From f48ca2618fed2193ffa929af7a44d3031dfb16dd Mon Sep 17 00:00:00 2001 From: Chi Wang Date: Fri, 8 Oct 2021 16:09:43 -0700 Subject: [PATCH] warning -> info for low cost partial config (#231) * warning -> info for low cost partial config #195, #110 * when n_estimators < 0, use trained_estimator's * log debug info * test random seed * remove "objective"; avoid ZeroDivisionError * hp config to estimator params * check type of searcher * default n_jobs * try import * Update searchalgo_auto.py * CLASSIFICATION * auto_augment flag * min_sample_size * make catboost optional --- README.md | 4 +- flaml/automl.py | 66 ++- flaml/data.py | 11 +- flaml/ml.py | 4 +- flaml/model.py | 349 +++++-------- flaml/nlp/hpo/searchalgo_auto.py | 128 +++-- flaml/onlineml/trial_runner.py | 325 +++++++----- flaml/searcher/blendsearch.py | 38 +- flaml/searcher/flow2.py | 256 +++++---- flaml/searcher/online_searcher.py | 264 ++++++---- flaml/searcher/search_thread.py | 2 +- flaml/tune/README.md | 62 ++- flaml/tune/trial_runner.py | 38 +- flaml/tune/tune.py | 43 +- notebook/flaml_automl.ipynb | 825 +++++++++++++++--------------- notebook/flaml_lightgbm.ipynb | 606 ++++++++++------------ notebook/flaml_xgboost.ipynb | 582 ++++++++++----------- setup.py | 4 +- test/test_automl.py | 105 ++-- test/test_python_log.py | 54 +- test/test_training_log.py | 18 +- test/tune/test_searcher.py | 13 +- 22 files changed, 1938 insertions(+), 1859 deletions(-) diff --git a/README.md b/README.md index 5937a76bb..45e3a7145 100644 --- a/README.md +++ b/README.md @@ -103,7 +103,7 @@ print(automl.model) ```python from flaml import AutoML -from sklearn.datasets import load_boston +from sklearn.datasets import fetch_california_housing # Initialize an AutoML instance automl = AutoML() # Specify automl goal and constraint @@ -113,7 +113,7 @@ automl_settings = { "task": 'regression', "log_file_name": "test/boston.log", } -X_train, y_train = load_boston(return_X_y=True) +X_train, y_train = fetch_california_housing(return_X_y=True) # Train with labeled input data automl.fit(X_train=X_train, y_train=y_train, **automl_settings) diff --git a/flaml/automl.py b/flaml/automl.py index d5441f23d..1b117a8ca 100644 --- a/flaml/automl.py +++ b/flaml/automl.py @@ -36,7 +36,7 @@ from .config import ( N_SPLITS, SAMPLE_MULTIPLY_FACTOR, ) -from .data import concat +from .data import concat, CLASSIFICATION from . import tune from .training_log import training_log_reader, training_log_writer @@ -619,7 +619,8 @@ class AutoML: if issparse(X_train_all): X_train_all = X_train_all.tocsr() if ( - self._state.task in ("binary", "multi") + self._state.task in CLASSIFICATION + and self._auto_augment and self._state.fit_kwargs.get("sample_weight") is None and self._split_type not in ["time", "group"] ): @@ -725,7 +726,7 @@ class AutoML: y_train, y_val = y_train_all[train_idx], y_train_all[val_idx] self._state.groups = self._state.groups_all[train_idx] self._state.groups_val = self._state.groups_all[val_idx] - elif self._state.task in ("binary", "multi"): + elif self._state.task in CLASSIFICATION: # for classification, make sure the labels are complete in both # training and validation data label_set, first = np.unique(y_train_all, return_index=True) @@ -904,10 +905,11 @@ class AutoML: n_splits=N_SPLITS, split_type=None, groups=None, - n_jobs=1, + n_jobs=-1, train_best=True, train_full=False, record_id=-1, + auto_augment=True, **fit_kwargs, ): """Retrain from log file @@ -943,7 +945,8 @@ class AutoML: groups: None or array-like | Group labels (with matching length to y_train) or groups counts (with sum equal to length of y_train) for training data. - n_jobs: An integer of the number of threads for training. + n_jobs: An integer of the number of threads for training. Use all + available resources when n_jobs == -1. train_best: A boolean of whether to train the best config in the time budget; if false, train the last config in the budget. train_full: A boolean of whether to train on the full data. If true, @@ -952,6 +955,8 @@ class AutoML: be retrained. By default `record_id = -1` which means this will be ignored. `record_id = 0` corresponds to the first trial, and when `record_id >= 0`, `time_budget` will be ignored. + auto_augment: boolean, default=True | Whether to automatically + augment rare classes. **fit_kwargs: Other key word arguments to pass to fit() function of the searched learners, such as sample_weight. """ @@ -1018,6 +1023,7 @@ class AutoML: elif eval_method == "auto": eval_method = self._decide_eval_method(time_budget) self.modelcount = 0 + self._auto_augment = auto_augment self._prepare_data(eval_method, split_ratio, n_splits) self._state.time_budget = None self._state.n_jobs = n_jobs @@ -1032,7 +1038,7 @@ class AutoML: self._state.task = get_classification_objective( len(np.unique(self._y_train_all)) ) - if self._state.task in ("binary", "multi"): + if self._state.task in CLASSIFICATION: assert split_type in [None, "stratified", "uniform", "time", "group"] self._split_type = ( split_type or self._state.groups is None and "stratified" or "group" @@ -1191,7 +1197,7 @@ class AutoML: Returns: A float for the minimal sample size or None """ - return MIN_SAMPLE_TRAIN if self._sample else None + return self._min_sample_size if self._sample else None @property def max_resource(self) -> Optional[float]: @@ -1282,7 +1288,7 @@ class AutoML: sample_weight_val=None, groups_val=None, groups=None, - verbose=1, + verbose=3, retrain_full=True, split_type=None, learner_selector="sample", @@ -1291,8 +1297,10 @@ class AutoML: seed=None, n_concurrent_trials=1, keep_search_state=False, - append_log=False, early_stop=False, + append_log=False, + auto_augment=True, + min_sample_size=MIN_SAMPLE_TRAIN, **fit_kwargs, ): """Find a model for a given task @@ -1375,7 +1383,7 @@ class AutoML: groups: None or array-like | Group labels (with matching length to y_train) or groups counts (with sum equal to length of y_train) for training data. - verbose: int, default=1 | Controls the verbosity, higher means more + verbose: int, default=3 | Controls the verbosity, higher means more messages. retrain_full: bool or str, default=True | whether to retrain the selected model on the full training data when using holdout. @@ -1412,8 +1420,12 @@ class AutoML: saving. early_stop: boolean, default=False | Whether to stop early if the search is considered to converge. - append_log: boolean, default=False | whetehr to directly append the log + append_log: boolean, default=False | Whetehr to directly append the log records to the input log file if it exists. + auto_augment: boolean, default=True | Whether to automatically + augment rare classes. + min_sample_size: int, default=MIN_SAMPLE_TRAIN | the minimal sample + size when sample=True. **fit_kwargs: Other key word arguments to pass to fit() function of the searched learners, such as sample_weight. Include period as a key word argument for 'forecast' task. @@ -1435,8 +1447,8 @@ class AutoML: self._learner_selector = learner_selector old_level = logger.getEffectiveLevel() self.verbose = verbose - if verbose == 0: - logger.setLevel(logging.WARNING) + # if verbose == 0: + logger.setLevel(50 - verbose * 10) if (not mlflow or not mlflow.active_run()) and not logger.handlers: # Add the console handler. _ch = logging.StreamHandler() @@ -1457,12 +1469,14 @@ class AutoML: and (eval_method == "holdout" and self._state.X_val is None) or (eval_method == "cv") ) + self._auto_augment = auto_augment + self._min_sample_size = min_sample_size self._prepare_data(eval_method, split_ratio, n_splits) self._sample = ( sample and task != "rank" and eval_method != "cv" - and (MIN_SAMPLE_TRAIN * SAMPLE_MULTIPLY_FACTOR < self._state.data_size) + and (self._min_sample_size * SAMPLE_MULTIPLY_FACTOR < self._state.data_size) ) if "auto" == metric: if "binary" in self._state.task: @@ -1584,8 +1598,8 @@ class AutoML: for state in self._search_states.values(): if state.trained_estimator: del state.trained_estimator - if verbose == 0: - logger.setLevel(old_level) + # if verbose == 0: + logger.setLevel(old_level) def _search_parallel(self): try: @@ -1631,6 +1645,8 @@ class AutoML: points_to_evaluate=points_to_evaluate, ) else: + self._state.time_from_start = time.time() - self._start_time_flag + time_left = self._state.time_budget - self._state.time_from_start search_alg = SearchAlgo( metric="val_loss", space=space, @@ -1645,13 +1661,9 @@ class AutoML: ], metric_constraints=self.metric_constraints, seed=self._seed, + time_budget_s=time_left, ) search_alg = ConcurrencyLimiter(search_alg, self._n_concurrent_trials) - self._state.time_from_start = time.time() - self._start_time_flag - time_left = self._state.time_budget - self._state.time_from_start - search_alg.set_search_properties( - None, None, config={"time_budget_s": time_left} - ) resources_per_trial = ( {"cpu": self._state.n_jobs} if self._state.n_jobs > 1 else None ) @@ -1782,7 +1794,7 @@ class AutoML: search_space = search_state.search_space if self._sample: prune_attr = "FLAML_sample_size" - min_resource = MIN_SAMPLE_TRAIN + min_resource = self._min_sample_size max_resource = self._state.data_size else: prune_attr = min_resource = max_resource = None @@ -1840,10 +1852,10 @@ class AutoML: else: search_space = None if self._hpo_method in ("bs", "cfo", "cfocat"): - search_state.search_alg.set_search_properties( + search_state.search_alg.searcher.set_search_properties( metric=None, mode=None, - config={ + setting={ "metric_target": self._state.best_loss, }, ) @@ -1852,7 +1864,7 @@ class AutoML: search_state.training_function, search_alg=search_state.search_alg, time_budget_s=min(budget_left, self._state.train_time_limit), - verbose=max(self.verbose - 1, 0), + verbose=max(self.verbose - 3, 0), use_ray=False, ) time_used = time.time() - start_run_time @@ -2077,7 +2089,7 @@ class AutoML: logger.info(estimators) if len(estimators) <= 1: return - if self._state.task in ("binary", "multi"): + if self._state.task in CLASSIFICATION: from sklearn.ensemble import StackingClassifier as Stacker else: from sklearn.ensemble import StackingRegressor as Stacker @@ -2184,7 +2196,7 @@ class AutoML: speed = delta_loss / delta_time if speed: estimated_cost = max(2 * gap / speed, estimated_cost) - estimated_cost == estimated_cost or 1e-10 + estimated_cost == estimated_cost or 1e-9 inv.append(1 / estimated_cost) else: estimated_cost = self._eci[i] diff --git a/flaml/data.py b/flaml/data.py index cf278c2d3..258f10b9b 100644 --- a/flaml/data.py +++ b/flaml/data.py @@ -1,15 +1,18 @@ """! - * Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved. + * Copyright (c) Microsoft Corporation. All rights reserved. * Licensed under the MIT License. """ import numpy as np from scipy.sparse import vstack, issparse import pandas as pd + from .training_log import training_log_reader from datetime import datetime +CLASSIFICATION = ("binary", "multi", "classification") + def load_openml_dataset( dataset_id, data_dir=None, random_state=0, dataset_format="dataframe" @@ -300,11 +303,7 @@ class DataTransformer: ) self._drop = drop - if task in ( - "binary", - "multi", - "classification", - ) or not pd.api.types.is_numeric_dtype(y): + if task in CLASSIFICATION or not pd.api.types.is_numeric_dtype(y): from sklearn.preprocessing import LabelEncoder self.label_transformer = LabelEncoder() diff --git a/flaml/ml.py b/flaml/ml.py index 19ff9c26c..b2d149565 100644 --- a/flaml/ml.py +++ b/flaml/ml.py @@ -33,7 +33,7 @@ from .model import ( ARIMA, SARIMAX, ) -from .data import group_counts +from .data import CLASSIFICATION, group_counts import logging @@ -301,7 +301,7 @@ def evaluate_model_CV( valid_fold_num = total_fold_num = 0 n = kf.get_n_splits() X_train_split, y_train_split = X_train_all, y_train_all - if task in ("binary", "multi"): + if task in CLASSIFICATION: labels = np.unique(y_train_all) else: labels = None diff --git a/flaml/model.py b/flaml/model.py index cdce9812a..6488c56d7 100644 --- a/flaml/model.py +++ b/flaml/model.py @@ -13,11 +13,11 @@ from lightgbm import LGBMClassifier, LGBMRegressor, LGBMRanker from scipy.sparse import issparse import pandas as pd from . import tune -from .data import group_counts +from .data import group_counts, CLASSIFICATION import logging -logger = logging.getLogger(__name__) +logger = logging.getLogger("flaml.automl") class BaseEstimator: @@ -30,24 +30,23 @@ class BaseEstimator: for both regression and classification """ - def __init__(self, task="binary", **params): + def __init__(self, task="binary", **config): """Constructor Args: task: A string of the task type, one of 'binary', 'multi', 'regression', 'rank', 'forecast' - n_jobs: An integer of the number of parallel threads - params: A dictionary of the hyperparameter names and values + config: A dictionary containing the hyperparameter names + and 'n_jobs' as keys. n_jobs is the number of parallel threads. """ - self.params = params + self.params = self.config2params(config) self.estimator_class = self._model = None self._task = task - if "_estimator_type" in params: - self._estimator_type = params["_estimator_type"] - del self.params["_estimator_type"] + if "_estimator_type" in config: + self._estimator_type = self.params.pop("_estimator_type") else: self._estimator_type = ( - "classifier" if task in ("binary", "multi") else "regressor" + "classifier" if task in CLASSIFICATION else "regressor" ) def get_params(self, deep=False): @@ -83,8 +82,9 @@ class BaseEstimator: current_time = time.time() if "groups" in kwargs: kwargs = kwargs.copy() + groups = kwargs.pop("groups") if self._task == "rank": - kwargs["group"] = group_counts(kwargs["groups"]) + kwargs["group"] = group_counts(groups) # groups_val = kwargs.get('groups_val') # if groups_val is not None: # kwargs['eval_group'] = [group_counts(groups_val)] @@ -92,10 +92,13 @@ class BaseEstimator: # (kwargs['X_val'], kwargs['y_val'])] # kwargs['verbose'] = False # del kwargs['groups_val'], kwargs['X_val'], kwargs['y_val'] - del kwargs["groups"] X_train = self._preprocess(X_train) model = self.estimator_class(**self.params) + if logger.level == logging.DEBUG: + logger.debug(f"flaml.model - {model} fit started") model.fit(X_train, y_train, **kwargs) + if logger.level == logging.DEBUG: + logger.debug(f"flaml.model - {model} fit finished") train_time = time.time() - current_time self._model = model return train_time @@ -143,9 +146,8 @@ class BaseEstimator: Each element at (i,j) is the probability for instance i to be in class j """ - assert self._task in ( - "binary", - "multi", + assert ( + self._task in CLASSIFICATION ), "predict_prob() only for classification task." X_test = self._preprocess(X_test) return self._model.predict_proba(X_test) @@ -160,9 +162,10 @@ class BaseEstimator: Returns: A dictionary of the search space. Each key is the name of a hyperparameter, and value is a dict with - its domain and init_value (optional), cat_hp_cost (optional) + its domain (required) and low_cost_init_value, init_value, + cat_hp_cost (if applicable). e.g., - {'domain': tune.randint(lower=1, upper=10), 'init_value': 1} + {'domain': tune.randint(lower=1, upper=10), 'init_value': 1}. """ return {} @@ -171,11 +174,11 @@ class BaseEstimator: """[optional method] memory size of the estimator in bytes Args: - config - the dict of the hyperparameter config + config - A dict of the hyperparameter config. Returns: A float of the memory size required by the estimator to train the - given config + given config. """ return 1.0 @@ -189,10 +192,21 @@ class BaseEstimator: """[optional method] initialize the class""" pass + def config2params(self, config: dict) -> dict: + """[optional method] config dict to params dict + + Args: + config - A dict of the hyperparameter config. + + Returns: + A dict that will be passed to self.estimator_class's constructor. + """ + return config.copy() + class SKLearnEstimator(BaseEstimator): - def __init__(self, task="binary", **params): - super().__init__(task, **params) + def __init__(self, task="binary", **config): + super().__init__(task, **config) def _preprocess(self, X): if isinstance(X, pd.DataFrame): @@ -255,39 +269,22 @@ class LGBMEstimator(BaseEstimator): }, } + def config2params(cls, config: dict) -> dict: + params = config.copy() + if "log_max_bin" in params: + params["max_bin"] = (1 << params.pop("log_max_bin")) - 1 + return params + @classmethod def size(cls, config): num_leaves = int(round(config.get("num_leaves") or config["max_leaves"])) n_estimators = int(round(config["n_estimators"])) return (num_leaves * 3 + (num_leaves - 1) * 4 + 1.0) * n_estimators * 8 - def __init__(self, task="binary", log_max_bin=8, **params): - super().__init__(task, **params) - if "objective" not in self.params: - # Default: ‘regression’ for LGBMRegressor, - # ‘binary’ or ‘multiclass’ for LGBMClassifier - objective = "regression" - if "binary" in task: - objective = "binary" - elif "multi" in task: - objective = "multiclass" - elif "rank" == task: - objective = "lambdarank" - self.params["objective"] = objective - if "n_estimators" in self.params: - self.params["n_estimators"] = int(round(self.params["n_estimators"])) - if "num_leaves" in self.params: - self.params["num_leaves"] = int(round(self.params["num_leaves"])) - if "min_child_samples" in self.params: - self.params["min_child_samples"] = int( - round(self.params["min_child_samples"]) - ) - if "max_bin" not in self.params: - self.params["max_bin"] = 1 << int(round(log_max_bin)) - 1 + def __init__(self, task="binary", **config): + super().__init__(task, **config) if "verbose" not in self.params: self.params["verbose"] = -1 - # if "subsample_freq" not in self.params: - # self.params['subsample_freq'] = 1 if "regression" == task: self.estimator_class = LGBMRegressor elif "rank" == task: @@ -355,7 +352,7 @@ class LGBMEstimator(BaseEstimator): if self.params["n_estimators"] > 0: self._fit(X_train, y_train, **kwargs) else: - self.params["n_estimators"] = n_iter + self.params["n_estimators"] = self._model.n_estimators train_time = time.time() - start_time return train_time @@ -415,56 +412,30 @@ class XGBoostEstimator(SKLearnEstimator): def cost_relative2lgbm(cls): return 1.6 + def config2params(cls, config: dict) -> dict: + params = config.copy() + params["max_depth"] = params.get("max_depth", 0) + params["grow_policy"] = params.get("grow_policy", "lossguide") + params["booster"] = params.get("booster", "gbtree") + params["use_label_encoder"] = params.get("use_label_encoder", False) + params["tree_method"] = params.get("tree_method", "hist") + if "n_jobs" in config: + params["nthread"] = params.pop("n_jobs") + return params + def __init__( self, task="regression", - all_thread=False, - n_jobs=1, - n_estimators=4, - max_leaves=4, - subsample=1.0, - min_child_weight=1, - learning_rate=0.1, - reg_lambda=1.0, - reg_alpha=0.0, - colsample_bylevel=1.0, - colsample_bytree=1.0, - tree_method="auto", - **params, + **config, ): - super().__init__(task, **params) - self._n_estimators = int(round(n_estimators)) - self.params.update( - { - "max_leaves": int(round(max_leaves)), - "max_depth": params.get("max_depth", 0), - "grow_policy": params.get("grow_policy", "lossguide"), - "tree_method": tree_method, - "verbosity": params.get("verbosity", 0), - "nthread": n_jobs, - "learning_rate": float(learning_rate), - "subsample": float(subsample), - "reg_alpha": float(reg_alpha), - "reg_lambda": float(reg_lambda), - "min_child_weight": float(min_child_weight), - "booster": params.get("booster", "gbtree"), - "colsample_bylevel": float(colsample_bylevel), - "colsample_bytree": float(colsample_bytree), - "objective": params.get("objective"), - } - ) - if all_thread: - del self.params["nthread"] - - def get_params(self, deep=False): - params = super().get_params() - params["n_jobs"] = params["nthread"] - return params + super().__init__(task, **config) + self.params["verbosity"] = 0 def fit(self, X_train, y_train, budget=None, **kwargs): start_time = time.time() - if not issparse(X_train): - self.params["tree_method"] = "hist" + if issparse(X_train): + self.params["tree_method"] = "auto" + else: X_train = self._preprocess(X_train) if "sample_weight" in kwargs: dtrain = xgb.DMatrix(X_train, label=y_train, weight=kwargs["sample_weight"]) @@ -478,8 +449,10 @@ class XGBoostEstimator(SKLearnEstimator): obj = objective if "objective" in self.params: del self.params["objective"] - self._model = xgb.train(self.params, dtrain, self._n_estimators, obj=obj) + _n_estimators = self.params.pop("n_estimators") + self._model = xgb.train(self.params, dtrain, _n_estimators, obj=obj) self.params["objective"] = objective + self.params["n_estimators"] = _n_estimators del dtrain train_time = time.time() - start_time return train_time @@ -502,54 +475,29 @@ class XGBoostSklearnEstimator(SKLearnEstimator, LGBMEstimator): def cost_relative2lgbm(cls): return XGBoostEstimator.cost_relative2lgbm() + def config2params(cls, config: dict) -> dict: + params = config.copy() + params["max_depth"] = 0 + params["grow_policy"] = params.get("grow_policy", "lossguide") + params["booster"] = params.get("booster", "gbtree") + params["use_label_encoder"] = params.get("use_label_encoder", False) + params["tree_method"] = params.get("tree_method", "hist") + return params + def __init__( self, task="binary", - n_jobs=1, - n_estimators=4, - max_leaves=4, - subsample=1.0, - min_child_weight=1, - learning_rate=0.1, - reg_lambda=1.0, - reg_alpha=0.0, - colsample_bylevel=1.0, - colsample_bytree=1.0, - tree_method="hist", - **params, + **config, ): - super().__init__(task, **params) - del self.params["objective"] - del self.params["max_bin"] + super().__init__(task, **config) del self.params["verbose"] - self.params.update( - { - "n_estimators": int(round(n_estimators)), - "max_leaves": int(round(max_leaves)), - "max_depth": 0, - "grow_policy": params.get("grow_policy", "lossguide"), - "tree_method": tree_method, - "n_jobs": n_jobs, - "verbosity": 0, - "learning_rate": float(learning_rate), - "subsample": float(subsample), - "reg_alpha": float(reg_alpha), - "reg_lambda": float(reg_lambda), - "min_child_weight": float(min_child_weight), - "booster": params.get("booster", "gbtree"), - "colsample_bylevel": float(colsample_bylevel), - "colsample_bytree": float(colsample_bytree), - "use_label_encoder": params.get("use_label_encoder", False), - } - ) + self.params["verbosity"] = 0 self.estimator_class = xgb.XGBRegressor if "rank" == task: self.estimator_class = xgb.XGBRanker - elif task in ("binary", "multi"): + elif task in CLASSIFICATION: self.estimator_class = xgb.XGBClassifier - self._time_per_iter = None - self._train_size = 0 def fit(self, X_train, y_train, budget=None, **kwargs): if issparse(X_train): @@ -578,7 +526,7 @@ class RandomForestEstimator(SKLearnEstimator, LGBMEstimator): "low_cost_init_value": 4, }, } - if task in ("binary", "multi"): + if task in CLASSIFICATION: space["criterion"] = { "domain": tune.choice(["gini", "entropy"]), # 'init_value': 'gini', @@ -589,36 +537,24 @@ class RandomForestEstimator(SKLearnEstimator, LGBMEstimator): def cost_relative2lgbm(cls): return 2.0 + def config2params(cls, config: dict) -> dict: + params = config.copy() + if "max_leaves" in params: + params["max_leaf_nodes"] = params.get( + "max_leaf_nodes", params.pop("max_leaves") + ) + return params + def __init__( self, task="binary", - n_jobs=1, - n_estimators=4, - max_features=1.0, - criterion="gini", - max_leaves=4, **params, ): super().