-import torch
-import torch.nn as nn
-import torchmetrics
-from ..utils.mamba_arch import Mamba
-from ..utils.config import MambularConfig
-import pytorch_lightning as pl
-
-
-[docs]class BaseMambularClassifier(pl.LightningModule):
- """
- A base class for building classification models using the Mambular architecture within the PyTorch Lightning framework.
-
- This class integrates various components such as embeddings for categorical and numerical features, the Mambular model
- for processing sequences of embeddings, and a classification head for prediction. It supports multi-class and binary classification tasks.
-
- Parameters
- ----------
- num_classes : int
- The number of classes in the classification task. For binary classification, this should be 2.
- config : MambularConfig
- An instance of MambularConfig containing configuration parameters for the Mambular model.
- cat_feature_info : dict, optional
- A dictionary mapping the names of categorical features to their number of unique categories.
- This information is used to configure embedding layers for categorical features. Defaults to None.
- num_feature_info : dict, optional
- A dictionary mapping the names of numerical features to the size of their input dimensions.
- This information is used to configure embedding layers for numerical features. Defaults to None.
- lr : float, optional
- The learning rate for the optimizer. Defaults to 1e-03.
- lr_patience : int, optional
- The number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
- weight_decay : float, optional
- Weight decay (L2 penalty) parameter for the optimizer. Defaults to 0.025.
- lr_factor : float, optional
- Factor by which the learning rate will be reduced. Defaults to 0.75.
-
- Attributes
- ----------
- embedding_activation : nn.Module
- The activation function to be applied after the linear transformation of numerical features.
- num_embeddings : nn.ModuleList
- A list of sequential modules, each corresponding to an embedding layer for a numerical feature.
- cat_embeddings : nn.ModuleList
- A list of embedding layers, each corresponding to a categorical feature.
- mamba : Mamba
- The Mambular model for processing sequences of embeddings.
- norm_f : nn.Module
- A normalization layer applied after the Mambular model.
- tabular_head : nn.Linear
- A linear layer for predicting the class labels from the aggregated embedding representation.
- pooling_method : str
- The method used to aggregate embeddings across features. Supported methods are 'avg', 'max', and 'sum'.
- loss_fct : nn.Module
- The loss function used for training the model, configured based on the number of classes.
- acc : torchmetrics.Accuracy
- A metric for computing the accuracy of predictions.
- auroc : torchmetrics.AUROC
- A metric for computing the Area Under the Receiver Operating Characteristic curve.
- precision : torchmetrics.Precision
- A metric for computing the precision of predictions.
-
- Methods
- -------
- forward(cat_features, num_features)
- Defines the forward pass of the model, processing both categorical and numerical features, aggregating embeddings,
- and producing predictions.
- training_step(batch, batch_idx)
- Performs a single training step, computing the loss and logging metrics for the training set.
- validation_step(batch, batch_idx)
- Performs a single validation step, computing the loss and logging metrics for the validation set.
- configure_optimizers()
- Configures the model's optimizers and learning rate schedulers.
- """
-
-
-
-
-
-
- def __init__(
- self,
- num_classes,
- config: MambularConfig,
- cat_feature_info: dict = None,
- num_feature_info: dict = None,
- lr=1e-03,
- lr_patience=10,
- weight_decay=0.025,
- lr_factor=0.75,
- ):
- super().__init__()
-
- self.config = config
- self.num_classes = 1 if num_classes == 2 else num_classes
- self.lr = lr
- self.lr_patience = lr_patience
- self.weight_decay = weight_decay
- self.lr_factor = lr_factor
- self.cat_feature_info = cat_feature_info
- self.num_feature_info = num_feature_info
-
- activations = {
- "relu": nn.ReLU(),
- "tanh": nn.Tanh(),
- "sigmoid": nn.Sigmoid(),
- "leaky_relu": nn.LeakyReLU(),
- "elu": nn.ELU(),
- "selu": nn.SELU(),
- "gelu": nn.GELU(),
- "softplus": nn.Softplus(),
- "leakyrelu": nn.LeakyReLU(),
- "linear": nn.Identity(),
- }
-
- self.embedding_activation = activations.get(
- self.config.num_embedding_activation.lower()
- )
- if self.embedding_activation is None:
- raise ValueError(
- f"Unsupported activation function: {self.config.num_embedding_activation}"
- )
-
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(input_shape, self.config.d_model, bias=False),
- nn.BatchNorm1d(self.config.d_model),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for feature_name, input_shape in num_feature_info.items()
- ]
- )
-
- # Create embedding layers for categorical features based on cat_feature_info
- self.cat_embeddings = nn.ModuleList(
- [
- nn.Embedding(num_categories + 1, self.config.d_model)
- for feature_name, num_categories in cat_feature_info.items()
- ]
- )
-
- self.mamba = Mamba(self.config)
- self.norm_f = self.config.norm(self.config.d_model)
-
- mlp_activation_fn = activations.get(
- self.config.tabular_head_activation.lower(), nn.Identity()
- )
- mlp_layers = []
- input_dim = self.config.d_model # Initial input dimension
-
- # Iterate over the specified units for each layer in the MLP
- for units in self.config.tabular_head_units:
- mlp_layers.append(nn.Linear(input_dim, units))
- mlp_layers.append(mlp_activation_fn)
- mlp_layers.append(nn.Dropout(self.config.tabular_head_dropout))
- input_dim = units
-
- # Add the final linear layer to map to a single output value
- mlp_layers.append(nn.Linear(input_dim, self.num_classes))
-
- # Combine all layers into a Sequential module
- self.tabular_head = nn.Sequential(*mlp_layers)
-
- self.pooling_method = self.config.pooling_method
- self.cls_token = nn.Parameter(torch.zeros(1, 1, self.config.d_model))
-
- if self.num_classes > 2:
- self.loss_fct = nn.CrossEntropyLoss()
- self.acc = torchmetrics.Accuracy(
- task="multiclass", num_classes=self.num_classes
- )
- self.auroc = torchmetrics.AUROC(
- task="multiclass", num_classes=self.num_classes
- )
- self.precision = torchmetrics.Precision(
- task="multiclass", num_classes=self.num_classes
- )
- else:
- self.loss_fct = torch.nn.BCEWithLogitsLoss()
- self.acc = torchmetrics.Accuracy(task="binary")
- self.auroc = torchmetrics.AUROC(task="binary")
- self.precision = torchmetrics.Precision(task="binary")
-
-[docs] def forward(self, cat_features, num_features):
- """
- Defines the forward pass of the classifier.
-
- Parameters
- ----------
- cat_features : Tensor
- Tensor containing the categorical features.
- num_features : Tensor
- Tensor containing the numerical features.
-
- Returns
- -------
- Tensor
- The output predictions of the model.
- """
- batch_size = (
- cat_features[0].size(0)
- if cat_features is not None
- else num_features[0].size(0)
- )
- cls_tokens = self.cls_token.expand(batch_size, -1, -1)
-
- # Process categorical features if present
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- else:
- cat_embeddings = None
-
- # Process numerical features if present
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = [
- emb(num_features[i]) for i, emb in enumerate(self.num_embeddings)
- ]
- num_embeddings = torch.stack(num_embeddings, dim=1)
- else:
- num_embeddings = None
-
- # Combine embeddings if both types are present, otherwise use whichever is available
- if cat_embeddings is not None and num_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings, num_embeddings], dim=1)
- elif cat_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings], dim=1)
- elif num_embeddings is not None:
- x = torch.cat([cls_tokens, num_embeddings], dim=1)
- else:
- raise ValueError("No features provided to the model.")
-
- x = self.mamba(x)
-
- # Apply pooling based on the specified method
- if self.pooling_method == "avg":
- x = torch.mean(x, dim=1)
- elif self.pooling_method == "max":
- x, _ = torch.max(x, dim=1)
- elif self.pooling_method == "sum":
- x = torch.sum(x, dim=1)
- elif self.pooling_method == "cls":
- x = x[:, 0]
- else:
- raise ValueError(f"Invalid pooling method: {self.pooling_method}")
-
- x = self.norm_f(x)
- preds = self.tabular_head(x)
-
- return preds
-
-[docs] def training_step(self, batch, batch_idx):
- """
- Processes a single batch during training, computes the loss and logs training metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
- """
-
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- if self.num_classes == 1:
- labels = labels.unsqueeze(
- 1
- ).float() # Reshape for binary classification loss calculation
-
- loss = self.loss_fct(preds, labels)
- self.log("train_loss", loss)
- # Calculate and log training accuracy
-
- acc = self.acc(preds, labels.int())
- self.log(
- "train_acc",
- acc,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- # Calculate and log AUROC
- auroc = self.auroc(preds, labels.int())
- self.log(
- "train_auroc",
- auroc,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- # Calculate and log precision
- precision = self.precision(preds, labels.int())
- self.log(
- "train_precision",
- precision,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- return loss
-
-[docs] def validation_step(self, batch, batch_idx):
- """
- Processes a single batch during validation, computes the loss and logs validation metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
- """
-
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- if self.num_classes == 1:
- labels = labels.unsqueeze(
- 1
- ).float() # Reshape for binary classification loss calculation
-
- loss = self.loss_fct(preds, labels)
- self.log("val_loss", loss)
- # Calculate and log training accuracy
-
- acc = self.acc(preds, labels.int())
- self.log(
- "val_acc",
- acc,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- auroc = self.auroc(preds, labels.int())
- self.log(
- "val_auroc", auroc, on_step=False, on_epoch=True, prog_bar=True, logger=True
- )
-
- # Calculate and log precision
- precision = self.precision(preds, labels.int())
- self.log(
- "val_precision",
- precision,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
-[docs] def configure_optimizers(self):
- """
- Sets up the model's optimizer and learning rate scheduler based on the configurations provided.
-
- Returns
- -------
- dict
- A dictionary containing the optimizer and lr_scheduler configurations.
- """
- optimizer = torch.optim.Adam(
- self.parameters(), lr=self.lr, weight_decay=self.config.weight_decay
- )
- scheduler = {
- "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(
- optimizer,
- mode="min",
- factor=self.lr_factor,
- patience=self.lr_patience,
- verbose=True,
- ),
- "monitor": "val_loss", # Name of the metric to monitor
- "interval": "epoch",
- "frequency": 1,
- }
-
- return {"optimizer": optimizer, "lr_scheduler": scheduler}
-
-import torch
-import torch.nn as nn
-from ..utils.mamba_arch import Mamba
-from ..utils.config import MambularConfig
-import pytorch_lightning as pl
-from ..utils.distributions import (
- NormalDistribution,
- NegativeBinomialDistribution,
- GammaDistribution,
- StudentTDistribution,
- PoissonDistribution,
- InverseGammaDistribution,
- BetaDistribution,
- DirichletDistribution,
- CategoricalDistribution,
-)
-
-
-[docs]class BaseMambularLSS(pl.LightningModule):
- """
- A base module for likelihood-based statistical learning (LSS) models built on PyTorch Lightning,
- integrating the Mamba architecture for tabular data. This module is designed to accommodate various
- statistical distribution families for different types of regression and classification tasks.
-
- Parameters
- ----------
- family : str
- The name of the statistical distribution family to be used for modeling. Supported families include
- 'normal', 'poisson', 'gamma', 'beta', 'dirichlet', 'studentt', 'negativebinom', 'inversegamma', and 'categorical'.
- config : MambularConfig
- An instance of MambularConfig containing configuration parameters for the model architecture.
- cat_feature_info : dict, optional
- A dictionary mapping the names of categorical features to their number of unique categories. Defaults to None.
- num_feature_info : dict, optional
- A dictionary mapping the names of numerical features to their number of dimensions after embedding. Defaults to None.
- lr : float, optional
- The initial learning rate for the optimizer. Defaults to 1e-03.
- lr_patience : int, optional
- The number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
- weight_decay : float, optional
- Weight decay (L2 penalty) coefficient. Defaults to 0.025.
- lr_factor : float, optional
- Factor by which the learning rate will be reduced. Defaults to 0.75.
- **distribution_params :
- Additional parameters specific to the chosen statistical distribution family.
-
- Attributes
- ----------
- mamba : Mamba
- The core neural network module implementing the Mamba architecture.
- norm_f : nn.Module
- Normalization layer applied after the Mamba block.
- tabular_head : nn.Linear
- Final linear layer mapping the features to the parameters of the chosen statistical distribution.
- loss_fct : callable
- The loss function derived from the chosen statistical distribution.
-
- Methods
- -------
- forward(cat_features, num_features)
- Defines the forward pass of the model.
- training_step(batch, batch_idx)
- Processes a single batch during training.
- validation_step(batch, batch_idx)
- Processes a single batch during validation.
- configure_optimizers()
- Sets up the model's optimizer and learning rate scheduler.
- """
-
-
-
-
- def __init__(
- self,
- family,
- config: MambularConfig,
- cat_feature_info: dict = None,
- num_feature_info: dict = None,
- lr=1e-03,
- lr_patience=10,
- weight_decay=0.025,
- lr_factor=0.75,
- **distribution_params,
- ):
- super().__init__()
-
- self.config = config
- self.lr = lr
- self.lr_patience = lr_patience
- self.weight_decay = weight_decay
- self.lr_factor = lr_factor
- self.cat_feature_info = cat_feature_info
- self.num_feature_info = num_feature_info
-
- activations = {
- "relu": nn.ReLU(),
- "tanh": nn.Tanh(),
- "sigmoid": nn.Sigmoid(),
- "leaky_relu": nn.LeakyReLU(),
- "elu": nn.ELU(),
- "selu": nn.SELU(),
- "gelu": nn.GELU(),
- "softplus": nn.Softplus(),
- "leakyrelu": nn.LeakyReLU(),
- "linear": nn.Identity(),
- }
-
- distribution_classes = {
- "normal": NormalDistribution,
- "poisson": PoissonDistribution,
- "gamma": GammaDistribution,
- "beta": BetaDistribution,
- "dirichlet": DirichletDistribution,
- "studentt": StudentTDistribution,
- "negativebinom": NegativeBinomialDistribution,
- "inversegamma": InverseGammaDistribution,
- "categorical": CategoricalDistribution,
- }
-
- if family in distribution_classes:
- # Pass additional distribution_params to the constructor of the distribution class
- self.family = distribution_classes[family](**distribution_params)
- else:
- raise ValueError("Unsupported family: {}".format(family))
-
- self.embedding_activation = activations.get(
- self.config.num_embedding_activation.lower()
- )
- if self.embedding_activation is None:
- raise ValueError(
- f"Unsupported activation function: {self.config.num_embedding_activation}"
- )
-
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(input_shape, self.config.d_model, bias=False),
- nn.BatchNorm1d(self.config.d_model),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for feature_name, input_shape in num_feature_info.items()
- ]
- )
-
- # Create embedding layers for categorical features based on cat_feature_info
- self.cat_embeddings = nn.ModuleList(
- [
- nn.Embedding(num_categories + 1, self.config.d_model)
- for feature_name, num_categories in cat_feature_info.items()
- ]
- )
-
- mlp_activation_fn = activations.get(
- self.config.tabular_head_activation.lower(), nn.Identity()
- )
-
- self.mamba = Mamba(self.config)
- self.norm_f = self.config.norm(self.config.d_model)
- mlp_layers = []
- input_dim = self.config.d_model # Initial input dimension
-
- # Iterate over the specified units for each layer in the MLP
- for units in self.config.tabular_head_units:
- mlp_layers.append(nn.Linear(input_dim, units))
- mlp_layers.append(mlp_activation_fn)
- mlp_layers.append(nn.Dropout(self.config.tabular_head_dropout))
- input_dim = units
-
- # Add the final linear layer to map to #distributional param output values
- mlp_layers.append(nn.Linear(input_dim, self.family.param_count))
-
- # Combine all layers into a Sequential module
- self.tabular_head = nn.Sequential(*mlp_layers)
-
- self.loss_fct = lambda predictions, y_true: self.family.compute_loss(
- predictions, y_true
- )
-
- self.cls_token = nn.Parameter(torch.zeros(1, 1, self.config.d_model))
- self.pooling_method = self.config.pooling_method
-
-[docs] def forward(self, cat_features, num_features):
- """
- Defines the forward pass of the model, processing both categorical and numerical features,
- and returning predictions based on the configured statistical distribution.
-
- Parameters
- ----------
- cat_features : Tensor
- Tensor containing the categorical features.
- num_features : Tensor
- Tensor containing the numerical features.
-
- Returns
- -------
- Tensor
- The predictions of the model, typically the parameters of the chosen statistical distribution.
- """
-
- batch_size = (
- cat_features[0].size(0)
- if cat_features is not None
- else num_features[0].size(0)
- )
- cls_tokens = self.cls_token.expand(batch_size, -1, -1)
-
- # Process categorical features if present
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- else:
- cat_embeddings = None
-
- # Process numerical features if present
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = [
- emb(num_features[i]) for i, emb in enumerate(self.num_embeddings)
- ]
- num_embeddings = torch.stack(num_embeddings, dim=1)
- else:
- num_embeddings = None
-
- # Combine embeddings if both types are present, otherwise use whichever is available
- if cat_embeddings is not None and num_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings, num_embeddings], dim=1)
- elif cat_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings], dim=1)
- elif num_embeddings is not None:
- x = torch.cat([cls_tokens, num_embeddings], dim=1)
- else:
- raise ValueError("No features provided to the model.")
-
- x = self.mamba(x)
-
- # Apply pooling based on the specified method
- if self.pooling_method == "avg":
- x = torch.mean(x, dim=1)
- elif self.pooling_method == "max":
- x, _ = torch.max(x, dim=1)
- elif self.pooling_method == "sum":
- x = torch.sum(x, dim=1)
- elif self.pooling_method == "cls":
- x = x[:, 0]
- else:
- raise ValueError(f"Invalid pooling method: {self.pooling_method}")
-
- x = self.norm_f(x)
- preds = self.tabular_head(x)
-
- return preds
-
-[docs] def training_step(self, batch, batch_idx):
- """
- Processes a single batch during training, computes the loss using the distribution-specific loss function,
- and logs training metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
-
- Returns
- -------
- Tensor
- The computed loss for the batch.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- loss = self.loss_fct(preds, labels)
- self.log(
- "train_loss",
- loss,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
- return loss
-
-[docs] def validation_step(self, batch, batch_idx):
- """
- Processes a single batch during validation, computes the loss using the distribution-specific loss function,
- and logs validation metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
- """
-
-
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- loss = self.loss_fct(preds, labels)
- self.log(
- "val_loss",
- loss,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
- return loss
-
-[docs] def configure_optimizers(self):
- """
- Sets up the model's optimizer and learning rate scheduler based on the configurations provided.
-
- Returns
- -------
- dict
- A dictionary containing the optimizer and lr_scheduler configurations.
- """
- optimizer = torch.optim.Adam(
- self.parameters(), lr=self.lr, weight_decay=self.config.weight_decay
- )
- scheduler = {
- "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(
- optimizer,
- mode="min",
- factor=self.lr_factor,
- patience=self.lr_patience,
- verbose=True,
- ),
- "monitor": "val_loss", # Name of the metric to monitor
- "interval": "epoch",
- "frequency": 1,
- }
-
- return {"optimizer": optimizer, "lr_scheduler": scheduler}
-
-import torch
-import torch.nn as nn
-from ..utils.mamba_arch import Mamba
-from ..utils.config import MambularConfig
-import pytorch_lightning as pl
-import torchmetrics
-
-
-[docs]class BaseEmbeddingMambularClassifier(pl.LightningModule):
- """
- A specialized classification module for protein data, built on PyTorch Lightning and integrating the Mamba architecture.
- It supports embeddings for categorical features and can process raw or embedded numerical features, making it suitable
- for complex protein sequence data.
-
- Parameters
- ----------
- config : MambularConfig
- Configuration parameters for the model architecture.
- cat_feature_info : dict, optional
- Information about categorical features, mapping feature names to the number of unique categories.
- num_feature_info : dict, optional
- Information about numerical features, mapping feature names to their number of dimensions after embedding.
- lr : float, optional
- Learning rate for the optimizer. Defaults to 1e-03.
- lr_patience : int, optional
- Number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
- weight_decay : float, optional
- Weight decay coefficient for regularization in the optimizer. Defaults to 0.025.
- lr_factor : float, optional
- Factor by which the learning rate will be reduced by the scheduler. Defaults to 0.75.
- seq_size : int, optional
- Size of sequence chunks for processing numerical features. Relevant when `raw_embeddings` is False.
- raw_embeddings : bool, optional
- Indicates whether to use raw numerical features directly or to process them into embeddings. Defaults to False.
-
- Attributes
- ----------
- mamba : Mamba
- The core neural network module implementing the Mamba architecture.
- norm_f : nn.Module
- Normalization layer applied after the Mamba block.
- tabular_head : nn.Linear
- Final linear layer mapping the features to the target.
-
- Methods
- -------
- forward(cat_features, num_features)
- Defines the forward pass of the model.
- training_step(batch, batch_idx)
- Processes a single batch during training.
- validation_step(batch, batch_idx)
- Processes a single batch during validation.
- configure_optimizers()
- Sets up the model's optimizer and learning rate scheduler.
- """
-
- def __init__(
- self,
- num_classes,
- config: MambularConfig,
- cat_feature_info: dict = None,
- num_feature_info: dict = None,
- lr=1e-03,
- lr_patience=10,
- weight_decay=0.025,
- lr_factor=0.75,
- seq_size: int = 20,
- raw_embeddings=False,
- ):
- super().__init__()
-
- self.config = config
- self.num_classes = 1 if num_classes == 2 else num_classes
- self.lr = lr
- self.lr_patience = lr_patience
- self.weight_decay = weight_decay
- self.lr_factor = lr_factor
- self.cat_feature_info = cat_feature_info
- self.num_feature_info = num_feature_info
- self.seq_size = seq_size
- self.raw_embeddings = raw_embeddings
-
- activations = {
- "relu": nn.ReLU(),
- "tanh": nn.Tanh(),
- "sigmoid": nn.Sigmoid(),
- "leaky_relu": nn.LeakyReLU(),
- "elu": nn.ELU(),
- "selu": nn.SELU(),
- "gelu": nn.GELU(),
- "softplus": nn.Softplus(),
- "leakyrelu": nn.LeakyReLU(),
- "linear": nn.Identity(),
- }
-
- if not self.raw_embeddings:
- data_size = len(num_feature_info.items())
- num_embedding_modules = data_size // self.seq_size
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(self.seq_size, self.config.d_model, bias=False),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for _ in range(num_embedding_modules)
- ]
- )
- else:
- data_size = len(num_feature_info.items())
- num_embedding_modules = data_size
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(1, self.config.d_model, bias=False),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for _ in range(num_embedding_modules)
- ]
- )
-
- self.cat_embeddings = nn.ModuleList(
- [
- nn.Embedding(num_categories + 1, self.config.d_model)
- for feature_name, num_categories in cat_feature_info.items()
- ]
- )
-
- self.mamba = Mamba(self.config)
- self.norm_f = self.config.norm(self.config.d_model)
- mlp_activation_fn = activations.get(
- self.config.tabular_head_activation.lower(), nn.Identity()
- )
-
- # Dynamically create MLP layers based on config.tabular_units
- mlp_layers = []
- input_dim = self.config.d_model # Initial input dimension
-
- # Iterate over the specified units for each layer in the MLP
- for units in self.config.tabular_head_units:
- mlp_layers.append(nn.Linear(input_dim, units))
- mlp_layers.append(mlp_activation_fn)
- mlp_layers.append(nn.Dropout(self.config.tabular_head_dropout))
- input_dim = units
-
- # Add the final linear layer to map to a single output value
- mlp_layers.append(nn.Linear(input_dim, self.num_classes))
-
- # Combine all layers into a Sequential module
- self.tabular_head = nn.Sequential(*mlp_layers)
-
- self.pooling_method = self.config.pooling_method
- self.cls_token = nn.Parameter(torch.zeros(1, 1, self.config.d_model))
-
- if self.config.layer_norm_after_embedding:
- self.embedding_norm = nn.LayerNorm(self.config.d_model)
-
- if self.num_classes > 2:
- self.loss_fct = nn.CrossEntropyLoss()
- self.acc = torchmetrics.Accuracy(
- task="multiclass", num_classes=self.num_classes
- )
- self.auroc = torchmetrics.AUROC(
- task="multiclass", num_classes=self.num_classes
- )
- self.precision = torchmetrics.Precision(
- task="multiclass", num_classes=self.num_classes
- )
- else:
- self.loss_fct = torch.nn.BCEWithLogitsLoss()
- self.acc = torchmetrics.Accuracy(task="binary")
- self.auroc = torchmetrics.AUROC(task="binary")
- self.precision = torchmetrics.Precision(task="binary")
-
-[docs] def forward(self, cat_features, num_features):
- """
- Defines the forward pass of the model, processing both categorical and numerical features,
- and returning regression predictions.
-
- Parameters
- ----------
- cat_features : Tensor
- Tensor containing the categorical features.
- num_features : Tensor
- Tensor containing the numerical features or raw sequence data, depending on `raw_embeddings`.
-
- Returns
- -------
- Tensor
- The output predictions of the model for regression tasks.
- """
- batch_size = (
- cat_features[0].size(0) if cat_features != [] else num_features[0].size(0)
- )
- cls_tokens = self.cls_token.expand(batch_size, -1, -1)
- # Process categorical features if present
- if not self.raw_embeddings:
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- else:
- cat_embeddings = None
-
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = []
- # Iterate through the num_embeddings, taking slices of num_features for each
- for i, emb in enumerate(self.num_embeddings):
- # Calculate start and end indices for slicing the list
- start_idx = i * self.seq_size
- end_idx = start_idx + self.seq_size
-
- # Slice the num_features list to get the current chunk
- current_chunk = num_features[start_idx:end_idx]
-
- # If the current_chunk is not empty, process it
- if current_chunk:
- # Concatenate tensors in the current chunk along dimension 1
- chunk_tensor = torch.cat(current_chunk, dim=1)
- # Apply the embedding layer to the chunk_tensor
- num_embeddings.append(emb(chunk_tensor))
-
- # Stack the resulting embeddings along the second dimension if num_embeddings is not empty
- if num_embeddings:
- num_embeddings = torch.stack(num_embeddings, dim=1)
- else:
- num_embeddings = None
-
- else:
- # Process categorical features if present
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- if self.config.layer_norm_after_embedding:
- cat_embeddings = self.embedding_norm(cat_embeddings)
- else:
- cat_embeddings = None
-
- # Process numerical features if present
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = [
- emb(num_features[i]) for i, emb in enumerate(self.num_embeddings)
- ]
- num_embeddings = torch.stack(num_embeddings, dim=1)
- if self.config.layer_norm_after_embedding:
- num_embeddings = self.embedding_norm(num_embeddings)
- else:
- num_embeddings = None
-
- # Combine embeddings if both types are present, otherwise use whichever is available
- if cat_embeddings is not None and num_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings, num_embeddings], dim=1)
- elif cat_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings], dim=1)
- elif num_embeddings is not None:
- x = torch.cat([cls_tokens, num_embeddings], dim=1)
- else:
- raise ValueError("No features provided to the model.")
-
- x = self.mamba(x)
-
- # Apply pooling based on the specified method
- if self.pooling_method == "avg":
- x = torch.mean(x, dim=1)
- elif self.pooling_method == "max":
- x, _ = torch.max(x, dim=1)
- elif self.pooling_method == "sum":
- x = torch.sum(x, dim=1)
- elif self.pooling_method == "cls_token":
- x = x[:, 0]
- else:
- raise ValueError(f"Invalid pooling method: {self.pooling_method}")
-
- x = self.norm_f(x)
- preds = self.tabular_head(x)
-
- return preds
-
-[docs] def training_step(self, batch, batch_idx):
- """
- Processes a single batch during training, computes the loss, and logs training metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
-
- Returns
- -------
- Tensor
- The computed loss for the batch.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- if self.num_classes == 1:
- labels = labels.unsqueeze(
- 1
- ).float() # Reshape for binary classification loss calculation
-
- loss = self.loss_fct(preds, labels)
- self.log("train_loss", loss)
- # Calculate and log training accuracy
-
- acc = self.acc(preds, labels.int())
- self.log(
- "train_acc",
- acc,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- # Calculate and log AUROC
- auroc = self.auroc(preds, labels.int())
- self.log(
- "train_auroc",
- auroc,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- # Calculate and log precision
- precision = self.precision(preds, labels.int())
- self.log(
- "train_precision",
- precision,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- return loss
-
-[docs] def validation_step(self, batch, batch_idx):
- """
- Processes a single batch during validation, computes the loss, and logs validation metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- if self.num_classes == 1:
- labels = labels.unsqueeze(
- 1
- ).float() # Reshape for binary classification loss calculation
-
- loss = self.loss_fct(preds, labels)
- self.log("val_loss", loss)
- # Calculate and log training accuracy
-
- acc = self.acc(preds, labels.int())
- self.log(
- "val_acc",
- acc,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- auroc = self.auroc(preds, labels.int())
- self.log(
- "val_auroc", auroc, on_step=False, on_epoch=True, prog_bar=True, logger=True
- )
-
- # Calculate and log precision
- precision = self.precision(preds, labels.int())
- self.log(
- "val_precision",
- precision,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
-[docs] def configure_optimizers(self):
- """
- Sets up the model's optimizer and learning rate scheduler based on the configurations provided.
-
- Returns
- -------
- dict
- A dictionary containing the optimizer and lr_scheduler configurations.
- """
- optimizer = torch.optim.Adam(
- self.parameters(), lr=self.lr, weight_decay=self.config.weight_decay
- )
- scheduler = {
- "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(
- optimizer,
- mode="min",
- factor=self.lr_factor,
- patience=self.lr_patience,
- verbose=True,
- ),
- "monitor": "val_loss", # Name of the metric to monitor
- "interval": "epoch",
- "frequency": 1,
- }
-
- return {"optimizer": optimizer, "lr_scheduler": scheduler}
-
-import torch
-import torch.nn as nn
-from ..utils.mamba_arch import Mamba
-from ..utils.config import MambularConfig
-import pytorch_lightning as pl
-
-
-[docs]class BaseEmbeddingMambularRegressor(pl.LightningModule):
- """
- A specialized regression module for protein data, built on PyTorch Lightning and integrating the Mamba architecture.
- It supports embeddings for categorical features and can process raw or embedded numerical features, making it suitable
- for complex protein sequence data.
-
- Parameters
- ----------
- config : MambularConfig
- Configuration parameters for the model architecture.
- cat_feature_info : dict, optional
- Information about categorical features, mapping feature names to the number of unique categories. Defaults to None.
- num_feature_info : dict, optional
- Information about numerical features, mapping feature names to their number of dimensions after embedding. Defaults to None.
- lr : float, optional
- Learning rate for the optimizer. Defaults to 1e-03.
- lr_patience : int, optional
- Number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
- weight_decay : float, optional
- Weight decay coefficient for regularization in the optimizer. Defaults to 0.025.
- lr_factor : float, optional
- Factor by which the learning rate will be reduced by the scheduler. Defaults to 0.75.
- seq_size : int, optional
- Size of sequence chunks for processing numerical features. Relevant when `raw_embeddings` is False.
- raw_embeddings : bool, optional
- Indicates whether to use raw numerical features directly or to process them into embeddings. Defaults to False.
-
- Attributes
- ----------
- mamba : Mamba
- The core neural network module implementing the Mamba architecture.
- norm_f : nn.Module
- Normalization layer applied after the Mamba block.
- tabular_head : nn.Linear
- Final linear layer mapping the features to the regression target.
- loss_fct : nn.MSELoss
- The loss function for regression tasks.
-
- Methods
- -------
- forward(cat_features, num_features)
- Defines the forward pass of the model.
- training_step(batch, batch_idx)
- Processes a single batch during training.
- validation_step(batch, batch_idx)
- Processes a single batch during validation.
- configure_optimizers()
- Sets up the model's optimizer and learning rate scheduler.
- """
-
- def __init__(
- self,
- config: MambularConfig,
- cat_feature_info: dict = None,
- num_feature_info: dict = None,
- lr=1e-03,
- lr_patience=10,
- weight_decay=0.025,
- lr_factor=0.75,
- seq_size: int = 20,
- raw_embeddings=False,
- ):
- super().__init__()
-
- self.config = config
- self.lr = lr
- self.lr_patience = lr_patience
- self.weight_decay = weight_decay
- self.lr_factor = lr_factor
- self.cat_feature_info = cat_feature_info
- self.num_feature_info = num_feature_info
- self.seq_size = seq_size
- self.raw_embeddings = raw_embeddings
-
- activations = {
- "relu": nn.ReLU(),
- "tanh": nn.Tanh(),
- "sigmoid": nn.Sigmoid(),
- "leaky_relu": nn.LeakyReLU(),
- "elu": nn.ELU(),
- "selu": nn.SELU(),
- "gelu": nn.GELU(),
- "softplus": nn.Softplus(),
- "leakyrelu": nn.LeakyReLU(),
- "linear": nn.Identity(),
- }
-
- self.embedding_activation = activations.get(
- self.config.num_embedding_activation.lower()
- )
- if self.embedding_activation is None:
- raise ValueError(
- f"Unsupported activation function: {self.config.num_embedding_activation}"
- )
-
- if not self.raw_embeddings:
- data_size = len(num_feature_info.items())
- num_embedding_modules = data_size // self.seq_size
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(self.seq_size, self.config.d_model, bias=False),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for _ in range(num_embedding_modules)
- ]
- )
- else:
- data_size = len(num_feature_info.items())
- num_embedding_modules = data_size
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(1, self.config.d_model, bias=False),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for _ in range(num_embedding_modules)
- ]
- )
-
- self.cat_embeddings = nn.ModuleList(
- [
- nn.Embedding(num_categories + 1, self.config.d_model)
- for feature_name, num_categories in cat_feature_info.items()
- ]
- )
-
- self.mamba = Mamba(self.config)
- self.norm_f = self.config.norm(self.config.d_model)
- mlp_activation_fn = activations.get(
- self.config.tabular_head_activation.lower(), nn.Identity()
- )
-
- # Dynamically create MLP layers based on config.tabular_units
- mlp_layers = []
- input_dim = self.config.d_model # Initial input dimension
-
- # Iterate over the specified units for each layer in the MLP
- for units in self.config.tabular_head_units:
- mlp_layers.append(nn.Linear(input_dim, units))
- mlp_layers.append(mlp_activation_fn)
- mlp_layers.append(nn.Dropout(self.config.tabular_head_dropout))
- input_dim = units
-
- # Add the final linear layer to map to a single output value
- mlp_layers.append(nn.Linear(input_dim, 1))
-
- # Combine all layers into a Sequential module
- self.tabular_head = nn.Sequential(*mlp_layers)
-
- self.pooling_method = self.config.pooling_method
- self.cls_token = nn.Parameter(torch.zeros(1, 1, self.config.d_model))
-
- if self.config.layer_norm_after_embedding:
- self.embedding_norm = nn.LayerNorm(self.config.d_model)
-
- self.loss_fct = nn.MSELoss()
-
-[docs] def forward(self, cat_features, num_features):
- """
- Defines the forward pass of the model, processing both categorical and numerical features,
- and returning regression predictions.
-
- Parameters
- ----------
- cat_features : Tensor
- Tensor containing the categorical features.
- num_features : Tensor
- Tensor containing the numerical features or raw sequence data, depending on `raw_embeddings`.
-
- Returns
- -------
- Tensor
- The output predictions of the model for regression tasks.
- """
- batch_size = (
- cat_features[0].size(0) if cat_features != [] else num_features[0].size(0)
- )
- cls_tokens = self.cls_token.expand(batch_size, -1, -1)
- # Process categorical features if present
- if not self.raw_embeddings:
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- else:
- cat_embeddings = None
-
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = []
- # Iterate through the num_embeddings, taking slices of num_features for each
- for i, emb in enumerate(self.num_embeddings):
- # Calculate start and end indices for slicing the list
- start_idx = i * self.seq_size
- end_idx = start_idx + self.seq_size
-
- # Slice the num_features list to get the current chunk
- current_chunk = num_features[start_idx:end_idx]
-
- # If the current_chunk is not empty, process it
- if current_chunk:
- # Concatenate tensors in the current chunk along dimension 1
- chunk_tensor = torch.cat(current_chunk, dim=1)
- # Apply the embedding layer to the chunk_tensor
- num_embeddings.append(emb(chunk_tensor))
-
- # Stack the resulting embeddings along the second dimension if num_embeddings is not empty
- if num_embeddings:
- num_embeddings = torch.stack(num_embeddings, dim=1)
- else:
- num_embeddings = None
-
- else:
- # Process categorical features if present
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- if self.config.layer_norm_after_embedding:
- cat_embeddings = self.embedding_norm(cat_embeddings)
- else:
- cat_embeddings = None
-
- # Process numerical features if present
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = [
- emb(num_features[i]) for i, emb in enumerate(self.num_embeddings)
- ]
- num_embeddings = torch.stack(num_embeddings, dim=1)
- if self.config.layer_norm_after_embedding:
- num_embeddings = self.embedding_norm(num_embeddings)
- else:
- num_embeddings = None
-
- # Combine embeddings if both types are present, otherwise use whichever is available
- if cat_embeddings is not None and num_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings, num_embeddings], dim=1)
- elif cat_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings], dim=1)
- elif num_embeddings is not None:
- x = torch.cat([cls_tokens, num_embeddings], dim=1)
- else:
- raise ValueError("No features provided to the model.")
-
- x = self.mamba(x)
-
- # Apply pooling based on the specified method
- if self.pooling_method == "avg":
- x = torch.mean(x, dim=1)
- elif self.pooling_method == "max":
- x, _ = torch.max(x, dim=1)
- elif self.pooling_method == "sum":
- x = torch.sum(x, dim=1)
- elif self.pooling_method == "cls_token":
- x = x[:, 0]
- else:
- raise ValueError(f"Invalid pooling method: {self.pooling_method}")
-
- x = self.norm_f(x)
- preds = self.tabular_head(x)
-
- return preds
-
-[docs] def training_step(self, batch, batch_idx):
- """
- Processes a single batch during training, computes the loss, and logs training metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
-
- Returns
- -------
- Tensor
- The computed loss for the batch.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- loss = self.loss_fct(preds.squeeze(), labels.float())
- self.log(
- "train_loss",
- loss,
- on_step=True,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
- return loss
-
-[docs] def validation_step(self, batch, batch_idx):
- """
- Processes a single batch during validation, computes the loss, and logs validation metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- loss = self.loss_fct(preds.squeeze(), labels.float())
- self.log(
- "val_loss",
- loss,
- on_step=True,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- return loss
-
-[docs] def configure_optimizers(self):
- """
- Sets up the model's optimizer and learning rate scheduler based on the configurations provided.
-
- Returns
- -------
- dict
- A dictionary containing the optimizer and lr_scheduler configurations.
- """
- optimizer = torch.optim.Adam(
- self.parameters(), lr=self.lr, weight_decay=self.config.weight_decay
- )
- scheduler = {
- "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(
- optimizer,
- mode="min",
- factor=self.lr_factor,
- patience=self.lr_patience,
- verbose=True,
- ),
- "monitor": "val_loss", # Name of the metric to monitor
- "interval": "epoch",
- "frequency": 1,
- }
-
- return {"optimizer": optimizer, "lr_scheduler": scheduler}
-
-import torch
-import torch.nn as nn
-from ..utils.mamba_arch import Mamba
-from ..utils.config import MambularConfig
-import pytorch_lightning as pl
-
-
-[docs]class BaseMambularRegressor(pl.LightningModule):
- """
- A base regression module for tabular data built on PyTorch Lightning. It incorporates embeddings
- for categorical and numerical features with a configurable architecture provided by MambularConfig.
- This module is designed for regression tasks.
-
- Parameters
- ----------
- config : MambularConfig
- An instance of MambularConfig containing configuration parameters for the model architecture.
- cat_feature_info : dict, optional
- A dictionary mapping the names of categorical features to their number of unique categories. Defaults to None.
- num_feature_info : dict, optional
- A dictionary mapping the names of numerical features to their number of dimensions after embedding. Defaults to None.
- lr : float, optional
- The initial learning rate for the optimizer. Defaults to 1e-03.
- lr_patience : int, optional
- The number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
- weight_decay : float, optional
- Weight decay (L2 penalty) coefficient. Defaults to 0.025.
- lr_factor : float, optional
- Factor by which the learning rate will be reduced. Defaults to 0.75.
-
- Attributes
- ----------
- mamba : Mamba
- The core neural network module implementing the Mamba architecture.
