paranormal-or-skeptic-ISI-public/run_pytorch.py

#!/usr/bin/env python
# coding: utf-8

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from sklearn.model_selection import train_test_split

from sklearn.datasets import fetch_20newsgroups
# https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import accuracy_score


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import numpy as np
import gensim
import torch
import pandas as pd
from gensim.test.utils import common_texts
from gensim.models import Word2Vec
import csv


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train_x = pd.read_csv('train/in.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
train_y = pd.read_csv('train/expected.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
dev_x = pd.read_csv('dev-0/in.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
dev_y = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
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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print(len(train_x))


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print(len(train_y))


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train_y = train_y[0]


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dev_y = dev_y[0]


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print(type(train_y))


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train_y = train_y.to_numpy()


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dev_y = dev_y.to_numpy()


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train_x.head


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dev_x.head()


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train_x = train_x[0]


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vec_model = Word2Vec(train_x, vector_size=100, window=5, min_count=1, workers=4)


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def w2v(model, data):
    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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w2v()


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dev_x = dev_x[0]
test_x = test_x[0]


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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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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])
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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X_dev0_w2v = vectorize(vec_model,dev_x)
X_test_w2v = vectorize(vec_model,test_x)


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class NeuralNetworkModel(torch.nn.Module):

    def __init__(self):
        super(NeuralNetworkModel, self).__init__()
        self.fc1 = torch.nn.Linear(FEAUTERES,500)
        self.fc2 = torch.nn.Linear(500,1)

    def forward(self, x):
        x = self.fc1(x)
        x = torch.relu(x)
        x = self.fc2(x)
        x = torch.sigmoid(x)
        return x


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criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)


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def get_loss_acc(model, X_dataset, Y_dataset):
    loss_score = 0
    acc_score = 0
    items_total = 0
    model.eval()
    for i in range(0, Y_dataset.shape[0], BATCH_SIZE):
        X = np.array(X_dataset[i:i+BATCH_SIZE]).astype(np.float32)
        X = torch.tensor(X)
        Y = Y_dataset[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]

        loss = criterion(Y_predictions, Y)

        loss_score += loss.item() * Y.shape[0]
    return (loss_score / items_total), (acc_score / items_total)


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def predict(model, data):
    model.eval()
    predictions = []
    for x in data:
        X = torch.tensor(np.array(x).astype(np.float32))
        Y_predictions = model(X)
        if Y_predictions[0] > 0.5:
            predictions.append("1")
        else:
            predictions.append("0")
    return predictions


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FEAUTERES = 100


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BATCH_SIZE = 5


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nn_model = NeuralNetworkModel()


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for epoch in range(7):
    loss_score = 0
    acc_score = 0
    items_total = 0
    nn_model.train()
    for i in range(0, train_y.shape[0], BATCH_SIZE):
        X = vec_x_train[i:i+BATCH_SIZE]
        X = torch.tensor(X)
        Y = train_y[i:i+BATCH_SIZE]
        Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)
        Y_predictions = nn_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]

    display(epoch)
    display(get_loss_acc(nn_model,vec_x_train, train_y))
    display(get_loss_acc(nn_model, vec_x_dev, dev_y))


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dev_pred = predict(nn_model, vec_x_dev)
test_pred = predict(nn_model, vec_x_test)


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dev_pred


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dev_pred = [int(i) for i in dev_pred]
test_pred = [int(i) for i in test_pred]


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dev_pred = np.array(dev_pred)
test_pred = np.array(test_pred)


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dev_pred


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np.savetxt("dev-0/out.tsv",dev_pred, delimiter="\t", fmt='%d')


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np.savetxt("test-A/out.tsv",test_pred, delimiter="\t", fmt='%d')


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