_synth.append | ||
| append_y_synth = y_synth.append | ||
| mean_ir = self._get_mean_imbalance_ratio() | ||
| if sparse.issparse(y): | ||
| y_synth = None | ||
| for label in self.unique_labels: | ||
| irlbl = self._get_imbalance_ratio_per_label(label, y_resampled) | ||
| if irlbl > mean_ir: | ||
| min_bag = self._get_all_instances_of_label(label) | ||
| for sample in min_bag: | ||
| distances = self._calc_distances(sample, min_bag) | ||
| distances = np.sort(distances, order='distance') | ||
| neighbours = distances[:self.k_neighbors] | ||
| ref_neigh = np.random.choice(neighbours, 1)[0] | ||
| X_new, y_new = self._create_new_sample( | ||
| sample, ref_neigh[1], [x[1] for x in neighbours]) | ||
| append_X_synth(X_new) | ||
| y_resambled = sparse.vstack((y_resampled, y_new)) | ||
| return np.concatenate((X_resampled, np.array(X_synth))), y_resampled | ||
| else: | ||
| for index, label in np.ndenumerate(self.unique_labels): | ||
| irlbl = self._get_imbalance_ratio_per_label(label, y_resampled) | ||
| if irlbl > mean_ir: | ||
| min_bag = self._get_all_instances_of_label(label) | ||
| for sample in min_bag: | ||
| distances = self._calc_distances(sample, min_bag) | ||
| distances = np.sort(distances, order='distance') | ||
| neighbours = distances[:self.k_neighbors] | ||
| ref_neigh = np.random.choice(neighbours, 1)[0] | ||
| X_new, y_new = self._create_new_sample( | ||
| sample, ref_neigh[1], [x[1] for x in neighbours]) | ||
| append_X_synth(X_new) | ||
| append_y_synth(y_new) | ||
| return np.concatenate((X_resampled, np.array(X_synth))), np.array(y_resampled.tolist()+y_synth) | ||
| def _validate_estimator(self): | ||
| categorical_features = np.asarray(self.categorical_features) | ||
| if categorical_features.dtype.name == "bool": | ||
| self.categorical_features_ = np.flatnonzero(categorical_features) | ||
| else: | ||
| if any( | ||
| [ | ||
| cat not in np.arange(self.n_features_) | ||
| for cat in categorical_features | ||
| ] | ||
| ): | ||
| raise ValueError( | ||
| "Some of the categorical indices are out of range. Indices" | ||
| " should be between 0 and {}".format(self.n_features_) | ||
| ) | ||
| self.categorical_features_ = categorical_features | ||
| self.continuous_features_ = np.setdiff1d( | ||
| np.arange(self.n_features_), self.categorical_features_ | ||
| ) | ||
| def _collect_unique_labels(self, y): | ||
| """A support function that flattens the labelsets and return one set of unique labels""" | ||
| return np.unique(np.array([a for x in y for a in (x if isinstance(x, list) else [x])])) | ||
| def _create_new_sample(self, sample_id, ref_neigh_id, neighbour_ids): | ||
| sample = self.features[sample_id] | ||
| synth_sample = np.copy(sample) | ||
| ref_neigh = self.features[ref_neigh_id] | ||
| sample_labels = self.labels[sample_id] | ||
| for i in range(synth_sample.shape[0]): | ||
| if i in self.continuous_features_: | ||
| diff = ref_neigh[i]-sample[i] | ||
| offset = diff*random.uniform(0, 1) | ||
| synth_sample[i] = sample[i]+offset | ||
| if i in self.categorical_features_: | ||
| synth_sample[i] = self._get_most_frequent_value( | ||
| self.features[neighbour_ids, i]) | ||
| X = synth_sample | ||
| if sparse.issparse(self.labels): | ||
| neighbours_labels = self.labels[neighbour_ids] | ||
| possible_labels = neighbours_labels.sum(axis=0) | ||
| y = np.zeros((1, len(self.unique_labels))) | ||
| if self.sampling_strategy_ == 'ranking': | ||
| head_index = int((self.k_neighbors + 1)/2) | ||
| choosen_labels = possible_labels.nonzero()[1][:head_index] | ||
| y[0, choosen_labels] = 1 | ||
| if self.sampling_strategy_ == 'union': | ||
| choosen_labels = possible_labels.nonzero()[0] | ||
| y[choosen_labels] = 1 | ||
| if self.sampling_strategy_ == 'intersection': | ||
| choosen_labels = sparse.find(possible_labels == len(neighbours_labels)) | ||
| y[choosen_labels] = 1 | ||
| y = sparse.csr_matrix(y) | ||
| else: | ||
| neighbours_labels = [] | ||
| for ni in neighbour_ids: | ||
| neighbours_labels.append(self.labels[ni].tolist()) | ||
| labels = [] # sample_labels.tolist() | ||
| labels += [a for x in neighbours_labels for a in ( | ||
| x if isinstance(x, list) else [x])] | ||
| labels = list(set(labels)) | ||
| if self.sampling_strategy_ == 'ranking': | ||
| head_index = int((self.k_neighbors + 1)/2) | ||