- norm_f : nn.Module
- Normalization layer applied after the Mamba block.
- tabular_head : nn.Linear
- Final linear layer mapping the features to a single output for regression tasks.
- train_mse : torchmetrics.MeanSquaredError
- Metric computation module for training Mean Squared Error.
- val_mse : torchmetrics.MeanSquaredError
- Metric computation module for validation Mean Squared Error.
- loss_fct : torch.nn.MSELoss
- The loss function for regression tasks.
-
- Methods
- -------
- forward(cat_features, num_features)
- Defines the forward pass of the model.
- training_step(batch, batch_idx)
- Processes a single batch during training.
- validation_step(batch, batch_idx)
- Processes a single batch during validation.
- configure_optimizers()
- Sets up the model's optimizer and learning rate scheduler.
- """
-
-
- def __init__(
- self,
- config: MambularConfig,
- cat_feature_info: dict = None,
- num_feature_info: dict = None,
- lr=1e-03,
- lr_patience=10,
- weight_decay=0.025,
- lr_factor=0.75,
- ):
- super().__init__()
-
- self.config = config
- self.lr = lr
- self.lr_patience = lr_patience
- self.weight_decay = weight_decay
- self.lr_factor = lr_factor
- self.cat_feature_info = cat_feature_info
- self.num_feature_info = num_feature_info
-
- activations = {
- "relu": nn.ReLU(),
- "tanh": nn.Tanh(),
- "sigmoid": nn.Sigmoid(),
- "leaky_relu": nn.LeakyReLU(),
- "elu": nn.ELU(),
- "selu": nn.SELU(),
- "gelu": nn.GELU(),
- "softplus": nn.Softplus(),
- "leakyrelu": nn.LeakyReLU(),
- "linear": nn.Identity(),
- }
-
- self.embedding_activation = activations.get(
- self.config.num_embedding_activation.lower()
- )
- if self.embedding_activation is None:
- raise ValueError(
- f"Unsupported activation function: {self.config.num_embedding_activation}"
- )
-
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(input_shape, self.config.d_model, bias=False),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for feature_name, input_shape in num_feature_info.items()
- ]
- )
-
- # Create embedding layers for categorical features based on cat_feature_info
- self.cat_embeddings = nn.ModuleList(
- [
- nn.Embedding(num_categories + 1, self.config.d_model)
- for feature_name, num_categories in cat_feature_info.items()
- ]
- )
-
- self.mamba = Mamba(self.config)
- self.norm_f = self.config.norm(self.config.d_model)
- mlp_activation_fn = activations.get(
- self.config.tabular_head_activation.lower(), nn.Identity()
- )
-
- # Dynamically create MLP layers based on config.tabular_units
- mlp_layers = []
- input_dim = self.config.d_model # Initial input dimension
-
- # Iterate over the specified units for each layer in the MLP
- for units in self.config.tabular_head_units:
- mlp_layers.append(nn.Linear(input_dim, units))
- mlp_layers.append(mlp_activation_fn)
- mlp_layers.append(nn.Dropout(self.config.tabular_head_dropout))
- input_dim = units
-
- # Add the final linear layer to map to a single output value
- mlp_layers.append(nn.Linear(input_dim, 1))
-
- # Combine all layers into a Sequential module
- self.tabular_head = nn.Sequential(*mlp_layers)
-
- self.pooling_method = self.config.pooling_method
- self.cls_token = nn.Parameter(torch.zeros(1, 1, self.config.d_model))
-
- self.loss_fct = nn.MSELoss()
-
- if self.config.layer_norm_after_embedding:
- self.embedding_norm = nn.LayerNorm(self.config.d_model)
-
-[docs] def forward(self, cat_features, num_features):
- """
- Defines the forward pass of the regressor.
-
- Parameters
- ----------
- cat_features : Tensor
- Tensor containing the categorical features.
- num_features : Tensor
- Tensor containing the numerical features.
-
- Returns
- -------
- Tensor
- The output predictions of the model for regression tasks.
- """
-
- batch_size = (
- cat_features[0].size(0) if cat_features != [] else num_features[0].size(0)
- )
- cls_tokens = self.cls_token.expand(batch_size, -1, -1)
-
- # Process categorical features if present
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- if self.config.layer_norm_after_embedding:
- cat_embeddings = self.embedding_norm(cat_embeddings)
- else:
- cat_embeddings = None
-
- # Process numerical features if present
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = [
- emb(num_features[i]) for i, emb in enumerate(self.num_embeddings)
- ]
- num_embeddings = torch.stack(num_embeddings, dim=1)
- if self.config.layer_norm_after_embedding:
- num_embeddings = self.embedding_norm(num_embeddings)
- else:
- num_embeddings = None
-
- # Combine embeddings if both types are present, otherwise use whichever is available
-
- if cat_embeddings is not None and num_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings, num_embeddings], dim=1)
- elif cat_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings], dim=1)
- elif num_embeddings is not None:
- x = torch.cat([cls_tokens, num_embeddings], dim=1)
- else:
- raise ValueError("No features provided to the model.")
-
- x = self.mamba(x)
-
- # Apply pooling based on the specified method
- if self.pooling_method == "avg":
- x = torch.mean(x, dim=1)
- elif self.pooling_method == "max":
- x, _ = torch.max(x, dim=1)
- elif self.pooling_method == "sum":
- x = torch.sum(x, dim=1)
- elif self.pooling_method == "cls_token":
- x = x[:, 0]
- else:
- raise ValueError(f"Invalid pooling method: {self.pooling_method}")
-
- x = self.norm_f(x)
- preds = self.tabular_head(x)
-
- return preds
-
-[docs] def training_step(self, batch, batch_idx):
- """
- Defines the forward pass of the regressor.
-
- Parameters
- ----------
- cat_features : Tensor
- Tensor containing the categorical features.
- num_features : Tensor
- Tensor containing the numerical features.
-
- Returns
- -------
- Tensor
- The output predictions of the model for regression tasks.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- loss = self.loss_fct(preds.squeeze(), labels.float())
- self.log(
- "train_loss",
- loss,
- on_step=True,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
- return loss
-
-[docs] def validation_step(self, batch, batch_idx):
- """
- Processes a single batch during validation, computes the loss, and logs validation metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- loss = self.loss_fct(preds.squeeze(), labels.float())
- self.log(
- "val_loss",
- loss,
- on_step=True,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- return loss
-
-[docs] def configure_optimizers(self):
- """
- Sets up the model's optimizer and learning rate scheduler based on the configurations provided.
-
- Returns
- -------
- dict
- A dictionary containing the optimizer and lr_scheduler configurations.
- """
- optimizer = torch.optim.Adam(
- self.parameters(), lr=self.lr, weight_decay=self.config.weight_decay
- )
- scheduler = {
- "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(
- optimizer,
- mode="min",
- factor=self.lr_factor,
- patience=self.lr_patience,
- verbose=True,
- ),
- "monitor": "val_loss", # Name of the metric to monitor
- "interval": "epoch",
- "frequency": 1,
- }
-
- return {"optimizer": optimizer, "lr_scheduler": scheduler}
-
-from sklearn.model_selection import train_test_split
-import pytorch_lightning as pl
-import torch
-from torch.utils.data import DataLoader
-import numpy as np
-from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
-from ..base_models.classifier import BaseMambularClassifier
-from ..utils.config import MambularConfig
-from ..utils.preprocessor import Preprocessor
-from ..utils.dataset import MambularDataModule, MambularDataset
-from sklearn.base import BaseEstimator
-import pandas as pd
-from sklearn.metrics import accuracy_score
-
-
-[docs]class MambularClassifier(BaseEstimator):
- """
- A classifier that mimics scikit-learn's API using PyTorch Lightning and a custom architecture.
-
- This classifier is designed to work with tabular data and provides a flexible interface for specifying model
- configurations and preprocessing steps. It integrates smoothly with scikit-learn's utilities, such as cross-validation
- and grid search.
-
- Parameters
- ----------
- **kwargs : Various
- Accepts any number of keyword arguments that are passed to the MambularConfig and Preprocessor classes.
- Known configuration arguments for the model are extracted based on a predefined list, and the rest are
- passed to the Preprocessor.
-
- Attributes
- ----------
- config : MambularConfig
- Configuration object that holds model-specific settings.
- preprocessor : Preprocessor
- Preprocessor object for handling feature preprocessing like normalization and encoding.
- model : BaseMambularClassifier or None
- The underlying PyTorch Lightning model, instantiated upon calling the `fit` method.
- """
-
- def __init__(self, **kwargs):
- # Known config arguments
- print("Received kwargs:", kwargs)
- config_arg_names = [
- "d_model",
- "n_layers",
- "dt_rank",
- "output_dimension",
- "pooling_method",
- "norm",
- "cls",
- "dt_min",
- "dt_max",
- "dropout",
- "bias",
- "weight_decay",
- "conv_bias",
- "d_state",
- "expand_factor",
- "d_conv",
- "dt_init",
- "dt_scale",
- "dt_init_floor",
- "tabular_head_units",
- "tabular_head_activation",
- "tabular_head_dropout",
- "num_emebedding_activation",
- "layer_norm_after_embedding",
- ]
- self.config_kwargs = {k: v for k, v in kwargs.items() if k in config_arg_names}
- self.config = MambularConfig(**self.config_kwargs)
-
- # The rest are assumed to be preprocessor arguments
- preprocessor_kwargs = {
- k: v for k, v in kwargs.items() if k not in config_arg_names
- }
- self.preprocessor = Preprocessor(**preprocessor_kwargs)
- self.model = None
-
- def get_params(self, deep=True):
- """
- Get parameters for this estimator.
-
- Parameters
- ----------
- deep : bool, default=True
- If True, will return the parameters for this estimator and contained subobjects that are estimators.
-
- Returns
- -------
- params : dict
- Parameter names mapped to their values.
- """
-
- params = self.config_kwargs # Parameters used to initialize MambularConfig
-
- # If deep=True, include parameters from nested components like preprocessor
- if deep:
- # Assuming Preprocessor has a get_params method
- preprocessor_params = {
- "preprocessor__" + key: value
- for key, value in self.preprocessor.get_params().items()
- }
- params.update(preprocessor_params)
-
- return params
-
- def set_params(self, **parameters):
- """
- Set the parameters of this estimator.
-
- Parameters
- ----------
- **parameters : dict
- Estimator parameters.
-
- Returns
- -------
- self : object
- Estimator instance.
- """
- # Update config_kwargs with provided parameters
- valid_config_keys = self.config_kwargs.keys()
- config_updates = {k: v for k, v in parameters.items() if k in valid_config_keys}
- self.config_kwargs.update(config_updates)
-
- # Update the config object
- for key, value in config_updates.items():
- setattr(self.config, key, value)
-
- # Handle preprocessor parameters (prefixed with 'preprocessor__')
- preprocessor_params = {
- k.split("__")[1]: v
- for k, v in parameters.items()
- if k.startswith("preprocessor__")
- }
- if preprocessor_params:
- # Assuming Preprocessor has a set_params method
- self.preprocessor.set_params(**preprocessor_params)
-
- return self
-
- def split_data(self, X, y, val_size, random_state):
- """
- Split the dataset into training and validation sets.
-
- Parameters
- ----------
- X : array-like of shape (n_samples, n_features)
- The input samples.
- y : array-like of shape (n_samples,)
- The target values.
- val_size : float
- The proportion of the dataset to include in the validation split.
- random_state : int
- Controls the shuffling applied to the data before applying the split.
-
- Returns
- -------
- X_train, X_val, y_train, y_val : arrays
- The split datasets.
- """
- X_train, X_val, y_train, y_val = train_test_split(
- X, y, test_size=val_size, random_state=random_state
- )
-
- return X_train, X_val, y_train, y_val
-
- def preprocess_data(self, X_train, y_train, X_val, y_val, batch_size, shuffle):
- """
- Preprocess the training and validation data and create corresponding DataLoaders.
-
- Parameters
- ----------
- X_train : array-like of shape (n_samples, n_features)
- The training input samples.
- y_train : array-like of shape (n_samples,)
- The training target values.
- X_val : array-like of shape (n_samples, n_features)
- The validation input samples.
- y_val : array-like of shape (n_samples,)
- The validation target values.
- batch_size : int
- Size of mini-batches for the DataLoader.
- shuffle : bool
- Whether to shuffle the training data before splitting into batches.
-
- Returns
- -------
- data_module : MambularDataModule
- An instance of MambularDataModule containing training and validation DataLoaders.
- """
-
- train_preprocessed_data = self.preprocessor.fit_transform(X_train, y_train)
- val_preprocessed_data = self.preprocessor.transform(X_val)
-
- # Update feature info based on the actual processed data
- (
- self.cat_feature_info,
- self.num_feature_info,
- ) = self.preprocessor.get_feature_info()
-
- # Initialize lists for tensors
- train_cat_tensors = []
- train_num_tensors = []
- val_cat_tensors = []
- val_num_tensors = []
-
- # Populate tensors for categorical features, if present in processed data
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[cat_key], dtype=torch.long)
- )
- if cat_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- if binned_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features, if present in processed data
- for key in self.num_feature_info:
- num_key = "num_" + key # Assuming numerical keys are prefixed with 'num_'
- if num_key in train_preprocessed_data:
- train_num_tensors.append(
- torch.tensor(train_preprocessed_data[num_key], dtype=torch.float)
- )
- if num_key in val_preprocessed_data:
- val_num_tensors.append(
- torch.tensor(val_preprocessed_data[num_key], dtype=torch.float)
- )
-
- train_labels = torch.tensor(y_train, dtype=torch.long)
- val_labels = torch.tensor(y_val, dtype=torch.long)
-
- # Create datasets
- train_dataset = MambularDataset(
- train_cat_tensors, train_num_tensors, train_labels, regression=False
- )
- val_dataset = MambularDataset(
- val_cat_tensors, val_num_tensors, val_labels, regression=False
- )
-
- # Create dataloaders
- train_dataloader = DataLoader(
- train_dataset, batch_size=batch_size, shuffle=shuffle
- )
- val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
-
- return MambularDataModule(train_dataloader, val_dataloader)
-
- def preprocess_test_data(self, X):
- """
- Preprocesses the test data and creates tensors for categorical and numerical features.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- Test feature set.
-
- Returns
- -------
- cat_tensors : list of Tensors
- List of tensors for each categorical feature.
- num_tensors : list of Tensors
- List of tensors for each numerical feature.
- """
- processed_data = self.preprocessor.transform(X)
-
- # Initialize lists for tensors
- cat_tensors = []
- num_tensors = []
-
- # Populate tensors for categorical features
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features
- for key in self.num_feature_info:
- num_key = "num_" + key # Assuming numerical keys are prefixed with 'num_'
- if num_key in processed_data:
- num_tensors.append(
- torch.tensor(processed_data[num_key], dtype=torch.float)
- )
-
- return cat_tensors, num_tensors
-
- def fit(
- self,
- X,
- y,
- val_size=0.2,
- X_val=None,
- y_val=None,
- max_epochs=100,
- random_state=101,
- batch_size=64,
- shuffle=True,
- patience=10,
- monitor="val_loss",
- mode="min",
- lr=1e-3,
- lr_patience=10,
- factor=0.75,
- weight_decay=0.025,
- **trainer_kwargs
- ):
- """
- Fit the model to the given training data, optionally using a separate validation set.
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The training input samples.
- y : array-like of shape (n_samples,) or (n_samples, n_outputs)
- The target values (class labels in classification, real numbers in regression).
- val_size : float, default=0.2
- The proportion of the dataset to include in the validation split if `X_val` is None. Ignored if `X_val` is provided.
- X_val : array-like or pd.DataFrame of shape (n_samples, n_features), optional
- The validation input samples. If provided, `X` and `y` are not split and this data is used for validation.
- y_val : array-like of shape (n_samples,) or (n_samples, n_outputs), optional
- The validation target values. Required if `X_val` is provided.
- max_epochs : int, default=100
- Maximum number of epochs for training.
- random_state : int, default=101
- Seed used by the random number generator for shuffling the data if `X_val` is not provided.
- batch_size : int, default=64
- Number of samples per gradient update.
- shuffle : bool, default=True
- Whether to shuffle the training data before each epoch if `X_val` is not provided.
- patience : int, default=10
- Number of epochs with no improvement after which training will be stopped if using early stopping.
- monitor : str, default="val_loss"
- Quantity to be monitored for early stopping.
- mode : str, default="min"
- One of {"min", "max"}. In "min" mode, training will stop when the quantity monitored has stopped decreasing; in "max" mode, it will stop when the quantity monitored has stopped increasing.
- lr : float, default=1e-3
- Learning rate for the optimizer.
- lr_patience : int, default=10
- Number of epochs with no improvement after which the learning rate will be reduced.
- factor : float, default=0.75
- Factor by which the learning rate will be reduced. new_lr = lr * factor.
- weight_decay : float, default=0.025
- Weight decay (L2 penalty) parameter.
- **trainer_kwargs : dict
- Additional keyword arguments to be passed to the PyTorch Lightning Trainer constructor.
-
- Returns
- -------
- self : object
- The fitted estimator.
- """
-
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- if X_val:
- if not isinstance(X_val, pd.DataFrame):
- X_val = pd.DataFrame(X_val)
-
- num_classes = len(np.unique(y))
-
- if not X_val:
- X_train, X_val, y_train, y_val = self.split_data(
- X, y, val_size, random_state
- )
-
- data_module = self.preprocess_data(
- X_train, y_train, X_val, y_val, batch_size, shuffle
- )
-
- self.model = BaseMambularClassifier(
- num_classes=num_classes,
- config=self.config,
- cat_feature_info=self.cat_feature_info,
- num_feature_info=self.num_feature_info,
- lr=lr,
- lr_patience=lr_patience,
- lr_factor=factor,
- weight_decay=weight_decay,
- )
-
- early_stop_callback = EarlyStopping(
- monitor=monitor, min_delta=0.00, patience=patience, verbose=False, mode=mode
- )
-
- checkpoint_callback = ModelCheckpoint(
- monitor="val_loss", # Adjust according to your validation metric
- mode="min",
- save_top_k=1,
- dirpath="model_checkpoints", # Specify the directory to save checkpoints
- filename="best_model",
- )
-
- # Initialize the trainer and train the model
- trainer = pl.Trainer(
- max_epochs=max_epochs,
- callbacks=[early_stop_callback, checkpoint_callback],
- **trainer_kwargs
- )
- trainer.fit(self.model, data_module)
-
- best_model_path = checkpoint_callback.best_model_path
- if best_model_path:
- checkpoint = torch.load(best_model_path)
- self.model.load_state_dict(checkpoint["state_dict"])
-
- return self
-
- def predict(self, X):
- """
- Predict the class labels for the given input samples.
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples to predict.
-
- Returns
- -------
- predictions : ndarray of shape (n_samples,)
- Predicted class labels for each input sample.
-
- Notes
- -----
- The method preprocesses the input data using the same preprocessor used during training,
- sets the model to evaluation mode, and then performs inference to predict the class labels.
- The predictions are converted from a PyTorch tensor to a NumPy array before being returned.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- logits = self.model(cat_tensors, num_tensors)
- predictions = torch.argmax(logits, dim=1)
-
- # Convert predictions to NumPy array and return
- return predictions.cpu().numpy()
-
- def predict_proba(self, X):
- """
- Predict class probabilities for the given input samples.
-
- Example
- -------
- from sklearn.metrics import accuracy_score, precision_score, f1_score, roc_auc_score
-
- # Define the metrics you want to evaluate
- metrics = {
- 'Accuracy': (accuracy_score, False),
- 'Precision': (precision_score, False),
- 'F1 Score': (f1_score, False),
- 'AUC Score': (roc_auc_score, True)
- }
-
- # Assuming 'X_test' and 'y_test' are your test dataset and labels
- # Evaluate using the specified metrics
- results = classifier.evaluate(X_test, y_test, metrics=metrics)
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples for which to predict class probabilities.
-
- Returns
- -------
- probabilities : ndarray of shape (n_samples, n_classes)
- Predicted class probabilities for each input sample.
-
- Notes
- -----
- The method preprocesses the input data using the same preprocessor used during training,
- sets the model to evaluation mode, and then performs inference to predict the class probabilities.
- Softmax is applied to the logits to obtain probabilities, which are then converted from a PyTorch tensor
- to a NumPy array before being returned.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- logits = self.model(cat_tensors, num_tensors)
- probabilities = torch.softmax(logits, dim=1)
-
- # Convert probabilities to NumPy array and return
- return probabilities.cpu().numpy()
-
- def evaluate(self, X, y_true, metrics=None):
- """
- Evaluate the model on the given data using specified metrics.
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples to predict.
- y_true : array-like of shape (n_samples,)
- The true class labels against which to evaluate the predictions.
- metrics : dict
- A dictionary where keys are metric names and values are tuples containing the metric function
- and a boolean indicating whether the metric requires probability scores (True) or class labels (False).
-
- Returns
- -------
- scores : dict
- A dictionary with metric names as keys and their corresponding scores as values.
-
- Notes
- -----
- This method uses either the `predict` or `predict_proba` method depending on the metric requirements.
- """
- # Ensure input is in the correct format
- if metrics is None:
- metrics = {"Accuracy": (accuracy_score, False)}
-
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
-
- # Initialize dictionary to store results
- scores = {}
-
- # Generate class probabilities if any metric requires them
- if any(use_proba for _, use_proba in metrics.values()):
- probabilities = self.predict_proba(X)
-
- # Generate class labels if any metric requires them
- if any(not use_proba for _, use_proba in metrics.values()):
- predictions = self.predict(X)
-
- # Compute each metric
- for metric_name, (metric_func, use_proba) in metrics.items():
- if use_proba:
- scores[metric_name] = metric_func(y_true, probabilities)
- else:
- scores[metric_name] = metric_func(y_true, predictions)
-
- return scores
-
-from sklearn.model_selection import train_test_split
-import pytorch_lightning as pl
-import torch
-from torch.utils.data import DataLoader
-from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
-from ..base_models.distributional import BaseMambularLSS
-from ..utils.config import MambularConfig
-from ..utils.preprocessor import Preprocessor
-from ..utils.dataset import MambularDataModule, MambularDataset
-from sklearn.base import BaseEstimator
-import pandas as pd
-from ..utils.distributional_metrics import (
- poisson_deviance,
- gamma_deviance,
- beta_brier_score,
- dirichlet_error,
- student_t_loss,
- negative_binomial_deviance,
- inverse_gamma_loss,
-)
-from sklearn.metrics import mean_squared_error, accuracy_score
-import numpy as np
-import properscoring as ps
-
-
-[docs]class MambularLSS(BaseEstimator):
- """
- MambularLSS is a machine learning estimator that is designed for structured data,
- incorporating both preprocessing and a deep learning model. The estimator
- integrates configurable components for data preprocessing and the neural network model,
- facilitating end-to-end training and prediction workflows.
-
- The initialization of this class separates configuration arguments for the model and
- the preprocessor, allowing for flexible adjustment of parameters.
-
- Attributes
- ----------
- config : MambularConfig
- Configuration object containing model-specific parameters.
- preprocessor : Preprocessor
- Preprocessor object for data preprocessing steps.
- model : torch.nn.Module
- The neural network model, initialized based on 'config'.
-
- Parameters
- ----------
- **kwargs : Arbitrary keyword arguments, divided into configuration for the model and
- preprocessing. Recognized keys include model parameters such as 'd_model',
- 'n_layers', etc., and any additional keys are assumed to be preprocessor arguments.
- """
-
- def __init__(self, **kwargs):
- # Known config arguments
- config_arg_names = [
- "d_model",
- "n_layers",
- "dt_rank",
- "output_dimension",
- "pooling_method",
- "norm",
- "cls",
- "dt_min",
- "dt_max",
- "dropout",
- "bias",
- "weight_decay",
- "conv_bias",
- "d_state",
- "expand_factor",
- "d_conv",
- "dt_init",
- "dt_scale",
- "dt_init_floor",
- "tabular_head_units",
- "tabular_head_activation",
- "tabular_head_dropout",
- "num_emebedding_activation",
- ]
- config_kwargs = {k: v for k, v in kwargs.items() if k in config_arg_names}
- self.config = MambularConfig(**config_kwargs)
-
- # The rest are assumed to be preprocessor arguments
- preprocessor_kwargs = {
- k: v for k, v in kwargs.items() if k not in config_arg_names
- }
- self.preprocessor = Preprocessor(**preprocessor_kwargs)
- self.model = None
-
- def get_params(self, deep=True):
- """
- Get parameters for this estimator, optionally including parameters from nested components
- like the preprocessor.
-
- Parameters
- ----------
- deep : bool, default=True
- If True, return parameters of nested components.
-
- Returns
- -------
- dict
- A dictionary mapping parameter names to their values. For nested components,
- parameter names are prefixed accordingly (e.g., 'preprocessor__<param_name>').
- """
-
- params = self.config_kwargs # Parameters used to initialize MambularConfig
-
- # If deep=True, include parameters from nested components like preprocessor
- if deep:
- # Assuming Preprocessor has a get_params method
- preprocessor_params = {
- "preprocessor__" + key: value
- for key, value in self.preprocessor.get_params().items()
- }
- params.update(preprocessor_params)
-
- return params
-
- def set_params(self, **parameters):
- """
- Set the parameters of this estimator, allowing for modifications to both the configuration
- and preprocessor parameters. Parameters not recognized as configuration arguments are
- assumed to be preprocessor arguments.
-
- Parameters
- ----------
- **parameters: Arbitrary keyword arguments where keys are parameter names and values
- are the new parameter values.
-
- Returns
- -------
- self: This instance with updated parameters.
- """
- # Update config_kwargs with provided parameters
- valid_config_keys = self.config_kwargs.keys()
- config_updates = {k: v for k, v in parameters.items() if k in valid_config_keys}
- self.config_kwargs.update(config_updates)
-
- # Update the config object
- for key, value in config_updates.items():
- setattr(self.config, key, value)
-
- # Handle preprocessor parameters (prefixed with 'preprocessor__')
- preprocessor_params = {
- k.split("__")[1]: v
- for k, v in parameters.items()
- if k.startswith("preprocessor__")
- }
- if preprocessor_params:
- # Assuming Preprocessor has a set_params method
- self.preprocessor.set_params(**preprocessor_params)
-
- return self
-
- def split_data(self, X, y, val_size, random_state):
- """
- Split the dataset into training and validation sets.
-
- Parameters
- ----------
- X : array-like
- Features of the dataset.
- y : array-like
- Target values.
- val_size : float
- The proportion of the dataset to include in the validation split.
- random_state : int, optional
- The seed used by the random number generator for reproducibility.
-
- Returns
- -------
- tuple
- A tuple containing split datasets (X_train, X_val, y_train, y_val).
- """
- X_train, X_val, y_train, y_val = train_test_split(
- X, y, test_size=val_size, random_state=random_state
- )
-
- return X_train, X_val, y_train, y_val
-
- def preprocess_data(self, X_train, y_train, X_val, y_val, batch_size, shuffle):
- """
- Preprocess the training and validation data, fit the preprocessor on the training data,
- and transform both training and validation data. This method also initializes tensors
- for categorical and numerical features and labels, and prepares DataLoader objects for
- both datasets.
-
- Parameters
- ----------
- X_train : array-like
- Training features.
- y_train : array-like
- Training target values.
- X_val : array-like
- Validation features.
- y_val : array-like
- Validation target values.
- batch_size : int
- Batch size for DataLoader objects.
- shuffle : bool
- Whether to shuffle the training data in the DataLoader.
-
- Returns
- -------
- MambularDataModule
- An object containing DataLoaders for training and validation datasets.
- """
- train_preprocessed_data = self.preprocessor.fit_transform(X_train, y_train)
- val_preprocessed_data = self.preprocessor.transform(X_val)
-
- # Update feature info based on the actual processed data
- (
- self.cat_feature_info,
- self.num_feature_info,
- ) = self.preprocessor.get_feature_info()
-
- # Initialize lists for tensors
- train_cat_tensors = []
- train_num_tensors = []
- val_cat_tensors = []
- val_num_tensors = []
-
- # Populate tensors for categorical features, if present in processed data
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[cat_key], dtype=torch.long)
- )
- if cat_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- if binned_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features, if present in processed data
- for key in self.num_feature_info:
- num_key = "num_" + key # Assuming numerical keys are prefixed with 'num_'
- if num_key in train_preprocessed_data:
- train_num_tensors.append(
- torch.tensor(train_preprocessed_data[num_key], dtype=torch.float)
- )
- if num_key in val_preprocessed_data:
- val_num_tensors.append(
- torch.tensor(val_preprocessed_data[num_key], dtype=torch.float)
- )
-
- train_labels = torch.tensor(y_train, dtype=torch.float)
- val_labels = torch.tensor(y_val, dtype=torch.float)
-
- # Create datasets
- train_dataset = MambularDataset(
- train_cat_tensors, train_num_tensors, train_labels
- )
- val_dataset = MambularDataset(val_cat_tensors, val_num_tensors, val_labels)
-
- # Create dataloaders
- train_dataloader = DataLoader(
- train_dataset, batch_size=batch_size, shuffle=shuffle
- )
- val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
-
- return MambularDataModule(train_dataloader, val_dataloader)
-
- def preprocess_test_data(self, X):
- """
- Preprocess test data using the fitted preprocessor. This method prepares tensors for
- categorical and numerical features based on the preprocessed test data.
-
- Parameters
- ----------
- X : array-like
- Test features to preprocess.
-
- Returns
- -------
- tuple
- A tuple containing lists of tensors for categorical and numerical features.
- """
- processed_data = self.preprocessor.transform(X)
-
- # Initialize lists for tensors
- cat_tensors = []
- num_tensors = []
-
- # Populate tensors for categorical features
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features
- for key in self.num_feature_info:
- num_key = "num_" + key # Assuming numerical keys are prefixed with 'num_'
- if num_key in processed_data:
- num_tensors.append(
- torch.tensor(processed_data[num_key], dtype=torch.float)
- )
-
- return cat_tensors, num_tensors
-
- def fit(
- self,
- X,
- y,
- family,
- val_size=0.2,
- X_val=None,
- y_val=None,
- max_epochs=100,
- random_state=101,
- batch_size=64,
- shuffle=True,
- patience=10,
- monitor="val_loss",
- mode="min",
- lr=1e-3,
- lr_patience=10,
- factor=0.75,
- weight_decay=0.025,
- **trainer_kwargs,
- ):
- """
- Fits the model to the provided data, using the specified loss distribution family for the prediction task.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- Training features.
- y : array-like, shape (n_samples,) or (n_samples, n_targets)
- Target values for training.
- family : str
- The name of the distribution family to use for the loss function. Examples include 'normal' for regression tasks.
- val_size : float, default=0.2
- Proportion of the dataset to include in the validation split if `X_val` is None.
- X_val : DataFrame or array-like, shape (n_samples, n_features), optional
- Validation features. If provided, `X` and `y` are not split.
- y_val : array-like, shape (n_samples,) or (n_samples, n_targets), optional
- Validation target values. Required if `X_val` is provided.
- max_epochs : int, default=100
- Maximum number of epochs for training.
- random_state : int, default=101
- Seed used by the random number generator for shuffling the data.
- batch_size : int, default=64
- Number of samples per gradient update.
- shuffle : bool, default=True
- Whether to shuffle the training data before each epoch.
- patience : int, default=10
- Number of epochs with no improvement on the validation metric to wait before early stopping.
- monitor : str, default="val_loss"
- The metric to monitor for early stopping.
- mode : str, default="min"
- In 'min' mode, training will stop when the quantity monitored has stopped decreasing;
- in 'max' mode, it will stop when the quantity monitored has stopped increasing.
- lr : float, default=1e-3
- Learning rate for the optimizer.
- lr_patience : int, default=10
- Number of epochs with no improvement on the validation metric to wait before reducing the learning rate.
- factor : float, default=0.75
- Factor by which the learning rate will be reduced.
- weight_decay : float, default=0.025
- Weight decay (L2 penalty) parameter.
- **trainer_kwargs : dict
- Additional keyword arguments for PyTorch Lightning's Trainer class.
-
- Returns
- -------
- self : object
- The fitted estimator.
- """
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- if X_val:
- if not isinstance(X_val, pd.DataFrame):
- X_val = pd.DataFrame(X_val)
-
- if not X_val:
- X_train, X_val, y_train, y_val = self.split_data(
- X, y, val_size, random_state
- )
-
- data_module = self.preprocess_data(
- X_train, y_train, X_val, y_val, batch_size, shuffle
- )
-
- self.model = BaseMambularLSS(
- family=family,
- config=self.config,
- cat_feature_info=self.cat_feature_info,
- num_feature_info=self.num_feature_info,
- lr=lr,
- lr_patience=lr_patience,
- lr_factor=factor,
- weight_decay=weight_decay,
- )
-
- early_stop_callback = EarlyStopping(
- monitor=monitor, min_delta=0.00, patience=patience, verbose=False, mode=mode
- )
-
- checkpoint_callback = ModelCheckpoint(
- monitor="val_loss",
- mode="min",
- save_top_k=1,
- dirpath="model_checkpoints",
- filename="best_model",
- )
-
- # Initialize the trainer and train the model
- trainer = pl.Trainer(
- max_epochs=max_epochs,
- callbacks=[early_stop_callback, checkpoint_callback],
- **trainer_kwargs,
- )
- trainer.fit(self.model, data_module)
-
- best_model_path = checkpoint_callback.best_model_path
- if best_model_path:
- checkpoint = torch.load(best_model_path)
- self.model.load_state_dict(checkpoint["state_dict"])
-
- return self
-
- def predict(self, X):
- """
- Predicts target values for the given input samples using the fitted model.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- The input samples for which to predict target values.
-
- Returns
- -------
- predictions : ndarray, shape (n_samples,) or (n_samples, n_distributional_parameters)
- The predicted target values.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- predictions = self.model(cat_tensors, num_tensors)
-
- # Convert predictions to NumPy array and return
- return predictions.cpu().numpy()
-
- def evaluate(self, X, y_true, metrics=None, distribution_family=None):
- """
- Evaluate the model on the given data using specified metrics tailored to the distribution type.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- Input samples.
- y_true : DataFrame or array-like, shape (n_samples,) or (n_samples, n_outputs)
- True target values.
- metrics : dict, optional
- A dictionary where keys are metric names and values are the metric functions.
- If None, default metrics based on the detected or specified distribution_family are used.
- distribution_family : str, optional
- Specifies the distribution family the model is predicting for. If None, it will attempt to infer based
- on the model's settings.
-
- Returns
- -------
- scores : dict
- A dictionary with metric names as keys and their corresponding scores as values.
- """
- # Infer distribution family from model settings if not provided
- if distribution_family is None:
- distribution_family = getattr(self.model, "distribution_family", "normal")
-
- # Setup default metrics if none are provided
- if metrics is None:
- metrics = self.get_default_metrics(distribution_family)
-
- # Make predictions
- predictions = self.predict(X)
-
- # Initialize dictionary to store results
- scores = {}
-
- # Compute each metric
- for metric_name, metric_func in metrics.items():
- scores[metric_name] = metric_func(y_true, predictions)
-
- return scores
-
- def get_default_metrics(self, distribution_family):
- """
- Provides default metrics based on the distribution family.
-
- Parameters
- ----------
- distribution_family : str
- The distribution family for which to provide default metrics.
-
- Returns
- -------
- metrics : dict
- A dictionary of default metric functions.
- """
- default_metrics = {
- "normal": {
- "MSE": lambda y, pred: mean_squared_error(y, pred[:, 0]),
- "CRPS": lambda y, pred: np.mean(
- [
- ps.crps_gaussian(y[i], mu=pred[i, 0], sig=np.sqrt(pred[i, 1]))
- for i in range(len(y))
- ]
- ),
- },
- "poisson": {"Poisson Deviance": poisson_deviance},
- "gamma": {"Gamma Deviance": gamma_deviance},
- "beta": {"Brier Score": beta_brier_score},
- "dirichlet": {"Dirichlet Error": dirichlet_error},
- "studentt": {"Student-T Loss": student_t_loss},
- "negativebinom": {"Negative Binomial Deviance": negative_binomial_deviance},
- "inversegamma": {"Inverse Gamma Loss": inverse_gamma_loss},
- "categorical": {"Accuracy": accuracy_score},
- }
- return default_metrics.get(distribution_family, {})
-
-from sklearn.model_selection import train_test_split
-import pytorch_lightning as pl
-import torch
-from torch.utils.data import DataLoader
-from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
-from ..base_models.embedding_classifier import BaseEmbeddingMambularClassifier
-from ..utils.config import MambularConfig
-from ..utils.preprocessor import Preprocessor
-from ..utils.dataset import MambularDataModule, EmbeddingMambularDataset
-from sklearn.base import BaseEstimator
-import pandas as pd
-from sklearn.decomposition import PCA
-import numpy as np
-from sklearn.metrics import accuracy_score
-
-
-[docs]class EmbeddingMambularClassifier(BaseEstimator):
- """
- Provides an scikit-learn-like interface for the ProteinMambularClassifier, making it compatible with
- scikit-learn's utilities and workflow. This class encapsulates the PyTorch Lightning model, preprocessing,
- and data loading, offering methods for fitting, predicting, and probability estimation in a manner akin
- to scikit-learn's API.
-
- Parameters
- ----------
- **kwargs : Configuration parameters that can include both MambularConfig settings and preprocessing
- options. Any unrecognized parameters are passed to the preprocessor.
-
- Attributes
- ----------
- config : MambularConfig
- Configuration object for the model, storing architecture-specific parameters.
- preprocessor : Preprocessor
- Object handling data preprocessing steps such as feature encoding and normalization.
- model : ProteinMambularClassifier
- The underlying neural network model, instantiated during the `fit` method.
- """
-
- def __init__(self, **kwargs):
- # Known config arguments
- config_arg_names = [
- "d_model",
- "n_layers",
- "dt_rank",
- "output_dimension",
- "pooling_method",
- "norm",
- "cls",
- "dt_min",
- "dt_max",
- "dropout",
- "bias",
- "weight_decay",
- "conv_bias",
- "d_state",
- "expand_factor",
- "d_conv",
- "dt_init",
- "dt_scale",
- "dt_init_floor",
- ]
- config_kwargs = {k: v for k, v in kwargs.items() if k in config_arg_names}
- self.config = MambularConfig(**config_kwargs)
-
- # The rest are assumed to be preprocessor arguments
- preprocessor_kwargs = {
- k: v for k, v in kwargs.items() if k not in config_arg_names
- }
- self.preprocessor = Preprocessor(**preprocessor_kwargs)
- self.model = None
-
- def get_params(self, deep=True):
- """
- Get parameters for this estimator.
-
- Parameters
- ----------
- deep : bool, default=True
- If True, will return the parameters for this estimator and contained subobjects that are estimators.
-
- Returns
- -------
- params : dict
- Parameter names mapped to their values.
- """
- params = self.config_kwargs # Parameters used to initialize MambularConfig
-
- # If deep=True, include parameters from nested components like preprocessor
- if deep:
- # Assuming Preprocessor has a get_params method
- preprocessor_params = {
- "preprocessor__" + key: value
- for key, value in self.preprocessor.get_params().items()
- }
- params.update(preprocessor_params)
-
- return params
-
- def set_params(self, **parameters):
- """
- Set the parameters of this estimator.
-
- Parameters
- ----------
- **parameters : dict
- Estimator parameters.
-
- Returns
- -------
- self : object
- Estimator instance.
- """
- # Update config_kwargs with provided parameters
- valid_config_keys = self.config_kwargs.keys()
- config_updates = {k: v for k, v in parameters.items() if k in valid_config_keys}
- self.config_kwargs.update(config_updates)
-
- # Update the config object
- for key, value in config_updates.items():
- setattr(self.config, key, value)
-
- # Handle preprocessor parameters (prefixed with 'preprocessor__')
- preprocessor_params = {
- k.split("__")[1]: v
- for k, v in parameters.items()
- if k.startswith("preprocessor__")
- }
- if preprocessor_params:
- # Assuming Preprocessor has a set_params method
- self.preprocessor.set_params(**preprocessor_params)
-
- return self
-
- def split_data(self, X, y, val_size, random_state):
- """
- Split the dataset into training and validation sets.
-
- Parameters
- ----------
- X : array-like of shape (n_samples, n_features)
- The input samples.
- y : array-like of shape (n_samples,)
- The target values.
- val_size : float
- The proportion of the dataset to include in the validation split.
- random_state : int
- Controls the shuffling applied to the data before applying the split.