__init__(task, **params) - del self.params["objective"] - del self.params["max_bin"] - self.params.update( - { - "n_estimators": int(round(n_estimators)), - "n_jobs": n_jobs, - "verbose": 0, - "max_features": float(max_features), - "max_leaf_nodes": params.get("max_leaf_nodes", int(round(max_leaves))), - } - ) + self.params["verbose"] = 0 self.estimator_class = RandomForestRegressor - if task in ("binary", "multi"): + if task in CLASSIFICATION: self.estimator_class = RandomForestClassifier - self.params["criterion"] = criterion - - def get_params(self, deep=False): - params = super().get_params() - return params class ExtraTreeEstimator(RandomForestEstimator): @@ -648,21 +584,16 @@ class LRL1Classifier(SKLearnEstimator): def cost_relative2lgbm(cls): return 160 - def __init__(self, task="binary", n_jobs=1, tol=0.0001, C=1.0, **params): - super().__init__(task, **params) - self.params.update( - { - "penalty": params.get("penalty", "l1"), - "tol": float(tol), - "C": float(C), - "solver": params.get("solver", "saga"), - "n_jobs": n_jobs, - } - ) - assert task in ( - "binary", - "multi", - ), "LogisticRegression for classification task only" + def config2params(cls, config: dict) -> dict: + params = config.copy() + params["tol"] = params.get("tol", 0.0001) + params["solver"] = params.get("solver", "saga") + params["penalty"] = params.get("penalty", "l1") + return params + + def __init__(self, task="binary", **config): + super().__init__(task, **config) + assert task in CLASSIFICATION, "LogisticRegression for classification task only" self.estimator_class = LogisticRegression @@ -675,21 +606,16 @@ class LRL2Classifier(SKLearnEstimator): def cost_relative2lgbm(cls): return 25 - def __init__(self, task="binary", n_jobs=1, tol=0.0001, C=1.0, **params): - super().__init__(task, **params) - self.params.update( - { - "penalty": params.get("penalty", "l2"), - "tol": float(tol), - "C": float(C), - "solver": params.get("solver", "lbfgs"), - "n_jobs": n_jobs, - } - ) - assert task in ( - "binary", - "multi", - ), "LogisticRegression for classification task only" + def config2params(cls, config: dict) -> dict: + params = config.copy() + params["tol"] = params.get("tol", 0.0001) + params["solver"] = params.get("solver", "lbfgs") + params["penalty"] = params.get("penalty", "l2") + return params + + def __init__(self, task="binary", **config): + super().__init__(task, **config) + assert task in CLASSIFICATION, "LogisticRegression for classification task only" self.estimator_class = LogisticRegression @@ -749,39 +675,33 @@ class CatBoostEstimator(BaseEstimator): X = X.to_numpy() return X + def config2params(cls, config: dict) -> dict: + params = config.copy() + params["n_estimators"] = params.get("n_estimators", 8192) + if "n_jobs" in params: + params["thread_count"] = params.pop("n_jobs") + return params + def __init__( self, task="binary", - n_jobs=1, - n_estimators=8192, - learning_rate=0.1, - early_stopping_rounds=4, - **params, + **config, ): - super().__init__(task, **params) + super().__init__(task, **config) self.params.update( { - "early_stopping_rounds": int(round(early_stopping_rounds)), - "n_estimators": n_estimators, - "learning_rate": learning_rate, - "thread_count": n_jobs, - "verbose": params.get("verbose", False), - "random_seed": params.get("random_seed", 10242048), + "verbose": config.get("verbose", False), + "random_seed": config.get("random_seed", 10242048), } ) from catboost import CatBoostRegressor self.estimator_class = CatBoostRegressor - if task in ("binary", "multi"): + if task in CLASSIFICATION: from catboost import CatBoostClassifier self.estimator_class = CatBoostClassifier - def get_params(self, deep=False): - params = super().get_params() - params["n_jobs"] = params["thread_count"] - return params - def fit(self, X_train, y_train, budget=None, **kwargs): import shutil @@ -881,7 +801,7 @@ class CatBoostEstimator(BaseEstimator): kwargs["sample_weight"] = weight self._model = model else: - self.params["n_estimators"] = n_iter + self.params["n_estimators"] = self._model.tree_count_ # except CatBoostError: # self._model = None train_time = time.time() - start_time @@ -904,22 +824,21 @@ class KNeighborsEstimator(BaseEstimator): def cost_relative2lgbm(cls): return 30 - def __init__(self, task="binary", n_jobs=1, n_neighbors=5, **params): - super().__init__(task, **params) - self.params.update( - { - "n_neighbors": int(round(n_neighbors)), - "weights": params.get("weights", "distance"), - "n_jobs": n_jobs, - } - ) - from sklearn.neighbors import KNeighborsRegressor + def config2params(cls, config: dict) -> dict: + params = config.copy() + params["weights"] = params.get("weights", "distance") + return params - self.estimator_class = KNeighborsRegressor - if task in ("binary", "multi"): + def __init__(self, task="binary", **config): + super().__init__(task, **config) + if task in CLASSIFICATION: from sklearn.neighbors import KNeighborsClassifier self.estimator_class = KNeighborsClassifier + else: + from sklearn.neighbors import KNeighborsRegressor + + self.estimator_class = KNeighborsRegressor def _preprocess(self, X): if isinstance(X, pd.DataFrame): @@ -963,9 +882,7 @@ class Prophet(BaseEstimator): } return space - def __init__(self, task="forecast", **params): - if "n_jobs" in params: - params.pop("n_jobs") + def __init__(self, task="forecast", n_jobs=1, **params): super().__init__(task, **params) def _join(self, X_train, y_train): diff --git a/flaml/nlp/hpo/searchalgo_auto.py b/flaml/nlp/hpo/searchalgo_auto.py index 4a180cfac..441150026 100644 --- a/flaml/nlp/hpo/searchalgo_auto.py +++ b/flaml/nlp/hpo/searchalgo_auto.py @@ -13,7 +13,7 @@ SEARCH_ALGO_MAPPING = OrderedDict( ("bs", BlendSearch), ("grid", None), ("gridbert", None), - ("rs", None) + ("rs", None), ] ) @@ -35,14 +35,16 @@ class AutoSearchAlgorithm: ) @classmethod - def from_method_name(cls, - search_algo_name, - search_algo_args_mode, - hpo_search_space, - time_budget, - metric_name, - metric_mode_name, - **custom_hpo_args): + def from_method_name( + cls, + search_algo_name, + search_algo_args_mode, + hpo_search_space, + time_budget, + metric_name, + metric_mode_name, + **custom_hpo_args + ): """ Instantiating one of the search algorithm classes based on the search algorithm name, search algorithm argument mode, hpo search space and other keyword args @@ -68,7 +70,9 @@ class AutoSearchAlgorithm: {"points_to_evaluate": [{"learning_rate": 1e-5, "num_train_epochs": 10}]) """ - assert hpo_search_space, "hpo_search_space needs to be specified for calling AutoSearchAlgorithm.from_method_name" + assert ( + hpo_search_space + ), "hpo_search_space needs to be specified for calling AutoSearchAlgorithm.from_method_name" if not search_algo_name: # TODO coverage search_algo_name = "grid" @@ -83,9 +87,15 @@ class AutoSearchAlgorithm: of the constructor function """ this_search_algo_kwargs = None - allowed_arguments = SEARCH_ALGO_MAPPING[search_algo_name].__init__.__code__.co_varnames - allowed_custom_args = {key: custom_hpo_args[key] for key in custom_hpo_args.keys() if - key in allowed_arguments} + allowed_arguments = SEARCH_ALGO_MAPPING[ + search_algo_name + ].__init__.__code__.co_varnames + custom_hpo_args["time_budget_s"] = time_budget + allowed_custom_args = { + key: custom_hpo_args[key] + for key in custom_hpo_args.keys() + if key in allowed_arguments + } """ If the search_algo_args_mode is "dft", set the args to the default args, e.g.,the default args for @@ -94,26 +104,34 @@ class AutoSearchAlgorithm: """ if search_algo_args_mode == "dft": # TODO coverage - this_search_algo_kwargs = DEFAULT_SEARCH_ALGO_ARGS_MAPPING[search_algo_name]( + this_search_algo_kwargs = DEFAULT_SEARCH_ALGO_ARGS_MAPPING[ + search_algo_name + ]( "dft", metric_name, metric_mode_name, hpo_search_space=hpo_search_space, - **allowed_custom_args) + **allowed_custom_args + ) elif search_algo_args_mode == "cus": - this_search_algo_kwargs = DEFAULT_SEARCH_ALGO_ARGS_MAPPING[search_algo_name]( + this_search_algo_kwargs = DEFAULT_SEARCH_ALGO_ARGS_MAPPING[ + search_algo_name + ]( "cus", metric_name, metric_mode_name, hpo_search_space=hpo_search_space, - **allowed_custom_args) + **allowed_custom_args + ) """ returning the hpo algorithm with the arguments """ - search_algo = SEARCH_ALGO_MAPPING[search_algo_name](**this_search_algo_kwargs) + search_algo = SEARCH_ALGO_MAPPING[search_algo_name]( + **this_search_algo_kwargs + ) if search_algo_name == "bs": - search_algo.set_search_properties(config={"time_budget_s": time_budget}) + search_algo.set_search_properties() return search_algo raise ValueError( "Unrecognized method {} for this kind of AutoSearchAlgorithm: {}.\n" @@ -125,29 +143,39 @@ class AutoSearchAlgorithm: @staticmethod def grid2list(grid_config): # TODO coverage - key_val_list = [[(key, each_val) for each_val in val_list['grid_search']] - for (key, val_list) in grid_config.items()] + key_val_list = [ + [(key, each_val) for each_val in val_list["grid_search"]] + for (key, val_list) in grid_config.items() + ] config_list = [dict(x) for x in itertools.product(*key_val_list)] return config_list -def get_search_algo_args_optuna(search_args_mode, - metric_name, - metric_mode_name, - hpo_search_space=None, - **custom_hpo_args): +def get_search_algo_args_optuna( + search_args_mode, + metric_name, + metric_mode_name, + hpo_search_space=None, + **custom_hpo_args +): # TODO coverage return {} -def default_search_algo_args_bs(search_args_mode, - metric_name, - metric_mode_name, - hpo_search_space=None, - **custom_hpo_args): - assert hpo_search_space, "hpo_search_space needs to be specified for calling AutoSearchAlgorithm.from_method_name" - if "num_train_epochs" in hpo_search_space and \ - isinstance(hpo_search_space["num_train_epochs"], ray.tune.sample.Categorical): +def default_search_algo_args_bs( + search_args_mode, + metric_name, + metric_mode_name, + hpo_search_space=None, + time_budget_s=None, + **custom_hpo_args +): + assert ( + hpo_search_space + ), "hpo_search_space needs to be specified for calling AutoSearchAlgorithm.from_method_name" + if "num_train_epochs" in hpo_search_space and isinstance( + hpo_search_space["num_train_epochs"], ray.tune.sample.Categorical + ): min_epoch = min(hpo_search_space["num_train_epochs"].categories) else: # TODO coverage @@ -156,31 +184,38 @@ def default_search_algo_args_bs(search_args_mode, default_search_algo_args = { "low_cost_partial_config": { "num_train_epochs": min_epoch, - "per_device_train_batch_size": max(hpo_search_space["per_device_train_batch_size"].categories), + "per_device_train_batch_size": max( + hpo_search_space["per_device_train_batch_size"].categories + ), }, "space": hpo_search_space, "metric": metric_name, - "mode": metric_mode_name + "mode": metric_mode_name, + "time_budget_s": time_budget_s, } if search_args_mode == "cus": default_search_algo_args.update(custom_hpo_args) return default_search_algo_args -def default_search_algo_args_grid_search(search_args_mode, - metric_name, - metric_mode_name, - hpo_search_space=None, - **custom_hpo_args): +def default_search_algo_args_grid_search( + search_args_mode, + metric_name, + metric_mode_name, + hpo_search_space=None, + **custom_hpo_args +): # TODO coverage return {} -def default_search_algo_args_random_search(search_args_mode, - metric_name, - metric_mode_name, - hpo_search_space=None, - **custom_hpo_args): +def default_search_algo_args_random_search( + search_args_mode, + metric_name, + metric_mode_name, + hpo_search_space=None, + **custom_hpo_args +): # TODO coverage return {} @@ -191,6 +226,5 @@ DEFAULT_SEARCH_ALGO_ARGS_MAPPING = OrderedDict( ("cfo", default_search_algo_args_bs), ("bs", default_search_algo_args_bs), ("grid", default_search_algo_args_grid_search), - ("gridbert", default_search_algo_args_random_search) ] ) diff --git a/flaml/onlineml/trial_runner.py b/flaml/onlineml/trial_runner.py index a5e584e81..d1cc6e542 100644 --- a/flaml/onlineml/trial_runner.py +++ b/flaml/onlineml/trial_runner.py @@ -1,10 +1,10 @@ -import time import numpy as np import math from flaml.tune import Trial from flaml.scheduler import TrialScheduler import logging + logger = logging.getLogger(__name__) @@ -45,16 +45,18 @@ class OnlineTrialRunner: Status change routine of a trial Trial.PENDING -> (Trial.RUNNING -> Trial.PAUSED -> Trial.RUNNING -> ...) -> Trial.TERMINATED(optional) """ + RANDOM_SEED = 123456 WARMSTART_NUM = 100 - def __init__(self, - max_live_model_num: int, - searcher=None, - scheduler=None, - champion_test_policy='loss_ucb', - **kwargs - ): + def __init__( + self, + max_live_model_num: int, + searcher=None, + scheduler=None, + champion_test_policy="loss_ucb", + **kwargs + ): """Constructor Args: @@ -64,7 +66,7 @@ class OnlineTrialRunner: Required methods of the searcher: - next_trial() Generate the next trial to add. - - set_search_properties(metric: Optional[str], mode: Optional[str], config: dict) + - set_search_properties(metric: Optional[str], mode: Optional[str], config: Optional[dict], setting: Optional[dict]) Generate new challengers based on the current champion and update the challenger list - on_trial_result(trial_id: str, result: Dict) Reprot results to the scheduler. @@ -87,8 +89,8 @@ class OnlineTrialRunner: self._scheduler = scheduler self._champion_test_policy = champion_test_policy self._max_live_model_num = max_live_model_num - self._remove_worse = kwargs.get('remove_worse', True) - self._bound_trial_num = kwargs.get('bound_trial_num', False) + self._remove_worse = kwargs.get("remove_worse", True) + self._bound_trial_num = kwargs.get("bound_trial_num", False) self._no_model_persistence = True # stores all the trials added to the OnlineTrialRunner @@ -103,21 +105,19 @@ class OnlineTrialRunner: # initially schedule up to max_live_model_num of live models and # set the first trial as the champion (which is done inside self.step()) self._total_steps = 0 - logger.info('init step %s', self._max_live_model_num) + logger.info("init step %s", self._max_live_model_num) # TODO: add more comments self.step() assert self._champion_trial is not None @property def champion_trial(self) -> Trial: - """The champion trial - """ + """The champion trial""" return self._champion_trial @property def running_trials(self): - """The running/'live' trials - """ + """The running/'live' trials""" return self._running_trials def step(self, data_sample=None, prediction_trial_tuple=None): @@ -147,7 +147,10 @@ class OnlineTrialRunner: # ***********Update running trials with observation*************************** if data_sample is not None: self._total_steps += 1 - prediction_made, prediction_trial = prediction_trial_tuple[0], prediction_trial_tuple[1] + prediction_made, prediction_trial = ( + prediction_trial_tuple[0], + prediction_trial_tuple[1], + ) # assert prediction_trial.status == Trial.RUNNING trials_to_pause = [] for trial in list(self._running_trials): @@ -156,16 +159,27 @@ class OnlineTrialRunner: else: y_predicted = prediction_made trial.train_eval_model_online(data_sample, y_predicted) - logger.debug('running trial at iter %s %s %s %s %s %s', self._total_steps, - trial.trial_id, trial.result.loss_avg, trial.result.loss_cb, - trial.result.resource_used, trial.resource_lease) + logger.debug( + "running trial at iter %s %s %s %s %s %s", + self._total_steps, + trial.trial_id, + trial.result.loss_avg, + trial.result.loss_cb, + trial.result.resource_used, + trial.resource_lease, + ) # report result to the searcher self._searcher.on_trial_result(trial.trial_id, trial.result) # report result to the scheduler and the scheduler makes a decision about # the running status of the trial decision = self._scheduler.on_trial_result(self, trial, trial.result) # set the status of the trial according to the decision made by the scheduler - logger.debug('trial decision %s %s at step %s', decision, trial.trial_id, self._total_steps) + logger.debug( + "trial decision %s %s at step %s", + decision, + trial.trial_id, + self._total_steps, + ) if decision == TrialScheduler.STOP: self.stop_trial(trial) elif decision == TrialScheduler.PAUSE: @@ -191,38 +205,45 @@ class OnlineTrialRunner: else: break - def get_top_running_trials(self, top_ratio=None, top_metric='ucb') -> list: - """Get a list of trial ids, whose performance is among the top running trials - """ - running_valid_trials = [trial for trial in self._running_trials if - trial.result is not None] + def get_top_running_trials(self, top_ratio=None, top_metric="ucb") -> list: + """Get a list of trial ids, whose performance is among the top running trials""" + running_valid_trials = [ + trial for trial in self._running_trials if trial.result is not None + ] if not running_valid_trials: return if top_ratio is None: top_number = 0 elif isinstance(top_ratio, float): top_number = math.ceil(len(running_valid_trials) * top_ratio) - elif isinstance(top_ratio, str) and 'best' in top_ratio: + elif isinstance(top_ratio, str) and "best" in top_ratio: top_number = 1 else: raise NotImplementedError - if 'ucb' in top_metric: - test_attribute = 'loss_ucb' - elif 'avg' in top_metric: - test_attribute = 'loss_avg' - elif 'lcb' in top_metric: - test_attribute = 'loss_lcb' + if "ucb" in top_metric: + test_attribute = "loss_ucb" + elif "avg" in top_metric: + test_attribute = "loss_avg" + elif "lcb" in top_metric: + test_attribute = "loss_lcb" else: raise NotImplementedError top_running_valid_trials = [] - logger.info('Running trial ids %s', [trial.trial_id for trial in running_valid_trials]) + logger.info( + "Running trial ids %s", [trial.trial_id for trial in running_valid_trials] + ) self._random_state.shuffle(running_valid_trials) - results = [trial.result.get_score(test_attribute) for trial in running_valid_trials] - sorted_index = np.argsort(np.array(results)) # sorted result (small to large) index + results = [ + trial.result.get_score(test_attribute) for trial in running_valid_trials + ] + # sorted result (small to large) index + sorted_index = np.argsort(np.array(results)) for i in range(min(top_number, len(running_valid_trials))): top_running_valid_trials.append(running_valid_trials[sorted_index[i]]) - logger.info('Top running ids %s', [trial.trial_id for trial in top_running_valid_trials]) + logger.info( + "Top running ids %s", [trial.trial_id for trial in top_running_valid_trials] + ) return top_running_valid_trials def _add_trial_from_searcher(self): @@ -234,12 +255,25 @@ class OnlineTrialRunner: """ # (optionally) upper bound the number of trials in the OnlineTrialRunner if self._bound_trial_num and self._first_challenger_pool_size is not None: - active_trial_size = len([t for t in self._trials if t.status != Trial.TERMINATED]) - trial_num_upper_bound = int(round((np.log10(self._total_steps) + 1) * self._first_challenger_pool_size) - ) if self._first_challenger_pool_size else np.inf + active_trial_size = len( + [t for t in self._trials if t.status != Trial.TERMINATED] + ) + trial_num_upper_bound = ( + int( + round( + (np.log10(self._total_steps) + 1) + * self._first_challenger_pool_size + ) + ) + if self._first_challenger_pool_size + else np.inf + ) if active_trial_size > trial_num_upper_bound: - logger.info('Not adding new trials: %s exceeds trial limit %s.', - active_trial_size, trial_num_upper_bound) + logger.info( + "Not adding new trials: %s exceeds trial limit %s.", + active_trial_size, + trial_num_upper_bound, + ) return None # output one trial from the trial pool (new challenger pool) maintained in the searcher @@ -253,7 +287,7 @@ class OnlineTrialRunner: # a valid trial is added. # Assumption on self._searcher: the first trial generated is the champion trial if self._champion_trial is None: - logger.info('Initial set up of the champion trial %s', trial.config) + logger.info("Initial set up of the champion trial %s", trial.config) self._set_champion(trial) else: self._all_new_challengers_added = True @@ -261,14 +295,15 @@ class OnlineTrialRunner: self._first_challenger_pool_size = len(self._trials) def _champion_test(self): - """Perform tests again the latest champion, including bette_than tests and worse_than tests - """ + """Perform tests again the latest champion, including bette_than tests and worse_than tests""" # for BetterThan test, we only need to compare the best challenger with the champion self._get_best_challenger() if self._best_challenger_trial is not None: assert self._best_challenger_trial.trial_id != self._champion_trial.trial_id # test whether a new champion is found and set the trial properties accordingly - is_new_champion_found = self._better_than_champion_test(self._best_challenger_trial) + is_new_champion_found = self._better_than_champion_test( + self._best_challenger_trial + ) if is_new_champion_found: self._set_champion(new_champion_trial=self._best_challenger_trial) @@ -278,39 +313,47 @@ class OnlineTrialRunner: for trial_to_test in self._trials: if trial_to_test.status != Trial.TERMINATED: worse_than_champion = self._worse_than_champion_test( - self._champion_trial, trial_to_test, self.WARMSTART_NUM) + self._champion_trial, trial_to_test, self.WARMSTART_NUM + ) if worse_than_champion: to_stop.append(trial_to_test) # we want to ensure there are at least #max_live_model_num of challengers remaining - max_to_stop_num = len([t for t in self._trials if t.status != Trial.TERMINATED] - ) - self._max_live_model_num + max_to_stop_num = ( + len([t for t in self._trials if t.status != Trial.TERMINATED]) + - self._max_live_model_num + ) for i in range(min(max_to_stop_num, len(to_stop))): self.stop_trial(to_stop[i]) def _get_best_challenger(self): - """Get the 'best' (in terms of the champion_test_policy) challenger under consideration. - """ + """Get the 'best' (in terms of the champion_test_policy) challenger under consideration.""" if self._champion_test_policy is None: return - if 'ucb' in self._champion_test_policy: - test_attribute = 'loss_ucb' - elif 'avg' in self._champion_test_policy: - test_attribute = 'loss_avg' + if "ucb" in self._champion_test_policy: + test_attribute = "loss_ucb" + elif "avg" in self._champion_test_policy: + test_attribute = "loss_avg" else: raise NotImplementedError - active_trials = [trial for trial in self._trials if - (trial.status != Trial.TERMINATED - and trial.trial_id != self._champion_trial.trial_id - and trial.result is not None)] + active_trials = [ + trial + for trial in self._trials + if ( + trial.status != Trial.TERMINATED + and trial.trial_id != self._champion_trial.trial_id + and trial.result is not None + ) + ] if active_trials: self._random_state.shuffle(active_trials) - results = [trial.result.get_score(test_attribute) for trial in active_trials] + results = [ + trial.result.get_score(test_attribute) for trial in active_trials + ] best_index = np.argmin(results) self._best_challenger_trial = active_trials[best_index] def _set_champion(self, new_champion_trial): - """Set the status of the existing trials once a new champion is found. - """ + """Set the status of the existing trials once a new champion is found.""" assert new_champion_trial is not None is_init_update = False if self._champion_trial is