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dev-0/out.tsv
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dev-0/out.tsv
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run_pytorch.py
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run_pytorch.py
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#!/usr/bin/env python
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# coding: utf-8
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# In[3]:
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import fetch_20newsgroups
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# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.metrics import accuracy_score
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# In[71]:
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import numpy as np
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import gensim
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import torch
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import pandas as pd
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from gensim.test.utils import common_texts
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from gensim.models import Word2Vec
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import csv
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# In[84]:
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train_x = pd.read_csv('train/in.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
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train_y = pd.read_csv('train/expected.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
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dev_x = pd.read_csv('dev-0/in.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
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dev_y = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
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test_x = pd.read_csv('test-A/in.tsv', header=None, sep='\t',quoting=csv.QUOTE_NONE, error_bad_lines=False)
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# In[85]:
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print(len(train_x))
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# In[86]:
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print(len(train_y))
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# In[87]:
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train_y = train_y[0]
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# In[100]:
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dev_y = dev_y[0]
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# In[88]:
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print(type(train_y))
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# In[89]:
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train_y = train_y.to_numpy()
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# In[102]:
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dev_y = dev_y.to_numpy()
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# In[90]:
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train_x.head
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# In[91]:
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dev_x.head()
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# In[92]:
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train_x = train_x[0]
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# In[93]:
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vec_model = Word2Vec(train_x, vector_size=100, window=5, min_count=1, workers=4)
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# In[ ]:
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def w2v(model, data):
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return np.array([np.mean([model.wv[word] if word in model.wv.key_to_index else np.zeros(100, dtype=float) for word in doc], axis=0) for doc in data])
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# In[ ]:
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w2v()
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# In[96]:
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dev_x = dev_x[0]
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test_x = test_x[0]
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# In[95]:
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vec_x_train = np.array([np.mean([vec_model.wv[word] if word in vec_model.wv.key_to_index else np.zeros(100, dtype=float) for word in doc], axis=0) for doc in train_x])
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# In[97]:
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vec_x_dev = np.array([np.mean([vec_model.wv[word] if word in vec_model.wv.key_to_index else np.zeros(100, dtype=float) for word in doc], axis=0) for doc in dev_x])
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vec_x_test = np.array([np.mean([vec_model.wv[word] if word in vec_model.wv.key_to_index else np.zeros(100, dtype=float) for word in doc], axis=0) for doc in test_x])
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# In[36]:
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X_dev0_w2v = vectorize(vec_model,dev_x)
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X_test_w2v = vectorize(vec_model,test_x)
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# In[7]:
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class NeuralNetworkModel(torch.nn.Module):
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def __init__(self):
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super(NeuralNetworkModel, self).__init__()
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self.fc1 = torch.nn.Linear(FEAUTERES,500)
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self.fc2 = torch.nn.Linear(500,1)
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def forward(self, x):
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x = self.fc1(x)
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x = torch.relu(x)
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x = self.fc2(x)
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x = torch.sigmoid(x)
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return x
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# In[37]:
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criterion = torch.nn.BCELoss()
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optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)
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# In[8]:
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def get_loss_acc(model, X_dataset, Y_dataset):
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loss_score = 0
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acc_score = 0
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items_total = 0
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model.eval()
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for i in range(0, Y_dataset.shape[0], BATCH_SIZE):
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X = np.array(X_dataset[i:i+BATCH_SIZE]).astype(np.float32)
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X = torch.tensor(X)
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Y = Y_dataset[i:i+BATCH_SIZE]
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Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)
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Y_predictions = model(X)
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acc_score += torch.sum((Y_predictions > 0.5) == Y).item()
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items_total += Y.shape[0]
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loss = criterion(Y_predictions, Y)
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loss_score += loss.item() * Y.shape[0]
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return (loss_score / items_total), (acc_score / items_total)
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# In[9]:
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def predict(model, data):
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model.eval()
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predictions = []
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for x in data:
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X = torch.tensor(np.array(x).astype(np.float32))
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Y_predictions = model(X)
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if Y_predictions[0] > 0.5:
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predictions.append("1")
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else:
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predictions.append("0")
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return predictions
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# In[18]:
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FEAUTERES = 100
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# In[62]:
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BATCH_SIZE = 5
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# In[58]:
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nn_model = NeuralNetworkModel()
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# In[103]:
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for epoch in range(7):
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loss_score = 0
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acc_score = 0
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items_total = 0
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nn_model.train()
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for i in range(0, train_y.shape[0], BATCH_SIZE):
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X = vec_x_train[i:i+BATCH_SIZE]
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X = torch.tensor(X)
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Y = train_y[i:i+BATCH_SIZE]
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Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)
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Y_predictions = nn_model(X)
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acc_score += torch.sum((Y_predictions > 0.5) == Y).item()
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items_total += Y.shape[0]
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optimizer.zero_grad()
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loss = criterion(Y_predictions, Y)
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loss.backward()
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optimizer.step()
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loss_score += loss.item() * Y.shape[0]
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display(epoch)
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display(get_loss_acc(nn_model,vec_x_train, train_y))
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display(get_loss_acc(nn_model, vec_x_dev, dev_y))
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# In[104]:
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dev_pred = predict(nn_model, vec_x_dev)
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test_pred = predict(nn_model, vec_x_test)
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# In[105]:
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dev_pred
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# In[119]:
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dev_pred = [int(i) for i in dev_pred]
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test_pred = [int(i) for i in test_pred]
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# In[120]:
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dev_pred = np.array(dev_pred)
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test_pred = np.array(test_pred)
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# In[117]:
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dev_pred
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# In[121]:
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np.savetxt("dev-0/out.tsv",dev_pred, delimiter="\t", fmt='%d')
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# In[122]:
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np.savetxt("test-A/out.tsv",test_pred, delimiter="\t", fmt='%d')
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# In[ ]:
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8650
test-A/out.tsv
8650
test-A/out.tsv
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