done

skrypt.py

@@ -0,0 +1,90 @@
+import numpy as np
+import torch
+from nltk.tokenize import word_tokenize
+import nltk
+from gensim import models
+from pandas import DataFrame
+import csv
+
+eph = 5
+BATCH_SIZE = 1
+
+class MyNeuralnn_model(torch.nn.Module):
+    def __init__(self):
+        super(MyNeuralnn_model, self).__init__()
+        self.fc1 = torch.nn.Linear(300, 600)
+        self.fc2 = torch.nn.Linear(600, 1)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = torch.relu(x)
+        x = self.fc2(x)
+        x = torch.sigmoid(x)
+        return x
+
+nn_model = MyNeuralnn_model()
+criterion = torch.nn.BCELoss()
+optimizer = torch.optim.SGD(nn_model.parameters(), lr=0.01)
+word2vec = models.KeyedVectors.load_word2vec_format(r"GoogleNews-vectors-negative300.bin.gz", binary=True, limit = 100000)
+
+def vectorize(document):
+    return np.mean([word2vec[word] for word in document if word in word2vec] or [np.zeros(300)], axis=0)
+
+Xtrain = []
+with open("train/in.tsv", 'r', encoding="utf-8") as train:
+    for line in csv.reader(train, delimiter="\t"):
+        Xtrain.append(line[0].lower())
+Xtrain = [word_tokenize(x) for x in Xtrain]
+Xtrain = [vectorize(document) for document in Xtrain]
+
+Ytrain = []
+with open("train/expected.tsv", 'r', encoding="utf-8") as train:
+    for line in csv.reader(train, delimiter="\t"):
+        Ytrain.append(line[0].lower())
+Ytrain = DataFrame(Ytrain)
+
+Xdev = []
+with open("dev-0/in.tsv", 'r', encoding="utf-8") as train:
+    for line in csv.reader(train, delimiter="\t"):
+        Xdev.append(line[0].lower())
+Xdev = [word_tokenize(x) for x in Xdev]
+Xdev = [vectorize(x) for x in Xdev]
+
+Xtest = []
+with open("test-A/in.tsv", 'r', encoding="utf-8") as train:
+    for line in csv.reader(train, delimiter="\t"):
+        Xtest.append(line[0].lower())
+Xtest = [word_tokenize(x) for x in Xtest]
+Xtest = [vectorize(x) for x in Xtest]
+
+
+for epoch in range(eph):
+    nn_model.train()
+    for i in range(0, Ytrain.shape[0], BATCH_SIZE):
+        x = Xtrain[i :i + BATCH_SIZE]
+        x = torch.tensor(x).float()
+        y = Ytrain[i :i + BATCH_SIZE]
+        y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)
+        y_predictions = nn_model(x)
+
+        loss = criterion(y_predictions, y)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+with torch.no_grad():
+#   Predykcja dla Dev
+  with open('dev-0/out.tsv', 'w', encoding="utf-8") as dev_out:
+    for i in range(0, len(Xdev), BATCH_SIZE):
+        x = Xdev[i :i + BATCH_SIZE]
+        x = torch.tensor(x).float()
+        predict = nn_model(x) > 0.5
+        dev_out.write(str(predict.to(torch.int32)[0].item()) + '\n')
+# Predykcja dla test
+  with open('test-A/out.tsv', 'w', encoding="utf-8") as test_A:
+    for i in range(0, len(Xtest), BATCH_SIZE):
+        x = Xtest[i :i + BATCH_SIZE]
+        x = torch.tensor(x).float()
+        predict = nn_model(x) > 0.5
+        test_A.write(str(predict.to(torch.int32)[0].item()) + '\n')
+