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Adding tabular regression pipeline (#85)
* removed old supported_tasks dictionary from heads, added some docstrings and some small fixes * removed old supported_tasks attribute and updated doc strings in base backbone and base head components * removed old supported_tasks attribute from network backbones * put time series backbones in separate files, add doc strings and refactored search space arguments * split image networks into separate files, add doc strings and refactor search space * fix typo * add an intial simple backbone test similar to the network head test * fix flake8 * fixed imports in backbones and heads * added new network backbone and head tests * enabled tests for adding custom backbones and heads, added required properties to base head and base backbone * adding tabular regression pipeline * fix flake8 * adding tabular regression pipeline * fix flake8 * fix regression test * fix indentation and comments, undo change in base network * pipeline fitting tests now check the expected output shape dynamically based on the input data * refactored trainer tests, added trainer test for regression * remove regression from mixup unitest * use pandas unique instead of numpy * [IMPORTANT] added proper target casting based on task type to base trainer * adding tabular regression task to api * adding tabular regression example, some small fixes * new/more tests for tabular regression * fix mypy and flake8 errors from merge * fix issues with new weighted loss and regression tasks * change tabular column transformer to use net fit_dictionary_tabular fixture * fixing tests, replaced num_classes with output_shape * fixes after merge * adding voting regressor wrapper * fix mypy and flake * updated example * lower r2 target * address comments * increasing timeout * increase number of labels in test_losses because it occasionally failed if one class was not in the labels * lower regression lr in score test until seeding properly works * fix randomization in feature validator test
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import os | ||
import uuid | ||
from typing import Any, Callable, Dict, List, Optional, Union | ||
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import numpy as np | ||
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import pandas as pd | ||
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from autoPyTorch.api.base_task import BaseTask | ||
from autoPyTorch.constants import ( | ||
TABULAR_REGRESSION, | ||
TASK_TYPES_TO_STRING | ||
) | ||
from autoPyTorch.data.tabular_validator import TabularInputValidator | ||
from autoPyTorch.datasets.base_dataset import BaseDataset | ||
from autoPyTorch.datasets.resampling_strategy import ( | ||
CrossValTypes, | ||
HoldoutValTypes, | ||
) | ||
from autoPyTorch.datasets.tabular_dataset import TabularDataset | ||
from autoPyTorch.pipeline.tabular_regression import TabularRegressionPipeline | ||
from autoPyTorch.utils.backend import Backend | ||
from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates | ||
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class TabularRegressionTask(BaseTask): | ||
""" | ||
Tabular Regression API to the pipelines. | ||
Args: | ||
seed (int): seed to be used for reproducibility. | ||
n_jobs (int), (default=1): number of consecutive processes to spawn. | ||
logging_config (Optional[Dict]): specifies configuration | ||
for logging, if None, it is loaded from the logging.yaml | ||
ensemble_size (int), (default=50): Number of models added to the ensemble built by | ||
Ensemble selection from libraries of models. | ||
Models are drawn with replacement. | ||
ensemble_nbest (int), (default=50): only consider the ensemble_nbest | ||
models to build the ensemble | ||
max_models_on_disc (int), (default=50): maximum number of models saved to disc. | ||
Also, controls the size of the ensemble as any additional models will be deleted. | ||
Must be greater than or equal to 1. | ||
temporary_directory (str): folder to store configuration output and log file | ||
output_directory (str): folder to store predictions for optional test set | ||
delete_tmp_folder_after_terminate (bool): determines whether to delete the temporary directory, | ||
when finished | ||
include_components (Optional[Dict]): If None, all possible components are used. | ||
Otherwise specifies set of components to use. | ||
exclude_components (Optional[Dict]): If None, all possible components are used. | ||
Otherwise specifies set of components not to use. Incompatible with include | ||
components | ||
""" | ||
