3 files changed, 219 insertions, 116 deletions

diff --git a/WMD_matching.py b/WMD_matching.py
index 59b64f9..ea496b8 100644
--- a/WMD_matching.py
+++ b/WMD_matching.py
@@ -1,15 +1,19 @@
 import argparse
-import numpy as np
+import csv
 import random
+
+import numpy as np
 from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
 from sklearn.preprocessing import normalize
-from Wasserstein_Distance import Wasserstein_Matcher
-from Wasserstein_Distance import load_embeddings, clean_corpus_using_embeddings_vocabulary
-import csv
+
+from Wasserstein_Distance import (Wasserstein_Matcher,
+                                  clean_corpus_using_embeddings_vocabulary,
+                                  load_embeddings)
+
 
 def main(args):
 
-    np.seterr(divide='ignore') # POT has issues with divide by zero errors
+    np.seterr(divide='ignore')  # POT has issues with divide by zero errors
     source_lang = args.source_lang
     target_lang = args.target_lang
 
@@ -25,32 +29,38 @@ def main(args):
     mode = args.mode
     runfor = list()
 
-    if (mode == 'all'):
-        runfor.extend(['wmd','snk'])
+    if mode == 'all':
+        runfor.extend(['wmd', 'snk'])
     else:
         runfor.append(mode)
 
-    defs_source = [line.rstrip('\n') for line in open(source_defs_filename, encoding='utf8')]
-    defs_target = [line.rstrip('\n') for line in open(target_defs_filename, encoding='utf8')]
+    defs_source = [
+        line.rstrip('\n')
+        for line in open(source_defs_filename, encoding='utf8')
+    ]
+    defs_target = [
+        line.rstrip('\n')
+        for line in open(target_defs_filename, encoding='utf8')
+    ]
 
     clean_src_corpus, clean_src_vectors, src_keys = clean_corpus_using_embeddings_vocabulary(
-            set(vectors_source.keys()),
-            defs_source,
-            vectors_source,
-            source_lang,
-            )
+        set(vectors_source.keys()),
+        defs_source,
+        vectors_source,
+        source_lang,
+    )
 
     clean_target_corpus, clean_target_vectors, target_keys = clean_corpus_using_embeddings_vocabulary(
-            set(vectors_target.keys()),
-            defs_target,
-            vectors_target,
-            target_lang,
-            )
+        set(vectors_target.keys()),
+        defs_target,
+        vectors_target,
+        target_lang,
+    )
 
     take = args.instances
 
     common_keys = set(src_keys).intersection(set(target_keys))
-    take = min(len(common_keys), take) # you can't sample more than length
+    take = min(len(common_keys), take)  # you can't sample more than length
     experiment_keys = random.sample(common_keys, take)
 
     instances = len(experiment_keys)
@@ -58,13 +68,18 @@ def main(args):
     clean_src_corpus = list(clean_src_corpus[experiment_keys])
     clean_target_corpus = list(clean_target_corpus[experiment_keys])
 
-    if (not batch):
-        print(f'{source_lang} - {target_lang} : document sizes: {len(clean_src_corpus)}, {len(clean_target_corpus)}')
+    if not batch:
+        print(
+            f'{source_lang} - {target_lang} : document sizes: {len(clean_src_corpus)}, {len(clean_target_corpus)}'
+        )
 
     del vectors_source, vectors_target, defs_source, defs_target
 
     vec = CountVectorizer().fit(clean_src_corpus + clean_target_corpus)
-    common = [word for word in vec.get_feature_names() if word in clean_src_vectors or word in clean_target_vectors]
+    common = [
+        word for word in vec.get_feature_names()
+        if word in clean_src_vectors or word in clean_target_vectors
+    ]
     W_common = []
     for w in common:
         if w in clean_src_vectors:
@@ -72,8 +87,10 @@ def main(args):
         else:
             W_common.append(np.array(clean_target_vectors[w]))
 
-    if (not batch):
-        print(f'{source_lang} - {target_lang}: the vocabulary size is {len(W_common)}')
+    if not batch:
+        print(
+            f'{source_lang} - {target_lang}: the vocabulary size is {len(W_common)}'
+        )
 
     W_common = np.array(W_common)
     W_common = normalize(W_common)
@@ -82,26 +99,28 @@ def main(args):
     X_train_idf = vect.transform(clean_src_corpus)
     X_test_idf = vect.transform(clean_target_corpus)
 