None: @@ -324,21 +367,20 @@ class OnlineTrialRunner: trial.set_checked_under_current_champion(False) self._champion_trial = new_champion_trial self._all_new_challengers_added = False - logger.info('Set the champion as %s', self._champion_trial.trial_id) + logger.info("Set the champion as %s", self._champion_trial.trial_id) if not is_init_update: self._champion_update_times += 1 # calling set_search_properties of searcher will trigger # new challenger generation. we do not do this for init champion # as this step is already done when first constructing the searcher - self._searcher.set_search_properties(None, None, - {self._searcher.CHAMPION_TRIAL_NAME: self._champion_trial} - ) + self._searcher.set_search_properties( + setting={self._searcher.CHAMPION_TRIAL_NAME: self._champion_trial} + ) else: self._champion_update_times = 0 def get_trials(self) -> list: - """Return the list of trials managed by this TrialRunner. - """ + """Return the list of trials managed by this TrialRunner.""" return self._trials def add_trial(self, new_trial): @@ -357,8 +399,12 @@ class OnlineTrialRunner: if trial.trial_id == new_trial.trial_id: trial.set_checked_under_current_champion(True) return - logger.info('adding trial at iter %s, %s %s', self._total_steps, new_trial.trial_id, - len(self._trials)) + logger.info( + "adding trial at iter %s, %s %s", + self._total_steps, + new_trial.trial_id, + len(self._trials), + ) self._trials.append(new_trial) self._scheduler.on_trial_add(self, new_trial) @@ -369,8 +415,11 @@ class OnlineTrialRunner: if trial.status in [Trial.ERROR, Trial.TERMINATED]: return else: - logger.info('Terminating trial %s, with trial result %s', - trial.trial_id, trial.result) + logger.info( + "Terminating trial %s, with trial result %s", + trial.trial_id, + trial.result, + ) trial.set_status(Trial.TERMINATED) # clean up model and result trial.clean_up_model() @@ -385,10 +434,15 @@ class OnlineTrialRunner: if trial.status in [Trial.ERROR, Trial.TERMINATED]: return else: - logger.info('Pausing trial %s, with trial loss_avg: %s, loss_cb: %s, loss_ucb: %s,\ - resource_lease: %s', trial.trial_id, trial.result.loss_avg, - trial.result.loss_cb, trial.result.loss_avg + trial.result.loss_cb, - trial.resource_lease) + logger.info( + "Pausing trial %s, with trial loss_avg: %s, loss_cb: %s, loss_ucb: %s,\ + resource_lease: %s", + trial.trial_id, + trial.result.loss_avg, + trial.result.loss_cb, + trial.result.loss_avg + trial.result.loss_cb, + trial.resource_lease, + ) trial.set_status(Trial.PAUSED) # clean up model and result if no model persistence if self._no_model_persistence: @@ -413,11 +467,15 @@ class OnlineTrialRunner: A bool indicating whether a new champion is found """ if trial_to_test.result is not None and self._champion_trial.result is not None: - if 'ucb' in self._champion_test_policy: - return self._test_lcb_ucb(self._champion_trial, trial_to_test, self.WARMSTART_NUM) - elif 'avg' in self._champion_test_policy: - return self._test_avg_loss(self._champion_trial, trial_to_test, self.WARMSTART_NUM) - elif 'martingale' in self._champion_test_policy: + if "ucb" in self._champion_test_policy: + return self._test_lcb_ucb( + self._champion_trial, trial_to_test, self.WARMSTART_NUM + ) + elif "avg" in self._champion_test_policy: + return self._test_avg_loss( + self._champion_trial, trial_to_test, self.WARMSTART_NUM + ) + elif "martingale" in self._champion_test_policy: return self._test_martingale(self._champion_trial, trial_to_test) else: raise NotImplementedError @@ -426,22 +484,38 @@ class OnlineTrialRunner: @staticmethod def _worse_than_champion_test(champion_trial, trial, warmstart_num=1) -> bool: - """Test whether the input trial is worse than the champion_trial - """ + """Test whether the input trial is worse than the champion_trial""" if trial.result is not None and trial.result.resource_used >= warmstart_num: if trial.result.loss_lcb > champion_trial.result.loss_ucb: - logger.info('=========trial %s is worse than champion %s=====', - trial.trial_id, champion_trial.trial_id) - logger.info('trial %s %s %s', trial.config, trial.result, trial.resource_lease) - logger.info('trial loss_avg:%s, trial loss_cb %s', trial.result.loss_avg, - trial.result.loss_cb) - logger.info('champion loss_avg:%s, champion loss_cb %s', champion_trial.result.loss_avg, - champion_trial.result.loss_cb) - logger.info('champion %s', champion_trial.config) - logger.info('trial loss_avg_recent:%s, trial loss_cb %s', trial.result.loss_avg_recent, - trial.result.loss_cb) - logger.info('champion loss_avg_recent:%s, champion loss_cb %s', - champion_trial.result.loss_avg_recent, champion_trial.result.loss_cb) + logger.info( + "=========trial %s is worse than champion %s=====", + trial.trial_id, + champion_trial.trial_id, + ) + logger.info( + "trial %s %s %s", trial.config, trial.result, trial.resource_lease + ) + logger.info( + "trial loss_avg:%s, trial loss_cb %s", + trial.result.loss_avg, + trial.result.loss_cb, + ) + logger.info( + "champion loss_avg:%s, champion loss_cb %s", + champion_trial.result.loss_avg, + champion_trial.result.loss_cb, + ) + logger.info("champion %s", champion_trial.config) + logger.info( + "trial loss_avg_recent:%s, trial loss_cb %s", + trial.result.loss_avg_recent, + trial.result.loss_cb, + ) + logger.info( + "champion loss_avg_recent:%s, champion loss_cb %s", + champion_trial.result.loss_avg_recent, + champion_trial.result.loss_cb, + ) return True return False @@ -452,18 +526,35 @@ class OnlineTrialRunner: """ assert trial.trial_id != champion_trial.trial_id if trial.result.resource_used >= warmstart_num: - if trial.result.loss_ucb < champion_trial.result.loss_lcb - champion_trial.result.loss_cb: - logger.info('======new champion condition satisfied: using lcb vs ucb=====') - logger.info('new champion trial %s %s %s', - trial.trial_id, trial.result.resource_used, trial.resource_lease) - logger.info('new champion trial loss_avg:%s, trial loss_cb %s', - trial.result.loss_avg, trial.result.loss_cb) - logger.info('old champion trial %s %s %s', - champion_trial.trial_id, champion_trial.result.resource_used, - champion_trial.resource_lease,) - logger.info('old champion loss avg %s, loss cb %s', - champion_trial.result.loss_avg, - champion_trial.result.loss_cb) + if ( + trial.result.loss_ucb + < champion_trial.result.loss_lcb - champion_trial.result.loss_cb + ): + logger.info( + "======new champion condition satisfied: using lcb vs ucb=====" + ) + logger.info( + "new champion trial %s %s %s", + trial.trial_id, + trial.result.resource_used, + trial.resource_lease, + ) + logger.info( + "new champion trial loss_avg:%s, trial loss_cb %s", + trial.result.loss_avg, + trial.result.loss_cb, + ) + logger.info( + "old champion trial %s %s %s", + champion_trial.trial_id, + champion_trial.result.resource_used, + champion_trial.resource_lease, + ) + logger.info( + "old champion loss avg %s, loss cb %s", + champion_trial.result.loss_avg, + champion_trial.result.loss_cb, + ) return True return False @@ -475,13 +566,19 @@ class OnlineTrialRunner: assert trial.trial_id != champion_trial.trial_id if trial.result.resource_used >= warmstart_num: if trial.result.loss_avg < champion_trial.result.loss_avg: - logger.info('=====new champion condition satisfied using avg loss=====') - logger.info('trial %s', trial.config) - logger.info('trial loss_avg:%s, trial loss_cb %s', - trial.result.loss_avg, trial.result.loss_cb) - logger.info('champion loss_avg:%s, champion loss_cb %s', - champion_trial.result.loss_avg, champion_trial.result.loss_cb) - logger.info('champion %s', champion_trial.config) + logger.info("=====new champion condition satisfied using avg loss=====") + logger.info("trial %s", trial.config) + logger.info( + "trial loss_avg:%s, trial loss_cb %s", + trial.result.loss_avg, + trial.result.loss_cb, + ) + logger.info( + "champion loss_avg:%s, champion loss_cb %s", + champion_trial.result.loss_avg, + champion_trial.result.loss_cb, + ) + logger.info("champion %s", champion_trial.config) return True return False diff --git a/flaml/searcher/blendsearch.py b/flaml/searcher/blendsearch.py index ee6dc5340..e4e20130c 100644 --- a/flaml/searcher/blendsearch.py +++ b/flaml/searcher/blendsearch.py @@ -129,11 +129,12 @@ class BlendSearch(Searcher): self._metric, self._mode = metric, mode init_config = low_cost_partial_config or {} if not init_config: - logger.warning( + logger.info( "No low-cost partial config given to the search algorithm. " "For cost-frugal search, " "consider providing low-cost values for cost-related hps via " - "'low_cost_partial_config'." + "'low_cost_partial_config'. More info can be found at " + "https://github.com/microsoft/FLAML/wiki/About-%60low_cost_partial_config%60" ) if evaluated_rewards and mode: self._points_to_evaluate = [] @@ -228,6 +229,7 @@ class BlendSearch(Searcher): metric: Optional[str] = None, mode: Optional[str] = None, config: Optional[Dict] = None, + setting: Optional[Dict] = None, ) -> bool: metric_changed = mode_changed = False if metric and self._metric != metric: @@ -264,22 +266,22 @@ class BlendSearch(Searcher): ) self._gs.space = self._ls.space self._init_search() - if config: - # CFO doesn't need these settings - if "time_budget_s" in config: - self._time_budget_s = config["time_budget_s"] # budget from now - now = time.time() - self._time_used += now - self._start_time - self._start_time = now - self._set_deadline() - if "metric_target" in config: - self._metric_target = config.get("metric_target") - if "num_samples" in config: - self._num_samples = ( - config["num_samples"] - + len(self._result) - + len(self._trial_proposed_by) - ) + if setting: + # CFO doesn't need these settings + if "time_budget_s" in setting: + self._time_budget_s = setting["time_budget_s"] # budget from now + now = time.time() + self._time_used += now - self._start_time + self._start_time = now + self._set_deadline() + if "metric_target" in setting: + self._metric_target = setting.get("metric_target") + if "num_samples" in setting: + self._num_samples = ( + setting["num_samples"] + + len(self._result) + + len(self._trial_proposed_by) + ) return True def _set_deadline(self): diff --git a/flaml/searcher/flow2.py b/flaml/searcher/flow2.py index 7e3b49cd9..9057c6584 100644 --- a/flaml/searcher/flow2.py +++ b/flaml/searcher/flow2.py @@ -1,14 +1,16 @@ -'''! - * Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved. +"""! + * Copyright (c) Microsoft Corporation. All rights reserved. * Licensed under the MIT License. See LICENSE file in the * project root for license information. -''' +""" from flaml.tune.sample import Domain from typing import Dict, Optional, Tuple import numpy as np + try: from ray import __version__ as ray_version - assert ray_version >= '1.0.0' + + assert ray_version >= "1.0.0" from ray.tune.suggest import Searcher from ray.tune.suggest.variant_generator import generate_variants from ray.tune import sample @@ -22,28 +24,30 @@ from ..tune.space import complete_config, denormalize, normalize import logging + logger = logging.getLogger(__name__) class FLOW2(Searcher): - '''Local search algorithm FLOW2, with adaptive step size - ''' + """Local search algorithm FLOW2, with adaptive step size""" STEPSIZE = 0.1 STEP_LOWER_BOUND = 0.0001 - def __init__(self, - init_config: dict, - metric: Optional[str] = None, - mode: Optional[str] = None, - space: Optional[dict] = None, - prune_attr: Optional[str] = None, - min_resource: Optional[float] = None, - max_resource: Optional[float] = None, - resource_multiple_factor: Optional[float] = 4, - cost_attr: Optional[str] = 'time_total_s', - seed: Optional[int] = 20): - '''Constructor + def __init__( + self, + init_config: dict, + metric: Optional[str] = None, + mode: Optional[str] = None, + space: Optional[dict] = None, + prune_attr: Optional[str] = None, + min_resource: Optional[float] = None, + max_resource: Optional[float] = None, + resource_multiple_factor: Optional[float] = 4, + cost_attr: Optional[str] = "time_total_s", + seed: Optional[int] = 20, + ): + """Constructor Args: init_config: a dictionary of a partial or full initial config, @@ -79,20 +83,18 @@ class FLOW2(Searcher): used for increasing resource. cost_attr: A string of the attribute used for cost. seed: An integer of the random seed. - ''' + """ if mode: assert mode in ["min", "max"], "`mode` must be 'min' or 'max'." else: mode = "min" - super(FLOW2, self).__init__( - metric=metric, - mode=mode) + super(FLOW2, self).__init__(metric=metric, mode=mode) # internally minimizes, so "max" => -1 if mode == "max": - self.metric_op = -1. + self.metric_op = -1.0 elif mode == "min": - self.metric_op = 1. + self.metric_op = 1.0 self.space = space or {} self._space = flatten_dict(self.space, prevent_delimiter=True) self._random = np.random.RandomState(seed) @@ -106,7 +108,7 @@ class FLOW2(Searcher): self.max_resource = max_resource self._resource = None self._step_lb = np.Inf - if space: + if space is not None: self._init_search() def _init_search(self): @@ -115,9 +117,10 @@ class FLOW2(Searcher): self._unordered_cat_hp = {} hier = False for key, domain in self._space.items(): - assert not (isinstance(domain, dict) and 'grid_search' in domain), \ - f"{key}'s domain is grid search, not supported in FLOW^2." - if callable(getattr(domain, 'get_sampler', None)): + assert not ( + isinstance(domain, dict) and "grid_search" in domain + ), f"{key}'s domain is grid search, not supported in FLOW^2." + if callable(getattr(domain, "get_sampler", None)): self._tunable_keys.append(key) sampler = domain.get_sampler() # the step size lower bound for uniform variables doesn't depend @@ -125,12 +128,14 @@ class FLOW2(Searcher): if isinstance(sampler, sample.Quantized): q = sampler.q sampler = sampler.get_sampler() - if str(sampler) == 'Uniform': + if str(sampler) == "Uniform": self._step_lb = min( - self._step_lb, q / (domain.upper - domain.lower)) - elif isinstance(domain, sample.Integer) and str(sampler) == 'Uniform': + self._step_lb, q / (domain.upper - domain.lower) + ) + elif isinstance(domain, sample.Integer) and str(sampler) == "Uniform": self._step_lb = min( - self._step_lb, 1.0 / (domain.upper - 1 - domain.lower)) + self._step_lb, 1.0 / (domain.upper - 1 - domain.lower) + ) if isinstance(domain, sample.Categorical): if not domain.ordered: self._unordered_cat_hp[key] = len(domain.categories) @@ -139,13 +144,12 @@ class FLOW2(Searcher): if isinstance(cat, dict): hier = True break - if str(sampler) != 'Normal': + if str(sampler) != "Normal": self._bounded_keys.append(key) if not hier: self._space_keys = sorted(self._tunable_keys) self.hierarchical = hier - if (self.prune_attr and self.prune_attr not in self._space - and self.max_resource): + if self.prune_attr and self.prune_attr not in self._space and self.max_resource: self.min_resource = self.min_resource or self._min_resource() self._resource = self._round(self.min_resource) if not hier: @@ -169,10 +173,11 @@ class FLOW2(Searcher): if self.step > self.step_ub: self.step = self.step_ub # maximal # consecutive no improvements - self.dir = 2**(min(9, self.dim)) + self.dir = 2 ** (min(9, self.dim)) self._configs = {} # dict from trial_id to (config, stepsize) self._K = 0 - self._iter_best_config = self.trial_count_proposed = self.trial_count_complete = 1 + self._iter_best_config = 1 + self.trial_count_proposed = self.trial_count_complete = 1 self._num_proposedby_incumbent = 0 self._reset_times = 0 # record intermediate trial cost @@ -196,14 +201,18 @@ class FLOW2(Searcher): if isinstance(sampler, sample.Quantized): q = sampler.q sampler_inner = sampler.get_sampler() - if str(sampler_inner) == 'LogUniform': + if str(sampler_inner) == "LogUniform": step_lb = min( - step_lb, np.log(1.0 + q / self.best_config[key]) - / np.log(domain.upper / domain.lower)) - elif isinstance(domain, sample.Integer) and str(sampler) == 'LogUniform': + step_lb, + np.log(1.0 + q / self.best_config[key]) + / np.log(domain.upper / domain.lower), + ) + elif isinstance(domain, sample.Integer) and str(sampler) == "LogUniform": step_lb = min( - step_lb, np.log(1.0 + 1.0 / self.best_config[key]) - / np.log((domain.upper - 1) / domain.lower)) + step_lb, + np.log(1.0 + 1.0 / self.best_config[key]) + / np.log((domain.upper - 1) / domain.lower), + ) if np.isinf(step_lb): step_lb = self.STEP_LOWER_BOUND else: @@ -215,13 +224,11 @@ class FLOW2(Searcher): return self._resource def _min_resource(self) -> float: - ''' automatically decide minimal resource - ''' + """automatically decide minimal resource""" return self.max_resource / np.pow(self.resource_multiple_factor, 5) def _round(self, resource) -> float: - ''' round the resource to self.max_resource if close to it - ''' + """round the resource to self.max_resource if close to it""" if resource * self.resource_multiple_factor > self.max_resource: return self.max_resource return resource @@ -231,70 +238,83 @@ class FLOW2(Searcher): return vec def complete_config( - self, partial_config: Dict, - lower: Optional[Dict] = None, upper: Optional[Dict] = None + self, + partial_config: Dict, + lower: Optional[Dict] = None, + upper: Optional[Dict] = None, ) -> Tuple[Dict, Dict]: - ''' generate a complete config from the partial config input + """generate a complete config from the partial config input add minimal resource to config if available - ''' + """ disturb = self._reset_times and partial_config == self.init_config # if not the first time to complete init_config, use random gaussian config, space = complete_config( - partial_config, self.space, self, disturb, lower, upper) + partial_config, self.space, self, disturb, lower, upper + ) if partial_config == self.init_config: self._reset_times += 1 if self._resource: config[self.prune_attr] = self.min_resource return config, space - def create(self, init_config: Dict, obj: float, cost: float, space: Dict - ) -> Searcher: + def create( + self, init_config: Dict, obj: float, cost: float, space: Dict + ) -> Searcher: # space is the subspace where the init_config is located flow2 = self.__class__( - init_config, self.metric, self.mode, - space, self.prune_attr, - self.min_resource, self.max_resource, - self.resource_multiple_factor, self.cost_attr, self.seed + 1) + init_config, + self.metric, + self.mode, + space, + self.prune_attr, + self.min_resource, + self.max_resource, + self.resource_multiple_factor, + self.cost_attr, + self.seed + 1, + ) flow2.best_obj = obj * self.metric_op # minimize internally flow2.cost_incumbent = cost self.seed += 1 return flow2 def normalize(self, config, recursive=False) -> Dict: - ''' normalize each dimension in config to [0,1] - ''' + """normalize each dimension in config to [0,1]""" return normalize( - config, self._space, self.best_config, self.incumbent, recursive) + config, self._space, self.best_config, self.incumbent, recursive + ) def denormalize(self, config): - ''' denormalize each dimension in config from [0,1] - ''' + """denormalize each dimension in config from [0,1]""" return denormalize( - config, self._space, self.best_config, self.incumbent, self._random) + config, self._space, self.best_config, self.incumbent, self._random + ) - def set_search_properties(self, - metric: Optional[str] = None, - mode: Optional[str] = None, - config: Optional[Dict] = None) -> bool: + def set_search_properties( + self, + metric: Optional[str] = None, + mode: Optional[str] = None, + config: Optional[Dict] = None, + ) -> bool: if metric: self._metric = metric if mode: assert mode in ["min", "max"], "`mode` must be 'min' or 'max'." self._mode = mode if mode == "max": - self.metric_op = -1. + self.metric_op = -1.0 elif mode == "min": - self.metric_op = 1. + self.metric_op = 1.0 if config: self.space = config self._space = flatten_dict(self.space) self._init_search() return True - def on_trial_complete(self, trial_id: str, result: Optional[Dict] = None, - error: bool = False): - ''' compare with incumbent - ''' + def on_trial_complete( + self, trial_id: str, result: Optional[Dict] = None, error: bool = False + ): + # compare with incumbent # if better, move, reset num_complete and num_proposed # if not better and num_complete >= 2*dim, num_allowed += 2 self.trial_count_complete += 1 @@ -329,15 +349,19 @@ class FLOW2(Searcher): if proposed_by == self.incumbent: # proposed by current incumbent and no better self._num_complete4incumbent += 1 - cost = result.get( - self.cost_attr) if result else self._trial_cost.get(trial_id) + cost = ( + result.get(self.cost_attr) if result else self._trial_cost.get(trial_id) + ) if cost: self._cost_complete4incumbent += cost - if self._num_complete4incumbent >= 2 * self.dim and \ - self._num_allowed4incumbent == 0: + if ( + self._num_complete4incumbent >= 2 * self.dim + and self._num_allowed4incumbent == 0 + ): self._num_allowed4incumbent = 2 if self._num_complete4incumbent == self.dir and ( - not self._resource or self._resource == self.max_resource): + not self._resource or self._resource == self.max_resource + ): # check stuck condition if using max resource self._num_complete4incumbent -= 2 if self._num_allowed4incumbent < 2: @@ -345,8 +369,7 @@ class FLOW2(Searcher): # elif proposed_by: del self._proposed_by[trial_id] def on_trial_result(self, trial_id: str, result: Dict): - ''' early update of incumbent - ''' + """early update of incumbent""" if result: obj = result.get(self._metric) if obj: @@ -373,27 +396,32 @@ class FLOW2(Searcher): def rand_vector_unit_sphere(self, dim, trunc=0) -> np.ndarray: vec = self._random.normal(0, 1, dim) if 0 < trunc < dim: - vec[np.abs(vec).argsort()[:dim - trunc]] = 0 + vec[np.abs(vec).argsort()[: dim - trunc]] = 0 mag = np.linalg.norm(vec) return vec / mag def suggest(self, trial_id: str) -> Optional[Dict]: - ''' suggest a new config, one of the following cases: + """suggest a new config, one of the following cases: 1. same incumbent, increase resource 2. same resource, move from the incumbent to a random direction 3. same resource, move from the incumbent to the opposite direction #TODO: better decouple FLOW2 config suggestion and stepsize update - ''' + """ self.trial_count_proposed += 1 - if self._num_complete4incumbent > 0 and self.cost_incumbent and \ - self._resource and self._resource < self.max_resource and ( + if ( + self._num_complete4incumbent > 0 + and self.cost_incumbent + and self._resource + and self._resource < self.max_resource + and ( self._cost_complete4incumbent - >= self.cost_incumbent * self.resource_multiple_factor): + >= self.cost_incumbent * self.resource_multiple_factor + ) + ): # consider increasing resource using sum eval cost of complete # configs old_resource = self._resource - self._resource = self._round( - self._resource * self.resource_multiple_factor) + self._resource = self._round(self._resource * self.resource_multiple_factor) self.cost_incumbent *= self._resource / old_resource config = self.best_config.copy() config[self.prune_attr] = self._resource @@ -409,8 +437,9 @@ class FLOW2(Searcher): self._direction_tried = None else: # propose a new direction - self._direction_tried = self.rand_vector_unit_sphere( - self.dim, self._trunc) * self.step + self._direction_tried = ( + self.rand_vector_unit_sphere(self.dim, self._trunc) * self.step + ) for i, key in