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def __init__( | ||
self, | ||
seed: int = 1, | ||
n_jobs: int = 1, | ||
logging_config: Optional[Dict] = None, | ||
ensemble_size: int = 50, | ||
ensemble_nbest: int = 50, | ||
max_models_on_disc: int = 50, | ||
temporary_directory: Optional[str] = None, | ||
output_directory: Optional[str] = None, | ||
delete_tmp_folder_after_terminate: bool = True, | ||
delete_output_folder_after_terminate: bool = True, | ||
include_components: Optional[Dict] = None, | ||
exclude_components: Optional[Dict] = None, | ||
resampling_strategy: Union[CrossValTypes, HoldoutValTypes] = HoldoutValTypes.holdout_validation, | ||
resampling_strategy_args: Optional[Dict[str, Any]] = None, | ||
backend: Optional[Backend] = None, | ||
search_space_updates: Optional[HyperparameterSearchSpaceUpdates] = None | ||
): | ||
super().__init__( | ||
seed=seed, | ||
n_jobs=n_jobs, | ||
logging_config=logging_config, | ||
ensemble_size=ensemble_size, | ||
ensemble_nbest=ensemble_nbest, | ||
max_models_on_disc=max_models_on_disc, | ||
temporary_directory=temporary_directory, | ||
output_directory=output_directory, | ||
delete_tmp_folder_after_terminate=delete_tmp_folder_after_terminate, | ||
delete_output_folder_after_terminate=delete_output_folder_after_terminate, | ||
include_components=include_components, | ||
exclude_components=exclude_components, | ||
backend=backend, | ||
resampling_strategy=resampling_strategy, | ||
resampling_strategy_args=resampling_strategy_args, | ||
search_space_updates=search_space_updates, | ||
task_type=TASK_TYPES_TO_STRING[TABULAR_REGRESSION], | ||
) | ||
|
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def _get_required_dataset_properties(self, dataset: BaseDataset) -> Dict[str, Any]: | ||
if not isinstance(dataset, TabularDataset): | ||
raise ValueError("Dataset is incompatible for the given task,: {}".format( | ||
type(dataset) | ||
)) | ||
return {'task_type': dataset.task_type, | ||
'output_type': dataset.output_type, | ||
'issparse': dataset.issparse, | ||
'numerical_columns': dataset.numerical_columns, | ||
'categorical_columns': dataset.categorical_columns} | ||
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def build_pipeline(self, dataset_properties: Dict[str, Any]) -> TabularRegressionPipeline: | ||
return TabularRegressionPipeline(dataset_properties=dataset_properties) | ||
|
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def search(self, | ||
optimize_metric: str, | ||
X_train: Optional[Union[List, pd.DataFrame, np.ndarray]] = None, | ||
y_train: Optional[Union[List, pd.DataFrame, np.ndarray]] = None, | ||
X_test: Optional[Union[List, pd.DataFrame, np.ndarray]] = None, | ||
y_test: Optional[Union[List, pd.DataFrame, np.ndarray]] = None, | ||
dataset_name: Optional[str] = None, | ||
budget_type: Optional[str] = None, | ||
budget: Optional[float] = None, | ||
total_walltime_limit: int = 100, | ||
func_eval_time_limit: int = 60, | ||
traditional_per_total_budget: float = 0.1, | ||
memory_limit: Optional[int] = 4096, | ||
smac_scenario_args: Optional[Dict[str, Any]] = None, | ||
get_smac_object_callback: Optional[Callable] = None, | ||
all_supported_metrics: bool = True, | ||
precision: int = 32, | ||
disable_file_output: List = [], | ||
load_models: bool = True, | ||
) -> 'BaseTask': | ||
""" | ||
Search for the best pipeline configuration for the given dataset. | ||
Fit both optimizes the machine learning models and builds an ensemble out of them. | ||
To disable ensembling, set ensemble_size==0. | ||
using the optimizer. | ||
Args: | ||
X_train, y_train, X_test, y_test: Union[np.ndarray, List, pd.DataFrame] | ||
A pair of features (X_train) and targets (y_train) used to fit a | ||
pipeline. Additionally, a holdout of this pairs (X_test, y_test) can | ||
be provided to track the generalization performance of each stage. | ||
optimize_metric (str): name of the metric that is used to | ||
evaluate a pipeline. | ||
budget_type (Optional[str]): | ||
Type of budget to be used when fitting the pipeline. | ||
Either 'epochs' or 'runtime'. If not provided, uses | ||
the default in the pipeline config ('epochs') | ||
budget (Optional[float]): | ||
Budget to fit a single run of the pipeline. If not | ||
provided, uses the default in the pipeline config | ||
total_walltime_limit (int), (default=100): Time limit | ||
in seconds for the search of appropriate models. | ||
By increasing this value, autopytorch has a higher | ||
chance of finding better models. | ||
func_eval_time_limit (int), (default=60): Time limit | ||
for a single call to the machine learning model. | ||
Model fitting will be terminated if the machine | ||
