pep-8

Author: stevenxxiu

.editorconfig

@@ -14,3 +14,4 @@ multi_line_output = 5
 order_by_type = true
 known_third_party=numpy,scipy,matplotlib,sklearn,joblib,theano,gensim,lasagne
 not_skip=__init__.py
+skip=/senti/score.py

senti/data/twitter.py

@@ -2,7 +2,6 @@
 
 import html
 import json
-import logging
 import os
 import re
 from contextlib import ExitStack, closing

senti/models/base/nn.py

@@ -100,7 +100,7 @@ def fit(
             params = lasagne.layers.get_all_params(self.network)
             best_perf, best_params = None, None
             epoch_iter = EpochIterator(
-                self.gen_batches, (docs, y), (epoch_size + self.batch_size - 1)//self.batch_size
+                self.gen_batches, (docs, y), (epoch_size + self.batch_size - 1) // self.batch_size
                 if epoch_size else None
             )
             for i, batches, update_params in zip(range(max_epochs), epoch_iter, update_params_iter):

senti/preprocess/twokenize.py

@@ -189,8 +189,8 @@ def regex_or(*items):
 edge_punct = "[" + edge_punct_chars + "]"
 not_edge_punct = "[a-zA-Z0-9]"  # content characters
 off_edge = r"(^|$|:|;|\s|\.|,)"  # colon here gets "(hello):" ==> "( hello ):"
-edge_punct_left = re.compile(off_edge + "("+edge_punct+"+)("+not_edge_punct+")", re.UNICODE)
-edge_punct_Right = re.compile("("+not_edge_punct+")("+edge_punct+"+)" + off_edge, re.UNICODE)
+edge_punct_left = re.compile(off_edge + "(" + edge_punct + "+)(" + not_edge_punct + ")", re.UNICODE)
+edge_punct_Right = re.compile("(" + not_edge_punct + ")(" + edge_punct + "+)" + off_edge, re.UNICODE)
 
 
 def split_edge_punct(input_):
@@ -236,7 +236,7 @@ def simple_tokenize(text):
     # Group the indices and map them to their respective portion of the string
     split_goods = []
     for i in range(0, len(indices), 2):
-        good_str = split_punct_text[indices[i]:indices[i+1]]
+        good_str = split_punct_text[indices[i]:indices[i + 1]]
         split_str = good_str.strip().split(" ")
         split_goods.append(split_str)
 

senti/score.py

@@ -1,5 +1,6 @@
 
-import matplotlib; matplotlib.use('Agg')
+import matplotlib
+matplotlib.use('Agg')
 from contextlib import closing
 
 import matplotlib.pyplot as plt

senti/senti_models.py

@@ -1,5 +1,4 @@
 
-import itertools
 import logging
 import re
 from types import SimpleNamespace
@@ -73,9 +72,9 @@ def fit_voting(self):
         # assume w_0=1 as w is invariant to scaling
         w = basinhopping(
             lambda w_: -(dev_label_indexes == np.argmax((
-                all_probs_first + all_probs_rest*w_.reshape((len(w_), 1, 1))
+                all_probs_first + all_probs_rest * w_.reshape((len(w_), 1, 1))
             ).sum(axis=0), axis=1)).sum(), get_rng().uniform(0, 1, len(classifiers) - 1), niter=1000,
-            minimizer_kwargs=dict(method='L-BFGS-B', bounds=[(0, None)]*(len(classifiers) - 1))
+            minimizer_kwargs=dict(method='L-BFGS-B', bounds=[(0, None)] * (len(classifiers) - 1))
         ).x
         w = np.hstack([[1], w])
         w /= w.sum()
@@ -366,7 +365,7 @@ def _fit_rnn_embedding(self):
             batch_size=128, emb_X=emb_char.X, lstm_params=(300, 300), output_size=emb_char.X.shape[1]
         )
         cf_char.fit(
-            ft_char.transform(emb_word.vocab), emb_word.syn0, epoch_size=10**3, max_epochs=2*300,
+            ft_char.transform(emb_word.vocab), emb_word.syn0, epoch_size=10**3, max_epochs=2 * 300,
             update_params_iter=geometric_learning_rates(0.01, 0.5, 10)
         )
         return cf_char

senti/transforms/transforms.py

@@ -56,4 +56,4 @@ def transform(docs):
 class Proportion(BaseEstimator, EmptyFitMixin):
     @staticmethod
     def transform(docs):
-        return vstack(sparse_sum(doc, axis=0)/doc.shape[0] for doc in docs)
+        return vstack(sparse_sum(doc, axis=0) / doc.shape[0] for doc in docs)

senti/utils/cache.py

@@ -42,6 +42,7 @@ def _cached_fit(self, cls, params, X, X_hash, *args, **kwargs):
         self.__wrapped__.fit(X, *args, **kwargs)
         return self.__wrapped__
 
+    # noinspection PyAttributeOutsideInit
     @skip_empty_fit
     def fit(self, X, *args, **kwargs):
         params = self.__wrapped__.get_params(deep=True)

senti/utils/numpy_.py

@@ -10,10 +10,10 @@
 def sparse_sum(X, axis):
     if axis == 0:
         n = X.shape[0]
-        return sparse.csr_matrix((np.ones(n), np.arange(n), (0, n)))*X
+        return sparse.csr_matrix((np.ones(n), np.arange(n), (0, n))) * X
     elif axis == 1:
         n = X.shape[1]
-        return X*sparse.csr_matrix((np.ones(n), np.arange(n), (0, n)))
+        return X * sparse.csr_matrix((np.ones(n), np.arange(n), (0, n)))
 
 
 def vstack(Xs):

senti/utils/sr.py

@@ -55,8 +55,8 @@ def __init__(self, srs, n=None):
 
     def __iter__(self):
         with reset_sr(*self.srs):
-            m = None if self.n is None else round(self.n/len(self.srs))
-            m_last = None if self.n is None else self.n - m*(len(self.srs) - 1)
+            m = None if self.n is None else round(self.n / len(self.srs))
+            m_last = None if self.n is None else self.n - m * (len(self.srs) - 1)
             for i, sr in enumerate(self.srs):
                 if m is None:
                     yield from sr