-
- Returns
- -------
- X_train, X_val, y_train, y_val : arrays
- The split datasets.
- """
- X_train, X_val, y_train, y_val = train_test_split(
- X, y, test_size=val_size, random_state=random_state
- )
-
- return X_train, X_val, y_train, y_val
-
- def preprocess_data(self, X_train, y_train, X_val, y_val, batch_size, shuffle):
- """
- Preprocess the training and validation data and create corresponding DataLoaders.
-
- Parameters
- ----------
- X_train : array-like of shape (n_samples, n_features)
- The training input samples.
- y_train : array-like of shape (n_samples,)
- The training target values.
- X_val : array-like of shape (n_samples, n_features)
- The validation input samples.
- y_val : array-like of shape (n_samples,)
- The validation target values.
- batch_size : int
- Size of mini-batches for the DataLoader.
- shuffle : bool
- Whether to shuffle the training data before splitting into batches.
-
- Returns
- -------
- data_module : MambularDataModule
- An instance of MambularDataModule containing training and validation DataLoaders.
- """
- train_preprocessed_data = self.preprocessor.fit_transform(X_train, y_train)
- val_preprocessed_data = self.preprocessor.transform(X_val)
-
- # Update feature info based on the actual processed data
- (
- self.cat_feature_info,
- self.num_feature_info,
- ) = self.preprocessor.get_feature_info()
-
- # Initialize lists for tensors
- train_cat_tensors = []
- train_num_tensors = []
- val_cat_tensors = []
- val_num_tensors = []
-
- # Populate tensors for categorical features, if present in processed data
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[cat_key], dtype=torch.long)
- )
- if cat_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- if binned_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features, if present in processed data
- for key in self.num_feature_info:
- num_key = "num_" + str(
- key
- ) # Assuming numerical keys are prefixed with 'num_'
- if num_key in train_preprocessed_data:
- train_num_tensors.append(
- torch.tensor(train_preprocessed_data[num_key], dtype=torch.float)
- )
- if num_key in val_preprocessed_data:
- val_num_tensors.append(
- torch.tensor(val_preprocessed_data[num_key], dtype=torch.float)
- )
-
- train_labels = torch.tensor(y_train, dtype=torch.long)
- val_labels = torch.tensor(y_val, dtype=torch.long)
-
- # Create datasets
- train_dataset = EmbeddingMambularDataset(
- train_cat_tensors, train_num_tensors, train_labels, regression=False
- )
- val_dataset = EmbeddingMambularDataset(
- val_cat_tensors, val_num_tensors, val_labels, regression=False
- )
-
- # Create dataloaders
- train_dataloader = DataLoader(
- train_dataset, batch_size=batch_size, shuffle=shuffle
- )
- val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
-
- return MambularDataModule(train_dataloader, val_dataloader)
-
- def preprocess_test_data(self, X):
- """
- Preprocesses the test data and creates tensors for categorical and numerical features.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- Test feature set.
-
- Returns
- -------
- cat_tensors : list of Tensors
- List of tensors for each categorical feature.
- num_tensors : list of Tensors
- List of tensors for each numerical feature.
- """
- processed_data = self.preprocessor.transform(X)
-
- # Initialize lists for tensors
- cat_tensors = []
- num_tensors = []
-
- # Populate tensors for categorical features
- for key in self.cat_feature_info:
- cat_key = "cat_" + str(
- key
- ) # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + str(key) # for binned features
- if binned_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features
- for key in self.num_feature_info:
- num_key = "num_" + str(
- key
- ) # Assuming numerical keys are prefixed with 'num_'
- if num_key in processed_data:
- num_tensors.append(
- torch.tensor(processed_data[num_key], dtype=torch.float)
- )
-
- return cat_tensors, num_tensors
-
- def fit(
- self,
- X,
- y,
- val_size=0.2,
- X_val=None,
- y_val=None,
- max_epochs=100,
- random_state=101,
- batch_size=64,
- shuffle=True,
- patience=10,
- monitor="val_loss",
- mode="min",
- lr=1e-3,
- lr_patience=10,
- factor=0.75,
- weight_decay=0.025,
- raw_embeddings=False,
- seq_size=20,
- pca=False,
- reduced_dims=16,
- **trainer_kwargs
- ):
- """
- Fits the model to the given dataset.
-
- Parameters
- ----------
- X : pandas DataFrame or array-like
- Feature matrix for training.
- y : array-like
- Target vector.
- val_size : float, optional
- Fraction of the data to use for validation if X_val is None.
- X_val : pandas DataFrame or array-like, optional
- Feature matrix for validation.
- y_val : array-like, optional
- Target vector for validation.
- max_epochs : int, default=100
- Maximum number of epochs for training.
- random_state : int, optional
- Seed for random number generators.
- batch_size : int, default=32
- Size of batches for training and validation.
- shuffle : bool, default=True
- Whether to shuffle training data before each epoch.
- patience : int, default=10
- Patience for early stopping based on val_loss.
- monitor : str, default='val_loss'
- Metric to monitor for early stopping.
- mode : str, default='min'
- Mode for early stopping ('min' or 'max').
- lr : float, default=0.001
- Learning rate for the optimizer.
- lr_patience : int, default=5
- Patience for learning rate reduction.
- factor : float, default=0.1
- Factor for learning rate reduction.
- weight_decay : float, default=0.0
- Weight decay for the optimizer.
- raw_embeddings : bool, default=False
- Whether to use raw features or embeddings.
- seq_size : int, optional
- Sequence size for embeddings, relevant if raw_embeddings is False.
- **trainer_kwargs : dict
- Additional arguments for the PyTorch Lightning Trainer.
-
- Returns
- -------
- self : object
- The fitted estimator.
- """
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- if X_val:
- if not isinstance(X_val, pd.DataFrame):
- X_val = pd.DataFrame(X_val)
-
- # Apply PCA if indicated
- if pca:
- pca_transformer = PCA(n_components=reduced_dims)
- X = pca_transformer.fit_transform(
- X
- ) # Fit and transform the PCA on the complete dataset
- if X_val is not None:
- X_val = pca_transformer.transform(
- X_val
- ) # Transform validation data with the same PCA model
-
- raw_embeddings = True
-
- if not X_val:
- X_train, X_val, y_train, y_val = self.split_data(
- X, y, val_size, random_state
- )
- else:
- X_train = X
- y_train = y
-
- data_module = self.preprocess_data(
- X_train, y_train, X_val, y_val, batch_size, shuffle
- )
-
- if raw_embeddings:
- self.config.d_model = X.shape[1]
-
- num_classes = len(np.unique(y))
-
- self.model = BaseEmbeddingMambularClassifier(
- num_classes=num_classes,
- config=self.config,
- cat_feature_info=self.cat_feature_info,
- num_feature_info=self.num_feature_info,
- lr=lr,
- lr_patience=lr_patience,
- lr_factor=factor,
- weight_decay=weight_decay,
- raw_embeddings=raw_embeddings,
- seq_size=seq_size,
- )
-
- early_stop_callback = EarlyStopping(
- monitor=monitor, min_delta=0.00, patience=patience, verbose=False, mode=mode
- )
-
- checkpoint_callback = ModelCheckpoint(
- monitor="val_loss", # Adjust according to your validation metric
- mode="min",
- save_top_k=1,
- dirpath="model_checkpoints", # Specify the directory to save checkpoints
- filename="best_model",
- )
-
- # Initialize the trainer and train the model
- trainer = pl.Trainer(
- max_epochs=max_epochs,
- callbacks=[early_stop_callback, checkpoint_callback],
- **trainer_kwargs
- )
- trainer.fit(self.model, data_module)
-
- best_model_path = checkpoint_callback.best_model_path
- if best_model_path:
- checkpoint = torch.load(best_model_path)
- self.model.load_state_dict(checkpoint["state_dict"])
-
- return self
-
- def predict(self, X):
- """
- Predict the class labels for the given input samples.
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples to predict.
-
- Returns
- -------
- predictions : ndarray of shape (n_samples,)
- Predicted class labels for each input sample.
-
- Notes
- -----
- The method preprocesses the input data using the same preprocessor used during training,
- sets the model to evaluation mode, and then performs inference to predict the class labels.
- The predictions are converted from a PyTorch tensor to a NumPy array before being returned.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
-
- if hasattr(self, "pca_transformer"):
- X = pd.DataFrame(self.pca_transformer.transform(X))
-
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- logits = self.model(cat_tensors, num_tensors)
- predictions = torch.argmax(logits, dim=1)
-
- # Convert predictions to NumPy array and return
- return predictions.cpu().numpy()
-
- def predict_proba(self, X):
- """
- Predict class probabilities for the given input samples.
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples for which to predict class probabilities.
-
- Returns
- -------
- probabilities : ndarray of shape (n_samples, n_classes)
- Predicted class probabilities for each input sample.
-
- Notes
- -----
- The method preprocesses the input data using the same preprocessor used during training,
- sets the model to evaluation mode, and then performs inference to predict the class probabilities.
- Softmax is applied to the logits to obtain probabilities, which are then converted from a PyTorch tensor
- to a NumPy array before being returned.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- logits = self.model(cat_tensors, num_tensors)
- probabilities = torch.softmax(logits, dim=1)
-
- # Convert probabilities to NumPy array and return
- return probabilities.cpu().numpy()
-
- def evaluate(self, X, y_true, metrics=None):
- """
- Evaluate the model on the given data using specified metrics.
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples to predict.
- y_true : array-like of shape (n_samples,)
- The true class labels against which to evaluate the predictions.
- metrics : dict
- A dictionary where keys are metric names and values are tuples containing the metric function
- and a boolean indicating whether the metric requires probability scores (True) or class labels (False).
-
- Returns
- -------
- scores : dict
- A dictionary with metric names as keys and their corresponding scores as values.
-
- Notes
- -----
- This method uses either the `predict` or `predict_proba` method depending on the metric requirements.
- """
- # Ensure input is in the correct format
- if metrics is None:
- metrics = {"Accuracy": (accuracy_score, False)}
-
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
-
- # Initialize dictionary to store results
- scores = {}
-
- # Generate class probabilities if any metric requires them
- if any(use_proba for _, use_proba in metrics.values()):
- probabilities = self.predict_proba(X)
-
- # Generate class labels if any metric requires them
- if any(not use_proba for _, use_proba in metrics.values()):
- predictions = self.predict(X)
-
- # Compute each metric
- for metric_name, (metric_func, use_proba) in metrics.items():
- if use_proba:
- scores[metric_name] = metric_func(y_true, probabilities)
- else:
- scores[metric_name] = metric_func(y_true, predictions)
-
- return scores
-
-from sklearn.model_selection import train_test_split
-import pytorch_lightning as pl
-import torch
-from torch.utils.data import DataLoader
-from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
-from ..base_models.embedding_regressor import BaseEmbeddingMambularRegressor
-from ..utils.config import MambularConfig
-from ..utils.preprocessor import Preprocessor
-from ..utils.dataset import MambularDataModule, EmbeddingMambularDataset
-from sklearn.base import BaseEstimator
-import pandas as pd
-from sklearn.decomposition import PCA
-from sklearn.metrics import mean_squared_error
-
-
-[docs]class EmbeddingMambularRegressor(BaseEstimator):
- """
- An sklearn-like interface for the ProteinMambularRegressor, making it compatible with sklearn's utilities
- and workflows. This class wraps the PyTorch Lightning model and preprocessor, providing methods for fitting,
- predicting, and setting/getting parameters in a way that mimics sklearn's API.
-
- Parameters
- ----------
- **kwargs : Keyword arguments that can include both configuration parameters for the MambularConfig and
- parameters for the preprocessor.
-
- Attributes
- ----------
- config : MambularConfig
- Configuration object containing model-specific parameters.
- preprocessor : Preprocessor
- Preprocessor object for data preprocessing steps.
- model : ProteinMambularRegressor
- The neural network model, initialized after the `fit` method is called.
- """
-
- def __init__(self, **kwargs):
- # Known config arguments
- config_arg_names = [
- "d_model",
- "n_layers",
- "dt_rank",
- "output_dimension",
- "pooling_method",
- "norm",
- "cls",
- "dt_min",
- "dt_max",
- "dropout",
- "bias",
- "weight_decay",
- "conv_bias",
- "d_state",
- "expand_factor",
- "d_conv",
- "dt_init",
- "dt_scale",
- "dt_init_floor",
- ]
- config_kwargs = {k: v for k, v in kwargs.items() if k in config_arg_names}
- self.config = MambularConfig(**config_kwargs)
-
- # The rest are assumed to be preprocessor arguments
- preprocessor_kwargs = {
- k: v for k, v in kwargs.items() if k not in config_arg_names
- }
-
- if not "numerical_preprocessing" in preprocessor_kwargs.keys():
- preprocessor_kwargs["numerical_preprocessing"] = "normalization"
- self.preprocessor = Preprocessor(**preprocessor_kwargs)
- self.model = None
-
- def get_params(self, deep=True):
- """
- Get parameters for this estimator. Overrides the BaseEstimator method.
-
- Parameters
- ----------
- deep : bool, default=True
- If True, returns the parameters for this estimator and contained sub-objects that are estimators.
-
- Returns
- -------
- params : dict
- Parameter names mapped to their values.
- """
- params = self.config_kwargs # Parameters used to initialize MambularConfig
-
- # If deep=True, include parameters from nested components like preprocessor
- if deep:
- # Assuming Preprocessor has a get_params method
- preprocessor_params = {
- "preprocessor__" + key: value
- for key, value in self.preprocessor.get_params().items()
- }
- params.update(preprocessor_params)
-
- return params
-
- def set_params(self, **parameters):
- """
- Set the parameters of this estimator. Overrides the BaseEstimator method.
-
- Parameters
- ----------
- **parameters : dict
- Estimator parameters to be set.
-
- Returns
- -------
- self : object
- The instance with updated parameters.
- """
- # Update config_kwargs with provided parameters
- valid_config_keys = self.config_kwargs.keys()
- config_updates = {k: v for k, v in parameters.items() if k in valid_config_keys}
- self.config_kwargs.update(config_updates)
-
- # Update the config object
- for key, value in config_updates.items():
- setattr(self.config, key, value)
-
- # Handle preprocessor parameters (prefixed with 'preprocessor__')
- preprocessor_params = {
- k.split("__")[1]: v
- for k, v in parameters.items()
- if k.startswith("preprocessor__")
- }
- if preprocessor_params:
- # Assuming Preprocessor has a set_params method
- self.preprocessor.set_params(**preprocessor_params)
-
- return self
-
- def split_data(self, X, y, val_size, random_state):
- """
- Splits the dataset into training and validation sets.
-
- Parameters
- ----------
- X : array-like or DataFrame, shape (n_samples, n_features)
- Input features.
- y : array-like, shape (n_samples,) or (n_samples, n_targets)
- Target values.
- val_size : float
- The proportion of the dataset to include in the validation split.
- random_state : int
- Controls the shuffling applied to the data before applying the split.
-
- Returns
- -------
- X_train, X_val, y_train, y_val : arrays
- The split datasets.
- """
- X_train, X_val, y_train, y_val = train_test_split(
- X, y, test_size=val_size, random_state=random_state
- )
-
- return X_train, X_val, y_train, y_val
-
- def preprocess_data(self, X_train, y_train, X_val, y_val, batch_size, shuffle):
- """
- Preprocesses the training and validation data, and creates DataLoaders for them.
-
- Parameters
- ----------
- X_train : DataFrame or array-like, shape (n_samples_train, n_features)
- Training feature set.
- y_train : array-like, shape (n_samples_train,)
- Training target values.
- X_val : DataFrame or array-like, shape (n_samples_val, n_features)
- Validation feature set.
- y_val : array-like, shape (n_samples_val,)
- Validation target values.
- batch_size : int
- Size of batches for the DataLoader.
- shuffle : bool
- Whether to shuffle the training data in the DataLoader.
-
- Returns
- -------
- data_module : MambularDataModule
- An instance of MambularDataModule containing the training and validation DataLoaders.
- """
- train_preprocessed_data = self.preprocessor.fit_transform(X_train, y_train)
- val_preprocessed_data = self.preprocessor.transform(X_val)
-
- # Update feature info based on the actual processed data
- (
- self.cat_feature_info,
- self.num_feature_info,
- ) = self.preprocessor.get_feature_info()
-
- # Initialize lists for tensors
- train_cat_tensors = []
- train_num_tensors = []
- val_cat_tensors = []
- val_num_tensors = []
-
- # Populate tensors for categorical features, if present in processed data
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[cat_key], dtype=torch.long)
- )
- if cat_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- if binned_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features, if present in processed data
- for key in self.num_feature_info:
- num_key = "num_" + str(
- key
- ) # Assuming numerical keys are prefixed with 'num_'
- if num_key in train_preprocessed_data:
- train_num_tensors.append(
- torch.tensor(train_preprocessed_data[num_key], dtype=torch.float)
- )
- if num_key in val_preprocessed_data:
- val_num_tensors.append(
- torch.tensor(val_preprocessed_data[num_key], dtype=torch.float)
- )
-
- train_labels = torch.tensor(y_train, dtype=torch.float)
- val_labels = torch.tensor(y_val, dtype=torch.float)
-
- # Create datasets
- train_dataset = EmbeddingMambularDataset(
- train_cat_tensors, train_num_tensors, train_labels
- )
- val_dataset = EmbeddingMambularDataset(
- val_cat_tensors, val_num_tensors, val_labels
- )
-
- # Create dataloaders
- train_dataloader = DataLoader(
- train_dataset, batch_size=batch_size, shuffle=shuffle
- )
- val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
-
- return MambularDataModule(train_dataloader, val_dataloader)
-
- def preprocess_test_data(self, X):
- """
- Preprocesses the test data and creates tensors for categorical and numerical features.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- Test feature set.
-
- Returns
- -------
- cat_tensors : list of Tensors
- List of tensors for each categorical feature.
- num_tensors : list of Tensors
- List of tensors for each numerical feature.
- """
- processed_data = self.preprocessor.transform(X)
-
- # Initialize lists for tensors
- cat_tensors = []
- num_tensors = []
-
- # Populate tensors for categorical features
- for key in self.cat_feature_info:
- cat_key = "cat_" + str(
- key
- ) # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + str(key) # for binned features
- if binned_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features
- for key in self.num_feature_info:
- num_key = "num_" + str(
- key
- ) # Assuming numerical keys are prefixed with 'num_'
- if num_key in processed_data:
- num_tensors.append(
- torch.tensor(processed_data[num_key], dtype=torch.float)
- )
-
- return cat_tensors, num_tensors
-
- def fit(
- self,
- X,
- y,
- val_size=0.2,
- X_val=None,
- y_val=None,
- max_epochs=100,
- random_state=101,
- batch_size=64,
- shuffle=True,
- patience=10,
- monitor="val_loss",
- mode="min",
- lr=1e-3,
- lr_patience=10,
- factor=0.75,
- weight_decay=0.025,
- raw_embeddings=False,
- seq_size=20,
- pca=False,
- **trainer_kwargs
- ):
- """
- Fits the ProteinMambularRegressor model to the training data.
-
- Parameters
- ----------
- X : array-like or DataFrame
- The training input samples.
- y : array-like
- The target values (class labels for classification, real numbers for regression).
- val_size : float, optional
- The proportion of the dataset to include in the validation split if `X_val` is not provided.
- X_val : array-like or DataFrame, optional
- The validation input samples.
- y_val : array-like, optional
- The validation target values.
- max_epochs : int, optional
- The maximum number of epochs for training.
- random_state : int, optional
- The seed used by the random number generator.
- batch_size : int, optional
- Size of the batches for training.
- shuffle : bool, optional
- Whether to shuffle the training data.
- patience : int, optional
- Patience for early stopping.
- monitor : str, optional
- Quantity to be monitored for early stopping.
- mode : str, optional
- One of {'auto', 'min', 'max'}. In 'min' mode, training will stop when the quantity monitored has stopped decreasing;
- in 'max' mode, it will stop when the quantity monitored has stopped increasing.
- lr : float, optional
- Learning rate for the optimizer.
- lr_patience : int, optional
- Number of epochs with no improvement after which the learning rate will be reduced.
- factor : float, optional
- Factor by which the learning rate will be reduced.
- weight_decay : float, optional
- Weight decay coefficient for regularization in the optimizer.
- raw_embeddings : bool, optional
- Whether to use raw numerical features directly or to process them into embeddings.
- seq_size : int, optional
- The sequence size for processing numerical features when not using raw embeddings.
- **trainer_kwargs : dict
- Additional keyword arguments for the PyTorch Lightning Trainer.
-
- Returns
- -------
- self : object
- Returns an instance of self.
- """
-
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- if X_val:
- if not isinstance(X_val, pd.DataFrame):
- X_val = pd.DataFrame(X_val)
-
- # Apply PCA if indicated
- if pca:
- self.pca_transformer = PCA(n_components=seq_size)
- X = pd.DataFrame(
- self.pca_transformer.fit_transform(X)
- ) # Fit and transform the PCA on the complete dataset
- if X_val is not None:
- X_val = pd.DataFrame(
- self.pca_transformer.transform(X_val)
- ) # Transform validation data with the same PCA model
-
- raw_embeddings = True
-
- if not X_val:
- X_train, X_val, y_train, y_val = self.split_data(
- X, y, val_size, random_state
- )
- else:
- X_train = X
- y_train = y
-
- data_module = self.preprocess_data(
- X_train, y_train, X_val, y_val, batch_size, shuffle
- )
-
- if raw_embeddings:
- self.config.d_model = X.shape[1]
-
- self.model = BaseEmbeddingMambularRegressor(
- config=self.config,
- cat_feature_info=self.cat_feature_info,
- num_feature_info=self.num_feature_info,
- lr=lr,
- lr_patience=lr_patience,
- lr_factor=factor,
- weight_decay=weight_decay,
- raw_embeddings=raw_embeddings,
- seq_size=seq_size,
- )
-
- early_stop_callback = EarlyStopping(
- monitor=monitor, min_delta=0.00, patience=patience, verbose=False, mode=mode
- )
-
- checkpoint_callback = ModelCheckpoint(
- monitor="val_loss", # Adjust according to your validation metric
- mode="min",
- save_top_k=1,
- dirpath="model_checkpoints", # Specify the directory to save checkpoints
- filename="best_model",
- )
-
- # Initialize the trainer and train the model
- trainer = pl.Trainer(
- max_epochs=max_epochs,
- callbacks=[early_stop_callback, checkpoint_callback],
- **trainer_kwargs
- )
- trainer.fit(self.model, data_module)
-
- best_model_path = checkpoint_callback.best_model_path
- if best_model_path:
- checkpoint = torch.load(best_model_path)
- self.model.load_state_dict(checkpoint["state_dict"])
-
- return self
-
- def predict(self, X):
- """
- Predicts target values for the given input samples.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- The input samples for which to predict target values.
-
- Returns
- -------
- predictions : ndarray, shape (n_samples,) or (n_samples, n_outputs)
- The predicted target values.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
-
- if hasattr(self, "pca_transformer"):
- X = pd.DataFrame(self.pca_transformer.transform(X))
-
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- predictions = self.model(cat_tensors, num_tensors)
-
- # Convert predictions to NumPy array and return
- return predictions.cpu().numpy()
-
- def evaluate(self, X, y_true, metrics=None):
- """
- Evaluate the model on the given data using specified metrics.
-
- Example:
- metrics = {
- 'Mean Squared Error': mean_squared_error,
- 'R2 Score': r2_score
- }
-
- # Assuming 'X_test' and 'y_test' are your test dataset and labels
- # Evaluate using the specified metrics
- results = regressor.evaluate(X_test, y_test, metrics=metrics)
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples to predict.
- y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
- The true target values against which to evaluate the predictions.
- metrics : dict
- A dictionary where keys are metric names and values are the metric functions.
-
- Returns
- -------
- scores : dict
- A dictionary with metric names as keys and their corresponding scores as values.
-
- Notes
- -----
- This method uses the `predict` method to generate predictions and computes each metric.
- """
- if metrics is None:
- metrics = {"Mean Squared Error": mean_squared_error}
-
- # Ensure input is in the correct format
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
-
- # Generate predictions using the trained model
- predictions = self.predict(X)
-
- # Initialize dictionary to store results
- scores = {}
-
- # Compute each metric
- for metric_name, metric_func in metrics.items():
- scores[metric_name] = metric_func(y_true, predictions)
-
- return scores
-
-from sklearn.model_selection import train_test_split
-import pytorch_lightning as pl
-import torch
-from torch.utils.data import DataLoader
-from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
-from ..base_models.regressor import BaseMambularRegressor
-from ..utils.config import MambularConfig
-from ..utils.preprocessor import Preprocessor
-from ..utils.dataset import MambularDataModule, MambularDataset
-from sklearn.base import BaseEstimator
-import pandas as pd
-from sklearn.metrics import mean_squared_error
-
-
-[docs]class MambularRegressor(BaseEstimator):
- """
- A regressor implemented using PyTorch Lightning that follows the scikit-learn API conventions. This class is designed
- to work with tabular data, offering a straightforward way to specify model configurations and preprocessing steps. It
- integrates seamlessly with scikit-learn's tools such as cross-validation and grid search.
-
- Parameters
- ----------
- **kwargs : Various
- Accepts any number of keyword arguments. Arguments recognized as model configuration options are passed to the
- MambularConfig constructor. Remaining arguments are assumed to be preprocessor options and passed to the
- Preprocessor constructor.
-
- Attributes
- ----------
- config : MambularConfig
- An object storing the configuration settings for the model.
- preprocessor : Preprocessor
- An object responsible for preprocessing the input data, such as encoding categorical variables and scaling numerical features.
- model : BaseMambularRegressor or None
- The underlying regression model, which is a PyTorch Lightning module. It is instantiated when the `fit` method is called.
- """
-
- def __init__(self, **kwargs):
- # Known config arguments
- print("Received kwargs:", kwargs)
- config_arg_names = [
- "d_model",
- "n_layers",
- "dt_rank",
- "output_dimension",
- "pooling_method",
- "norm",
- "cls",
- "dt_min",
- "dt_max",
- "dropout",
- "bias",
- "weight_decay",
- "conv_bias",
- "d_state",
- "expand_factor",
- "d_conv",
- "dt_init",
- "dt_scale",
- "dt_init_floor",
- "tabular_head_units",
- "tabular_head_activation",
- "tabular_head_dropout",
- "num_emebedding_activation",
- "layer_norm_after_embedding",
- ]
- self.config_kwargs = {k: v for k, v in kwargs.items() if k in config_arg_names}
- self.config = MambularConfig(**self.config_kwargs)
-
- # The rest are assumed to be preprocessor arguments
- preprocessor_kwargs = {
- k: v for k, v in kwargs.items() if k not in config_arg_names
- }
- self.preprocessor = Preprocessor(**preprocessor_kwargs)
- self.model = None
-
- def get_params(self, deep=True):
- """
- Get parameters for this estimator. Overrides the BaseEstimator method.
-
- Parameters
- ----------
- deep : bool, default=True
- If True, returns the parameters for this estimator and contained sub-objects that are estimators.
-
- Returns
- -------
- params : dict
- Parameter names mapped to their values.
- """
- params = self.config_kwargs # Parameters used to initialize MambularConfig
-
- # If deep=True, include parameters from nested components like preprocessor
- if deep:
- # Assuming Preprocessor has a get_params method
- preprocessor_params = {
- "preprocessor__" + key: value
- for key, value in self.preprocessor.get_params().items()
- }
- params.update(preprocessor_params)
-
- return params
-
- def set_params(self, **parameters):
- """
- Set the parameters of this estimator. Overrides the BaseEstimator method.
-
- Parameters
- ----------
- **parameters : dict
- Estimator parameters to be set.
-
- Returns
- -------
- self : object
- The instance with updated parameters.
- """
- # Update config_kwargs with provided parameters
- valid_config_keys = self.config_kwargs.keys()
- config_updates = {k: v for k, v in parameters.items() if k in valid_config_keys}
- self.config_kwargs.update(config_updates)
-
- # Update the config object
- for key, value in config_updates.items():
- setattr(self.config, key, value)
-
- # Handle preprocessor parameters (prefixed with 'preprocessor__')
- preprocessor_params = {
- k.split("__")[1]: v
- for k, v in parameters.items()
- if k.startswith("preprocessor__")
- }
- if preprocessor_params:
- # Assuming Preprocessor has a set_params method
- self.preprocessor.set_params(**preprocessor_params)
-
- return self
-
- def split_data(self, X, y, val_size, random_state):
- """
- Splits the dataset into training and validation sets.
-
- Parameters
- ----------
- X : array-like or DataFrame, shape (n_samples, n_features)
- Input features.
- y : array-like, shape (n_samples,) or (n_samples, n_targets)
- Target values.
- val_size : float
- The proportion of the dataset to include in the validation split.
- random_state : int
- Controls the shuffling applied to the data before applying the split.
-
- Returns
- -------
- X_train, X_val, y_train, y_val : arrays
- The split datasets.
- """
- X_train, X_val, y_train, y_val = train_test_split(
- X, y, test_size=val_size, random_state=random_state
- )
-
- return X_train, X_val, y_train, y_val
-
- def preprocess_data(self, X_train, y_train, X_val, y_val, batch_size, shuffle):
- """
- Preprocesses the training and validation data, and creates DataLoaders for them.
-
- Parameters
- ----------
- X_train : DataFrame or array-like, shape (n_samples_train, n_features)
- Training feature set.
- y_train : array-like, shape (n_samples_train,)
- Training target values.
- X_val : DataFrame or array-like, shape (n_samples_val, n_features)
- Validation feature set.
- y_val : array-like, shape (n_samples_val,)
- Validation target values.
- batch_size : int
- Size of batches for the DataLoader.
- shuffle : bool
- Whether to shuffle the training data in the DataLoader.
-
- Returns
- -------
- data_module : MambularDataModule
- An instance of MambularDataModule containing the training and validation DataLoaders.
- """
- train_preprocessed_data = self.preprocessor.fit_transform(X_train, y_train)
- val_preprocessed_data = self.preprocessor.transform(X_val)
-
- # Update feature info based on the actual processed data
- (
- self.cat_feature_info,
- self.num_feature_info,
- ) = self.preprocessor.get_feature_info()
-
- # Initialize lists for tensors
- train_cat_tensors = []
- train_num_tensors = []
- val_cat_tensors = []
- val_num_tensors = []
-
- # Populate tensors for categorical features, if present in processed data
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[cat_key], dtype=torch.long)
- )
- if cat_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- if binned_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features, if present in processed data
- for key in self.num_feature_info:
- num_key = "num_" + key # Assuming numerical keys are prefixed with 'num_'
- if num_key in train_preprocessed_data:
- train_num_tensors.append(
- torch.tensor(train_preprocessed_data[num_key], dtype=torch.float)
- )
- if num_key in val_preprocessed_data:
- val_num_tensors.append(
- torch.tensor(val_preprocessed_data[num_key], dtype=torch.float)
- )
-
- train_labels = torch.tensor(y_train, dtype=torch.float)
- val_labels = torch.tensor(y_val, dtype=torch.float)
-
- # Create datasets
- train_dataset = MambularDataset(
- train_cat_tensors, train_num_tensors, train_labels
- )
- val_dataset = MambularDataset(val_cat_tensors, val_num_tensors, val_labels)
-
- # Create dataloaders
- train_dataloader = DataLoader(
- train_dataset, batch_size=batch_size, shuffle=shuffle
- )
- val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
-
- return MambularDataModule(train_dataloader, val_dataloader)
-
- def preprocess_test_data(self, X):
- """
- Preprocesses the test data and creates tensors for categorical and numerical features.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- Test feature set.
-
- Returns
- -------
- cat_tensors : list of Tensors
- List of tensors for each categorical feature.
- num_tensors : list of Tensors
- List of tensors for each numerical feature.
- """
- processed_data = self.preprocessor.transform(X)
-
- # Initialize lists for tensors
- cat_tensors = []
- num_tensors = []
-
- # Populate tensors for categorical features
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features
- for key in self.num_feature_info:
- num_key = "num_" + key # Assuming numerical keys are prefixed with 'num_'
- if num_key in processed_data:
- num_tensors.append(
- torch.tensor(processed_data[num_key], dtype=torch.float)
- )
-
- return cat_tensors, num_tensors
-
- def fit(
- self,
- X,
- y,
- val_size=0.2,
- X_val=None,
- y_val=None,
- max_epochs=100,
- random_state=101,
- batch_size=128,
- shuffle=True,
- patience=10,
- monitor="val_loss",
- mode="min",
- lr=1e-3,
- lr_patience=5,
- factor=0.75,
- weight_decay=1e-06,
- **trainer_kwargs
- ):
- """
- Trains the regression model using the provided training data. Optionally, a separate validation set can be used.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- The training input samples.
- y : array-like, shape (n_samples,) or (n_samples, n_targets)
- The target values (real numbers).
- val_size : float, default=0.2
- The proportion of the dataset to include in the validation split if `X_val` is None. Ignored if `X_val` is provided.
- X_val : DataFrame or array-like, shape (n_samples, n_features), optional
- The validation input samples. If provided, `X` and `y` are not split and this data is used for validation.
- y_val : array-like, shape (n_samples,) or (n_samples, n_targets), optional
- The validation target values. Required if `X_val` is provided.
- max_epochs : int, default=100
- Maximum number of epochs for training.
- random_state : int, default=101
- Controls the shuffling applied to the data before applying the split.
- batch_size : int, default=64
- Number of samples per gradient update.
- shuffle : bool, default=True
- Whether to shuffle the training data before each epoch.
- patience : int, default=10
- Number of epochs with no improvement on the validation loss to wait before early stopping.
- monitor : str, default="val_loss"
- The metric to monitor for early stopping.
- mode : str, default="min"
- Whether the monitored metric should be minimized (`min`) or maximized (`max`).
- lr : float, default=1e-3
- Learning rate for the optimizer.
- lr_patience : int, default=10
- Number of epochs with no improvement on the validation loss to wait before reducing the learning rate.
- factor : float, default=0.75
- Factor by which the learning rate will be reduced.
- weight_decay : float, default=0.025
- Weight decay (L2 penalty) coefficient.
- **trainer_kwargs : Additional keyword arguments for PyTorch Lightning's Trainer class.
-
- Returns
- -------
- self : object
- The fitted regressor.
- """
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- if X_val:
- if not isinstance(X_val, pd.DataFrame):
- X_val = pd.DataFrame(X_val)
-
- if not X_val:
- X_train, X_val, y_train, y_val = self.split_data(
- X, y, val_size, random_state
- )
-
- data_module = self.preprocess_data(
- X_train, y_train, X_val, y_val, batch_size, shuffle
- )
-
- self.model = BaseMambularRegressor(
- config=self.config,
- cat_feature_info=self.cat_feature_info,
- num_feature_info=self.num_feature_info,
- lr=lr,
- lr_patience=lr_patience,
- lr_factor=factor,
- weight_decay=weight_decay,
- )
-
- early_stop_callback = EarlyStopping(
- monitor=monitor, min_delta=0.00, patience=patience, verbose=False, mode=mode
- )
-
- checkpoint_callback = ModelCheckpoint(
- monitor="val_loss", # Adjust according to your validation metric
- mode="min",
- save_top_k=1,
- dirpath="model_checkpoints", # Specify the directory to save checkpoints
- filename="best_model",
- )
-
- # Initialize the trainer and train the model
- trainer = pl.Trainer(
- max_epochs=max_epochs,
- callbacks=[early_stop_callback, checkpoint_callback],
- **trainer_kwargs
- )
- trainer.fit(self.model, data_module)
-
- best_model_path = checkpoint_callback.best_model_path
- if best_model_path:
- checkpoint = torch.load(best_model_path)
- self.model.load_state_dict(checkpoint["state_dict"])
-
- return self
-
- def predict(self, X):
- """
- Predicts target values for the given input samples.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- The input samples for which to predict target values.
-
- Returns
- -------
- predictions : ndarray, shape (n_samples,) or (n_samples, n_outputs)
- The predicted target values.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- predictions = self.model(cat_tensors, num_tensors)
-
- # Convert predictions to NumPy array and return
- return predictions.cpu().numpy()
-
- def evaluate(self, X, y_true, metrics=None):
- """
- Evaluate the model on the given data using specified metrics.
-
- Example:
- metrics = {
- 'Mean Squared Error': mean_squared_error,
- 'R2 Score': r2_score
- }
-
- # Assuming 'X_test' and 'y_test' are your test dataset and labels
- # Evaluate using the specified metrics
- results = regressor.evaluate(X_test, y_test, metrics=metrics)
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples to predict.
- y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
- The true target values against which to evaluate the predictions.
- metrics : dict
- A dictionary where keys are metric names and values are the metric functions.
-
- Returns
- -------
- scores : dict
- A dictionary with metric names as keys and their corresponding scores as values.
-
- Notes
- -----
- This method uses the `predict` method to generate predictions and computes each metric.