enumerate(self._tunable_keys): move[key] += self._direction_tried[i] self._project(move) @@ -442,7 +471,8 @@ class FLOW2(Searcher): break self._same = same if self._num_proposedby_incumbent == self.dir and ( - not self._resource or self._resource == self.max_resource): + not self._resource or self._resource == self.max_resource + ): # check stuck condition if using max resource self._num_proposedby_incumbent -= 2 self._init_phase = False @@ -459,11 +489,11 @@ class FLOW2(Searcher): # random for i, key in enumerate(self._tunable_keys): if self._direction_tried[i] != 0: - for _, generated in generate_variants({'config': { - key: self._space[key] - }}): - if generated['config'][key] != best_config[key]: - config[key] = generated['config'][key] + for _, generated in generate_variants( + {"config": {key: self._space[key]}} + ): + if generated["config"][key] != best_config[key]: + config[key] = generated["config"][key] return unflatten_dict(config) break else: @@ -477,8 +507,7 @@ class FLOW2(Searcher): return unflatten_dict(config) def _project(self, config): - ''' project normalized config in the feasible region and set prune_attr - ''' + """project normalized config in the feasible region and set prune_attr""" for key in self._bounded_keys: value = config[key] config[key] = max(0, min(1, value)) @@ -487,14 +516,13 @@ class FLOW2(Searcher): @property def can_suggest(self) -> bool: - ''' can't suggest if 2*dim configs have been proposed for the incumbent - while fewer are completed - ''' + """can't suggest if 2*dim configs have been proposed for the incumbent + while fewer are completed + """ return self._num_allowed4incumbent > 0 def config_signature(self, config, space: Dict = None) -> tuple: - ''' return the signature tuple of a config - ''' + """return the signature tuple of a config""" config = flatten_dict(config) if space: space = flatten_dict(space) @@ -514,8 +542,11 @@ class FLOW2(Searcher): if self.hierarchical: # can't remove constant for hierarchical search space, # e.g., learner - if not (domain is None or type(domain) in (str, int, float) - or isinstance(domain, sample.Domain)): + if not ( + domain is None + or type(domain) in (str, int, float) + or isinstance(domain, sample.Domain) + ): # not domain or hashable # get rid of list type for hierarchical search space. continue @@ -527,16 +558,14 @@ class FLOW2(Searcher): @property def converged(self) -> bool: - ''' return whether the local search has converged - ''' + """return whether the local search has converged""" if self._num_complete4incumbent < self.dir - 2: return False # check stepsize after enough configs are completed return self.step < self.step_lower_bound def reach(self, other: Searcher) -> bool: - ''' whether the incumbent can reach the incumbent of other - ''' + """whether the incumbent can reach the incumbent of other""" config1, config2 = self.best_config, other.best_config incumbent1, incumbent2 = self.incumbent, other.incumbent if self._resource and config1[self.prune_attr] > config2[self.prune_attr]: @@ -547,6 +576,9 @@ class FLOW2(Searcher): if config1[key] != config2.get(key): return False delta = np.array( - [incumbent1[key] - incumbent2.get(key, np.inf) - for key in self._tunable_keys]) + [ + incumbent1[key] - incumbent2.get(key, np.inf) + for key in self._tunable_keys + ] + ) return np.linalg.norm(delta) <= self.step diff --git a/flaml/searcher/online_searcher.py b/flaml/searcher/online_searcher.py index 836717ab0..e90f68281 100644 --- a/flaml/searcher/online_searcher.py +++ b/flaml/searcher/online_searcher.py @@ -20,14 +20,19 @@ class BaseSearcher: on_trial_complete() """ - def __init__(self, - metric: Optional[str] = None, - mode: Optional[str] = None, - ): + def __init__( + self, + metric: Optional[str] = None, + mode: Optional[str] = None, + ): pass - def set_search_properties(self, metric: Optional[str] = None, mode: Optional[str] = None, - config: Optional[Dict] = None): + def set_search_properties( + self, + metric: Optional[str] = None, + mode: Optional[str] = None, + config: Optional[Dict] = None, + ): if metric: self._metric = metric if mode: @@ -66,6 +71,7 @@ class ChampionFrontierSearcher(BaseSearcher): (although not the same searcher_trial_id). searcher_trial_id will be used in suggest() """ + # ****the following constants are used when generating new challengers in # the _query_config_oracle function # how many item to add when doing the expansion @@ -84,25 +90,26 @@ class ChampionFrontierSearcher(BaseSearcher): # 0.95 of the previous best config's loss. # NOTE: this setting depends on the assumption that (and thus # _query_config_oracle) is only triggered when a better champion is found. - CFO_SEARCHER_METRIC_NAME = 'pseudo_loss' + CFO_SEARCHER_METRIC_NAME = "pseudo_loss" CFO_SEARCHER_LARGE_LOSS = 1e6 # the random seed used in generating numerical hyperparamter configs (when CFO is not used) NUM_RANDOM_SEED = 111 - CHAMPION_TRIAL_NAME = 'champion_trial' + CHAMPION_TRIAL_NAME = "champion_trial" TRIAL_CLASS = VowpalWabbitTrial - def __init__(self, - init_config: Dict, - space: Optional[Dict] = None, - metric: Optional[str] = None, - mode: Optional[str] = None, - random_seed: Optional[int] = 2345, - online_trial_args: Optional[Dict] = {}, - nonpoly_searcher_name: Optional[str] = 'CFO' - ): - '''Constructor + def __init__( + self, + init_config: Dict, + space: Optional[Dict] = None, + metric: Optional[str] = None, + mode: Optional[str] = None, + random_seed: Optional[int] = 2345, + online_trial_args: Optional[Dict] = {}, + nonpoly_searcher_name: Optional[str] = "CFO", + ): + """Constructor Args: init_config: dict @@ -113,7 +120,7 @@ class ChampionFrontierSearcher(BaseSearcher): online_trial_args: dict nonpoly_searcher_name: A string to specify the search algorithm for nonpoly hyperparameters - ''' + """ self._init_config = init_config self._space = space self._seed = random_seed @@ -122,44 +129,62 @@ class ChampionFrontierSearcher(BaseSearcher): self._random_state = np.random.RandomState(self._seed) self._searcher_for_nonpoly_hp = {} - self._space_of_nonpoly_hp = {} + # dicts to remember the mapping between searcher_trial_id and trial_id - self._searcher_trialid_to_trialid = {} # key: searcher_trial_id, value: trial_id - self._trialid_to_searcher_trial_id = {} # value: trial_id, key: searcher_trial_id + self._space_of_nonpoly_hp = {} + + # key: searcher_trial_id, value: trial_id + self._searcher_trialid_to_trialid = {} + + # value: trial_id, key: searcher_trial_id + self._trialid_to_searcher_trial_id = {} + self._challenger_list = [] # initialize the search in set_search_properties - self.set_search_properties(config={self.CHAMPION_TRIAL_NAME: None}, init_call=True) - logger.debug('using random seed %s in config oracle', self._seed) + self.set_search_properties( + setting={self.CHAMPION_TRIAL_NAME: None}, init_call=True + ) + logger.debug("using random seed %s in config oracle", self._seed) - def set_search_properties(self, metric: Optional[str] = None, - mode: Optional[str] = None, - config: Optional[Dict] = {}, - init_call: Optional[bool] = False): - """Construct search space with given config, and setup the search - """ + def set_search_properties( + self, + metric: Optional[str] = None, + mode: Optional[str] = None, + config: Optional[Dict] = {}, + setting: Optional[Dict] = {}, + init_call: Optional[bool] = False, + ): + """Construct search space with given config, and setup the search""" super().set_search_properties(metric, mode, config) # *********Use ConfigOralce (i.e, self._generate_new_space to generate list of new challengers) - logger.info('champion trial %s', config) - champion_trial = config.get(self.CHAMPION_TRIAL_NAME, None) + logger.info("setting %s", setting) + champion_trial = setting.get(self.CHAMPION_TRIAL_NAME, None) if champion_trial is None: champion_trial = self._create_trial_from_config(self._init_config) # generate a new list of challenger trials - new_challenger_list = self._query_config_oracle(champion_trial.config, - champion_trial.trial_id, - self._trialid_to_searcher_trial_id[champion_trial.trial_id]) + new_challenger_list = self._query_config_oracle( + champion_trial.config, + champion_trial.trial_id, + self._trialid_to_searcher_trial_id[champion_trial.trial_id], + ) # add the newly generated challengers to existing challengers # there can be duplicates and we check duplicates when calling next_trial() self._challenger_list = self._challenger_list + new_challenger_list # add the champion as part of the new_challenger_list when called initially if init_call: self._challenger_list.append(champion_trial) - logger.critical('Created challengers from champion %s', champion_trial.trial_id) - logger.critical('New challenger size %s, %s', len(self._challenger_list), - [t.trial_id for t in self._challenger_list]) + logger.info( + "**Important** Created challengers from champion %s", + champion_trial.trial_id, + ) + logger.info( + "New challenger size %s, %s", + len(self._challenger_list), + [t.trial_id for t in self._challenger_list], + ) def next_trial(self): - """Return a trial from the _challenger_list - """ + """Return a trial from the _challenger_list""" next_trial = None if self._challenger_list: next_trial = self._challenger_list.pop() @@ -175,8 +200,9 @@ class ChampionFrontierSearcher(BaseSearcher): self._trialid_to_searcher_trial_id[trial.trial_id] = searcher_trial_id return trial - def _query_config_oracle(self, seed_config, seed_config_trial_id, - seed_config_searcher_trial_id=None) -> List[Trial]: + def _query_config_oracle( + self, seed_config, seed_config_trial_id, seed_config_searcher_trial_id=None + ) -> List[Trial]: """Give the seed config, generate a list of new configs (which are supposed to include at least one config that has better performance than the input seed_config) """ @@ -189,12 +215,16 @@ class ChampionFrontierSearcher(BaseSearcher): config_domain = self._space[k] if isinstance(config_domain, PolynomialExpansionSet): # get candidate configs for hyperparameters of the PolynomialExpansionSet type - partial_new_configs = self._generate_independent_hp_configs(k, v, config_domain) + partial_new_configs = self._generate_independent_hp_configs( + k, v, config_domain + ) if partial_new_configs: hyperparameter_config_groups.append(partial_new_configs) # does not have searcher_trial_ids searcher_trial_ids_groups.append([]) - elif isinstance(config_domain, Float) or isinstance(config_domain, Categorical): + elif isinstance(config_domain, Float) or isinstance( + config_domain, Categorical + ): # otherwise we need to deal with them in group nonpoly_config[k] = v if k not in self._space_of_nonpoly_hp: @@ -204,38 +234,57 @@ class ChampionFrontierSearcher(BaseSearcher): if nonpoly_config: new_searcher_trial_ids = [] partial_new_nonpoly_configs = [] - if 'CFO' in self._nonpoly_searcher_name: + if "CFO" in self._nonpoly_searcher_name: if seed_config_trial_id not in self._searcher_for_nonpoly_hp: - self._searcher_for_nonpoly_hp[seed_config_trial_id] = CFO(space=self._space_of_nonpoly_hp, - points_to_evaluate=[nonpoly_config], - metric=self.CFO_SEARCHER_METRIC_NAME, - ) + self._searcher_for_nonpoly_hp[seed_config_trial_id] = CFO( + space=self._space_of_nonpoly_hp, + points_to_evaluate=[nonpoly_config], + metric=self.CFO_SEARCHER_METRIC_NAME, + ) # initialize the search in set_search_properties - self._searcher_for_nonpoly_hp[seed_config_trial_id].set_search_properties( - config={'metric_target': self.CFO_SEARCHER_LARGE_LOSS}) + self._searcher_for_nonpoly_hp[ + seed_config_trial_id + ].set_search_properties( + setting={"metric_target": self.CFO_SEARCHER_LARGE_LOSS} + ) # We need to call this for once, such that the seed config in points_to_evaluate will be called # to be tried - self._searcher_for_nonpoly_hp[seed_config_trial_id].suggest(seed_config_searcher_trial_id) + self._searcher_for_nonpoly_hp[seed_config_trial_id].suggest( + seed_config_searcher_trial_id + ) # assuming minimization - if self._searcher_for_nonpoly_hp[seed_config_trial_id].metric_target is None: + if ( + self._searcher_for_nonpoly_hp[seed_config_trial_id].metric_target + is None + ): pseudo_loss = self.CFO_SEARCHER_LARGE_LOSS else: - pseudo_loss = self._searcher_for_nonpoly_hp[seed_config_trial_id].metric_target * 0.95 + pseudo_loss = ( + self._searcher_for_nonpoly_hp[ + seed_config_trial_id + ].metric_target + * 0.95 + ) pseudo_result_to_report = {} for k, v in nonpoly_config.items(): - pseudo_result_to_report['config/' + str(k)] = v + pseudo_result_to_report["config/" + str(k)] = v pseudo_result_to_report[self.CFO_SEARCHER_METRIC_NAME] = pseudo_loss - pseudo_result_to_report['time_total_s'] = 1 - self._searcher_for_nonpoly_hp[seed_config_trial_id].on_trial_complete(seed_config_searcher_trial_id, - result=pseudo_result_to_report) + pseudo_result_to_report["time_total_s"] = 1 + self._searcher_for_nonpoly_hp[seed_config_trial_id].on_trial_complete( + seed_config_searcher_trial_id, result=pseudo_result_to_report + ) while len(partial_new_nonpoly_configs) < self.NUMERICAL_NUM: # suggest multiple times new_searcher_trial_id = Trial.generate_id() new_searcher_trial_ids.append(new_searcher_trial_id) - suggestion = self._searcher_for_nonpoly_hp[seed_config_trial_id].suggest(new_searcher_trial_id) + suggestion = self._searcher_for_nonpoly_hp[ + seed_config_trial_id + ].suggest(new_searcher_trial_id) if suggestion is not None: partial_new_nonpoly_configs.append(suggestion) - logger.info('partial_new_nonpoly_configs %s', partial_new_nonpoly_configs) + logger.info( + "partial_new_nonpoly_configs %s", partial_new_nonpoly_configs + ) else: raise NotImplementedError if partial_new_nonpoly_configs: @@ -244,9 +293,11 @@ class ChampionFrontierSearcher(BaseSearcher): # ----------- coordinate generation of new challengers in the case of multiple groups new_trials = [] for i in range(len(hyperparameter_config_groups)): - logger.info('hyperparameter_config_groups[i] %s %s', - len(hyperparameter_config_groups[i]), - hyperparameter_config_groups[i]) + logger.info( + "hyperparameter_config_groups[i] %s %s", + len(hyperparameter_config_groups[i]), + hyperparameter_config_groups[i], + ) for j, new_partial_config in enumerate(hyperparameter_config_groups[i]): new_seed_config = seed_config.copy() new_seed_config.update(new_partial_config) @@ -260,32 +311,55 @@ class ChampionFrontierSearcher(BaseSearcher): new_searcher_trial_id = searcher_trial_ids_groups[i][j] else: new_searcher_trial_id = None - new_trial = self._create_trial_from_config(new_seed_config, new_searcher_trial_id) + new_trial = self._create_trial_from_config( + new_seed_config, new_searcher_trial_id + ) new_trials.append(new_trial) - logger.info('new_configs %s', [t.trial_id for t in new_trials]) + logger.info("new_configs %s", [t.trial_id for t in new_trials]) return new_trials - def _generate_independent_hp_configs(self, hp_name, current_config_value, config_domain) -> List: + def _generate_independent_hp_configs( + self, hp_name, current_config_value, config_domain + ) -> List: if isinstance(config_domain, PolynomialExpansionSet): - seed_interactions = list(current_config_value) + list(config_domain.init_monomials) - logger.critical('Seed namespaces (singletons and interactions): %s', seed_interactions) - logger.info('current_config_value %s %s', current_config_value, seed_interactions) - configs = self._generate_poly_expansion_sets(seed_interactions, - self.EXPANSION_ORDER, - config_domain.allow_self_inter, - config_domain.highest_poly_order, - self.POLY_EXPANSION_ADDITION_NUM, - ) + seed_interactions = list(current_config_value) + list( + config_domain.init_monomials + ) + logger.info( + "**Important** Seed namespaces (singletons and interactions): %s", + seed_interactions, + ) + logger.info("current_config_value %s", current_config_value) + configs = self._generate_poly_expansion_sets( + seed_interactions, + self.EXPANSION_ORDER, + config_domain.allow_self_inter, + config_domain.highest_poly_order, + self.POLY_EXPANSION_ADDITION_NUM, + ) else: raise NotImplementedError configs_w_key = [{hp_name: hp_config} for hp_config in configs] return configs_w_key - def _generate_poly_expansion_sets(self, seed_interactions, order, allow_self_inter, - highest_poly_order, interaction_num_to_add): - champion_all_combinations = self._generate_all_comb(seed_interactions, order, allow_self_inter, highest_poly_order) - space = sorted(list(itertools.combinations( - champion_all_combinations, interaction_num_to_add))) + def _generate_poly_expansion_sets( + self, + seed_interactions, + order, + allow_self_inter, + highest_poly_order, + interaction_num_to_add, + ): + champion_all_combinations = self._generate_all_comb( + seed_interactions, order, allow_self_inter, highest_poly_order + ) + space = sorted( + list( + itertools.combinations( + champion_all_combinations, interaction_num_to_add + ) + ) + ) self._random_state.shuffle(space) candidate_configs = [set(seed_interactions) | set(item) for item in space] final_candidate_configs = [] @@ -295,9 +369,12 @@ class ChampionFrontierSearcher(BaseSearcher): return final_candidate_configs @staticmethod - def _generate_all_comb(seed_interactions: list, seed_interaction_order: int, - allow_self_inter: Optional[bool] = False, - highest_poly_order: Optional[int] = None): + def _generate_all_comb( + seed_interactions: list, + seed_interaction_order: int, + allow_self_inter: Optional[bool] = False, + highest_poly_order: Optional[int] = None, + ): """Generate new interactions by doing up to seed_interaction_order on the seed_interactions Args: @@ -312,8 +389,7 @@ class ChampionFrontierSearcher(BaseSearcher): """ def get_interactions(list1, list2): - """Get combinatorial list of tuples - """ + """Get combinatorial list of tuples""" new_list = [] for i in list1: for j in list2: @@ -321,19 +397,18 @@ class ChampionFrontierSearcher(BaseSearcher): # 'abc' 'cba' 'bca' are all 'abc' # this is done to ensure we can use the config as the signature # of the trial, i.e., trial id. - new_interaction = ''.join(sorted(i + j)) + new_interaction = "".join(sorted(i + j)) if new_interaction not in new_list: new_list.append(new_interaction) return new_list def strip_self_inter(s): - """Remove duplicates in an interaction string - """ + """Remove duplicates in an interaction string""" if len(s) == len(set(s)): return s else: # return ''.join(sorted(set(s))) - new_s = '' + new_s = "" char_list = [] for i in s: if i not in char_list: @@ -351,10 +426,15 @@ class ChampionFrontierSearcher(BaseSearcher): all_interactions_no_self_inter = [] for s in all_interactions: s_no_inter = strip_self_inter(s) - if len(s_no_inter) > 1 and s_no_inter not in all_interactions_no_self_inter: + if ( + len(s_no_inter) > 1 + and s_no_inter not in all_interactions_no_self_inter + ): all_interactions_no_self_inter.append(s_no_inter) all_interactions = all_interactions_no_self_inter if highest_poly_order is not None: - all_interactions = [c for c in all_interactions if len(c) <= highest_poly_order] - logger.info('all_combinations %s', all_interactions) + all_interactions = [ + c for c in all_interactions if len(c) <= highest_poly_order + ] + logger.info("all_combinations %s", all_interactions) return all_interactions diff --git a/flaml/searcher/search_thread.py b/flaml/searcher/search_thread.py index f3118c753..e04dccdf9 100644 --- a/flaml/searcher/search_thread.py +++ b/flaml/searcher/search_thread.py @@ -54,7 +54,7 @@ class SearchThread: @classmethod def set_eps(cls, time_budget_s): - cls._eps = max(min(time_budget_s / 1000.0, 1.0), 1e-10) + cls._eps = max(min(time_budget_s / 1000.0, 1.0), 1e-9) def suggest(self, trial_id: str) -> Optional[Dict]: ''' use the suggest() of the underlying search algorithm diff --git a/flaml/tune/README.md b/flaml/tune/README.md index aada353da..a0d5c6a83 100644 --- a/flaml/tune/README.md +++ b/flaml/tune/README.md @@ -1,6 +1,6 @@ # Economical Hyperparameter Optimization -`flaml.tune` is a module for economical hyperparameter tuning. It frees users from manually tuning many hyperparameters for a software, such as machine learning training procedures. +`flaml.tune` is a module for economical hyperparameter tuning. It frees users from manually tuning many hyperparameters for a software, such as machine learning training procedures. It can be used standalone, or together with ray tune or nni. * Example for sequential tuning (recommended when compute resource is limited and each trial can consume all the resources): @@ -18,8 +18,8 @@ def evaluate_config(config): # and the cost could be related to certain hyperparameters # in this example, we assume it's proportional to x time.sleep(config['x']/100000) - # use tune.report to report the metric to optimize - tune.report(metric=metric) + # use tune.report to report the metric to optimize + tune.report(metric=metric) analysis = tune.run( evaluate_config, # the function to evaluate a config @@ -33,7 +33,7 @@ analysis = tune.run( num_samples=-1, # the maximal number of configs to try, -1 means infinite time_budget_s=60, # the time budget in seconds local_dir='logs/', # the local directory to store logs - # verbose=0, # verbosity + # verbose=0, # verbosity # use_ray=True, # uncomment when performing parallel tuning using ray ) @@ -57,8 +57,8 @@ def evaluate_config(config): # and the cost could be related to certain hyperparameters # in this example, we assume it's proportional to x time.sleep(config['x']/100000) - # use tune.report to report the metric to optimize - tune.report(metric=metric) + # use tune.report to report the metric to optimize + tune.report(metric=metric) # provide a time budget (in seconds) for the tuning process time_budget_s = 60 @@ -77,25 +77,25 @@ cfo = CFO(low_cost_partial_config=low_cost_partial_config) blendsearch = BlendSearch( metric="metric", mode="min", space=config_search_space, - low_cost_partial_config=low_cost_partial_config) + low_cost_partial_config=low_cost_partial_config, + time_budget_s=time_budget_s +) # NOTE: when using BlendSearch as a search_alg in ray tune, you need to -# configure the 'time_budget_s' for BlendSearch accordingly as follows such that +# configure the 'time_budget_s' for BlendSearch accordingly such that # BlendSearch is aware of the time budget. This step is not needed when -# BlendSearch is used as the search_alg in flaml.tune as it is already done -# automatically in flaml. Also, this step needs to be done after the search -# space is passed to BlendSearch and before raytune.run. -blendsearch.set_search_properties(config={"time_budget_s": time_budget_s}) +# BlendSearch is used as the search_alg in flaml.tune as it is done +# automatically in flaml. analysis = raytune.run( evaluate_config, # the function to evaluate a config config=config_search_space, metric='metric', # the name of the metric used for optimization mode='min', # the optimization mode, 'min' or 'max' - num_samples=-1, # the maximal number of configs to try, -1 means infinite + num_samples=-1, # the maximal number of configs to try, -1 means infinite time_budget_s=time_budget_s, # the time budget in seconds local_dir='logs/', # the local directory to store logs search_alg=blendsearch # or cfo - ) +) print(analysis.best_trial.last_result) # the best trial's result print(analysis.best_config) # the best config @@ -107,11 +107,10 @@ print(analysis.best_config) # the best config $nnictl create --config ./config.yml ``` -* For more examples, please check out +* For more examples, please check out [notebooks](https://github.com/microsoft/FLAML/tree/main/notebook/). - -`flaml` offers two HPO methods: CFO and BlendSearch. +`flaml` offers two HPO methods: CFO and BlendSearch. `flaml.tune` uses BlendSearch by default. ## CFO: Frugal Optimization for Cost-related Hyperparameters @@ -121,27 +120,27 @@ $nnictl create --config ./config.yml

