def test_init(self, char_level, num_tokens, separator, token_to_index, expected_token_to_index): tokenizer = data.Tokenizer(char_level=char_level, num_tokens=num_tokens, token_to_index=token_to_index) assert tokenizer.separator == separator assert tokenizer.token_to_index == expected_token_to_index
def test_save_and_load(self): with tempfile.TemporaryDirectory() as dp: tokenizer = data.Tokenizer( char_level=False, token_to_index={"<PAD>": 0, "<UNK>": 1, "hello": 2, "world": 3} ) fp = Path(dp, "label_encoder.json") tokenizer.save(fp=fp) tokenizer = data.Tokenizer.load(fp=fp) assert len(tokenizer) == 4
def test_fit_on_texts(self, char_level, num_tokens, texts, vocab_size): tokenizer = data.Tokenizer(char_level=char_level, num_tokens=num_tokens) tokenizer.fit_on_texts(texts=texts) assert len(tokenizer) == vocab_size
def setup_method(self): """Called before every method.""" self.tokenizer = data.Tokenizer(char_level=True, num_tokens=None)
class TestTokenizer: def setup_method(self): """Called before every method.""" self.tokenizer = data.Tokenizer(char_level=True, num_tokens=None) def teardown_method(self): """Called after every method.""" del self.tokenizer @pytest.mark.parametrize( "char_level, num_tokens, separator, token_to_index, expected_token_to_index", [ (True, None, "", None, {"<PAD>": 0, "<UNK>": 1}), (False, None, " ", None, {"<PAD>": 0, "<UNK>": 1}), ( False, None, " ", {"<PAD>": 0, "<UNK>": 1, "hello": 2}, {"<PAD>": 0, "<UNK>": 1, "hello": 2}, ), ], ) def test_init(self, char_level, num_tokens, separator, token_to_index, expected_token_to_index): tokenizer = data.Tokenizer( char_level=char_level, num_tokens=num_tokens, token_to_index=token_to_index ) assert tokenizer.separator == separator assert tokenizer.token_to_index == expected_token_to_index def test_len(self): assert len(self.tokenizer) == 2 def test_str(self): assert str(self.tokenizer) == f"<Tokenizer(num_tokens={len(self.tokenizer)})>" @pytest.mark.parametrize( "char_level, num_tokens, texts, vocab_size", [(False, None, ["hello world", "goodbye"], 5), (False, 4, ["hello world", "goodbye"], 4)], ) def test_fit_on_texts(self, char_level, num_tokens, texts, vocab_size): tokenizer = data.Tokenizer(char_level=char_level, num_tokens=num_tokens) tokenizer.fit_on_texts(texts=texts) assert len(tokenizer) == vocab_size @pytest.mark.parametrize( "tokenizer, texts, sequences, decoded", [ ( data.Tokenizer( char_level=False, token_to_index={"<PAD>": 0, "<UNK>": 1, "hello": 2, "world": 3}, ), ["hello world", "hi world", "apple"], [[2, 3], [1, 3], [1]], ["hello world", "<UNK> world", "<UNK>"], ), ( data.Tokenizer( char_level=True, token_to_index={"<PAD>": 0, "<UNK>": 1, " ": 2, "a": 3, "b": 4} ), ["ab", "b", "a x ab"], [[3, 4], [4], [3, 2, 1, 2, 3, 4]], ["ab", "b", "a <UNK> ab"], ), ], ) def test_encode_decode(self, tokenizer, texts, sequences, decoded): assert tokenizer.texts_to_sequences(texts=texts) == sequences assert tokenizer.sequences_to_texts(sequences=sequences) == decoded def test_save_and_load(self): with tempfile.TemporaryDirectory() as dp: tokenizer = data.Tokenizer( char_level=False, token_to_index={"<PAD>": 0, "<UNK>": 1, "hello": 2, "world": 3} ) fp = Path(dp, "label_encoder.json") tokenizer.save(fp=fp) tokenizer = data.Tokenizer.load(fp=fp) assert len(tokenizer) == 4