- """
- if metrics is None:
- metrics = {"Mean Squared Error": mean_squared_error}
-
- # Ensure input is in the correct format
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
-
- # Generate predictions using the trained model
- predictions = self.predict(X)
-
- # Initialize dictionary to store results
- scores = {}
-
- # Compute each metric
- for metric_name, metric_func in metrics.items():
- scores[metric_name] = metric_func(y_true, predictions)
-
- return scores
-Short
- */ - .o-tooltip--left { - position: relative; - } - - .o-tooltip--left:after { - opacity: 0; - visibility: hidden; - position: absolute; - content: attr(data-tooltip); - padding: .2em; - font-size: .8em; - left: -.2em; - background: grey; - color: white; - white-space: nowrap; - z-index: 2; - border-radius: 2px; - transform: translateX(-102%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); -} - -.o-tooltip--left:hover:after { - display: block; - opacity: 1; - visibility: visible; - transform: translateX(-100%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); - transition-delay: .5s; -} - -/* By default the copy button shouldn't show up when printing a page */ -@media print { - button.copybtn { - display: none; - } -} diff --git a/docs/$READTHEDOCS_OUTPUT/html/_static/copybutton.js b/docs/$READTHEDOCS_OUTPUT/html/_static/copybutton.js deleted file mode 100644 index 2ea7ff3..0000000 --- a/docs/$READTHEDOCS_OUTPUT/html/_static/copybutton.js +++ /dev/null @@ -1,248 +0,0 @@ -// Localization support -const messages = { - 'en': { - 'copy': 'Copy', - 'copy_to_clipboard': 'Copy to clipboard', - 'copy_success': 'Copied!', - 'copy_failure': 'Failed to copy', - }, - 'es' : { - 'copy': 'Copiar', - 'copy_to_clipboard': 'Copiar al portapapeles', - 'copy_success': '¡Copiado!', - 'copy_failure': 'Error al copiar', - }, - 'de' : { - 'copy': 'Kopieren', - 'copy_to_clipboard': 'In die Zwischenablage kopieren', - 'copy_success': 'Kopiert!', - 'copy_failure': 'Fehler beim Kopieren', - }, - 'fr' : { - 'copy': 'Copier', - 'copy_to_clipboard': 'Copier dans le presse-papier', - 'copy_success': 'Copié !', - 'copy_failure': 'Échec de la copie', - }, - 'ru': { - 'copy': 'Скопировать', - 'copy_to_clipboard': 'Скопировать в буфер', - 'copy_success': 'Скопировано!', - 'copy_failure': 'Не удалось скопировать', - }, - 'zh-CN': { - 'copy': '复制', - 'copy_to_clipboard': '复制到剪贴板', - 'copy_success': '复制成功!', - 'copy_failure': '复制失败', - }, - 'it' : { - 'copy': 'Copiare', - 'copy_to_clipboard': 'Copiato negli appunti', - 'copy_success': 'Copiato!', - 'copy_failure': 'Errore durante la copia', - } -} - -let locale = 'en' -if( document.documentElement.lang !== undefined - && messages[document.documentElement.lang] !== undefined ) { - locale = document.documentElement.lang -} - -let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; -if (doc_url_root == '#') { - doc_url_root = ''; -} - -/** - * SVG files for our copy buttons - */ -let iconCheck = `` - -// If the user specified their own SVG use that, otherwise use the default -let iconCopy = ``; -if (!iconCopy) { - iconCopy = `` -} - -/** - * Set up copy/paste for code blocks - */ - -const runWhenDOMLoaded = cb => { - if (document.readyState != 'loading') { - cb() - } else if (document.addEventListener) { - document.addEventListener('DOMContentLoaded', cb) - } else { - document.attachEvent('onreadystatechange', function() { - if (document.readyState == 'complete') cb() - }) - } -} - -const codeCellId = index => `codecell${index}` - -// Clears selected text since ClipboardJS will select the text when copying -const clearSelection = () => { - if (window.getSelection) { - window.getSelection().removeAllRanges() - } else if (document.selection) { - document.selection.empty() - } -} - -// Changes tooltip text for a moment, then changes it back -// We want the timeout of our `success` class to be a bit shorter than the -// tooltip and icon change, so that we can hide the icon before changing back. -var timeoutIcon = 2000; -var timeoutSuccessClass = 1500; - -const temporarilyChangeTooltip = (el, oldText, newText) => { - el.setAttribute('data-tooltip', newText) - el.classList.add('success') - // Remove success a little bit sooner than we change the tooltip - // So that we can use CSS to hide the copybutton first - setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) - setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) -} - -// Changes the copy button icon for two seconds, then changes it back -const temporarilyChangeIcon = (el) => { - el.innerHTML = iconCheck; - setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) -} - -const addCopyButtonToCodeCells = () => { - // If ClipboardJS hasn't loaded, wait a bit and try again. This - // happens because we load ClipboardJS asynchronously. - if (window.ClipboardJS === undefined) { - setTimeout(addCopyButtonToCodeCells, 250) - return - } - - // Add copybuttons to all of our code cells - const COPYBUTTON_SELECTOR = 'div.highlight pre'; - const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) - codeCells.forEach((codeCell, index) => { - const id = codeCellId(index) - codeCell.setAttribute('id', id) - - const clipboardButton = id => - `` - codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) - }) - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} - - -var copyTargetText = (trigger) => { - var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - - // get filtered text - let exclude = '.linenos'; - - let text = filterText(target, exclude); - return formatCopyText(text, '', false, true, true, true, '', '') -} - - // Initialize with a callback so we can modify the text before copy - const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) - - // Update UI with error/success messages - clipboard.on('success', event => { - clearSelection() - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) - temporarilyChangeIcon(event.trigger) - }) - - clipboard.on('error', event => { - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) - }) -} - -runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/docs/$READTHEDOCS_OUTPUT/html/_static/copybutton_funcs.js b/docs/$READTHEDOCS_OUTPUT/html/_static/copybutton_funcs.js deleted file mode 100644 index dbe1aaa..0000000 --- a/docs/$READTHEDOCS_OUTPUT/html/_static/copybutton_funcs.js +++ /dev/null @@ -1,73 +0,0 @@ -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -export function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} diff --git a/docs/$READTHEDOCS_OUTPUT/html/_static/css/badge_only.css b/docs/$READTHEDOCS_OUTPUT/html/_static/css/badge_only.css deleted file mode 100644 index c718cee..0000000 --- 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" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - localStorage.removeItem("sphinx_highlight_terms") - }, - - initEscapeListener: () => { - // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; - if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { - SphinxHighlight.hideSearchWords(); - event.preventDefault(); - } - }); - }, -}; - -_ready(SphinxHighlight.highlightSearchWords); -_ready(SphinxHighlight.initEscapeListener); diff --git a/docs/$READTHEDOCS_OUTPUT/html/_static/togglebutton.css b/docs/$READTHEDOCS_OUTPUT/html/_static/togglebutton.css deleted file mode 100644 index 54a6787..0000000 --- a/docs/$READTHEDOCS_OUTPUT/html/_static/togglebutton.css +++ /dev/null @@ -1,160 +0,0 @@ -/** - * Admonition-based toggles - */ - -/* Visibility of the target */ -.admonition.toggle .admonition-title ~ * { - transition: opacity .3s, height .3s; -} - -/* Toggle buttons inside admonitions so we see the title */ -.admonition.toggle { - position: relative; -} - -/* Titles should cut off earlier to avoid overlapping w/ button */ -.admonition.toggle .admonition-title { - padding-right: 25%; - cursor: pointer; -} - -/* Hovering will cause a slight shift in color to make it feel interactive */ -.admonition.toggle .admonition-title:hover { - box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 1%); -} - -/* Hovering will cause a slight shift in color to make it feel interactive */ -.admonition.toggle .admonition-title:active { - box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 3%); -} - -/* Remove extra whitespace below the admonition title when hidden */ -.admonition.toggle-hidden { - padding-bottom: 0; -} - -.admonition.toggle-hidden .admonition-title { - margin-bottom: 0; -} - -/* hides all the content of a page until de-toggled */ -.admonition.toggle-hidden .admonition-title ~ * { - height: 0; - margin: 0; - opacity: 0; - visibility: hidden; -} - -/* General button style and position*/ -button.toggle-button { - /** - * Background and shape. 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-This class integrates various components such as embeddings for categorical and numerical features, the Mambular model -for processing sequences of embeddings, and a classification head for prediction. It supports multi-class and binary classification tasks.
-num_classes (int) – The number of classes in the classification task. For binary classification, this should be 2.
config (MambularConfig) – An instance of MambularConfig containing configuration parameters for the Mambular model.
cat_feature_info (dict, optional) – A dictionary mapping the names of categorical features to their number of unique categories. -This information is used to configure embedding layers for categorical features. Defaults to None.
num_feature_info (dict, optional) – A dictionary mapping the names of numerical features to the size of their input dimensions. -This information is used to configure embedding layers for numerical features. Defaults to None.
lr (float, optional) – The learning rate for the optimizer. Defaults to 1e-03.
lr_patience (int, optional) – The number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
weight_decay (float, optional) – Weight decay (L2 penalty) parameter for the optimizer. Defaults to 0.025.
lr_factor (float, optional) – Factor by which the learning rate will be reduced. Defaults to 0.75.
The activation function to be applied after the linear transformation of numerical features.
-nn.Module
-A list of sequential modules, each corresponding to an embedding layer for a numerical feature.
-nn.ModuleList
-A list of embedding layers, each corresponding to a categorical feature.
-nn.ModuleList
-The Mambular model for processing sequences of embeddings.
-Mamba
-A normalization layer applied after the Mambular model.
-nn.Module
-A linear layer for predicting the class labels from the aggregated embedding representation.
-nn.Linear
-The method used to aggregate embeddings across features. Supported methods are ‘avg’, ‘max’, and ‘sum’.
-str
-The loss function used for training the model, configured based on the number of classes.
-nn.Module
-A metric for computing the accuracy of predictions.
-torchmetrics.Accuracy
-A metric for computing the Area Under the Receiver Operating Characteristic curve.
-torchmetrics.AUROC
-A metric for computing the precision of predictions.
-torchmetrics.Precision
-Defines the forward pass of the model, processing both categorical and numerical features, aggregating embeddings, -and producing predictions.
-Performs a single training step, computing the loss and logging metrics for the training set.
-Performs a single validation step, computing the loss and logging metrics for the validation set.
-Configures the model’s optimizers and learning rate schedulers.
-automatic_optimizationIf set to False you are responsible for calling .backward(), .step(), .zero_grad().
current_epochThe current epoch in the Trainer, or 0 if not attached.
example_input_arrayThe example input array is a specification of what the module can consume in the forward() method.
global_rankThe index of the current process across all nodes and devices.
-global_stepTotal training batches seen across all epochs.
-hparamsThe collection of hyperparameters saved with save_hyperparameters().
hparams_initialThe collection of hyperparameters saved with save_hyperparameters().
local_rankThe index of the current process within a single node.
-loggerReference to the logger object in the Trainer.
-loggersReference to the list of loggers in the Trainer.
-on_gpuReturns True if this model is currently located on a GPU.
strict_loadingDetermines how Lightning loads this model using .load_state_dict(…, strict=model.strict_loading).
-Methods
-
|
-Add a child module to the current module. |
-
|
-Gather tensors or collections of tensors from multiple processes. |
-
|
-Apply |
-
|
-Called to perform backward on the loss returned in |
-
|
-Casts all floating point parameters and buffers to |
-
|
-Return an iterator over module buffers. |
-
|
-Return an iterator over immediate children modules. |
-
|
-Handles gradient clipping internally. |
-
|
-Compile this Module's forward using |
-
|
-Configure model-specific callbacks. |
-
|
-Perform gradient clipping for the optimizer parameters. |
-
|
-Hook to create modules in a strategy and precision aware context. |
-
| - | Sets up the model's optimizer and learning rate scheduler based on the configurations provided. |
-
|
-Deprecated. |
-
|
-See |
-
|
-Moves all model parameters and buffers to the GPU. |
-
|
-See |
-
|
-Set the module in evaluation mode. |
-
|
-Set the extra representation of the module. |
-
|
-See |
-
|
-Defines the forward pass of the classifier. |
-
|
-Freeze all params for inference. |
-
|
-Return the buffer given by |
-
|
-Return any extra state to include in the module's state_dict. |
-
|
-Return the parameter given by |
-
|
-Return the submodule given by |
-
|
-See |
-
|
-Move all model parameters and buffers to the IPU. |
-
|
-Primary way of loading a model from a checkpoint. |
-
|
-Copy parameters and buffers from |
-
|
-Log a key, value pair. |
-
|
-Log a dictionary of values at once. |
-
|
-Override this method to adjust the default way the |
-
|
-Returns the learning rate scheduler(s) that are being used during training. |
-
|
-Call this directly from your |
-
|
-Return an iterator over all modules in the network. |
-
|
-Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself. |
-
|
-Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself. |
-
|
-Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself. |
-
|
-Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself. |
-
|
-Called after |
-
|
-Override to alter or apply batch augmentations to your batch after it is transferred to the device. |
-
|
-Called before |
-
|
-Override to alter or apply batch augmentations to your batch before it is transferred to the device. |
-
|
-Called before |
-
|
-Called after |
-
|
-Called at the very end of fit. |
-
|
-Called at the very beginning of fit. |
-
|
-Called by Lightning to restore your model. |
-
|
-Called in the predict loop after the batch. |
-
|
-Called in the predict loop before anything happens for that batch. |
-
|
-Called at the end of predicting. |
-
|
-Called at the end of predicting. |
-
|
-Called at the beginning of predicting. |
-
|
-Called when the predict loop starts. |
-
|
-Called at the beginning of predicting. |
-
|
-Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save. |
-
|
-Called in the test loop after the batch. |
-
|
-Called in the test loop before anything happens for that batch. |
-
|
-Called at the end of testing. |
-
|
-Called in the test loop at the very end of the epoch. |
-
|
-Called in the test loop at the very beginning of the epoch. |
-
|
-Called when the test loop starts. |
-
|
-Called when the test loop ends. |
-
|
-Called at the beginning of testing. |
-
|
-Called in the training loop after the batch. |
-
|
-Called in the training loop before anything happens for that batch. |
-
|
-Called at the end of training before logger experiment is closed. |
-
|
-Called in the training loop at the very end of the epoch. |
-
|
-Called in the training loop at the very beginning of the epoch. |
-
|
-Called at the beginning of training after sanity check. |
-
|
-Called in the validation loop after the batch. |
-
|
-Called in the validation loop before anything happens for that batch. |
-
|
-Called at the end of validation. |
-
|
-Called in the validation loop at the very end of the epoch. |
-
|
-Called in the validation loop at the very beginning of the epoch. |
-
|
-Called when the validation loop starts. |
-
|
-Called when the validation loop ends. |
-
|
-Called by the training loop to release gradients before entering the validation loop. |
-
|
-Called at the beginning of validation. |
-
|
-Override this method to adjust the default way the |
-
|
-Override this method to change the default behaviour of |
-
|
-Returns the optimizer(s) that are being used during training. |
-
|
-Return an iterator over module parameters. |
-
|
-An iterable or collection of iterables specifying prediction samples. |
-
|
-Step function called during |
-
|
-Use this to download and prepare data. |
-
|
-Prints only from process 0. |
-
|
-Register a backward hook on the module. |
-
|
-Add a buffer to the module. |
-
|
-Register a forward hook on the module. |
-
|
-Register a forward pre-hook on the module. |
-
|
-Register a backward hook on the module. |
-
|
-Register a backward pre-hook on the module. |
-
|
-Register a post hook to be run after module's |
-
|
-Alias for |
-
|
-Add a parameter to the module. |
-
|
-Register a pre-hook for the |
-
|
-Change if autograd should record operations on parameters in this module. |
-
|
-Save arguments to |
-
|
-Set extra state contained in the loaded state_dict. |
-
|
-Called at the beginning of fit (train + validate), validate, test, or predict. |
-
|
-See |
-
|
-Return a dictionary containing references to the whole state of the module. |
-
|
-Called at the end of fit (train + validate), validate, test, or predict. |
-
|
-An iterable or collection of iterables specifying test samples. |
-
|
-Operates on a single batch of data from the test set. |
-
|
-See |
-
|
-Move the parameters and buffers to the specified device without copying storage. |
-
|
-Saves the model in ONNX format. |
-
|
-By default compiles the whole model to a |
-
|
-Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup. |
-
|
-Set the module in training mode. |
-
|
-An iterable or collection of iterables specifying training samples. |
-
|
-Processes a single batch during training, computes the loss and logs training metrics. |
-
|
-Override this hook if your |
-
|
-See |
-
|
-Unfreeze all parameters for training. |
-
|
-Resets the state of required gradients that were toggled with |
-
|
-An iterable or collection of iterables specifying validation samples. |
-
|
-Processes a single batch during validation, computes the loss and logs validation metrics. |
-
|
-Move all model parameters and buffers to the XPU. |
-
|
-Reset gradients of all model parameters. |
-
__call__ |
-- |
Sets up the model’s optimizer and learning rate scheduler based on the configurations provided.
-A dictionary containing the optimizer and lr_scheduler configurations.
-dict
-Defines the forward pass of the classifier.
-cat_features (Tensor) – Tensor containing the categorical features.
num_features (Tensor) – Tensor containing the numerical features.
The output predictions of the model.
-Tensor
-Processes a single batch during training, computes the loss and logs training metrics.
-batch (tuple) – A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
batch_idx (int) – The index of the batch within the epoch.
Processes a single batch during validation, computes the loss and logs validation metrics.
-batch (tuple) – A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
batch_idx (int) – The index of the batch within the epoch.
A base module for likelihood-based statistical learning (LSS) models built on PyTorch Lightning, -integrating the Mamba architecture for tabular data. This module is designed to accommodate various -statistical distribution families for different types of regression and classification tasks.
-family (str) – The name of the statistical distribution family to be used for modeling. Supported families include -‘normal’, ‘poisson’, ‘gamma’, ‘beta’, ‘dirichlet’, ‘studentt’, ‘negativebinom’, ‘inversegamma’, and ‘categorical’.
config (MambularConfig) – An instance of MambularConfig containing configuration parameters for the model architecture.
cat_feature_info (dict, optional) – A dictionary mapping the names of categorical features to their number of unique categories. Defaults to None.
num_feature_info (dict, optional) – A dictionary mapping the names of numerical features to their number of dimensions after embedding. Defaults to None.
lr (float, optional) – The initial learning rate for the optimizer. Defaults to 1e-03.
lr_patience (int, optional) – The number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
weight_decay (float, optional) – Weight decay (L2 penalty) coefficient. Defaults to 0.025.
lr_factor (float, optional) – Factor by which the learning rate will be reduced. Defaults to 0.75.
**distribution_params – Additional parameters specific to the chosen statistical distribution family.
The core neural network module implementing the Mamba architecture.
-Mamba
-Normalization layer applied after the Mamba block.
-nn.Module
-Final linear layer mapping the features to the parameters of the chosen statistical distribution.
-nn.Linear
-The loss function derived from the chosen statistical distribution.
-callable
-automatic_optimizationIf set to False you are responsible for calling .backward(), .step(), .zero_grad().
current_epochThe current epoch in the Trainer, or 0 if not attached.
example_input_arrayThe example input array is a specification of what the module can consume in the forward() method.
global_rankThe index of the current process across all nodes and devices.
-global_stepTotal training batches seen across all epochs.
-hparamsThe collection of hyperparameters saved with save_hyperparameters().
hparams_initialThe collection of hyperparameters saved with save_hyperparameters().
local_rankThe index of the current process within a single node.
-loggerReference to the logger object in the Trainer.
-loggersReference to the list of loggers in the Trainer.
-on_gpuReturns True if this model is currently located on a GPU.
strict_loadingDetermines how Lightning loads this model using .load_state_dict(…, strict=model.strict_loading).
-Methods
-
|
-Add a child module to the current module. |
-
|
-Gather tensors or collections of tensors from multiple processes. |
-
|
-Apply |
-
|
-Called to perform backward on the loss returned in |
-
|
-Casts all floating point parameters and buffers to |
-
|
-Return an iterator over module buffers. |
-
|
-Return an iterator over immediate children modules. |
-
|
-Handles gradient clipping internally. |
-
|
-Compile this Module's forward using |
-
|
-Configure model-specific callbacks. |
-
|
-Perform gradient clipping for the optimizer parameters. |
-
|
-Hook to create modules in a strategy and precision aware context. |
-
| - | Sets up the model's optimizer and learning rate scheduler based on the configurations provided. |
-
|
-Deprecated. |
-
|
-See |
-
|
-Moves all model parameters and buffers to the GPU. |
-
|
-See |
-
|
-Set the module in evaluation mode. |
-
|
-Set the extra representation of the module. |
-
|
-See |
-
|
-Defines the forward pass of the model, processing both categorical and numerical features, and returning predictions based on the configured statistical distribution. |
-
|
-Freeze all params for inference. |
-
|
-Return the buffer given by |
-
|
-Return any extra state to include in the module's state_dict. |
-
|
-Return the parameter given by |
-
|
-Return the submodule given by |
-
|
-See |
-
|
-Move all model parameters and buffers to the IPU. |
-
|
-Primary way of loading a model from a checkpoint. |
-
|
-Copy parameters and buffers from |
-
|
-Log a key, value pair. |
-
|
-Log a dictionary of values at once. |
-
|
-Override this method to adjust the default way the |
-
|
-Returns the learning rate scheduler(s) that are being used during training. |
-
|
-Call this directly from your |
-
|
-Return an iterator over all modules in the network. |
-
|
-Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself. |
-
|
-Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself. |
-
|
-Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself. |
-
|
-Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself. |
-
|
-Called after |
-
|
-Override to alter or apply batch augmentations to your batch after it is transferred to the device. |
-
|
-Called before |
-
|
-Override to alter or apply batch augmentations to your batch before it is transferred to the device. |
-
|
-Called before |
-
|
-Called after |
-
|
-Called at the very end of fit. |
-
|
-Called at the very beginning of fit. |
-
|
-Called by Lightning to restore your model. |
-
|
-Called in the predict loop after the batch. |
-
|
-Called in the predict loop before anything happens for that batch. |
-
|
-Called at the end of predicting. |
-
|
-Called at the end of predicting. |
-
|
-Called at the beginning of predicting. |
-
|
-Called when the predict loop starts. |
-
|
-Called at the beginning of predicting. |
-
|
-Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save. |
-
|
-Called in the test loop after the batch. |
-
|
-Called in the test loop before anything happens for that batch. |
-
|
-Called at the end of testing. |
-
|
-Called in the test loop at the very end of the epoch. |
-
|
-Called in the test loop at the very beginning of the epoch. |
-
|
-Called when the test loop starts. |
-
|
-Called when the test loop ends. |
-
|
-Called at the beginning of testing. |
-
|
-Called in the training loop after the batch. |
-
|
-Called in the training loop before anything happens for that batch. |
-
|
-Called at the end of training before logger experiment is closed. |
-
|
-Called in the training loop at the very end of the epoch. |
-
|
-Called in the training loop at the very beginning of the epoch. |
-
|
-Called at the beginning of training after sanity check. |
-
|
-Called in the validation loop after the batch. |
-
|
-Called in the validation loop before anything happens for that batch. |
-
|
-Called at the end of validation. |
-
|
-Called in the validation loop at the very end of the epoch. |
-
|
-Called in the validation loop at the very beginning of the epoch. |
-
|
-Called when the validation loop starts. |
-
|
-Called when the validation loop ends. |
-
|
-Called by the training loop to release gradients before entering the validation loop. |
-
|
-Called at the beginning of validation. |
-
|
-Override this method to adjust the default way the |
-
|
-Override this method to change the default behaviour of |
-
|
-Returns the optimizer(s) that are being used during training. |
-
|
-Return an iterator over module parameters. |
-
|
-An iterable or collection of iterables specifying prediction samples. |
-
|
-Step function called during |
-
|
-Use this to download and prepare data. |
-
|
-Prints only from process 0. |
-
|
-Register a backward hook on the module. |
-
|
-Add a buffer to the module. |
-
|
-Register a forward hook on the module. |
-
|
-Register a forward pre-hook on the module. |
-
|
-Register a backward hook on the module. |
-
|
-Register a backward pre-hook on the module. |
-
|
-Register a post hook to be run after module's |
-
|
-Alias for |
-
|
-Add a parameter to the module. |
-
|
-Register a pre-hook for the |
-
|
-Change if autograd should record operations on parameters in this module. |
-
|
-Save arguments to |
-
|
-Set extra state contained in the loaded state_dict. |
-
|
-Called at the beginning of fit (train + validate), validate, test, or predict. |
-
|
-See |
-
|
-Return a dictionary containing references to the whole state of the module. |
-
|
-Called at the end of fit (train + validate), validate, test, or predict. |
-
|
-An iterable or collection of iterables specifying test samples. |
-
|
-Operates on a single batch of data from the test set. |
-
|
-See |
-
|
-Move the parameters and buffers to the specified device without copying storage. |
-
|
-Saves the model in ONNX format. |
-
|
-By default compiles the whole model to a |
-
|
-Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup. |
-
|
-Set the module in training mode. |
-
|
-An iterable or collection of iterables specifying training samples. |
-
|
-Processes a single batch during training, computes the loss using the distribution-specific loss function, and logs training metrics. |
-
|
-Override this hook if your |
-
|
-See |
-
|
-Unfreeze all parameters for training. |
-
|
-Resets the state of required gradients that were toggled with |
-
|
-An iterable or collection of iterables specifying validation samples. |
-
|
-Processes a single batch during validation, computes the loss using the distribution-specific loss function, and logs validation metrics. |
-
|
-Move all model parameters and buffers to the XPU. |
-
|
-Reset gradients of all model parameters. |
-
__call__ |
-- |
Sets up the model’s optimizer and learning rate scheduler based on the configurations provided.
-A dictionary containing the optimizer and lr_scheduler configurations.
-dict
-Defines the forward pass of the model, processing both categorical and numerical features, -and returning predictions based on the configured statistical distribution.
-cat_features (Tensor) – Tensor containing the categorical features.
num_features (Tensor) – Tensor containing the numerical features.
The predictions of the model, typically the parameters of the chosen statistical distribution.
-Tensor
-Processes a single batch during training, computes the loss using the distribution-specific loss function, -and logs training metrics.
-batch (tuple) – A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
batch_idx (int) – The index of the batch within the epoch.
The computed loss for the batch.
-Tensor
-Processes a single batch during validation, computes the loss using the distribution-specific loss function, -and logs validation metrics.
-batch (tuple) – A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
batch_idx (int) – The index of the batch within the epoch.
A specialized classification module for protein data, built on PyTorch Lightning and integrating the Mamba architecture. -It supports embeddings for categorical features and can process raw or embedded numerical features, making it suitable -for complex protein sequence data.
-config (MambularConfig) – Configuration parameters for the model architecture.
cat_feature_info (dict, optional) – Information about categorical features, mapping feature names to the number of unique categories.
num_feature_info (dict, optional) – Information about numerical features, mapping feature names to their number of dimensions after embedding.
lr (float, optional) – Learning rate for the optimizer. Defaults to 1e-03.
lr_patience (int, optional) – Number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
weight_decay (float, optional) – Weight decay coefficient for regularization in the optimizer. Defaults to 0.025.
lr_factor (float, optional) – Factor by which the learning rate will be reduced by the scheduler. Defaults to 0.75.
seq_size (int, optional) – Size of sequence chunks for processing numerical features. Relevant when raw_embeddings is False.
raw_embeddings (bool, optional) – Indicates whether to use raw numerical features directly or to process them into embeddings. Defaults to False.
The core neural network module implementing the Mamba architecture.
-Mamba
-Normalization layer applied after the Mamba block.
-nn.Module
-Final linear layer mapping the features to the target.
-nn.Linear
-automatic_optimizationIf set to False you are responsible for calling .backward(), .step(), .zero_grad().
current_epochThe current epoch in the Trainer, or 0 if not attached.
example_input_arrayThe example input array is a specification of what the module can consume in the forward() method.
global_rankThe index of the current process across all nodes and devices.
-global_stepTotal training batches seen across all epochs.
-hparamsThe collection of hyperparameters saved with save_hyperparameters().
hparams_initialThe collection of hyperparameters saved with save_hyperparameters().
local_rankThe index of the current process within a single node.
-loggerReference to the logger object in the Trainer.
-loggersReference to the list of loggers in the Trainer.
-on_gpuReturns True if this model is currently located on a GPU.
strict_loadingDetermines how Lightning loads this model using .load_state_dict(…, strict=model.strict_loading).
-Methods
-
|
-Add a child module to the current module. |
-
|
-Gather tensors or collections of tensors from multiple processes. |
-
|
-Apply |
-
|
-Called to perform backward on the loss returned in |
-
|
-Casts all floating point parameters and buffers to |
-
|
-Return an iterator over module buffers. |
-
|
-Return an iterator over immediate children modules. |
-
|
-Handles gradient clipping internally. |
-
|
-Compile this Module's forward using |
-
|
-Configure model-specific callbacks. |
-
|
-Perform gradient clipping for the optimizer parameters. |
-
|
-Hook to create modules in a strategy and precision aware context. |
-
| - | Sets up the model's optimizer and learning rate scheduler based on the configurations provided. |
-
|
-Deprecated. |
-
|
-See |
-
|
-Moves all model parameters and buffers to the GPU. |
-
|
-See |
-
|
-Set the module in evaluation mode. |
-
|
-Set the extra representation of the module. |
-
|
-See |
-
|
-Defines the forward pass of the model, processing both categorical and numerical features, and returning regression predictions. |
-
|
-Freeze all params for inference. |
-
|
-Return the buffer given by |
-
|
-Return any extra state to include in the module's state_dict. |
-
|
-Return the parameter given by |
-
|
-Return the submodule given by |
-
|
-See |
-
|
-Move all model parameters and buffers to the IPU. |
-
|
-Primary way of loading a model from a checkpoint. |
-
|
-Copy parameters and buffers from |
-
|
-Log a key, value pair. |
-
|
-Log a dictionary of values at once. |
-
|
-Override this method to adjust the default way the |
-
|
-Returns the learning rate scheduler(s) that are being used during training. |
-
|
-Call this directly from your |
-
|
-Return an iterator over all modules in the network. |
-
|
-Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself. |
-
|
-Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself. |
-
|
-Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself. |
-
|
-Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself. |
-
|
-Called after |
-
|
-Override to alter or apply batch augmentations to your batch after it is transferred to the device. |
-
|
-Called before |
-
|
-Override to alter or apply batch augmentations to your batch before it is transferred to the device. |
-
|
-Called before |
-
|
-Called after |
-
|
-Called at the very end of fit. |
-
|
-Called at the very beginning of fit. |
-
|
-Called by Lightning to restore your model. |
-
|
-Called in the predict loop after the batch. |
-
|
-Called in the predict loop before anything happens for that batch. |
-
|
-Called at the end of predicting. |
-
|
-Called at the end of predicting. |
-
|
-Called at the beginning of predicting. |
-
|
-Called when the predict loop starts. |
-
|
-Called at the beginning of predicting. |
-
|
-Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save. |
-
|
-Called in the test loop after the batch. |
-
|
-Called in the test loop before anything happens for that batch. |
-
|
-Called at the end of testing. |
-
|
-Called in the test loop at the very end of the epoch. |
-
|
-Called in the test loop at the very beginning of the epoch. |
-
|
-Called when the test loop starts. |
-
|
-Called when the test loop ends. |
-
|
-Called at the beginning of testing. |
-
|
-Called in the training loop after the batch. |
-
|
-Called in the training loop before anything happens for that batch. |
-
|
-Called at the end of training before logger experiment is closed. |
-
|
-Called in the training loop at the very end of the epoch. |
-
|
-Called in the training loop at the very beginning of the epoch. |
-
|
-Called at the beginning of training after sanity check. |
-
|
-Called in the validation loop after the batch. |
-
|
-Called in the validation loop before anything happens for that batch. |
-
|
-Called at the end of validation. |
-
|
-Called in the validation loop at the very end of the epoch. |
-
|
-Called in the validation loop at the very beginning of the epoch. |
-
|
-Called when the validation loop starts. |
-
|
-Called when the validation loop ends. |
-
|
-Called by the training loop to release gradients before entering the validation loop. |
-
|
-Called at the beginning of validation. |
-
|
-Override this method to adjust the default way the |
-
|
-Override this method to change the default behaviour of |
-
|
-Returns the optimizer(s) that are being used during training. |
-
|
-Return an iterator over module parameters. |
-
|
-An iterable or collection of iterables specifying prediction samples. |
-
|
-Step function called during |
-
|
-Use this to download and prepare data. |
-
|
-Prints only from process 0. |
-
|
-Register a backward hook on the module. |
-
|
-Add a buffer to the module. |
-
|
-Register a forward hook on the module. |
-
|
-Register a forward pre-hook on the module. |
-
|
-Register a backward hook on the module. |
-
|
-Register a backward pre-hook on the module. |
-
|
-Register a post hook to be run after module's |
-
|
-Alias for |
-
|
-Add a parameter to the module. |
-
|
-Register a pre-hook for the |
-
|
-Change if autograd should record operations on parameters in this module. |
-
|
-Save arguments to |
-
|
-Set extra state contained in the loaded state_dict. |
-
|
-Called at the beginning of fit (train + validate), validate, test, or predict. |
-
|
-See |
-
|
-Return a dictionary containing references to the whole state of the module. |
-
|
-Called at the end of fit (train + validate), validate, test, or predict. |
-
|
-An iterable or collection of iterables specifying test samples. |
-
|
-Operates on a single batch of data from the test set. |
-
|
-See |
-
|
-Move the parameters and buffers to the specified device without copying storage. |
-
|
-Saves the model in ONNX format. |
-
|
-By default compiles the whole model to a |
-
|
-Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup. |
-
|
-Set the module in training mode. |
-
|
-An iterable or collection of iterables specifying training samples. |
-
|
-Processes a single batch during training, computes the loss, and logs training metrics. |
-
|
-Override this hook if your |
-
|
-See |
-
|
-Unfreeze all parameters for training. |
-
|
-Resets the state of required gradients that were toggled with |
-
|
-An iterable or collection of iterables specifying validation samples. |
-
|
-Processes a single batch during validation, computes the loss, and logs validation metrics. |
-
|
-Move all model parameters and buffers to the XPU. |
-
|
-Reset gradients of all model parameters. |
-
__call__ |
-- |
Sets up the model’s optimizer and learning rate scheduler based on the configurations provided.
-A dictionary containing the optimizer and lr_scheduler configurations.
-dict
-Defines the forward pass of the model, processing both categorical and numerical features, -and returning regression predictions.
-cat_features (Tensor) – Tensor containing the categorical features.
num_features (Tensor) – Tensor containing the numerical features or raw sequence data, depending on raw_embeddings.
The output predictions of the model for regression tasks.
-Tensor
-Processes a single batch during training, computes the loss, and logs training metrics.
-batch (tuple) – A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
batch_idx (int) – The index of the batch within the epoch.
The computed loss for the batch.
-Tensor
-Processes a single batch during validation, computes the loss, and logs validation metrics.
-batch (tuple) – A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
batch_idx (int) – The index of the batch within the epoch.
A specialized regression module for protein data, built on PyTorch Lightning and integrating the Mamba architecture. -It supports embeddings for categorical features and can process raw or embedded numerical features, making it suitable -for complex protein sequence data.
-config (MambularConfig) – Configuration parameters for the model architecture.
cat_feature_info (dict, optional) – Information about categorical features, mapping feature names to the number of unique categories. Defaults to None.
num_feature_info (dict, optional) – Information about numerical features, mapping feature names to their number of dimensions after embedding. Defaults to None.
lr (float, optional) – Learning rate for the optimizer. Defaults to 1e-03.
lr_patience (int, optional) – Number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
weight_decay (float, optional) – Weight decay coefficient for regularization in the optimizer. Defaults to 0.025.
lr_factor (float, optional) – Factor by which the learning rate will be reduced by the scheduler. Defaults to 0.75.
seq_size (int, optional) – Size of sequence chunks for processing numerical features. Relevant when raw_embeddings is False.
raw_embeddings (bool, optional) – Indicates whether to use raw numerical features directly or to process them into embeddings. Defaults to False.
The core neural network module implementing the Mamba architecture.
-Mamba
-Normalization layer applied after the Mamba block.
-nn.Module
-Final linear layer mapping the features to the regression target.
-nn.Linear
-The loss function for regression tasks.
-nn.MSELoss
-automatic_optimizationIf set to False you are responsible for calling .backward(), .step(), .zero_grad().
current_epochThe current epoch in the Trainer, or 0 if not attached.
example_input_arrayThe example input array is a specification of what the module can consume in the forward() method.
global_rankThe index of the current process across all nodes and devices.
-global_stepTotal training batches seen across all epochs.
-hparamsThe collection of hyperparameters saved with save_hyperparameters().
hparams_initialThe collection of hyperparameters saved with save_hyperparameters().
local_rankThe index of the current process within a single node.
-loggerReference to the logger object in the Trainer.
-loggersReference to the list of loggers in the Trainer.
-on_gpuReturns True if this model is currently located on a GPU.
strict_loadingDetermines how Lightning loads this model using .load_state_dict(…, strict=model.strict_loading).
-Methods
-
|
-Add a child module to the current module. |
-
|
-Gather tensors or collections of tensors from multiple processes. |
-
|
-Apply |
-
|
-Called to perform backward on the loss returned in |
-
|
-Casts all floating point parameters and buffers to |
-
|
-Return an iterator over module buffers. |
-
|
-Return an iterator over immediate children modules. |
-
|
-Handles gradient clipping internally. |
-
|
-Compile this Module's forward using |
-
|
-Configure model-specific callbacks. |
-
|
-Perform gradient clipping for the optimizer parameters. |
-
|
-Hook to create modules in a strategy and precision aware context. |
-
| - | Sets up the model's optimizer and learning rate scheduler based on the configurations provided. |
-
|
-Deprecated. |
-
|
-See |
-
|
-Moves all model parameters and buffers to the GPU. |
-
|
-See |
-
|
-Set the module in evaluation mode. |
-
|
-Set the extra representation of the module. |
-
|
-See |
-
|
-Defines the forward pass of the model, processing both categorical and numerical features, and returning regression predictions. |
-
|
-Freeze all params for inference. |
-
|
-Return the buffer given by |
-
|
-Return any extra state to include in the module's state_dict. |
-
|
-Return the parameter given by |
-
|
-Return the submodule given by |
-
|
-See |
-
|
-Move all model parameters and buffers to the IPU. |
-
|
-Primary way of loading a model from a checkpoint. |
-
|
-Copy parameters and buffers from |
-
|
-Log a key, value pair. |
-
|
-Log a dictionary of values at once. |
-
|
-Override this method to adjust the default way the |
-
|
-Returns the learning rate scheduler(s) that are being used during training. |
-
|
-Call this directly from your |
-
|
-Return an iterator over all modules in the network. |
-
|
-Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself. |
-
|
-Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself. |
-
|
-Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself. |
-
|
-Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself. |
-
|
-Called after |
-
|
-Override to alter or apply batch augmentations to your batch after it is transferred to the device. |
-
|
-Called before |
-
|
-Override to alter or apply batch augmentations to your batch before it is transferred to the device. |
-
|
-Called before |
-
|
-Called after |
-
|
-Called at the very end of fit. |
-
|
-Called at the very beginning of fit. |
-
|
-Called by Lightning to restore your model. |
-
|
-Called in the predict loop after the batch. |
-
|
-Called in the predict loop before anything happens for that batch. |
-
|
-Called at the end of predicting. |
-
|
-Called at the end of predicting. |
-
|
-Called at the beginning of predicting. |
-
|
-Called when the predict loop starts. |
-
|
-Called at the beginning of predicting. |
-
|
-Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save. |
-
|
-Called in the test loop after the batch. |
-
|
-Called in the test loop before anything happens for that batch. |
-
|
-Called at the end of testing. |
-
|
-Called in the test loop at the very end of the epoch. |
-
|
-Called in the test loop at the very beginning of the epoch. |
-
|
-Called when the test loop starts. |
-
|
-Called when the test loop ends. |
-
|
-Called at the beginning of testing. |
-
|
-Called in the training loop after the batch. |
-
|
-Called in the training loop before anything happens for that batch. |
-
|
-Called at the end of training before logger experiment is closed. |
-
|
-Called in the training loop at the very end of the epoch. |
-
|
-Called in the training loop at the very beginning of the epoch. |
-
|
-Called at the beginning of training after sanity check. |
-
|
-Called in the validation loop after the batch. |
-
|
-Called in the validation loop before anything happens for that batch. |
-
|
-Called at the end of validation. |
-
|
-Called in the validation loop at the very end of the epoch. |
-
|
-Called in the validation loop at the very beginning of the epoch. |
-
|
-Called when the validation loop starts. |
-
|
-Called when the validation loop ends. |
-
|
-Called by the training loop to release gradients before entering the validation loop. |
-
|
-Called at the beginning of validation. |
-
|
-Override this method to adjust the default way the |
-
|
-Override this method to change the default behaviour of |
-
|
-Returns the optimizer(s) that are being used during training. |
-
|
-Return an iterator over module parameters. |
-
|
-An iterable or collection of iterables specifying prediction samples. |
-
|
-Step function called during |
-
|
-Use this to download and prepare data. |
-
|
-Prints only from process 0. |
-
|
-Register a backward hook on the module. |
-
|
-Add a buffer to the module. |
-
|
-Register a forward hook on the module. |
-
|
-Register a forward pre-hook on the module. |
-
|
-Register a backward hook on the module. |
-
|
-Register a backward pre-hook on the module. |
-
|
-Register a post hook to be run after module's |
-
|
-Alias for |
-
|
-Add a parameter to the module. |
-
|
-Register a pre-hook for the |
-
|
-Change if autograd should record operations on parameters in this module. |
-
|
-Save arguments to |
-
|
-Set extra state contained in the loaded state_dict. |
-
|
-Called at the beginning of fit (train + validate), validate, test, or predict. |
-
|
-See |
-
|
-Return a dictionary containing references to the whole state of the module. |
-
|
-Called at the end of fit (train + validate), validate, test, or predict. |
-
|
-An iterable or collection of iterables specifying test samples. |
-
|
-Operates on a single batch of data from the test set. |
-
|
-See |
-
|
-Move the parameters and buffers to the specified device without copying storage. |
-
|
-Saves the model in ONNX format. |
-
|
-By default compiles the whole model to a |
-
|
-Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup. |
-
|
-Set the module in training mode. |
-
|
-An iterable or collection of iterables specifying training samples. |
-
|
-Processes a single batch during training, computes the loss, and logs training metrics. |
-
|
-Override this hook if your |
-
|
-See |
-
|
-Unfreeze all parameters for training. |
-
|
-Resets the state of required gradients that were toggled with |
-
|
-An iterable or collection of iterables specifying validation samples. |
-
|
-Processes a single batch during validation, computes the loss, and logs validation metrics. |
-
|
-Move all model parameters and buffers to the XPU. |
-
|
-Reset gradients of all model parameters. |
-
__call__ |
-- |
Sets up the model’s optimizer and learning rate scheduler based on the configurations provided.
-A dictionary containing the optimizer and lr_scheduler configurations.
-dict
-Defines the forward pass of the model, processing both categorical and numerical features, -and returning regression predictions.
-cat_features (Tensor) – Tensor containing the categorical features.
num_features (Tensor) – Tensor containing the numerical features or raw sequence data, depending on raw_embeddings.
The output predictions of the model for regression tasks.
-Tensor
-Processes a single batch during training, computes the loss, and logs training metrics.
-batch (tuple) – A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
batch_idx (int) – The index of the batch within the epoch.
The computed loss for the batch.
-Tensor
-Processes a single batch during validation, computes the loss, and logs validation metrics.
-batch (tuple) – A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
batch_idx (int) – The index of the batch within the epoch.
A base regression module for tabular data built on PyTorch Lightning. It incorporates embeddings -for categorical and numerical features with a configurable architecture provided by MambularConfig. -This module is designed for regression tasks.
