diff --git a/pages/students/2016/lukas_pokryvka/dp2021/yelp/script.py b/pages/students/2016/lukas_pokryvka/dp2021/yelp/script.py
index 52da238091..b0629a18b7 100644
--- a/pages/students/2016/lukas_pokryvka/dp2021/yelp/script.py
+++ b/pages/students/2016/lukas_pokryvka/dp2021/yelp/script.py
@@ -1 +1,748 @@
-# README
\ No newline at end of file
+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]))
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