diff --git a/README.md b/README.md index 29f57feb..ecf45eeb 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,17 @@ Mambular is a Python library for tabular deep learning. It includes models that leverage the Mamba (State Space Model) architecture, as well as other popular models like TabTransformer, FTTransformer, TabM and tabular ResNets. Check out our paper `Mambular: A Sequential Model for Tabular Deep Learning`, available [here](https://arxiv.org/abs/2408.06291). Also check out our paper introducing [TabulaRNN](https://arxiv.org/pdf/2411.17207) and analyzing the efficiency of NLP inspired tabular models. +

⚡ What's New ⚡

+ + + + +

Table of Contents

- [🏃 Quickstart](#-quickstart) @@ -30,7 +41,6 @@ Mambular is a Python library for tabular deep learning. It includes models that - [🛠️ Installation](#️-installation) - [🚀 Usage](#-usage) - [💻 Implement Your Own Model](#-implement-your-own-model) -- [Custom Training](#custom-training) - [🏷️ Citation](#️-citation) - [License](#license) @@ -103,6 +113,7 @@ pip install mamba-ssm

Preprocessing

Mambular simplifies data preprocessing with a range of tools designed for easy transformation of tabular data. +Specify a default method, or a dictionary defining individual preprocessing methods for each feature.

Data Type Detection and Transformation

@@ -116,6 +127,7 @@ Mambular simplifies data preprocessing with a range of tools designed for easy t - **Polynomial Features**: Automatically generates polynomial and interaction terms for numerical features, enhancing the ability to capture higher-order relationships. - **Box-Cox & Yeo-Johnson Transformations**: Performs power transformations to stabilize variance and normalize distributions. - **Custom Binning**: Enables user-defined bin edges for precise discretization of numerical data. +- **Pre-trained Encoding**: Use sentence transformers to encode categorical features. @@ -147,6 +159,28 @@ preds = model.predict(X) preds = model.predict_proba(X) ``` +Get latent representations for each feature: +```python +# simple encoding +model.encode(X) +``` + +Use unstructured data: +```python +# load pretrained models +image_model = ... +nlp_model = ... + +# create embeddings +img_embs = image_model.encode(images) +txt_embs = nlp_model.encode(texts) + +# fit model on tabular data and unstructured data +model.fit(X_train, y_train, embeddings=[img_embs, txt_embs]) +``` + + +

Hyperparameter Optimization

Since all of the models are sklearn base estimators, you can use the built-in hyperparameter optimizatino from sklearn. @@ -222,9 +256,11 @@ MambularLSS allows you to model the full distribution of a response variable, no - **studentt**: For data with heavier tails, useful with small samples. - **negativebinom**: For over-dispersed count data. - **inversegamma**: Often used as a prior in Bayesian inference. +- **johnsonsu**: Four parameter distribution defining location, scale, kurtosis and skewness. - **categorical**: For data with more than two categories. - **Quantile**: For quantile regression using the pinball loss. + These distribution classes make MambularLSS versatile in modeling various data types and distributions. @@ -269,13 +305,16 @@ Here's how you can implement a custom model with Mambular: ```python from dataclasses import dataclass + from mambular.configs import BaseConfig @dataclass - class MyConfig: + class MyConfig(BaseConfig): lr: float = 1e-04 lr_patience: int = 10 weight_decay: float = 1e-06 - lr_factor: float = 0.1 + n_layers: int = 4 + pooling_method:str = "avg + ``` 2. **Second, define your model:** @@ -290,22 +329,32 @@ Here's how you can implement a custom model with Mambular: class MyCustomModel(BaseModel): def __init__( self, - cat_feature_info, - num_feature_info, + feature_information: tuple, num_classes: int = 1, config=None, **kwargs, ): - super().