nn lr part

main.py

@@ -0,0 +1,107 @@
+import numpy
+from sklearn.preprocessing import LabelEncoder
+from sklearn.naive_bayes import GaussianNB, MultinomialNB
+from sklearn.pipeline import Pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer
+import torch
+from gensim import downloader
+from nltk.tokenize import word_tokenize
+import pandas as pd
+
+class NetworkModel(torch.nn.Module):
+
+    def __init__(self, input_size, hidden_size, num_classes):
+        super(NetworkModel, self).__init__()
+        self.fc1 = torch.nn.Linear(input_size, hidden_size)
+        self.fc2 = torch.nn.Linear(hidden_size, num_classes)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = torch.relu(x)
+        x = self.fc2(x)
+        x = torch.sigmoid(x)
+        return x
+
+word2vec = downloader.load("word2vec-google-news-300")
+
+def word2vecOnDoc(document):
+    return numpy.mean(
+        [word2vec[token] for token in document if token in word2vec] or [numpy.zeros(300)],
+        axis=0,
+    )
+
+def prepareData(data):
+    data = [word_tokenize(row) for row in data.content.str.lower()]
+    data = [word2vecOnDoc(document) for document in data]
+    return data
+
+def trainModel(trainFileIn, trainFileExpected):
+    inData = pd.read_table(
+        trainFileIn,
+        error_bad_lines=False,
+        header=None,
+        quoting=3,
+        usecols=["content"],
+        names=["content", "id"],
+        nrows=225000,
+    )
+    expectedData = pd.read_table(
+        trainFileExpected,
+        error_bad_lines=False,
+        header=None,
+        quoting=3,
+        usecols=["label"],
+        names=["label"],
+        nrows=225000,
+    )
+
+   # expectedData = prepareData(expectedData)
+    inData = prepareData(inData)
+    networkModel = NetworkModel(300, 300, 1)
+    criterion = torch.nn.BCELoss()
+    optim = torch.optim.SGD(networkModel.parameters(), lr=0.02)
+    epochs = 1
+    batchSize = 2
+
+    for _ in range(epochs):
+        networkModel.train()
+        for i in range(0, expectedData.shape[0], batchSize):
+            x = inData[i : i + batchSize]
+            x = torch.tensor(x)
+            y = expectedData[i : i + batchSize]
+            y = torch.tensor(y.astype(numpy.float32).to_numpy()).reshape(-1, 1)
+            outputs = networkModel(x.float())
+            loss = criterion(outputs, y)
+#            print(loss)
+            optim.zero_grad()
+            loss.backward()
+            optim.step()
+    return networkModel
+
+def evaluateModel(model, inFile, outFile):
+    inData = pd.read_table(
+        inFile,
+        error_bad_lines=False,
+        header=None,
+        quoting=3,
+        usecols=["content"],
+        names=["content", "id"],
+    )
+
+    inData = prepareData(inData)
+    batchSize = 2
+    pred = []
+    with torch.no_grad():
+        for i in range(0, len(inData), batchSize):
+            x = inData[i : i + batchSize]
+            x = torch.tensor(x)
+            outputs = model(x.float())
+            prediction = outputs >= 0.5
+            pred += prediction.tolist()
+    numpy.asarray(pred, dtype=numpy.int32).tofile(outFile, sep="\n")
+
+model = trainModel("train/in.tsv", "train/expected.tsv")
+evaluateModel(model, "dev-0/in.tsv", "dev-0/out.tsv")
+evaluateModel(model, "test-A/in.tsv", "test-A/out.tsv")
+
+