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* version update post release v1.2.2 * automl option * import pandas * remove automl.utils * default * test * type hint and version update * dependency update * link to open in colab * use packging.version to close #725 --------- Co-authored-by: Li Jiang <lijiang1@microsoft.com> Co-authored-by: Li Jiang <bnujli@gmail.com>
2.4 KiB
2.4 KiB
As FLAML's AutoML module can be used a transformer in the Sklearn's pipeline we can get all the benefits of pipeline.
Prerequisites
Install the [automl] option.
pip install "flaml[automl] openml"
Load data
from flaml.automl.data import load_openml_dataset
# Download [Airlines dataset](https://www.openml.org/d/1169) from OpenML. The task is to predict whether a given flight will be delayed, given the information of the scheduled departure.
X_train, X_test, y_train, y_test = load_openml_dataset(
dataset_id=1169, data_dir='./', random_state=1234, dataset_format='array')
Create a pipeline
from sklearn import set_config
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from flaml import AutoML
set_config(display='diagram')
imputer = SimpleImputer()
standardizer = StandardScaler()
automl = AutoML()
automl_pipeline = Pipeline([
("imputuer",imputer),
("standardizer", standardizer),
("automl", automl)
])
automl_pipeline
Run AutoML in the pipeline
automl_settings = {
"time_budget": 60, # total running time in seconds
"metric": "accuracy", # primary metrics can be chosen from: ['accuracy', 'roc_auc', 'roc_auc_weighted', 'roc_auc_ovr', 'roc_auc_ovo', 'f1', 'log_loss', 'mae', 'mse', 'r2'] Check the documentation for more details (https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#optimization-metric)
"task": "classification", # task type
"estimator_list": ["xgboost", "catboost", "lgbm"],
"log_file_name": "airlines_experiment.log", # flaml log file
}
pipeline_settings = {
f"automl__{key}": value for key, value in automl_settings.items()
}
automl_pipeline.fit(X_train, y_train, **pipeline_settings)
Get the automl object from the pipeline
automl = automl_pipeline.steps[2][1]
# Get the best config and best learner
print('Best ML leaner:', automl.best_estimator)
print('Best hyperparmeter config:', automl.best_config)
print('Best accuracy on validation data: {0:.4g}'.format(1 - automl.best_loss))
print('Training duration of best run: {0:.4g} s'.format(automl.best_config_train_time))