__init__(**kwargs) - self.save_hyperparameters(ignore=["cat_feature_info", "num_feature_info"]) + super().__init__(**kwargs) + self.save_hyperparameters(ignore=["feature_information"]) + self.returns_ensemble = False + + # embedding layer + self.embedding_layer = EmbeddingLayer( + *feature_information, + config=config, + ) - input_dim = get_feature_dimensions(num_feature_info, cat_feature_info) + input_dim = np.sum( + [len(info) * self.hparams.d_model for info in feature_information] + ) self.linear = nn.Linear(input_dim, num_classes) - def forward(self, num_features, cat_features): - x = num_features + cat_features - x = torch.cat(x, dim=1) + def forward(self, *data) -> torch.Tensor: + x = self.embedding_layer(*data) + B, S, D = x.shape + x = x.reshape(B, S * D) + # Pass through linear layer output = self.linear(x) @@ -329,60 +378,11 @@ Here's how you can implement a custom model with Mambular: ```python regressor = MyRegressor(numerical_preprocessing="ple") regressor.fit(X_train, y_train, max_epochs=50) + + regressor.evaluate(X_test, y_test) ``` -# Custom Training -If you prefer to setup custom training, preprocessing and evaluation, you can simply use the `mambular.base_models`. -Just be careful that all basemodels expect lists of features as inputs. More precisely as list for numerical features and a list for categorical features. A custom training loop, with random data could look like this. -```python -import torch -import torch.nn as nn -import torch.optim as optim -from mambular.base_models import Mambular -from mambular.configs import DefaultMambularConfig - -# Dummy data and configuration -cat_feature_info = { - "cat1": { - "preprocessing": "imputer -> continuous_ordinal", - "dimension": 1, - "categories": 4, - } -} # Example categorical feature information -num_feature_info = { - "num1": {"preprocessing": "imputer -> scaler", "dimension": 1, "categories": None} -} # Example numerical feature information -num_classes = 1 -config = DefaultMambularConfig() # Use the desired configuration - -# Initialize model, loss function, and optimizer -model = Mambular(cat_feature_info, num_feature_info, num_classes, config) -criterion = nn.MSELoss() # Use MSE for regression; change as appropriate for your task -optimizer = optim.Adam(model.parameters(), lr=0.001) - -# Example training loop -for epoch in range(10): # Number of epochs - model.train() - optimizer.zero_grad() - - # Dummy Data - num_features = [torch.randn(32, 1) for _ in num_feature_info] - cat_features = [torch.randint(0, 5, (32,)) for _ in cat_feature_info] - labels = torch.randn(32, num_classes) - - # Forward pass - outputs = model(num_features, cat_features) - loss = criterion(outputs, labels) - - # Backward pass and optimization - loss.backward() - optimizer.step() - - # Print loss for monitoring - print(f"Epoch [{epoch+1}/10], Loss: {loss.item():.4f}") - -``` # 🏷️ Citation diff --git a/mambular/configs/__init__.py b/mambular/configs/__init__.py index b2e18708..cdda37ef 100644 --- a/mambular/configs/__init__.py +++ b/mambular/configs/__init__.py @@ -10,6 +10,7 @@ from .tabm_config import DefaultTabMConfig from .tabtransformer_config import DefaultTabTransformerConfig from .tabularnn_config import DefaultTabulaRNNConfig +from .base_config import BaseConfig __all__ = [ "DefaultFTTransformerConfig", @@ -24,4 +25,5 @@ "DefaultTabMConfig", "DefaultTabTransformerConfig", "DefaultTabulaRNNConfig", + "BaseConfig" ] diff --git