-    vect_tf = CountVectorizer(vocabulary=common, dtype=np.double)
-    vect_tf.fit(clean_src_corpus + clean_target_corpus)
-    X_train_tf = vect_tf.transform(clean_src_corpus)
-    X_test_tf = vect_tf.transform(clean_target_corpus)
-
     for metric in runfor:
-        if (not batch):
+        if not batch:
             print(f'{metric}: {source_lang} - {target_lang}')
 
-        clf = Wasserstein_Matcher(W_embed=W_common, n_neighbors=5, n_jobs=14, sinkhorn=(metric == 'snk'))
+        clf = Wasserstein_Matcher(W_embed=W_common,
+                                  n_neighbors=5,
+                                  n_jobs=14,
+                                  sinkhorn=(metric == 'snk'))
         clf.fit(X_train_idf[:instances], np.ones(instances))
-        row_ind, col_ind, a = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances)
+        row_ind, col_ind, _ = clf.kneighbors(X_test_idf[:instances],
+                                             n_neighbors=instances)
         result = zip(row_ind, col_ind)
-        p_at_one = len([x for x,y in result if x == y])
+        p_at_one = len([x for x, y in result if x == y])
         percentage = p_at_one / instances * 100
 
-        if (not batch):
+        if not batch:
             print(f'P @ 1: {p_at_one}\ninstances: {instances}\n{percentage}%')
         else:
-            fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{p_at_one}', f'{percentage}']
+            fields = [
+                f'{source_lang}', f'{target_lang}', f'{instances}',
+                f'{p_at_one}', f'{percentage}'
+            ]
             with open(f'{metric}_matching_results.csv', 'a') as f:
                 writer = csv.writer(f)
                 writer.writerow(fields)
@@ -109,16 +128,31 @@ def main(args):
 
 if __name__ == "__main__":
 
-    parser = argparse.ArgumentParser(description='matching using wmd and wasserstein distance')
+    parser = argparse.ArgumentParser(
+        description='matching using wmd and wasserstein distance')
     parser.add_argument('source_lang', help='source language short name')
     parser.add_argument('target_lang', help='target language short name')
     parser.add_argument('source_vector', help='path of the source vector')
     parser.add_argument('target_vector', help='path of the target vector')
     parser.add_argument('source_defs', help='path of the source definitions')
     parser.add_argument('target_defs', help='path of the target definitions')
-    parser.add_argument('-b', '--batch', action='store_true', help='running in batch (store results in csv) or running a single instance (output the results)')
-    parser.add_argument('mode', choices=['all', 'wmd', 'snk'], default='all', help='which methods to run')
-    parser.add_argument('-n', '--instances', help='number of instances in each language to retrieve', default=1000, type=int)
+    parser.add_argument(
+        '-b',
+        '--batch',
+        action='store_true',
+        help=
+        'running in batch (store results in csv) or running a single instance (output the results)'
+    )
+    parser.add_argument('mode',
+                        choices=['all', 'wmd', 'snk'],
+                        default='all',
+                        help='which methods to run')
+    parser.add_argument(
+        '-n',
+        '--instances',
+        help='number of instances in each language to retrieve',
+        default=1000,
+        type=int)
 
     args = parser.parse_args()
 
diff --git a/WMD_retrieval.py b/WMD_retrieval.py
index f32372f..3328023 100644
--- a/WMD_retrieval.py
+++ b/WMD_retrieval.py
@@ -1,15 +1,19 @@
 import argparse
-import numpy as np
+import csv
 import random
+
+import numpy as np
 from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
 from sklearn.preprocessing import normalize
-from Wasserstein_Distance import Wasserstein_Retriever
-from Wasserstein_Distance import load_embeddings, clean_corpus_using_embeddings_vocabulary, mrr_precision_at_k
-import csv
+
+from Wasserstein_Distance import (Wasserstein_Retriever,
+                                  clean_corpus_using_embeddings_vocabulary,
+                                  load_embeddings)
+
 
 def main(args):
 
-    np.seterr(divide='ignore') # POT has issues with divide by zero errors
+    np.seterr(divide='ignore')  # POT has issues with divide by zero errors
     source_lang = args.source_lang
     target_lang = args.target_lang
 
@@ -25,32 +29,38 @@ def main(args):
     mode = args.mode
     runfor = list()
 
-    if (mode == 'all'):
-        runfor.extend(['wmd','snk'])
+    if mode == 'all':
+        runfor.extend(['wmd', 'snk'])
     else:
         runfor.append(mode)
 
