from argparse import Namespace from collections import Counter import json import os import re import string import numpy as np import pandas as pd import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.utils.data import Dataset, DataLoader from tqdm.notebook import tqdm class Vocabulary(object): """Class to process text and extract vocabulary for mapping""" def __init__(self, token_to_idx=None, add_unk=True, unk_token="<UNK>"): """ Args: token_to_idx (dict): a pre-existing map of tokens to indices add_unk (bool): a flag that indicates whether to add the UNK token unk_token (str): the UNK token to add into the Vocabulary """ if token_to_idx is None: token_to_idx = {} self._token_to_idx = token_to_idx self._idx_to_token = {idx: token for token, idx in self._token_to_idx.items()} self._add_unk = add_unk self._unk_token = unk_token self.unk_index = -1 if add_unk: self.unk_index = self.add_token(unk_token) def to_serializable(self): """ returns a dictionary that can be serialized """ return {'token_to_idx': self._token_to_idx, 'add_unk': self._add_unk, 'unk_token': self._unk_token} @classmethod def from_serializable(cls, contents): """ instantiates the Vocabulary from a serialized dictionary """ return cls(**contents) def add_token(self, token): """Update mapping dicts based on the token. Args: token (str): the item to add into the Vocabulary Returns: index (int): the integer corresponding to the token """ if token in self._token_to_idx: index = self._token_to_idx[token] else: index = len(self._token_to_idx) self._token_to_idx[token] = index self._idx_to_token[index] = token return index def add_many(self, tokens): """Add a list of tokens into the Vocabulary Args: tokens (list): a list of string tokens Returns: indices (list): a list of indices corresponding to the tokens """ return [self.add_token(token) for token in tokens] def lookup_token(self, token): """Retrieve the index associated with the token or the UNK index if token isn't present. Args: token (str): the token to look up Returns: index (int): the index corresponding to the token Notes: `unk_index` needs to be >=0 (having been added into the Vocabulary) for the UNK functionality """ if self.unk_index >= 0: return self._token_to_idx.get(token, self.unk_index) else: return self._token_to_idx[token] def lookup_index(self, index): """Return the token associated with the index Args: index (int): the index to look up Returns: token (str): the token corresponding to the index Raises: KeyError: if the index is not in the Vocabulary """ if index not in self._idx_to_token: raise KeyError("the index (%d) is not in the Vocabulary" % index) return self._idx_to_token[index] def __str__(self): return "<Vocabulary(size=%d)>" % len(self) def __len__(self): return len(self._token_to_idx) class ReviewVectorizer(object): """ The Vectorizer which coordinates the Vocabularies and puts them to use""" def __init__(self, review_vocab, rating_vocab): """ Args: review_vocab (Vocabulary): maps words to integers rating_vocab (Vocabulary): maps class labels to integers """ self.review_vocab = review_vocab self.rating_vocab = rating_vocab def vectorize(self, review): """Create a collapsed one-hit vector for the review Args: review (str): the review Returns: one_hot (np.ndarray): the collapsed one-hot encoding """ one_hot = np.zeros(len(self.review_vocab), dtype=np.float32) for token in review.split(" "): if token not in string.punctuation: one_hot[self.review_vocab.lookup_token(token)] = 1 return one_hot @classmethod def from_dataframe(cls, review_df, cutoff=25): """Instantiate the vectorizer from the dataset dataframe Args: review_df (pandas.DataFrame): the review dataset cutoff (int): the parameter for frequency-based filtering Returns: an instance of the ReviewVectorizer """ review_vocab = Vocabulary(add_unk=True) rating_vocab = Vocabulary(add_unk=False) # Add ratings for rating in sorted(set(review_df.rating)): rating_vocab.add_token(rating) # Add top words if count > provided count word_counts = Counter() for review in review_df.review: for word in review.split(" "): if word not in string.punctuation: word_counts[word] += 1 for word, count in word_counts.items(): if count > cutoff: review_vocab.add_token(word) return cls(review_vocab, rating_vocab) @classmethod def from_serializable(cls, contents): """Instantiate a ReviewVectorizer from a serializable dictionary Args: contents (dict): the serializable dictionary Returns: an instance of the ReviewVectorizer class """ review_vocab = Vocabulary.from_serializable(contents['review_vocab']) rating_vocab = Vocabulary.from_serializable(contents['rating_vocab']) return