main.py

@@ -1,135 +0,0 @@
-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')