Tidy up Wasserstein classes into one file

1 files changed, 0 insertions, 73 deletions

diff --git a/Wass_Retriever.py b/Wass_Retriever.py
deleted file mode 100644
index 036cf93..0000000
--- a/Wass_Retriever.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import ot
-from sklearn.preprocessing import normalize
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.metrics import euclidean_distances
-from sklearn.externals.joblib import Parallel, delayed
-from sklearn.utils import check_array
-from sklearn.metrics.scorer import check_scoring
-from pathos.multiprocessing import ProcessingPool as Pool
-from sklearn.metrics import euclidean_distances
-import numpy as np
-
-class Wasserstein_Retriever(KNeighborsClassifier):
-    """
-    Implements a nearest neighbors classifier for input distributions using the Wasserstein distance as metric.
-    Source and target distributions are l_1 normalized before computing the Wasserstein distance.
-    Wasserstein is parametrized by the distances between the individual points of the distributions.
-    """
-    def __init__(self, W_embed, n_neighbors=1, n_jobs=1, verbose=False, sinkhorn= False, sinkhorn_reg=0.1):
-        """
-        Initialization of the class.
-        Arguments
-        ---------
-        W_embed: embeddings of the words, np.array
-        verbose: True/False
-        """
-        self.sinkhorn = sinkhorn
-        self.sinkhorn_reg = sinkhorn_reg
-        self.W_embed = W_embed
-        self.verbose = verbose
-        super(Wasserstein_Retriever, self).__init__(n_neighbors=n_neighbors, n_jobs=n_jobs, metric='precomputed', algorithm='brute')
-
-    def _wmd(self, i, row, X_train):
-        union_idx = np.union1d(X_train[i].indices, row.indices)
-        W_minimal = self.W_embed[union_idx]
-        W_dist = euclidean_distances(W_minimal)
-        bow_i = X_train[i, union_idx].A.ravel()
-        bow_j = row[:, union_idx].A.ravel()
-        if self.sinkhorn:
-            return  ot.sinkhorn2(bow_i, bow_j, W_dist, self.sinkhorn_reg, numItermax=50, method='sinkhorn_stabilized',)[0]
-        else:
-            return  ot.emd2(bow_i, bow_j, W_dist)
-
-    def _wmd_row(self, row):
-        X_train = self._fit_X
-        n_samples_train = X_train.shape[0]
-        return [self._wmd(i, row, X_train) for i in range(n_samples_train)]
-
-    def _pairwise_wmd(self, X_test, X_train=None):
-        n_samples_test = X_test.shape[0]
-
-        if X_train is None:
-            X_train = self._fit_X
-        pool = Pool(nodes=self.n_jobs) # Parallelization of the calculation of the distances
-        dist  = pool.map(self._wmd_row, X_test)
-        return np.array(dist)
-
-    def fit(self, X, y):
-        X = check_array(X, accept_sparse='csr', copy=True)
-        X = normalize(X, norm='l1', copy=False)
-        return super(Wasserstein_Retriever, self).fit(X, y)
-
-    def predict(self, X):
-        X = check_array(X, accept_sparse='csr', copy=True)
-        X = normalize(X, norm='l1', copy=False)
-        dist = self._pairwise_wmd(X)
-        return super(Wasserstein_Retriever, self).predict(dist)
-
-    def kneighbors(self, X, n_neighbors=1):
-        X = check_array(X, accept_sparse='csr', copy=True)
-        X = normalize(X, norm='l1', copy=False)
-        dist = self._pairwise_wmd(X)
-        return super(Wasserstein_Retriever, self).kneighbors(dist, n_neighbors)
-