a/mambular/models/sklearn_base_regressor.py b/mambular/models/sklearn_base_regressor.py index 258e084f..210b785e 100644 --- a/mambular/models/sklearn_base_regressor.py +++ b/mambular/models/sklearn_base_regressor.py @@ -14,11 +14,7 @@ from ..base_models.lightning_wrapper import TaskModel from ..data_utils.datamodule import MambularDataModule from ..preprocessing import Preprocessor -from ..utils.config_mapper import ( - activation_mapper, - get_search_space, - round_to_nearest_16, -) +from ..utils.config_mapper import activation_mapper, get_search_space, round_to_nearest_16 class SklearnBaseRegressor(BaseEstimator): @@ -42,15 +38,11 @@ def __init__(self, model, config, **kwargs): ] self.config_kwargs = { - k: v - for k, v in kwargs.items() - if k not in self.preprocessor_arg_names and not k.startswith("optimizer") + k: v for k, v in kwargs.items() if k not in self.preprocessor_arg_names and not k.startswith("optimizer") } self.config = config(**self.config_kwargs) - preprocessor_kwargs = { - k: v for k, v in kwargs.items() if k in self.preprocessor_arg_names - } + preprocessor_kwargs = {k: v for k, v in kwargs.items() if k in self.preprocessor_arg_names} self.preprocessor = Preprocessor(**preprocessor_kwargs) self.base_model = model @@ -70,8 +62,7 @@ def __init__(self, model, config, **kwargs): self.optimizer_kwargs = { k: v for k, v in kwargs.items() - if k - not in ["lr", "weight_decay", "patience", "lr_patience", "optimizer_type"] + if k not in ["lr", "weight_decay", "patience", "lr_patience", "optimizer_type"] and k.startswith("optimizer_") } @@ -92,10 +83,7 @@ def get_params(self, deep=True): params.update(self.config_kwargs) if deep: - preprocessor_params = { - "prepro__" + key: value - for key, value in self.preprocessor.get_params().items() - } + preprocessor_params = {"prepro__" + key: value for key, value in self.preprocessor.get_params().items()} params.update(preprocessor_params) return params @@ -113,14 +101,8 @@ def set_params(self, **parameters): self : object Estimator instance. """ - config_params = { - k: v for k, v in parameters.items() if not k.startswith("prepro__") - } - preprocessor_params = { - k.split("__")[1]: v - for k, v in parameters.items() - if k.startswith("prepro__") - } + config_params = {k: v for k, v in parameters.items() if not k.startswith("prepro__")} + preprocessor_params = {k.split("__")[1]: v for k, v in parameters.items() if k.startswith("prepro__")} if config_params: self.config_kwargs.update(config_params) @@ -240,13 +222,9 @@ def build_model( self.data_module.embedding_feature_info, ), lr=lr if lr is not None else self.config.lr, - lr_patience=( - lr_patience if lr_patience is not None else self.config.lr_patience - ), + lr_patience=(lr_patience if lr_patience is not None else self.config.lr_patience), lr_factor=lr_factor if lr_factor is not None else self.config.lr_factor, - weight_decay=( - weight_decay if weight_decay is not None else self.config.weight_decay - ), + weight_decay=(weight_decay if weight_decay is not None else self.config.weight_decay), train_metrics=train_metrics, val_metrics=val_metrics, optimizer_type=self.optimizer_type, @@ -277,9 +255,7 @@ def get_number_of_params(self, requires_grad=True): If the model has not been built prior to calling this method. """ if not self.built: - raise ValueError( - "The model must be built before the number of parameters can be estimated" - ) + raise ValueError("The model must be built before the number of parameters can be estimated") else: if requires_grad: return sum(p.numel() for p in self.task_model.parameters() if p.requires_grad) # type: ignore @@ -456,7 +432,7 @@ def