-config (MambularConfig) – An instance of MambularConfig containing configuration parameters for the model architecture.
cat_feature_info (dict, optional) – A dictionary mapping the names of categorical features to their number of unique categories. Defaults to None.
num_feature_info (dict, optional) – A dictionary mapping the names of numerical features to their number of dimensions after embedding. Defaults to None.
lr (float, optional) – The initial learning rate for the optimizer. Defaults to 1e-03.
lr_patience (int, optional) – The number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
weight_decay (float, optional) – Weight decay (L2 penalty) coefficient. Defaults to 0.025.
lr_factor (float, optional) – Factor by which the learning rate will be reduced. Defaults to 0.75.
The core neural network module implementing the Mamba architecture.
-Mamba
-Normalization layer applied after the Mamba block.
-nn.Module
-Final linear layer mapping the features to a single output for regression tasks.
-nn.Linear
-Metric computation module for training Mean Squared Error.
-torchmetrics.MeanSquaredError
-Metric computation module for validation Mean Squared Error.
-torchmetrics.MeanSquaredError
-The loss function for regression tasks.
-torch.nn.MSELoss
-automatic_optimizationIf set to False you are responsible for calling .backward(), .step(), .zero_grad().
current_epochThe current epoch in the Trainer, or 0 if not attached.
example_input_arrayThe example input array is a specification of what the module can consume in the forward() method.
global_rankThe index of the current process across all nodes and devices.
-global_stepTotal training batches seen across all epochs.
-hparamsThe collection of hyperparameters saved with save_hyperparameters().
hparams_initialThe collection of hyperparameters saved with save_hyperparameters().
local_rankThe index of the current process within a single node.
-loggerReference to the logger object in the Trainer.
-loggersReference to the list of loggers in the Trainer.
-on_gpuReturns True if this model is currently located on a GPU.
strict_loadingDetermines how Lightning loads this model using .load_state_dict(…, strict=model.strict_loading).
-Methods
-
|
-Add a child module to the current module. |
-
|
-Gather tensors or collections of tensors from multiple processes. |
-
|
-Apply |
-
|
-Called to perform backward on the loss returned in |
-
|
-Casts all floating point parameters and buffers to |
-
|
-Return an iterator over module buffers. |
-
|
-Return an iterator over immediate children modules. |
-
|
-Handles gradient clipping internally. |
-
|
-Compile this Module's forward using |
-
|
-Configure model-specific callbacks. |
-
|
-Perform gradient clipping for the optimizer parameters. |
-
|
-Hook to create modules in a strategy and precision aware context. |
-
| - | Sets up the model's optimizer and learning rate scheduler based on the configurations provided. |
-
|
-Deprecated. |
-
|
-See |
-
|
-Moves all model parameters and buffers to the GPU. |
-
|
-See |
-
|
-Set the module in evaluation mode. |
-
|
-Set the extra representation of the module. |
-
|
-See |
-
|
-Defines the forward pass of the regressor. |
-
|
-Freeze all params for inference. |
-
|
-Return the buffer given by |
-
|
-Return any extra state to include in the module's state_dict. |
-
|
-Return the parameter given by |
-
|
-Return the submodule given by |
-
|
-See |
-
|
-Move all model parameters and buffers to the IPU. |
-
|
-Primary way of loading a model from a checkpoint. |
-
|
-Copy parameters and buffers from |
-
|
-Log a key, value pair. |
-
|
-Log a dictionary of values at once. |
-
|
-Override this method to adjust the default way the |
-
|
-Returns the learning rate scheduler(s) that are being used during training. |
-
|
-Call this directly from your |
-
|
-Return an iterator over all modules in the network. |
-
|
-Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself. |
-
|
-Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself. |
-
|
-Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself. |
-
|
-Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself. |
-
|
-Called after |
-
|
-Override to alter or apply batch augmentations to your batch after it is transferred to the device. |
-
|
-Called before |
-
|
-Override to alter or apply batch augmentations to your batch before it is transferred to the device. |
-
|
-Called before |
-
|
-Called after |
-
|
-Called at the very end of fit. |
-
|
-Called at the very beginning of fit. |
-
|
-Called by Lightning to restore your model. |
-
|
-Called in the predict loop after the batch. |
-
|
-Called in the predict loop before anything happens for that batch. |
-
|
-Called at the end of predicting. |
-
|
-Called at the end of predicting. |
-
|
-Called at the beginning of predicting. |
-
|
-Called when the predict loop starts. |
-
|
-Called at the beginning of predicting. |
-
|
-Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save. |
-
|
-Called in the test loop after the batch. |
-
|
-Called in the test loop before anything happens for that batch. |
-
|
-Called at the end of testing. |
-
|
-Called in the test loop at the very end of the epoch. |
-
|
-Called in the test loop at the very beginning of the epoch. |
-
|
-Called when the test loop starts. |
-
|
-Called when the test loop ends. |
-
|
-Called at the beginning of testing. |
-
|
-Called in the training loop after the batch. |
-
|
-Called in the training loop before anything happens for that batch. |
-
|
-Called at the end of training before logger experiment is closed. |
-
|
-Called in the training loop at the very end of the epoch. |
-
|
-Called in the training loop at the very beginning of the epoch. |
-
|
-Called at the beginning of training after sanity check. |
-
|
-Called in the validation loop after the batch. |
-
|
-Called in the validation loop before anything happens for that batch. |
-
|
-Called at the end of validation. |
-
|
-Called in the validation loop at the very end of the epoch. |
-
|
-Called in the validation loop at the very beginning of the epoch. |
-
|
-Called when the validation loop starts. |
-
|
-Called when the validation loop ends. |
-
|
-Called by the training loop to release gradients before entering the validation loop. |
-
|
-Called at the beginning of validation. |
-
|
-Override this method to adjust the default way the |
-
|
-Override this method to change the default behaviour of |
-
|
-Returns the optimizer(s) that are being used during training. |
-
|
-Return an iterator over module parameters. |
-
|
-An iterable or collection of iterables specifying prediction samples. |
-
|
-Step function called during |
-
|
-Use this to download and prepare data. |
-
|
-Prints only from process 0. |
-
|
-Register a backward hook on the module. |
-
|
-Add a buffer to the module. |
-
|
-Register a forward hook on the module. |
-
|
-Register a forward pre-hook on the module. |
-
|
-Register a backward hook on the module. |
-
|
-Register a backward pre-hook on the module. |
-
|
-Register a post hook to be run after module's |
-
|
-Alias for |
-
|
-Add a parameter to the module. |
-
|
-Register a pre-hook for the |
-
|
-Change if autograd should record operations on parameters in this module. |
-
|
-Save arguments to |
-
|
-Set extra state contained in the loaded state_dict. |
-
|
-Called at the beginning of fit (train + validate), validate, test, or predict. |
-
|
-See |
-
|
-Return a dictionary containing references to the whole state of the module. |
-
|
-Called at the end of fit (train + validate), validate, test, or predict. |
-
|
-An iterable or collection of iterables specifying test samples. |
-
|
-Operates on a single batch of data from the test set. |
-
|
-See |
-
|
-Move the parameters and buffers to the specified device without copying storage. |
-
|
-Saves the model in ONNX format. |
-
|
-By default compiles the whole model to a |
-
|
-Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup. |
-
|
-Set the module in training mode. |
-
|
-An iterable or collection of iterables specifying training samples. |
-
|
-Defines the forward pass of the regressor. |
-
|
-Override this hook if your |
-
|
-See |
-
|
-Unfreeze all parameters for training. |
-
|
-Resets the state of required gradients that were toggled with |
-
|
-An iterable or collection of iterables specifying validation samples. |
-
|
-Processes a single batch during validation, computes the loss, and logs validation metrics. |
-
|
-Move all model parameters and buffers to the XPU. |
-
|
-Reset gradients of all model parameters. |
-
__call__ |
-- |
Sets up the model’s optimizer and learning rate scheduler based on the configurations provided.
-A dictionary containing the optimizer and lr_scheduler configurations.
-dict
-Defines the forward pass of the regressor.
-cat_features (Tensor) – Tensor containing the categorical features.
num_features (Tensor) – Tensor containing the numerical features.
The output predictions of the model for regression tasks.
-Tensor
-Defines the forward pass of the regressor.
-cat_features (Tensor) – Tensor containing the categorical features.
num_features (Tensor) – Tensor containing the numerical features.
The output predictions of the model for regression tasks.
-Tensor
-Processes a single batch during validation, computes the loss, and logs validation metrics.
-batch (tuple) – A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
batch_idx (int) – The index of the batch within the epoch.
This module provides base classes for the Mambular models.
-Functionality |
-Description |
-
|---|---|
|
-Multi-class and binary classification tasks. |
-
|
-Various statistical distribution families for different types of regression and classification tasks. |
-
|
-Specialized classification module for complex protein sequence data. |
-
|
-Specialized regression module for complex protein sequence data. |
-
|
-Regression tasks. |
-
A classifier that mimics scikit-learn’s API using PyTorch Lightning and a custom architecture.
-This classifier is designed to work with tabular data and provides a flexible interface for specifying model -configurations and preprocessing steps. It integrates smoothly with scikit-learn’s utilities, such as cross-validation -and grid search.
-**kwargs (Various) – Accepts any number of keyword arguments that are passed to the MambularConfig and Preprocessor classes. -Known configuration arguments for the model are extracted based on a predefined list, and the rest are -passed to the Preprocessor.
-Configuration object that holds model-specific settings.
-MambularConfig
-Preprocessor object for handling feature preprocessing like normalization and encoding.
-Preprocessor
-The underlying PyTorch Lightning model, instantiated upon calling the fit method.
-BaseMambularClassifier or None
-Methods
-
|
-Evaluate the model on the given data using specified metrics. |
-
|
-Fit the model to the given training data, optionally using a separate validation set. |
-
|
-Get metadata routing of this object. |
-
|
-Get parameters for this estimator. |
-
|
-Predict the class labels for the given input samples. |
-
|
-Predict class probabilities for the given input samples. |
-
|
-Preprocess the training and validation data and create corresponding DataLoaders. |
-
|
-Preprocesses the test data and creates tensors for categorical and numerical features. |
-
|
-Request metadata passed to the |
-
|
-Set the parameters of this estimator. |
-
|
-Split the dataset into training and validation sets. |
-
MambularLSS is a machine learning estimator that is designed for structured data, -incorporating both preprocessing and a deep learning model. The estimator -integrates configurable components for data preprocessing and the neural network model, -facilitating end-to-end training and prediction workflows.
-The initialization of this class separates configuration arguments for the model and -the preprocessor, allowing for flexible adjustment of parameters.
-Configuration object containing model-specific parameters.
-MambularConfig
-Preprocessor object for data preprocessing steps.
-Preprocessor
-The neural network model, initialized based on ‘config’.
-torch.nn.Module
-**kwargs (Arbitrary keyword arguments, divided into configuration for the model and) – preprocessing. Recognized keys include model parameters such as ‘d_model’, -‘n_layers’, etc., and any additional keys are assumed to be preprocessor arguments.
-Methods
-
|
-Evaluate the model on the given data using specified metrics tailored to the distribution type. |
-
|
-Fits the model to the provided data, using the specified loss distribution family for the prediction task. |
-
|
-Provides default metrics based on the distribution family. |
-
|
-Get metadata routing of this object. |
-
|
-Get parameters for this estimator, optionally including parameters from nested components like the preprocessor. |
-
|
-Predicts target values for the given input samples using the fitted model. |
-
|
-Preprocess the training and validation data, fit the preprocessor on the training data, and transform both training and validation data. |
-
|
-Preprocess test data using the fitted preprocessor. |
-
|
-Request metadata passed to the |
-
|
-Set the parameters of this estimator, allowing for modifications to both the configuration and preprocessor parameters. |
-
|
-Split the dataset into training and validation sets. |
-
Provides an scikit-learn-like interface for the ProteinMambularClassifier, making it compatible with -scikit-learn’s utilities and workflow. This class encapsulates the PyTorch Lightning model, preprocessing, -and data loading, offering methods for fitting, predicting, and probability estimation in a manner akin -to scikit-learn’s API.
-**kwargs (Configuration parameters that can include both MambularConfig settings and preprocessing) – options. Any unrecognized parameters are passed to the preprocessor.
-Configuration object for the model, storing architecture-specific parameters.
-MambularConfig
-Object handling data preprocessing steps such as feature encoding and normalization.
-Preprocessor
-The underlying neural network model, instantiated during the fit method.
-ProteinMambularClassifier
-Methods
-
|
-Evaluate the model on the given data using specified metrics. |
-
|
-Fits the model to the given dataset. |
-
|
-Get metadata routing of this object. |
-
|
-Get parameters for this estimator. |
-
|
-Predict the class labels for the given input samples. |
-
|
-Predict class probabilities for the given input samples. |
-
|
-Preprocess the training and validation data and create corresponding DataLoaders. |
-
|
-Preprocesses the test data and creates tensors for categorical and numerical features. |
-
|
-Request metadata passed to the |
-
|
-Set the parameters of this estimator. |
-
|
-Split the dataset into training and validation sets. |
-
An sklearn-like interface for the ProteinMambularRegressor, making it compatible with sklearn’s utilities -and workflows. This class wraps the PyTorch Lightning model and preprocessor, providing methods for fitting, -predicting, and setting/getting parameters in a way that mimics sklearn’s API.
-**kwargs (Keyword arguments that can include both configuration parameters for the MambularConfig and) – parameters for the preprocessor.
-Configuration object containing model-specific parameters.
-MambularConfig
-Preprocessor object for data preprocessing steps.
-Preprocessor
-The neural network model, initialized after the fit method is called.
-ProteinMambularRegressor
-Methods
-
|
-Evaluate the model on the given data using specified metrics. |
-
|
-Fits the ProteinMambularRegressor model to the training data. |
-
|
-Get metadata routing of this object. |
-
|
-Get parameters for this estimator. |
-
|
-Predicts target values for the given input samples. |
-
|
-Preprocesses the training and validation data, and creates DataLoaders for them. |
-
|
-Preprocesses the test data and creates tensors for categorical and numerical features. |
-
|
-Request metadata passed to the |
-
|
-Set the parameters of this estimator. |
-
|
-Splits the dataset into training and validation sets. |
-
A regressor implemented using PyTorch Lightning that follows the scikit-learn API conventions. This class is designed -to work with tabular data, offering a straightforward way to specify model configurations and preprocessing steps. It -integrates seamlessly with scikit-learn’s tools such as cross-validation and grid search.
-**kwargs (Various) – Accepts any number of keyword arguments. Arguments recognized as model configuration options are passed to the -MambularConfig constructor. Remaining arguments are assumed to be preprocessor options and passed to the -Preprocessor constructor.
-An object storing the configuration settings for the model.
-MambularConfig
-An object responsible for preprocessing the input data, such as encoding categorical variables and scaling numerical features.
-Preprocessor
-The underlying regression model, which is a PyTorch Lightning module. It is instantiated when the fit method is called.
-BaseMambularRegressor or None
-Methods
-
|
-Evaluate the model on the given data using specified metrics. |
-
|
-Trains the regression model using the provided training data. |
-
|
-Get metadata routing of this object. |
-
|
-Get parameters for this estimator. |
-
|
-Predicts target values for the given input samples. |
-
|
-Preprocesses the training and validation data, and creates DataLoaders for them. |
-
|
-Preprocesses the test data and creates tensors for categorical and numerical features. |
-
|
-Request metadata passed to the |
-
|
-Set the parameters of this estimator. |
-
|
-Splits the dataset into training and validation sets. |
-
This module provides classes for the Mambular models that adhere to scikit-learn’s BaseEstimator interface.
-Functionality |
-Description |
-
|---|---|
|
-Multi-class and binary classification tasks. |
-
|
-Various statistical distribution families for different types of regression and classification tasks. |
-
|
-Specialized classification module for complex protein sequence data. |
-
|
-Specialized regression module for complex protein sequence data. |
-
|
-Regression tasks. |
-
| - | - |
| - | - |
| - | - |
| - |
Mambular is a Python package that brings the power of Mamba architectures to tabular data, offering a suite of deep learning models for regression, classification, and distributional regression tasks. Designed with ease of use in mind, Mambular models adhere to scikit-learn’s BaseEstimator interface, making them highly compatible with the familiar scikit-learn ecosystem. This means you can fit, predict, and transform using Mambular models just as you would with any traditional scikit-learn model, but with the added performance and flexibility of deep learning.
Comprehensive Model Suite: Includes modules for regression (MambularRegressor), classification (MambularClassifier), and distributional regression (MambularLSS), catering to a wide range of tabular data tasks.
State-of-the-Art Architectures: Leverages the Mamba architecture, known for its effectiveness in handling sequential and time-series data within a state-space modeling framework, adapted here for tabular data.
Seamless Integration: Designed to work effortlessly with scikit-learn, allowing for easy inclusion in existing machine learning pipelines, cross-validation, and hyperparameter tuning workflows.
Extensive Preprocessing: Comes with a powerful preprocessing module that supports a broad array of data transformation techniques, ensuring that your data is optimally prepared for model training.
Sklearn-like API: The familiar scikit-learn fit, predict, and predict_proba methods mean minimal learning curve for those already accustomed to scikit-learn.
PyTorch Lightning Under the Hood: Built on top of PyTorch Lightning, Mambular models benefit from streamlined training processes, easy customization, and advanced features like distributed training and 16-bit precision.
Install Mambular using pip:
-pip install mambular
-Mambular elevates the preprocessing stage of model development, employing a sophisticated suite of techniques to ensure your data is in the best shape for the Mamba architectures. Our preprocessing module is designed to be both powerful and intuitive, offering a range of options to transform your tabular data efficiently.
-Mambular automatically identifies the type of each feature in your dataset, applying the most suitable transformations to numerical and categorical variables. This includes:
-Ordinal Encoding: Categorical features are seamlessly transformed into numerical values, preserving their inherent order and making them model-ready.
One-Hot Encoding: For nominal data, Mambular employs one-hot encoding to capture the presence or absence of categories without imposing ordinality.
Binning: Numerical features can be discretized into bins, a useful technique for handling continuous variables in certain modeling contexts.
Decision Tree Binning: Optionally, Mambular can use decision trees to find the optimal binning strategy for numerical features, enhancing model interpretability and performance.
Normalization: Mambular can easily handle numerical features without specifically turning them into categorical features. Standard preprocessing steps such as normalization per feature are possible
Standardization: Similarly, Standardization instead of Normalization can be used.
Our preprocessing pipeline gracefully handles missing data, employing strategies like mean imputation for numerical features and mode imputation for categorical ones, ensuring that your models receive complete data inputs without manual intervention.
-While Mambular excels in automating the preprocessing workflow, it also offers flexibility. You can customize the preprocessing steps to fit the unique needs of your dataset, ensuring that you’re not locked into a one-size-fits-all approach.
-By integrating Mambular’s preprocessing module into your workflow, you’re not just preparing your data for deep learning; you’re optimizing it for excellence. This commitment to data quality is what sets Mambular apart, making it an indispensable tool in your machine learning arsenal.
-Fitting a model in mambular is as simple as it gets. All models in mambular are sklearn BaseEstimators. Thus the .fit method is implemented for all of them. Additionally, this allows for using all other sklearn inherent methods such as their built in hyperparameter optimization tools.
from mambular.models import MambularClassifier
-# Initialize and fit your model
-model = MambularClassifier(
- dropout=0.01,
- d_model=128,
- n_layers=6,
- numerical_preprocessing="normalization",
-)
-
-# X can be a dataframe or something that can be easily transformed into a pd.DataFrame as a np.array
-model.fit(X, y, max_epochs=500, lr=1e-03, patience=25)
-Predictions are also easily obtained:
-# simple predictions
-preds = model.predict(X)
-
-# Predict probabilities
-preds = model.predict_proba(X)
-Mambular introduces a cutting-edge approach to distributional regression through its MambularLSS module, empowering users to model the full distribution of a response variable, not just its mean. This method is particularly valuable in scenarios where understanding the variability, skewness, or kurtosis of the response distribution is as crucial as predicting its central tendency.
Full Distribution Modeling: Unlike traditional regression models that predict a single value (e.g., the mean), MambularLSS models the entire distribution of the response variable. This allows for more informative predictions, including quantiles, variance, and higher moments.
Customizable Distribution Types: MambularLSS supports a variety of distribution families (e.g., Gaussian, Poisson, Binomial), making it adaptable to different types of response variables, from continuous to count data.
Location, Scale, Shape Parameters: The model predicts parameters corresponding to the location, scale, and shape of the distribution, offering a nuanced understanding of the data’s underlying distributional characteristics.
Enhanced Predictive Uncertainty: By modeling the full distribution, MambularLSS provides richer information on predictive uncertainty, enabling more robust decision-making processes in uncertain environments.
MambularLSS offers a wide range of distribution classes to cater to various statistical modeling needs. The available distribution classes include:
normal: Normal Distribution for modeling continuous data with a symmetric distribution around the mean.
poisson: Poisson Distribution for modeling count data that represents the number of events occurring within a fixed interval.
gamma: Gamma Distribution for modeling continuous data that is skewed and bounded at zero, often used for waiting times.
beta: Beta Distribution for modeling data that is bounded between 0 and 1, useful for proportions and percentages.
dirichlet: Dirichlet Distribution for modeling multivariate data where individual components are correlated, and the sum is constrained to 1.
studentt: Student’s T-Distribution for modeling data with heavier tails than the normal distribution, useful when the sample size is small.
negativebinom: Negative Binomial Distribution for modeling count data with over-dispersion relative to the Poisson distribution.
inversegamma: Inverse Gamma Distribution, often used as a prior distribution in Bayesian inference for scale parameters.
categorical: Categorical Distribution for modeling categorical data with more than two categories.
These distribution classes allow MambularLSS to flexibly model a wide variety of data types and distributions, providing users with the tools needed to capture the full complexity of their data.
Risk Assessment: In finance or insurance, understanding the range and likelihood of potential losses is as important as predicting average outcomes.
Demand Forecasting: For inventory management, capturing the variability in product demand helps in optimizing stock levels.
Personalized Medicine: In healthcare, distributional regression can predict a range of possible patient responses to a treatment, aiding in personalized therapy planning.
To integrate distributional regression into your workflow with MambularLSS, start by initializing the model with your desired configuration, similar to other Mambular models:
from mambular.models import MambularLSS
-
-# Initialize the MambularLSS model
-model = MambularLSS(
- dropout=0.2,
- d_model=256,
- n_layers=4,
-
-)
-
-# Fit the model to your data
-model.fit(
- X,
- y,
- max_epochs=300,
- lr=1e-03,
- patience=10,
- family="normal" # define your distribution
- )
-Please follow the steps below for installing mambular
Install from the source:
-cd mamba-tabular
-pip install .
-Note: Make sure you in the same directory where setup.py file resides.
This package is so far not available in PyPi.
-Mambular is a Python package that brings the power of Mamba architectures to tabular data, offering a suite of deep learning models for regression, classification, and distributional regression tasks. Designed with ease of use in mind, Mambular models adhere to scikit-learn’s BaseEstimator interface, making them highly compatible with the familiar scikit-learn ecosystem. This means you can fit, predict, and transform using Mambular models just as you would with any traditional scikit-learn model, but with the added performance and flexibility of deep learning.
Comprehensive Model Suite: Includes modules for regression (MambularRegressor), classification (MambularClassifier), and distributional regression (MambularLSS), catering to a wide range of tabular data tasks.
State-of-the-Art Architectures: Leverages the Mamba architecture, known for its effectiveness in handling sequential and time-series data within a state-space modeling framework, adapted here for tabular data.
Seamless Integration: Designed to work effortlessly with scikit-learn, allowing for easy inclusion in existing machine learning pipelines, cross-validation, and hyperparameter tuning workflows.
Extensive Preprocessing: Comes with a powerful preprocessing module that supports a broad array of data transformation techniques, ensuring that your data is optimally prepared for model training.
Sklearn-like API: The familiar scikit-learn fit, predict, and predict_proba methods mean minimal learning curve for those already accustomed to scikit-learn.
PyTorch Lightning Under the Hood: Built on top of PyTorch Lightning, Mambular models benefit from streamlined training processes, easy customization, and advanced features like distributed training and 16-bit precision.
Install Mambular using pip:
-pip install mambular
-Mambular elevates the preprocessing stage of model development, employing a sophisticated suite of techniques to ensure your data is in the best shape for the Mamba architectures. Our preprocessing module is designed to be both powerful and intuitive, offering a range of options to transform your tabular data efficiently.
-Mambular automatically identifies the type of each feature in your dataset, applying the most suitable transformations to numerical and categorical variables. This includes:
-Ordinal Encoding: Categorical features are seamlessly transformed into numerical values, preserving their inherent order and making them model-ready.
One-Hot Encoding: For nominal data, Mambular employs one-hot encoding to capture the presence or absence of categories without imposing ordinality.
Binning: Numerical features can be discretized into bins, a useful technique for handling continuous variables in certain modeling contexts.
Decision Tree Binning: Optionally, Mambular can use decision trees to find the optimal binning strategy for numerical features, enhancing model interpretability and performance.
Normalization: Mambular can easily handle numerical features without specifically turning them into categorical features. Standard preprocessing steps such as normalization per feature are possible
Standardization: Similarly, Standardization instead of Normalization can be used.
Our preprocessing pipeline gracefully handles missing data, employing strategies like mean imputation for numerical features and mode imputation for categorical ones, ensuring that your models receive complete data inputs without manual intervention.
-While Mambular excels in automating the preprocessing workflow, it also offers flexibility. You can customize the preprocessing steps to fit the unique needs of your dataset, ensuring that you’re not locked into a one-size-fits-all approach.
-By integrating Mambular’s preprocessing module into your workflow, you’re not just preparing your data for deep learning; you’re optimizing it for excellence. This commitment to data quality is what sets Mambular apart, making it an indispensable tool in your machine learning arsenal.
-Fitting a model in mambular is as simple as it gets. All models in mambular are sklearn BaseEstimators. Thus the .fit method is implemented for all of them. Additionally, this allows for using all other sklearn inherent methods such as their built in hyperparameter optimization tools.
from mambular.models import MambularClassifier
-# Initialize and fit your model
-model = MambularClassifier(
- dropout=0.01,
- d_model=128,
- n_layers=6,
- numerical_preprocessing="normalization",
-)
-
-# X can be a dataframe or something that can be easily transformed into a pd.DataFrame as a np.array
-model.fit(X, y, max_epochs=500, lr=1e-03, patience=25)
-Predictions are also easily obtained:
-# simple predictions
-preds = model.predict(X)
-
-# Predict probabilities
-preds = model.predict_proba(X)
-Mambular introduces a cutting-edge approach to distributional regression through its MambularLSS module, empowering users to model the full distribution of a response variable, not just its mean. This method is particularly valuable in scenarios where understanding the variability, skewness, or kurtosis of the response distribution is as crucial as predicting its central tendency.
Full Distribution Modeling: Unlike traditional regression models that predict a single value (e.g., the mean), MambularLSS models the entire distribution of the response variable. This allows for more informative predictions, including quantiles, variance, and higher moments.
Customizable Distribution Types: MambularLSS supports a variety of distribution families (e.g., Gaussian, Poisson, Binomial), making it adaptable to different types of response variables, from continuous to count data.
Location, Scale, Shape Parameters: The model predicts parameters corresponding to the location, scale, and shape of the distribution, offering a nuanced understanding of the data’s underlying distributional characteristics.
Enhanced Predictive Uncertainty: By modeling the full distribution, MambularLSS provides richer information on predictive uncertainty, enabling more robust decision-making processes in uncertain environments.
MambularLSS offers a wide range of distribution classes to cater to various statistical modeling needs. The available distribution classes include:
normal: Normal Distribution for modeling continuous data with a symmetric distribution around the mean.
poisson: Poisson Distribution for modeling count data that represents the number of events occurring within a fixed interval.
gamma: Gamma Distribution for modeling continuous data that is skewed and bounded at zero, often used for waiting times.
beta: Beta Distribution for modeling data that is bounded between 0 and 1, useful for proportions and percentages.
dirichlet: Dirichlet Distribution for modeling multivariate data where individual components are correlated, and the sum is constrained to 1.
studentt: Student’s T-Distribution for modeling data with heavier tails than the normal distribution, useful when the sample size is small.
negativebinom: Negative Binomial Distribution for modeling count data with over-dispersion relative to the Poisson distribution.
inversegamma: Inverse Gamma Distribution, often used as a prior distribution in Bayesian inference for scale parameters.
categorical: Categorical Distribution for modeling categorical data with more than two categories.
These distribution classes allow MambularLSS to flexibly model a wide variety of data types and distributions, providing users with the tools needed to capture the full complexity of their data.
Risk Assessment: In finance or insurance, understanding the range and likelihood of potential losses is as important as predicting average outcomes.
Demand Forecasting: For inventory management, capturing the variability in product demand helps in optimizing stock levels.
Personalized Medicine: In healthcare, distributional regression can predict a range of possible patient responses to a treatment, aiding in personalized therapy planning.
To integrate distributional regression into your workflow with MambularLSS, start by initializing the model with your desired configuration, similar to other Mambular models:
from mambular.models import MambularLSS
-
-# Initialize the MambularLSS model
-model = MambularLSS(
- dropout=0.2,
- d_model=256,
- n_layers=4,
-
-)
-
-# Fit the model to your data
-model.fit(
- X,
- y,
- max_epochs=300,
- lr=1e-03,
- patience=10,
- family="normal" # define your distribution
- )
-
-Example code for implementing the models:
- -
-import torch
-import torch.nn as nn
-import torchmetrics
-from ..utils.mamba_arch import Mamba
-from ..utils.config import MambularConfig
-import pytorch_lightning as pl
-
-
-[docs]class BaseMambularClassifier(pl.LightningModule):
- """
- A base class for building classification models using the Mambular architecture within the PyTorch Lightning framework.
-
- This class integrates various components such as embeddings for categorical and numerical features, the Mambular model
- for processing sequences of embeddings, and a classification head for prediction. It supports multi-class and binary classification tasks.
-
- Parameters
- ----------
- num_classes : int
- The number of classes in the classification task. For binary classification, this should be 2.
- config : MambularConfig
- An instance of MambularConfig containing configuration parameters for the Mambular model.
- cat_feature_info : dict, optional
- A dictionary mapping the names of categorical features to their number of unique categories.
- This information is used to configure embedding layers for categorical features. Defaults to None.
- num_feature_info : dict, optional
- A dictionary mapping the names of numerical features to the size of their input dimensions.
- This information is used to configure embedding layers for numerical features. Defaults to None.
- lr : float, optional
- The learning rate for the optimizer. Defaults to 1e-03.
- lr_patience : int, optional
- The number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
- weight_decay : float, optional
- Weight decay (L2 penalty) parameter for the optimizer. Defaults to 0.025.
- lr_factor : float, optional
- Factor by which the learning rate will be reduced. Defaults to 0.75.
-
- Attributes
- ----------
- embedding_activation : nn.Module
- The activation function to be applied after the linear transformation of numerical features.
- num_embeddings : nn.ModuleList
- A list of sequential modules, each corresponding to an embedding layer for a numerical feature.
- cat_embeddings : nn.ModuleList
- A list of embedding layers, each corresponding to a categorical feature.
- mamba : Mamba
- The Mambular model for processing sequences of embeddings.
- norm_f : nn.Module
- A normalization layer applied after the Mambular model.
- tabular_head : nn.Linear
- A linear layer for predicting the class labels from the aggregated embedding representation.
- pooling_method : str
- The method used to aggregate embeddings across features. Supported methods are 'avg', 'max', and 'sum'.
- loss_fct : nn.Module
- The loss function used for training the model, configured based on the number of classes.
- acc : torchmetrics.Accuracy
- A metric for computing the accuracy of predictions.
- auroc : torchmetrics.AUROC
- A metric for computing the Area Under the Receiver Operating Characteristic curve.
- precision : torchmetrics.Precision
- A metric for computing the precision of predictions.
-
- Methods
- -------
- forward(cat_features, num_features)
- Defines the forward pass of the model, processing both categorical and numerical features, aggregating embeddings,
- and producing predictions.
- training_step(batch, batch_idx)
- Performs a single training step, computing the loss and logging metrics for the training set.
- validation_step(batch, batch_idx)
- Performs a single validation step, computing the loss and logging metrics for the validation set.
- configure_optimizers()
- Configures the model's optimizers and learning rate schedulers.
- """
-
-
-
-
-
-
- def __init__(
- self,
- num_classes,
- config: MambularConfig,
- cat_feature_info: dict = None,
- num_feature_info: dict = None,
- lr=1e-03,
- lr_patience=10,
- weight_decay=0.025,
- lr_factor=0.75,
- ):
- super().__init__()
-
- self.config = config
- self.num_classes = 1 if num_classes == 2 else num_classes
- self.lr = lr
- self.lr_patience = lr_patience
- self.weight_decay = weight_decay
- self.lr_factor = lr_factor
- self.cat_feature_info = cat_feature_info
- self.num_feature_info = num_feature_info
-
- activations = {
- "relu": nn.ReLU(),
- "tanh": nn.Tanh(),
- "sigmoid": nn.Sigmoid(),
- "leaky_relu": nn.LeakyReLU(),
- "elu": nn.ELU(),
- "selu": nn.SELU(),
- "gelu": nn.GELU(),
- "softplus": nn.Softplus(),
- "leakyrelu": nn.LeakyReLU(),
- "linear": nn.Identity(),
- }
-
- self.embedding_activation = activations.get(
- self.config.num_embedding_activation.lower()
- )
- if self.embedding_activation is None:
- raise ValueError(
- f"Unsupported activation function: {self.config.num_embedding_activation}"
- )
-
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(input_shape, self.config.d_model, bias=False),
- nn.BatchNorm1d(self.config.d_model),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for feature_name, input_shape in num_feature_info.items()
- ]
- )
-
- # Create embedding layers for categorical features based on cat_feature_info
- self.cat_embeddings = nn.ModuleList(
- [
- nn.Embedding(num_categories + 1, self.config.d_model)
- for feature_name, num_categories in cat_feature_info.items()
- ]
- )
-
- self.mamba = Mamba(self.config)
- self.norm_f = self.config.norm(self.config.d_model)
-
- mlp_activation_fn = activations.get(
- self.config.tabular_head_activation.lower(), nn.Identity()
- )
- mlp_layers = []
- input_dim = self.config.d_model # Initial input dimension
-
- # Iterate over the specified units for each layer in the MLP
- for units in self.config.tabular_head_units:
- mlp_layers.append(nn.Linear(input_dim, units))
- mlp_layers.append(mlp_activation_fn)
- mlp_layers.append(nn.Dropout(self.config.tabular_head_dropout))
- input_dim = units
-
- # Add the final linear layer to map to a single output value
- mlp_layers.append(nn.Linear(input_dim, self.num_classes))
-
- # Combine all layers into a Sequential module
- self.tabular_head = nn.Sequential(*mlp_layers)
-
- self.pooling_method = self.config.pooling_method
- self.cls_token = nn.Parameter(torch.zeros(1, 1, self.config.d_model))
-
- if self.num_classes > 2:
- self.loss_fct = nn.CrossEntropyLoss()
- self.acc = torchmetrics.Accuracy(
- task="multiclass", num_classes=self.num_classes
- )
- self.auroc = torchmetrics.AUROC(
- task="multiclass", num_classes=self.num_classes
- )
- self.precision = torchmetrics.Precision(
- task="multiclass", num_classes=self.num_classes
- )
- else:
- self.loss_fct = torch.nn.BCEWithLogitsLoss()
- self.acc = torchmetrics.Accuracy(task="binary")
- self.auroc = torchmetrics.AUROC(task="binary")
- self.precision = torchmetrics.Precision(task="binary")
-
-[docs] def forward(self, cat_features, num_features):
- """
- Defines the forward pass of the classifier.
-
- Parameters
- ----------
- cat_features : Tensor
- Tensor containing the categorical features.
- num_features : Tensor
- Tensor containing the numerical features.
-
- Returns
- -------
- Tensor
- The output predictions of the model.
- """
- batch_size = (
- cat_features[0].size(0)
- if cat_features is not None
- else num_features[0].size(0)
- )
- cls_tokens = self.cls_token.expand(batch_size, -1, -1)
-
- # Process categorical features if present
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- else:
- cat_embeddings = None
-
- # Process numerical features if present
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = [
- emb(num_features[i]) for i, emb in enumerate(self.num_embeddings)
- ]
- num_embeddings = torch.stack(num_embeddings, dim=1)
- else:
- num_embeddings = None
-
- # Combine embeddings if both types are present, otherwise use whichever is available
- if cat_embeddings is not None and num_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings, num_embeddings], dim=1)
- elif cat_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings], dim=1)
- elif num_embeddings is not None:
- x = torch.cat([cls_tokens, num_embeddings], dim=1)
- else:
- raise ValueError("No features provided to the model.")
-
- x = self.mamba(x)
-
- # Apply pooling based on the specified method
- if self.pooling_method == "avg":
- x = torch.mean(x, dim=1)
- elif self.pooling_method == "max":
- x, _ = torch.max(x, dim=1)
- elif self.pooling_method == "sum":
- x = torch.sum(x, dim=1)
- elif self.pooling_method == "cls":
- x = x[:, 0]
- else:
- raise ValueError(f"Invalid pooling method: {self.pooling_method}")
-
- x = self.norm_f(x)
- preds = self.tabular_head(x)
-
- return preds
-
-[docs] def training_step(self, batch, batch_idx):
- """
- Processes a single batch during training, computes the loss and logs training metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
- """
-
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- if self.num_classes == 1:
- labels = labels.unsqueeze(
- 1
- ).float() # Reshape for binary classification loss calculation
-
- loss = self.loss_fct(preds, labels)
- self.log("train_loss", loss)
- # Calculate and log training accuracy
-
- acc = self.acc(preds, labels.int())
- self.log(
- "train_acc",
- acc,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- # Calculate and log AUROC
- auroc = self.auroc(preds, labels.int())
- self.log(
- "train_auroc",
- auroc,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- # Calculate and log precision
- precision = self.precision(preds, labels.int())
- self.log(
- "train_precision",
- precision,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- return loss
-
-[docs] def validation_step(self, batch, batch_idx):
- """
- Processes a single batch during validation, computes the loss and logs validation metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
- """
-
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- if self.num_classes == 1:
- labels = labels.unsqueeze(
- 1
- ).float() # Reshape for binary classification loss calculation
-
- loss = self.loss_fct(preds, labels)
- self.log("val_loss", loss)
- # Calculate and log training accuracy
-
- acc = self.acc(preds, labels.int())
- self.log(
- "val_acc",
- acc,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- auroc = self.auroc(preds, labels.int())
- self.log(
- "val_auroc", auroc, on_step=False, on_epoch=True, prog_bar=True, logger=True
- )
-
- # Calculate and log precision
- precision = self.precision(preds, labels.int())
- self.log(
- "val_precision",
- precision,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
-[docs] def configure_optimizers(self):
- """
- Sets up the model's optimizer and learning rate scheduler based on the configurations provided.
-
- Returns
- -------
- dict
- A dictionary containing the optimizer and lr_scheduler configurations.
- """
- optimizer = torch.optim.Adam(
- self.parameters(), lr=self.lr, weight_decay=self.config.weight_decay
- )
- scheduler = {
- "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(
- optimizer,
- mode="min",
- factor=self.lr_factor,
- patience=self.lr_patience,
- verbose=True,
- ),
- "monitor": "val_loss", # Name of the metric to monitor
- "interval": "epoch",
- "frequency": 1,
- }
-
- return {"optimizer": optimizer, "lr_scheduler": scheduler}
-
-import torch
-import torch.nn as nn
-from ..utils.mamba_arch import Mamba
-from ..utils.config import MambularConfig
-import pytorch_lightning as pl
-from ..utils.distributions import (
- NormalDistribution,
- NegativeBinomialDistribution,
- GammaDistribution,
- StudentTDistribution,
- PoissonDistribution,
- InverseGammaDistribution,
- BetaDistribution,
- DirichletDistribution,
- CategoricalDistribution,
-)
-
-
-[docs]class BaseMambularLSS(pl.LightningModule):
- """
- A base module for likelihood-based statistical learning (LSS) models built on PyTorch Lightning,
- integrating the Mamba architecture for tabular data. This module is designed to accommodate various
- statistical distribution families for different types of regression and classification tasks.
-
- Parameters
- ----------
- family : str
- The name of the statistical distribution family to be used for modeling. Supported families include
- 'normal', 'poisson', 'gamma', 'beta', 'dirichlet', 'studentt', 'negativebinom', 'inversegamma', and 'categorical'.
