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da2cd7ca89
* Added spark support for parallel training. * Added tests and fixed a bug * Added more tests and updated docs * Updated setup.py and docs * Added customize_learner and tests * Update spark tests and setup.py * Update docs and verbose * Update logging, fix issue in cloud notebook * Update github workflow for spark tests * Update github workflow * Remove hack of handling _choice_ * Allow for failures * Fix tests, update docs * Update setup.py * Update Dockerfile for Spark * Update tests, remove some warnings * Add test for notebooks, update utils * Add performance test for Spark * Fix lru_cache maxsize * Fix test failures on some platforms * Fix coverage report failure * resovle PR comments * resovle PR comments 2nd round * resovle PR comments 3rd round * fix lint and rename test class * resovle PR comments 4th round * refactor customize_learner to broadcast_code
125 lines
3.6 KiB
Python
125 lines
3.6 KiB
Python
from flaml.tune.spark.utils import broadcast_code
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custom_code = """
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from flaml import tune
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from flaml.automl.model import LGBMEstimator, XGBoostSklearnEstimator, SKLearnEstimator
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from flaml.automl.data import CLASSIFICATION, get_output_from_log
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class MyRegularizedGreedyForest(SKLearnEstimator):
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def __init__(self, task="binary", **config):
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super().__init__(task, **config)
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if task in CLASSIFICATION:
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from rgf.sklearn import RGFClassifier
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self.estimator_class = RGFClassifier
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else:
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from rgf.sklearn import RGFRegressor
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self.estimator_class = RGFRegressor
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@classmethod
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def search_space(cls, data_size, task):
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space = {
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"max_leaf": {
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"domain": tune.lograndint(lower=4, upper=data_size[0]),
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"init_value": 4,
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},
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"n_iter": {
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"domain": tune.lograndint(lower=1, upper=data_size[0]),
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"init_value": 1,
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},
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"n_tree_search": {
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"domain": tune.lograndint(lower=1, upper=32768),
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"init_value": 1,
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},
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"opt_interval": {
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"domain": tune.lograndint(lower=1, upper=10000),
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"init_value": 100,
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},
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"learning_rate": {"domain": tune.loguniform(lower=0.01, upper=20.0)},
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"min_samples_leaf": {
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"domain": tune.lograndint(lower=1, upper=20),
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"init_value": 20,
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},
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}
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return space
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@classmethod
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def size(cls, config):
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max_leaves = int(round(config.get("max_leaf", 1)))
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n_estimators = int(round(config.get("n_iter", 1)))
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return (max_leaves * 3 + (max_leaves - 1) * 4 + 1.0) * n_estimators * 8
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@classmethod
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def cost_relative2lgbm(cls):
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return 1.0
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class MyLargeXGB(XGBoostSklearnEstimator):
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@classmethod
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def search_space(cls, **params):
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return {
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"n_estimators": {
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"domain": tune.lograndint(lower=4, upper=32768),
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"init_value": 32768,
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"low_cost_init_value": 4,
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},
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"max_leaves": {
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"domain": tune.lograndint(lower=4, upper=3276),
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"init_value": 3276,
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"low_cost_init_value": 4,
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},
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}
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class MyLargeLGBM(LGBMEstimator):
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@classmethod
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def search_space(cls, **params):
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return {
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"n_estimators": {
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"domain": tune.lograndint(lower=4, upper=32768),
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"init_value": 32768,
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"low_cost_init_value": 4,
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},
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"num_leaves": {
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"domain": tune.lograndint(lower=4, upper=3276),
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"init_value": 3276,
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"low_cost_init_value": 4,
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},
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}
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def custom_metric(
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X_val,
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y_val,
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estimator,
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labels,
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X_train,
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y_train,
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weight_val=None,
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weight_train=None,
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config=None,
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groups_val=None,
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groups_train=None,
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):
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from sklearn.metrics import log_loss
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import time
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start = time.time()
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y_pred = estimator.predict_proba(X_val)
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pred_time = (time.time() - start) / len(X_val)
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val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val)
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y_pred = estimator.predict_proba(X_train)
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train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train)
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alpha = 0.5
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return val_loss * (1 + alpha) - alpha * train_loss, {
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"val_loss": val_loss,
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"train_loss": train_loss,
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"pred_time": pred_time,
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}
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"""
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_ = broadcast_code(custom_code=custom_code)
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