log reg

main.py

@@ -1,41 +1,103 @@
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
 import pandas as pd
 import numpy as np
-from stop_words import get_stop_words
+from gensim import downloader
+import torch
+from nltk.tokenize import word_tokenize
 
-stop_words = get_stop_words('polish')
+class LogisticRegressionModel(torch.nn.Module):
+    def __init__(self, input_size):
+        super(LogisticRegressionModel, self).__init__()
+        self.l1 = torch.nn.Linear(input_size, 500)
+        self.l2 = torch.nn.Linear(500, 1)
+
+    def forward(self, x):
+        x = self.l1(x)
+        x = torch.relu(x)
+        x = self.l2(x)
+        x = torch.sigmoid(x)
+        return x
 
-v = TfidfVectorizer(stop_words=None)
-naive_bayes=MultinomialNB()
 
 ball_train = pd.read_csv('train/train.tsv', sep='\t', error_bad_lines=False, header=None)
-
 y_train = pd.DataFrame(ball_train[0])
 x_train = pd.DataFrame(ball_train[1])
 x_np=x_train.to_numpy()
 x_np = [str(item) for item in x_np]
-
-x_train=v.fit_transform(x_np)
-
-naive_bayes.fit(x_train, y_train)
+x_train=[word_tokenize(i) for i in x_np]
 
 ball_dev = pd.read_csv('dev-0/in.tsv', sep='\t', error_bad_lines=False, header=None)
-
 X_dev = pd.DataFrame(ball_dev)
 X_dev_np=X_dev.to_numpy()
 X_dev_np = [str(item) for item in X_dev_np]
-X_dev=v.transform(X_dev_np)
-
-Y_dev_predicted = naive_bayes.predict(X_dev)
-pd.DataFrame(Y_dev_predicted).to_csv('dev-0/out.tsv', sep='\t', index=False, header=False)
-
+X_dev=[word_tokenize(i) for i in X_dev_np]
 
 ball_test=pd.read_csv('test-A/in.tsv', sep='\t', error_bad_lines=False, header=None)
 X_test = pd.DataFrame(ball_test)
 X_test_np=X_test.to_numpy()
 X_test_np = [str(item) for item in X_test_np]
-X_test=v.transform(X_test_np)
+X_test=[word_tokenize(i) for i in X_test_np]
 
-Y_test_predicted = naive_bayes.predict(X_test)
+w2v = downloader.load('word2vec-google-news-300')
+
+x_train = [np.mean([w2v[word] for word in content if word in w2v] or [np.zeros(300)], axis=0) for content in x_train]
+
+X_dev = [np.mean([w2v[word] for word in content if word in w2v] or [np.zeros(300)], axis=0) for content in X_dev]
+
+X_test = [np.mean([w2v[word] for word in content if word in w2v] or [np.zeros(300)], axis=0) for content in X_test]
+
+
+lr_model = LogisticRegressionModel(300)
+
+BATCH_SIZE = 5
+criterion = torch.nn.BCELoss()
+optimizer = torch.optim.SGD(lr_model.parameters(), lr = 0.1)
+loss_score = 0
+acc_score = 0
+items_total = 0
+lr_model.train()
+for i in range(0, y_train.shape[0], BATCH_SIZE):
+    X = x_train[i:i + BATCH_SIZE]
+    X = torch.tensor(X)
+    Y = y_train[i:i + BATCH_SIZE]
+    Y = torch.tensor(Y.astype(np.float32).to_numpy()).reshape(-1, 1)
+    Y_predictions = lr_model(X.float())
+    acc_score += torch.sum((Y_predictions > 0.5) == Y).item()
+    items_total += Y.shape[0]
+    optimizer.zero_grad()
+    loss = criterion(Y_predictions, Y)
+    loss.backward()
+    optimizer.step()
+    loss_score += loss.item() * Y.shape[0]
+
+
+Y_dev_predicted, Y_test_predicted = [], []
+lr_model.eval()
+with torch.no_grad():
+    for i in range(0, len(X_dev), BATCH_SIZE):
+        X = X_dev[i:i+BATCH_SIZE]
+        X = torch.tensor(X)
+        outputs = lr_model(X.float())
+        prediction = (outputs > 0.5)
+        Y_dev_predicted += prediction.tolist()
+    for i in range(0, len(X_test), BATCH_SIZE):
+        X = X_test[i:i+BATCH_SIZE]
+        X = torch.tensor(X)
+        outputs = lr_model(X.float())
+        prediction = (outputs > 0.5)
+        Y_test_predicted += prediction.tolist()
+
+for i in range(0, len(Y_dev_predicted)):
+    if Y_dev_predicted[i]==[True]:
+        Y_dev_predicted[i]=1
+    else:
+        Y_dev_predicted[i]=0
+
+for i in range(0, len(Y_test_predicted)):
+    if Y_test_predicted[i]==[True]:
+        Y_test_predicted[i]=1
+    else:
+        Y_test_predicted[i]=0
+
+
+pd.DataFrame(Y_dev_predicted).to_csv('dev-0/out.tsv', sep='\t', index=False, header=False)
 pd.DataFrame(Y_test_predicted).to_csv('test-A/out.tsv', sep='\t', index=False, header=False)