Upload train.py

train.py

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+
+import torch
+from torch import nn
+import nltk_u
+import pandas as pd
+from pathlib import Path
+import matplotlib.pyplot as plt
+from torch.nn.modules.loss import CrossEntropyLoss
+from torch.utils.data import DataLoader, TensorDataset
+from sklearn.metrics import accuracy_score, f1_score
+from sklearn.model_selection import train_test_split
+#####################################################################################################################
+
+def preprocess_data(label_X, target_y):
+    preprocessed= TensorDataset(label_X, target_y)
+    return preprocessed
+
+def dataloader(dataset, batch_size, shuffle, num_workers):
+    dataloader= DataLoader(dataset=dataset,
+                           batch_size=batch_size,
+                           shuffle= shuffle,
+                           num_workers=num_workers)
+    return (dataloader)
+
+class RNN_model(nn.Module):
+  def __init__(self):
+    super().__init__()
+
+    self.rnn= nn.RNN(input_size=1477, hidden_size=240,num_layers=1, nonlinearity= 'relu', bias= True).to(device)
+    self.output= nn.Linear(in_features=240, out_features=24).to(device)
+
+  def forward(self, x):
+    y, hidden= self.rnn(x)
+    x= self.output(y).to(device)
+    return(x)
+#####################################################################################################################
+# import data
+df= pd.read_csv('Symptom2Disease_1.csv')
+
+target=['Psoriasis', 'Varicose Veins', 'Typhoid', 'Chicken pox',
+       'Impetigo', 'Dengue', 'Fungal infection', 'Common Cold',
+       'Pneumonia', 'Dimorphic Hemorrhoids', 'Arthritis', 'Acne',
+       'Bronchial Asthma', 'Hypertension', 'Migraine',
+       'Cervical spondylosis', 'Jaundice', 'Malaria',
+       'urinary tract infection', 'allergy',
+       'gastroesophageal reflux disease', 'drug reaction',
+       'peptic ulcer disease', 'diabetes']
+target_dict= {i:j for i,j in enumerate(sorted(target))}
+df['label']= df['label'].replace({j:i for i,j in enumerate(sorted(target))})
+df.drop('Unnamed: 0', axis= 1, inplace= True)
+df.duplicated().sum()
+df[df.duplicated]
+df.drop_duplicates(inplace= True)
+df['label'].value_counts()
+#####################################################################################################################
+train_data, test_data= train_test_split(df, test_size=0.15, random_state=42 )
+train_data['label'].value_counts().sort_index()
+test_data['label'].value_counts().sort_index()
+#vectorizer= nltk_u.vectorizer()
+from sklearn.feature_extraction.text import TfidfVectorizer
+from spacy.lang.de.stop_words import STOP_WORDS
+vectorizer = TfidfVectorizer(stop_words=list(STOP_WORDS))
+vectorizer.fit(train_data.text)
+vectorizer.get_feature_names_out()[: 100]
+vectorizer= vectorizer
+data_input= vectorizer.transform(train_data.text)
+test_data_input= vectorizer.transform(test_data.text)
+#####################################################################################################################
+# Convert vectors to tensors
+input_data_tensors= torch.tensor(data_input.toarray()).to(torch.float32)
+test_data_tensors= torch.tensor(test_data_input.toarray()).to(torch.float32)
+train_data_output= torch.tensor(train_data['label'].values)
+test_data_output= torch.tensor(test_data['label'].values)
+train_dataset= preprocess_data(input_data_tensors, train_data_output)
+test_dataset= preprocess_data(test_data_tensors, test_data_output)
+train_dataloader= dataloader(dataset=train_dataset,
+                                             batch_size=32, shuffle= True, num_workers=2)
+test_dataloader= dataloader(dataset=test_dataset,
+                                             batch_size=32, shuffle= False, num_workers=2)
+text, target= next(iter(train_dataloader))
+#####################################################################################################################
+if torch.cuda.is_available():
+    device = "cuda"
+    print(f'################################################################# device: {device}#################################################################')
+else:
+    device = "cpu"
+#####################################################################################################################
+model= RNN_model()
+loss_fn= CrossEntropyLoss()
+optimizer= torch.optim.SGD(model.parameters(), lr= 0.1, weight_decay=0)
+#####################################################################################################################
+## train model
+epoch= 500
+
+results= {
+      "train_loss": [],
+      "train_accuracy": [],
+      "test_loss": [],
+      "test_accuracy": []
+      }
+
+for i in range(epoch):
+  train_loss=0
+  train_acc=0
+  for batch, (X, y) in enumerate(train_dataloader):
+    X, y= X.to(device), y.to(device)
+    # Train the model
+    model.train()
+    optimizer.zero_grad()
+    y_logits= model(X)
+    # Calculate the loss
+    loss= loss_fn(y_logits, y)
+    train_loss += loss
+    # ypreds
+    y_preds= torch.argmax(torch.softmax(y_logits, dim=1), dim=1)
+    accuracy = accuracy_score(y.cpu(), y_preds.cpu())
+    train_acc += accuracy
+    # zero grad
+    optimizer.zero_grad()
+    # Loss backward
+    loss.backward()
+    # Optimizer step
+    optimizer.step()
+  train_loss /= len(train_dataloader)
+  train_acc /=len(train_dataloader)
+  test_loss = 0
+  test_acc=0
+  model.eval()
+  with torch.inference_mode():
+    for X, y in test_dataloader:
+      X, y= X.to(device), y.to(device)
+      y_logits= model(X)
+      loss= loss_fn(y_logits, y)
+      test_loss += loss
+      test_preds= torch.argmax(torch.softmax(y_logits, dim=1), dim=1)
+      accuracy = accuracy_score(y.cpu(), test_preds.cpu())
+      test_acc += accuracy
+    test_loss /= len(test_dataloader)
+    test_acc /= len(test_dataloader)
+
+  results['train_loss'].append(train_loss)
+  results['train_accuracy'].append(train_acc)
+  results['test_loss'].append(test_loss)
+  results['test_accuracy'].append(test_acc)
+  if i % 50 == 0:
+    print(f"\nTrain loss: {train_loss:.5f} | Train Acc: {train_acc:.5f} | Test loss: {test_loss:.5f} | Test Acc: {test_acc:.5f} |")
+
+#####################################################################################################################
+'''
+plt.figure(figsize=(10,5))
+plt.subplot(1,2,1)
+plt.plot(results['train_loss'], label= 'train')
+plt.plot(results['test_loss'], label= 'test')
+plt.title('loss curve for train and test')
+plt.legend()
+plt.subplot(1,2,2)
+plt.plot(results['train_accuracy'], label= 'train')
+plt.plot(results['test_accuracy'], label= 'test')
+plt.title('accuracy score for train and test')
+plt.legend()
+'''
+#####################################################################################################################
+new_data= 'I have been having burning pain anytime i am peeing, what could be the issue?'
+transformed_new= vectorizer.transform([new_data])
+transformed_new= torch.tensor(transformed_new.toarray()).to(torch.float32)
+model.eval()
+with torch.inference_mode():
+  y_logits=model(transformed_new)
+  test_preds= torch.argmax(torch.softmax(y_logits, dim=1), dim=1)
+  test_pred= target_dict[test_preds.item()]
+  print(f'based on your symptoms, I believe you are having {test_pred}')
+
+target_dir_path = Path('Models')
+target_dir_path.mkdir(parents=True,
+                      exist_ok=True)
+model_path= target_dir_path / 'symtom2disease_model.pth'
+torch.save(obj=model.state_dict(),f= model_path)
+#####################################################################################################################