-CFO uses the randomized direct search method FLOW2 with adaptive stepsize and random restart. +CFO uses the randomized direct search method FLOW2 with adaptive stepsize and random restart. It requires a low-cost initial point as input if such point exists. The search begins with the low-cost initial point and gradually move to high cost region if needed. The local search method has a provable convergence -rate and bounded cost. +rate and bounded cost. -About FLOW2: FLOW2 is a simple yet effective randomized direct search method. +About FLOW2: FLOW2 is a simple yet effective randomized direct search method. It is an iterative optimization method that can optimize for black-box functions. FLOW2 only requires pairwise comparisons between function values to perform iterative update. Comparing to existing HPO methods, FLOW2 has the following appealing properties: + 1. It is applicable to general black-box functions with a good convergence rate in terms of loss. -3. It provides theoretical guarantees on the total evaluation cost incurred. +1. It provides theoretical guarantees on the total evaluation cost incurred. The GIFs attached below demonstrate an example search trajectory of FLOW2 shown in the loss and evaluation cost (i.e., the training time ) space respectively. From the demonstration, we can see that (1) FLOW2 can quickly move toward the low-loss region, showing good convergence property and (2) FLOW2 tends to avoid exploring the high-cost region until necessary.

- +

Figure 1. FLOW2 in tuning the # of leaves and the # of trees for XGBoost. The two background heatmaps show the loss and cost distribution of all configurations. The black dots are the points evaluated in FLOW2. Black dots connected by lines are points that yield better loss performance when evaluated.

- Example: ```python @@ -152,9 +151,9 @@ tune.run(... ``` Recommended scenario: there exist cost-related hyperparameters and a low-cost -initial point is known before optimization. +initial point is known before optimization. If the search space is complex and CFO gets trapped into local optima, consider -using BlendSearch. +using BlendSearch. ## BlendSearch: Economical Hyperparameter Optimization With Blended Search Strategy @@ -167,7 +166,7 @@ BlendSearch combines local search with global search. It leverages the frugality of CFO and the space exploration ability of global search methods such as Bayesian optimization. Like CFO, BlendSearch requires a low-cost initial point as input if such point exists, and starts the search from there. Different from -CFO, BlendSearch will not wait for the local search to fully converge before +CFO, BlendSearch will not wait for the local search to fully converge before trying new start points. The new start points are suggested by the global search method and filtered based on their distance to the existing points in the cost-related dimensions. BlendSearch still gradually increases the trial cost. @@ -184,19 +183,18 @@ tune.run(... ) ``` -- Recommended scenario: cost-related hyperparameters exist, a low-cost +* Recommended scenario: cost-related hyperparameters exist, a low-cost initial point is known, and the search space is complex such that local search is prone to be stuck at local optima. - -- Suggestion about using larger search space in BlendSearch: +* Suggestion about using larger search space in BlendSearch: In hyperparameter optimization, a larger search space is desirable because it is more likely to include the optimal configuration (or one of the optimal configurations) in hindsight. However the performance (especially anytime performance) of most existing HPO methods is undesirable if the cost of the configurations in the search space has a large variation. Thus hand-crafted small search spaces (with relatively homogeneous cost) are often used in practice for these methods, which is subject to idiosyncrasy. BlendSearch combines the benefits of local search and global search, which enables a smart (economical) way of deciding where to explore in the search space even though it is larger than necessary. This allows users to specify a larger search space in BlendSearch, which is often easier and a better practice than narrowing down the search space by hand. For more technical details, please check our papers. * [Frugal Optimization for Cost-related Hyperparameters](https://arxiv.org/abs/2005.01571). Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021. -``` +```bibtex @inproceedings{wu2021cfo, title={Frugal Optimization for Cost-related Hyperparameters}, author={Qingyun Wu and Chi Wang and Silu Huang}, @@ -207,11 +205,11 @@ For more technical details, please check our papers. * [Economical Hyperparameter Optimization With Blended Search Strategy](https://www.microsoft.com/en-us/research/publication/economical-hyperparameter-optimization-with-blended-search-strategy/). Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021. -``` +```bibtex @inproceedings{wang2021blendsearch, title={Economical Hyperparameter Optimization With Blended Search Strategy}, author={Chi Wang and Qingyun Wu and Silu Huang and Amin Saied}, year={2021}, booktitle={ICLR'21}, } -``` \ No newline at end of file +``` diff --git a/flaml/tune/trial_runner.py b/flaml/tune/trial_runner.py index 690e57df3..75c5181ea 100644 --- a/flaml/tune/trial_runner.py +++ b/flaml/tune/trial_runner.py @@ -1,9 +1,10 @@ -'''! - * Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved. +"""! + * Copyright (c) Microsoft Corporation. All rights reserved. * Licensed under the MIT License. See LICENSE file in the * project root for license information. -''' +""" from typing import Optional + # try: # from ray import __version__ as ray_version # assert ray_version >= '1.0.0' @@ -11,20 +12,19 @@ from typing import Optional # except (ImportError, AssertionError): from .trial import Trial import logging + logger = logging.getLogger(__name__) -class Nologger(): - '''Logger without logging - ''' +class Nologger: + """Logger without logging""" def on_result(self, result): pass class SimpleTrial(Trial): - '''A simple trial class - ''' + """A simple trial class""" def __init__(self, config, trial_id=None): self.trial_id = Trial.generate_id() if trial_id is None else trial_id @@ -49,10 +49,13 @@ class BaseTrialRunner: Note that the caller usually should not mutate trial state directly. """ - def __init__(self, - search_alg=None, scheduler=None, - metric: Optional[str] = None, - mode: Optional[str] = 'min'): + def __init__( + self, + search_alg=None, + scheduler=None, + metric: Optional[str] = None, + mode: Optional[str] = "min", + ): self._search_alg = search_alg self._scheduler_alg = scheduler self._trials = [] @@ -89,12 +92,12 @@ class BaseTrialRunner: trial.set_status(Trial.PAUSED) def stop_trial(self, trial): - """Stops trial. - """ + """Stops trial.""" if trial.status not in [Trial.ERROR, Trial.TERMINATED]: if self._scheduler_alg: self._scheduler_alg.on_trial_complete( - self, trial.trial_id, trial.last_result) + self, trial.trial_id, trial.last_result + ) self._search_alg.on_trial_complete(trial.trial_id, trial.last_result) trial.set_status(Trial.TERMINATED) elif self._scheduler_alg: @@ -102,8 +105,7 @@ class BaseTrialRunner: class SequentialTrialRunner(BaseTrialRunner): - """Implementation of the sequential trial runner - """ + """Implementation of the sequential trial runner""" def step(self) -> Trial: """Runs one step of the trial event loop. @@ -114,7 +116,7 @@ class SequentialTrialRunner(BaseTrialRunner): """ trial_id = Trial.generate_id() config = self._search_alg.suggest(trial_id) - if config: + if config is not None: trial = SimpleTrial(config, trial_id) self.add_trial(trial) trial.set_status(Trial.RUNNING) diff --git a/flaml/tune/tune.py b/flaml/tune/tune.py index 1d2db41ec..22a7cbd11 100644 --- a/flaml/tune/tune.py +++ b/flaml/tune/tune.py @@ -13,7 +13,10 @@ try: assert ray_version >= "1.0.0" from ray.tune.analysis import ExperimentAnalysis as EA + + ray_import = True except (ImportError, AssertionError): + ray_import = False from .analysis import ExperimentAnalysis as EA from .result import DEFAULT_METRIC import logging @@ -278,9 +281,9 @@ def run( else: logger.setLevel(logging.CRITICAL) - if search_alg is None: - from ..searcher.blendsearch import BlendSearch + from ..searcher.blendsearch import BlendSearch + if search_alg is None: search_alg = BlendSearch( metric=metric or DEFAULT_METRIC, mode=mode, @@ -299,16 +302,27 @@ def run( metric_constraints=metric_constraints, ) else: - search_alg.set_search_properties(metric, mode, config) if metric is None or mode is None: metric = metric or search_alg.metric mode = mode or search_alg.mode - if time_budget_s or num_samples > 0: - search_alg.set_search_properties( - None, - None, - config={"time_budget_s": time_budget_s, "num_samples": num_samples}, - ) + if ray_import: + from ray.tune.suggest import ConcurrencyLimiter + else: + from flaml.searcher.suggestion import ConcurrencyLimiter + searcher = ( + search_alg.searcher + if isinstance(search_alg, ConcurrencyLimiter) + else search_alg + ) + if isinstance(searcher, BlendSearch): + setting = {} + if time_budget_s: + setting["time_budget_s"] = time_budget_s + if num_samples > 0: + setting["num_samples"] = num_samples + searcher.set_search_properties(metric, mode, config, setting) + else: + searcher.set_search_properties(metric, mode, config) scheduler = None if report_intermediate_result: params = {} @@ -321,15 +335,10 @@ def run( params["grace_period"] = min_resource if reduction_factor: params["reduction_factor"] = reduction_factor - try: - from ray import __version__ as ray_version - - assert ray_version >= "1.0.0" + if ray_import: from ray.tune.schedulers import ASHAScheduler scheduler = ASHAScheduler(**params) - except (ImportError, AssertionError): - pass if use_ray: try: from ray import tune @@ -392,7 +401,9 @@ def run( else: fail += 1 # break with ub consecutive failures if fail == ub: - logger.warning("fail to sample a trial for 10 times in a row, stopping.") + logger.warning( + f"fail to sample a trial for {max_failure} times in a row, stopping." + ) if verbose > 0: logger.handlers.clear() return ExperimentAnalysis(_runner.get_trials(), metric=metric, mode=mode) diff --git a/notebook/flaml_automl.ipynb b/notebook/flaml_automl.ipynb index 99336f19c..71c4b33d0 100644 --- a/notebook/flaml_automl.ipynb +++ b/notebook/flaml_automl.ipynb @@ -3,7 +3,7 @@ { "cell_type": "markdown", "source": [ - "Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved. \n", + "Copyright (c) Microsoft Corporation. All rights reserved. \n", "\n", "Licensed under the MIT License.\n", "\n", @@ -13,8 +13,7 @@ "## 1. Introduction\n", "\n", "FLAML is a Python library (https://github.com/microsoft/FLAML) designed to automatically produce accurate machine learning models \n", - "with low computational cost. It is fast and cheap. The simple and lightweight design makes it easy \n", - "to use and extend, such as adding new learners. FLAML can \n", + "with low computational cost. It is fast and cheap. The simple and lightweight design makes it easy to use and extend, such as adding new learners. FLAML can \n", "- serve as an economical AutoML engine,\n", "- be used as a fast hyperparameter tuning tool, or \n", "- be embedded in self-tuning software that requires low latency & resource in repetitive\n", @@ -37,7 +36,10 @@ "cell_type": "code", "execution_count": null, "source": [ - "!pip install flaml[notebook];" + "!pip install flaml[notebook];\n", + "# from v0.6.6, catboost is made an optional dependency to build conda package.\n", + "# to install catboost, you can uncomment and run:\n", + "# !pip install flaml[catboost]" ], "outputs": [], "metadata": {} @@ -117,7 +119,7 @@ " \"time_budget\": 240, # total running time in seconds\n", " \"metric\": 'accuracy', # can be: 'r2', 'rmse', 'mae', 'mse', 'accuracy', 'roc_auc', 'roc_auc_ovr',\n", " # 'roc_auc_ovo', 'log_loss', 'mape', 'f1', 'ap', 'ndcg', 'micro_f1', 'macro_f1'\n", - " \"task\": 'classification', # task type \n", + " \"task\": 'classification', # task type\n", " \"log_file_name\": 'airlines_experiment.log', # flaml log file\n", " \"seed\": 7654321, # random seed\n", "}" @@ -141,270 +143,166 @@ "output_type": "stream", "name": "stderr", "text": [ - 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"[flaml.automl: 08-31 00:57:21] {1532} INFO - iteration 114, current learner extra_tree\n", - "[flaml.automl: 08-31 00:57:21] {1689} INFO - at 229.0s,\tbest extra_tree's error=0.3555,\tbest lgbm's error=0.3300\n", - "[flaml.automl: 08-31 00:57:21] {1532} INFO - iteration 115, current learner lrl1\n", - "/home/dmx/miniconda2/envs/blend/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", - " warnings.warn(\"The max_iter was reached which means \"\n", - "[flaml.automl: 08-31 00:57:22] {1689} INFO - at 229.9s,\tbest lrl1's error=0.4334,\tbest lgbm's error=0.3300\n", - "[flaml.automl: 08-31 00:57:22] {1532} INFO - iteration 116, current learner lgbm\n", - "[flaml.automl: 08-31 00:57:25] {1689} INFO - at 232.4s,\tbest lgbm's error=0.3300,\tbest lgbm's error=0.3300\n", - "[flaml.automl: 08-31 00:57:25] {1532} INFO - iteration 117, current learner lgbm\n", - "[flaml.automl: 08-31 00:57:34] {1689} INFO - at 241.7s,\tbest lgbm's error=0.3300,\tbest lgbm's error=0.3300\n", - "[flaml.automl: 08-31 00:57:34] {1766} INFO - selected model: LGBMClassifier(colsample_bytree=0.3841266992710469,\n", - " learning_rate=0.04886499949999022, max_bin=512,\n", - " min_child_samples=4, n_estimators=220, num_leaves=270,\n", - " objective='binary', reg_alpha=0.0009765625,\n", - " reg_lambda=0.07539015928723636, verbose=-1)\n", - "[flaml.automl: 08-31 00:57:43] {1814} INFO - retrain lgbm for 9.1s\n", - "[flaml.automl: 08-31 00:57:43] {1817} INFO - retrained model: LGBMClassifier(colsample_bytree=0.3841266992710469,\n", - " learning_rate=0.04886499949999022, max_bin=512,\n", - " min_child_samples=4, n_estimators=220, num_leaves=270,\n", - " objective='binary', reg_alpha=0.0009765625,\n", - " reg_lambda=0.07539015928723636, verbose=-1)\n", - "[flaml.automl: 08-31 00:57:43] {1364} INFO - fit succeeded\n", - "[flaml.automl: 08-31 00:57:43] {1365} INFO - Time taken to find the best model: 241.71747207641602\n", - "[flaml.automl: 08-31 00:57:43] {1370} WARNING - Time taken to find the best model is 101% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n" + "[flaml.automl: 10-08 15:16:48] {1966} INFO - at 239.5s,\testimator lrl1's best error=0.4339,\tbest estimator lgbm's best error=0.3296\n", + "[flaml.automl: 10-08 15:16:48] {1777} INFO - iteration 65, current learner rf\n", + "[flaml.automl: 10-08 15:16:48] {1966} INFO - at 239.6s,\testimator rf's best error=0.3717,\tbest estimator lgbm's best error=0.3296\n", + "[flaml.automl: 10-08 15:16:48] {1777} INFO - iteration 66, current learner rf\n", + "[flaml.automl: 10-08 15:16:48] {1966} INFO - at 239.6s,\testimator rf's best error=0.3717,\tbest estimator lgbm's best error=0.3296\n", + "[flaml.automl: 10-08 15:16:48] {1777} INFO - iteration 67, current learner extra_tree\n", + "[flaml.automl: 10-08 15:16:48] {1966} INFO - at 239.7s,\testimator extra_tree's best error=0.3753,\tbest estimator lgbm's best error=0.3296\n", + "[flaml.automl: 10-08 15:16:48] {1777} INFO - iteration 68, current learner rf\n", + "[flaml.automl: 10-08 15:16:48] {1966} INFO - at 239.8s,\testimator rf's best error=0.3717,\tbest estimator lgbm's best error=0.3296\n", + "[flaml.automl: 10-08 15:16:48] {2073} INFO - selected model: LGBMClassifier(colsample_bytree=0.7263265270618353,\n", + " learning_rate=0.19240592731562936, max_bin=511,\n", + " min_child_samples=101, n_estimators=334, num_leaves=50,\n", + " reg_alpha=0.042474252908075376, reg_lambda=0.44574701224719,\n", + " verbose=-1)\n", + "[flaml.automl: 10-08 15:16:59] {2136} INFO - retrain lgbm for 10.5s\n", + "[flaml.automl: 10-08 15:16:59] {2142} INFO - retrained model: LGBMClassifier(colsample_bytree=0.7263265270618353,\n", + " learning_rate=0.19240592731562936, max_bin=511,\n", + " min_child_samples=101, n_estimators=334, num_leaves=50,\n", + " reg_alpha=0.042474252908075376, reg_lambda=0.44574701224719,\n", + " verbose=-1)\n", + "[flaml.automl: 10-08 15:16:59] {1571} INFO - fit succeeded\n", + "[flaml.automl: 10-08 15:16:59] {1572} INFO - Time taken to find the best model: 79.82886719703674\n" ] } ], @@ -444,9 +342,9 @@ "name": "stdout", "text": [ "Best ML leaner: lgbm\n", - "Best hyperparmeter config: {'n_estimators': 220, 'num_leaves': 270, 'min_child_samples': 4, 'learning_rate': 0.04886499949999022, 'log_max_bin': 10, 'colsample_bytree': 0.3841266992710469, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.07539015928723636, 'FLAML_sample_size': 364083}\n", - "Best accuracy on validation data: 0.67\n", - "Training duration of best run: 9.323 s\n" + "Best hyperparmeter config: {'n_estimators': 334, 'num_leaves': 50, 'min_child_samples': 101, 'learning_rate': 0.19240592731562936, 'log_max_bin': 9, 'colsample_bytree': 0.7263265270618353, 'reg_alpha': 0.042474252908075376, 'reg_lambda': 0.44574701224719, 'FLAML_sample_size': 364083}\n", + "Best accuracy on validation data: 0.6704\n", + "Training duration of best run: 11.24 s\n" ] } ], @@ -468,11 +366,11 @@ "output_type": "execute_result", "data": { "text/plain": [ - "LGBMClassifier(colsample_bytree=0.3841266992710469,\n", - " learning_rate=0.04886499949999022, max_bin=512,\n", - " min_child_samples=4, n_estimators=220, num_leaves=270,\n", - " objective='binary', reg_alpha=0.0009765625,\n", - " reg_lambda=0.07539015928723636, verbose=-1)" + "LGBMClassifier(colsample_bytree=0.7263265270618353,\n", + " learning_rate=0.19240592731562936, max_bin=511,\n", + " min_child_samples=101, n_estimators=334, num_leaves=50,\n", + " reg_alpha=0.042474252908075376, reg_lambda=0.44574701224719,\n", + " verbose=-1)" ] }, "metadata": {}, @@ -555,9 +453,9 @@ "output_type": "stream", "name": "stdout", "text": [ - "accuracy = 0.6729231864497278\n", - "roc_auc = 0.7261961112785199\n", - "log_loss = 0.6033707263741326\n" + "accuracy = 0.6713287750470908\n", + "roc_auc = 0.7249878990284184\n", + "log_loss = 0.6035815508574605\n" ] } ], @@ -597,18 +495,15 @@ "name": "stdout", "text": [ "{'Current Learner': 'lgbm', 'Current Sample': 10000, 'Current Hyper-parameters': {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 20, 'learning_rate': 0.09999999999999995, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0, 'FLAML_sample_size': 10000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 20, 'learning_rate': 0.09999999999999995, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0, 'FLAML_sample_size': 10000}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 10000, 'Current Hyper-parameters': {'n_estimators': 12, 'num_leaves': 4, 'min_child_samples': 15, 'learning_rate': 0.2712162364070373, 'log_max_bin': 10, 'colsample_bytree': 0.9285002286474459, 'reg_alpha': 0.002668211515123386, 'reg_lambda': 0.5215467339232843, 'FLAML_sample_size': 10000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 12, 'num_leaves': 4, 'min_child_samples': 15, 'learning_rate': 0.2712162364070373, 'log_max_bin': 10, 'colsample_bytree': 0.9285002286474459, 'reg_alpha': 0.002668211515123386, 'reg_lambda': 0.5215467339232843, 'FLAML_sample_size': 10000}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 10000, 'Current Hyper-parameters': {'n_estimators': 14, 'num_leaves': 5, 'min_child_samples': 9, 'learning_rate': 0.2835381908967212, 'log_max_bin': 9, 'colsample_bytree': 0.8304072431299575, 'reg_alpha': 0.0014132988481787994, 'reg_lambda': 0.033183495034912504, 'FLAML_sample_size': 10000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 14, 'num_leaves': 5, 'min_child_samples': 9, 'learning_rate': 0.2835381908967212, 'log_max_bin': 9, 'colsample_bytree': 0.8304072431299575, 'reg_alpha': 0.0014132988481787994, 'reg_lambda': 0.033183495034912504, 'FLAML_sample_size': 10000}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 10000, 'Current Hyper-parameters': {'n_estimators': 53, 'num_leaves': 4, 'min_child_samples': 7, 'learning_rate': 0.15662398373030859, 'log_max_bin': 10, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 10000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 53, 'num_leaves': 4, 'min_child_samples': 7, 'learning_rate': 0.15662398373030859, 'log_max_bin': 10, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 10000}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 40000, 'Current Hyper-parameters': {'n_estimators': 53, 'num_leaves': 4, 'min_child_samples': 7, 'learning_rate': 0.15662398373030859, 'log_max_bin': 10, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 40000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 53, 'num_leaves': 4, 'min_child_samples': 7, 'learning_rate': 0.15662398373030859, 'log_max_bin': 10, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 40000}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 40000, 'Current Hyper-parameters': {'n_estimators': 89, 'num_leaves': 4, 'min_child_samples': 6, 'learning_rate': 0.2915468353191124, 'log_max_bin': 10, 'colsample_bytree': 0.8291836310024803, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.022917008702549507, 'FLAML_sample_size': 40000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 89, 'num_leaves': 4, 'min_child_samples': 6, 'learning_rate': 0.2915468353191124, 'log_max_bin': 10, 'colsample_bytree': 0.8291836310024803, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.022917008702549507, 'FLAML_sample_size': 40000}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 40000, 'Current Hyper-parameters': {'n_estimators': 53, 'num_leaves': 19, 'min_child_samples': 7, 'learning_rate': 0.15662398373030845, 'log_max_bin': 9, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.006958608037974516, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 40000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 53, 'num_leaves': 19, 'min_child_samples': 7, 'learning_rate': 0.15662398373030845, 'log_max_bin': 9, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.006958608037974516, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 40000}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 364083, 'Current Hyper-parameters': {'n_estimators': 53, 'num_leaves': 19, 'min_child_samples': 7, 'learning_rate': 0.15662398373030845, 'log_max_bin': 9, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.006958608037974516, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 364083}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 53, 'num_leaves': 19, 'min_child_samples': 7, 'learning_rate': 0.15662398373030845, 'log_max_bin': 9, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.006958608037974516, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 