-    defs_source = [line.rstrip('\n') for line in open(source_defs_filename, encoding='utf8')]
-    defs_target = [line.rstrip('\n') for line in open(target_defs_filename, encoding='utf8')]
+    defs_source = [
+        line.rstrip('\n')
+        for line in open(source_defs_filename, encoding='utf8')
+    ]
+    defs_target = [
+        line.rstrip('\n')
+        for line in open(target_defs_filename, encoding='utf8')
+    ]
 
     clean_src_corpus, clean_src_vectors, src_keys = clean_corpus_using_embeddings_vocabulary(
-            set(vectors_source.keys()),
-            defs_source,
-            vectors_source,
-            source_lang,
-            )
+        set(vectors_source.keys()),
+        defs_source,
+        vectors_source,
+        source_lang,
+    )
 
     clean_target_corpus, clean_target_vectors, target_keys = clean_corpus_using_embeddings_vocabulary(
-            set(vectors_target.keys()),
-            defs_target,
-            vectors_target,
-            target_lang,
-            )
+        set(vectors_target.keys()),
+        defs_target,
+        vectors_target,
+        target_lang,
+    )
 
     take = args.instances
 
     common_keys = set(src_keys).intersection(set(target_keys))
-    take = min(len(common_keys), take) # you can't sample more than length
+    take = min(len(common_keys), take)  # you can't sample more than length
     experiment_keys = random.sample(common_keys, take)
 
     instances = len(experiment_keys)
@@ -58,13 +68,18 @@ def main(args):
     clean_src_corpus = list(clean_src_corpus[experiment_keys])
     clean_target_corpus = list(clean_target_corpus[experiment_keys])
 
-    if (not batch):
-        print(f'{source_lang} - {target_lang} : document sizes: {len(clean_src_corpus)}, {len(clean_target_corpus)}')
+    if not batch:
+        print(
+            f'{source_lang} - {target_lang} : document sizes: {len(clean_src_corpus)}, {len(clean_target_corpus)}'
+        )
 
     del vectors_source, vectors_target, defs_source, defs_target
 
     vec = CountVectorizer().fit(clean_src_corpus + clean_target_corpus)
-    common = [word for word in vec.get_feature_names() if word in clean_src_vectors or word in clean_target_vectors]
+    common = [
+        word for word in vec.get_feature_names()
+        if word in clean_src_vectors or word in clean_target_vectors
+    ]
     W_common = []
     for w in common:
         if w in clean_src_vectors:
@@ -72,8 +87,10 @@ def main(args):
         else:
             W_common.append(np.array(clean_target_vectors[w]))
 
-    if (not batch):
-        print(f'{source_lang} - {target_lang}: the vocabulary size is {len(W_common)}')
+    if not batch:
+        print(
+            f'{source_lang} - {target_lang}: the vocabulary size is {len(W_common)}'
+        )
 
     W_common = np.array(W_common)
     W_common = normalize(W_common)
@@ -82,26 +99,28 @@ def main(args):
     X_train_idf = vect.transform(clean_src_corpus)
     X_test_idf = vect.transform(clean_target_corpus)
 
-    vect_tf = CountVectorizer(vocabulary=common, dtype=np.double)
-    vect_tf.fit(clean_src_corpus + clean_target_corpus)
-    X_train_tf = vect_tf.transform(clean_src_corpus)
-    X_test_tf = vect_tf.transform(clean_target_corpus)
-
     for metric in runfor:
-        if (not batch):
+        if not batch:
             print(f'{metric} - tfidf: {source_lang} - {target_lang}')
 
-        clf = Wasserstein_Retriever(W_embed=W_common, n_neighbors=5, n_jobs=14, sinkhorn=(metric == 'snk'))
+        clf = Wasserstein_Retriever(W_embed=W_common,
+                                    n_neighbors=5,
+                                    n_jobs=14,
+                                    sinkhorn=(metric == 'snk'))
         clf.fit(X_train_idf[:instances], np.ones(instances))
         # dist, preds = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances)
         # mrr, p_at_one = mrr_precision_at_k(list(range(len(preds))), preds)
         # percentage = p_at_one * 100
-        p_at_one, percentage  = clf.align(X_test_idf[:instances], n_neighbors=instances)
+        p_at_one, percentage = clf.align(X_test_idf[:instances],
+                                         n_neighbors=instances)
 