predict(self, X, embeddings=None, device=None): predictions_list = self.trainer.predict(self.task_model, self.data_module) # Concatenate predictions from all batches - predictions = torch.cat(predictions_list, dim=0) + predictions = torch.cat(predictions_list, dim=0) # type: ignore # Check if ensemble is used if getattr(self.base_model, "returns_ensemble", False): # If using ensemble @@ -553,9 +529,7 @@ def encode(self, X, embeddings=None, batch_size=64): # Process data in batches encoded_outputs = [] for batch in tqdm(data_loader): - embeddings = self.task_model.base_model.encode( - batch - ) # Call your encode function + embeddings = self.task_model.base_model.encode(batch) # Call your encode function encoded_outputs.append(embeddings) # Concatenate all encoded outputs @@ -633,13 +607,11 @@ def optimize_hparams( best_val_loss = float("inf") if X_val is not None and y_val is not None: - val_loss = self.evaluate( - X_val, y_val, metrics={"Mean Squared Error": mean_squared_error} - )["Mean Squared Error"] - else: - val_loss = self.trainer.validate(self.task_model, self.data_module)[0][ - "val_loss" + val_loss = self.evaluate(X_val, y_val, metrics={"Mean Squared Error": mean_squared_error})[ + "Mean Squared Error" ] + else: + val_loss = self.trainer.validate(self.task_model, self.data_module)[0]["val_loss"] best_val_loss = val_loss best_epoch_val_loss = self.task_model.epoch_val_loss_at( # type: ignore @@ -665,9 +637,7 @@ def _objective(hyperparams): if param_value in activation_mapper: setattr(self.config, key, activation_mapper[param_value]) else: - raise ValueError( - f"Unknown activation function: {param_value}" - ) + raise ValueError(f"Unknown activation function: {param_value}") else: setattr(self.config, key, param_value) @@ -689,9 +659,7 @@ def _objective(hyperparams): # Dynamically set the early pruning threshold if prune_by_epoch: - early_pruning_threshold = ( - best_epoch_val_loss * 1.5 - ) # Prune based on specific epoch loss + early_pruning_threshold = best_epoch_val_loss * 1.5 # Prune based on specific epoch loss else: # Prune based on the best overall validation loss early_pruning_threshold = best_val_loss * 1.5 @@ -702,19 +670,15 @@ def _objective(hyperparams): try: # Wrap the risky operation (model fitting) in a try-except block - self.fit( - X, y, X_val=X_val, y_val=y_val, max_epochs=max_epochs, rebuild=False - ) + self.fit(X, y, X_val=X_val, y_val=y_val, max_epochs=max_epochs, rebuild=False) # Evaluate validation loss if X_val is not None and y_val is not None: - val_loss = self.evaluate( - X_val, y_val, metrics={"Mean Squared Error": mean_squared_error} - )["Mean Squared Error"] + val_loss = self.evaluate(X_val, y_val, metrics={"Mean Squared Error": mean_squared_error})[ + "Mean Squared Error" + ] else: - val_loss = self.trainer.validate(self.task_model, self.data_module)[ - 0 - ]["val_loss"] + val_loss = self.trainer.validate(self.task_model, self.data_module)[0]["val_loss"] # Pruning based on validation loss at specific epoch epoch_val_loss = self.task_model.epoch_val_loss_at( # type: ignore @@ -731,21 +695,15 @@ def _objective(hyperparams): except Exception as e: # Penalize the hyperparameter configuration with a large value - print( - f"Error encountered during fit with hyperparameters {hyperparams}: {e}" - ) - return ( - best_val_loss * 100 - ) # Large value to discourage this configuration + print(f"Error encountered during fit with hyperparameters {hyperparams}: {e}") + return best_val_loss * 100 # Large value to discourage this configuration # Perform Bayesian optimization using scikit-optimize result = gp_minimize(_objective, param_space, n_calls=time, random_state=42) # Update the model with the best-found hyperparameters best_hparams = result.x # type: ignore - head_layer_sizes = ( - [] if "head_layer_sizes" in self.config.