first applictation

main.py

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+from gensim.models import KeyedVectors
+import nltk
+import numpy as np
+import os
+import gensim
+from gensim.models import Word2Vec
+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
+import torch
+import csv
+
+# Assigning data from files to variables
+train = pd.read_table('train/train.tsv', error_bad_lines=False,
+                      sep='\t', quoting=csv.QUOTE_NONE, header=None)
+x_dev = pd.read_table('dev-0/in.tsv', error_bad_lines=False,
+                      sep='\t', header=None, quoting=csv.QUOTE_NONE)
+y_dev = pd.read_table('dev-0/expected.tsv', error_bad_lines=False,
+                      sep='\t', header=None, quoting=csv.QUOTE_NONE)
+x_test = pd.read_table('test-A/in.tsv', error_bad_lines=False,
+                       sep='\t', header=None, quoting=csv.QUOTE_NONE)
+
+# Data split na x i y
+x_train = train[1].values
+y_train = train[0].values
+x_dev = x_dev[0].values
+x_test = x_test[0].values
+
+# I needed this only once
+# nltk.download('punkt')
+
+
+# Tokenization
+def tokenize(data):
+    new_data = [nltk.word_tokenize(x) for x in data]
+
+    for doc in new_data:
+        i = 0
+        while i < len(doc):
+            if doc[i].isalpha():
+                doc[i] = doc[i].lower()
+            else:
+                del doc[i]
+            i += 1
+    return new_data
+
+
+x_train_tokenized = tokenize(x_train)
+x_dev_tokenized = tokenize(x_dev)
+x_test_tokenized = tokenize(x_test)
+
+# trained custom model form wiki-forms-all-100-skipg-ns
+# run only on first try
+# http://dsmodels.nlp.ipipan.waw.pl/dsmodels/wiki-forms-all-100-skipg-ns.txt.gz
+# word2vec = KeyedVectors.load_word2vec_format(
+#     'wiki-forms-all-100-skipg-ns.txt.gz', binary=False)
+# word2vec.save("word2vec.bin")
+word2vec = KeyedVectors.load("word2vec.bin")
+
+x_train = [np.mean([word2vec[word] for word in content if word in word2vec] or [
+                   np.zeros(100)], axis=0) for content in x_train]
+x_train_tensor = torch.tensor(
+    np.array(x_train, dtype=np.float32).astype(np.float32))
+x_train_vec = np.array(x_train, dtype=np.float32)
+
+x_dev = [np.mean([word2vec[word] for word in content if word in word2vec] or [
+                 np.zeros(100)], axis=0) for content in x_dev]
+x_dev_vec = np.array(x_dev, dtype=np.float32)
+
+
+x_test = [np.mean([word2vec[word] for word in content if word in word2vec] or [
+                  np.zeros(100)], axis=0) for content in x_test]
+x_test_vec = np.array(x_test, dtype=np.float32)
+
+
+class NNModel(torch.nn.Module):
+
+    def __init__(self):
+        super(NNModel, self).__init__()
+        self.fc1 = torch.nn.Linear(100, 200)
+        self.fc2 = torch.nn.Linear(200, 1)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = torch.relu(x)
+        x = self.fc2(x)
+        x = torch.sigmoid(x)
+        return x
+
+
+model = NNModel()
+
+criterion = torch.nn.BCELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+
+batch_size = 12
+for epoch in range(10):
+    loss_score = 0
+    acc_score = 0
+    items_total = 0
+    model.train()
+
+    for i in range(0, y_train.shape[0], batch_size):
+        X = x_train_vec[i:i + batch_size]
+        X = torch.tensor(X.astype(np.float32))
+        Y = y_train[i:i + batch_size]
+        Y = torch.tensor(Y.astype(np.float32)).reshape(-1, 1)
+        Y_predictions = model(X)
+        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]
+    print(epoch)
+
+# Generation
+y_pred_dev = model(torch.tensor(x_dev_vec.astype(np.float32)))
+y_pred_dev = y_pred_dev.cpu().detach().numpy()
+y_pred_dev = (y_pred_dev > 0.5)
+y_pred_dev = np.asarray(y_pred_dev, dtype=np.int32)
+y_pred_dev.tofile('dev-0/out.tsv', sep='\n')
+
+
+y_pred_test = model(torch.tensor(x_dev_vec.astype(np.float32)))
+y_pred_test = y_pred_test.cpu().detach().numpy()
+y_pred_test = (y_pred_test > 0.5)
+y_pred_test = np.asarray(y_pred_test, dtype=np.int32)
+y_pred_test.tofile('test-A/out.tsv', sep='\n')