-        if (not batch):
+        if not batch:
             print(f'P @ 1: {p_at_one}\ninstances: {instances}\n{percentage}%')
         else:
-            fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{p_at_one}', f'{percentage}']
+            fields = [
+                f'{source_lang}', f'{target_lang}', f'{instances}',
+                f'{p_at_one}', f'{percentage}'
+            ]
             with open(f'{metric}_retrieval_result.csv', 'a') as f:
                 writer = csv.writer(f)
                 writer.writerow(fields)
@@ -109,16 +128,31 @@ def main(args):
 
 if __name__ == "__main__":
 
-    parser = argparse.ArgumentParser(description='run retrieval using wmd or snk')
+    parser = argparse.ArgumentParser(
+        description='run retrieval using wmd or snk')
     parser.add_argument('source_lang', help='source language short name')
     parser.add_argument('target_lang', help='target language short name')
     parser.add_argument('source_vector', help='path of the source vector')
     parser.add_argument('target_vector', help='path of the target vector')
     parser.add_argument('source_defs', help='path of the source definitions')
     parser.add_argument('target_defs', help='path of the target definitions')
-    parser.add_argument('-b', '--batch', action='store_true', help='running in batch (store results in csv) or running a single instance (output the results)')
-    parser.add_argument('mode', choices=['all', 'wmd', 'snk'], default='all', help='which methods to run')
-    parser.add_argument('-n', '--instances', help='number of instances in each language to retrieve', default=1000, type=int)
+    parser.add_argument(
+        '-b',
+        '--batch',
+        action='store_true',
+        help=
+        'running in batch (store results in csv) or running a single instance (output the results)'
+    )
+    parser.add_argument('mode',
+                        choices=['all', 'wmd', 'snk'],
+                        default='all',
+                        help='which methods to run')
+    parser.add_argument(
+        '-n',
+        '--instances',
+        help='number of instances in each language to retrieve',
+        default=1000,
+        type=int)
 
     args = parser.parse_args()
 
diff --git a/Wasserstein_Distance.py b/Wasserstein_Distance.py
index 08439d2..161c13c 100644
--- a/Wasserstein_Distance.py
+++ b/Wasserstein_Distance.py
@@ -1,15 +1,14 @@
-import ot
-from sklearn.preprocessing import normalize
-from lapjv import lapjv
-from sklearn.neighbors import KNeighborsClassifier
+import numpy as np
 from sklearn.metrics import euclidean_distances
-from sklearn.externals.joblib import Parallel, delayed
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.preprocessing import normalize
 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
+
+import ot
+from lapjv import lapjv
 from mosestokenizer import MosesTokenizer
+from pathos.multiprocessing import ProcessingPool as Pool
+
 
 class Wasserstein_Matcher(KNeighborsClassifier):
     """
@@ -17,7 +16,13 @@ class Wasserstein_Matcher(KNeighborsClassifier):
     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):
+    def __init__(self,
+                 W_embed,
+                 n_neighbors=1,
+                 n_jobs=1,
+                 verbose=False,
+                 sinkhorn=False,
+                 sinkhorn_reg=0.1):
         """
         Initialization of the class.
         Arguments
@@ -29,7 +34,10 @@ class Wasserstein_Matcher(KNeighborsClassifier):
         self.sinkhorn_reg = sinkhorn_reg
         self.W_embed = W_embed
         self.verbose = verbose
-        super(Wasserstein_Matcher, self).__init__(n_neighbors=n_neighbors, n_jobs=n_jobs, metric='precomputed', algorithm='brute')
+        super(Wasserstein_Matcher, 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)
@@ -38,9 +46,16 @@ class Wasserstein_Matcher(KNeighborsClassifier):
         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]
+            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)
+            return ot.emd2(bow_i, bow_j, W_dist)
 
     def _wmd_row(self, row):
         X_train = self._fit_X
@@ -52,28 +67,31 @@ class Wasserstein_Matcher(KNeighborsClassifier):
 
         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)
+        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_train_idf
-        X = check_array(X, accept_sparse='csr', copy=True) # check if array is sparse
+    def fit(self, X, y):  # X_train_idf
+        X = check_array(X, accept_sparse='csr',
+                        copy=True)  # check if array is sparse
         X = normalize(X, norm='l1', copy=False)
-        return super(Wasserstein_Matcher, self).fit(X, y) # X_train_idf, np_ones(document collection size)
+        return super(Wasserstein_Matcher, self).fit(
+            X, y)  # X_train_idf, np_ones(document collection size)
 