__dataclass_fields__ else None - ) + head_layer_sizes = [] if "head_layer_sizes" in self.config.__dataclass_fields__ else None layer_sizes = [] if "layer_sizes" in self.config.__dataclass_fields__ else None # Iterate over the best hyperparameters found by optimization diff --git a/mambular/preprocessing/preprocessor.py b/mambular/preprocessing/preprocessor.py index 0e69f815..44c21193 100644 --- a/mambular/preprocessing/preprocessor.py +++ b/mambular/preprocessing/preprocessor.py @@ -120,14 +120,10 @@ def __init__( ): self.n_bins = n_bins self.numerical_preprocessing = ( - numerical_preprocessing.lower() - if numerical_preprocessing is not None - else "none" + numerical_preprocessing.lower() if numerical_preprocessing is not None else "none" ) self.categorical_preprocessing = ( - categorical_preprocessing.lower() - if categorical_preprocessing is not None - else "none" + categorical_preprocessing.lower() if categorical_preprocessing is not None else "none" ) if self.numerical_preprocessing not in [ "ple", @@ -251,19 +247,13 @@ def _detect_column_types(self, X): numerical_features.append(col) else: if isinstance(self.cat_cutoff, float): - cutoff_condition = ( - num_unique_values / total_samples - ) < self.cat_cutoff + cutoff_condition = (num_unique_values / total_samples) < self.cat_cutoff elif isinstance(self.cat_cutoff, int): cutoff_condition = num_unique_values < self.cat_cutoff else: - raise ValueError( - "cat_cutoff should be either a float or an integer." - ) + raise ValueError("cat_cutoff should be either a float or an integer.") - if X[col].dtype.kind not in "iufc" or ( - X[col].dtype.kind == "i" and cutoff_condition - ): + if X[col].dtype.kind not in "iufc" or (X[col].dtype.kind == "i" and cutoff_condition): categorical_features.append(col) else: numerical_features.append(col) @@ -276,11 +266,9 @@ def _fit_embeddings(self, embeddings): self.embedding_dimensions = {} if isinstance(embeddings, np.ndarray): self.embedding_dimensions["embeddings_1"] = embeddings.shape[1] - elif isinstance(embeddings, list) and all( - isinstance(e, np.ndarray) for e in embeddings - ): + elif isinstance(embeddings, list) and all(isinstance(e, np.ndarray) for e in embeddings): for idx, e in enumerate(embeddings): - self.embedding_dimensions[f"embedding_{idx+1}"] = e.shape[1] + self.embedding_dimensions[f"embedding_{idx + 1}"] = e.shape[1] else: self.embeddings = False @@ -310,9 +298,7 @@ def fit(self, X, y=None, embeddings=None): if numerical_features: for feature in numerical_features: - feature_preprocessing = self.feature_preprocessing.get( - feature, self.numerical_preprocessing - ) + feature_preprocessing = self.feature_preprocessing.get(feature, self.numerical_preprocessing) # extended the annotation list if new transformer is added, either from sklearn or custom numeric_transformer_steps: list[ @@ -348,11 +334,7 @@ def fit(self, X, y=None, embeddings=None): ( "discretizer", KBinsDiscretizer( - n_bins=( - bins - if isinstance(bins, int) - else len(bins) - 1 - ), + n_bins=(bins if isinstance(bins, int) else len(bins) - 1), encode="ordinal", strategy=self.binning_strategy, # type: ignore subsample=200_000 if len(X) > 200_000 else None, @@ -381,17 +363,13 @@ def fit(self, X, y=None, embeddings=None): numeric_transformer_steps.append(("scaler", StandardScaler())) elif feature_preprocessing == "minmax": - numeric_transformer_steps.append( - ("minmax", MinMaxScaler(feature_range=(-1, 1))) - ) + numeric_transformer_steps.append(("minmax", MinMaxScaler(feature_range=(-1, 