- config : MambularConfig
- An instance of MambularConfig containing configuration parameters for the model architecture.
- cat_feature_info : dict, optional
- A dictionary mapping the names of categorical features to their number of unique categories. Defaults to None.
- num_feature_info : dict, optional
- A dictionary mapping the names of numerical features to their number of dimensions after embedding. Defaults to None.
- lr : float, optional
- The initial learning rate for the optimizer. Defaults to 1e-03.
- lr_patience : int, optional
- The number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
- weight_decay : float, optional
- Weight decay (L2 penalty) coefficient. Defaults to 0.025.
- lr_factor : float, optional
- Factor by which the learning rate will be reduced. Defaults to 0.75.
- **distribution_params :
- Additional parameters specific to the chosen statistical distribution family.
-
- Attributes
- ----------
- mamba : Mamba
- The core neural network module implementing the Mamba architecture.
- norm_f : nn.Module
- Normalization layer applied after the Mamba block.
- tabular_head : nn.Linear
- Final linear layer mapping the features to the parameters of the chosen statistical distribution.
- loss_fct : callable
- The loss function derived from the chosen statistical distribution.
-
- Methods
- -------
- forward(cat_features, num_features)
- Defines the forward pass of the model.
- training_step(batch, batch_idx)
- Processes a single batch during training.
- validation_step(batch, batch_idx)
- Processes a single batch during validation.
- configure_optimizers()
- Sets up the model's optimizer and learning rate scheduler.
- """
-
-
-
-
- def __init__(
- self,
- family,
- config: MambularConfig,
- cat_feature_info: dict = None,
- num_feature_info: dict = None,
- lr=1e-03,
- lr_patience=10,
- weight_decay=0.025,
- lr_factor=0.75,
- **distribution_params,
- ):
- super().__init__()
-
- self.config = config
- self.lr = lr
- self.lr_patience = lr_patience
- self.weight_decay = weight_decay
- self.lr_factor = lr_factor
- self.cat_feature_info = cat_feature_info
- self.num_feature_info = num_feature_info
-
- activations = {
- "relu": nn.ReLU(),
- "tanh": nn.Tanh(),
- "sigmoid": nn.Sigmoid(),
- "leaky_relu": nn.LeakyReLU(),
- "elu": nn.ELU(),
- "selu": nn.SELU(),
- "gelu": nn.GELU(),
- "softplus": nn.Softplus(),
- "leakyrelu": nn.LeakyReLU(),
- "linear": nn.Identity(),
- }
-
- distribution_classes = {
- "normal": NormalDistribution,
- "poisson": PoissonDistribution,
- "gamma": GammaDistribution,
- "beta": BetaDistribution,
- "dirichlet": DirichletDistribution,
- "studentt": StudentTDistribution,
- "negativebinom": NegativeBinomialDistribution,
- "inversegamma": InverseGammaDistribution,
- "categorical": CategoricalDistribution,
- }
-
- if family in distribution_classes:
- # Pass additional distribution_params to the constructor of the distribution class
- self.family = distribution_classes[family](**distribution_params)
- else:
- raise ValueError("Unsupported family: {}".format(family))
-
- self.embedding_activation = activations.get(
- self.config.num_embedding_activation.lower()
- )
- if self.embedding_activation is None:
- raise ValueError(
- f"Unsupported activation function: {self.config.num_embedding_activation}"
- )
-
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(input_shape, self.config.d_model, bias=False),
- nn.BatchNorm1d(self.config.d_model),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for feature_name, input_shape in num_feature_info.items()
- ]
- )
-
- # Create embedding layers for categorical features based on cat_feature_info
- self.cat_embeddings = nn.ModuleList(
- [
- nn.Embedding(num_categories + 1, self.config.d_model)
- for feature_name, num_categories in cat_feature_info.items()
- ]
- )
-
- mlp_activation_fn = activations.get(
- self.config.tabular_head_activation.lower(), nn.Identity()
- )
-
- self.mamba = Mamba(self.config)
- self.norm_f = self.config.norm(self.config.d_model)
- mlp_layers = []
- input_dim = self.config.d_model # Initial input dimension
-
- # Iterate over the specified units for each layer in the MLP
- for units in self.config.tabular_head_units:
- mlp_layers.append(nn.Linear(input_dim, units))
- mlp_layers.append(mlp_activation_fn)
- mlp_layers.append(nn.Dropout(self.config.tabular_head_dropout))
- input_dim = units
-
- # Add the final linear layer to map to #distributional param output values
- mlp_layers.append(nn.Linear(input_dim, self.family.param_count))
-
- # Combine all layers into a Sequential module
- self.tabular_head = nn.Sequential(*mlp_layers)
-
- self.loss_fct = lambda predictions, y_true: self.family.compute_loss(
- predictions, y_true
- )
-
- self.cls_token = nn.Parameter(torch.zeros(1, 1, self.config.d_model))
- self.pooling_method = self.config.pooling_method
-
-[docs] def forward(self, cat_features, num_features):
- """
- Defines the forward pass of the model, processing both categorical and numerical features,
- and returning predictions based on the configured statistical distribution.
-
- Parameters
- ----------
- cat_features : Tensor
- Tensor containing the categorical features.
- num_features : Tensor
- Tensor containing the numerical features.
-
- Returns
- -------
- Tensor
- The predictions of the model, typically the parameters of the chosen statistical distribution.
- """
-
- batch_size = (
- cat_features[0].size(0)
- if cat_features is not None
- else num_features[0].size(0)
- )
- cls_tokens = self.cls_token.expand(batch_size, -1, -1)
-
- # Process categorical features if present
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- else:
- cat_embeddings = None
-
- # Process numerical features if present
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = [
- emb(num_features[i]) for i, emb in enumerate(self.num_embeddings)
- ]
- num_embeddings = torch.stack(num_embeddings, dim=1)
- else:
- num_embeddings = None
-
- # Combine embeddings if both types are present, otherwise use whichever is available
- if cat_embeddings is not None and num_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings, num_embeddings], dim=1)
- elif cat_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings], dim=1)
- elif num_embeddings is not None:
- x = torch.cat([cls_tokens, num_embeddings], dim=1)
- else:
- raise ValueError("No features provided to the model.")
-
- x = self.mamba(x)
-
- # Apply pooling based on the specified method
- if self.pooling_method == "avg":
- x = torch.mean(x, dim=1)
- elif self.pooling_method == "max":
- x, _ = torch.max(x, dim=1)
- elif self.pooling_method == "sum":
- x = torch.sum(x, dim=1)
- elif self.pooling_method == "cls":
- x = x[:, 0]
- else:
- raise ValueError(f"Invalid pooling method: {self.pooling_method}")
-
- x = self.norm_f(x)
- preds = self.tabular_head(x)
-
- return preds
-
-[docs] def training_step(self, batch, batch_idx):
- """
- Processes a single batch during training, computes the loss using the distribution-specific loss function,
- and logs training metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
-
- Returns
- -------
- Tensor
- The computed loss for the batch.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- loss = self.loss_fct(preds, labels)
- self.log(
- "train_loss",
- loss,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
- return loss
-
-[docs] def validation_step(self, batch, batch_idx):
- """
- Processes a single batch during validation, computes the loss using the distribution-specific loss function,
- and logs validation metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
- """
-
-
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- loss = self.loss_fct(preds, labels)
- self.log(
- "val_loss",
- loss,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
- return loss
-
-[docs] def configure_optimizers(self):
- """
- Sets up the model's optimizer and learning rate scheduler based on the configurations provided.
-
- Returns
- -------
- dict
- A dictionary containing the optimizer and lr_scheduler configurations.
- """
- optimizer = torch.optim.Adam(
- self.parameters(), lr=self.lr, weight_decay=self.config.weight_decay
- )
- scheduler = {
- "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(
- optimizer,
- mode="min",
- factor=self.lr_factor,
- patience=self.lr_patience,
- verbose=True,
- ),
- "monitor": "val_loss", # Name of the metric to monitor
- "interval": "epoch",
- "frequency": 1,
- }
-
- return {"optimizer": optimizer, "lr_scheduler": scheduler}
-
-import torch
-import torch.nn as nn
-from ..utils.mamba_arch import Mamba
-from ..utils.config import MambularConfig
-import pytorch_lightning as pl
-import torchmetrics
-
-
-[docs]class BaseEmbeddingMambularClassifier(pl.LightningModule):
- """
- A specialized classification module for protein data, built on PyTorch Lightning and integrating the Mamba architecture.
- It supports embeddings for categorical features and can process raw or embedded numerical features, making it suitable
- for complex protein sequence data.
-
- Parameters
- ----------
- config : MambularConfig
- Configuration parameters for the model architecture.
- cat_feature_info : dict, optional
- Information about categorical features, mapping feature names to the number of unique categories.
- num_feature_info : dict, optional
- Information about numerical features, mapping feature names to their number of dimensions after embedding.
- lr : float, optional
- Learning rate for the optimizer. Defaults to 1e-03.
- lr_patience : int, optional
- Number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
- weight_decay : float, optional
- Weight decay coefficient for regularization in the optimizer. Defaults to 0.025.
- lr_factor : float, optional
- Factor by which the learning rate will be reduced by the scheduler. Defaults to 0.75.
- seq_size : int, optional
- Size of sequence chunks for processing numerical features. Relevant when `raw_embeddings` is False.
- raw_embeddings : bool, optional
- Indicates whether to use raw numerical features directly or to process them into embeddings. Defaults to False.
-
- Attributes
- ----------
- mamba : Mamba
- The core neural network module implementing the Mamba architecture.
- norm_f : nn.Module
- Normalization layer applied after the Mamba block.
- tabular_head : nn.Linear
- Final linear layer mapping the features to the target.
-
- Methods
- -------
- forward(cat_features, num_features)
- Defines the forward pass of the model.
- training_step(batch, batch_idx)
- Processes a single batch during training.
- validation_step(batch, batch_idx)
- Processes a single batch during validation.
- configure_optimizers()
- Sets up the model's optimizer and learning rate scheduler.
- """
-
- def __init__(
- self,
- num_classes,
- config: MambularConfig,
- cat_feature_info: dict = None,
- num_feature_info: dict = None,
- lr=1e-03,
- lr_patience=10,
- weight_decay=0.025,
- lr_factor=0.75,
- seq_size: int = 20,
- raw_embeddings=False,
- ):
- super().__init__()
-
- self.config = config
- self.num_classes = 1 if num_classes == 2 else num_classes
- self.lr = lr
- self.lr_patience = lr_patience
- self.weight_decay = weight_decay
- self.lr_factor = lr_factor
- self.cat_feature_info = cat_feature_info
- self.num_feature_info = num_feature_info
- self.seq_size = seq_size
- self.raw_embeddings = raw_embeddings
-
- activations = {
- "relu": nn.ReLU(),
- "tanh": nn.Tanh(),
- "sigmoid": nn.Sigmoid(),
- "leaky_relu": nn.LeakyReLU(),
- "elu": nn.ELU(),
- "selu": nn.SELU(),
- "gelu": nn.GELU(),
- "softplus": nn.Softplus(),
- "leakyrelu": nn.LeakyReLU(),
- "linear": nn.Identity(),
- }
-
- if not self.raw_embeddings:
- data_size = len(num_feature_info.items())
- num_embedding_modules = data_size // self.seq_size
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(self.seq_size, self.config.d_model, bias=False),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for _ in range(num_embedding_modules)
- ]
- )
- else:
- data_size = len(num_feature_info.items())
- num_embedding_modules = data_size
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(1, self.config.d_model, bias=False),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for _ in range(num_embedding_modules)
- ]
- )
-
- self.cat_embeddings = nn.ModuleList(
- [
- nn.Embedding(num_categories + 1, self.config.d_model)
- for feature_name, num_categories in cat_feature_info.items()
- ]
- )
-
- self.mamba = Mamba(self.config)
- self.norm_f = self.config.norm(self.config.d_model)
- mlp_activation_fn = activations.get(
- self.config.tabular_head_activation.lower(), nn.Identity()
- )
-
- # Dynamically create MLP layers based on config.tabular_units
- mlp_layers = []
- input_dim = self.config.d_model # Initial input dimension
-
- # Iterate over the specified units for each layer in the MLP
- for units in self.config.tabular_head_units:
- mlp_layers.append(nn.Linear(input_dim, units))
- mlp_layers.append(mlp_activation_fn)
- mlp_layers.append(nn.Dropout(self.config.tabular_head_dropout))
- input_dim = units
-
- # Add the final linear layer to map to a single output value
- mlp_layers.append(nn.Linear(input_dim, self.num_classes))
-
- # Combine all layers into a Sequential module
- self.tabular_head = nn.Sequential(*mlp_layers)
-
- self.pooling_method = self.config.pooling_method
- self.cls_token = nn.Parameter(torch.zeros(1, 1, self.config.d_model))
-
- if self.config.layer_norm_after_embedding:
- self.embedding_norm = nn.LayerNorm(self.config.d_model)
-
- if self.num_classes > 2:
- self.loss_fct = nn.CrossEntropyLoss()
- self.acc = torchmetrics.Accuracy(
- task="multiclass", num_classes=self.num_classes
- )
- self.auroc = torchmetrics.AUROC(
- task="multiclass", num_classes=self.num_classes
- )
- self.precision = torchmetrics.Precision(
- task="multiclass", num_classes=self.num_classes
- )
- else:
- self.loss_fct = torch.nn.BCEWithLogitsLoss()
- self.acc = torchmetrics.Accuracy(task="binary")
- self.auroc = torchmetrics.AUROC(task="binary")
- self.precision = torchmetrics.Precision(task="binary")
-
-[docs] def forward(self, cat_features, num_features):
- """
- Defines the forward pass of the model, processing both categorical and numerical features,
- and returning regression predictions.
-
- Parameters
- ----------
- cat_features : Tensor
- Tensor containing the categorical features.
- num_features : Tensor
- Tensor containing the numerical features or raw sequence data, depending on `raw_embeddings`.
-
- Returns
- -------
- Tensor
- The output predictions of the model for regression tasks.
- """
- batch_size = (
- cat_features[0].size(0) if cat_features != [] else num_features[0].size(0)
- )
- cls_tokens = self.cls_token.expand(batch_size, -1, -1)
- # Process categorical features if present
- if not self.raw_embeddings:
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- else:
- cat_embeddings = None
-
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = []
- # Iterate through the num_embeddings, taking slices of num_features for each
- for i, emb in enumerate(self.num_embeddings):
- # Calculate start and end indices for slicing the list
- start_idx = i * self.seq_size
- end_idx = start_idx + self.seq_size
-
- # Slice the num_features list to get the current chunk
- current_chunk = num_features[start_idx:end_idx]
-
- # If the current_chunk is not empty, process it
- if current_chunk:
- # Concatenate tensors in the current chunk along dimension 1
- chunk_tensor = torch.cat(current_chunk, dim=1)
- # Apply the embedding layer to the chunk_tensor
- num_embeddings.append(emb(chunk_tensor))
-
- # Stack the resulting embeddings along the second dimension if num_embeddings is not empty
- if num_embeddings:
- num_embeddings = torch.stack(num_embeddings, dim=1)
- else:
- num_embeddings = None
-
- else:
- # Process categorical features if present
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- if self.config.layer_norm_after_embedding:
- cat_embeddings = self.embedding_norm(cat_embeddings)
- else:
- cat_embeddings = None
-
- # Process numerical features if present
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = [
- emb(num_features[i]) for i, emb in enumerate(self.num_embeddings)
- ]
- num_embeddings = torch.stack(num_embeddings, dim=1)
- if self.config.layer_norm_after_embedding:
- num_embeddings = self.embedding_norm(num_embeddings)
- else:
- num_embeddings = None
-
- # Combine embeddings if both types are present, otherwise use whichever is available
- if cat_embeddings is not None and num_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings, num_embeddings], dim=1)
- elif cat_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings], dim=1)
- elif num_embeddings is not None:
- x = torch.cat([cls_tokens, num_embeddings], dim=1)
- else:
- raise ValueError("No features provided to the model.")
-
- x = self.mamba(x)
-
- # Apply pooling based on the specified method
- if self.pooling_method == "avg":
- x = torch.mean(x, dim=1)
- elif self.pooling_method == "max":
- x, _ = torch.max(x, dim=1)
- elif self.pooling_method == "sum":
- x = torch.sum(x, dim=1)
- elif self.pooling_method == "cls_token":
- x = x[:, 0]
- else:
- raise ValueError(f"Invalid pooling method: {self.pooling_method}")
-
- x = self.norm_f(x)
- preds = self.tabular_head(x)
-
- return preds
-
-[docs] def training_step(self, batch, batch_idx):
- """
- Processes a single batch during training, computes the loss, and logs training metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
-
- Returns
- -------
- Tensor
- The computed loss for the batch.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- if self.num_classes == 1:
- labels = labels.unsqueeze(
- 1
- ).float() # Reshape for binary classification loss calculation
-
- loss = self.loss_fct(preds, labels)
- self.log("train_loss", loss)
- # Calculate and log training accuracy
-
- acc = self.acc(preds, labels.int())
- self.log(
- "train_acc",
- acc,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- # Calculate and log AUROC
- auroc = self.auroc(preds, labels.int())
- self.log(
- "train_auroc",
- auroc,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- # Calculate and log precision
- precision = self.precision(preds, labels.int())
- self.log(
- "train_precision",
- precision,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- return loss
-
-[docs] def validation_step(self, batch, batch_idx):
- """
- Processes a single batch during validation, computes the loss, and logs validation metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- if self.num_classes == 1:
- labels = labels.unsqueeze(
- 1
- ).float() # Reshape for binary classification loss calculation
-
- loss = self.loss_fct(preds, labels)
- self.log("val_loss", loss)
- # Calculate and log training accuracy
-
- acc = self.acc(preds, labels.int())
- self.log(
- "val_acc",
- acc,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- auroc = self.auroc(preds, labels.int())
- self.log(
- "val_auroc", auroc, on_step=False, on_epoch=True, prog_bar=True, logger=True
- )
-
- # Calculate and log precision
- precision = self.precision(preds, labels.int())
- self.log(
- "val_precision",
- precision,
- on_step=False,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
-[docs] def configure_optimizers(self):
- """
- Sets up the model's optimizer and learning rate scheduler based on the configurations provided.
-
- Returns
- -------
- dict
- A dictionary containing the optimizer and lr_scheduler configurations.
- """
- optimizer = torch.optim.Adam(
- self.parameters(), lr=self.lr, weight_decay=self.config.weight_decay
- )
- scheduler = {
- "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(
- optimizer,
- mode="min",
- factor=self.lr_factor,
- patience=self.lr_patience,
- verbose=True,
- ),
- "monitor": "val_loss", # Name of the metric to monitor
- "interval": "epoch",
- "frequency": 1,
- }
-
- return {"optimizer": optimizer, "lr_scheduler": scheduler}
-
-import torch
-import torch.nn as nn
-from ..utils.mamba_arch import Mamba
-from ..utils.config import MambularConfig
-import pytorch_lightning as pl
-
-
-[docs]class BaseEmbeddingMambularRegressor(pl.LightningModule):
- """
- A specialized regression module for protein data, built on PyTorch Lightning and integrating the Mamba architecture.
- It supports embeddings for categorical features and can process raw or embedded numerical features, making it suitable
- for complex protein sequence data.
-
- Parameters
- ----------
- config : MambularConfig
- Configuration parameters for the model architecture.
- cat_feature_info : dict, optional
- Information about categorical features, mapping feature names to the number of unique categories. Defaults to None.
- num_feature_info : dict, optional
- Information about numerical features, mapping feature names to their number of dimensions after embedding. Defaults to None.
- lr : float, optional
- Learning rate for the optimizer. Defaults to 1e-03.
- lr_patience : int, optional
- Number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
- weight_decay : float, optional
- Weight decay coefficient for regularization in the optimizer. Defaults to 0.025.
- lr_factor : float, optional
- Factor by which the learning rate will be reduced by the scheduler. Defaults to 0.75.
- seq_size : int, optional
- Size of sequence chunks for processing numerical features. Relevant when `raw_embeddings` is False.
- raw_embeddings : bool, optional
- Indicates whether to use raw numerical features directly or to process them into embeddings. Defaults to False.
-
- Attributes
- ----------
- mamba : Mamba
- The core neural network module implementing the Mamba architecture.
- norm_f : nn.Module
- Normalization layer applied after the Mamba block.
- tabular_head : nn.Linear
- Final linear layer mapping the features to the regression target.
- loss_fct : nn.MSELoss
- The loss function for regression tasks.
-
- Methods
- -------
- forward(cat_features, num_features)
- Defines the forward pass of the model.
- training_step(batch, batch_idx)
- Processes a single batch during training.
- validation_step(batch, batch_idx)
- Processes a single batch during validation.
- configure_optimizers()
- Sets up the model's optimizer and learning rate scheduler.
- """
-
- def __init__(
- self,
- config: MambularConfig,
- cat_feature_info: dict = None,
- num_feature_info: dict = None,
- lr=1e-03,
- lr_patience=10,
- weight_decay=0.025,
- lr_factor=0.75,
- seq_size: int = 20,
- raw_embeddings=False,
- ):
- super().__init__()
-
- self.config = config
- self.lr = lr
- self.lr_patience = lr_patience
- self.weight_decay = weight_decay
- self.lr_factor = lr_factor
- self.cat_feature_info = cat_feature_info
- self.num_feature_info = num_feature_info
- self.seq_size = seq_size
- self.raw_embeddings = raw_embeddings
-
- activations = {
- "relu": nn.ReLU(),
- "tanh": nn.Tanh(),
- "sigmoid": nn.Sigmoid(),
- "leaky_relu": nn.LeakyReLU(),
- "elu": nn.ELU(),
- "selu": nn.SELU(),
- "gelu": nn.GELU(),
- "softplus": nn.Softplus(),
- "leakyrelu": nn.LeakyReLU(),
- "linear": nn.Identity(),
- }
-
- self.embedding_activation = activations.get(
- self.config.num_embedding_activation.lower()
- )
- if self.embedding_activation is None:
- raise ValueError(
- f"Unsupported activation function: {self.config.num_embedding_activation}"
- )
-
- if not self.raw_embeddings:
- data_size = len(num_feature_info.items())
- num_embedding_modules = data_size // self.seq_size
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(self.seq_size, self.config.d_model, bias=False),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for _ in range(num_embedding_modules)
- ]
- )
- else:
- data_size = len(num_feature_info.items())
- num_embedding_modules = data_size
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(1, self.config.d_model, bias=False),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for _ in range(num_embedding_modules)
- ]
- )
-
- self.cat_embeddings = nn.ModuleList(
- [
- nn.Embedding(num_categories + 1, self.config.d_model)
- for feature_name, num_categories in cat_feature_info.items()
- ]
- )
-
- self.mamba = Mamba(self.config)
- self.norm_f = self.config.norm(self.config.d_model)
- mlp_activation_fn = activations.get(
- self.config.tabular_head_activation.lower(), nn.Identity()
- )
-
- # Dynamically create MLP layers based on config.tabular_units
- mlp_layers = []
- input_dim = self.config.d_model # Initial input dimension
-
- # Iterate over the specified units for each layer in the MLP
- for units in self.config.tabular_head_units:
- mlp_layers.append(nn.Linear(input_dim, units))
- mlp_layers.append(mlp_activation_fn)
- mlp_layers.append(nn.Dropout(self.config.tabular_head_dropout))
- input_dim = units
-
- # Add the final linear layer to map to a single output value
- mlp_layers.append(nn.Linear(input_dim, 1))
-
- # Combine all layers into a Sequential module
- self.tabular_head = nn.Sequential(*mlp_layers)
-
- self.pooling_method = self.config.pooling_method
- self.cls_token = nn.Parameter(torch.zeros(1, 1, self.config.d_model))
-
- if self.config.layer_norm_after_embedding:
- self.embedding_norm = nn.LayerNorm(self.config.d_model)
-
- self.loss_fct = nn.MSELoss()
-
-[docs] def forward(self, cat_features, num_features):
- """
- Defines the forward pass of the model, processing both categorical and numerical features,
- and returning regression predictions.
-
- Parameters
- ----------
- cat_features : Tensor
- Tensor containing the categorical features.
- num_features : Tensor
- Tensor containing the numerical features or raw sequence data, depending on `raw_embeddings`.
-
- Returns
- -------
- Tensor
- The output predictions of the model for regression tasks.
- """
- batch_size = (
- cat_features[0].size(0) if cat_features != [] else num_features[0].size(0)
- )
- cls_tokens = self.cls_token.expand(batch_size, -1, -1)
- # Process categorical features if present
- if not self.raw_embeddings:
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- else:
- cat_embeddings = None
-
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = []
- # Iterate through the num_embeddings, taking slices of num_features for each
- for i, emb in enumerate(self.num_embeddings):
- # Calculate start and end indices for slicing the list
- start_idx = i * self.seq_size
- end_idx = start_idx + self.seq_size
-
- # Slice the num_features list to get the current chunk
- current_chunk = num_features[start_idx:end_idx]
-
- # If the current_chunk is not empty, process it
- if current_chunk:
- # Concatenate tensors in the current chunk along dimension 1
- chunk_tensor = torch.cat(current_chunk, dim=1)
- # Apply the embedding layer to the chunk_tensor
- num_embeddings.append(emb(chunk_tensor))
-
- # Stack the resulting embeddings along the second dimension if num_embeddings is not empty
- if num_embeddings:
- num_embeddings = torch.stack(num_embeddings, dim=1)
- else:
- num_embeddings = None
-
- else:
- # Process categorical features if present
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- if self.config.layer_norm_after_embedding:
- cat_embeddings = self.embedding_norm(cat_embeddings)
- else:
- cat_embeddings = None
-
- # Process numerical features if present
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = [
- emb(num_features[i]) for i, emb in enumerate(self.num_embeddings)
- ]
- num_embeddings = torch.stack(num_embeddings, dim=1)
- if self.config.layer_norm_after_embedding:
- num_embeddings = self.embedding_norm(num_embeddings)
- else:
- num_embeddings = None
-
- # Combine embeddings if both types are present, otherwise use whichever is available
- if cat_embeddings is not None and num_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings, num_embeddings], dim=1)
- elif cat_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings], dim=1)
- elif num_embeddings is not None:
- x = torch.cat([cls_tokens, num_embeddings], dim=1)
- else:
- raise ValueError("No features provided to the model.")
-
- x = self.mamba(x)
-
- # Apply pooling based on the specified method
- if self.pooling_method == "avg":
- x = torch.mean(x, dim=1)
- elif self.pooling_method == "max":
- x, _ = torch.max(x, dim=1)
- elif self.pooling_method == "sum":
- x = torch.sum(x, dim=1)
- elif self.pooling_method == "cls_token":
- x = x[:, 0]
- else:
- raise ValueError(f"Invalid pooling method: {self.pooling_method}")
-
- x = self.norm_f(x)
- preds = self.tabular_head(x)
-
- return preds
-
-[docs] def training_step(self, batch, batch_idx):
- """
- Processes a single batch during training, computes the loss, and logs training metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
-
- Returns
- -------
- Tensor
- The computed loss for the batch.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- loss = self.loss_fct(preds.squeeze(), labels.float())
- self.log(
- "train_loss",
- loss,
- on_step=True,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
- return loss
-
-[docs] def validation_step(self, batch, batch_idx):
- """
- Processes a single batch during validation, computes the loss, and logs validation metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- loss = self.loss_fct(preds.squeeze(), labels.float())
- self.log(
- "val_loss",
- loss,
- on_step=True,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- return loss
-
-[docs] def configure_optimizers(self):
- """
- Sets up the model's optimizer and learning rate scheduler based on the configurations provided.
-
- Returns
- -------
- dict
- A dictionary containing the optimizer and lr_scheduler configurations.
- """
- optimizer = torch.optim.Adam(
- self.parameters(), lr=self.lr, weight_decay=self.config.weight_decay
- )
- scheduler = {
- "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(
- optimizer,
- mode="min",
- factor=self.lr_factor,
- patience=self.lr_patience,
- verbose=True,
- ),
- "monitor": "val_loss", # Name of the metric to monitor
- "interval": "epoch",
- "frequency": 1,
- }
-
- return {"optimizer": optimizer, "lr_scheduler": scheduler}
-
-import torch
-import torch.nn as nn
-from ..utils.mamba_arch import Mamba
-from ..utils.config import MambularConfig
-import pytorch_lightning as pl
-
-
-[docs]class BaseMambularRegressor(pl.LightningModule):
- """
- A base regression module for tabular data built on PyTorch Lightning. It incorporates embeddings
- for categorical and numerical features with a configurable architecture provided by MambularConfig.
- This module is designed for regression tasks.
-
- Parameters
- ----------
- config : MambularConfig
- An instance of MambularConfig containing configuration parameters for the model architecture.
- cat_feature_info : dict, optional
- A dictionary mapping the names of categorical features to their number of unique categories. Defaults to None.
- num_feature_info : dict, optional
- A dictionary mapping the names of numerical features to their number of dimensions after embedding. Defaults to None.
- lr : float, optional
- The initial learning rate for the optimizer. Defaults to 1e-03.
- lr_patience : int, optional
- The number of epochs with no improvement after which learning rate will be reduced. Defaults to 10.
- weight_decay : float, optional
- Weight decay (L2 penalty) coefficient. Defaults to 0.025.
- lr_factor : float, optional
- Factor by which the learning rate will be reduced. Defaults to 0.75.
-
- Attributes
- ----------
- mamba : Mamba
- The core neural network module implementing the Mamba architecture.
- norm_f : nn.Module
- Normalization layer applied after the Mamba block.
- tabular_head : nn.Linear
- Final linear layer mapping the features to a single output for regression tasks.
- train_mse : torchmetrics.MeanSquaredError
- Metric computation module for training Mean Squared Error.
- val_mse : torchmetrics.MeanSquaredError
- Metric computation module for validation Mean Squared Error.
- loss_fct : torch.nn.MSELoss
- The loss function for regression tasks.
-
- Methods
- -------
- forward(cat_features, num_features)
- Defines the forward pass of the model.
- training_step(batch, batch_idx)
- Processes a single batch during training.
- validation_step(batch, batch_idx)
- Processes a single batch during validation.
- configure_optimizers()
- Sets up the model's optimizer and learning rate scheduler.
- """
-
-
- def __init__(
- self,
- config: MambularConfig,
- cat_feature_info: dict = None,
- num_feature_info: dict = None,
- lr=1e-03,
- lr_patience=10,
- weight_decay=0.025,
- lr_factor=0.75,
- ):
- super().__init__()
-
- self.config = config
- self.lr = lr
- self.lr_patience = lr_patience
- self.weight_decay = weight_decay
- self.lr_factor = lr_factor
- self.cat_feature_info = cat_feature_info
- self.num_feature_info = num_feature_info
-
- activations = {
- "relu": nn.ReLU(),
- "tanh": nn.Tanh(),
- "sigmoid": nn.Sigmoid(),
- "leaky_relu": nn.LeakyReLU(),
- "elu": nn.ELU(),
- "selu": nn.SELU(),
- "gelu": nn.GELU(),
- "softplus": nn.Softplus(),
- "leakyrelu": nn.LeakyReLU(),
- "linear": nn.Identity(),
- }
-
- self.embedding_activation = activations.get(
- self.config.num_embedding_activation.lower()
- )
- if self.embedding_activation is None:
- raise ValueError(
- f"Unsupported activation function: {self.config.num_embedding_activation}"
- )
-
- self.num_embeddings = nn.ModuleList(
- [
- nn.Sequential(
- nn.Linear(input_shape, self.config.d_model, bias=False),
- self.embedding_activation, # Example using ReLU as the activation function, change as needed
- )
- for feature_name, input_shape in num_feature_info.items()
- ]
- )
-
- # Create embedding layers for categorical features based on cat_feature_info
- self.cat_embeddings = nn.ModuleList(
- [
- nn.Embedding(num_categories + 1, self.config.d_model)
- for feature_name, num_categories in cat_feature_info.items()
- ]
- )
-
- self.mamba = Mamba(self.config)
- self.norm_f = self.config.norm(self.config.d_model)
- mlp_activation_fn = activations.get(
- self.config.tabular_head_activation.lower(), nn.Identity()
- )
-
- # Dynamically create MLP layers based on config.tabular_units
- mlp_layers = []
- input_dim = self.config.d_model # Initial input dimension
-
- # Iterate over the specified units for each layer in the MLP
- for units in self.config.tabular_head_units:
- mlp_layers.append(nn.Linear(input_dim, units))
- mlp_layers.append(mlp_activation_fn)
- mlp_layers.append(nn.Dropout(self.config.tabular_head_dropout))
- input_dim = units
-
- # Add the final linear layer to map to a single output value
- mlp_layers.append(nn.Linear(input_dim, 1))
-
- # Combine all layers into a Sequential module
- self.tabular_head = nn.Sequential(*mlp_layers)
-
- self.pooling_method = self.config.pooling_method
- self.cls_token = nn.Parameter(torch.zeros(1, 1, self.config.d_model))
-
- self.loss_fct = nn.MSELoss()
-
- if self.config.layer_norm_after_embedding:
- self.embedding_norm = nn.LayerNorm(self.config.d_model)
-
-[docs] def forward(self, cat_features, num_features):
- """
- Defines the forward pass of the regressor.
-
- Parameters
- ----------
- cat_features : Tensor
- Tensor containing the categorical features.
- num_features : Tensor
- Tensor containing the numerical features.
-
- Returns
- -------
- Tensor
- The output predictions of the model for regression tasks.
- """
-
- batch_size = (
- cat_features[0].size(0) if cat_features != [] else num_features[0].size(0)
- )
- cls_tokens = self.cls_token.expand(batch_size, -1, -1)
-
- # Process categorical features if present
- if len(self.cat_embeddings) > 0 and cat_features:
- cat_embeddings = [
- emb(cat_features[i]) for i, emb in enumerate(self.cat_embeddings)
- ]
- cat_embeddings = torch.stack(cat_embeddings, dim=1)
- cat_embeddings = torch.squeeze(cat_embeddings, dim=2)
- if self.config.layer_norm_after_embedding:
- cat_embeddings = self.embedding_norm(cat_embeddings)
- else:
- cat_embeddings = None
-
- # Process numerical features if present
- if len(self.num_embeddings) > 0 and num_features:
- num_embeddings = [
- emb(num_features[i]) for i, emb in enumerate(self.num_embeddings)
- ]
- num_embeddings = torch.stack(num_embeddings, dim=1)
- if self.config.layer_norm_after_embedding:
- num_embeddings = self.embedding_norm(num_embeddings)
- else:
- num_embeddings = None
-
- # Combine embeddings if both types are present, otherwise use whichever is available
-
- if cat_embeddings is not None and num_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings, num_embeddings], dim=1)
- elif cat_embeddings is not None:
- x = torch.cat([cls_tokens, cat_embeddings], dim=1)
- elif num_embeddings is not None:
- x = torch.cat([cls_tokens, num_embeddings], dim=1)
- else:
- raise ValueError("No features provided to the model.")
-
- x = self.mamba(x)
-
- # Apply pooling based on the specified method
- if self.pooling_method == "avg":
- x = torch.mean(x, dim=1)
- elif self.pooling_method == "max":
- x, _ = torch.max(x, dim=1)
- elif self.pooling_method == "sum":
- x = torch.sum(x, dim=1)
- elif self.pooling_method == "cls_token":
- x = x[:, 0]
- else:
- raise ValueError(f"Invalid pooling method: {self.pooling_method}")
-
- x = self.norm_f(x)
- preds = self.tabular_head(x)
-
- return preds
-
-[docs] def training_step(self, batch, batch_idx):
- """
- Defines the forward pass of the regressor.
-
- Parameters
- ----------
- cat_features : Tensor
- Tensor containing the categorical features.
- num_features : Tensor
- Tensor containing the numerical features.
-
- Returns
- -------
- Tensor
- The output predictions of the model for regression tasks.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- loss = self.loss_fct(preds.squeeze(), labels.float())
- self.log(
- "train_loss",
- loss,
- on_step=True,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
- return loss
-
-[docs] def validation_step(self, batch, batch_idx):
- """
- Processes a single batch during validation, computes the loss, and logs validation metrics.
-
- Parameters
- ----------
- batch : tuple
- A batch of data from the DataLoader, containing numerical features, categorical features, and labels.
- batch_idx : int
- The index of the batch within the epoch.
- """
- num_features, cat_features, labels = batch
- preds = self(num_features, cat_features)
-
- loss = self.loss_fct(preds.squeeze(), labels.float())
- self.log(
- "val_loss",
- loss,
- on_step=True,
- on_epoch=True,
- prog_bar=True,
- logger=True,
- )
-
- return loss
-
-[docs] def configure_optimizers(self):
- """
- Sets up the model's optimizer and learning rate scheduler based on the configurations provided.
-
- Returns
- -------
- dict
- A dictionary containing the optimizer and lr_scheduler configurations.
- """
- optimizer = torch.optim.Adam(
- self.parameters(), lr=self.lr, weight_decay=self.config.weight_decay
- )
- scheduler = {
- "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(
- optimizer,
- mode="min",
- factor=self.lr_factor,
- patience=self.lr_patience,
- verbose=True,
- ),
- "monitor": "val_loss", # Name of the metric to monitor
- "interval": "epoch",
- "frequency": 1,
- }
-
- return {"optimizer": optimizer, "lr_scheduler": scheduler}
-
-from sklearn.model_selection import train_test_split
-import pytorch_lightning as pl
-import torch
-from torch.utils.data import DataLoader
-import numpy as np
-from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
-from ..base_models.classifier import BaseMambularClassifier
-from ..utils.config import MambularConfig
-from ..utils.preprocessor import Preprocessor
-from ..utils.dataset import MambularDataModule, MambularDataset
-from sklearn.base import BaseEstimator
-import pandas as pd
-from sklearn.metrics import accuracy_score
-
-
-[docs]class MambularClassifier(BaseEstimator):
- """
- A classifier that mimics scikit-learn's API using PyTorch Lightning and a custom architecture.
-
- This classifier is designed to work with tabular data and provides a flexible interface for specifying model
- configurations and preprocessing steps. It integrates smoothly with scikit-learn's utilities, such as cross-validation
- and grid search.
-
- Parameters
- ----------
- **kwargs : Various
- Accepts any number of keyword arguments that are passed to the MambularConfig and Preprocessor classes.
- Known configuration arguments for the model are extracted based on a predefined list, and the rest are
- passed to the Preprocessor.
-
- Attributes
- ----------
- config : MambularConfig
- Configuration object that holds model-specific settings.
- preprocessor : Preprocessor
- Preprocessor object for handling feature preprocessing like normalization and encoding.
- model : BaseMambularClassifier or None
- The underlying PyTorch Lightning model, instantiated upon calling the `fit` method.
- """
-
- def __init__(self, **kwargs):
- # Known config arguments
- print("Received kwargs:", kwargs)
- config_arg_names = [
- "d_model",
- "n_layers",
- "dt_rank",
- "output_dimension",
- "pooling_method",
- "norm",
- "cls",
- "dt_min",
- "dt_max",
- "dropout",
- "bias",
- "weight_decay",
- "conv_bias",
- "d_state",
- "expand_factor",
- "d_conv",
- "dt_init",
- "dt_scale",
- "dt_init_floor",
- "tabular_head_units",
- "tabular_head_activation",
- "tabular_head_dropout",
- "num_emebedding_activation",
- "layer_norm_after_embedding",
- ]
- self.config_kwargs = {k: v for k, v in kwargs.items() if k in config_arg_names}
- self.config = MambularConfig(**self.config_kwargs)
-
- # The rest are assumed to be preprocessor arguments
- preprocessor_kwargs = {
- k: v for k, v in kwargs.items() if k not in config_arg_names
- }
- self.preprocessor = Preprocessor(**preprocessor_kwargs)
- self.model = None
-
- def get_params(self, deep=True):
- """
- Get parameters for this estimator.
-
- Parameters
- ----------
- deep : bool, default=True
- If True, will return the parameters for this estimator and contained subobjects that are estimators.
-
- Returns
- -------
- params : dict
- Parameter names mapped to their values.
- """
-
- params = self.config_kwargs # Parameters used to initialize MambularConfig
-
- # If deep=True, include parameters from nested components like preprocessor
- if deep:
- # Assuming Preprocessor has a get_params method
- preprocessor_params = {
- "preprocessor__" + key: value
- for key, value in self.preprocessor.get_params().items()
- }
- params.update(preprocessor_params)
-
- return params
-
- def set_params(self, **parameters):
- """
- Set the parameters of this estimator.