364083}}\n", - "{'Current Learner': 'catboost', 'Current Sample': 40000, 'Current Hyper-parameters': {'early_stopping_rounds': 11, 'learning_rate': 0.0943289179113066, 'FLAML_sample_size': 40000}, 'Best Learner': 'catboost', 'Best Hyper-parameters': {'early_stopping_rounds': 11, 'learning_rate': 0.0943289179113066, 'FLAML_sample_size': 40000}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 364083, 'Current Hyper-parameters': {'n_estimators': 112, 'num_leaves': 42, 'min_child_samples': 6, 'learning_rate': 0.05081630216512539, 'log_max_bin': 8, 'colsample_bytree': 0.6140029119098487, 'reg_alpha': 0.0043659867548350275, 'reg_lambda': 0.00988067243709054, 'FLAML_sample_size': 364083}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 112, 'num_leaves': 42, 'min_child_samples': 6, 'learning_rate': 0.05081630216512539, 'log_max_bin': 8, 'colsample_bytree': 0.6140029119098487, 'reg_alpha': 0.0043659867548350275, 'reg_lambda': 0.00988067243709054, 'FLAML_sample_size': 364083}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 364083, 'Current Hyper-parameters': {'n_estimators': 224, 'num_leaves': 19, 'min_child_samples': 3, 'learning_rate': 0.06277721620788371, 'log_max_bin': 9, 'colsample_bytree': 0.6641909243388362, 'reg_alpha': 0.02046640007359354, 'reg_lambda': 0.021186644668220945, 'FLAML_sample_size': 364083}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 224, 'num_leaves': 19, 'min_child_samples': 3, 'learning_rate': 0.06277721620788371, 'log_max_bin': 9, 'colsample_bytree': 0.6641909243388362, 'reg_alpha': 0.02046640007359354, 'reg_lambda': 0.021186644668220945, 'FLAML_sample_size': 364083}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 364083, 'Current Hyper-parameters': {'n_estimators': 159, 'num_leaves': 54, 'min_child_samples': 3, 'learning_rate': 0.15480526013767984, 'log_max_bin': 9, 'colsample_bytree': 0.4565737938156385, 'reg_alpha': 0.014844095616079196, 'reg_lambda': 0.01191495998733094, 'FLAML_sample_size': 364083}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 159, 'num_leaves': 54, 'min_child_samples': 3, 'learning_rate': 0.15480526013767984, 'log_max_bin': 9, 'colsample_bytree': 0.4565737938156385, 'reg_alpha': 0.014844095616079196, 'reg_lambda': 0.01191495998733094, 'FLAML_sample_size': 364083}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 364083, 'Current Hyper-parameters': {'n_estimators': 100, 'num_leaves': 310, 'min_child_samples': 2, 'learning_rate': 0.0958942820044505, 'log_max_bin': 10, 'colsample_bytree': 0.40118043723920377, 'reg_alpha': 0.006532533034382694, 'reg_lambda': 0.02014962736208268, 'FLAML_sample_size': 364083}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 100, 'num_leaves': 310, 'min_child_samples': 2, 'learning_rate': 0.0958942820044505, 'log_max_bin': 10, 'colsample_bytree': 0.40118043723920377, 'reg_alpha': 0.006532533034382694, 'reg_lambda': 0.02014962736208268, 'FLAML_sample_size': 364083}}\n" + "{'Current Learner': 'lgbm', 'Current Sample': 10000, 'Current Hyper-parameters': {'n_estimators': 4, 'num_leaves': 14, 'min_child_samples': 15, 'learning_rate': 0.22841390623808822, 'log_max_bin': 9, 'colsample_bytree': 1.0, 'reg_alpha': 0.0014700173967242716, 'reg_lambda': 7.624911621832711, 'FLAML_sample_size': 10000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 4, 'num_leaves': 14, 'min_child_samples': 15, 'learning_rate': 0.22841390623808822, 'log_max_bin': 9, 'colsample_bytree': 1.0, 'reg_alpha': 0.0014700173967242716, 'reg_lambda': 7.624911621832711, 'FLAML_sample_size': 10000}}\n", + "{'Current Learner': 'lgbm', 'Current Sample': 10000, 'Current Hyper-parameters': {'n_estimators': 4, 'num_leaves': 25, 'min_child_samples': 12, 'learning_rate': 0.5082200481556802, 'log_max_bin': 8, 'colsample_bytree': 0.9696263001275751, 'reg_alpha': 0.0028107036379524425, 'reg_lambda': 3.716898117989413, 'FLAML_sample_size': 10000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 4, 'num_leaves': 25, 'min_child_samples': 12, 'learning_rate': 0.5082200481556802, 'log_max_bin': 8, 'colsample_bytree': 0.9696263001275751, 'reg_alpha': 0.0028107036379524425, 'reg_lambda': 3.716898117989413, 'FLAML_sample_size': 10000}}\n", + "{'Current Learner': 'lgbm', 'Current Sample': 10000, 'Current Hyper-parameters': {'n_estimators': 23, 'num_leaves': 14, 'min_child_samples': 15, 'learning_rate': 0.22841390623808822, 'log_max_bin': 9, 'colsample_bytree': 1.0, 'reg_alpha': 0.0014700173967242718, 'reg_lambda': 7.624911621832699, 'FLAML_sample_size': 10000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 23, 'num_leaves': 14, 'min_child_samples': 15, 'learning_rate': 0.22841390623808822, 'log_max_bin': 9, 'colsample_bytree': 1.0, 'reg_alpha': 0.0014700173967242718, 'reg_lambda': 7.624911621832699, 'FLAML_sample_size': 10000}}\n", + "{'Current Learner': 'lgbm', 'Current Sample': 10000, 'Current Hyper-parameters': {'n_estimators': 101, 'num_leaves': 12, 'min_child_samples': 24, 'learning_rate': 0.07647794276357095, 'log_max_bin': 10, 'colsample_bytree': 1.0, 'reg_alpha': 0.001749539645587163, 'reg_lambda': 4.373760956394571, 'FLAML_sample_size': 10000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 101, 'num_leaves': 12, 'min_child_samples': 24, 'learning_rate': 0.07647794276357095, 'log_max_bin': 10, 'colsample_bytree': 1.0, 'reg_alpha': 0.001749539645587163, 'reg_lambda': 4.373760956394571, 'FLAML_sample_size': 10000}}\n", + "{'Current Learner': 'lgbm', 'Current Sample': 40000, 'Current Hyper-parameters': {'n_estimators': 101, 'num_leaves': 12, 'min_child_samples': 24, 'learning_rate': 0.07647794276357095, 'log_max_bin': 10, 'colsample_bytree': 1.0, 'reg_alpha': 0.001749539645587163, 'reg_lambda': 4.373760956394571, 'FLAML_sample_size': 40000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 101, 'num_leaves': 12, 'min_child_samples': 24, 'learning_rate': 0.07647794276357095, 'log_max_bin': 10, 'colsample_bytree': 1.0, 'reg_alpha': 0.001749539645587163, 'reg_lambda': 4.373760956394571, 'FLAML_sample_size': 40000}}\n", + "{'Current Learner': 'lgbm', 'Current Sample': 40000, 'Current Hyper-parameters': {'n_estimators': 361, 'num_leaves': 11, 'min_child_samples': 32, 'learning_rate': 0.13528717598813866, 'log_max_bin': 9, 'colsample_bytree': 0.9851977789068981, 'reg_alpha': 0.0038372002422749616, 'reg_lambda': 0.25113531892556773, 'FLAML_sample_size': 40000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 361, 'num_leaves': 11, 'min_child_samples': 32, 'learning_rate': 0.13528717598813866, 'log_max_bin': 9, 'colsample_bytree': 0.9851977789068981, 'reg_alpha': 0.0038372002422749616, 'reg_lambda': 0.25113531892556773, 'FLAML_sample_size': 40000}}\n", + "{'Current Learner': 'lgbm', 'Current Sample': 364083, 'Current Hyper-parameters': {'n_estimators': 361, 'num_leaves': 11, 'min_child_samples': 32, 'learning_rate': 0.13528717598813866, 'log_max_bin': 9, 'colsample_bytree': 0.9851977789068981, 'reg_alpha': 0.0038372002422749616, 'reg_lambda': 0.25113531892556773, 'FLAML_sample_size': 364083}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 361, 'num_leaves': 11, 'min_child_samples': 32, 'learning_rate': 0.13528717598813866, 'log_max_bin': 9, 'colsample_bytree': 0.9851977789068981, 'reg_alpha': 0.0038372002422749616, 'reg_lambda': 0.25113531892556773, 'FLAML_sample_size': 364083}}\n", + "{'Current Learner': 'lgbm', 'Current Sample': 364083, 'Current Hyper-parameters': {'n_estimators': 654, 'num_leaves': 27, 'min_child_samples': 61, 'learning_rate': 0.0705835177602005, 'log_max_bin': 10, 'colsample_bytree': 0.8629551479851468, 'reg_alpha': 0.016562972790870267, 'reg_lambda': 0.25883390536609663, 'FLAML_sample_size': 364083}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 654, 'num_leaves': 27, 'min_child_samples': 61, 'learning_rate': 0.0705835177602005, 'log_max_bin': 10, 'colsample_bytree': 0.8629551479851468, 'reg_alpha': 0.016562972790870267, 'reg_lambda': 0.25883390536609663, 'FLAML_sample_size': 364083}}\n", + "{'Current Learner': 'lgbm', 'Current Sample': 364083, 'Current Hyper-parameters': {'n_estimators': 334, 'num_leaves': 50, 'min_child_samples': 101, 'learning_rate': 0.19240592731562936, 'log_max_bin': 9, 'colsample_bytree': 0.7263265270618353, 'reg_alpha': 0.042474252908075376, 'reg_lambda': 0.44574701224719, 'FLAML_sample_size': 364083}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 334, 'num_leaves': 50, 'min_child_samples': 101, 'learning_rate': 0.19240592731562936, 'log_max_bin': 9, 'colsample_bytree': 0.7263265270618353, 'reg_alpha': 0.042474252908075376, 'reg_lambda': 0.44574701224719, 'FLAML_sample_size': 364083}}\n" ] } ], @@ -637,11 +532,10 @@ { "output_type": "display_data", "data": { + "image/png": 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ML3wjItZJuhJoj4il6b4/kNQB9AHzImKLpLcB/y5pJ0kSu6bCcY8RbXxTI905SaK5qZGbPnJyFSIys5FMmcZBfoXk0tOvkAxSB3An8KmI2Fx8eJVraWmJ9vb2aodRqPIxCIDGUQ1cfc40j0GY2X6RtDIiWvL2VXIV0yaS7qHSyRqB9wC39HuQFaKUBC5dvJptfTtp9lVMZlagSrqYSje9zQQuILnx7cc4QVTFnBnNuwak3a1kZkUaMEFIeifwQeAsYDnJYPGrI+K3QxCbmZlVUb8JQlIX8CTJ4PTfRMTzkh53cjAzqw8DXea6GBgPfAB4r6RDeWltajMzG+H6TRAR8WngeJK5mE4DOoGx6eyrhw1NeGZmVi0D3iiXrkF9d0TMJUkWF5BMuPfEEMRmZmZVVNFVTADpdBi3Abell7qamdkIVnGCyIqI/DkfbL95llYzqzX7lSBscHmWVjOrRU4QNWBfZ2n1DKRmNhQqWQ9iMjAPOC5bPyLO6Pcg2yf7OkurZyA1s6FQSQviFuBrwLUkM67aIPMsrWZWiypZD2JHRHw1IpZHxMrSo/DI6si8mVNoHNWwW1njqAbmzZxSpYjMzCprQXxP0seAW4EXS4UR8WxhUQ0jg3H1kWdpNbNaVEmCuDD9Oi9TFsCrBz+c4WUwrz7yLK1mVmsqWQ/i+KEIZDga7DWifXWSmdWSSq5iGgX8BfCOtOhHwL+nd1bXtcFeI9pXJ5lZLamki+mrwCjg39LtP0nL/ryooIYLX31kZiNZJVcxvTkiLoyIu9LHRcCbiw5sOPDVR2Y2klWSIPok/Z/ShqRX4/shgGRg+epzpnFQQ/JjbG5q5OpzpvnqIzMbESrpYpoH3C1pAyCSO6ovKjSqYcRXH5nZSFXJVUx3SpoElPpNOiPixYGOMTOz4W+gNanPiIi7JJ1TtusESUTEkoJjMzOzKhqoBfFO4C7gvTn7AnCCMDMbwfpNEBHxD+nTKyPi8ew+Sb55zsxshKvkKqb/zilbPNiBmJlZbRloDOI1wOuAw8vGIUYDhxQdWC0rn6DvkFEHMOawg6sdlpnZoBpoDGIK8B6gid3HIZ4HLi4yqFqWN0HfAapyUGZmBRhoDOK7wHclnRwR9w9hTDUtb4K+nQFPPZs/L5OZ2XBVyY1yqyR9nKS7aVfXUkR8uLCoathgT9BnZlarKhmk/hbwe8BM4B5gAkk3U10a39SYW97cT7mZ2XBVSYI4ISI+C7wQEf8J/F/gLZWcXNIsSZ2S1ku6rJ8650nqkLRO0g1l+0ZL6pL0r5W83lDwBH1mVi8q6WIqrfvQI+n1wC+Bo/Z2kKQGYCFwJtAFrJC0NCI6MnUmAfOBUyJiq6Ty834OuLeCGIeMlwc1s3pRSYJYJOkI4LPAUuAw4PIKjjsJWB8RGwAkfQeYDXRk6lwMLIyIrQARsam0Q9KbgKOB24GWCl5vyHiCPjOrB5VM1vf19Ok97Ns61M3AU5ntLvbsmpoMIOk+oAG4IiJul3QA8EXgj4F39fcCkuYCcwGOPfbYfQjNzMz2ZqAb5S4Z6MCI+NIgvf4k4DSSwe97JU0jSQzLIqJL6v8mg4hYBCwCaGlpiUGIx8zMUgO1IF6Zfp1CsoLc0nT7vcDyCs7dDRyT2Z6QlmV1AQ+m61s/LulRkoRxMnCqpI+RdGkdJOk3EZE70G1mZoNvoBvl/hFA0r3AiRHxfLp9BfD9Cs69ApiUTuzXDZwPfLCsTitwAfBNSWNIupw2RMQflSpI+hDQ4uRgZja0KrnM9WhgW2Z7W1o2oIjYAXwCaAMeAW6OiHWSrpR0dlqtDdgiqQO4G5gXEVv25RswM7NiVHIV0/XAckm3pttzgOsqOXlELAOWlZVdnnkewCXpo79zXFfp65mZ2eCp5CqmqyT9ADg1LbooIlYVG5aZmVXbQFcxjY6I5yQdCTyRPkr7joyIZ4sPz8zMqmWgFsQNJNN9ryRZYrRE6fa+3BNhZmbDzEBXMb0n/erlRc3M6tBAXUwnDnRgRDw0+OGYmVmtGKiL6YsD7AvgjEGOxczMashAXUynD2UgZmZWWyq5D4J0mu+p7L6i3PVFBWVmZtW31wQh6R9IJtObSnLT27uBH5PcQGdmZiNUJVNtnAv8PvDLiLgIeCNweKFRmZlZ1VWSIHojYiewQ9JoYBO7z9Jad1pXdbPqyR4efPxZTrnmLlpXlU9Sa2Y2/FUyBtEuqQm4luSmud8A9xcaVQ1rXdXN/CVr2Na3E4Dunl7mL1kD4GVHzWxE6bcFIWmhpFMi4mMR0RMRXyNZX/rCtKupLi1o66R3e99uZb3b+1jQ1lmliMzMijFQC+JR4AuSxgE3Azd6kj7Y2NO7T+VmZsNVvy2IiPhyRJwMvBPYAnxD0s8k/YOkyUMWYY0Z39S4T+VmZsPVXgepI+IXEfH5iJhBsvrbHJIFgOrSvJlTaBzVsFtZ46gG5s2cUqWIzMyKsdcEIelASe+V9G3gB0AncE7hkdWoOTOaufqcaRzUkPzompsaufqcaR6gNrMRZ6DJ+s4kaTGcBSwHvgPMjYgXhii2mjVnRjM3Ln8SgJs+cnKVozEzK8ZAg9TzSdaE+OuI2DpE8ZiZWY0YaLI+z9ZqZlbHKrmT2szM6pAThJmZ5XKCMDOzXE4QZmaWywnCzMxyOUGYmVkuJwgzM8vlBGFmZrmcIMzMLJcThJmZ5XKCMDOzXE4QZmaWq9AEIWmWpE5J6yVd1k+d8yR1SFon6Ya07DhJD0l6OC3/aJFxmpnZngaa7vtlkdQALATOBLqAFZKWRkRHps4kkmnFT4mIrZKOSnc9DZwcES9KOgxYmx67sah4zcxsd0W2IE4C1kfEhojYRrLg0OyyOhcDC0vrTUTEpvTrtoh4Ma1zcMFxmplZjiL/8TYDT2W2u9KyrMnAZEn3SXpA0qzSDknHSFqdnuPzea0HSXMltUtq37x5cwHfgplZ/ar2J/MDgUnAaSTLm14rqQkgIp6KiDcAJwAXSjq6/OCIWBQRLRHRMnbs2CEM28xs5CsyQXQDx2S2J6RlWV3A0ojYHhGPA4+SJIxd0pbDWuDUAmM1M7MyRSaIFcAkScdLOgg4H1haVqeVpPWApDEkXU4bJE2Q1JiWHwG8HegsMFYzMytTWIKIiB3AJ4A24BHg5ohYJ+lKSWen1dqALZI6gLuBeRGxBXgt8KCknwL3AF+IiDVFxWpmZnsq7DJXgIhYBiwrK7s88zyAS9JHts4dwBuKjM3MzAZW7UFqMzOrUU4QZmaWywnCzMxyOUGYmVkuJwgzM8vlBGFmZrmcIMzMLJcThJmZ5XKCMDOzXE4QZmaWywnCzMxyOUGYmVkuJwgzM8vlBGFmZrmcIMzMLFeh60EMJ62rulnQ1snGnl7GNzUyb+YU5sxornZYZmZV4wRBkhzmL1lD7/Y+ALp7epm/JFnAzknCzOqVEwSwoK1zV3Io6d3ex6WLV3Pj8idzj+l4+jmmjhs9FOGZmVWFxyCAjT29ueXb+nb2e8zUcaOZPd2tCzMbudyCAMY3NdKdkySamxq56SMnVyEiM7PqcwsCmDdzCo2jGnYraxzVwLyZU6oUkZlZ9bkFwUsD0ZcuXs22vp00+yomMzMniJI5M5p3DUi7W8nMzAlit/sfRjUcwDFHNlY7JDOzmlDXYxCl+x+6e3oJkquWHn/mBVpXdVc7NDOzqqvrBJF3/8POSMrNzOpdXSeI/u5/6K/czKye1HWCGN+UP97QX7mZWT2p6wTh+x/MzPpX11cxle5z8CyuZmZ7qusEAUmScEIwM9tTXXcxmZlZ/wpNEJJmSeqUtF7SZf3UOU9Sh6R1km5Iy6ZLuj8tWy3pA0XGaWZmeyqsi0lSA7AQOBPoAlZIWhoRHZk6k4D5wCkRsVXSUemu3wJ/GhE/lzQeWCmpLSJ6iorXzMx2V2QL4iRgfURsiIhtwHeA2WV1LgYWRsRWgIjYlH59NCJ+nj7fCGwCxhYYq5mZlSkyQTQDT2W2u9KyrMnAZEn3SXpA0qzyk0g6CTgIeCxn31xJ7ZLaN2/ePIihm5lZta9iOhCYBJwGTADulTSt1JUkaRzwLeDCiNhjebeIWAQsSutulvSLvbzeGOCZwQt/UDm2/ePY9o9j2z8jMbbj+ttRZILoBo7JbE9Iy7K6gAcjYjvwuKRHSRLGCkmjge8Dn4mIB/b2YhGx1y4oSe0R0VLpNzCUHNv+cWz7x7Htn3qLrcguphXAJEnHSzoIOB9YWlanlaT1gKQxJF1OG9L6twLXR8TiAmM0M7N+FJYgImIH8AmgDXgEuDki1km6UtLZabU2YIukDuBuYF5EbAHOA94BfEjSw+ljelGxmpnZngodg4iIZcCysrLLM88DuCR9ZOv8F/BfBYS0qIBzDhbHtn8c2/5xbPunrmJT8j/azMxsd55qw8zMcjlBmJlZrrpIEJXMCTXE8XxD0iZJazNlR0q6Q9LP069HVCGuYyTdnZkb61M1FNshkpZL+mka2z+m5cdLejB9b29Kr4CrCkkNklZJuq2WYpP0hKQ16cUe7WlZ1d/TNI4mSYsl/UzSI5JOroXYJE3JXCDzsKTnJH26FmJL4/ur9O9graQb07+PQf99G/EJIjMn1LuBqcAFkqZWNyquA8rvGr8MuDMiJgF3pttDbQfw1xExFXgr8PH0Z1ULsb0InBERbwSmA7MkvRX4PPAvEXECsBX4syrEVvIpkiv2SmopttMjYnrmOvlaeE8BvgzcHhGvAd5I8vOremwR0Zn+vKYDbyKZH+7WWohNUjPwl0BLRLweaCC5jWDwf98iYkQ/gJOBtsz2fGB+DcQ1EVib2e4ExqXPxwGdNRDjd0kmW6yp2IBXAA8BbyG5c/TAvPd6iGOaQPIP4wzgNkA1FNsTwJiysqq/p8DhwOOkF8vUUmxl8fwBcF+txMZL0xgdSXIl6m3AzCJ+30Z8C4LK5oSqBUdHxNPp818CR1czGEkTgRnAg9RIbGkXzsMkkzfeQTI/V08k99xAdd/b/wdcCpSmhHkVtRNbAD+UtFLS3LSsFt7T44HNwDfTrrmvSzq0RmLLOh+4MX1e9dgiohv4AvAk8DTwa2AlBfy+1UOCGHYi+QhQteuPJR0G/Dfw6Yh4LruvmrFFRF8kTf4JJLMFv6YacZST9B5gU0SsrHYs/Xh7RJxI0s36cUnvyO6s4nt6IHAi8NWImAG8QFmXTQ38LRwEnA3cUr6vWrGl4x6zSRLseOBQ9uyyHhT1kCAqmROqFvwqnZywNEnhpmoEIWkUSXL4dkQsqaXYSiKZzPFukmZ0k6TSDZ/Vem9PAc6W9ATJtPZnkPSt10JspU+cRDKd/q0kybUW3tMuoCsiHky3F5MkjFqIreTdwEMR8at0uxZiexfweERsjmQeuyUkv4OD/vtWDwmikjmhasFS4ML0+YUk/f9DSpKA/wAeiYgv1VhsYyU1pc8bScZGHiFJFOdWM7aImB8REyJiIsnv110R8Ue1EJukQyW9svScpD99LTXwnkbEL4GnJE1Ji34f6KiF2DIu4KXuJaiN2J4E3irpFenfbOnnNvi/b9Uc/BnCQZ2zgEdJ+qw/UwPx3EjSd7id5FPUn5H0Wd8J/Bz4H+DIKsT1dpIm82rg4fRxVo3E9gZgVRrbWuDytPzVwHJgPUk3wMFVfm9PA26rldjSGH6aPtaVfv9r4T1N45gOtKfvaytwRA3FdiiwBTg8U1Yrsf0j8LP0b+FbwMFF/L55qg0zM8tVD11MZma2H5wgzMwslxOEmZnlcoIwM7NcThBmZpbLCcKGBUn/IunTme02SV/PbH9R0iX5R4Ok6ySdmz7/kaQ9FneXNErSNelMnQ9Jul/Su9N9TyhZN31f4971uv3sX5jOFtohqTcze+i5kpaV7v0YTJLGlWac7Wf/QZLuzdx0ZXXKCcKGi/uAtwFIOgAYA7wus/9twE9e5mt8jmQCttdHMjXFHOCVL/OcA4qIj0cyfchZwGORziAaEYsj4qxI7hofbJcA1w4Q0zaSa/0/UMBr2zDiBGHDxU9IptaAJDGsBZ6XdISkg4HXAg9JulzSinSe/EXpnaZ7JekVwMXAJyPiRYCI+FVE3JxT95L0/GvLWjV/Kmm1kjUrvpVz3OfSFkVDhTE9IWmMpFVa5x0AAALySURBVIlK1ku4TtKjkr4t6V2S7ktbOyel9Q9VstbI8nTyu9n9nPoPgdvTY16X1n84jX1SWqcV+KNK4rSRy01IGxYiYqOkHZKOJWkt3E8yW+XJJLNZromIbZL+NSKuBEj/Sb8H+F4FL3EC8GSUTU5YTtKbgItIphoX8KCke4BtwN8Db4uIZyQdWXbcApLWyEWxf3enngC8H/gwyfQxHyS58/1s4O9IWjufIZnm48Np19RySf8TES9k4jge2FpKgsBHgS9HxLfTqWhKyWst8Ob9iNNGELcgbDj5CUlyKCWI+zPb96V1TleyqtYakknzXpd3opfh7cCtEfFCRPyGZKK0U9PXuiUingGIiGczx3yWZLqGj+5ncoBkcrY1EbGTZMqMO9NzrSFZWwSSeZYuUzIl+o+AQ4Bjy84zjmSK7ZL7gb+T9LfAcRHRm8bfB2wrzeNk9ckJwoaT0jjENJJPuA+QtCDeBvxE0iHAvwHnRsQ0kn72Qyo893rgWEmjBz3q5BP/m8pbFfvoxczznZntnbzUEyDgDzPjGMdGRHaFO4BeMj+TiLiBpBXSCyyTdEam7sHA715GzDbMOUHYcPITki6jZyNZG+JZoIkkSfyEl/7xPaNkTYt+rx4qFxG/JZnJ9stpV0tpBtn3l1X9X2BOOpPmocD70rK7gPdLelV6bDYZ3A5cA3y/4E/kbcAnS+Mukmbk1HmUl1ocSHo1sCEivkIy++cb0vJXAc9EMp201SknCBtO1pBcvfRAWdmvI+KZ9Iqfa0laF20kn9z3xd+TdL90SFpLspRj+YJJD5GsKb6cZLW9r0fEqohYB1wF3CPpp8CXyo67JY1taTpdeRE+B4wCVktal27vJh2PeEzSCWnRecDatFvq9cD1afnpwPcLitOGCc/malZnJL0PeFNE/P0AdZYAl0XEo0MXmdUaX8VkVmci4tZSV1ietIut1cnB3IIwM7NcHoMwM7NcThBmZpbLCcLMzHI5QZiZWS4nCDMzy/X/AUdLzLp17HVCAAAAAElFTkSuQmCC", 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" + ] }, "metadata": { "needs_background": "light" @@ -771,7 +665,7 @@ "source": [ "print('default xgboost accuracy', '=', 1 - sklearn_metric_loss_score('accuracy', y_pred_xgb, y_test))\n", "print('default lgbm accuracy', '=', 1 - sklearn_metric_loss_score('accuracy', y_pred_lgbm, y_test))\n", - "print('flaml accuracy', '=', 1 - sklearn_metric_loss_score('accuracy', y_pred, y_test))" + "print('flaml (4min) accuracy', '=', 1 - sklearn_metric_loss_score('accuracy', y_pred, y_test))" ], "outputs": [ { @@ -780,7 +674,7 @@ "text": [ "default xgboost accuracy = 0.6676060098186078\n", "default lgbm accuracy = 0.6602346380315323\n", - "flaml accuracy = 0.6729231864497278\n" + "flaml (4min) accuracy = 0.6713287750470908\n" ] } ], @@ -840,37 +734,23 @@ "\n", "\n", "class MyRegularizedGreedyForest(SKLearnEstimator):\n", - "\n", - "\n", - " def __init__(self, task='binary', n_jobs=1, **params):\n", + " def __init__(self, task='binary', **config):\n", " '''Constructor\n", " \n", " Args:\n", " task: A string of the task type, one of\n", " 'binary', 'multi', 'regression'\n", - " n_jobs: An integer of the number of parallel threads\n", - " params: A dictionary of the hyperparameter names and values\n", + " config: A dictionary containing the hyperparameter names\n", + " and 'n_jobs' as keys. n_jobs is the number of parallel threads.\n", " '''\n", "\n", - " super().__init__(task, **params)\n", + " super().__init__(task, **config)\n", "\n", - " '''task=regression for RGFRegressor; \n", - " binary or multiclass for RGFClassifier'''\n", - " if 'regression' in task:\n", - " self.estimator_class = RGFRegressor\n", - " else:\n", + " '''task=binary or multi for classification task'''\n", + " if task in (\"binary\", \"multi\"):\n", " self.estimator_class = RGFClassifier\n", - "\n", - " # convert to int for integer hyperparameters\n", - " self.params = {\n", - " \"n_jobs\": n_jobs,\n", - " 'max_leaf': int(params['max_leaf']),\n", - " 'n_iter': int(params['n_iter']),\n", - " 'n_tree_search': int(params['n_tree_search']),\n", - " 'opt_interval': int(params['opt_interval']),\n", - " 'learning_rate': params['learning_rate'],\n", - " 'min_samples_leaf': int(params['min_samples_leaf'])\n", - " } \n", + " else:\n", + " self.estimator_class = RGFRegressor\n", "\n", " @classmethod\n", " def search_space(cls, data_size, task):\n", @@ -879,9 +759,10 @@ " Returns:\n", " A dictionary of the search space. \n", " Each key is the name of a hyperparameter, and value is a dict with\n", - " its domain and init_value (optional), cat_hp_cost (optional) \n", - " e.g., \n", - " {'domain': tune.randint(lower=1, upper=10), 'init_value': 1}\n", + " its domain (required) and low_cost_init_value, init_value,\n", + " cat_hp_cost (if applicable).\n", + " e.g.,\n", + " {'domain': tune.randint(lower=1, upper=10), 'init_value': 1}.\n", " '''\n", " space = { \n", " 'max_leaf': {'domain': tune.lograndint(lower=4, upper=data_size), 'init_value': 4, 'low_cost_init_value': 4},\n", @@ -963,7 +844,92 @@ "\n", "automl.fit(X_train = X_train, y_train = y_train, **settings)" ], - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[flaml.automl: 10-08 15:17:57] {1458} INFO - Data split method: stratified\n", + "[flaml.automl: 10-08 15:17:57] {1462} INFO - Evaluation method: holdout\n", + "[flaml.automl: 10-08 15:17:57] {1510} INFO - Minimizing error metric: 1-accuracy\n", + "[flaml.automl: 10-08 15:17:57] {1547} INFO - List of ML learners in AutoML Run: ['RGF', 'lgbm', 'rf', 'xgboost']\n", + "[flaml.automl: 10-08 15:17:57] {1777} INFO - iteration 0, current learner RGF\n", + "/home/dmx/miniconda2/envs/test/lib/python3.8/site-packages/rgf/utils.py:224: UserWarning: Cannot find FastRGF executable files. FastRGF estimators will be unavailable for usage.\n", + " warnings.warn(\"Cannot find FastRGF executable files. \"\n", + "[flaml.automl: 10-08 15:17:59] {1894} INFO - Estimated sufficient time budget=718418s. 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Consider increasing the time budget.\n" + ] + } + ], "metadata": { "slideshow": { "slide_type": "slide" @@ -1034,104 +1000,121 @@ "output_type": "stream", "name": "stderr", "text": [ - "[flaml.automl: 08-31 00:58:32] {1279} INFO - Evaluation method: holdout\n", - "[flaml.automl: 08-31 00:58:32] {1312} INFO - Minimizing error metric: customized metric\n", - "[flaml.automl: 08-31 00:58:32] {1338} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'catboost', 'xgboost', 'extra_tree', 'lrl1']\n", - "[flaml.automl: 08-31 00:58:32] {1532} INFO - iteration 0, current learner lgbm\n", - "[flaml.automl: 08-31 00:58:32] {1689} INFO - at 1.3s,\tbest lgbm's error=0.6646,\tbest lgbm's error=0.6646\n", - "[flaml.automl: 08-31 00:58:32] {1532} INFO - iteration 1, current learner lgbm\n", - "[flaml.automl: 08-31 00:58:33] {1689} INFO - at 1.4s,\tbest lgbm's error=0.6646,\tbest lgbm's error=0.6646\n", - "[flaml.automl: 08-31 00:58:33] {1532} INFO - iteration 2, current learner lgbm\n", - "[flaml.automl: 08-31 00:58:33] {1689} INFO - 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at 10.0s,\tbest catboost's error=0.6846,\tbest lgbm's error=0.6366\n", - "[flaml.automl: 08-31 00:58:41] {1766} INFO - selected model: LGBMClassifier(colsample_bytree=0.9318698026248824,\n", - " learning_rate=0.3731008541260285, max_bin=256,\n", - " min_child_samples=18, n_estimators=4, num_leaves=19,\n", - " objective='binary', reg_alpha=0.04956056407320672,\n", - " reg_lambda=3.319594476328555, verbose=-1)\n", - "[flaml.automl: 08-31 00:58:41] {1820} INFO - not retraining because the time budget is too small.\n", - "[flaml.automl: 08-31 00:58:41] {1364} INFO - fit succeeded\n", - "[flaml.automl: 08-31 00:58:41] {1365} INFO - Time taken to find the best model: 7.795238971710205\n", - "[flaml.automl: 08-31 00:58:41] {1370} WARNING - Time taken to find the best model is 78% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n" + "[flaml.automl: 10-08 15:18:07] {1458} INFO - Data split method: stratified\n", + "[flaml.automl: 10-08 15:18:07] {1462} INFO - Evaluation method: holdout\n", + "[flaml.automl: 10-08 15:18:07] {1510} INFO - Minimizing error metric: customized metric\n", + "[flaml.automl: 10-08 15:18:07] {1547} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'lrl1']\n", + "[flaml.automl: 10-08 15:18:07] {1777} INFO - iteration 0, current learner lgbm\n", + "[flaml.automl: 10-08 15:18:07] {1894} INFO - Estimated sufficient time budget=33595s. 