1)))) elif feature_preprocessing == "quantile": numeric_transformer_steps.append( ( "quantile", - QuantileTransformer( - n_quantiles=self.n_bins, random_state=101 - ), + QuantileTransformer(n_quantiles=self.n_bins, random_state=101), ) ) @@ -399,9 +377,7 @@ def fit(self, X, y=None, embeddings=None): if self.scaling_strategy == "standardization": numeric_transformer_steps.append(("scaler", StandardScaler())) elif self.scaling_strategy == "minmax": - numeric_transformer_steps.append( - ("minmax", MinMaxScaler(feature_range=(-1, 1))) - ) + numeric_transformer_steps.append(("minmax", MinMaxScaler(feature_range=(-1, 1)))) numeric_transformer_steps.append( ( "polynomial", @@ -416,9 +392,7 @@ def fit(self, X, y=None, embeddings=None): if self.scaling_strategy == "standardization": numeric_transformer_steps.append(("scaler", StandardScaler())) elif self.scaling_strategy == "minmax": - numeric_transformer_steps.append( - ("minmax", MinMaxScaler(feature_range=(-1, 1))) - ) + numeric_transformer_steps.append(("minmax", MinMaxScaler(feature_range=(-1, 1)))) numeric_transformer_steps.append( ( "splines", @@ -437,9 +411,7 @@ def fit(self, X, y=None, embeddings=None): if self.scaling_strategy == "standardization": numeric_transformer_steps.append(("scaler", StandardScaler())) elif self.scaling_strategy == "minmax": - numeric_transformer_steps.append( - ("minmax", MinMaxScaler(feature_range=(-1, 1))) - ) + numeric_transformer_steps.append(("minmax", MinMaxScaler(feature_range=(-1, 1)))) numeric_transformer_steps.append( ( "rbf", @@ -456,9 +428,7 @@ def fit(self, X, y=None, embeddings=None): if self.scaling_strategy == "standardization": numeric_transformer_steps.append(("scaler", StandardScaler())) elif self.scaling_strategy == "minmax": - numeric_transformer_steps.append( - ("minmax", MinMaxScaler(feature_range=(-1, 1))) - ) + numeric_transformer_steps.append(("minmax", MinMaxScaler(feature_range=(-1, 1)))) numeric_transformer_steps.append( ( "sigmoid", @@ -471,21 +441,16 @@ def fit(self, X, y=None, embeddings=None): ) ) - elif feature_preprocessing == "ple": - numeric_transformer_steps.append( - ("minmax", MinMaxScaler(feature_range=(-1, 1))) - ) - numeric_transformer_steps.append( - ("ple", PLE(n_bins=self.n_bins, task=self.task)) - ) + numeric_transformer_steps.append(("minmax", MinMaxScaler(feature_range=(-1, 1)))) + numeric_transformer_steps.append(("ple", PLE(n_bins=self.n_bins, task=self.task))) elif feature_preprocessing == "box-cox": numeric_transformer_steps.append( - ("minmax", MinMaxScaler(feature_range=(1e-03, 1))) + ("minmax", MinMaxScaler(feature_range=(1e-03, 1))) # type: ignore ) numeric_transformer_steps.append( - ("check_positive", MinMaxScaler(feature_range=(1e-3, 1))) + ("check_positive", MinMaxScaler(feature_range=(1e-3, 1))) # type: ignore ) numeric_transformer_steps.append( ( @@ -516,9 +481,7 @@ def fit(self, X, y=None, embeddings=None): if categorical_features: for feature in categorical_features: - feature_preprocessing = self.feature_preprocessing.get( - feature, self.categorical_preprocessing - ) + feature_preprocessing = self.feature_preprocessing.get(feature, self.categorical_preprocessing) if feature_preprocessing == "int": # Use ContinuousOrdinalEncoder for "int" categorical_transformer = Pipeline( @@ -553,18 +516,12 @@ def fit(self, X, y=None, embeddings=None): ] ) else: - raise ValueError( - f"Unknown categorical_preprocessing type: {feature_preprocessing}" - ) + raise ValueError(f"Unknown categorical_preprocessing type: {feature_preprocessing}") # Append the transformer for the current categorical feature - transformers.append( - (f"cat_{feature}", categorical_transformer, [feature]) - ) + transformers.append((f"cat_{feature}", categorical_transformer, [feature])) - self.column_transformer = ColumnTransformer( - transformers=transformers, remainder="passthrough" - ) + self.column_transformer = ColumnTransformer(transformers=transformers, remainder="passthrough") self.column_transformer.fit(X, y) self.fitted = True @@ -590,17 +547,13 @@ def _get_decision_tree_bins(self, X, y, numerical_features): bins = [] for feature in numerical_features: tree_model = ( - DecisionTreeClassifier(max_depth=3) - if y.dtype.kind in "bi" - else DecisionTreeRegressor(max_depth=3) + DecisionTreeClassifier(max_depth=3) if y.dtype.kind in "bi" else DecisionTreeRegressor(max_depth=3) ) tree_model.fit(X[[feature]], y) thresholds = tree_model.tree_.threshold[tree_model.tree_.feature != -2] # type: ignore bin_edges = np.sort(np.unique(thresholds)) - bins.append( - np.concatenate(([X[feature].min()], bin_edges, [X[feature].max()])) - ) + bins.append(np.concatenate(([X[feature].min()], bin_edges, [X[feature].max()]))) return bins def transform(self, X, embeddings=None): @@ -634,30 +587,27 @@ def transform(self, X, embeddings=None): # Now let's convert this into a dictionary of arrays, one per column transformed_dict = self._split_transformed_output(X, transformed_X) if embeddings is not None: - assert self.embeddings is True, "self.embeddings should be True but is not." + if not self.embeddings: + raise ValueError("self.embeddings should be True but is not.") if isinstance(embeddings, np.ndarray): - assert ( - self.embedding_dimensions["embedding_1"] == embeddings.shape[1] - ), ( - f"Expected embedding dimension {self.embedding_dimensions['embeddings']}, " - f"but got {embeddings.shape[1]}" - ) + if self.embedding_dimensions["embedding_1"] != embeddings.shape[1]: + raise ValueError( + f"Expected embedding dimension {self.embedding_dimensions['embedding_1']}, " + f"but got {embeddings.shape[1]}" + ) transformed_dict["embedding_1"] = embeddings.astype(np.float32) - elif isinstance(embeddings, list) and all( - isinstance(e, np.ndarray) for e in embeddings - ): + elif isinstance(embeddings, list) and all(isinstance(e, np.ndarray) for e in embeddings): for idx, e in enumerate(embeddings): - key = f"embedding_{idx+1}" - assert self.embedding_dimensions[key] == e.shape[1], ( - f"Expected embedding dimension {self.embedding_dimensions[key]} for {key}, " - f"but got {e.shape[1]}" - ) + key = f"embedding_{idx + 1}" + if self.embedding_dimensions[key] != e.shape[1]: + raise ValueError( + f"Expected embedding dimension {self.embedding_dimensions[key]} for {key}, but got {e.shape[1]}" + ) transformed_dict[key] = e.astype(np.float32) else: - assert ( - self.embeddings is False - ), "self.embeddings should be False when embeddings are None." + if self.embeddings is not False: + raise ValueError("self.embeddings should be False when embeddings are None.") self.embeddings = False return transformed_dict @@ -790,9 +740,7 @@ def get_feature_info(self, verbose=True): "categories": None, } if verbose: - print( - f"Numerical Feature: {feature_name}, Info: {numerical_feature_info[feature_name]}" - ) + print(f"Numerical Feature: {feature_name}, Info: {numerical_feature_info[feature_name]}") elif "continuous_ordinal" in steps: step = transformer_pipeline.named_steps["continuous_ordinal"] @@ -842,9 +790,7 @@ def get_feature_info(self, verbose=True): "categories": None, } if verbose: - print( - f"Feature: {feature_name}, Info: {preprocessing_type}, Dimension: {dimension}" - ) + print(f"Feature: {feature_name}, 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