feed forward pytorch

run_nn.py

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+import lzma
+# from sklearn.metrics import accuracy_score
+import numpy as np
+import gensim
+import gensim.downloader
+import torch
+import pandas as pd
+import re
+
+BATCH_SIZE = 5
+
+
+class LogisticRegressionModel(torch.nn.Module):
+
+    def __init__(self):
+        super(LogisticRegressionModel, self).__init__()
+        self.layer_one = torch.nn.Linear(100, 500)
+        self.layer_two = torch.nn.Linear(500, 1)
+
+    def forward(self, x):
+        x = self.layer_one(x)
+        x = torch.relu(x)
+        x = self.layer_two(x)
+        x = torch.sigmoid(x)
+        return x
+
+
+def get_data(file_name, data_type):
+    lines = []
+    if data_type == "tsv":
+        with open(file_name, encoding="utf-8") as file:
+            for line in file.readlines():
+                lines.append(int(line.replace("\n", "")))
+    else:
+        with lzma.open(f"{file_name}.{data_type}") as file:
+            for line in file.readlines():
+                lines.append(line.rstrip().decode("utf-8"))
+    return lines
+
+
+def clean_data(x_data):
+    for sentance in range(len(x_data)):
+        x_data[sentance] = re.sub(r"\\n"," ", x_data[sentance])
+        x_data[sentance] = re.sub(r"\W"," ", x_data[sentance])
+    return x_data
+
+
+def train_model(word2vector):
+    x_data = clean_data(get_data(f"train/in.tsv", "xz"))
+    Y_train = pd.Series(get_data(f"train/expected.tsv", "tsv"))
+    X_train = [np.mean([word2vector[word] for word in sentance.split() if word in word2vector] or [np.zeros(100)], axis=0) for sentance in x_data]
+    lr_model = LogisticRegressionModel()
+
+    acc_score = 0
+    items_total = 0
+    lr_model.train()
+    for i in range(0, len(Y_train), BATCH_SIZE):
+        #print(i, end=", ")
+        X = X_train[i:i+BATCH_SIZE]
+        X = torch.tensor(np.array(X))
+
+        Y = Y_train[i:i+BATCH_SIZE]
+        Y = torch.tensor(Y.astype(np.float32).to_numpy()).reshape(-1,1)
+        Y_predictions = lr_model(X.float())
+
+        acc_score += torch.sum((Y_predictions > 0.5) == Y).item()
+        items_total += len(Y_train) 
+        criterion = torch.nn.BCELoss()
+        optimizer = torch.optim.SGD(lr_model.parameters(), lr = 0.1)
+
+        optimizer.zero_grad()
+        loss = criterion(Y_predictions, Y)
+        loss.backward()
+        optimizer.step()
+    #print(f"acc score: {acc_score}")
+    return lr_model
+
+
+def prediction(lr_model, word2vector, name_of_file):
+    x_data = clean_data(get_data(f"{name_of_file}/in.tsv", "xz"))
+    x_data = [np.mean([word2vector[word] for word in sentance.split() if word in word2vector] or [np.zeros(100)], axis=0) for sentance in x_data]
+             
+    y_predictions = []
+    with torch.no_grad():
+        for i in range(0, len(x_data), BATCH_SIZE):
+            x = x_data[i:i + BATCH_SIZE]
+            prediction_x = lr_model(torch.tensor(x).float())
+            y_predictions.extend((prediction_x > 0.5))
+    
+    results = []
+    for result in y_predictions:
+        results.append(result.int()[0].item())
+    
+    with open(f"{name_of_file}\out.tsv", "w", encoding="UTF-8") as file_out:
+        for single_pred in results:
+            file_out.writelines(f"{str(single_pred)}\n")
+
+
+word2vector = gensim.downloader.load("glove-wiki-gigaword-100")
+
+lr_model = train_model(word2vector)
+prediction(lr_model, word2vector, "dev-0")
+prediction(lr_model, word2vector, "test-A")
+
+
+'''y_true = []
+with open("dev-0/expected.tsv", encoding='utf-8') as file:
+            for line in file.readlines():
+                y_true.append(line)
+y_pred = []
+with open("dev-0/out.tsv", encoding='utf-8') as file:
+            for line in file.readlines():
+                y_pred.append(line)
+print(accuracy_score(y_true, y_pred))'''
+
+