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selected model: XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1.0,\n", + " colsample_bynode=1, colsample_bytree=0.7942569492674472, gamma=0,\n", + " gpu_id=-1, grow_policy='lossguide', importance_type='gain',\n", + " interaction_constraints='', learning_rate=0.6413547778096401,\n", + " max_delta_step=0, max_depth=0, max_leaves=17,\n", + " min_child_weight=13.753540541999772, missing=nan,\n", + " monotone_constraints='()', n_estimators=4, n_jobs=-1,\n", + " num_parallel_tree=1, random_state=0,\n", + " reg_alpha=0.016714365103792518, reg_lambda=0.4874780682949813,\n", + " scale_pos_weight=1, subsample=1.0, tree_method='hist',\n", + " use_label_encoder=False, validate_parameters=1, verbosity=0)\n", + "[flaml.automl: 10-08 15:18:18] {2136} INFO - retrain xgboost for 1.8s\n", + "[flaml.automl: 10-08 15:18:18] {2142} INFO - retrained model: XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1.0,\n", + " colsample_bynode=1, colsample_bytree=0.7942569492674472, gamma=0,\n", + " gpu_id=-1, grow_policy='lossguide', importance_type='gain',\n", + " interaction_constraints='', learning_rate=0.6413547778096401,\n", + " max_delta_step=0, max_depth=0, max_leaves=17,\n", + " min_child_weight=13.753540541999772, missing=nan,\n", + " monotone_constraints='()', n_estimators=4, n_jobs=-1,\n", + " num_parallel_tree=1, random_state=0,\n", + " reg_alpha=0.016714365103792518, reg_lambda=0.4874780682949813,\n", + " scale_pos_weight=1, subsample=1.0, tree_method='hist',\n", + " use_label_encoder=False, validate_parameters=1, verbosity=0)\n", + "[flaml.automl: 10-08 15:18:18] {1571} INFO - fit succeeded\n", + "[flaml.automl: 10-08 15:18:18] {1572} INFO - Time taken to find the best model: 10.063513994216919\n", + "[flaml.automl: 10-08 15:18:18] {1583} WARNING - Time taken to find the best model is 101% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n" ] } ], @@ -1140,11 +1123,11 @@ ], "metadata": { "interpreter": { - "hash": "0cfea3304185a9579d09e0953576b57c8581e46e6ebc6dfeb681bc5a511f7544" + "hash": "ea9f131eb1b7663628f6445553ba215a834e2f0b4d18774746f0f47938ce4671" }, "kernelspec": { "name": "python3", - "display_name": "Python 3.8.0 64-bit ('blend': conda)" + "display_name": "Python 3.8.0 64-bit ('test': conda)" }, "language_info": { "codemirror_mode": { diff --git a/notebook/flaml_lightgbm.ipynb b/notebook/flaml_lightgbm.ipynb index e92f1da0e..529d20bf0 100644 --- a/notebook/flaml_lightgbm.ipynb +++ b/notebook/flaml_lightgbm.ipynb @@ -3,7 +3,7 @@ { "cell_type": "markdown", "source": [ - "Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved. \n", + "Copyright (c) Microsoft Corporation. All rights reserved. \n", "\n", "Licensed under the MIT License.\n", "\n", @@ -142,112 +142,76 @@ "output_type": "stream", "name": "stderr", "text": [ - "[flaml.automl: 08-22 21:09:17] {1130} INFO - Evaluation method: cv\n", - "[flaml.automl: 08-22 21:09:17] {634} INFO - Using RepeatedKFold\n", - "[flaml.automl: 08-22 21:09:17] {1155} INFO - Minimizing error metric: 1-r2\n", - "[flaml.automl: 08-22 21:09:17] {1175} INFO - List of ML learners in AutoML Run: ['lgbm']\n", - "[flaml.automl: 08-22 21:09:17] {1358} INFO - iteration 0, current learner lgbm\n", - "[flaml.automl: 08-22 21:09:18] {1515} INFO - at 0.5s,\tbest lgbm's error=0.7385,\tbest lgbm's error=0.7385\n", - "[flaml.automl: 08-22 21:09:18] {1358} INFO - iteration 1, current learner lgbm\n", - "[flaml.automl: 08-22 21:09:18] {1515} INFO - at 0.7s,\tbest lgbm's error=0.7385,\tbest lgbm's error=0.7385\n", - "[flaml.automl: 08-22 21:09:18] {1358} INFO - iteration 2, current learner lgbm\n", - "[flaml.automl: 08-22 21:09:18] {1515} INFO - 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Consider increasing the time budget.\n" + "[flaml.automl: 09-29 23:10:08] {1446} INFO - Data split method: uniform\n", + "[flaml.automl: 09-29 23:10:08] {1450} INFO - Evaluation method: cv\n", + "[flaml.automl: 09-29 23:10:08] {1496} INFO - Minimizing error metric: 1-r2\n", + "[flaml.automl: 09-29 23:10:08] {1533} INFO - List of ML learners in AutoML Run: ['lgbm']\n", + "[flaml.automl: 09-29 23:10:08] {1763} INFO - iteration 0, current learner lgbm\n", + "[flaml.automl: 09-29 23:10:08] {1880} INFO - Estimated sufficient time budget=3832s. 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LGBMRegressor(colsample_bytree=0.8025848209352517,\n", + " learning_rate=0.09100963138990395, max_bin=255,\n", + " min_child_samples=42, n_estimators=363, num_leaves=216,\n", + " reg_alpha=0.001113000336715291, reg_lambda=76.50614276906414,\n", + " verbose=-1)\n", + "[flaml.automl: 09-29 23:14:10] {2122} INFO - retrain lgbm for 1.9s\n", + "[flaml.automl: 09-29 23:14:10] {2128} INFO - retrained model: LGBMRegressor(colsample_bytree=0.8025848209352517,\n", + " learning_rate=0.09100963138990395, max_bin=255,\n", + " min_child_samples=42, n_estimators=363, num_leaves=216,\n", + " reg_alpha=0.001113000336715291, reg_lambda=76.50614276906414,\n", + " verbose=-1)\n", + "[flaml.automl: 09-29 23:14:10] {1557} INFO - fit succeeded\n", + "[flaml.automl: 09-29 23:14:10] {1558} INFO - Time taken to find the best model: 35.49429440498352\n" ] } ], @@ -283,9 +247,9 @@ "output_type": "stream", "name": "stdout", "text": [ - "Best hyperparmeter config: {'n_estimators': 2120, 'num_leaves': 92, 'min_child_samples': 9, 'learning_rate': 0.011556686284183076, 'log_max_bin': 10, 'colsample_bytree': 0.6513228229604555, 'reg_alpha': 0.024999216167840198, 'reg_lambda': 0.01918323581702806}\n", - "Best r2 on validation data: 0.8465\n", - "Training duration of best run: 35.16 s\n" + "Best hyperparmeter config: {'n_estimators': 363, 'num_leaves': 216, 'min_child_samples': 42, 'learning_rate': 0.09100963138990395, 'log_max_bin': 8, 'colsample_bytree': 0.8025848209352517, 'reg_alpha': 0.001113000336715291, 'reg_lambda': 76.50614276906414}\n", + "Best r2 on validation data: 0.8436\n", + "Training duration of best run: 9.511 s\n" ] } ], @@ -300,18 +264,18 @@ "cell_type": "code", "execution_count": 6, "source": [ - "automl.model.estimator\n" + "automl.model.estimator" ], "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "LGBMRegressor(colsample_bytree=0.6513228229604555,\n", - " learning_rate=0.011556686284183076, max_bin=512,\n", - " min_child_samples=9, n_estimators=2120, num_leaves=92,\n", - " objective='regression', reg_alpha=0.024999216167840198,\n", - " reg_lambda=0.01918323581702806, verbose=-1)" + "LGBMRegressor(colsample_bytree=0.8025848209352517,\n", + " learning_rate=0.09100963138990395, max_bin=255,\n", + " min_child_samples=42, n_estimators=363, num_leaves=216,\n", + " reg_alpha=0.001113000336715291, reg_lambda=76.50614276906414,\n", + " verbose=-1)" ] }, "metadata": {}, @@ -345,11 +309,10 @@ { "output_type": "display_data", "data": { + "image/png": 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" + ] }, "metadata": { "needs_background": "light" @@ -388,8 +351,8 @@ "output_type": "stream", "name": "stdout", "text": [ - "Predicted labels [144012.37488361 251501.98425004 147503.3849682 ... 219178.01297482\n", - " 213834.88677304 272956.11149784]\n", + "Predicted labels [143391.65036598 245535.13731975 153171.44071644 ... 184354.52735665\n", + " 235510.49470402 282617.22858849]\n", "True labels 14740 136900.0\n", "10101 241300.0\n", "20566 200700.0\n", @@ -427,9 +390,9 @@ "output_type": "stream", "name": "stdout", "text": [ - "r2 = 0.8540590968156087\n", - "mse = 1929120783.4023921\n", - "mae = 28944.167002684408\n" + "r2 = 0.8505434326525669\n", + "mse = 1975592613.1389656\n", + "mae = 29471.536046101864\n" ] } ], @@ -457,15 +420,13 @@ "name": "stdout", "text": [ "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 20, 'learning_rate': 0.09999999999999995, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 20, 'learning_rate': 0.09999999999999995, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 12, 'learning_rate': 0.26770501231052046, 'log_max_bin': 7, 'colsample_bytree': 1.0, 'reg_alpha': 0.001348364934537134, 'reg_lambda': 1.4442580148221913}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 12, 'learning_rate': 0.26770501231052046, 'log_max_bin': 7, 'colsample_bytree': 1.0, 'reg_alpha': 0.001348364934537134, 'reg_lambda': 1.4442580148221913}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 11, 'num_leaves': 4, 'min_child_samples': 9, 'learning_rate': 0.7260594590615893, 'log_max_bin': 9, 'colsample_bytree': 0.9285002286474459, 'reg_alpha': 0.0036840681931986645, 'reg_lambda': 0.7532480505730402}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 11, 'num_leaves': 4, 'min_child_samples': 9, 'learning_rate': 0.7260594590615893, 'log_max_bin': 9, 'colsample_bytree': 0.9285002286474459, 'reg_alpha': 0.0036840681931986645, 'reg_lambda': 0.7532480505730402}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 13, 'num_leaves': 5, 'min_child_samples': 5, 'learning_rate': 0.7590459488450945, 'log_max_bin': 8, 'colsample_bytree': 0.8304072431299575, 'reg_alpha': 0.001951378031519758, 'reg_lambda': 0.04792552866398477}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 13, 'num_leaves': 5, 'min_child_samples': 5, 'learning_rate': 0.7590459488450945, 'log_max_bin': 8, 'colsample_bytree': 0.8304072431299575, 'reg_alpha': 0.001951378031519758, 'reg_lambda': 0.04792552866398477}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 44, 'num_leaves': 4, 'min_child_samples': 4, 'learning_rate': 0.41929025492645006, 'log_max_bin': 8, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.009280655005879927}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 44, 'num_leaves': 4, 'min_child_samples': 4, 'learning_rate': 0.41929025492645006, 'log_max_bin': 8, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.009280655005879927}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 141, 'num_leaves': 17, 'min_child_samples': 3, 'learning_rate': 0.17402065726724145, 'log_max_bin': 8, 'colsample_bytree': 0.6649148062238498, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.006761362450996487}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 141, 'num_leaves': 17, 'min_child_samples': 3, 'learning_rate': 0.17402065726724145, 'log_max_bin': 8, 'colsample_bytree': 0.6649148062238498, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.006761362450996487}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 88, 'num_leaves': 70, 'min_child_samples': 4, 'learning_rate': 0.09348689572544734, 'log_max_bin': 7, 'colsample_bytree': 0.5967846088487322, 'reg_alpha': 0.006958608037974516, 'reg_lambda': 0.001895876878997586}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 88, 'num_leaves': 70, 'min_child_samples': 4, 'learning_rate': 0.09348689572544734, 'log_max_bin': 7, 'colsample_bytree': 0.5967846088487322, 'reg_alpha': 0.006958608037974516, 'reg_lambda': 0.001895876878997586}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 166, 'num_leaves': 34, 'min_child_samples': 2, 'learning_rate': 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'reg_lambda': 0.006874802748054271}}\n", - "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 256, 'num_leaves': 79, 'min_child_samples': 4, 'learning_rate': 0.06020420143131026, 'log_max_bin': 10, 'colsample_bytree': 0.6501336877031868, 'reg_alpha': 0.11324823332770402, 'reg_lambda': 0.007122448821650475}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 256, 'num_leaves': 79, 'min_child_samples': 4, 'learning_rate': 0.06020420143131026, 'log_max_bin': 10, 'colsample_bytree': 0.6501336877031868, 'reg_alpha': 0.11324823332770402, 'reg_lambda': 0.007122448821650475}}\n" + "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 4, 'num_leaves': 12, 'min_child_samples': 15, 'learning_rate': 0.2284139062380884, 'log_max_bin': 9, 'colsample_bytree': 1.0, 'reg_alpha': 0.0014700173967242716, 'reg_lambda': 7.624911621832711}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': 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'log_max_bin': 9, 'colsample_bytree': 1.0, 'reg_alpha': 0.0014700173967242718, 'reg_lambda': 7.624911621832699}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 20, 'num_leaves': 12, 'min_child_samples': 15, 'learning_rate': 0.2284139062380884, 'log_max_bin': 9, 'colsample_bytree': 1.0, 'reg_alpha': 0.0014700173967242718, 'reg_lambda': 7.624911621832699}}\n", + "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 78, 'num_leaves': 10, 'min_child_samples': 24, 'learning_rate': 0.07647794276357107, 'log_max_bin': 10, 'colsample_bytree': 1.0, 'reg_alpha': 0.001749539645587163, 'reg_lambda': 4.373760956394571}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 78, 'num_leaves': 10, 'min_child_samples': 24, 'learning_rate': 0.07647794276357107, 'log_max_bin': 10, 'colsample_bytree': 1.0, 'reg_alpha': 0.001749539645587163, 'reg_lambda': 4.373760956394571}}\n", + "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 171, 'num_leaves': 15, 'min_child_samples': 49, 'learning_rate': 0.09991937598563264, 'log_max_bin': 9, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009940547384005775, 'reg_lambda': 1.1214041135390789}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 171, 'num_leaves': 15, 'min_child_samples': 49, 'learning_rate': 0.09991937598563264, 'log_max_bin': 9, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009940547384005775, 'reg_lambda': 1.1214041135390789}}\n", + "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 230, 'num_leaves': 61, 'min_child_samples': 58, 'learning_rate': 0.11237861230007634, 'log_max_bin': 9, 'colsample_bytree': 0.9596144262255549, 'reg_alpha': 0.0009765625, 'reg_lambda': 20.911712312854934}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 230, 'num_leaves': 61, 'min_child_samples': 58, 'learning_rate': 0.11237861230007634, 'log_max_bin': 9, 'colsample_bytree': 0.9596144262255549, 'reg_alpha': 0.0009765625, 'reg_lambda': 20.911712312854934}}\n", + "{'Current Learner': 'lgbm', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 363, 'num_leaves': 216, 'min_child_samples': 42, 'learning_rate': 0.09100963138990395, 'log_max_bin': 8, 'colsample_bytree': 0.8025848209352517, 'reg_alpha': 0.001113000336715291, 'reg_lambda': 76.50614276906414}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 363, 'num_leaves': 216, 'min_child_samples': 42, 'learning_rate': 0.09100963138990395, 'log_max_bin': 8, 'colsample_bytree': 0.8025848209352517, 'reg_alpha': 0.001113000336715291, 'reg_lambda': 76.50614276906414}}\n" ] } ], @@ -493,11 +454,10 @@ { "output_type": "display_data", "data": { + "image/png": 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" + ] }, "metadata": { "needs_background": "light" @@ -523,14 +483,14 @@ "cell_type": "code", "execution_count": 13, "source": [ - "print('flaml r2', '=', 1 - sklearn_metric_loss_score('r2', y_pred, y_test))" + "print('flaml (4min) r2', '=', 1 - sklearn_metric_loss_score('r2', y_pred, y_test))" ], "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "flaml r2 = 0.8540590968156087\n" + "flaml (4min) r2 = 0.8505434326525669\n" ] } ], @@ -625,7 +585,7 @@ " \"objective\": \"regression\",\n", " \"metric\": \"regression\",\n", " \"verbosity\": -1,\n", - "}\n" + "}" ], "outputs": [], "metadata": {} @@ -642,89 +602,89 @@ "output_type": "stream", "name": "stderr", "text": [ - "\u001b[32m[I 2021-08-22 21:13:20,495]\u001b[0m A new study created in memory with name: no-name-11015170-733e-470d-817a-413f55382d0c\u001b[0m\n", - "feature_fraction, val_score: 1923826918.442117: 14%|#4 | 1/7 [00:01<00:08, 1.49s/it]\u001b[32m[I 2021-08-22 21:13:22,006]\u001b[0m Trial 0 finished with value: 1923826918.4421172 and parameters: {'feature_fraction': 0.8999999999999999}. Best is trial 0 with value: 1923826918.4421172.\u001b[0m\n", - "feature_fraction, val_score: 1923826918.442117: 29%|##8 | 2/7 [00:02<00:07, 1.44s/it]\u001b[32m[I 2021-08-22 21:13:23,335]\u001b[0m Trial 1 finished with value: 1935542284.5841475 and parameters: {'feature_fraction': 0.6}. Best is trial 0 with value: 1923826918.4421172.\u001b[0m\n", - "feature_fraction, val_score: 1923826918.442117: 43%|####2 | 3/7 [00:04<00:05, 1.44s/it]\u001b[32m[I 2021-08-22 21:13:24,762]\u001b[0m Trial 2 finished with value: 1959693958.979498 and parameters: {'feature_fraction': 0.7}. Best is trial 0 with value: 1923826918.4421172.\u001b[0m\n", - "feature_fraction, val_score: 1923826918.442117: 57%|#####7 | 4/7 [00:05<00:04, 1.38s/it]\u001b[32m[I 2021-08-22 21:13:26,021]\u001b[0m Trial 3 finished with value: 2237193094.1989536 and parameters: {'feature_fraction': 0.4}. Best is trial 0 with value: 1923826918.4421172.\u001b[0m\n", - "feature_fraction, val_score: 1923826918.442117: 71%|#######1 | 5/7 [00:06<00:02, 1.36s/it]\u001b[32m[I 2021-08-22 21:13:27,314]\u001b[0m Trial 4 finished with value: 1988198059.953293 and parameters: {'feature_fraction': 0.5}. Best is trial 0 with value: 1923826918.4421172.\u001b[0m\n", - "feature_fraction, val_score: 1923826918.442117: 86%|########5 | 6/7 [00:08<00:01, 1.37s/it]\u001b[32m[I 2021-08-22 21:13:28,719]\u001b[0m Trial 5 finished with value: 1959693958.979498 and parameters: {'feature_fraction': 0.8}. Best is trial 0 with value: 1923826918.4421172.\u001b[0m\n", - "feature_fraction, val_score: 1923826918.442117: 100%|##########| 7/7 [00:09<00:00, 1.42s/it]\u001b[32m[I 2021-08-22 21:13:30,240]\u001b[0m Trial 6 finished with value: 1961388150.442425 and parameters: {'feature_fraction': 1.0}. Best is trial 0 with value: 1923826918.4421172.\u001b[0m\n", - "feature_fraction, val_score: 1923826918.442117: 100%|##########| 7/7 [00:09<00:00, 1.39s/it]\n", - "num_leaves, val_score: 1902337773.833954: 5%|5 | 1/20 [00:02<00:41, 2.21s/it]\u001b[32m[I 2021-08-22 21:13:32,454]\u001b[0m Trial 7 finished with value: 1902337773.8339543 and parameters: {'num_leaves': 62}. Best is trial 7 with value: 1902337773.8339543.\u001b[0m\n", - "num_leaves, val_score: 1892120308.436302: 10%|# | 2/20 [00:04<00:42, 2.35s/it]\u001b[32m[I 2021-08-22 21:13:35,126]\u001b[0m Trial 8 finished with value: 1892120308.4363017 and parameters: {'num_leaves': 78}. Best is trial 8 with value: 1892120308.4363017.\u001b[0m\n", - "num_leaves, val_score: 1892120308.436302: 15%|#5 | 3/20 [00:06<00:34, 2.03s/it]\u001b[32m[I 2021-08-22 21:13:36,422]\u001b[0m Trial 9 finished with value: 1924633212.447515 and parameters: {'num_leaves': 26}. Best is trial 8 with value: 1892120308.4363017.\u001b[0m\n", - "num_leaves, val_score: 1892120308.436302: 20%|## | 4/20 [00:07<00:27, 1.70s/it]\u001b[32m[I 2021-08-22 21:13:37,340]\u001b[0m Trial 10 finished with value: 1975840134.2036633 and parameters: {'num_leaves': 12}. Best is trial 8 with value: 1892120308.4363017.\u001b[0m\n", - "num_leaves, val_score: 1892120308.436302: 25%|##5 | 5/20 [00:13<00:46, 3.12s/it]\u001b[32m[I 2021-08-22 21:13:43,773]\u001b[0m Trial 11 finished with value: 1923702276.2852578 and parameters: {'num_leaves': 204}. Best is trial 8 with value: 1892120308.4363017.\u001b[0m\n", - "num_leaves, val_score: 1892120308.436302: 30%|### | 6/20 [00:20<00:59, 4.24s/it]\u001b[32m[I 2021-08-22 21:13:50,646]\u001b[0m Trial 12 finished with value: 1939984702.0007648 and parameters: {'num_leaves': 214}. Best is trial 8 with value: 1892120308.4363017.\u001b[0m\n", - "num_leaves, val_score: 1892120308.436302: 35%|###5 | 7/20 [00:23<00:49, 3.79s/it]\u001b[32m[I 2021-08-22 21:13:53,368]\u001b[0m Trial 13 finished with value: 1942261187.1568937 and parameters: {'num_leaves': 61}. Best is trial 8 with value: 1892120308.4363017.\u001b[0m\n", - "num_leaves, val_score: 1892120308.436302: 40%|#### | 8/20 [00:33<01:09, 5.81s/it]\u001b[32m[I 2021-08-22 21:14:03,909]\u001b[0m Trial 14 finished with value: 1962322296.1656826 and parameters: {'num_leaves': 234}. Best is trial 8 with value: 1892120308.4363017.\u001b[0m\n", - "num_leaves, val_score: 1892120308.436302: 45%|####5 | 9/20 [00:38<01:00, 5.52s/it]\u001b[32m[I 2021-08-22 21:14:08,728]\u001b[0m Trial 15 finished with value: 1933575055.0360022 and parameters: {'num_leaves': 131}. Best is trial 8 with value: 1892120308.4363017.\u001b[0m\n", - "num_leaves, val_score: 1892120308.436302: 50%|##### | 10/20 [00:41<00:47, 4.70s/it]\u001b[32m[I 2021-08-22 21:14:11,527]\u001b[0m Trial 16 finished with value: 1907396468.702243 and parameters: {'num_leaves': 64}. Best is trial 8 with value: 1892120308.4363017.\u001b[0m\n", - "num_leaves, val_score: 1880656950.656438: 55%|#####5 | 11/20 [00:46<00:43, 4.83s/it]\u001b[32m[I 2021-08-22 21:14:16,641]\u001b[0m Trial 17 finished with value: 1880656950.6564376 and parameters: {'num_leaves': 141}. Best is trial 17 with value: 1880656950.6564376.\u001b[0m\n", - "num_leaves, val_score: 1880656950.656438: 60%|###### | 12/20 [00:51<00:38, 4.80s/it]\u001b[32m[I 2021-08-22 21:14:21,399]\u001b[0m Trial 18 finished with value: 1906428309.75546 and parameters: {'num_leaves': 139}. Best is trial 17 with value: 1880656950.6564376.\u001b[0m\n", - "num_leaves, val_score: 1880656950.656438: 65%|######5 | 13/20 [00:56<00:35, 5.07s/it]\u001b[32m[I 2021-08-22 21:14:27,074]\u001b[0m Trial 19 finished with value: 1897071192.2731016 and parameters: {'num_leaves': 161}. Best is trial 17 with value: 1880656950.6564376.\u001b[0m\n", - "num_leaves, val_score: 1880656950.656438: 70%|####### | 14/20 [01:01<00:28, 4.81s/it]\u001b[32m[I 2021-08-22 21:14:31,276]\u001b[0m Trial 20 finished with value: 1910775598.9420693 and parameters: {'num_leaves': 95}. Best is trial 17 with value: 1880656950.6564376.\u001b[0m\n", - "num_leaves, val_score: 1880656950.656438: 75%|#######5 | 15/20 [01:04<00:22, 4.44s/it]\u001b[32m[I 2021-08-22 21:14:34,857]\u001b[0m Trial 21 finished with value: 1890350018.7742429 and parameters: {'num_leaves': 101}. Best is trial 17 with value: 1880656950.6564376.\u001b[0m\n", - "num_leaves, val_score: 1874647481.354196: 80%|######## | 16/20 [01:10<00:19, 4.84s/it]\u001b[32m[I 2021-08-22 21:14:40,645]\u001b[0m Trial 22 finished with value: 1874647481.354196 and parameters: {'num_leaves': 174}. Best is trial 22 with value: 1874647481.354196.\u001b[0m\n", - "num_leaves, val_score: 1874647481.354196: 85%|########5 | 17/20 [01:16<00:15, 5.12s/it]\u001b[32m[I 2021-08-22 21:14:46,424]\u001b[0m Trial 23 finished with value: 1929626032.4915411 and parameters: {'num_leaves': 176}. Best is trial 22 with value: 1874647481.354196.\u001b[0m\n", - "num_leaves, val_score: 1874647481.354196: 90%|######### | 18/20 [01:22<00:10, 5.35s/it]\u001b[32m[I 2021-08-22 21:14:52,288]\u001b[0m Trial 24 finished with value: 1926786945.429698 and parameters: {'num_leaves': 177}. Best is trial 22 with value: 1874647481.354196.\u001b[0m\n", - "num_leaves, val_score: 1874647481.354196: 95%|#########5| 19/20 [01:30<00:06, 6.26s/it]\u001b[32m[I 2021-08-22 21:15:00,672]\u001b[0m Trial 25 finished with value: 1936436149.7610657 and parameters: {'num_leaves': 248}. Best is trial 22 with value: 1874647481.354196.\u001b[0m\n", - "num_leaves, val_score: 1870787631.458499: 100%|##########| 20/20 [01:35<00:00, 5.93s/it]\u001b[32m[I 2021-08-22 21:15:05,849]\u001b[0m Trial 26 finished with value: 1870787631.4584987 and parameters: {'num_leaves': 152}. Best is trial 26 with value: 1870787631.4584987.\u001b[0m\n", - "num_leaves, val_score: 1870787631.458499: 100%|##########| 20/20 [01:35<00:00, 4.78s/it]\n", - "bagging, val_score: 1870787631.458499: 10%|# | 1/10 [00:07<01:06, 7.43s/it]\u001b[32m[I 2021-08-22 21:15:13,289]\u001b[0m Trial 27 finished with value: 2237757312.870728 and parameters: {'bagging_fraction': 0.44000087334449334, 'bagging_freq': 6}. Best is trial 27 with value: 2237757312.870728.\u001b[0m\n", - "bagging, val_score: 1870787631.458499: 20%|## | 2/10 [00:15<01:01, 7.71s/it]\u001b[32m[I 2021-08-22 21:15:21,655]\u001b[0m Trial 28 finished with value: 2162729069.0272393 and parameters: {'bagging_fraction': 0.5075440331178458, 'bagging_freq': 3}. Best is trial 28 with value: 2162729069.0272393.\u001b[0m\n", - "bagging, val_score: 1870787631.458499: 30%|### | 3/10 [00:21<00:49, 7.05s/it]\u001b[32m[I 2021-08-22 21:15:27,150]\u001b[0m Trial 29 finished with value: 2003355452.8831115 and parameters: {'bagging_fraction': 0.757776235401641, 'bagging_freq': 1}. Best is trial 29 with value: 2003355452.8831115.