TabularRegressor¶

class flash.tabular.regression.model.TabularRegressor(parameters, embedding_sizes, cat_dims, num_features, backbone='tabnet', loss_fn=<function mse_loss>, optimizer='Adam', lr_scheduler=None, metrics=None, learning_rate=None, **backbone_kwargs)[source]¶

The TabularRegressor is a Task for classifying tabular data. For more details, see Tabular Classification.

Parameters

parameters¶ (Dict[str, Any]) – The parameters computed from the training data (can be obtained from the parameters attribute of the TabularRegressionData object containing your training data).
embedding_sizes¶ (list) – List of (num_classes, emb_dim) to form categorical embeddings.
cat_dims¶ (list) – Number of distinct values for each categorical column.
num_features¶ (int) – Number of columns in table.
backbone¶ (str) – name of the model to use.
loss_fn¶ (Callable) – Loss function for training, defaults to mean squared error.
optimizer¶ (TypeVar(OPTIMIZER_TYPE, str, Callable, Tuple[str, Dict[str, Any]], None)) – Optimizer to use for training.
lr_scheduler¶ (Optional[TypeVar(LR_SCHEDULER_TYPE, str, Callable, Tuple[str, Dict[str, Any]], Tuple[str, Dict[str, Any], Dict[str, Any]], None)]) – The LR scheduler to use during training.
metrics¶ (Optional[TypeVar(METRICS_TYPE, Metric, Mapping, Sequence, None)]) – Metrics to compute for training and evaluation. Can either be an metric from the torchmetrics package, a custom metric inherenting from torchmetrics.Metric, a callable function or a list/dict containing a combination of the aforementioned. In all cases, each metric needs to have the signature metric(preds,target) and return a single scalar tensor. Defaults to torchmetrics.Accuracy.
learning_rate¶ (Optional[float]) – Learning rate to use for training.
**backbone_kwargs¶ – Optional additional arguments for the model.

classmethod available_finetuning_strategies(cls)¶

Returns a list containing the keys of the available Finetuning Strategies.

Return type: List[str]

classmethod available_lr_schedulers(cls)¶

Returns a list containing the keys of the available LR schedulers.

Return type: List[str]

classmethod available_optimizers(cls)¶

Returns a list containing the keys of the available Optimizers.

Return type: List[str]

classmethod available_outputs(cls)¶

Returns the list of available outputs (that can be used during prediction or serving) for this Task.

Examples

..testsetup:

>>> from flash import Task

>>> print(Task.available_outputs())
['preds', 'raw']

Return type: List[str]

property data_parameters: Dict[str, Any]¶

Get the parameters computed from the training data used to create this TabularRegressor. Use these parameters to load data for evaluation / prediction.

Example:

import flash
from flash.core.data.utils import download_data
from flash.tabular import TabularRegressionData, TabularRegressor
download_data("https://pl-flash-data.s3.amazonaws.com/SeoulBikeData.csv", "./data")
model = TabularRegressor.load_from_checkpoint(
    "https://flash-weights.s3.amazonaws.com/0.7.0/tabular_regression_model.pt"
)
datamodule = TabularRegressionData.from_csv(
    predict_file="data/SeoulBikeData.csv",
    parameters=model.data_parameters,
    batch_size=8,
)
trainer = flash.Trainer()
trainer.predict(
    model,
    datamodule=datamodule,
)

Return type: Dict[str, Any]

classmethod load_from_checkpoint(cls, checkpoint_path, map_location=None, hparams_file=None, strict=True, **kwargs)¶

Primary way of loading a model from a checkpoint. When Lightning saves a checkpoint it stores the arguments passed to __init__ in the checkpoint under "hyper_parameters".

Any arguments specified through **kwargs will override args stored in "hyper_parameters".

Parameters

checkpoint_path¶ (Union[str, Path, IO]) – Path to checkpoint. This can also be a URL, or file-like object
map_location¶ (Union[device, str, int, Callable[[Union[device, str, int]], Union[device, str, int]], Dict[Union[device, str, int], Union[device, str, int]], None]) – If your checkpoint saved a GPU model and you now load on CPUs or a different number of GPUs, use this to map to the new setup. The behaviour is the same as in torch.load().
hparams_file¶ (Union[str, Path, None]) –
Optional path to a .yaml or .csv file with hierarchical structure as in this example:
```
drop_prob: 0.2
dataloader:
    batch_size: 32
```
You most likely won’t need this since Lightning will always save the hyperparameters to the checkpoint. However, if your checkpoint weights don’t have the hyperparameters saved, use this method to pass in a .yaml file with the hparams you’d like to use. These will be converted into a dict and passed into your LightningModule for use.

If your model’s hparams argument is Namespace and .yaml file has hierarchical structure, you need to refactor your model to treat hparams as dict.
strict¶ (bool) – Whether to strictly enforce that the keys in checkpoint_path match the keys returned by this module’s state dict.
**kwargs¶ – Any extra keyword args needed to init the model. Can also be used to override saved hyperparameter values.

Return type

Self

Returns

LightningModule instance with loaded weights and hyperparameters (if available).

Note

load_from_checkpoint is a class method. You should use your LightningModule class to call it instead of the LightningModule instance.

Example:

# load weights without mapping ...
model = MyLightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')

# or load weights mapping all weights from GPU 1 to GPU 0 ...
map_location = {'cuda:1':'cuda:0'}
model = MyLightningModule.load_from_checkpoint(
    'path/to/checkpoint.ckpt',
    map_location=map_location
)

# or load weights and hyperparameters from separate files.
model = MyLightningModule.load_from_checkpoint(
    'path/to/checkpoint.ckpt',
    hparams_file='/path/to/hparams_file.yaml'
)

# override some of the params with new values
model = MyLightningModule.load_from_checkpoint(
    PATH,
    num_layers=128,
    pretrained_ckpt_path=NEW_PATH,
)

# predict
pretrained_model.eval()
pretrained_model.freeze()
y_hat = pretrained_model(x)