cls(review_vocab=review_vocab, rating_vocab=rating_vocab) def to_serializable(self): """Create the serializable dictionary for caching Returns: contents (dict): the serializable dictionary """ return {'review_vocab': self.review_vocab.to_serializable(), 'rating_vocab': self.rating_vocab.to_serializable()} class ReviewDataset(Dataset): def __init__(self, review_df, vectorizer): """ Args: review_df (pandas.DataFrame): the dataset vectorizer (ReviewVectorizer): vectorizer instantiated from dataset """ self.review_df = review_df self._vectorizer = vectorizer self.train_df = self.review_df[self.review_df.split=='train'] self.train_size = len(self.train_df) self.val_df = self.review_df[self.review_df.split=='val'] self.validation_size = len(self.val_df) self.test_df = self.review_df[self.review_df.split=='test'] self.test_size = len(self.test_df) self._lookup_dict = {'train': (self.train_df, self.train_size), 'val': (self.val_df, self.validation_size), 'test': (self.test_df, self.test_size)} self.set_split('train') @classmethod def load_dataset_and_make_vectorizer(cls, review_csv): """Load dataset and make a new vectorizer from scratch Args: review_csv (str): location of the dataset Returns: an instance of ReviewDataset """ review_df = pd.read_csv(review_csv) train_review_df = review_df[review_df.split=='train'] return cls(review_df, ReviewVectorizer.from_dataframe(train_review_df)) @classmethod def load_dataset_and_load_vectorizer(cls, review_csv, vectorizer_filepath): """Load dataset and the corresponding vectorizer. Used in the case in the vectorizer has been cached for re-use Args: review_csv (str): location of the dataset vectorizer_filepath (str): location of the saved vectorizer Returns: an instance of ReviewDataset """ review_df = pd.read_csv(review_csv) vectorizer = cls.load_vectorizer_only(vectorizer_filepath) return cls(review_df, vectorizer) @staticmethod def load_vectorizer_only(vectorizer_filepath): """a static method for loading the vectorizer from file Args: vectorizer_filepath (str): the location of the serialized vectorizer Returns: an instance of ReviewVectorizer """ with open(vectorizer_filepath) as fp: return ReviewVectorizer.from_serializable(json.load(fp)) def save_vectorizer(self, vectorizer_filepath): """saves the vectorizer to disk using json Args: vectorizer_filepath (str): the location to save the vectorizer """ with open(vectorizer_filepath, "w") as fp: json.dump(self._vectorizer.to_serializable(), fp) def get_vectorizer(self): """ returns the vectorizer """ return self._vectorizer def set_split(self, split="train"): """ selects the splits in the dataset using a column in the dataframe Args: split (str): one of "train", "val", or "test" """ self._target_split = split self._target_df, self._target_size = self._lookup_dict[split] def __len__(self): return self._target_size def __getitem__(self, index): """the primary entry point method for PyTorch datasets Args: index (int): the index to the data point Returns: a dictionary holding the data point's features (x_data) and label (y_target) """ row = self._target_df.iloc[index] review_vector = \ self._vectorizer.vectorize(row.review) rating_index = \ self._vectorizer.rating_vocab.lookup_token(row.rating) return {'x_data': review_vector, 'y_target': rating_index} def get_num_batches(self, batch_size): """Given a batch size, return the number of batches in the dataset Args: batch_size (int) Returns: number of batches in the dataset """ return len(self) // batch_size def generate_batches(dataset, batch_size, shuffle=True, drop_last=True, device="cpu"): """ A generator function which wraps the PyTorch DataLoader. It will ensure each tensor is on the write device location. """ dataloader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last) for data_dict in dataloader: out_data_dict = {} for name, tensor in data_dict.items(): out_data_dict[name] = data_dict[name].to(device) yield out_data_dict class ReviewClassifier(nn.Module): """ a simple perceptron based classifier """ def __init__(self, num_features): """ Args: num_features (int): the size of the input feature vector """ super(ReviewClassifier, self).