paranormal_or_skeptic.py

@@ -1,109 +0,0 @@
-
-from nltk.util import pr
-import pandas as pd
-import numpy as np
-import torch
-from gensim import downloader
-from nltk.tokenize import word_tokenize
-import csv
-
-BATCH_SIZE = 5
-
-
-class NeuralNetworkModel(torch.nn.Module):
-
-    def __init__(self):
-        dim = 200
-        super(NeuralNetworkModel, self).__init__()
-        self.one = torch.nn.Linear(dim, 500)
-        self.two = torch.nn.Linear(500, 1)
-
-    def forward(self, x):
-        x = self.one(x)
-        x = torch.relu(x)
-        x = self.two(x)
-        x = torch.sigmoid(x)
-        return x
-
-
-def read_data():
-    x_labels = (pd.read_csv('in-header.tsv', sep='\t')).columns
-    y_labels = (pd.read_csv('out-header.tsv', sep='\t')).columns
-
-    x_train = pd.read_table('train/in.tsv', header=None, quoting=csv.QUOTE_NONE, names=x_labels)
-    y_train = pd.read_table('train/expected.tsv', header=None, quoting=csv.QUOTE_NONE, names=y_labels)
-    x_dev = pd.read_table('dev-0/in.tsv', header=None, quoting=csv.QUOTE_NONE, names=x_labels)
-    x_test = pd.read_table('test-A/in.tsv', header=None, quoting=csv.QUOTE_NONE, names=x_labels)
-
-    # remove some rows for faster development
-    remove_n = 200000
-    drop_indices = np.random.choice(x_train.index, remove_n, replace=False)
-    x_train = x_train.drop(drop_indices)
-    y_train = y_train.drop(drop_indices)
-
-    return x_labels, y_labels, x_train, y_train, x_dev, x_test
-
-
-def process_data(x_labels, y_labels, x_train, y_train, x_dev, x_test):
-    x_train = x_train[x_labels[0]].str.lower()
-    x_dev = x_dev[x_labels[0]].str.lower()
-    x_test = x_test[x_labels[0]].str.lower()
-    y_train = y_train[y_labels[0]]
-
-    x_train = [word_tokenize(x) for x in x_train]
-    x_dev = [word_tokenize(x) for x in x_dev]
-    x_test = [word_tokenize(x) for x in x_test]
-
-    w2v = downloader.load('glove-wiki-gigaword-200')
-
-    x_train = [np.mean([w2v[w] for w in d if w in w2v] or [np.zeros(200)], axis=0) for d in x_train]
-    x_dev = [np.mean([w2v[w] for w in d if w in w2v] or [np.zeros(200)], axis=0) for d in x_dev]
-    x_test = [np.mean([w2v[w] for w in d if w in w2v] or [np.zeros(200)], axis=0) for d in x_test]
-
-    return x_train, y_train, x_dev, x_test
-
-
-def predict(model, x_data, out_path):
-    y_out = []
-    model.eval()
-    with torch.no_grad():
-        for i in range(0, len(x_data), BATCH_SIZE):
-
-            x = x_data[i:i+BATCH_SIZE]
-            x = torch.tensor(x)
-            pred = nn_model(x.float())
-
-            y_pred = (pred > 0.5)
-            y_out.extend(y_pred)
-
-    y_data = np.asarray(y_out, dtype=np.int32)
-    pd.DataFrame(y_data).to_csv(out_path, sep='\t', index=False,  header=False)
-
-
-if __name__ == "__main__":
-    x_labels, y_labels, x_train, y_train, x_dev, x_test = read_data()
-
-    x_train, y_train, x_dev, x_test = process_data(x_labels, y_labels, x_train, y_train, x_dev, x_test)
-
-    nn_model = NeuralNetworkModel()
-    criterion = torch.nn.BCELoss()
-    optimizer = torch.optim.SGD(nn_model.parameters(), lr=0.1)
-
-    for epoch in range(5):
-        nn_model.train()
-
-        for i in range(0, y_train.shape[0], BATCH_SIZE):
-            X = x_train[i:i+BATCH_SIZE]
-            X = torch.tensor(X)
-            Y = y_train[i:i+BATCH_SIZE]
-            Y = torch.tensor(Y.astype(np.float32).to_numpy()).reshape(-1, 1)
-
-            Y_predictions = nn_model(X.float())
-
-            loss = criterion(Y_predictions, Y)
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-    predict(nn_model, x_dev, 'dev-0/out.tsv')
-    predict(nn_model, x_test, 'test-A/out.tsv')