-
- Parameters
- ----------
- **parameters : dict
- Estimator parameters.
-
- Returns
- -------
- self : object
- Estimator instance.
- """
- # Update config_kwargs with provided parameters
- valid_config_keys = self.config_kwargs.keys()
- config_updates = {k: v for k, v in parameters.items() if k in valid_config_keys}
- self.config_kwargs.update(config_updates)
-
- # Update the config object
- for key, value in config_updates.items():
- setattr(self.config, key, value)
-
- # Handle preprocessor parameters (prefixed with 'preprocessor__')
- preprocessor_params = {
- k.split("__")[1]: v
- for k, v in parameters.items()
- if k.startswith("preprocessor__")
- }
- if preprocessor_params:
- # Assuming Preprocessor has a set_params method
- self.preprocessor.set_params(**preprocessor_params)
-
- return self
-
- def split_data(self, X, y, val_size, random_state):
- """
- Split the dataset into training and validation sets.
-
- Parameters
- ----------
- X : array-like of shape (n_samples, n_features)
- The input samples.
- y : array-like of shape (n_samples,)
- The target values.
- val_size : float
- The proportion of the dataset to include in the validation split.
- random_state : int
- Controls the shuffling applied to the data before applying the split.
-
- Returns
- -------
- X_train, X_val, y_train, y_val : arrays
- The split datasets.
- """
- X_train, X_val, y_train, y_val = train_test_split(
- X, y, test_size=val_size, random_state=random_state
- )
-
- return X_train, X_val, y_train, y_val
-
- def preprocess_data(self, X_train, y_train, X_val, y_val, batch_size, shuffle):
- """
- Preprocess the training and validation data and create corresponding DataLoaders.
-
- Parameters
- ----------
- X_train : array-like of shape (n_samples, n_features)
- The training input samples.
- y_train : array-like of shape (n_samples,)
- The training target values.
- X_val : array-like of shape (n_samples, n_features)
- The validation input samples.
- y_val : array-like of shape (n_samples,)
- The validation target values.
- batch_size : int
- Size of mini-batches for the DataLoader.
- shuffle : bool
- Whether to shuffle the training data before splitting into batches.
-
- Returns
- -------
- data_module : MambularDataModule
- An instance of MambularDataModule containing training and validation DataLoaders.
- """
-
- train_preprocessed_data = self.preprocessor.fit_transform(X_train, y_train)
- val_preprocessed_data = self.preprocessor.transform(X_val)
-
- # Update feature info based on the actual processed data
- (
- self.cat_feature_info,
- self.num_feature_info,
- ) = self.preprocessor.get_feature_info()
-
- # Initialize lists for tensors
- train_cat_tensors = []
- train_num_tensors = []
- val_cat_tensors = []
- val_num_tensors = []
-
- # Populate tensors for categorical features, if present in processed data
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[cat_key], dtype=torch.long)
- )
- if cat_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- if binned_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features, if present in processed data
- for key in self.num_feature_info:
- num_key = "num_" + key # Assuming numerical keys are prefixed with 'num_'
- if num_key in train_preprocessed_data:
- train_num_tensors.append(
- torch.tensor(train_preprocessed_data[num_key], dtype=torch.float)
- )
- if num_key in val_preprocessed_data:
- val_num_tensors.append(
- torch.tensor(val_preprocessed_data[num_key], dtype=torch.float)
- )
-
- train_labels = torch.tensor(y_train, dtype=torch.long)
- val_labels = torch.tensor(y_val, dtype=torch.long)
-
- # Create datasets
- train_dataset = MambularDataset(
- train_cat_tensors, train_num_tensors, train_labels, regression=False
- )
- val_dataset = MambularDataset(
- val_cat_tensors, val_num_tensors, val_labels, regression=False
- )
-
- # Create dataloaders
- train_dataloader = DataLoader(
- train_dataset, batch_size=batch_size, shuffle=shuffle
- )
- val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
-
- return MambularDataModule(train_dataloader, val_dataloader)
-
- def preprocess_test_data(self, X):
- """
- Preprocesses the test data and creates tensors for categorical and numerical features.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- Test feature set.
-
- Returns
- -------
- cat_tensors : list of Tensors
- List of tensors for each categorical feature.
- num_tensors : list of Tensors
- List of tensors for each numerical feature.
- """
- processed_data = self.preprocessor.transform(X)
-
- # Initialize lists for tensors
- cat_tensors = []
- num_tensors = []
-
- # Populate tensors for categorical features
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features
- for key in self.num_feature_info:
- num_key = "num_" + key # Assuming numerical keys are prefixed with 'num_'
- if num_key in processed_data:
- num_tensors.append(
- torch.tensor(processed_data[num_key], dtype=torch.float)
- )
-
- return cat_tensors, num_tensors
-
- def fit(
- self,
- X,
- y,
- val_size=0.2,
- X_val=None,
- y_val=None,
- max_epochs=100,
- random_state=101,
- batch_size=64,
- shuffle=True,
- patience=10,
- monitor="val_loss",
- mode="min",
- lr=1e-3,
- lr_patience=10,
- factor=0.75,
- weight_decay=0.025,
- **trainer_kwargs
- ):
- """
- Fit the model to the given training data, optionally using a separate validation set.
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The training input samples.
- y : array-like of shape (n_samples,) or (n_samples, n_outputs)
- The target values (class labels in classification, real numbers in regression).
- val_size : float, default=0.2
- The proportion of the dataset to include in the validation split if `X_val` is None. Ignored if `X_val` is provided.
- X_val : array-like or pd.DataFrame of shape (n_samples, n_features), optional
- The validation input samples. If provided, `X` and `y` are not split and this data is used for validation.
- y_val : array-like of shape (n_samples,) or (n_samples, n_outputs), optional
- The validation target values. Required if `X_val` is provided.
- max_epochs : int, default=100
- Maximum number of epochs for training.
- random_state : int, default=101
- Seed used by the random number generator for shuffling the data if `X_val` is not provided.
- batch_size : int, default=64
- Number of samples per gradient update.
- shuffle : bool, default=True
- Whether to shuffle the training data before each epoch if `X_val` is not provided.
- patience : int, default=10
- Number of epochs with no improvement after which training will be stopped if using early stopping.
- monitor : str, default="val_loss"
- Quantity to be monitored for early stopping.
- mode : str, default="min"
- One of {"min", "max"}. In "min" mode, training will stop when the quantity monitored has stopped decreasing; in "max" mode, it will stop when the quantity monitored has stopped increasing.
- lr : float, default=1e-3
- Learning rate for the optimizer.
- lr_patience : int, default=10
- Number of epochs with no improvement after which the learning rate will be reduced.
- factor : float, default=0.75
- Factor by which the learning rate will be reduced. new_lr = lr * factor.
- weight_decay : float, default=0.025
- Weight decay (L2 penalty) parameter.
- **trainer_kwargs : dict
- Additional keyword arguments to be passed to the PyTorch Lightning Trainer constructor.
-
- Returns
- -------
- self : object
- The fitted estimator.
- """
-
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- if X_val:
- if not isinstance(X_val, pd.DataFrame):
- X_val = pd.DataFrame(X_val)
-
- num_classes = len(np.unique(y))
-
- if not X_val:
- X_train, X_val, y_train, y_val = self.split_data(
- X, y, val_size, random_state
- )
-
- data_module = self.preprocess_data(
- X_train, y_train, X_val, y_val, batch_size, shuffle
- )
-
- self.model = BaseMambularClassifier(
- num_classes=num_classes,
- config=self.config,
- cat_feature_info=self.cat_feature_info,
- num_feature_info=self.num_feature_info,
- lr=lr,
- lr_patience=lr_patience,
- lr_factor=factor,
- weight_decay=weight_decay,
- )
-
- early_stop_callback = EarlyStopping(
- monitor=monitor, min_delta=0.00, patience=patience, verbose=False, mode=mode
- )
-
- checkpoint_callback = ModelCheckpoint(
- monitor="val_loss", # Adjust according to your validation metric
- mode="min",
- save_top_k=1,
- dirpath="model_checkpoints", # Specify the directory to save checkpoints
- filename="best_model",
- )
-
- # Initialize the trainer and train the model
- trainer = pl.Trainer(
- max_epochs=max_epochs,
- callbacks=[early_stop_callback, checkpoint_callback],
- **trainer_kwargs
- )
- trainer.fit(self.model, data_module)
-
- best_model_path = checkpoint_callback.best_model_path
- if best_model_path:
- checkpoint = torch.load(best_model_path)
- self.model.load_state_dict(checkpoint["state_dict"])
-
- return self
-
- def predict(self, X):
- """
- Predict the class labels for the given input samples.
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples to predict.
-
- Returns
- -------
- predictions : ndarray of shape (n_samples,)
- Predicted class labels for each input sample.
-
- Notes
- -----
- The method preprocesses the input data using the same preprocessor used during training,
- sets the model to evaluation mode, and then performs inference to predict the class labels.
- The predictions are converted from a PyTorch tensor to a NumPy array before being returned.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- logits = self.model(cat_tensors, num_tensors)
- predictions = torch.argmax(logits, dim=1)
-
- # Convert predictions to NumPy array and return
- return predictions.cpu().numpy()
-
- def predict_proba(self, X):
- """
- Predict class probabilities for the given input samples.
-
- Example
- -------
- from sklearn.metrics import accuracy_score, precision_score, f1_score, roc_auc_score
-
- # Define the metrics you want to evaluate
- metrics = {
- 'Accuracy': (accuracy_score, False),
- 'Precision': (precision_score, False),
- 'F1 Score': (f1_score, False),
- 'AUC Score': (roc_auc_score, True)
- }
-
- # Assuming 'X_test' and 'y_test' are your test dataset and labels
- # Evaluate using the specified metrics
- results = classifier.evaluate(X_test, y_test, metrics=metrics)
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples for which to predict class probabilities.
-
- Returns
- -------
- probabilities : ndarray of shape (n_samples, n_classes)
- Predicted class probabilities for each input sample.
-
- Notes
- -----
- The method preprocesses the input data using the same preprocessor used during training,
- sets the model to evaluation mode, and then performs inference to predict the class probabilities.
- Softmax is applied to the logits to obtain probabilities, which are then converted from a PyTorch tensor
- to a NumPy array before being returned.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- logits = self.model(cat_tensors, num_tensors)
- probabilities = torch.softmax(logits, dim=1)
-
- # Convert probabilities to NumPy array and return
- return probabilities.cpu().numpy()
-
- def evaluate(self, X, y_true, metrics=None):
- """
- Evaluate the model on the given data using specified metrics.
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples to predict.
- y_true : array-like of shape (n_samples,)
- The true class labels against which to evaluate the predictions.
- metrics : dict
- A dictionary where keys are metric names and values are tuples containing the metric function
- and a boolean indicating whether the metric requires probability scores (True) or class labels (False).
-
- Returns
- -------
- scores : dict
- A dictionary with metric names as keys and their corresponding scores as values.
-
- Notes
- -----
- This method uses either the `predict` or `predict_proba` method depending on the metric requirements.
- """
- # Ensure input is in the correct format
- if metrics is None:
- metrics = {"Accuracy": (accuracy_score, False)}
-
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
-
- # Initialize dictionary to store results
- scores = {}
-
- # Generate class probabilities if any metric requires them
- if any(use_proba for _, use_proba in metrics.values()):
- probabilities = self.predict_proba(X)
-
- # Generate class labels if any metric requires them
- if any(not use_proba for _, use_proba in metrics.values()):
- predictions = self.predict(X)
-
- # Compute each metric
- for metric_name, (metric_func, use_proba) in metrics.items():
- if use_proba:
- scores[metric_name] = metric_func(y_true, probabilities)
- else:
- scores[metric_name] = metric_func(y_true, predictions)
-
- return scores
-
-from sklearn.model_selection import train_test_split
-import pytorch_lightning as pl
-import torch
-from torch.utils.data import DataLoader
-from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
-from ..base_models.distributional import BaseMambularLSS
-from ..utils.config import MambularConfig
-from ..utils.preprocessor import Preprocessor
-from ..utils.dataset import MambularDataModule, MambularDataset
-from sklearn.base import BaseEstimator
-import pandas as pd
-from ..utils.distributional_metrics import (
- poisson_deviance,
- gamma_deviance,
- beta_brier_score,
- dirichlet_error,
- student_t_loss,
- negative_binomial_deviance,
- inverse_gamma_loss,
-)
-from sklearn.metrics import mean_squared_error, accuracy_score
-import numpy as np
-import properscoring as ps
-
-
-[docs]class MambularLSS(BaseEstimator):
- """
- MambularLSS is a machine learning estimator that is designed for structured data,
- incorporating both preprocessing and a deep learning model. The estimator
- integrates configurable components for data preprocessing and the neural network model,
- facilitating end-to-end training and prediction workflows.
-
- The initialization of this class separates configuration arguments for the model and
- the preprocessor, allowing for flexible adjustment of parameters.
-
- Attributes
- ----------
- config : MambularConfig
- Configuration object containing model-specific parameters.
- preprocessor : Preprocessor
- Preprocessor object for data preprocessing steps.
- model : torch.nn.Module
- The neural network model, initialized based on 'config'.
-
- Parameters
- ----------
- **kwargs : Arbitrary keyword arguments, divided into configuration for the model and
- preprocessing. Recognized keys include model parameters such as 'd_model',
- 'n_layers', etc., and any additional keys are assumed to be preprocessor arguments.
- """
-
- def __init__(self, **kwargs):
- # Known config arguments
- config_arg_names = [
- "d_model",
- "n_layers",
- "dt_rank",
- "output_dimension",
- "pooling_method",
- "norm",
- "cls",
- "dt_min",
- "dt_max",
- "dropout",
- "bias",
- "weight_decay",
- "conv_bias",
- "d_state",
- "expand_factor",
- "d_conv",
- "dt_init",
- "dt_scale",
- "dt_init_floor",
- "tabular_head_units",
- "tabular_head_activation",
- "tabular_head_dropout",
- "num_emebedding_activation",
- ]
- config_kwargs = {k: v for k, v in kwargs.items() if k in config_arg_names}
- self.config = MambularConfig(**config_kwargs)
-
- # The rest are assumed to be preprocessor arguments
- preprocessor_kwargs = {
- k: v for k, v in kwargs.items() if k not in config_arg_names
- }
- self.preprocessor = Preprocessor(**preprocessor_kwargs)
- self.model = None
-
- def get_params(self, deep=True):
- """
- Get parameters for this estimator, optionally including parameters from nested components
- like the preprocessor.
-
- Parameters
- ----------
- deep : bool, default=True
- If True, return parameters of nested components.
-
- Returns
- -------
- dict
- A dictionary mapping parameter names to their values. For nested components,
- parameter names are prefixed accordingly (e.g., 'preprocessor__<param_name>').
- """
-
- params = self.config_kwargs # Parameters used to initialize MambularConfig
-
- # If deep=True, include parameters from nested components like preprocessor
- if deep:
- # Assuming Preprocessor has a get_params method
- preprocessor_params = {
- "preprocessor__" + key: value
- for key, value in self.preprocessor.get_params().items()
- }
- params.update(preprocessor_params)
-
- return params
-
- def set_params(self, **parameters):
- """
- Set the parameters of this estimator, allowing for modifications to both the configuration
- and preprocessor parameters. Parameters not recognized as configuration arguments are
- assumed to be preprocessor arguments.
-
- Parameters
- ----------
- **parameters: Arbitrary keyword arguments where keys are parameter names and values
- are the new parameter values.
-
- Returns
- -------
- self: This instance with updated parameters.
- """
- # Update config_kwargs with provided parameters
- valid_config_keys = self.config_kwargs.keys()
- config_updates = {k: v for k, v in parameters.items() if k in valid_config_keys}
- self.config_kwargs.update(config_updates)
-
- # Update the config object
- for key, value in config_updates.items():
- setattr(self.config, key, value)
-
- # Handle preprocessor parameters (prefixed with 'preprocessor__')
- preprocessor_params = {
- k.split("__")[1]: v
- for k, v in parameters.items()
- if k.startswith("preprocessor__")
- }
- if preprocessor_params:
- # Assuming Preprocessor has a set_params method
- self.preprocessor.set_params(**preprocessor_params)
-
- return self
-
- def split_data(self, X, y, val_size, random_state):
- """
- Split the dataset into training and validation sets.
-
- Parameters
- ----------
- X : array-like
- Features of the dataset.
- y : array-like
- Target values.
- val_size : float
- The proportion of the dataset to include in the validation split.
- random_state : int, optional
- The seed used by the random number generator for reproducibility.
-
- Returns
- -------
- tuple
- A tuple containing split datasets (X_train, X_val, y_train, y_val).
- """
- X_train, X_val, y_train, y_val = train_test_split(
- X, y, test_size=val_size, random_state=random_state
- )
-
- return X_train, X_val, y_train, y_val
-
- def preprocess_data(self, X_train, y_train, X_val, y_val, batch_size, shuffle):
- """
- Preprocess the training and validation data, fit the preprocessor on the training data,
- and transform both training and validation data. This method also initializes tensors
- for categorical and numerical features and labels, and prepares DataLoader objects for
- both datasets.
-
- Parameters
- ----------
- X_train : array-like
- Training features.
- y_train : array-like
- Training target values.
- X_val : array-like
- Validation features.
- y_val : array-like
- Validation target values.
- batch_size : int
- Batch size for DataLoader objects.
- shuffle : bool
- Whether to shuffle the training data in the DataLoader.
-
- Returns
- -------
- MambularDataModule
- An object containing DataLoaders for training and validation datasets.
- """
- train_preprocessed_data = self.preprocessor.fit_transform(X_train, y_train)
- val_preprocessed_data = self.preprocessor.transform(X_val)
-
- # Update feature info based on the actual processed data
- (
- self.cat_feature_info,
- self.num_feature_info,
- ) = self.preprocessor.get_feature_info()
-
- # Initialize lists for tensors
- train_cat_tensors = []
- train_num_tensors = []
- val_cat_tensors = []
- val_num_tensors = []
-
- # Populate tensors for categorical features, if present in processed data
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[cat_key], dtype=torch.long)
- )
- if cat_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- if binned_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features, if present in processed data
- for key in self.num_feature_info:
- num_key = "num_" + key # Assuming numerical keys are prefixed with 'num_'
- if num_key in train_preprocessed_data:
- train_num_tensors.append(
- torch.tensor(train_preprocessed_data[num_key], dtype=torch.float)
- )
- if num_key in val_preprocessed_data:
- val_num_tensors.append(
- torch.tensor(val_preprocessed_data[num_key], dtype=torch.float)
- )
-
- train_labels = torch.tensor(y_train, dtype=torch.float)
- val_labels = torch.tensor(y_val, dtype=torch.float)
-
- # Create datasets
- train_dataset = MambularDataset(
- train_cat_tensors, train_num_tensors, train_labels
- )
- val_dataset = MambularDataset(val_cat_tensors, val_num_tensors, val_labels)
-
- # Create dataloaders
- train_dataloader = DataLoader(
- train_dataset, batch_size=batch_size, shuffle=shuffle
- )
- val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
-
- return MambularDataModule(train_dataloader, val_dataloader)
-
- def preprocess_test_data(self, X):
- """
- Preprocess test data using the fitted preprocessor. This method prepares tensors for
- categorical and numerical features based on the preprocessed test data.
-
- Parameters
- ----------
- X : array-like
- Test features to preprocess.
-
- Returns
- -------
- tuple
- A tuple containing lists of tensors for categorical and numerical features.
- """
- processed_data = self.preprocessor.transform(X)
-
- # Initialize lists for tensors
- cat_tensors = []
- num_tensors = []
-
- # Populate tensors for categorical features
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features
- for key in self.num_feature_info:
- num_key = "num_" + key # Assuming numerical keys are prefixed with 'num_'
- if num_key in processed_data:
- num_tensors.append(
- torch.tensor(processed_data[num_key], dtype=torch.float)
- )
-
- return cat_tensors, num_tensors
-
- def fit(
- self,
- X,
- y,
- family,
- val_size=0.2,
- X_val=None,
- y_val=None,
- max_epochs=100,
- random_state=101,
- batch_size=64,
- shuffle=True,
- patience=10,
- monitor="val_loss",
- mode="min",
- lr=1e-3,
- lr_patience=10,
- factor=0.75,
- weight_decay=0.025,
- **trainer_kwargs,
- ):
- """
- Fits the model to the provided data, using the specified loss distribution family for the prediction task.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- Training features.
- y : array-like, shape (n_samples,) or (n_samples, n_targets)
- Target values for training.
- family : str
- The name of the distribution family to use for the loss function. Examples include 'normal' for regression tasks.
- val_size : float, default=0.2
- Proportion of the dataset to include in the validation split if `X_val` is None.
- X_val : DataFrame or array-like, shape (n_samples, n_features), optional
- Validation features. If provided, `X` and `y` are not split.
- y_val : array-like, shape (n_samples,) or (n_samples, n_targets), optional
- Validation target values. Required if `X_val` is provided.
- max_epochs : int, default=100
- Maximum number of epochs for training.
- random_state : int, default=101
- Seed used by the random number generator for shuffling the data.
- batch_size : int, default=64
- Number of samples per gradient update.
- shuffle : bool, default=True
- Whether to shuffle the training data before each epoch.
- patience : int, default=10
- Number of epochs with no improvement on the validation metric to wait before early stopping.
- monitor : str, default="val_loss"
- The metric to monitor for early stopping.
- mode : str, default="min"
- In 'min' mode, training will stop when the quantity monitored has stopped decreasing;
- in 'max' mode, it will stop when the quantity monitored has stopped increasing.
- lr : float, default=1e-3
- Learning rate for the optimizer.
- lr_patience : int, default=10
- Number of epochs with no improvement on the validation metric to wait before reducing the learning rate.
- factor : float, default=0.75
- Factor by which the learning rate will be reduced.
- weight_decay : float, default=0.025
- Weight decay (L2 penalty) parameter.
- **trainer_kwargs : dict
- Additional keyword arguments for PyTorch Lightning's Trainer class.
-
- Returns
- -------
- self : object
- The fitted estimator.
- """
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- if X_val:
- if not isinstance(X_val, pd.DataFrame):
- X_val = pd.DataFrame(X_val)
-
- if not X_val:
- X_train, X_val, y_train, y_val = self.split_data(
- X, y, val_size, random_state
- )
-
- data_module = self.preprocess_data(
- X_train, y_train, X_val, y_val, batch_size, shuffle
- )
-
- self.model = BaseMambularLSS(
- family=family,
- config=self.config,
- cat_feature_info=self.cat_feature_info,
- num_feature_info=self.num_feature_info,
- lr=lr,
- lr_patience=lr_patience,
- lr_factor=factor,
- weight_decay=weight_decay,
- )
-
- early_stop_callback = EarlyStopping(
- monitor=monitor, min_delta=0.00, patience=patience, verbose=False, mode=mode
- )
-
- checkpoint_callback = ModelCheckpoint(
- monitor="val_loss",
- mode="min",
- save_top_k=1,
- dirpath="model_checkpoints",
- filename="best_model",
- )
-
- # Initialize the trainer and train the model
- trainer = pl.Trainer(
- max_epochs=max_epochs,
- callbacks=[early_stop_callback, checkpoint_callback],
- **trainer_kwargs,
- )
- trainer.fit(self.model, data_module)
-
- best_model_path = checkpoint_callback.best_model_path
- if best_model_path:
- checkpoint = torch.load(best_model_path)
- self.model.load_state_dict(checkpoint["state_dict"])
-
- return self
-
- def predict(self, X):
- """
- Predicts target values for the given input samples using the fitted model.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- The input samples for which to predict target values.
-
- Returns
- -------
- predictions : ndarray, shape (n_samples,) or (n_samples, n_distributional_parameters)
- The predicted target values.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- predictions = self.model(cat_tensors, num_tensors)
-
- # Convert predictions to NumPy array and return
- return predictions.cpu().numpy()
-
- def evaluate(self, X, y_true, metrics=None, distribution_family=None):
- """
- Evaluate the model on the given data using specified metrics tailored to the distribution type.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- Input samples.
- y_true : DataFrame or array-like, shape (n_samples,) or (n_samples, n_outputs)
- True target values.
- metrics : dict, optional
- A dictionary where keys are metric names and values are the metric functions.
- If None, default metrics based on the detected or specified distribution_family are used.
- distribution_family : str, optional
- Specifies the distribution family the model is predicting for. If None, it will attempt to infer based
- on the model's settings.
-
- Returns
- -------
- scores : dict
- A dictionary with metric names as keys and their corresponding scores as values.
- """
- # Infer distribution family from model settings if not provided
- if distribution_family is None:
- distribution_family = getattr(self.model, "distribution_family", "normal")
-
- # Setup default metrics if none are provided
- if metrics is None:
- metrics = self.get_default_metrics(distribution_family)
-
- # Make predictions
- predictions = self.predict(X)
-
- # Initialize dictionary to store results
- scores = {}
-
- # Compute each metric
- for metric_name, metric_func in metrics.items():
- scores[metric_name] = metric_func(y_true, predictions)
-
- return scores
-
- def get_default_metrics(self, distribution_family):
- """
- Provides default metrics based on the distribution family.
-
- Parameters
- ----------
- distribution_family : str
- The distribution family for which to provide default metrics.
-
- Returns
- -------
- metrics : dict
- A dictionary of default metric functions.
- """
- default_metrics = {
- "normal": {
- "MSE": lambda y, pred: mean_squared_error(y, pred[:, 0]),
- "CRPS": lambda y, pred: np.mean(
- [
- ps.crps_gaussian(y[i], mu=pred[i, 0], sig=np.sqrt(pred[i, 1]))
- for i in range(len(y))
- ]
- ),
- },
- "poisson": {"Poisson Deviance": poisson_deviance},
- "gamma": {"Gamma Deviance": gamma_deviance},
- "beta": {"Brier Score": beta_brier_score},
- "dirichlet": {"Dirichlet Error": dirichlet_error},
- "studentt": {"Student-T Loss": student_t_loss},
- "negativebinom": {"Negative Binomial Deviance": negative_binomial_deviance},
- "inversegamma": {"Inverse Gamma Loss": inverse_gamma_loss},
- "categorical": {"Accuracy": accuracy_score},
- }
- return default_metrics.get(distribution_family, {})
-
-from sklearn.model_selection import train_test_split
-import pytorch_lightning as pl
-import torch
-from torch.utils.data import DataLoader
-from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
-from ..base_models.embedding_classifier import BaseEmbeddingMambularClassifier
-from ..utils.config import MambularConfig
-from ..utils.preprocessor import Preprocessor
-from ..utils.dataset import MambularDataModule, EmbeddingMambularDataset
-from sklearn.base import BaseEstimator
-import pandas as pd
-from sklearn.decomposition import PCA
-import numpy as np
-from sklearn.metrics import accuracy_score
-
-
-[docs]class EmbeddingMambularClassifier(BaseEstimator):
- """
- Provides an scikit-learn-like interface for the ProteinMambularClassifier, making it compatible with
- scikit-learn's utilities and workflow. This class encapsulates the PyTorch Lightning model, preprocessing,
- and data loading, offering methods for fitting, predicting, and probability estimation in a manner akin
- to scikit-learn's API.
-
- Parameters
- ----------
- **kwargs : Configuration parameters that can include both MambularConfig settings and preprocessing
- options. Any unrecognized parameters are passed to the preprocessor.
-
- Attributes
- ----------
- config : MambularConfig
- Configuration object for the model, storing architecture-specific parameters.
- preprocessor : Preprocessor
- Object handling data preprocessing steps such as feature encoding and normalization.
- model : ProteinMambularClassifier
- The underlying neural network model, instantiated during the `fit` method.
- """
-
- def __init__(self, **kwargs):
- # Known config arguments
- config_arg_names = [
- "d_model",
- "n_layers",
- "dt_rank",
- "output_dimension",
- "pooling_method",
- "norm",
- "cls",
- "dt_min",
- "dt_max",
- "dropout",
- "bias",
- "weight_decay",
- "conv_bias",
- "d_state",
- "expand_factor",
- "d_conv",
- "dt_init",
- "dt_scale",
- "dt_init_floor",
- ]
- config_kwargs = {k: v for k, v in kwargs.items() if k in config_arg_names}
- self.config = MambularConfig(**config_kwargs)
-
- # The rest are assumed to be preprocessor arguments
- preprocessor_kwargs = {
- k: v for k, v in kwargs.items() if k not in config_arg_names
- }
- self.preprocessor = Preprocessor(**preprocessor_kwargs)
- self.model = None
-
- def get_params(self, deep=True):
- """
- Get parameters for this estimator.
-
- Parameters
- ----------
- deep : bool, default=True
- If True, will return the parameters for this estimator and contained subobjects that are estimators.
-
- Returns
- -------
- params : dict
- Parameter names mapped to their values.
- """
- params = self.config_kwargs # Parameters used to initialize MambularConfig
-
- # If deep=True, include parameters from nested components like preprocessor
- if deep:
- # Assuming Preprocessor has a get_params method
- preprocessor_params = {
- "preprocessor__" + key: value
- for key, value in self.preprocessor.get_params().items()
- }
- params.update(preprocessor_params)
-
- return params
-
- def set_params(self, **parameters):
- """
- Set the parameters of this estimator.
-
- Parameters
- ----------
- **parameters : dict
- Estimator parameters.
-
- Returns
- -------
- self : object
- Estimator instance.
- """
- # Update config_kwargs with provided parameters
- valid_config_keys = self.config_kwargs.keys()
- config_updates = {k: v for k, v in parameters.items() if k in valid_config_keys}
- self.config_kwargs.update(config_updates)
-
- # Update the config object
- for key, value in config_updates.items():
- setattr(self.config, key, value)
-
- # Handle preprocessor parameters (prefixed with 'preprocessor__')
- preprocessor_params = {
- k.split("__")[1]: v
- for k, v in parameters.items()
- if k.startswith("preprocessor__")
- }
- if preprocessor_params:
- # Assuming Preprocessor has a set_params method
- self.preprocessor.set_params(**preprocessor_params)
-
- return self
-
- def split_data(self, X, y, val_size, random_state):
- """
- Split the dataset into training and validation sets.
-
- Parameters
- ----------
- X : array-like of shape (n_samples, n_features)
- The input samples.
- y : array-like of shape (n_samples,)
- The target values.
- val_size : float
- The proportion of the dataset to include in the validation split.
- random_state : int
- Controls the shuffling applied to the data before applying the split.
-
- Returns
- -------
- X_train, X_val, y_train, y_val : arrays
- The split datasets.
- """
- X_train, X_val, y_train, y_val = train_test_split(
- X, y, test_size=val_size, random_state=random_state
- )
-
- return X_train, X_val, y_train, y_val
-
- def preprocess_data(self, X_train, y_train, X_val, y_val, batch_size, shuffle):
- """
- Preprocess the training and validation data and create corresponding DataLoaders.
-
- Parameters
- ----------
- X_train : array-like of shape (n_samples, n_features)
- The training input samples.
- y_train : array-like of shape (n_samples,)
- The training target values.
- X_val : array-like of shape (n_samples, n_features)
- The validation input samples.
- y_val : array-like of shape (n_samples,)
- The validation target values.
- batch_size : int
- Size of mini-batches for the DataLoader.
- shuffle : bool
- Whether to shuffle the training data before splitting into batches.
-
- Returns
- -------
- data_module : MambularDataModule
- An instance of MambularDataModule containing training and validation DataLoaders.
- """
- train_preprocessed_data = self.preprocessor.fit_transform(X_train, y_train)
- val_preprocessed_data = self.preprocessor.transform(X_val)
-
- # Update feature info based on the actual processed data
- (
- self.cat_feature_info,
- self.num_feature_info,
- ) = self.preprocessor.get_feature_info()
-
- # Initialize lists for tensors
- train_cat_tensors = []
- train_num_tensors = []
- val_cat_tensors = []
- val_num_tensors = []
-
- # Populate tensors for categorical features, if present in processed data
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[cat_key], dtype=torch.long)
- )
- if cat_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- if binned_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features, if present in processed data
- for key in self.num_feature_info:
- num_key = "num_" + str(
- key
- ) # Assuming numerical keys are prefixed with 'num_'
- if num_key in train_preprocessed_data:
- train_num_tensors.append(
- torch.tensor(train_preprocessed_data[num_key], dtype=torch.float)
- )
- if num_key in val_preprocessed_data:
- val_num_tensors.append(
- torch.tensor(val_preprocessed_data[num_key], dtype=torch.float)
- )
-
- train_labels = torch.tensor(y_train, dtype=torch.long)
- val_labels = torch.tensor(y_val, dtype=torch.long)
-
- # Create datasets
- train_dataset = EmbeddingMambularDataset(
- train_cat_tensors, train_num_tensors, train_labels, regression=False
- )
- val_dataset = EmbeddingMambularDataset(
- val_cat_tensors, val_num_tensors, val_labels, regression=False
- )
-
- # Create dataloaders
- train_dataloader = DataLoader(
- train_dataset, batch_size=batch_size, shuffle=shuffle
- )
- val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
-
- return MambularDataModule(train_dataloader, val_dataloader)
-
- def preprocess_test_data(self, X):
- """
- Preprocesses the test data and creates tensors for categorical and numerical features.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- Test feature set.
-
- Returns
- -------
- cat_tensors : list of Tensors
- List of tensors for each categorical feature.
- num_tensors : list of Tensors
- List of tensors for each numerical feature.
- """
- processed_data = self.preprocessor.transform(X)
-
- # Initialize lists for tensors
- cat_tensors = []
- num_tensors = []
-
- # Populate tensors for categorical features
- for key in self.cat_feature_info:
- cat_key = "cat_" + str(
- key
- ) # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + str(key) # for binned features
- if binned_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features
- for key in self.num_feature_info:
- num_key = "num_" + str(
- key
- ) # Assuming numerical keys are prefixed with 'num_'
- if num_key in processed_data:
- num_tensors.append(
- torch.tensor(processed_data[num_key], dtype=torch.float)
- )
-
- return cat_tensors, num_tensors
-
- def fit(
- self,
- X,
- y,
- val_size=0.2,
- X_val=None,
- y_val=None,
- max_epochs=100,
- random_state=101,
- batch_size=64,
- shuffle=True,
- patience=10,
- monitor="val_loss",
- mode="min",
- lr=1e-3,
- lr_patience=10,
- factor=0.75,
- weight_decay=0.025,
- raw_embeddings=False,
- seq_size=20,
- pca=False,
- reduced_dims=16,
- **trainer_kwargs
- ):
- """
- Fits the model to the given dataset.
-
- Parameters
- ----------
- X : pandas DataFrame or array-like
- Feature matrix for training.
- y : array-like
- Target vector.
- val_size : float, optional
- Fraction of the data to use for validation if X_val is None.
- X_val : pandas DataFrame or array-like, optional
- Feature matrix for validation.
- y_val : array-like, optional
- Target vector for validation.
- max_epochs : int, default=100
- Maximum number of epochs for training.
- random_state : int, optional
- Seed for random number generators.
- batch_size : int, default=32
- Size of batches for training and validation.
- shuffle : bool, default=True
- Whether to shuffle training data before each epoch.
- patience : int, default=10
- Patience for early stopping based on val_loss.
- monitor : str, default='val_loss'
- Metric to monitor for early stopping.
- mode : str, default='min'
- Mode for early stopping ('min' or 'max').
- lr : float, default=0.001
- Learning rate for the optimizer.
- lr_patience : int, default=5
- Patience for learning rate reduction.
- factor : float, default=0.1
- Factor for learning rate reduction.
- weight_decay : float, default=0.0
- Weight decay for the optimizer.
- raw_embeddings : bool, default=False
- Whether to use raw features or embeddings.
- seq_size : int, optional
- Sequence size for embeddings, relevant if raw_embeddings is False.
- **trainer_kwargs : dict
- Additional arguments for the PyTorch Lightning Trainer.
-
- Returns
- -------
- self : object
- The fitted estimator.
- """
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- if X_val:
- if not isinstance(X_val, pd.DataFrame):
- X_val = pd.DataFrame(X_val)
-
- # Apply PCA if indicated
- if pca:
- pca_transformer = PCA(n_components=reduced_dims)
- X = pca_transformer.fit_transform(
- X
- ) # Fit and transform the PCA on the complete dataset
- if X_val is not None:
- X_val = pca_transformer.transform(
- X_val
- ) # Transform validation data with the same PCA model
-
- raw_embeddings = True
-
- if not X_val:
- X_train, X_val, y_train, y_val = self.split_data(
- X, y, val_size, random_state
- )
- else:
- X_train = X
- y_train = y
-
- data_module = self.preprocess_data(
- X_train, y_train, X_val, y_val, batch_size, shuffle
- )
-
- if raw_embeddings:
- self.config.d_model = X.shape[1]
-
- num_classes = len(np.unique(y))
-
- self.model = BaseEmbeddingMambularClassifier(
- num_classes=num_classes,
- config=self.config,
- cat_feature_info=self.cat_feature_info,
- num_feature_info=self.num_feature_info,
- lr=lr,
- lr_patience=lr_patience,
- lr_factor=factor,
- weight_decay=weight_decay,
- raw_embeddings=raw_embeddings,
- seq_size=seq_size,
- )
-
- early_stop_callback = EarlyStopping(
- monitor=monitor, min_delta=0.00, patience=patience, verbose=False, mode=mode
- )
-
- checkpoint_callback = ModelCheckpoint(
- monitor="val_loss", # Adjust according to your validation metric
- mode="min",
- save_top_k=1,
- dirpath="model_checkpoints", # Specify the directory to save checkpoints
- filename="best_model",
- )
-
- # Initialize the trainer and train the model
- trainer = pl.Trainer(
- max_epochs=max_epochs,
- callbacks=[early_stop_callback, checkpoint_callback],
- **trainer_kwargs
- )
- trainer.fit(self.model, data_module)
-
- best_model_path = checkpoint_callback.best_model_path
- if best_model_path:
- checkpoint = torch.load(best_model_path)
- self.model.load_state_dict(checkpoint["state_dict"])
-
- return self
-
- def predict(self, X):
- """
- Predict the class labels for the given input samples.
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples to predict.
-
- Returns
- -------
- predictions : ndarray of shape (n_samples,)
- Predicted class labels for each input sample.
-
- Notes
- -----
- The method preprocesses the input data using the same preprocessor used during training,
- sets the model to evaluation mode, and then performs inference to predict the class labels.
- The predictions are converted from a PyTorch tensor to a NumPy array before being returned.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
-
- if hasattr(self, "pca_transformer"):
- X = pd.DataFrame(self.pca_transformer.transform(X))
-
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- logits = self.model(cat_tensors, num_tensors)
- predictions = torch.argmax(logits, dim=1)
-
- # Convert predictions to NumPy array and return
- return predictions.cpu().numpy()
-
- def predict_proba(self, X):
- """
- Predict class probabilities for the given input samples.
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples for which to predict class probabilities.
-
- Returns
- -------
- probabilities : ndarray of shape (n_samples, n_classes)
- Predicted class probabilities for each input sample.
-
- Notes
- -----
- The method preprocesses the input data using the same preprocessor used during training,
- sets the model to evaluation mode, and then performs inference to predict the class probabilities.
- Softmax is applied to the logits to obtain probabilities, which are then converted from a PyTorch tensor
- to a NumPy array before being returned.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- logits = self.model(cat_tensors, num_tensors)
- probabilities = torch.softmax(logits, dim=1)
-
- # Convert probabilities to NumPy array and return
- return probabilities.cpu().numpy()
-
- def evaluate(self, X, y_true, metrics=None):
- """
- Evaluate the model on the given data using specified metrics.
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples to predict.
- y_true : array-like of shape (n_samples,)
- The true class labels against which to evaluate the predictions.
- metrics : dict
- A dictionary where keys are metric names and values are tuples containing the metric function
- and a boolean indicating whether the metric requires probability scores (True) or class labels (False).
-
- Returns
- -------
- scores : dict
- A dictionary with metric names as keys and their corresponding scores as values.
-
- Notes
- -----
- This method uses either the `predict` or `predict_proba` method depending on the metric requirements.