__init__() self.fc1 = nn.Linear(in_features=num_features, out_features=1) def forward(self, x_in, apply_sigmoid=False): """The forward pass of the classifier Args: x_in (torch.Tensor): an input data tensor. x_in.shape should be (batch, num_features) apply_sigmoid (bool): a flag for the sigmoid activation should be false if used with the Cross Entropy losses Returns: the resulting tensor. tensor.shape should be (batch,) """ y_out = self.fc1(x_in).squeeze() if apply_sigmoid: y_out = torch.sigmoid(y_out) return y_out def make_train_state(args): return {'stop_early': False, 'early_stopping_step': 0, 'early_stopping_best_val': 1e8, 'learning_rate': args.learning_rate, 'epoch_index': 0, 'train_loss': [], 'train_acc': [], 'val_loss': [], 'val_acc': [], 'test_loss': -1, 'test_acc': -1, 'model_filename': args.model_state_file} def update_train_state(args, model, train_state): """Handle the training state updates. Components: - Early Stopping: Prevent overfitting. - Model Checkpoint: Model is saved if the model is better :param args: main arguments :param model: model to train :param train_state: a dictionary representing the training state values :returns: a new train_state """ # Save one model at least if train_state['epoch_index'] == 0: torch.save(model.state_dict(), train_state['model_filename']) train_state['stop_early'] = False # Save model if performance improved elif train_state['epoch_index'] >= 1: loss_tm1, loss_t = train_state['val_loss'][-2:] # If loss worsened if loss_t >= train_state['early_stopping_best_val']: # Update step train_state['early_stopping_step'] += 1 # Loss decreased else: # Save the best model if loss_t < train_state['early_stopping_best_val']: torch.save(model.state_dict(), train_state['model_filename']) # Reset early stopping step train_state['early_stopping_step'] = 0 # Stop early ? train_state['stop_early'] = \ train_state['early_stopping_step'] >= args.early_stopping_criteria return train_state def compute_accuracy(y_pred, y_target): y_target = y_target.cpu() y_pred_indices = (torch.sigmoid(y_pred)>0.5).cpu().long()#.max(dim=1)[1] n_correct = torch.eq(y_pred_indices, y_target).sum().item() return n_correct / len(y_pred_indices) * 100 def set_seed_everywhere(seed, cuda): np.random.seed(seed) torch.manual_seed(seed) if cuda: torch.cuda.manual_seed_all(seed) def handle_dirs(dirpath): if not os.path.exists(dirpath): os.makedirs(dirpath) args = Namespace( # Data and Path information frequency_cutoff=25, model_state_file='model.pth', review_csv='data/yelp/reviews_with_splits_lite.csv', # review_csv='data/yelp/reviews_with_splits_full.csv', save_dir='model_storage/ch3/yelp/', vectorizer_file='vectorizer.json', # No Model hyper parameters # Training hyper parameters batch_size=128, early_stopping_criteria=5, learning_rate=0.001, num_epochs=100, seed=1337, # Runtime options catch_keyboard_interrupt=True, cuda=True, expand_filepaths_to_save_dir=True, reload_from_files=False, ) if args.expand_filepaths_to_save_dir: args.vectorizer_file = os.path.join(args.save_dir, args.vectorizer_file) args.model_state_file = os.path.join(args.save_dir, args.model_state_file) print("Expanded filepaths: ") print("\t{}".format(args.vectorizer_file)) print("\t{}".format(args.model_state_file)) # Check CUDA if not torch.cuda.is_available(): args.cuda = False if torch.cuda.device_count() > 1: print("Pouzivam", torch.cuda.device_count(), "graficke karty!") args.device = torch.device("cuda" if args.cuda else "cpu") # Set seed for reproducibility set_seed_everywhere(args.seed, args.cuda) # handle dirs handle_dirs(args.save_dir) if args.reload_from_files: # training from a checkpoint print("Loading dataset and vectorizer") dataset = ReviewDataset.load_dataset_and_load_vectorizer(args.review_csv, args.vectorizer_file) else: print("Loading dataset and creating vectorizer") # create dataset and vectorizer dataset = ReviewDataset.load_dataset_and_make_vectorizer(args.review_csv) dataset.save_vectorizer(args.vectorizer_file) vectorizer = dataset.get_vectorizer() classifier = ReviewClassifier(num_features=len(vectorizer.review_vocab)) classifier = nn.DataParallel(classifier) classifier = classifier.to(args.device) loss_func = nn.BCEWithLogitsLoss() optimizer = optim.Adam(classifier.parameters(), lr=args.learning_rate) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer, mode='min', factor=0.5, patience=1) train_state = make_train_state(args) epoch_bar = tqdm(desc='training routine', total=args.num_epochs, position=0) dataset.set_split('train') train_bar = tqdm(desc='split=train', total=dataset.get_num_batches(args.batch_size), position=1, leave=True) dataset.set_split('val') val_bar = tqdm(desc='split=val', total=dataset.get_num_batches(args.batch_size), position=1, leave=True) try: for epoch_index in range(args.num_epochs): train_state['epoch_index'] = epoch_index # Iterate over training dataset # setup: batch generator, set loss and acc to 0, set train mode on dataset.set_split('train') batch_generator = generate_batches(dataset, batch_size=args.batch_size, device=args.device) running_loss = 0.0 running_acc = 0.0 classifier.train() for