default.suggest

suggest_config

def suggest_config(task, X, y, estimator_or_predictor, location=None, k=None, meta_feature_fn=meta_feature)

Suggest a list of configs for the given task and training data.

The returned configs can be used as starting points for AutoML.fit(). FLAML_sample_size is removed from the configs.

suggest_learner

def suggest_learner(task, X, y, estimator_or_predictor="all", estimator_list=None, location=None)

Suggest best learner within estimator_list.

suggest_hyperparams

def suggest_hyperparams(task, X, y, estimator_or_predictor, location=None)

Suggest hyperparameter configurations and an estimator class.

The configurations can be used to initialize the estimator class like lightgbm.LGBMRegressor.

Example:

hyperparams, estimator_class = suggest_hyperparams("regression", X_train, y_train, "lgbm")
model = estimator_class(**hyperparams)  # estimator_class is LGBMRegressor
model.fit(X_train, y_train)

Arguments:

• task - A string of the task type, e.g., 'classification', 'regression', 'ts_forecast', 'rank', 'seq-classification', 'seq-regression'.
• X - A dataframe of training data in shape n*m. For 'ts_forecast' task, the first column of X_train must be the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric).
• y - A series of labels in shape n*1.
• estimator_or_predictor - A str of the learner name or a dict of the learned config predictor. If a dict, it contains:
  • "version": a str of the version number.
  • "preprocessing": a dictionary containing:
  • "center": a list of meta feature value offsets for normalization.
  • "scale": a list of meta feature scales to normalize each dimension.
  • "neighbors": a list of dictionaries. Each dictionary contains:
  • "features": a list of the normalized meta features for a neighbor.
  • "choice": an integer of the configuration id in the portfolio.
  • "portfolio": a list of dictionaries, each corresponding to a configuration:
  • "class": a str of the learner name.
  • "hyperparameters": a dict of the config. The key "FLAML_sample_size" will be ignored.
• location - (Optional) A str of the location containing mined portfolio file. Only valid when the portfolio is a str, by default the location is flaml/default.

Returns:

• hyperparams - A dict of the hyperparameter configurations.
• estiamtor_class - A class of the underlying estimator, e.g., lightgbm.LGBMClassifier.

preprocess_and_suggest_hyperparams

def preprocess_and_suggest_hyperparams(task, X, y, estimator_or_predictor, location=None)

Preprocess the data and suggest hyperparameters.

Example:

hyperparams, estimator_class, X, y, feature_transformer, label_transformer = \
    preprocess_and_suggest_hyperparams("classification", X_train, y_train, "xgb_limitdepth")
model = estimator_class(**hyperparams)  # estimator_class is XGBClassifier
model.fit(X, y)
X_test = feature_transformer.transform(X_test)
y_pred = label_transformer.inverse_transform(pd.Series(model.predict(X_test).astype(int)))

Arguments:

• task - A string of the task type, e.g., 'classification', 'regression', 'ts_forecast', 'rank', 'seq-classification', 'seq-regression'.
• X - A dataframe of training data in shape n*m. For 'ts_forecast' task, the first column of X_train must be the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric).
• y - A series of labels in shape n*1.
• estimator_or_predictor - A str of the learner name or a dict of the learned config predictor. "choose_xgb" means choosing between xgb_limitdepth and xgboost. If a dict, it contains:
  • "version": a str of the version number.
  • "preprocessing": a dictionary containing:
  • "center": a list of meta feature value offsets for normalization.
  • "scale": a list of meta feature scales to normalize each dimension.
  • "neighbors": a list of dictionaries. Each dictionary contains:
  • "features": a list of the normalized meta features for a neighbor.
  • "choice": a integer of the configuration id in the portfolio.
  • "portfolio": a list of dictionaries, each corresponding to a configuration:
  • "class": a str of the learner name.
  • "hyperparameters": a dict of the config. They key "FLAML_sample_size" will be ignored.
• location - (Optional) A str of the location containing mined portfolio file. Only valid when the portfolio is a str, by default the location is flaml/default.

Returns:

• hyperparams - A dict of the hyperparameter configurations.
• estiamtor_class - A class of the underlying estimator, e.g., lightgbm.LGBMClassifier.
• X - the preprocessed X.
• y - the preprocessed y.
• feature_transformer - a data transformer that can be applied to X_test.
• label_transformer - a label transformer that can be applied to y_test.