| y = labels[:head_index] | ||
| if self.sampling_strategy_ == 'union': | ||
| y = labels[:] | ||
| if self.sampling_strategy_ == 'intersection': | ||
| y = list(set.intersection(*neighbours_labels)) | ||
| return X, y | ||
| def _calc_distances(self, sample, min_bag): | ||
| def calc_dist(bag_sample): | ||
| nominal_distance = sum([self._get_vdm( | ||
| self.features[sample, cat], self.features[bag_sample, cat], cat)for cat in self.categorical_features_]) | ||
| ordinal_distance = sum([self._get_euclidean_distance( | ||
| self.features[sample, num], self.features[bag_sample, num])for num in self.continuous_features_]) | ||
| dist = sum([nominal_distance, ordinal_distance]) | ||
| return (dist, bag_sample) | ||
| distances = [calc_dist(bag_sample) for bag_sample in min_bag] | ||
| dtype = np.dtype([('distance', float), ('index', int)]) | ||
| return np.array(distances, dtype=dtype) | ||
| def _get_euclidean_distance(self, first, second): | ||
| euclidean_distance = np.linalg.norm(first-second) | ||
| return euclidean_distance | ||
| def _get_vdm(self, first, second, category): | ||
| """A support function to compute the Value Difference Metric(VDM) discribed in https://arxiv.org/pdf/cs/9701101.pdf""" | ||
| if sparse.issparse(self.features): | ||
| def f_sparse(c): | ||
| N_ax = len(sparse.find(self.features[:, category] == first)[0]) | ||
| N_ay = len(sparse.find( | ||
| self.features[:, category] == second)[0]) | ||
| c_instances = self._get_all_instances_of_label(c) | ||
| N_axc = len(sparse.find( | ||
| self.features[c_instances, category] == first)[0]) | ||
| N_ayc = len(sparse.find( | ||
| self.features[c_instances, category] == second)[0]) | ||
| p = np.square(np.abs((N_axc/N_ax)-(N_ayc/N_ay))) | ||
| return p | ||
| vdm = np.sum(np.array([f_sparse(c)for c in self.unique_labels])) | ||
| return vdm | ||
| category_rows = self.features[:, category] | ||
| N_ax = len(np.where(category_rows == first)) | ||
| N_ay = len(np.where(category_rows == second)) | ||
| def f(c): | ||
| class_instances = self._get_all_instances_of_label(c) | ||
| class_instance_rows = category_rows[class_instances] | ||
| N_axc = len(np.where(class_instance_rows == first)[0]) | ||
| N_ayc = len(np.where(class_instance_rows == second)[0]) | ||
| p = abs((N_axc/N_ax)-(N_ayc/N_ay)) | ||
| return p | ||
| vdm = np.array([f(c)for c in self.unique_labels]).sum() | ||
| return vdm | ||
| def _get_all_instances_of_label(self, label): | ||
| if sparse.issparse(self.labels): | ||
| return self.labels[:, label].nonzero()[0] | ||
| instance_ids = [] | ||
| append_instance_id = instance_ids.append | ||
| for i, label_set in enumerate(self.labels): | ||
| if label in label_set: | ||
| append_instance_id(i) | ||
| return np.array(instance_ids) | ||
| def _get_mean_imbalance_ratio(self): | ||
| ratio_sum = np.sum(np.array( | ||
| list(map(self._get_imbalance_ratio_per_label, self.unique_labels)))) | ||
| return ratio_sum/len(self.unique_labels) | ||
| def _get_imbalance_ratio_per_label(self, label, labels=None): | ||
| sum_h = self._sum_h | ||
| if labels is None: | ||
| sum_array = np.array([sum_h(l, self.labels) | ||
| for l in self.unique_labels]) | ||
| ratio = sum_array.max()/sum_h(label, self.labels) | ||
| else: | ||
| sum_array = np.array([sum_h(l, labels)for l in self.unique_labels]) | ||
| ratio = sum_array.max()/sum_h(label, labels) | ||
| return ratio | ||
| def _sum_h(self, label, labels): | ||
| if sparse.issparse(labels): | ||
| return labels[:, label].count_nonzero() | ||
| h_sum = 0 | ||
| def h(l, Y): | ||
| if l in Y: | ||
| return 1 | ||
| else: | ||
| return 0 | ||
| for label_set in labels: | ||
| h_sum += h(label, label_set) | ||
| return h_sum | ||
| def _get_label_frequencies(self, labels): | ||
| """"A support function to get the frequencies of labels""" | ||
| frequency_map = np.array(np.unique(labels, return_counts=True)).T | ||
| frequencies = np.array([x[1] for x in frequency_map]) | ||
| return frequencies | ||
| def _get_most_frequent_value(self, values): | ||
| """"A support function to get most frequent value if a list of values""" | ||
| uniques, indices = np.unique(values, return_inverse=True) | ||
| return uniques[np.argmax(np.bincount(indices))] |
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