Update dopset.py

dopset.py

@@ -1,80 +1,81 @@
-import string
-import torch
-import numpy as np
-from nltk.corpus import stopwords
-stop_words = set(stopwords.words('english'))
-import torch.nn as nn
-import pickle
-from transformers import BertTokenizer, BertModel, DistilBertTokenizer, DistilBertModel
-from sklearn.linear_model import LogisticRegression
-
-
-
-EMBEDDING_DIM = 64
-VOCAB_SIZE = 203310
-embedding_matrix = np.zeros((VOCAB_SIZE, EMBEDDING_DIM))
-embedding_layer = torch.nn.Embedding.from_pretrained(torch.FloatTensor(embedding_matrix))
-
-
-
-
-with open('lstm/vocab_to_int.txt', 'rb') as f:
-    vocab_to_int = pickle.load(f)
-
-class LSTMClassifier(nn.Module):
-    def __init__(self, embedding_dim: int, hidden_size: int = 32) -> None:
-        super().__init__()
-
-        self.embedding_dim = embedding_dim # создаем эмбединг сайз
-        self.hidden_size = hidden_size # создаем хидден сайз
-        self.embedding = embedding_layer # создаем слои модели
-
-        self.lstm = nn.LSTM(
-            input_size=self.embedding_dim,
-            hidden_size=self.hidden_size,
-            batch_first=True
-        )
-        
-        self.clf = nn.Linear(self.hidden_size, 1)
-    
-    def forward(self, x):
-        embedding = self.embedding(x)
-        _, (h_n, _) = self.lstm(embedding)
-        out = self.clf(h_n.squeeze())
-        return out  
-
-
-def data_preprocessing(text: str) -> str:
-    text = text.lower()
-    text = ''.join([c for c in text if c not in string.punctuation])# Remove punctuation
-    text = [word for word in text.split() if word not in stop_words] 
-    text = ' '.join(text)
-    return text
-
-
-
-def padding(review_int: list, seq_len: int) -> np.array:
-    features = np.zeros((len(review_int), seq_len), dtype = int)
-    for i, review in enumerate(review_int):
-        if len(review) <= seq_len:
-            zeros = list(np.zeros(seq_len - len(review)))
-            new = zeros + review
-        else:
-            new = review[: seq_len]
-        features[i, :] = np.array(new)
-            
-    return features
-
-def preprocess_single_string(input_string: str, seq_len: int, vocab_to_int: dict = vocab_to_int) -> list:
-    preprocessed_string = data_preprocessing(input_string)
-    result_list = []
-    for word in preprocessed_string.split():
-        try: 
-            result_list.append(vocab_to_int[word])
-        except KeyError as e:
-            print(f'{e}: not in dictionary!')
-    result_padded = padding([result_list], seq_len)[0]
-
-    return torch.tensor(result_padded)
-
-
+import string
+import torch
+import numpy as np
+from nltk.corpus import stopwords
+stop_words = set(stopwords.words('english'))
+import torch.nn as nn
+import pickle
+from transformers import BertTokenizer, BertModel, DistilBertTokenizer, DistilBertModel
+from sklearn.linear_model import LogisticRegression
+import nltk
+
+
+
+EMBEDDING_DIM = 64
+VOCAB_SIZE = 203310
+embedding_matrix = np.zeros((VOCAB_SIZE, EMBEDDING_DIM))
+embedding_layer = torch.nn.Embedding.from_pretrained(torch.FloatTensor(embedding_matrix))
+
+
+
+
+with open('lstm/vocab_to_int.txt', 'rb') as f:
+    vocab_to_int = pickle.load(f)
+
+class LSTMClassifier(nn.Module):
+    def __init__(self, embedding_dim: int, hidden_size: int = 32) -> None:
+        super().__init__()
+
+        self.embedding_dim = embedding_dim # создаем эмбединг сайз
+        self.hidden_size = hidden_size # создаем хидден сайз
+        self.embedding = embedding_layer # создаем слои модели
+
+        self.lstm = nn.LSTM(
+            input_size=self.embedding_dim,
+            hidden_size=self.hidden_size,
+            batch_first=True
+        )
+        
+        self.clf = nn.Linear(self.hidden_size, 1)
+    
+    def forward(self, x):
+        embedding = self.embedding(x)
+        _, (h_n, _) = self.lstm(embedding)
+        out = self.clf(h_n.squeeze())
+        return out  
+
+
+def data_preprocessing(text: str) -> str:
+    text = text.lower()
+    text = ''.join([c for c in text if c not in string.punctuation])# Remove punctuation
+    text = [word for word in text.split() if word not in stop_words] 
+    text = ' '.join(text)
+    return text
+
+
+
+def padding(review_int: list, seq_len: int) -> np.array:
+    features = np.zeros((len(review_int), seq_len), dtype = int)
+    for i, review in enumerate(review_int):
+        if len(review) <= seq_len:
+            zeros = list(np.zeros(seq_len - len(review)))
+            new = zeros + review
+        else:
+            new = review[: seq_len]
+        features[i, :] = np.array(new)
+            
+    return features
+
+def preprocess_single_string(input_string: str, seq_len: int, vocab_to_int: dict = vocab_to_int) -> list:
+    preprocessed_string = data_preprocessing(input_string)
+    result_list = []
+    for word in preprocessed_string.split():
+        try: 
+            result_list.append(vocab_to_int[word])
+        except KeyError as e:
+            print(f'{e}: not in dictionary!')
+    result_padded = padding([result_list], seq_len)[0]
+
+    return torch.tensor(result_padded)
+
+