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A new documentation website. And: * add actions for doc * update docstr * installation instructions for doc dev * unify README and Getting Started * rename notebook * doc about best_model_for_estimator #340 * docstr for keep_search_state #340 * DNN Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu> Co-authored-by: Z.sk <shaokunzhang@psu.edu>
2.1 KiB
2.1 KiB
As FLAML's AutoML module can be used a transformer in the Sklearn's pipeline we can get all the benefits of pipeline.
Load data
from flaml.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
settings = {
"time_budget": 60, # total running time in seconds
"metric": 'accuracy', # primary metrics can be chosen from: ['accuracy','roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'f1','log_loss','mae','mse','r2']
"task": 'classification', # task type
"estimator_list":['xgboost','catboost','lgbm'],
"log_file_name": 'airlines_experiment.log', # flaml log file
}
automl_pipeline.fit(X_train, y_train,
automl__time_budget=60,
automl__metric="accuracy")
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))