Datasets:
| # import os | |
| # import pandas as pd | |
| # import numpy as np | |
| # from sklearn.model_selection import train_test_split | |
| # from sklearn.feature_extraction.text import TfidfVectorizer | |
| # from sklearn.preprocessing import LabelEncoder | |
| # # from torch.utils.data import DataLoader, TensorDataset | |
| # from torch.utils.data import DataLoader, TensorDataset, WeightedRandomSampler | |
| # import torch | |
| # # Other imports remain the same | |
| # def create_weighted_sampler(labels): | |
| # class_counts = np.bincount(labels) | |
| # class_weights = 1. / class_counts | |
| # sample_weights = class_weights[labels] | |
| # return sample_weights # Return sample weights, not the sampler | |
| # # Function to read Finsen data | |
| # def read_finsen_data(file_path): | |
| # df = pd.read_csv(file_path) | |
| # texts = df['Content'].tolist() | |
| # labels = LabelEncoder().fit_transform(df['Category']) | |
| # return texts, labels | |
| # # Function to create train and validation DataLoaders | |
| # def get_train_valid_loader(batch_size=128, valid_size=0.1, get_val_temp=0, random_seed=42, shuffle=True, num_workers=4, pin_memory=False): | |
| # # texts, labels = read_finsen_data('./data/FinSen/finsen.csv') | |
| # texts, labels = read_finsen_data('./adafocal-main/data/FinSen/finsen.csv') | |
| # # Vectorize and split the dataset | |
| # vectorizer = TfidfVectorizer(stop_words='english', max_features=2000) | |
| # features = vectorizer.fit_transform(texts).toarray() | |
| # X_train, X_val, y_train, y_val = train_test_split(features, labels, test_size=valid_size, random_state=random_seed) | |
| # # Create weighted sampler for the training set to handle class imbalance | |
| # train_weights = create_weighted_sampler(y_train) | |
| # # train_sampler = WeightedRandomSampler(train_weights, len(train_weights)) | |
| # train_sampler = WeightedRandomSampler(weights=train_weights, num_samples=len(train_weights), replacement=True) | |
| # # Convert to tensors and create DataLoaders | |
| # train_dataset = TensorDataset(torch.tensor(X_train, dtype=torch.float32), torch.tensor(y_train, dtype=torch.long)) | |
| # val_dataset = TensorDataset(torch.tensor(X_val, dtype=torch.float32), torch.tensor(y_val, dtype=torch.long)) | |
| # # Use sampler for train_loader | |
| # train_loader = DataLoader(train_dataset, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers, pin_memory=pin_memory) | |
| # # Validation loader doesn't need weighted sampling | |
| # val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, pin_memory=pin_memory) | |
| # return train_loader, val_loader | |
| # # # Convert to tensors and create DataLoaders | |
| # # train_dataset = TensorDataset(torch.tensor(X_train, dtype=torch.float32), torch.tensor(y_train, dtype=torch.long)) | |
| # # val_dataset = TensorDataset(torch.tensor(X_val, dtype=torch.float32), torch.tensor(y_val, dtype=torch.long)) | |
| # # train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, pin_memory=pin_memory) | |
| # # val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, pin_memory=pin_memory) | |
| # # return train_loader, val_loader | |
| # # Function to create a test DataLoader | |
| # def get_test_loader(batch_size=128, shuffle=True, num_workers=4, pin_memory=False): | |
| # # texts, labels = read_finsen_data('./data/FinSen/finsen.csv') # Ensure this path is correct | |
| # texts, labels = read_finsen_data('./adafocal-main/data/FinSen/finsen.csv') | |
| # # Vectorize and split the dataset for test data | |
| # vectorizer = TfidfVectorizer(stop_words='english', max_features=2000) | |
| # features = vectorizer.fit_transform(texts).toarray() | |
| # _, X_test, _, y_test = train_test_split(features, labels, test_size=0.2, random_state=42, shuffle=True) | |
| # # Convert to tensors and create DataLoader | |
| # test_dataset = TensorDataset(torch.tensor(X_test, dtype=torch.float32), torch.tensor(y_test, dtype=torch.long)) | |
| # test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, pin_memory=pin_memory) | |
| # return test_loader | |
| import os | |
| import pandas as pd | |
| import numpy as np | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.preprocessing import LabelEncoder | |
| from torch.utils.data import DataLoader, TensorDataset, WeightedRandomSampler | |
| import torch | |
| def create_weighted_sampler(labels): | |
| class_counts = np.bincount(labels) | |
| class_weights = 1. / class_counts | |
| sample_weights = class_weights[labels] | |
| weighted_sampler = WeightedRandomSampler(weights=sample_weights, num_samples=len(sample_weights), replacement=True) | |
| return weighted_sampler | |
| def read_finsen_data(file_path): | |
| df = pd.read_csv(file_path) | |
| texts = df['Content'].tolist() | |
| labels = df['Category'].apply(lambda x: x.strip()).tolist() # Ensure labels are stripped of whitespace | |
| label_encoder = LabelEncoder() | |
| labels = label_encoder.fit_transform(labels) | |
| return texts, labels, label_encoder.classes_ | |
| def prepare_data_loaders(texts, labels, batch_size, valid_size, random_seed, shuffle, num_workers, pin_memory): | |
| vectorizer = TfidfVectorizer(stop_words='english', max_features=2000) | |
| features = vectorizer.fit_transform(texts).toarray() | |
| X_train, X_val_test, y_train, y_val_test = train_test_split(features, labels, test_size=valid_size + 0.1, random_state=random_seed) # Adjust valid_size + test_size | |
| X_val, X_test, y_val, y_test = train_test_split(X_val_test, y_val_test, test_size=0.5, random_state=random_seed) # Split remaining data equally for val and test | |
| train_sampler = create_weighted_sampler(y_train) | |
| train_dataset = TensorDataset(torch.tensor(X_train, dtype=torch.float32), torch.tensor(y_train, dtype=torch.long)) | |
| val_dataset = TensorDataset(torch.tensor(X_val, dtype=torch.float32), torch.tensor(y_val, dtype=torch.long)) | |
| test_dataset = TensorDataset(torch.tensor(X_test, dtype=torch.float32), torch.tensor(y_test, dtype=torch.long)) | |
| train_loader = DataLoader(train_dataset, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers, pin_memory=pin_memory) | |
| val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, pin_memory=pin_memory) | |
| test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, pin_memory=pin_memory) | |
| return train_loader, val_loader, test_loader | |
| def get_data_loaders(finsen_data_path, batch_size=128, valid_size=0.2, random_seed=42, shuffle=True, num_workers=4, pin_memory=False): | |
| texts, labels, _ = read_finsen_data(finsen_data_path) | |
| return prepare_data_loaders(texts, labels, batch_size, valid_size, random_seed, shuffle, num_workers, pin_memory) | |
| # Now, train_loader, val_loader, and test_loader can be used in training and evaluation. | |