batch_index, batch_dict in enumerate(batch_generator): # the training routine is these 5 steps: # -------------------------------------- # step 1. zero the gradients optimizer.zero_grad() # step 2. compute the output y_pred = classifier(x_in=batch_dict['x_data'].float()) # step 3. compute the loss loss = loss_func(y_pred, batch_dict['y_target'].float()) loss_t = loss.item() running_loss += (loss_t - running_loss) / (batch_index + 1) # step 4. use loss to produce gradients loss.backward() # step 5. use optimizer to take gradient step optimizer.step() # ----------------------------------------- # compute the accuracy acc_t = compute_accuracy(y_pred, batch_dict['y_target']) running_acc += (acc_t - running_acc) / (batch_index + 1) # update bar train_bar.set_postfix(loss=running_loss, acc=running_acc, epoch=epoch_index) train_bar.update() train_state['train_loss'].append(running_loss) train_state['train_acc'].append(running_acc) # Iterate over val dataset # setup: batch generator, set loss and acc to 0; set eval mode on dataset.set_split('val') batch_generator = generate_batches(dataset, batch_size=args.batch_size, device=args.device) running_loss = 0. running_acc = 0. classifier.eval() for batch_index, batch_dict in enumerate(batch_generator): # compute the output y_pred = classifier(x_in=batch_dict['x_data'].float()) # step 3. compute the loss loss = loss_func(y_pred, batch_dict['y_target'].float()) loss_t = loss.item() running_loss += (loss_t - running_loss) / (batch_index + 1) # compute the accuracy acc_t = compute_accuracy(y_pred, batch_dict['y_target']) running_acc += (acc_t - running_acc) / (batch_index + 1) val_bar.set_postfix(loss=running_loss, acc=running_acc, epoch=epoch_index) val_bar.update() train_state['val_loss'].append(running_loss) train_state['val_acc'].append(running_acc) train_state = update_train_state(args=args, model=classifier, train_state=train_state) scheduler.step(train_state['val_loss'][-1]) train_bar.n = 0 val_bar.n = 0 epoch_bar.update() if train_state['stop_early']: break train_bar.n = 0 val_bar.n = 0 epoch_bar.update() except KeyboardInterrupt: print("Exiting loop") classifier.load_state_dict(torch.load(train_state['model_filename'])) classifier = classifier.to(args.device) dataset.set_split('test') batch_generator = generate_batches(dataset, batch_size=args.batch_size, device=args.device) running_loss = 0. running_acc = 0. classifier.eval() for batch_index, batch_dict in enumerate(batch_generator): # compute the output y_pred = classifier(x_in=batch_dict['x_data'].float()) # compute the loss loss = loss_func(y_pred, batch_dict['y_target'].float()) loss_t = loss.item() running_loss += (loss_t - running_loss) / (batch_index + 1) # compute the accuracy acc_t = compute_accuracy(y_pred, batch_dict['y_target']) running_acc += (acc_t - running_acc) / (batch_index + 1) train_state['test_loss'] = running_loss train_state['test_acc'] = running_acc print("Test loss: {:.3f}".format(train_state['test_loss'])) print("Test Accuracy: {:.2f}".format(train_state['test_acc'])) def preprocess_text(text): text = text.lower() text = re.sub(r"([.,!?])", r" \1 ", text) text = re.sub(r"[^a-zA-Z.,!?]+", r" ", text) return text def predict_rating(review, classifier, vectorizer, decision_threshold=0.5): """Predict the rating of a review Args: review (str): the text of the review classifier (ReviewClassifier): the trained model vectorizer (ReviewVectorizer): the corresponding vectorizer decision_threshold (float): The numerical boundary which separates the rating classes """ review = preprocess_text(review) vectorized_review = torch.tensor(vectorizer.vectorize(review)) result = classifier(vectorized_review.view(1, -1)) probability_value = F.sigmoid(result).item() index = 1 if probability_value < decision_threshold: index = 0 return vectorizer.rating_vocab.lookup_index(index) test_review = "this is a pretty awesome book" classifier = classifier.cpu() prediction = predict_rating(test_review, classifier, vectorizer, decision_threshold=0.5) print("{} -> {}".format(test_review, prediction)) # Sort weights fc1_weights = classifier.fc1.weight.detach()[0] _, indices = torch.sort(fc1_weights, dim=0, descending=True) indices = indices.numpy().tolist() # Top 20 words print("Influential words in Positive Reviews:") print("--------------------------------------") for i in range(20): print(vectorizer.review_vocab.lookup_index(indices[i])) print("====\n\n\n") # Top 20 negative words print("Influential words in Negative Reviews:") print("--------------------------------------") indices.reverse() for i in range(20): print(vectorizer.review_vocab.lookup_index(indices[i]))