update

README.md

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+Skeptic vs paranormal subreddits
+================================
+
+Classify a reddit as either from Skeptic subreddit or one of the
+"paranormal" subreddits (Paranormal, UFOs, TheTruthIsHere, Ghosts,
+,Glitch-in-the-Matrix, conspiracytheories).
+
+Output label is the probability of a paranormal subreddit.
+
+Sources
+-------
+
+Data taken from <https://archive.org/details/2015_reddit_comments_corpus>.

config.txt

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+--metric Likelihood --metric Accuracy --metric F1 --metric F0:N<Precision> --metric F9999999:N<Recall>  --precision 4 --in-header in-header.tsv --out-header out-header.tsv

in-header.tsv

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+PostText	Timestamp

out-header.tsv

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+Label

program.py

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+import numpy as np
+import torch
+import gensim
+from sklearn import preprocessing
+import pandas as pd
+from gensim.test.utils import common_texts
+from gensim.models import Word2Vec
+
+with open("train/in.tsv") as f:
+    X_train = f.readlines()
+    with open("train/expected.tsv") as ff:
+        Y_train = ff.readlines() 
+        with open("test-A/in.tsv") as d:
+            X = d.readlines() 
+
+model = Word2Vec(X_train, min_count=1,size= 50,workers=3, window =3, sg = 1)
+X_train=model[X_train]
+X=model[X]
+FEAUTERES=10000
+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
+
+nn_model = NeuralNetworkModel()
+
+BATCH_SIZE = 5
+
+criterion = torch.nn.BCELoss()
+
+optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)
+
+for epoch in range(10):
+    loss_score = 0
+    acc_score = 0
+    items_total = 0
+    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.astype(np.float32).todense())
+        Y = Y_train[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] 
+        with open('test-A/out1.tsv', 'w') as file:
+            for e in Y_predictions:
+                file.write("%f\n" % e)
+
+