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Diffstat (limited to 'Wass_Matcher.py')
| -rw-r--r-- | Wass_Matcher.py | 76 |
1 files changed, 0 insertions, 76 deletions
diff --git a/Wass_Matcher.py b/Wass_Matcher.py deleted file mode 100644 index 44b29eb..0000000 --- a/Wass_Matcher.py +++ /dev/null | |||
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| 1 | import ot | ||
| 2 | from sklearn.preprocessing import normalize | ||
| 3 | from lapjv import lapjv | ||
| 4 | from sklearn.neighbors import KNeighborsClassifier | ||
| 5 | from sklearn.metrics import euclidean_distances | ||
| 6 | from sklearn.externals.joblib import Parallel, delayed | ||
| 7 | from sklearn.utils import check_array | ||
| 8 | from sklearn.metrics.scorer import check_scoring | ||
| 9 | from pathos.multiprocessing import ProcessingPool as Pool | ||
| 10 | from sklearn.metrics import euclidean_distances | ||
| 11 | import numpy as np | ||
| 12 | |||
| 13 | class Wasserstein_Matcher(KNeighborsClassifier): | ||
| 14 | """ | ||
| 15 | Implements a nearest neighbors classifier for input distributions using the Wasserstein distance as metric. | ||
| 16 | Source and target distributions are l_1 normalized before computing the Wasserstein distance. | ||
| 17 | Wasserstein is parametrized by the distances between the individual points of the distributions. | ||
| 18 | """ | ||
| 19 | def __init__(self, W_embed, n_neighbors=1, n_jobs=1, verbose=False, sinkhorn= False, sinkhorn_reg=0.1): | ||
| 20 | """ | ||
| 21 | Initialization of the class. | ||
| 22 | Arguments | ||
| 23 | --------- | ||
| 24 | W_embed: embeddings of the words, np.array | ||
| 25 | verbose: True/False | ||
| 26 | """ | ||
| 27 | self.sinkhorn = sinkhorn | ||
| 28 | self.sinkhorn_reg = sinkhorn_reg | ||
| 29 | self.W_embed = W_embed | ||
| 30 | self.verbose = verbose | ||
| 31 | super(Wasserstein_Matcher, self).__init__(n_neighbors=n_neighbors, n_jobs=n_jobs, metric='precomputed', algorithm='brute') | ||
| 32 | |||
| 33 | def _wmd(self, i, row, X_train): | ||
| 34 | union_idx = np.union1d(X_train[i].indices, row.indices) | ||
| 35 | W_minimal = self.W_embed[union_idx] | ||
| 36 | W_dist = euclidean_distances(W_minimal) | ||
| 37 | bow_i = X_train[i, union_idx].A.ravel() | ||
| 38 | bow_j = row[:, union_idx].A.ravel() | ||
| 39 | if self.sinkhorn: | ||
| 40 | return ot.sinkhorn2(bow_i, bow_j, W_dist, self.sinkhorn_reg, numItermax=50, method='sinkhorn_stabilized',)[0] | ||
| 41 | else: | ||
| 42 | return ot.emd2(bow_i, bow_j, W_dist) | ||
| 43 | |||
| 44 | def _wmd_row(self, row): | ||
| 45 | X_train = self._fit_X | ||
| 46 | n_samples_train = X_train.shape[0] | ||
| 47 | return [self._wmd(i, row, X_train) for i in range(n_samples_train)] | ||
| 48 | |||
| 49 | def _pairwise_wmd(self, X_test, X_train=None): | ||
| 50 | n_samples_test = X_test.shape[0] | ||
| 51 | |||
| 52 | if X_train is None: | ||
| 53 | X_train = self._fit_X | ||
| 54 | pool = Pool(nodes=self.n_jobs) # Parallelization of the calculation of the distances | ||
| 55 | dist = pool.map(self._wmd_row, X_test) | ||
| 56 | return np.array(dist) | ||
| 57 | |||
| 58 | def fit(self, X, y): # X_train_idf | ||
| 59 | X = check_array(X, accept_sparse='csr', copy=True) # check if array is sparse | ||
| 60 | X = normalize(X, norm='l1', copy=False) | ||
| 61 | return super(Wasserstein_Matcher, self).fit(X, y) # X_train_idf, np_ones(document collection size) | ||
| 62 | |||
| 63 | def predict(self, X): | ||
| 64 | X = check_array(X, accept_sparse='csr', copy=True) | ||
| 65 | X = normalize(X, norm='l1', copy=False) | ||
| 66 | dist = self._pairwise_wmd(X) | ||
| 67 | dist = dist * 1000 # for lapjv, small floating point numbers are evil | ||
| 68 | return super(Wasserstein_Matcher, self).predict(dist) | ||
| 69 | |||
| 70 | def kneighbors(self, X, n_neighbors=1): # X : X_train_idf | ||
| 71 | X = check_array(X, accept_sparse='csr', copy=True) | ||
| 72 | X = normalize(X, norm='l1', copy=False) | ||
| 73 | dist = self._pairwise_wmd(X) | ||
| 74 | dist = dist * 1000 # for lapjv, small floating point numbers are evil | ||
| 75 | return lapjv(dist) # and here is the matching part | ||
| 76 | |||