X=X_, y=y_, train_size=0.5) # View slices test_df = pd.DataFrame({ "text": X_test, "tags": label_encoder.decode(y_test) }) cv_transformers_df = slice_dataframe(test_df, cv_transformers) print(f"{len(cv_transformers_df)} projects") print(cv_transformers_df[["text", "tags"]].head()) short_text_df = slice_dataframe(test_df, short_text) print(f"{len(short_text_df)} projects") print(short_text_df[["text", "tags"]].head()) # 8. Tokenize inputs tokenizer = data.Tokenizer(char_level=args.char_level) tokenizer.fit_on_texts(texts=X_train) X_train = np.array(tokenizer.texts_to_sequences(X_train), dtype=object) X_val = np.array(tokenizer.texts_to_sequences(X_val), dtype=object) X_test = np.array(tokenizer.texts_to_sequences(X_test), dtype=object) # 9. Create dataloaders train_dataset = data.CNNTextDataset(X=X_train, y=y_train, max_filter_size=args.max_filter_size) val_dataset = data.CNNTextDataset(X=X_val, y=y_val, max_filter_size=args.max_filter_size) test_dataset = data.CNNTextDataset(X=X_test, y=y_test, max_filter_size=args.max_filter_size)
def run(params: Namespace, trial: optuna.trial._trial.Trial = None) -> Dict: """Operations for training. Args: params (Namespace): Input parameters for operations. trial (optuna.trial._trial.Trail, optional): Optuna optimization trial. Defaults to None. Returns: Artifacts to save and load for later. """ # 1. Set seed utils.set_seed(seed=params.seed) # 2. Set device device = utils.set_device(cuda=params.cuda) # 3. Load data projects_fp = Path(config.DATA_DIR, "projects.json") tags_fp = Path(config.DATA_DIR, "tags.json") projects = utils.load_dict(filepath=projects_fp) tags_dict = utils.list_to_dict(utils.load_dict(filepath=tags_fp), key="tag") df = pd.DataFrame(projects) if params.shuffle: df = df.sample(frac=1).reset_index(drop=True) df = df[:params.subset] # None = all samples # 4. Prepare data (feature engineering, filter, clean) df, tags_above_freq, tags_below_freq = data.prepare( df=df, include=list(tags_dict.keys()), exclude=config.EXCLUDED_TAGS, min_tag_freq=params.min_tag_freq, ) params.num_samples = len(df) # 5. Preprocess data df.text = df.text.apply(data.preprocess, lower=params.lower, stem=params.stem) # 6. Encode labels labels = df.tags label_encoder = data.MultiLabelLabelEncoder() label_encoder.fit(labels) y = label_encoder.encode(labels) # Class weights all_tags = list(itertools.chain.from_iterable(labels.values)) counts = np.bincount( [label_encoder.class_to_index[class_] for class_ in all_tags]) class_weights = {i: 1.0 / count for i, count in enumerate(counts)} # 7. Split data utils.set_seed(seed=params.seed) # needed for skmultilearn X = df.text.to_numpy() X_train, X_, y_train, y_ = data.iterative_train_test_split( X=X, y=y, train_size=params.train_size) X_val, X_test, y_val, y_test = data.iterative_train_test_split( X=X_, y=y_, train_size=0.5) test_df = pd.DataFrame({ "text": X_test, "tags": label_encoder.decode(y_test) }) # 8. Tokenize inputs tokenizer = data.Tokenizer(char_level=params.char_level) tokenizer.fit_on_texts(texts=X_train) X_train = np.array(tokenizer.texts_to_sequences(X_train), dtype=object) X_val = np.array(tokenizer.texts_to_sequences(X_val), dtype=object) X_test = np.array(tokenizer.texts_to_sequences(X_test), dtype=object) # 9. Create dataloaders train_dataset = data.CNNTextDataset(X=X_train, y=y_train, max_filter_size=params.max_filter_size) val_dataset = data.CNNTextDataset(X=X_val, y=y_val, max_filter_size=params.max_filter_size) train_dataloader = train_dataset.create_dataloader( batch_size=params.batch_size) val_dataloader = val_dataset.create_dataloader( batch_size=params.batch_size) # 10. Initialize model model = models.initialize_model( params=params, vocab_size=len(tokenizer), num_classes=len(label_encoder), device=device, ) # 11. Train model logger.info( f"Parameters: {json.dumps(params.__dict__, indent=2, cls=NumpyEncoder)}" ) params, model, loss = train.train( params=params, train_dataloader=train_dataloader, val_dataloader=val_dataloader, model=model, device=device, class_weights=class_weights, trial=trial, ) # 12. Evaluate model artifacts = { "params": params, "label_encoder": label_encoder, "tokenizer": tokenizer, "model": model, "loss": loss, } device = torch.device("cpu") y_true, y_pred, performance = eval.evaluate(df=test_df, artifacts=artifacts) artifacts["performance"] = performance return artifacts
