Czyszczenie kodu. Rozwiazane zadanie regresji logistycznej.

dev-0/out.tsv

@@ -948,7 +948,7 @@
 0
 0
 1
-1
+0
 1
 1
 0

mlrlrozwiazanie.py

@@ -1,18 +0,0 @@
-import gzip
-import gensim
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib.gridspec as gridspec
-from sklearn.preprocessing import LabelEncoder
-from sklearn.linear_model import LogisticRegression
-from gensim.models import Word2Vec
-
-w2v = gensim.models.Word2Vec(vector_size=100)
-w2v = Word2Vec.load("w2v.model")
-#w2v.wv.save_word2vec_format('world.txt', binary=False)
-
-#w2v.wv.load_word2vec_format('../../../ncexclude/nkjp+wiki-forms-all-100-cbow-hs.txt.gz', binary=False)
-
-#print(w2v.wv.most_similar(['gol']))
-print(w2v.wv.index_to_key)

mrlrozwiazanie.py

@@ -1,26 +0,0 @@
-import gzip
-import gensim
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib.gridspec as gridspec
-from sklearn.preprocessing import LabelEncoder
-from sklearn.linear_model import LogisticRegression
-from gensim.models import Word2Vec, KeyedVectors
-
-# train_X = [] 
-# train_y = []
-# with gzip.open('train/train.tsv.gz','r') as fin:        
-#     for line in fin:
-#         sline = line.decode('UTF-8').replace("\n", "").split("\t")
-#         train_y.append(sline[0])
-#         train_X.append(''.join(sline[1:]))
-
-# w2v = gensim.models.Word2Vec(list(train_X), vector_size=100, window=10, min_count=2, epochs=5, workers=2)
-
-#w2v = gensim.models.Word2Vec(vector_size=100)
-#w2v.wv.load_word2vec_format('../../../ncexclude/nkjp+wiki-forms-all-100-cbow-hs.txt.gz', binary=False)
-#w2v.wv.load_word2vec_format('../../../ncexclude/wiki-forms-all-100-skipg-ns.txt.gz', binary=False)
-
-w2v = KeyedVectors.load_word2vec_format('../../../ncexclude/wiki-forms-all-100-skipg-ns.txt.gz', binary=False)
-w2v.save("word2vec2.wordvectors")

rlrozwiazanie.py

@@ -1,18 +1,13 @@
-#import numpy as np
 import gzip
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
 from sklearn import metrics
 import pandas as pd
 import numpy as np
 
-import gensim
+from gensim.models import KeyedVectors
-from gensim.models import Word2Vec, Phrases, phrases, KeyedVectors
 
-from sklearn.linear_model import LogisticRegression
 import re
 import torch
-from torch.utils.data import Dataset, TensorDataset, DataLoader
+from torch.utils.data import TensorDataset, DataLoader
 
 def get_str_cleaned(str_dirty):
     punctuation = '!"#$%&\'()*+,-./:;<=>?@[\\\\]^_`{|}~'
@@ -22,7 +17,6 @@ def get_str_cleaned(str_dirty):
         new_str = new_str.replace(char,'')
     return new_str
 
-#df = pd.read_csv('sport-text-classification-ball-ISI-public/train/train.tsv.gz', compression='gzip', header=None, sep='\t', error_bad_lines=False)
 train_X = [] 
 train_y = []
 with gzip.open('train/train.tsv.gz','r') as fin:        
@@ -35,31 +29,23 @@ with gzip.open('train/train.tsv.gz','r') as fin:
             train_X.append(cleared)
 
 train_X_data = pd.DataFrame(train_X)
-#w2v = gensim.models.Word2Vec(vector_size=100)
+#Korpusy można pobrać z:
-# #w2v.wv.load_word2vec_format('../../../ncexclude/nkjp+wiki-forms-all-100-cbow-hs.txt.gz', binary=False)
+#http://dsmodels.nlp.ipipan.waw.pl/dsmodels/nkjp+wiki-forms-all-100-cbow-hs.txt.gz
-#w2v.wv.load_word2vec_format('../../../ncexclude/wiki-forms-all-100-skipg-ns.txt.gz', binary=False)
+#http://dsmodels.nlp.ipipan.waw.pl/dsmodels/wiki-forms-all-100-skipg-ns.txt.gz
-#w2v = Word2Vec.load("w2v.model")
+#w2v = KeyedVectors.load_word2vec_format('../../../ncexclude/nkjp+wiki-forms-all-100-cbow-hs.txt.gz', binary=False)
-#w2v.wv.init_sims()
+w2v = KeyedVectors.load_word2vec_format('../../../ncexclude/wiki-forms-all-100-skipg-ns.txt.gz', binary=False)
-#w2v.wv.load("word2vec.wordvectors")
+#w2v.save("word2vec.wordvectors")
-#w2v = KeyedVectors.load_word2vec_format('../../../ncexclude/wiki-forms-all-100-skipg-ns.txt.gz', binary=False)
+#w2v = KeyedVectors.load("word2vec.wordvectors")
-w2v = KeyedVectors.load("word2vec2.wordvectors")
+
-#print(list(w2v.index_to_key))
 def document_vector(doc):
-    """Create document vectors by averaging word vectors. Remove out-of-vocabulary words."""
-    #print(doc)
-    #doc = [word for word in doc if word in w2v.index_to_key]
     try:
         doc2 = []
         doc = doc.split(' ')
         for word in doc:
-            #print(get_str_cleaned(word))
-            #print(word)
-            #print(w2v.wv.index_to_key)
             if word in w2v:
                 doc2.append(word)
         return np.mean(w2v[doc2], axis=0)
     except:
-        print(doc)
         return np.zeros(100)
 
 train_X_data = train_X_data[train_X_data.columns[0]].apply(document_vector)
@@ -78,8 +64,6 @@ with open('dev-0/expected.tsv','r') as dev_expected_file:
 dev_X_data = pd.DataFrame(dev_X)
 dev_X_data = dev_X_data[dev_X_data.columns[0]].apply(document_vector)
 
-# X_train_vec = list(train_X_data['doc_vector'])
-# X_dev_vec = list(dev_X_data['doc_vector'])
 
 class LogisticRegressionModel(torch.nn.Module):
 
@@ -111,24 +95,6 @@ dev_loader = DataLoader(dataset=dev_dataset)
 
 n_epochs = 2
 
-# loss_score = 0
-# acc_score = 0
-# items_total = 0
-# for x_batch, y_batch in train_loader:
-#     lr_model.train()
-#             # Makes predictions
-#     yhat = lr_model(x_batch)
-#     # Computes loss
-#     loss = criterion(yhat, y_batch.unsqueeze(1))
-#     # Computes gradients
-#     loss.backward()
-#     # Updates parameters and zeroes gradients
-#     optimizer.step()
-#     optimizer.zero_grad()    
-
-#     loss_score += loss.item() * yhat.shape[0] 
-# print(loss_score)
-
 def make_train_step(model, loss_fn, optimizer):
     def train_step(x, y):
         model.train()