learning algorithm runs over the time limit. Set | ||
this value high enough so that typical machine | ||
learning algorithms can be fit on the training | ||
data. | ||
traditional_per_total_budget (float), (default=0.1): | ||
Percent of total walltime to be allocated for | ||
running traditional classifiers. | ||
memory_limit (Optional[int]), (default=4096): Memory | ||
limit in MB for the machine learning algorithm. autopytorch | ||
will stop fitting the machine learning algorithm if it tries | ||
to allocate more than memory_limit MB. If None is provided, | ||
no memory limit is set. In case of multi-processing, memory_limit | ||
will be per job. This memory limit also applies to the ensemble | ||
creation process. | ||
smac_scenario_args (Optional[Dict]): Additional arguments inserted | ||
into the scenario of SMAC. See the | ||
[SMAC documentation] (https://automl.github.io/SMAC3/master/options.html?highlight=scenario#scenario) | ||
get_smac_object_callback (Optional[Callable]): Callback function | ||
to create an object of class | ||
[smac.optimizer.smbo.SMBO](https://automl.github.io/SMAC3/master/apidoc/smac.optimizer.smbo.html). | ||
The function must accept the arguments scenario_dict, | ||
instances, num_params, runhistory, seed and ta. This is | ||
an advanced feature. Use only if you are familiar with | ||
[SMAC](https://automl.github.io/SMAC3/master/index.html). | ||
all_supported_metrics (bool), (default=True): if True, all | ||
metrics supporting current task will be calculated | ||
for each pipeline and results will be available via cv_results | ||
precision (int), (default=32): Numeric precision used when loading | ||
ensemble data. Can be either '16', '32' or '64'. | ||
disable_file_output (Union[bool, List]): | ||
load_models (bool), (default=True): Whether to load the | ||
models after fitting AutoPyTorch. | ||
Returns: | ||
self | ||
""" | ||
if dataset_name is None: | ||
dataset_name = str(uuid.uuid1(clock_seq=os.getpid())) | ||
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# we have to create a logger for at this point for the validator | ||
self._logger = self._get_logger(dataset_name) | ||
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# Create a validator object to make sure that the data provided by | ||
# the user matches the autopytorch requirements | ||
self.InputValidator = TabularInputValidator( | ||
is_classification=False, | ||
logger_port=self._logger_port, | ||
) | ||
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# Fit a input validator to check the provided data | ||
# Also, an encoder is fit to both train and test data, | ||
# to prevent unseen categories during inference | ||
self.InputValidator.fit(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test) | ||
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self.dataset = TabularDataset( | ||
X=X_train, Y=y_train, | ||
X_test=X_test, Y_test=y_test, | ||
validator=self.InputValidator, | ||
resampling_strategy=self.resampling_strategy, | ||
resampling_strategy_args=self.resampling_strategy_args, | ||
) | ||
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return self._search( | ||
dataset=self.dataset, | ||
optimize_metric=optimize_metric, | ||
budget_type=budget_type, | ||
budget=budget, | ||
total_walltime_limit=total_walltime_limit, | ||
func_eval_time_limit=func_eval_time_limit, | ||
traditional_per_total_budget=traditional_per_total_budget, | ||
memory_limit=memory_limit, | ||
smac_scenario_args=smac_scenario_args, | ||
get_smac_object_callback=get_smac_object_callback, | ||
all_supported_metrics=all_supported_metrics, | ||
precision=precision, | ||
disable_file_output=disable_file_output, | ||
load_models=load_models, | ||
) | ||
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def predict( | ||
self, | ||
X_test: np.ndarray, | ||
batch_size: Optional[int] = None, | ||
n_jobs: int = 1 | ||
) -> np.ndarray: | ||
if self.InputValidator is None or not self.InputValidator._is_fitted: | ||
raise ValueError("predict() is only supported after calling search. Kindly call first " | ||
"the estimator fit() method.") | ||
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X_test = self.InputValidator.feature_validator.transform(X_test) | ||
predicted_values = super().predict(X_test, batch_size=batch_size, | ||
n_jobs=n_jobs) | ||
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# Allow to predict in the original domain -- that is, the user is not interested | ||
# in our encoded values | ||
return self.InputValidator.target_validator.inverse_transform(predicted_values) |
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