autogen/test/spark/custom_mylearner.py

from flaml.tune.spark.utils import broadcast_code

custom_code = """
from flaml import tune
import time
from flaml.automl.model import LGBMEstimator, XGBoostSklearnEstimator, SKLearnEstimator
from flaml.automl.data import CLASSIFICATION, get_output_from_log

class MyRegularizedGreedyForest(SKLearnEstimator):
    def __init__(self, task="binary", **config):

        super().__init__(task, **config)

        if task in CLASSIFICATION:
            from rgf.sklearn import RGFClassifier

            self.estimator_class = RGFClassifier
        else:
            from rgf.sklearn import RGFRegressor

            self.estimator_class = RGFRegressor

    @classmethod
    def search_space(cls, data_size, task):
        space = {
            "max_leaf": {
                "domain": tune.lograndint(lower=4, upper=data_size[0]),
                "init_value": 4,
            },
            "n_iter": {
                "domain": tune.lograndint(lower=1, upper=data_size[0]),
                "init_value": 1,
            },
            "n_tree_search": {
                "domain": tune.lograndint(lower=1, upper=32768),
                "init_value": 1,
            },
            "opt_interval": {
                "domain": tune.lograndint(lower=1, upper=10000),
                "init_value": 100,
            },
            "learning_rate": {"domain": tune.loguniform(lower=0.01, upper=20.0)},
            "min_samples_leaf": {
                "domain": tune.lograndint(lower=1, upper=20),
                "init_value": 20,
            },
        }
        return space

    @classmethod
    def size(cls, config):
        max_leaves = int(round(config.get("max_leaf", 1)))
        n_estimators = int(round(config.get("n_iter", 1)))
        return (max_leaves * 3 + (max_leaves - 1) * 4 + 1.0) * n_estimators * 8

    @classmethod
    def cost_relative2lgbm(cls):
        return 1.0


class MyLargeXGB(XGBoostSklearnEstimator):
    @classmethod
    def search_space(cls, **params):
        return {
            "n_estimators": {
                "domain": tune.lograndint(lower=4, upper=32768),
                "init_value": 32768,
                "low_cost_init_value": 4,
            },
            "max_leaves": {
                "domain": tune.lograndint(lower=4, upper=3276),
                "init_value": 3276,
                "low_cost_init_value": 4,
            },
        }


class MyLargeLGBM(LGBMEstimator):
    @classmethod
    def search_space(cls, **params):
        return {
            "n_estimators": {
                "domain": tune.lograndint(lower=4, upper=32768),
                "init_value": 32768,
                "low_cost_init_value": 4,
            },
            "num_leaves": {
                "domain": tune.lograndint(lower=4, upper=3276),
                "init_value": 3276,
                "low_cost_init_value": 4,
            },
        }



def custom_metric(
    X_val,
    y_val,
    estimator,
    labels,
    X_train,
    y_train,
    weight_val=None,
    weight_train=None,
    config=None,
    groups_val=None,
    groups_train=None,
):
    from sklearn.metrics import log_loss
    import time

    start = time.time()
    y_pred = estimator.predict_proba(X_val)
    pred_time = (time.time() - start) / len(X_val)
    val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val)
    y_pred = estimator.predict_proba(X_train)
    train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train)
    alpha = 0.5
    return val_loss * (1 + alpha) - alpha * train_loss, {
        "val_loss": val_loss,
        "train_loss": train_loss,
        "pred_time": pred_time,
    }

def lazy_metric(
    X_val,
    y_val,
    estimator,
    labels,
    X_train,
    y_train,
    weight_val=None,
    weight_train=None,
    config=None,
    groups_val=None,
    groups_train=None,
):
    from sklearn.metrics import log_loss

    time.sleep(2)
    start = time.time()
    y_pred = estimator.predict_proba(X_val)
    pred_time = (time.time() - start) / len(X_val)
    val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val)
    y_pred = estimator.predict_proba(X_train)
    train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train)
    alpha = 0.5
    return val_loss * (1 + alpha) - alpha * train_loss, {
        "val_loss": val_loss,
        "train_loss": train_loss,
        "pred_time": pred_time,
    }
"""

_ = broadcast_code(custom_code=custom_code)