- """
- # Ensure input is in the correct format
- if metrics is None:
- metrics = {"Accuracy": (accuracy_score, False)}
-
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
-
- # Initialize dictionary to store results
- scores = {}
-
- # Generate class probabilities if any metric requires them
- if any(use_proba for _, use_proba in metrics.values()):
- probabilities = self.predict_proba(X)
-
- # Generate class labels if any metric requires them
- if any(not use_proba for _, use_proba in metrics.values()):
- predictions = self.predict(X)
-
- # Compute each metric
- for metric_name, (metric_func, use_proba) in metrics.items():
- if use_proba:
- scores[metric_name] = metric_func(y_true, probabilities)
- else:
- scores[metric_name] = metric_func(y_true, predictions)
-
- return scores
-
-from sklearn.model_selection import train_test_split
-import pytorch_lightning as pl
-import torch
-from torch.utils.data import DataLoader
-from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
-from ..base_models.embedding_regressor import BaseEmbeddingMambularRegressor
-from ..utils.config import MambularConfig
-from ..utils.preprocessor import Preprocessor
-from ..utils.dataset import MambularDataModule, EmbeddingMambularDataset
-from sklearn.base import BaseEstimator
-import pandas as pd
-from sklearn.decomposition import PCA
-from sklearn.metrics import mean_squared_error
-
-
-[docs]class EmbeddingMambularRegressor(BaseEstimator):
- """
- An sklearn-like interface for the ProteinMambularRegressor, making it compatible with sklearn's utilities
- and workflows. This class wraps the PyTorch Lightning model and preprocessor, providing methods for fitting,
- predicting, and setting/getting parameters in a way that mimics sklearn's API.
-
- Parameters
- ----------
- **kwargs : Keyword arguments that can include both configuration parameters for the MambularConfig and
- parameters for the preprocessor.
-
- Attributes
- ----------
- config : MambularConfig
- Configuration object containing model-specific parameters.
- preprocessor : Preprocessor
- Preprocessor object for data preprocessing steps.
- model : ProteinMambularRegressor
- The neural network model, initialized after the `fit` method is called.
- """
-
- def __init__(self, **kwargs):
- # Known config arguments
- config_arg_names = [
- "d_model",
- "n_layers",
- "dt_rank",
- "output_dimension",
- "pooling_method",
- "norm",
- "cls",
- "dt_min",
- "dt_max",
- "dropout",
- "bias",
- "weight_decay",
- "conv_bias",
- "d_state",
- "expand_factor",
- "d_conv",
- "dt_init",
- "dt_scale",
- "dt_init_floor",
- ]
- config_kwargs = {k: v for k, v in kwargs.items() if k in config_arg_names}
- self.config = MambularConfig(**config_kwargs)
-
- # The rest are assumed to be preprocessor arguments
- preprocessor_kwargs = {
- k: v for k, v in kwargs.items() if k not in config_arg_names
- }
-
- if not "numerical_preprocessing" in preprocessor_kwargs.keys():
- preprocessor_kwargs["numerical_preprocessing"] = "normalization"
- self.preprocessor = Preprocessor(**preprocessor_kwargs)
- self.model = None
-
- def get_params(self, deep=True):
- """
- Get parameters for this estimator. Overrides the BaseEstimator method.
-
- Parameters
- ----------
- deep : bool, default=True
- If True, returns the parameters for this estimator and contained sub-objects that are estimators.
-
- Returns
- -------
- params : dict
- Parameter names mapped to their values.
- """
- params = self.config_kwargs # Parameters used to initialize MambularConfig
-
- # If deep=True, include parameters from nested components like preprocessor
- if deep:
- # Assuming Preprocessor has a get_params method
- preprocessor_params = {
- "preprocessor__" + key: value
- for key, value in self.preprocessor.get_params().items()
- }
- params.update(preprocessor_params)
-
- return params
-
- def set_params(self, **parameters):
- """
- Set the parameters of this estimator. Overrides the BaseEstimator method.
-
- Parameters
- ----------
- **parameters : dict
- Estimator parameters to be set.
-
- Returns
- -------
- self : object
- The instance with updated parameters.
- """
- # Update config_kwargs with provided parameters
- valid_config_keys = self.config_kwargs.keys()
- config_updates = {k: v for k, v in parameters.items() if k in valid_config_keys}
- self.config_kwargs.update(config_updates)
-
- # Update the config object
- for key, value in config_updates.items():
- setattr(self.config, key, value)
-
- # Handle preprocessor parameters (prefixed with 'preprocessor__')
- preprocessor_params = {
- k.split("__")[1]: v
- for k, v in parameters.items()
- if k.startswith("preprocessor__")
- }
- if preprocessor_params:
- # Assuming Preprocessor has a set_params method
- self.preprocessor.set_params(**preprocessor_params)
-
- return self
-
- def split_data(self, X, y, val_size, random_state):
- """
- Splits the dataset into training and validation sets.
-
- Parameters
- ----------
- X : array-like or DataFrame, shape (n_samples, n_features)
- Input features.
- y : array-like, shape (n_samples,) or (n_samples, n_targets)
- Target values.
- val_size : float
- The proportion of the dataset to include in the validation split.
- random_state : int
- Controls the shuffling applied to the data before applying the split.
-
- Returns
- -------
- X_train, X_val, y_train, y_val : arrays
- The split datasets.
- """
- X_train, X_val, y_train, y_val = train_test_split(
- X, y, test_size=val_size, random_state=random_state
- )
-
- return X_train, X_val, y_train, y_val
-
- def preprocess_data(self, X_train, y_train, X_val, y_val, batch_size, shuffle):
- """
- Preprocesses the training and validation data, and creates DataLoaders for them.
-
- Parameters
- ----------
- X_train : DataFrame or array-like, shape (n_samples_train, n_features)
- Training feature set.
- y_train : array-like, shape (n_samples_train,)
- Training target values.
- X_val : DataFrame or array-like, shape (n_samples_val, n_features)
- Validation feature set.
- y_val : array-like, shape (n_samples_val,)
- Validation target values.
- batch_size : int
- Size of batches for the DataLoader.
- shuffle : bool
- Whether to shuffle the training data in the DataLoader.
-
- Returns
- -------
- data_module : MambularDataModule
- An instance of MambularDataModule containing the training and validation DataLoaders.
- """
- train_preprocessed_data = self.preprocessor.fit_transform(X_train, y_train)
- val_preprocessed_data = self.preprocessor.transform(X_val)
-
- # Update feature info based on the actual processed data
- (
- self.cat_feature_info,
- self.num_feature_info,
- ) = self.preprocessor.get_feature_info()
-
- # Initialize lists for tensors
- train_cat_tensors = []
- train_num_tensors = []
- val_cat_tensors = []
- val_num_tensors = []
-
- # Populate tensors for categorical features, if present in processed data
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[cat_key], dtype=torch.long)
- )
- if cat_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- if binned_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features, if present in processed data
- for key in self.num_feature_info:
- num_key = "num_" + str(
- key
- ) # Assuming numerical keys are prefixed with 'num_'
- if num_key in train_preprocessed_data:
- train_num_tensors.append(
- torch.tensor(train_preprocessed_data[num_key], dtype=torch.float)
- )
- if num_key in val_preprocessed_data:
- val_num_tensors.append(
- torch.tensor(val_preprocessed_data[num_key], dtype=torch.float)
- )
-
- train_labels = torch.tensor(y_train, dtype=torch.float)
- val_labels = torch.tensor(y_val, dtype=torch.float)
-
- # Create datasets
- train_dataset = EmbeddingMambularDataset(
- train_cat_tensors, train_num_tensors, train_labels
- )
- val_dataset = EmbeddingMambularDataset(
- val_cat_tensors, val_num_tensors, val_labels
- )
-
- # Create dataloaders
- train_dataloader = DataLoader(
- train_dataset, batch_size=batch_size, shuffle=shuffle
- )
- val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
-
- return MambularDataModule(train_dataloader, val_dataloader)
-
- def preprocess_test_data(self, X):
- """
- Preprocesses the test data and creates tensors for categorical and numerical features.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- Test feature set.
-
- Returns
- -------
- cat_tensors : list of Tensors
- List of tensors for each categorical feature.
- num_tensors : list of Tensors
- List of tensors for each numerical feature.
- """
- processed_data = self.preprocessor.transform(X)
-
- # Initialize lists for tensors
- cat_tensors = []
- num_tensors = []
-
- # Populate tensors for categorical features
- for key in self.cat_feature_info:
- cat_key = "cat_" + str(
- key
- ) # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + str(key) # for binned features
- if binned_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features
- for key in self.num_feature_info:
- num_key = "num_" + str(
- key
- ) # Assuming numerical keys are prefixed with 'num_'
- if num_key in processed_data:
- num_tensors.append(
- torch.tensor(processed_data[num_key], dtype=torch.float)
- )
-
- return cat_tensors, num_tensors
-
- def fit(
- self,
- X,
- y,
- val_size=0.2,
- X_val=None,
- y_val=None,
- max_epochs=100,
- random_state=101,
- batch_size=64,
- shuffle=True,
- patience=10,
- monitor="val_loss",
- mode="min",
- lr=1e-3,
- lr_patience=10,
- factor=0.75,
- weight_decay=0.025,
- raw_embeddings=False,
- seq_size=20,
- pca=False,
- **trainer_kwargs
- ):
- """
- Fits the ProteinMambularRegressor model to the training data.
-
- Parameters
- ----------
- X : array-like or DataFrame
- The training input samples.
- y : array-like
- The target values (class labels for classification, real numbers for regression).
- val_size : float, optional
- The proportion of the dataset to include in the validation split if `X_val` is not provided.
- X_val : array-like or DataFrame, optional
- The validation input samples.
- y_val : array-like, optional
- The validation target values.
- max_epochs : int, optional
- The maximum number of epochs for training.
- random_state : int, optional
- The seed used by the random number generator.
- batch_size : int, optional
- Size of the batches for training.
- shuffle : bool, optional
- Whether to shuffle the training data.
- patience : int, optional
- Patience for early stopping.
- monitor : str, optional
- Quantity to be monitored for early stopping.
- mode : str, optional
- One of {'auto', 'min', 'max'}. In 'min' mode, training will stop when the quantity monitored has stopped decreasing;
- in 'max' mode, it will stop when the quantity monitored has stopped increasing.
- lr : float, optional
- Learning rate for the optimizer.
- lr_patience : int, optional
- Number of epochs with no improvement after which the learning rate will be reduced.
- factor : float, optional
- Factor by which the learning rate will be reduced.
- weight_decay : float, optional
- Weight decay coefficient for regularization in the optimizer.
- raw_embeddings : bool, optional
- Whether to use raw numerical features directly or to process them into embeddings.
- seq_size : int, optional
- The sequence size for processing numerical features when not using raw embeddings.
- **trainer_kwargs : dict
- Additional keyword arguments for the PyTorch Lightning Trainer.
-
- Returns
- -------
- self : object
- Returns an instance of self.
- """
-
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- if X_val:
- if not isinstance(X_val, pd.DataFrame):
- X_val = pd.DataFrame(X_val)
-
- # Apply PCA if indicated
- if pca:
- self.pca_transformer = PCA(n_components=seq_size)
- X = pd.DataFrame(
- self.pca_transformer.fit_transform(X)
- ) # Fit and transform the PCA on the complete dataset
- if X_val is not None:
- X_val = pd.DataFrame(
- self.pca_transformer.transform(X_val)
- ) # Transform validation data with the same PCA model
-
- raw_embeddings = True
-
- if not X_val:
- X_train, X_val, y_train, y_val = self.split_data(
- X, y, val_size, random_state
- )
- else:
- X_train = X
- y_train = y
-
- data_module = self.preprocess_data(
- X_train, y_train, X_val, y_val, batch_size, shuffle
- )
-
- if raw_embeddings:
- self.config.d_model = X.shape[1]
-
- self.model = BaseEmbeddingMambularRegressor(
- config=self.config,
- cat_feature_info=self.cat_feature_info,
- num_feature_info=self.num_feature_info,
- lr=lr,
- lr_patience=lr_patience,
- lr_factor=factor,
- weight_decay=weight_decay,
- raw_embeddings=raw_embeddings,
- seq_size=seq_size,
- )
-
- early_stop_callback = EarlyStopping(
- monitor=monitor, min_delta=0.00, patience=patience, verbose=False, mode=mode
- )
-
- checkpoint_callback = ModelCheckpoint(
- monitor="val_loss", # Adjust according to your validation metric
- mode="min",
- save_top_k=1,
- dirpath="model_checkpoints", # Specify the directory to save checkpoints
- filename="best_model",
- )
-
- # Initialize the trainer and train the model
- trainer = pl.Trainer(
- max_epochs=max_epochs,
- callbacks=[early_stop_callback, checkpoint_callback],
- **trainer_kwargs
- )
- trainer.fit(self.model, data_module)
-
- best_model_path = checkpoint_callback.best_model_path
- if best_model_path:
- checkpoint = torch.load(best_model_path)
- self.model.load_state_dict(checkpoint["state_dict"])
-
- return self
-
- def predict(self, X):
- """
- Predicts target values for the given input samples.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- The input samples for which to predict target values.
-
- Returns
- -------
- predictions : ndarray, shape (n_samples,) or (n_samples, n_outputs)
- The predicted target values.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
-
- if hasattr(self, "pca_transformer"):
- X = pd.DataFrame(self.pca_transformer.transform(X))
-
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- predictions = self.model(cat_tensors, num_tensors)
-
- # Convert predictions to NumPy array and return
- return predictions.cpu().numpy()
-
- def evaluate(self, X, y_true, metrics=None):
- """
- Evaluate the model on the given data using specified metrics.
-
- Example:
- metrics = {
- 'Mean Squared Error': mean_squared_error,
- 'R2 Score': r2_score
- }
-
- # Assuming 'X_test' and 'y_test' are your test dataset and labels
- # Evaluate using the specified metrics
- results = regressor.evaluate(X_test, y_test, metrics=metrics)
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples to predict.
- y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
- The true target values against which to evaluate the predictions.
- metrics : dict
- A dictionary where keys are metric names and values are the metric functions.
-
- Returns
- -------
- scores : dict
- A dictionary with metric names as keys and their corresponding scores as values.
-
- Notes
- -----
- This method uses the `predict` method to generate predictions and computes each metric.
- """
- if metrics is None:
- metrics = {"Mean Squared Error": mean_squared_error}
-
- # Ensure input is in the correct format
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
-
- # Generate predictions using the trained model
- predictions = self.predict(X)
-
- # Initialize dictionary to store results
- scores = {}
-
- # Compute each metric
- for metric_name, metric_func in metrics.items():
- scores[metric_name] = metric_func(y_true, predictions)
-
- return scores
-
-from sklearn.model_selection import train_test_split
-import pytorch_lightning as pl
-import torch
-from torch.utils.data import DataLoader
-from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
-from ..base_models.regressor import BaseMambularRegressor
-from ..utils.config import MambularConfig
-from ..utils.preprocessor import Preprocessor
-from ..utils.dataset import MambularDataModule, MambularDataset
-from sklearn.base import BaseEstimator
-import pandas as pd
-from sklearn.metrics import mean_squared_error
-
-
-[docs]class MambularRegressor(BaseEstimator):
- """
- A regressor implemented using PyTorch Lightning that follows the scikit-learn API conventions. This class is designed
- to work with tabular data, offering a straightforward way to specify model configurations and preprocessing steps. It
- integrates seamlessly with scikit-learn's tools such as cross-validation and grid search.
-
- Parameters
- ----------
- **kwargs : Various
- Accepts any number of keyword arguments. Arguments recognized as model configuration options are passed to the
- MambularConfig constructor. Remaining arguments are assumed to be preprocessor options and passed to the
- Preprocessor constructor.
-
- Attributes
- ----------
- config : MambularConfig
- An object storing the configuration settings for the model.
- preprocessor : Preprocessor
- An object responsible for preprocessing the input data, such as encoding categorical variables and scaling numerical features.
- model : BaseMambularRegressor or None
- The underlying regression model, which is a PyTorch Lightning module. It is instantiated when the `fit` method is called.
- """
-
- def __init__(self, **kwargs):
- # Known config arguments
- print("Received kwargs:", kwargs)
- config_arg_names = [
- "d_model",
- "n_layers",
- "dt_rank",
- "output_dimension",
- "pooling_method",
- "norm",
- "cls",
- "dt_min",
- "dt_max",
- "dropout",
- "bias",
- "weight_decay",
- "conv_bias",
- "d_state",
- "expand_factor",
- "d_conv",
- "dt_init",
- "dt_scale",
- "dt_init_floor",
- "tabular_head_units",
- "tabular_head_activation",
- "tabular_head_dropout",
- "num_emebedding_activation",
- "layer_norm_after_embedding",
- ]
- self.config_kwargs = {k: v for k, v in kwargs.items() if k in config_arg_names}
- self.config = MambularConfig(**self.config_kwargs)
-
- # The rest are assumed to be preprocessor arguments
- preprocessor_kwargs = {
- k: v for k, v in kwargs.items() if k not in config_arg_names
- }
- self.preprocessor = Preprocessor(**preprocessor_kwargs)
- self.model = None
-
- def get_params(self, deep=True):
- """
- Get parameters for this estimator. Overrides the BaseEstimator method.
-
- Parameters
- ----------
- deep : bool, default=True
- If True, returns the parameters for this estimator and contained sub-objects that are estimators.
-
- Returns
- -------
- params : dict
- Parameter names mapped to their values.
- """
- params = self.config_kwargs # Parameters used to initialize MambularConfig
-
- # If deep=True, include parameters from nested components like preprocessor
- if deep:
- # Assuming Preprocessor has a get_params method
- preprocessor_params = {
- "preprocessor__" + key: value
- for key, value in self.preprocessor.get_params().items()
- }
- params.update(preprocessor_params)
-
- return params
-
- def set_params(self, **parameters):
- """
- Set the parameters of this estimator. Overrides the BaseEstimator method.
-
- Parameters
- ----------
- **parameters : dict
- Estimator parameters to be set.
-
- Returns
- -------
- self : object
- The instance with updated parameters.
- """
- # Update config_kwargs with provided parameters
- valid_config_keys = self.config_kwargs.keys()
- config_updates = {k: v for k, v in parameters.items() if k in valid_config_keys}
- self.config_kwargs.update(config_updates)
-
- # Update the config object
- for key, value in config_updates.items():
- setattr(self.config, key, value)
-
- # Handle preprocessor parameters (prefixed with 'preprocessor__')
- preprocessor_params = {
- k.split("__")[1]: v
- for k, v in parameters.items()
- if k.startswith("preprocessor__")
- }
- if preprocessor_params:
- # Assuming Preprocessor has a set_params method
- self.preprocessor.set_params(**preprocessor_params)
-
- return self
-
- def split_data(self, X, y, val_size, random_state):
- """
- Splits the dataset into training and validation sets.
-
- Parameters
- ----------
- X : array-like or DataFrame, shape (n_samples, n_features)
- Input features.
- y : array-like, shape (n_samples,) or (n_samples, n_targets)
- Target values.
- val_size : float
- The proportion of the dataset to include in the validation split.
- random_state : int
- Controls the shuffling applied to the data before applying the split.
-
- Returns
- -------
- X_train, X_val, y_train, y_val : arrays
- The split datasets.
- """
- X_train, X_val, y_train, y_val = train_test_split(
- X, y, test_size=val_size, random_state=random_state
- )
-
- return X_train, X_val, y_train, y_val
-
- def preprocess_data(self, X_train, y_train, X_val, y_val, batch_size, shuffle):
- """
- Preprocesses the training and validation data, and creates DataLoaders for them.
-
- Parameters
- ----------
- X_train : DataFrame or array-like, shape (n_samples_train, n_features)
- Training feature set.
- y_train : array-like, shape (n_samples_train,)
- Training target values.
- X_val : DataFrame or array-like, shape (n_samples_val, n_features)
- Validation feature set.
- y_val : array-like, shape (n_samples_val,)
- Validation target values.
- batch_size : int
- Size of batches for the DataLoader.
- shuffle : bool
- Whether to shuffle the training data in the DataLoader.
-
- Returns
- -------
- data_module : MambularDataModule
- An instance of MambularDataModule containing the training and validation DataLoaders.
- """
- train_preprocessed_data = self.preprocessor.fit_transform(X_train, y_train)
- val_preprocessed_data = self.preprocessor.transform(X_val)
-
- # Update feature info based on the actual processed data
- (
- self.cat_feature_info,
- self.num_feature_info,
- ) = self.preprocessor.get_feature_info()
-
- # Initialize lists for tensors
- train_cat_tensors = []
- train_num_tensors = []
- val_cat_tensors = []
- val_num_tensors = []
-
- # Populate tensors for categorical features, if present in processed data
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[cat_key], dtype=torch.long)
- )
- if cat_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in train_preprocessed_data:
- train_cat_tensors.append(
- torch.tensor(train_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- if binned_key in val_preprocessed_data:
- val_cat_tensors.append(
- torch.tensor(val_preprocessed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features, if present in processed data
- for key in self.num_feature_info:
- num_key = "num_" + key # Assuming numerical keys are prefixed with 'num_'
- if num_key in train_preprocessed_data:
- train_num_tensors.append(
- torch.tensor(train_preprocessed_data[num_key], dtype=torch.float)
- )
- if num_key in val_preprocessed_data:
- val_num_tensors.append(
- torch.tensor(val_preprocessed_data[num_key], dtype=torch.float)
- )
-
- train_labels = torch.tensor(y_train, dtype=torch.float)
- val_labels = torch.tensor(y_val, dtype=torch.float)
-
- # Create datasets
- train_dataset = MambularDataset(
- train_cat_tensors, train_num_tensors, train_labels
- )
- val_dataset = MambularDataset(val_cat_tensors, val_num_tensors, val_labels)
-
- # Create dataloaders
- train_dataloader = DataLoader(
- train_dataset, batch_size=batch_size, shuffle=shuffle
- )
- val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
-
- return MambularDataModule(train_dataloader, val_dataloader)
-
- def preprocess_test_data(self, X):
- """
- Preprocesses the test data and creates tensors for categorical and numerical features.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- Test feature set.
-
- Returns
- -------
- cat_tensors : list of Tensors
- List of tensors for each categorical feature.
- num_tensors : list of Tensors
- List of tensors for each numerical feature.
- """
- processed_data = self.preprocessor.transform(X)
-
- # Initialize lists for tensors
- cat_tensors = []
- num_tensors = []
-
- # Populate tensors for categorical features
- for key in self.cat_feature_info:
- cat_key = "cat_" + key # Assuming categorical keys are prefixed with 'cat_'
- if cat_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[cat_key], dtype=torch.long)
- )
-
- binned_key = "num_" + key # for binned features
- if binned_key in processed_data:
- cat_tensors.append(
- torch.tensor(processed_data[binned_key], dtype=torch.long)
- )
-
- # Populate tensors for numerical features
- for key in self.num_feature_info:
- num_key = "num_" + key # Assuming numerical keys are prefixed with 'num_'
- if num_key in processed_data:
- num_tensors.append(
- torch.tensor(processed_data[num_key], dtype=torch.float)
- )
-
- return cat_tensors, num_tensors
-
- def fit(
- self,
- X,
- y,
- val_size=0.2,
- X_val=None,
- y_val=None,
- max_epochs=100,
- random_state=101,
- batch_size=128,
- shuffle=True,
- patience=10,
- monitor="val_loss",
- mode="min",
- lr=1e-3,
- lr_patience=5,
- factor=0.75,
- weight_decay=1e-06,
- **trainer_kwargs
- ):
- """
- Trains the regression model using the provided training data. Optionally, a separate validation set can be used.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- The training input samples.
- y : array-like, shape (n_samples,) or (n_samples, n_targets)
- The target values (real numbers).
- val_size : float, default=0.2
- The proportion of the dataset to include in the validation split if `X_val` is None. Ignored if `X_val` is provided.
- X_val : DataFrame or array-like, shape (n_samples, n_features), optional
- The validation input samples. If provided, `X` and `y` are not split and this data is used for validation.
- y_val : array-like, shape (n_samples,) or (n_samples, n_targets), optional
- The validation target values. Required if `X_val` is provided.
- max_epochs : int, default=100
- Maximum number of epochs for training.
- random_state : int, default=101
- Controls the shuffling applied to the data before applying the split.
- batch_size : int, default=64
- Number of samples per gradient update.
- shuffle : bool, default=True
- Whether to shuffle the training data before each epoch.
- patience : int, default=10
- Number of epochs with no improvement on the validation loss to wait before early stopping.
- monitor : str, default="val_loss"
- The metric to monitor for early stopping.
- mode : str, default="min"
- Whether the monitored metric should be minimized (`min`) or maximized (`max`).
- lr : float, default=1e-3
- Learning rate for the optimizer.
- lr_patience : int, default=10
- Number of epochs with no improvement on the validation loss to wait before reducing the learning rate.
- factor : float, default=0.75
- Factor by which the learning rate will be reduced.
- weight_decay : float, default=0.025
- Weight decay (L2 penalty) coefficient.
- **trainer_kwargs : Additional keyword arguments for PyTorch Lightning's Trainer class.
-
- Returns
- -------
- self : object
- The fitted regressor.
- """
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- if X_val:
- if not isinstance(X_val, pd.DataFrame):
- X_val = pd.DataFrame(X_val)
-
- if not X_val:
- X_train, X_val, y_train, y_val = self.split_data(
- X, y, val_size, random_state
- )
-
- data_module = self.preprocess_data(
- X_train, y_train, X_val, y_val, batch_size, shuffle
- )
-
- self.model = BaseMambularRegressor(
- config=self.config,
- cat_feature_info=self.cat_feature_info,
- num_feature_info=self.num_feature_info,
- lr=lr,
- lr_patience=lr_patience,
- lr_factor=factor,
- weight_decay=weight_decay,
- )
-
- early_stop_callback = EarlyStopping(
- monitor=monitor, min_delta=0.00, patience=patience, verbose=False, mode=mode
- )
-
- checkpoint_callback = ModelCheckpoint(
- monitor="val_loss", # Adjust according to your validation metric
- mode="min",
- save_top_k=1,
- dirpath="model_checkpoints", # Specify the directory to save checkpoints
- filename="best_model",
- )
-
- # Initialize the trainer and train the model
- trainer = pl.Trainer(
- max_epochs=max_epochs,
- callbacks=[early_stop_callback, checkpoint_callback],
- **trainer_kwargs
- )
- trainer.fit(self.model, data_module)
-
- best_model_path = checkpoint_callback.best_model_path
- if best_model_path:
- checkpoint = torch.load(best_model_path)
- self.model.load_state_dict(checkpoint["state_dict"])
-
- return self
-
- def predict(self, X):
- """
- Predicts target values for the given input samples.
-
- Parameters
- ----------
- X : DataFrame or array-like, shape (n_samples, n_features)
- The input samples for which to predict target values.
-
- Returns
- -------
- predictions : ndarray, shape (n_samples,) or (n_samples, n_outputs)
- The predicted target values.
- """
- # Preprocess the data
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
- device = next(self.model.parameters()).device
- cat_tensors, num_tensors = self.preprocess_test_data(X)
- if isinstance(cat_tensors, list):
- cat_tensors = [tensor.to(device) for tensor in cat_tensors]
- else:
- cat_tensors = cat_tensors.to(device)
-
- if isinstance(num_tensors, list):
- num_tensors = [tensor.to(device) for tensor in num_tensors]
- else:
- num_tensors = num_tensors.to(device)
-
- # Set the model to evaluation mode
- self.model.eval()
-
- # Perform inference
- with torch.no_grad():
- predictions = self.model(cat_tensors, num_tensors)
-
- # Convert predictions to NumPy array and return
- return predictions.cpu().numpy()
-
- def evaluate(self, X, y_true, metrics=None):
- """
- Evaluate the model on the given data using specified metrics.
-
- Example:
- metrics = {
- 'Mean Squared Error': mean_squared_error,
- 'R2 Score': r2_score
- }
-
- # Assuming 'X_test' and 'y_test' are your test dataset and labels
- # Evaluate using the specified metrics
- results = regressor.evaluate(X_test, y_test, metrics=metrics)
-
- Parameters
- ----------
- X : array-like or pd.DataFrame of shape (n_samples, n_features)
- The input samples to predict.
- y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
- The true target values against which to evaluate the predictions.
- metrics : dict
- A dictionary where keys are metric names and values are the metric functions.
-
- Returns
- -------
- scores : dict
- A dictionary with metric names as keys and their corresponding scores as values.
-
- Notes
- -----
- This method uses the `predict` method to generate predictions and computes each metric.
- """
- if metrics is None:
- metrics = {"Mean Squared Error": mean_squared_error}
-
- # Ensure input is in the correct format
- if not isinstance(X, pd.DataFrame):
- X = pd.DataFrame(X)
-
- # Generate predictions using the trained model
- predictions = self.predict(X)
-
- # Initialize dictionary to store results
- scores = {}
-
- # Compute each metric
- for metric_name, metric_func in metrics.items():
- scores[metric_name] = metric_func(y_true, predictions)
-
- return scores
-Short
- */ - .o-tooltip--left { - position: relative; - } - - .o-tooltip--left:after { - opacity: 0; - visibility: hidden; - position: absolute; - content: attr(data-tooltip); - padding: .2em; - font-size: .8em; - left: -.2em; - background: grey; - color: white; - white-space: nowrap; - z-index: 2; - border-radius: 2px; - transform: translateX(-102%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); -} - -.o-tooltip--left:hover:after { - display: block; - opacity: 1; - visibility: visible; - transform: translateX(-100%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); - transition-delay: .5s; -} - -/* By default the copy button shouldn't show up when printing a page */ -@media print { - button.copybtn { - display: none; - } -} diff --git a/docs/_build/html/_static/copybutton.js b/docs/_build/html/_static/copybutton.js deleted file mode 100644 index 2ea7ff3..0000000 --- a/docs/_build/html/_static/copybutton.js +++ /dev/null @@ -1,248 +0,0 @@ -// Localization support -const messages = { - 'en': { - 'copy': 'Copy', - 'copy_to_clipboard': 'Copy to clipboard', - 'copy_success': 'Copied!', - 'copy_failure': 'Failed to copy', - }, - 'es' : { - 'copy': 'Copiar', - 'copy_to_clipboard': 'Copiar al portapapeles', - 'copy_success': '¡Copiado!', - 'copy_failure': 'Error al copiar', - }, - 'de' : { - 'copy': 'Kopieren', - 'copy_to_clipboard': 'In die Zwischenablage kopieren', - 'copy_success': 'Kopiert!', - 'copy_failure': 'Fehler beim Kopieren', - }, - 'fr' : { - 'copy': 'Copier', - 'copy_to_clipboard': 'Copier dans le presse-papier', - 'copy_success': 'Copié !', - 'copy_failure': 'Échec de la copie', - }, - 'ru': { - 'copy': 'Скопировать', - 'copy_to_clipboard': 'Скопировать в буфер', - 'copy_success': 'Скопировано!', - 'copy_failure': 'Не удалось скопировать', - }, - 'zh-CN': { - 'copy': '复制', - 'copy_to_clipboard': '复制到剪贴板', - 'copy_success': '复制成功!', - 'copy_failure': '复制失败', - }, - 'it' : { - 'copy': 'Copiare', - 'copy_to_clipboard': 'Copiato negli appunti', - 'copy_success': 'Copiato!', - 'copy_failure': 'Errore durante la copia', - } -} - -let locale = 'en' -if( document.documentElement.lang !== undefined - && messages[document.documentElement.lang] !== undefined ) { - locale = document.documentElement.lang -} - -let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; -if (doc_url_root == '#') { - doc_url_root = ''; -} - -/** - * SVG files for our copy buttons - */ -let iconCheck = `` - -// If the user specified their own SVG use that, otherwise use the default -let iconCopy = ``; -if (!iconCopy) { - iconCopy = `` -} - -/** - * Set up copy/paste for code blocks - */ - -const runWhenDOMLoaded = cb => { - if (document.readyState != 'loading') { - cb() - } else if (document.addEventListener) { - document.addEventListener('DOMContentLoaded', cb) - } else { - document.attachEvent('onreadystatechange', function() { - if (document.readyState == 'complete') cb() - }) - } -} - -const codeCellId = index => `codecell${index}` - -// Clears selected text since ClipboardJS will select the text when copying -const clearSelection = () => { - if (window.getSelection) { - window.getSelection().removeAllRanges() - } else if (document.selection) { - document.selection.empty() - } -} - -// Changes tooltip text for a moment, then changes it back -// We want the timeout of our `success` class to be a bit shorter than the -// tooltip and icon change, so that we can hide the icon before changing back. -var timeoutIcon = 2000; -var timeoutSuccessClass = 1500; - -const temporarilyChangeTooltip = (el, oldText, newText) => { - el.setAttribute('data-tooltip', newText) - el.classList.add('success') - // Remove success a little bit sooner than we change the tooltip - // So that we can use CSS to hide the copybutton first - setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) - setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) -} - -// Changes the copy button icon for two seconds, then changes it back -const temporarilyChangeIcon = (el) => { - el.innerHTML = iconCheck; - setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) -} - -const addCopyButtonToCodeCells = () => { - // If ClipboardJS hasn't loaded, wait a bit and try again. This - // happens because we load ClipboardJS asynchronously. - if (window.ClipboardJS === undefined) { - setTimeout(addCopyButtonToCodeCells, 250) - return - } - - // Add copybuttons to all of our code cells - const COPYBUTTON_SELECTOR = 'div.highlight pre'; - const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) - codeCells.forEach((codeCell, index) => { - const id = codeCellId(index) - codeCell.setAttribute('id', id) - - const clipboardButton = id => - `` - codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) - }) - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} - - -var copyTargetText = (trigger) => { - var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - - // get filtered text - let exclude = '.linenos'; - - let text = filterText(target, exclude); - return formatCopyText(text, '', false, true, true, true, '', '') -} - - // Initialize with a callback so we can modify the text before copy - const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) - - // Update UI with error/success messages - clipboard.on('success', event => { - clearSelection() - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) - temporarilyChangeIcon(event.trigger) - }) - - clipboard.on('error', event => { - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) - }) -} - -runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/docs/_build/html/_static/copybutton_funcs.js b/docs/_build/html/_static/copybutton_funcs.js deleted file mode 100644 index dbe1aaa..0000000 --- a/docs/_build/html/_static/copybutton_funcs.js +++ /dev/null @@ -1,73 +0,0 @@ -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -export function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} diff --git a/docs/_build/html/_static/css/badge_only.css b/docs/_build/html/_static/css/badge_only.css deleted file mode 100644 index c718cee..0000000 --- a/docs/_build/html/_static/css/badge_only.css 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" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - localStorage.removeItem("sphinx_highlight_terms") - }, - - initEscapeListener: () => { - // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; - if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { - SphinxHighlight.hideSearchWords(); - event.preventDefault(); - } - }); - }, -}; - -_ready(SphinxHighlight.highlightSearchWords); -_ready(SphinxHighlight.initEscapeListener); diff --git a/docs/_build/html/_static/togglebutton.css b/docs/_build/html/_static/togglebutton.css deleted file mode 100644 index 54a6787..0000000 --- a/docs/_build/html/_static/togglebutton.css +++ /dev/null @@ -1,160 +0,0 @@ -/** - * Admonition-based toggles - */ - -/* Visibility of the target */ -.admonition.toggle .admonition-title ~ * { - transition: opacity .3s, height .3s; -} - -/* Toggle buttons inside admonitions so we see the title */ -.admonition.toggle { - position: relative; -} - -/* Titles should cut off earlier to avoid overlapping w/ button */ -.admonition.toggle .admonition-title { - padding-right: 25%; - cursor: pointer; -} - -/* Hovering will cause a slight shift in color to make it feel interactive */ -.admonition.toggle .admonition-title:hover { - box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 1%); -} - -/* Hovering will cause a slight shift in color to make it feel interactive */ -.admonition.toggle .admonition-title:active { - box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 3%); -} - -/* Remove extra whitespace below the admonition title when hidden */ -.admonition.toggle-hidden { - padding-bottom: 0; -} - -.admonition.toggle-hidden .admonition-title { - margin-bottom: 0; -} - -/* hides all the content of a page until de-toggled */ -.admonition.toggle-hidden .admonition-title ~ * { - height: 0; - margin: 0; - opacity: 0; - visibility: hidden; -} - -/* General button style and position*/ -button.toggle-button { - /** - * Background and shape. By default there's no background - * but users can style as they wish - */ - background: none; - border: none; - outline: none; - - /* Positioning just inside the admonition title */ - position: absolute; - right: 0.5em; - padding: 0px; - border: none; - outline: none; -} - -/* Display the toggle hint on wide screens */ -@media (min-width: 768px) { - button.toggle-button.toggle-button-hidden:before { - content: attr(data-toggle-hint); /* This will be filled in by JS */ - font-size: .8em; - align-self: center; - } -} - -/* Icon behavior */ -.tb-icon { - transition: transform .2s ease-out; - height: 1.5em; - width: 1.5em; - stroke: currentColor; /* So that we inherit the color of other text */ -} - -/* The icon should point right when closed, down when open. */ -/* Open */ -.admonition.toggle button .tb-icon { - transform: rotate(90deg); -} - -/* Closed */ -.admonition.toggle button.toggle-button-hidden .tb-icon { - transform: rotate(0deg); -} - -/* With details toggles, we don't rotate the icon so it points right */ -details.toggle-details .tb-icon { - height: 1.4em; - width: 1.4em; - margin-top: 0.1em; /* To center the button vertically */ -} - - -/** - * Details-based toggles. - * In this case, we wrap elements with `.toggle` in a details block. - */ - -/* Details blocks */ -details.toggle-details { - margin: 1em 0; -} - - -details.toggle-details summary { - display: flex; - align-items: center; - cursor: pointer; - list-style: none; - border-radius: .2em; - border-left: 3px solid #1976d2; - background-color: rgb(204 204 204 / 10%); - padding: 0.2em 0.7em 0.3em 0.5em; /* Less padding on left because the SVG has left margin */ - font-size: 0.9em; -} - -details.toggle-details summary:hover { - background-color: rgb(204 204 204 / 20%); -} - -details.toggle-details summary:active { - background: rgb(204 204 204 / 28%); -} - -.toggle-details__summary-text { - margin-left: 0.2em; -} - -details.toggle-details[open] summary { - margin-bottom: .5em; -} - -details.toggle-details[open] summary .tb-icon { - transform: rotate(90deg); -} - -details.toggle-details[open] summary ~ * { - animation: toggle-fade-in .3s ease-out; -} - -@keyframes toggle-fade-in { - from {opacity: 0%;} - to {opacity: 100%;} -} - -/* Print rules - we hide all toggle button elements at print */ -@media print { - /* Always hide the summary so the button doesn't show up */ - details.toggle-details summary { - display: none; - } -} \ No newline at end of file diff --git a/docs/_build/html/_static/togglebutton.js b/docs/_build/html/_static/togglebutton.js deleted file mode 100644 index 215a7ee..0000000 --- a/docs/_build/html/_static/togglebutton.js +++ /dev/null @@ -1,187 +0,0 @@ -/** - * Add Toggle Buttons to elements - */ - -let toggleChevron = ` -`; - -var initToggleItems = () => { - var itemsToToggle = document.querySelectorAll(togglebuttonSelector); - console.log(`[togglebutton]: Adding toggle buttons to ${itemsToToggle.length} items`) - // Add the button to each admonition and hook up a callback to toggle visibility - itemsToToggle.forEach((item, index) => { - if (item.classList.contains("admonition")) { - // If it's an admonition block, then we'll add a button inside - // Generate unique IDs for this item - var toggleID = `toggle-${index}`; - var buttonID = `button-${toggleID}`; - - item.setAttribute('id', toggleID); - if (!item.classList.contains("toggle")){ - item.classList.add("toggle"); - } - // This is the button that will be added to each item to trigger the toggle - var collapseButton = ` - `; - - title = item.querySelector(".admonition-title") - title.insertAdjacentHTML("beforeend", collapseButton); - thisButton = document.getElementById(buttonID); - - // Add click handlers for the button + admonition title (if admonition) - admonitionTitle = document.querySelector(`#${toggleID} > .admonition-title`) - if (admonitionTitle) { - // If an admonition, then make the whole title block clickable - admonitionTitle.addEventListener('click', toggleClickHandler); - admonitionTitle.dataset.target = toggleID - admonitionTitle.dataset.button = buttonID - } else { - // If not an admonition then we'll listen for the button click - thisButton.addEventListener('click', toggleClickHandler); - } - - // Now hide the item for this toggle button unless explicitly noted to show - if (!item.classList.contains("toggle-shown")) { - toggleHidden(thisButton); - } - } else { - // If not an admonition, wrap the block in a