nn

main.py

@@ -1,24 +1,222 @@
-from sklearn import preprocessing
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.pipeline import make_pipeline
+#!/usr/bin/env python
+# coding: utf-8
 
+# In[1]:
+
+
+import csv
+
+
+# In[2]:
+
+
+get_ipython().system('pip install gensim')
+
+
+# In[17]:
+
+
+import nltk
+nltk.download('punkt')
+
+
+# In[9]:
+
+
+get_ipython().system('pip install nltk')
+
+
+# In[3]:
+
+
+get_ipython().system('pip install torch')
+
+
+# In[4]:
+
+
+import gensim.downloader
 import numpy as np
+import pandas as pd
+import torch
+
+
+# In[5]:
+
+
+import torch.nn as nn
+from nltk import word_tokenize
+
+
+# In[13]:
+
+
+header_names = ["content", "id", "label"]
+
+
+# In[23]:
+
+
+class FF(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(FF, self).__init__()
+        self.fc1 = nn.Linear(input_dim, hidden_dim)
+        self.relu1 = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
+        self.relu2 = nn.ReLU()
+        self.fc3 = nn.Linear(hidden_dim, output_dim)
+
+    def forward(self, x):
+        out = self.fc1(x)
+        out = self.relu1(out)
+        out = self.relu2(out)
+        out = self.fc3(out)
+        return torch.sigmoid(out)
+
+train_set_labels = pd.read_table(
+    "train/expected.tsv",
+    error_bad_lines=False,
+    quoting=csv.QUOTE_NONE,
+    header=None,
+    names=header_names[2:],
+)
+
+train_set_features = pd.read_table(
+    "train/in.tsv.xz",
+    error_bad_lines=False,
+    quoting=csv.QUOTE_NONE,
+    header=None,
+    names=header_names[:2],
+)
+
+
+test_set = pd.read_table(
+    "test-A/in.tsv.xz",
+    error_bad_lines=False,
+    header=None,
+    quoting=csv.QUOTE_NONE,
+    names=header_names[:2],
+)
+
+dev_set = pd.read_table(
+    "dev-0/in.tsv.xz",
+    error_bad_lines=False,
+    header=None,
+    quoting=csv.QUOTE_NONE,
+    names=header_names[:2],
+)
+X_train = train_set_features["content"].str.lower()
+y_train = train_set_labels["label"]
+
+X_dev = dev_set["content"].str.lower()
+X_test = test_set["content"].str.lower()
+X_train = [word_tokenize(content) for content in X_train]
+X_dev = [word_tokenize(content) for content in X_dev]
+X_test = [word_tokenize(content) for content in X_test]
+word2vec = gensim.downloader.load("word2vec-google-news-300")
+
+
+# In[24]:
+
+
+X_train = [
+    np.mean(
+        [word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
+        axis=0,
+    )
+    for content in X_train
+]
+X_dev = [
+    np.mean(
+        [word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
+        axis=0,
+    )
+    for content in X_dev
+]
+
+X_test = [
+    np.mean(
+        [word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
+        axis=0,
+    )
+    for content in X_test
+]
+hidden_layer = 650
+epochs = 15
+batch_size = 10
+
+
+# In[27]:
+
+
+output_dim = 1
+
+
+input_dim =300
+model = FF(input_dim, hidden_layer, output_dim)
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+criterion = torch.nn.BCELoss()
+
+
+# In[28]:
+
+
+for epoch in range(epochs):
+    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)
+
+        outputs = model(X.float())
+        loss = criterion(outputs, y)
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+test_prediction = []
+dev_prediction = []
+model.eval()
+with torch.no_grad():
+    for i in range(0, len(X_test), batch_size):
+        X = X_test[i : i + batch_size]
+        X = torch.tensor(X)
+
+        outputs = model(X.float())
+
+        prediction = outputs > 0.5
+        test_prediction += prediction.tolist()
+
+    for i in range(0, len(X_dev), batch_size):
+        X = X_dev[i : i + batch_size]
+        X = torch.tensor(X)
+
+        outputs = model(X.float())
+
+        prediction = outputs > 0.5
+        dev_prediction += prediction.tolist()
+
+test_prediction = np.asarray(test_prediction, dtype=np.int32)
+dev_prediction = np.asarray(dev_prediction, dtype=np.int32)
+test_prediction.tofile("./test-A/out.tsv", sep="\n")
+dev_prediction.tofile("./dev-0/out.tsv", sep="\n")
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
 
-eng = preprocessing.LabelEncoder()
 
-with open("train/in.tsv") as myFile:
-    tmp = myFile.readlines()
-    with open("train/expected.tsv") as finFile:
-        fin = finFile.readlines() 
-        fin = eng.fit_transform(fin)
-        with open("test-A/in.tsv") as tFile:
-            fic = tFile.readlines() 
-            gnb = make_pipeline(TfidfVectorizer(),MultinomialNB())
-            model = gnb.fit(tmp, fin)
-            fin_pred = model.predict(fic)
-            fin_pred = np.array(fin_pred)
-            np.set_printoptions(threshold = np.inf)
-            eFile = np.array2string(fin_pred.flatten(), precision = 2, separator = '\n',suppress_small = True)
-            myFile = open("test-A/out.tsv", "a")
-            myFile.write(eFile)