Panda3

main.py

@@ -1,222 +1,105 @@
-#!/usr/bin/env python
+# noinspection PyUnresolvedReferences
-# coding: utf-8
-
-# In[1]:
-
-
 import csv
-
+import torch
-
-# 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
+from nltk.util import pr
+from gensim import downloader
+from nltk.tokenize import word_tokenize
 
+BATCH_SIZE = 5
 
-# In[5]:
+class NeuralNetworkModel(torch.nn.Module):
 
-
+    def __init__(self):
-import torch.nn as nn
+        dim = 200
-from nltk import word_tokenize
+        super(NeuralNetworkModel, self).__init__()
-
+        self.one = torch.nn.Linear(dim, 500)
-
+        self.two = torch.nn.Linear(500, 1)
-# 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)
+        x = self.one(x)
-        out = self.relu1(out)
+        x = torch.relu(x)
-        out = self.relu2(out)
+        x = self.two(x)
-        out = self.fc3(out)
+        x = torch.sigmoid(x)
-        return torch.sigmoid(out)
+        return x
 
-train_set_labels = pd.read_table(
+def read_data():
-    "train/expected.tsv",
+    x_labels = (pd.read_csv('in-header.tsv', sep='\t')).columns
-    error_bad_lines=False,
+    y_labels = (pd.read_csv('out-header.tsv', sep='\t')).columns
-    quoting=csv.QUOTE_NONE,
-    header=None,
-    names=header_names[2:],
-)
 
-train_set_features = pd.read_table(
+    x_train = pd.read_table('train/in.tsv', header=None, quoting=csv.QUOTE_NONE, names=x_labels)
-    "train/in.tsv.xz",
+    y_train = pd.read_table('train/expected.tsv', header=None, quoting=csv.QUOTE_NONE, names=y_labels)
-    error_bad_lines=False,
+    x_dev = pd.read_table('dev-0/in.tsv', header=None, quoting=csv.QUOTE_NONE, names=x_labels)
-    quoting=csv.QUOTE_NONE,
+    x_test = pd.read_table('test-A/in.tsv', header=None, quoting=csv.QUOTE_NONE, names=x_labels)
-    header=None,
-    names=header_names[:2],
-)
 
+    # 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)
 
-test_set = pd.read_table(
+    return x_labels, y_labels, x_train, y_train, x_dev, x_test
-    "test-A/in.tsv.xz",
-    error_bad_lines=False,
-    header=None,
-    quoting=csv.QUOTE_NONE,
-    names=header_names[:2],
-)
 
-dev_set = pd.read_table(
+def process_data(x_labels, y_labels, x_train, y_train, x_dev, x_test):
-    "dev-0/in.tsv.xz",
+    x_train = x_train[x_labels[0]].str.lower()
-    error_bad_lines=False,
+    x_dev = x_dev[x_labels[0]].str.lower()
-    header=None,
+    x_test = x_test[x_labels[0]].str.lower()
-    quoting=csv.QUOTE_NONE,
+    y_train = y_train[y_labels[0]]
-    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_train = [word_tokenize(x) for x in x_train]
-X_test = test_set["content"].str.lower()
+    x_dev = [word_tokenize(x) for x in x_dev]
-X_train = [word_tokenize(content) for content in X_train]
+    x_test = [word_tokenize(x) for x in x_test]
-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")
 
+    w2v = downloader.load('glove-wiki-gigaword-200')
 
-# In[24]:
+    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
 
-X_train = [
+def predict(model, x_data, out_path):
-    np.mean(
+    y_out = []
-        [word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
+    model.eval()
-        axis=0,
+    with torch.no_grad():
-    )
+        for i in range(0, len(x_data), BATCH_SIZE):
-    for content in X_train
+            x = x_data[i:i + BATCH_SIZE]
-]
+            x = torch.tensor(x)
-X_dev = [
+            pred = nn_model(x.float())
-    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 = [
+            y_pred = (pred > 0.5)
-    np.mean(
+            y_out.extend(y_pred)
-        [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
 
+    y_data = np.asarray(y_out, dtype=np.int32)
+    pd.DataFrame(y_data).to_csv(out_path, sep='\t', index=False, header=False)
 
-# In[27]:
+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)
 
-output_dim = 1
+    nn_model = NeuralNetworkModel()
 
+    criterion = torch.nn.BCELoss()
+    optimizer = torch.optim.SGD(nn_model.parameters(), lr=0.1)
 
-input_dim =300
+    for epoch in range(5):
-model = FF(input_dim, hidden_layer, output_dim)
+        nn_model.train()
-optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
-criterion = torch.nn.BCELoss()
 
-
+        for i in range(0, y_train.shape[0], BATCH_SIZE):
-# In[28]:
+            X = x_train[i:i + BATCH_SIZE]
-
-
-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())
+            Y = y_train[i:i + BATCH_SIZE]
-        loss = criterion(outputs, y)
+            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()
-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[ ]:
-
-
-
 
+    predict(nn_model, x_dev, 'dev-0/out.tsv')
+    predict(nn_model, x_test, 'test-A/out.tsv')