     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)
-        dist = dist * 1000 # for lapjv, small floating point numbers are evil
+        dist = dist * 1000  # for lapjv, small floating point numbers are evil
         return super(Wasserstein_Matcher, self).predict(dist)
 
-    def kneighbors(self, X, n_neighbors=1): # X : X_train_idf
+    def kneighbors(self, X, n_neighbors=1):  # X : X_train_idf
         X = check_array(X, accept_sparse='csr', copy=True)
         X = normalize(X, norm='l1', copy=False)
         dist = self._pairwise_wmd(X)
-        dist = dist * 1000 # for lapjv, small floating point numbers are evil
-        return lapjv(dist) # and here is the matching part
+        dist = dist * 1000  # for lapjv, small floating point numbers are evil
+        return lapjv(dist)  # and here is the matching part
 
 
 class Wasserstein_Retriever(KNeighborsClassifier):
@@ -82,7 +100,13 @@ class Wasserstein_Retriever(KNeighborsClassifier):
     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):
+    def __init__(self,
+                 W_embed,
+                 n_neighbors=1,
+                 n_jobs=1,
+                 verbose=False,
+                 sinkhorn=False,
+                 sinkhorn_reg=0.1):
         """
         Initialization of the class.
         Arguments
@@ -94,7 +118,10 @@ class Wasserstein_Retriever(KNeighborsClassifier):
         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')
+        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)
@@ -103,9 +130,16 @@ class Wasserstein_Retriever(KNeighborsClassifier):
         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]
+            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)
+            return ot.emd2(bow_i, bow_j, W_dist)
 
     def _wmd_row(self, row):
         X_train = self._fit_X
@@ -117,8 +151,8 @@ class Wasserstein_Retriever(KNeighborsClassifier):
 
         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)
+        pool = Pool(nodes=self.n_jobs)
+        dist = pool.map(self._wmd_row, X_test)
         return np.array(dist)
 
     def fit(self, X, y):
@@ -144,8 +178,8 @@ class Wasserstein_Retriever(KNeighborsClassifier):
         precision at one and percentage values
 
         """
-        dist, preds = self.kneighbors(X, n_neighbors)
-        mrr, p_at_one = mrr_precision_at_k(list(range(len(preds))), preds)
+        _, preds = self.kneighbors(X, n_neighbors)
+        _, p_at_one = mrr_precision_at_k(list(range(len(preds))), preds)
         percentage = p_at_one * 100
         return (p_at_one, percentage)
 
@@ -168,7 +202,8 @@ def load_embeddings(path, dimension=300):
             fp.seek(0)
         for line in fp:
             elems = line.split()
-            vectors[" ".join(elems[:-dimension])] = " ".join(elems[-dimension:])
+            vectors[" ".join(elems[:-dimension])] = " ".join(
+                elems[-dimension:])
     return vectors
 
 
@@ -177,7 +212,7 @@ def clean_corpus_using_embeddings_vocabulary(
         corpus,
         vectors,
         language,
-        ):
+):
     '''
     Cleans corpus using the dictionary of embeddings.
     Any word without an associated embedding in the dictionary is ignored.
@@ -192,7 +227,8 @@ def clean_corpus_using_embeddings_vocabulary(
         for word in words:
             if word in words_we_want:
                 clean_doc.append(word + '__%s' % language)
-                clean_vectors[word + '__%s' % language] = np.array(vectors[word].split()).astype(np.float)
+                clean_vectors[word + '__%s' % language] = np.array(
+                    vectors[word].split()).astype(np.float)
         if len(clean_doc) > 3 and len(clean_doc) < 25:
             keys.append(key)
         clean_corpus.append(' '.join(clean_doc))
@@ -208,10 +244,9 @@ def mrr_precision_at_k(golden, preds, k_list=[1,]):
     precision_at = np.zeros(len(k_list))
     for key, elem in enumerate(golden):
         if elem in preds[key]:
-            location = np.where(preds[key]==elem)[0][0]
-            my_score += 1/(1+ location)
+            location = np.where(preds[key] == elem)[0][0]
+            my_score += 1 / (1 + location)
         for k_index, k_value in enumerate(k_list):
             if location < k_value:
                 precision_at[k_index] += 1
-    return my_score/len(golden), (precision_at/len(golden))[0]
-
+    return my_score / len(golden), (precision_at / len(golden))[0]