def train(params: Namespace, trial: optuna.trial._trial.Trial = None) -> Dict: """Operations for training. Args: params (Namespace): Input parameters for operations. trial (optuna.trial._trial.Trail, optional): Optuna optimization trial. Defaults to None. Returns: Artifacts to save and load for later. """ # Set up utils.set_seed(seed=params.seed) device = utils.set_device(cuda=params.cuda) # Load features features_fp = Path(config.DATA_DIR, "features.json") tags_fp = Path(config.DATA_DIR, "tags.json") features = utils.load_dict(filepath=features_fp) tags_dict = utils.list_to_dict(utils.load_dict(filepath=tags_fp), key="tag") df = pd.DataFrame(features) if params.shuffle: df = df.sample(frac=1).reset_index(drop=True) df = df[:params.subset] # None = all samples # Prepare data (filter, clean, etc.) df, tags_above_freq, tags_below_freq = data.prepare( df=df, include=list(tags_dict.keys()), exclude=config.EXCLUDED_TAGS, min_tag_freq=params.min_tag_freq, ) params.num_samples = len(df) # Preprocess data df.text = df.text.apply(data.preprocess, lower=params.lower, stem=params.stem) # Encode labels labels = df.tags label_encoder = data.MultiLabelLabelEncoder() label_encoder.fit(labels) y = label_encoder.encode(labels) # Class weights all_tags = list(itertools.chain.from_iterable(labels.values)) counts = np.bincount( [label_encoder.class_to_index[class_] for class_ in all_tags]) class_weights = {i: 1.0 / count for i, count in enumerate(counts)} # Split data utils.set_seed(seed=params.seed) # needed for skmultilearn X = df.text.to_numpy() X_train, X_, y_train, y_ = data.iterative_train_test_split( X=X, y=y, train_size=params.train_size) X_val, X_test, y_val, y_test = data.iterative_train_test_split( X=X_, y=y_, train_size=0.5) test_df = pd.DataFrame({ "text": X_test, "tags": label_encoder.decode(y_test) }) # Tokenize inputs tokenizer = data.Tokenizer(char_level=params.char_level) tokenizer.fit_on_texts(texts=X_train) X_train = np.array(tokenizer.texts_to_sequences(X_train), dtype=object) X_val = np.array(tokenizer.texts_to_sequences(X_val), dtype=object) X_test = np.array(tokenizer.texts_to_sequences(X_test), dtype=object) # Create dataloaders train_dataset = data.CNNTextDataset(X=X_train, y=y_train, max_filter_size=params.max_filter_size) val_dataset = data.CNNTextDataset(X=X_val, y=y_val, max_filter_size=params.max_filter_size) train_dataloader = train_dataset.create_dataloader( batch_size=params.batch_size) val_dataloader = val_dataset.create_dataloader( batch_size=params.batch_size) # Initialize model model = models.initialize_model( params=params, vocab_size=len(tokenizer), num_classes=len(label_encoder), device=device, ) # Train model logger.info( f"Parameters: {json.dumps(params.__dict__, indent=2, cls=NumpyEncoder)}" ) class_weights_tensor = torch.Tensor(np.array(list(class_weights.values()))) loss_fn = nn.BCEWithLogitsLoss(weight=class_weights_tensor) optimizer = torch.optim.Adam(model.parameters(), lr=params.lr) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=0.05, patience=5) # Trainer module trainer = Trainer( model=model, device=device, loss_fn=loss_fn, optimizer=optimizer, scheduler=scheduler, trial=trial, ) # Train best_val_loss, best_model = trainer.train(params.num_epochs, params.patience, train_dataloader, val_dataloader) # Find best threshold _, y_true, y_prob = trainer.eval_step(dataloader=train_dataloader) params.threshold = find_best_threshold(y_true=y_true, y_prob=y_prob) # Evaluate model artifacts = { "params": params, "label_encoder": label_encoder, "tokenizer": tokenizer, "model": best_model, "loss": best_val_loss, } device = torch.device("cpu") y_true, y_pred, performance = eval.evaluate(df=test_df, artifacts=artifacts) artifacts["performance"] = performance return artifacts