\u001b[0m\n", - "bagging, val_score: 1870787631.458499: 40%|#### | 4/10 [00:28<00:42, 7.02s/it]\u001b[32m[I 2021-08-22 21:15:34,105]\u001b[0m Trial 30 finished with value: 2169017536.089679 and parameters: {'bagging_fraction': 0.5470758964212703, 'bagging_freq': 4}. Best is trial 29 with value: 2003355452.8831115.\u001b[0m\n", - "bagging, val_score: 1870787631.458499: 50%|##### | 5/10 [00:33<00:32, 6.49s/it]\u001b[32m[I 2021-08-22 21:15:39,358]\u001b[0m Trial 31 finished with value: 1949886129.0973551 and parameters: {'bagging_fraction': 0.7729694462744219, 'bagging_freq': 1}. Best is trial 31 with value: 1949886129.0973551.\u001b[0m\n", - "bagging, val_score: 1870787631.458499: 60%|###### | 6/10 [00:40<00:26, 6.53s/it]\u001b[32m[I 2021-08-22 21:15:45,996]\u001b[0m Trial 32 finished with value: 2082597134.5380604 and parameters: {'bagging_fraction': 0.6293524485160634, 'bagging_freq': 6}. Best is trial 31 with value: 1949886129.0973551.\u001b[0m\n", - "bagging, val_score: 1870787631.458499: 70%|####### | 7/10 [00:46<00:19, 6.57s/it]\u001b[32m[I 2021-08-22 21:15:52,653]\u001b[0m Trial 33 finished with value: 2128522268.181099 and parameters: {'bagging_fraction': 0.5194460357854906, 'bagging_freq': 4}. Best is trial 31 with value: 1949886129.0973551.\u001b[0m\n", - "bagging, val_score: 1870787631.458499: 80%|######## | 8/10 [00:53<00:12, 6.49s/it]\u001b[32m[I 2021-08-22 21:15:58,939]\u001b[0m Trial 34 finished with value: 1972329936.356194 and parameters: {'bagging_fraction': 0.7021495661140726, 'bagging_freq': 2}. Best is trial 31 with value: 1949886129.0973551.\u001b[0m\n", - "bagging, val_score: 1870787631.458499: 90%|######### | 9/10 [00:58<00:06, 6.15s/it]\u001b[32m[I 2021-08-22 21:16:04,316]\u001b[0m Trial 35 finished with value: 2035847515.436036 and parameters: {'bagging_fraction': 0.7365019160691924, 'bagging_freq': 1}. Best is trial 31 with value: 1949886129.0973551.\u001b[0m\n", - "bagging, val_score: 1870787631.458499: 100%|##########| 10/10 [01:05<00:00, 6.27s/it]\u001b[32m[I 2021-08-22 21:16:10,869]\u001b[0m Trial 36 finished with value: 2089685881.7609503 and parameters: {'bagging_fraction': 0.5702856203071842, 'bagging_freq': 6}. Best is trial 31 with value: 1949886129.0973551.\u001b[0m\n", - "bagging, val_score: 1870787631.458499: 100%|##########| 10/10 [01:05<00:00, 6.50s/it]\n", - "feature_fraction_stage2, val_score: 1870787631.458499: 17%|#6 | 1/6 [00:05<00:25, 5.10s/it]\u001b[32m[I 2021-08-22 21:16:15,976]\u001b[0m Trial 37 finished with value: 1915845450.4267912 and parameters: {'feature_fraction': 0.9799999999999999}. Best is trial 37 with value: 1915845450.4267912.\u001b[0m\n", - "feature_fraction_stage2, val_score: 1870787631.458499: 33%|###3 | 2/6 [00:09<00:20, 5.02s/it]\u001b[32m[I 2021-08-22 21:16:20,814]\u001b[0m Trial 38 finished with value: 1870787631.4584987 and parameters: {'feature_fraction': 0.852}. Best is trial 38 with value: 1870787631.4584987.\u001b[0m\n", - "feature_fraction_stage2, val_score: 1870787631.458499: 50%|##### | 3/6 [00:14<00:14, 4.95s/it]\u001b[32m[I 2021-08-22 21:16:25,594]\u001b[0m Trial 39 finished with value: 1870787631.4584987 and parameters: {'feature_fraction': 0.8839999999999999}. Best is trial 38 with value: 1870787631.4584987.\u001b[0m\n", - "feature_fraction_stage2, val_score: 1870787631.458499: 67%|######6 | 4/6 [00:20<00:10, 5.08s/it]\u001b[32m[I 2021-08-22 21:16:30,990]\u001b[0m Trial 40 finished with value: 1915845450.4267912 and parameters: {'feature_fraction': 0.948}. Best is trial 38 with value: 1870787631.4584987.\u001b[0m\n", - "feature_fraction_stage2, val_score: 1870787631.458499: 83%|########3 | 5/6 [00:25<00:05, 5.19s/it]\u001b[32m[I 2021-08-22 21:16:36,418]\u001b[0m Trial 41 finished with value: 1870787631.4584987 and parameters: {'feature_fraction': 0.9159999999999999}. Best is trial 38 with value: 1870787631.4584987.\u001b[0m\n", - "feature_fraction_stage2, val_score: 1870787631.458499: 100%|##########| 6/6 [00:31<00:00, 5.39s/it]\u001b[32m[I 2021-08-22 21:16:42,282]\u001b[0m Trial 42 finished with value: 1870787631.4584987 and parameters: {'feature_fraction': 0.82}. Best is trial 38 with value: 1870787631.4584987.\u001b[0m\n", - "feature_fraction_stage2, val_score: 1870787631.458499: 100%|##########| 6/6 [00:31<00:00, 5.24s/it]\n", - "regularization_factors, val_score: 1870787631.458499: 5%|5 | 1/20 [00:05<01:41, 5.36s/it]\u001b[32m[I 2021-08-22 21:16:47,653]\u001b[0m Trial 43 finished with value: 1870787631.491234 and parameters: {'lambda_l1': 6.212193776886605e-06, 'lambda_l2': 3.009357838100163e-08}. Best is trial 43 with value: 1870787631.491234.\u001b[0m\n", - "regularization_factors, val_score: 1870787534.973267: 10%|# | 2/20 [00:12<01:44, 5.82s/it]\u001b[32m[I 2021-08-22 21:16:54,524]\u001b[0m Trial 44 finished with value: 1870787534.9732666 and parameters: {'lambda_l1': 4.443479994016017e-08, 'lambda_l2': 3.556819404354524e-05}. Best is trial 44 with value: 1870787534.9732666.\u001b[0m\n", - "regularization_factors, val_score: 1870787534.973267: 15%|#5 | 3/20 [00:17<01:36, 5.65s/it]\u001b[32m[I 2021-08-22 21:16:59,795]\u001b[0m Trial 45 finished with value: 1870787622.4979687 and parameters: {'lambda_l1': 0.014465195791714083, 'lambda_l2': 2.1021138174252987e-07}. Best is trial 44 with value: 1870787534.9732666.\u001b[0m\n", - "regularization_factors, val_score: 1870787534.973267: 20%|## | 4/20 [00:22<01:28, 5.52s/it]\u001b[32m[I 2021-08-22 21:17:05,006]\u001b[0m Trial 46 finished with value: 1870787619.5326774 and parameters: {'lambda_l1': 1.429709851157171e-06, 'lambda_l2': 4.419238564285042e-06}. Best is trial 44 with value: 1870787534.9732666.\u001b[0m\n", - "regularization_factors, val_score: 1870787534.973267: 25%|##5 | 5/20 [00:28<01:22, 5.51s/it]\u001b[32m[I 2021-08-22 21:17:10,496]\u001b[0m Trial 47 finished with value: 1870787590.2959824 and parameters: {'lambda_l1': 0.06926729801332972, 'lambda_l2': 1.6922599199508456e-08}. Best is trial 44 with value: 1870787534.9732666.\u001b[0m\n", - "regularization_factors, val_score: 1870787534.973267: 30%|### | 6/20 [00:33<01:16, 5.44s/it]\u001b[32m[I 2021-08-22 21:17:15,768]\u001b[0m Trial 48 finished with value: 1870787630.8453631 and parameters: {'lambda_l1': 2.052098028013423e-08, 'lambda_l2': 2.3337138419589934e-07}. Best is trial 44 with value: 1870787534.9732666.\u001b[0m\n", - "regularization_factors, val_score: 1870787534.973267: 35%|###5 | 7/20 [00:38<01:10, 5.39s/it]\u001b[32m[I 2021-08-22 21:17:21,046]\u001b[0m Trial 49 finished with value: 1876699891.9815595 and parameters: {'lambda_l1': 4.304944401004946e-06, 'lambda_l2': 0.0007656130560392606}. Best is trial 44 with value: 1870787534.9732666.\u001b[0m\n", - "regularization_factors, val_score: 1870787534.973267: 40%|#### | 8/20 [00:44<01:04, 5.37s/it]\u001b[32m[I 2021-08-22 21:17:26,372]\u001b[0m Trial 50 finished with value: 1894915643.8553405 and parameters: {'lambda_l1': 5.783302142631901e-07, 'lambda_l2': 0.005857904967523283}. Best is trial 44 with value: 1870787534.9732666.\u001b[0m\n", - "regularization_factors, val_score: 1870787534.973267: 45%|####5 | 9/20 [00:49<00:58, 5.32s/it]\u001b[32m[I 2021-08-22 21:17:31,570]\u001b[0m Trial 51 finished with value: 1877059069.215943 and parameters: {'lambda_l1': 2.9196198134708893, 'lambda_l2': 4.6093049397982125e-05}. Best is trial 44 with value: 1870787534.9732666.\u001b[0m\n", - "regularization_factors, val_score: 1853015384.453637: 50%|##### | 10/20 [00:54<00:53, 5.34s/it]\u001b[32m[I 2021-08-22 21:17:36,944]\u001b[0m Trial 52 finished with value: 1853015384.453637 and parameters: {'lambda_l1': 0.09558504914610533, 'lambda_l2': 3.220273228431258}. Best is trial 52 with value: 1853015384.453637.\u001b[0m\n", - "regularization_factors, val_score: 1853015384.453637: 55%|#####5 | 11/20 [01:00<00:49, 5.45s/it]\u001b[32m[I 2021-08-22 21:17:42,672]\u001b[0m Trial 53 finished with value: 1896992309.3706267 and parameters: {'lambda_l1': 6.575749289036579, 'lambda_l2': 7.662096538085835}. Best is trial 52 with value: 1853015384.453637.\u001b[0m\n", - "regularization_factors, val_score: 1853015384.453637: 60%|###### | 12/20 [01:06<00:44, 5.56s/it]\u001b[32m[I 2021-08-22 21:17:48,473]\u001b[0m Trial 54 finished with value: 1893493622.3798478 and parameters: {'lambda_l1': 0.0008722383951977965, 'lambda_l2': 0.13339065517857865}. Best is trial 52 with value: 1853015384.453637.\u001b[0m\n", - "regularization_factors, val_score: 1853015384.453637: 65%|######5 | 13/20 [01:12<00:39, 5.68s/it]\u001b[32m[I 2021-08-22 21:17:54,425]\u001b[0m Trial 55 finished with value: 1895459424.4650118 and parameters: {'lambda_l1': 0.046018652714269824, 'lambda_l2': 3.596577171855534}. Best is trial 52 with value: 1853015384.453637.\u001b[0m\n", - "regularization_factors, val_score: 1853015384.453637: 70%|####### | 14/20 [01:17<00:33, 5.57s/it]\u001b[32m[I 2021-08-22 21:17:59,760]\u001b[0m Trial 56 finished with value: 1902235965.6523015 and parameters: {'lambda_l1': 0.0004372538444200538, 'lambda_l2': 0.018403234102680837}. Best is trial 52 with value: 1853015384.453637.\u001b[0m\n", - "regularization_factors, val_score: 1853015384.453637: 75%|#######5 | 15/20 [01:23<00:27, 5.57s/it]\u001b[32m[I 2021-08-22 21:18:05,311]\u001b[0m Trial 57 finished with value: 1870787547.884662 and parameters: {'lambda_l1': 1.5644444125721077e-08, 'lambda_l2': 3.06684587723285e-05}. Best is trial 52 with value: 1853015384.453637.\u001b[0m\n", - "regularization_factors, val_score: 1853015384.453637: 80%|######## | 16/20 [01:28<00:22, 5.56s/it]\u001b[32m[I 2021-08-22 21:18:10,860]\u001b[0m Trial 58 finished with value: 1952628057.3679152 and parameters: {'lambda_l1': 0.41090501100060367, 'lambda_l2': 0.4784149571785825}. Best is trial 52 with value: 1853015384.453637.\u001b[0m\n", - "regularization_factors, val_score: 1853015384.453637: 85%|########5 | 17/20 [01:34<00:16, 5.66s/it]\u001b[32m[I 2021-08-22 21:18:16,748]\u001b[0m Trial 59 finished with value: 1874516545.805995 and parameters: {'lambda_l1': 4.63176126114126e-05, 'lambda_l2': 0.0002597320589400073}. Best is trial 52 with value: 1853015384.453637.\u001b[0m\n", - "regularization_factors, val_score: 1853015384.453637: 90%|######### | 18/20 [01:40<00:11, 5.80s/it]\u001b[32m[I 2021-08-22 21:18:22,861]\u001b[0m Trial 60 finished with value: 1870787615.0912282 and parameters: {'lambda_l1': 0.007392465833323452, 'lambda_l2': 4.3888082066628725e-06}. Best is trial 52 with value: 1853015384.453637.\u001b[0m\n", - "regularization_factors, val_score: 1853015384.453637: 95%|#########5| 19/20 [01:46<00:05, 5.72s/it]\u001b[32m[I 2021-08-22 21:18:28,403]\u001b[0m Trial 61 finished with value: 1859899631.3896043 and parameters: {'lambda_l1': 1.573671858757602e-07, 'lambda_l2': 0.0021763888923074476}. Best is trial 52 with value: 1853015384.453637.\u001b[0m\n", - "regularization_factors, val_score: 1853015384.453637: 100%|##########| 20/20 [01:51<00:00, 5.70s/it]\u001b[32m[I 2021-08-22 21:18:34,066]\u001b[0m Trial 62 finished with value: 1905701773.7289548 and parameters: {'lambda_l1': 0.7977530430827201, 'lambda_l2': 0.6661855838737094}. Best is trial 52 with value: 1853015384.453637.\u001b[0m\n", - "regularization_factors, val_score: 1853015384.453637: 100%|##########| 20/20 [01:51<00:00, 5.59s/it]\n", - "min_data_in_leaf, val_score: 1853015384.453637: 20%|## | 1/5 [00:04<00:19, 4.95s/it]\u001b[32m[I 2021-08-22 21:18:39,027]\u001b[0m Trial 63 finished with value: 1859286747.0773554 and parameters: {'min_child_samples': 10}. Best is trial 63 with value: 1859286747.0773554.\u001b[0m\n", - "min_data_in_leaf, val_score: 1853015384.453637: 40%|#### | 2/5 [00:09<00:14, 4.87s/it]\u001b[32m[I 2021-08-22 21:18:43,705]\u001b[0m Trial 64 finished with value: 1877906183.7743464 and parameters: {'min_child_samples': 5}. Best is trial 63 with value: 1859286747.0773554.\u001b[0m\n", - "min_data_in_leaf, val_score: 1853015384.453637: 60%|###### | 3/5 [00:15<00:10, 5.15s/it]\u001b[32m[I 2021-08-22 21:18:49,514]\u001b[0m Trial 65 finished with value: 1996406986.7947733 and parameters: {'min_child_samples': 100}. Best is trial 63 with value: 1859286747.0773554.\u001b[0m\n", - "min_data_in_leaf, val_score: 1853015384.453637: 80%|######## | 4/5 [00:22<00:05, 5.82s/it]\u001b[32m[I 2021-08-22 21:18:56,904]\u001b[0m Trial 66 finished with value: 1983678395.4383106 and parameters: {'min_child_samples': 50}. Best is trial 63 with value: 1859286747.0773554.\u001b[0m\n", - "min_data_in_leaf, val_score: 1853015384.453637: 100%|##########| 5/5 [00:28<00:00, 5.92s/it]\u001b[32m[I 2021-08-22 21:19:03,042]\u001b[0m Trial 67 finished with value: 1906448776.6538603 and parameters: {'min_child_samples': 25}. Best is trial 63 with value: 1859286747.0773554.\u001b[0m\n", - "min_data_in_leaf, val_score: 1853015384.453637: 100%|##########| 5/5 [00:28<00:00, 5.79s/it]" + "\u001b[32m[I 2021-09-29 23:14:13,542]\u001b[0m A new study created in memory with name: no-name-c5c149a5-8d21-451d-8907-5246d780db77\u001b[0m\n", + "feature_fraction, val_score: 2237193094.198954: 14%|#4 | 1/7 [00:01<00:09, 1.51s/it]\u001b[32m[I 2021-09-29 23:14:15,079]\u001b[0m Trial 0 finished with value: 2237193094.1989536 and parameters: {'feature_fraction': 0.4}. Best is trial 0 with value: 2237193094.1989536.\u001b[0m\n", + "feature_fraction, val_score: 1961388150.442425: 29%|##8 | 2/7 [00:03<00:07, 1.56s/it]\u001b[32m[I 2021-09-29 23:14:16,753]\u001b[0m Trial 1 finished with value: 1961388150.442425 and parameters: {'feature_fraction': 1.0}. Best is trial 1 with value: 1961388150.442425.\u001b[0m\n", + "feature_fraction, val_score: 1961388150.442425: 43%|####2 | 3/7 [00:04<00:06, 1.51s/it]\u001b[32m[I 2021-09-29 23:14:18,158]\u001b[0m Trial 2 finished with value: 1988198059.953293 and parameters: {'feature_fraction': 0.5}. Best is trial 1 with value: 1961388150.442425.\u001b[0m\n", + "feature_fraction, val_score: 1959693958.979498: 57%|#####7 | 4/7 [00:06<00:04, 1.52s/it]\u001b[32m[I 2021-09-29 23:14:19,698]\u001b[0m Trial 3 finished with value: 1959693958.979498 and parameters: {'feature_fraction': 0.8}. Best is trial 3 with value: 1959693958.979498.\u001b[0m\n", + "feature_fraction, val_score: 1923826918.442117: 71%|#######1 | 5/7 [00:07<00:03, 1.55s/it]\u001b[32m[I 2021-09-29 23:14:21,298]\u001b[0m Trial 4 finished with value: 1923826918.4421172 and parameters: {'feature_fraction': 0.8999999999999999}. Best is trial 4 with value: 1923826918.4421172.\u001b[0m\n", + "feature_fraction, val_score: 1923826918.442117: 86%|########5 | 6/7 [00:09<00:01, 1.54s/it]\u001b[32m[I 2021-09-29 23:14:22,813]\u001b[0m Trial 5 finished with value: 1959693958.979498 and parameters: {'feature_fraction': 0.7}. Best is trial 4 with value: 1923826918.4421172.\u001b[0m\n", + "feature_fraction, val_score: 1923826918.442117: 100%|##########| 7/7 [00:10<00:00, 1.50s/it]\u001b[32m[I 2021-09-29 23:14:24,238]\u001b[0m Trial 6 finished with value: 1935542284.5841475 and parameters: {'feature_fraction': 0.6}. Best is trial 4 with value: 1923826918.4421172.\u001b[0m\n", + "feature_fraction, val_score: 1923826918.442117: 100%|##########| 7/7 [00:10<00:00, 1.53s/it]\n", + "num_leaves, val_score: 1894793944.507313: 5%|5 | 1/20 [00:02<00:47, 2.51s/it]\u001b[32m[I 2021-09-29 23:14:26,757]\u001b[0m Trial 7 finished with value: 1894793944.5073128 and parameters: {'num_leaves': 63}. Best is trial 7 with value: 1894793944.5073128.\u001b[0m\n", + "num_leaves, val_score: 1894793944.507313: 10%|# | 2/20 [00:10<01:17, 4.28s/it]\u001b[32m[I 2021-09-29 23:14:35,165]\u001b[0m Trial 8 finished with value: 1927707783.0138607 and parameters: {'num_leaves': 231}. Best is trial 7 with value: 1894793944.5073128.\u001b[0m\n", + "num_leaves, val_score: 1894793944.507313: 15%|#5 | 3/20 [00:13<01:06, 3.90s/it]\u001b[32m[I 2021-09-29 23:14:38,165]\u001b[0m Trial 9 finished with value: 1898863555.537152 and parameters: {'num_leaves': 80}. Best is trial 7 with value: 1894793944.5073128.\u001b[0m\n", + "num_leaves, val_score: 1894793944.507313: 20%|## | 4/20 [00:20<01:13, 4.60s/it]\u001b[32m[I 2021-09-29 23:14:44,401]\u001b[0m Trial 10 finished with value: 1895572554.0884657 and parameters: {'num_leaves': 170}. Best is trial 7 with value: 1894793944.5073128.\u001b[0m\n", + "num_leaves, val_score: 1894793944.507313: 25%|##5 | 5/20 [00:27<01:23, 5.55s/it]\u001b[32m[I 2021-09-29 23:14:52,188]\u001b[0m Trial 11 finished with value: 1932937258.238013 and parameters: {'num_leaves': 221}. Best is trial 7 with value: 1894793944.5073128.\u001b[0m\n", + "num_leaves, val_score: 1881619881.755456: 30%|### | 6/20 [00:32<01:12, 5.19s/it]\u001b[32m[I 2021-09-29 23:14:56,509]\u001b[0m Trial 12 finished with value: 1881619881.7554562 and parameters: {'num_leaves': 116}. Best is trial 12 with value: 1881619881.7554562.\u001b[0m\n", + "num_leaves, val_score: 1881619881.755456: 35%|###5 | 7/20 [00:37<01:09, 5.33s/it]\u001b[32m[I 2021-09-29 23:15:02,188]\u001b[0m Trial 13 finished with value: 1896214256.4755397 and parameters: {'num_leaves': 149}. Best is trial 12 with value: 1881619881.7554562.\u001b[0m\n", + "num_leaves, val_score: 1881619881.755456: 40%|#### | 8/20 [00:46<01:16, 6.39s/it]\u001b[32m[I 2021-09-29 23:15:11,047]\u001b[0m Trial 14 finished with value: 1932801730.248241 and parameters: {'num_leaves': 249}. Best is trial 12 with value: 1881619881.7554562.\u001b[0m\n", + "num_leaves, val_score: 1874343820.223125: 45%|####5 | 9/20 [00:50<01:00, 5.49s/it]\u001b[32m[I 2021-09-29 23:15:14,420]\u001b[0m Trial 15 finished with value: 1874343820.2231247 and parameters: {'num_leaves': 90}. Best is trial 15 with value: 1874343820.2231247.\u001b[0m\n", + "num_leaves, val_score: 1874343820.223125: 50%|##### | 10/20 [00:51<00:43, 4.33s/it]\u001b[32m[I 2021-09-29 23:15:16,039]\u001b[0m Trial 16 finished with value: 1953979611.4522753 and parameters: {'num_leaves': 33}. Best is trial 15 with value: 1874343820.2231247.\u001b[0m\n", + "num_leaves, val_score: 1874343820.223125: 55%|#####5 | 11/20 [00:52<00:29, 3.24s/it]\u001b[32m[I 2021-09-29 23:15:16,762]\u001b[0m Trial 17 finished with value: 2096126435.5363083 and parameters: {'num_leaves': 6}. Best is trial 15 with value: 1874343820.2231247.\u001b[0m\n", + "num_leaves, val_score: 1874343820.223125: 60%|###### | 12/20 [00:56<00:28, 3.60s/it]\u001b[32m[I 2021-09-29 23:15:21,192]\u001b[0m Trial 18 finished with value: 1906288467.0368485 and parameters: {'num_leaves': 104}. Best is trial 15 with value: 1874343820.2231247.\u001b[0m\n", + "num_leaves, val_score: 1868351924.901373: 65%|######5 | 13/20 [01:02<00:28, 4.06s/it]\u001b[32m[I 2021-09-29 23:15:26,319]\u001b[0m Trial 19 finished with value: 1868351924.901373 and parameters: {'num_leaves': 123}. Best is trial 19 with value: 1868351924.901373.\u001b[0m\n", + "num_leaves, val_score: 1868351924.901373: 70%|####### | 14/20 [01:10<00:31, 5.25s/it]\u001b[32m[I 2021-09-29 23:15:34,344]\u001b[0m Trial 20 finished with value: 1914548906.8882365 and parameters: {'num_leaves': 182}. Best is trial 19 with value: 1868351924.901373.\u001b[0m\n", + "num_leaves, val_score: 1868351924.901373: 75%|#######5 | 15/20 [01:13<00:23, 4.62s/it]\u001b[32m[I 2021-09-29 23:15:37,498]\u001b[0m Trial 21 finished with value: 1893247951.8046007 and parameters: {'num_leaves': 66}. Best is trial 19 with value: 1868351924.901373.\u001b[0m\n", + "num_leaves, val_score: 1868351924.901373: 80%|######## | 16/20 [01:19<00:20, 5.14s/it]\u001b[32m[I 2021-09-29 23:15:43,859]\u001b[0m Trial 22 finished with value: 1869033871.381424 and parameters: {'num_leaves': 137}. Best is trial 19 with value: 1868351924.901373.\u001b[0m\n", + "num_leaves, val_score: 1868351924.901373: 85%|########5 | 17/20 [01:26<00:16, 5.59s/it]\u001b[32m[I 2021-09-29 23:15:50,493]\u001b[0m Trial 23 finished with value: 1906428309.75546 and parameters: {'num_leaves': 139}. Best is trial 19 with value: 1868351924.901373.\u001b[0m\n", + "num_leaves, val_score: 1868351924.901373: 90%|######### | 18/20 [01:33<00:12, 6.08s/it]\u001b[32m[I 2021-09-29 23:15:57,732]\u001b[0m Trial 24 finished with value: 1908820539.6759791 and parameters: {'num_leaves': 190}. Best is trial 19 with value: 1868351924.901373.\u001b[0m\n", + "num_leaves, val_score: 1863498501.613274: 95%|#########5| 19/20 [01:38<00:05, 5.89s/it]\u001b[32m[I 2021-09-29 23:16:03,159]\u001b[0m Trial 25 finished with value: 1863498501.6132743 and parameters: {'num_leaves': 132}. Best is trial 25 with value: 1863498501.6132743.\u001b[0m\n", + "num_leaves, val_score: 1863498501.613274: 100%|##########| 20/20 [01:45<00:00, 5.98s/it]\u001b[32m[I 2021-09-29 23:16:09,353]\u001b[0m Trial 26 finished with value: 1931430333.393386 and parameters: {'num_leaves': 163}. Best is trial 25 with value: 1863498501.6132743.\u001b[0m\n", + "num_leaves, val_score: 1863498501.613274: 100%|##########| 20/20 [01:45<00:00, 5.26s/it]\n", + "bagging, val_score: 1863498501.613274: 10%|# | 1/10 [00:06<00:54, 6.06s/it]\u001b[32m[I 2021-09-29 23:16:15,418]\u001b[0m Trial 27 finished with value: 1972994781.1627002 and parameters: {'bagging_fraction': 0.8189909368436051, 'bagging_freq': 5}. Best is trial 27 with value: 1972994781.1627002.\u001b[0m\n", + "bagging, val_score: 1863498501.613274: 20%|## | 2/10 [00:11<00:48, 6.01s/it]\u001b[32m[I 2021-09-29 23:16:21,308]\u001b[0m Trial 28 finished with value: 2049496930.4993417 and parameters: {'bagging_fraction': 0.7873861289934212, 'bagging_freq': 3}. Best is trial 27 with value: 1972994781.1627002.\u001b[0m\n", + "bagging, val_score: 1863498501.613274: 30%|### | 3/10 [00:18<00:42, 6.12s/it]\u001b[32m[I 2021-09-29 23:16:27,693]\u001b[0m Trial 29 finished with value: 2139768933.1393518 and parameters: {'bagging_fraction': 0.4388276153024021, 'bagging_freq': 2}. Best is trial 27 with value: 1972994781.1627002.\u001b[0m\n", + "bagging, val_score: 1863498501.613274: 40%|#### | 4/10 [00:24<00:37, 6.28s/it]\u001b[32m[I 2021-09-29 23:16:34,335]\u001b[0m Trial 30 finished with value: 2158142780.1248493 and parameters: {'bagging_fraction': 0.4239288962558818, 'bagging_freq': 2}. Best is trial 27 with value: 1972994781.1627002.\u001b[0m\n", + "bagging, val_score: 1863498501.613274: 50%|##### | 5/10 [00:31<00:31, 6.22s/it]\u001b[32m[I 2021-09-29 23:16:40,435]\u001b[0m Trial 31 finished with value: 2010657702.029828 and parameters: {'bagging_fraction': 0.715314699361536, 'bagging_freq': 6}. Best is trial 27 with value: 1972994781.1627002.\u001b[0m\n", + "bagging, val_score: 1863498501.613274: 60%|###### | 6/10 [00:36<00:24, 6.06s/it]\u001b[32m[I 2021-09-29 23:16:46,125]\u001b[0m Trial 32 finished with value: 1964933494.828049 and parameters: {'bagging_fraction': 0.7738253951287195, 'bagging_freq': 3}. Best is trial 32 with value: 1964933494.828049.\u001b[0m\n", + "bagging, val_score: 1863498501.613274: 70%|####### | 7/10 [00:42<00:18, 6.01s/it]\u001b[32m[I 2021-09-29 23:16:52,023]\u001b[0m Trial 33 finished with value: 2026997372.947912 and parameters: {'bagging_fraction': 0.745596764852547, 'bagging_freq': 5}. Best is trial 32 with value: 1964933494.828049.\u001b[0m\n", + "bagging, val_score: 1863498501.613274: 80%|######## | 8/10 [00:47<00:11, 5.65s/it]\u001b[32m[I 2021-09-29 23:16:56,834]\u001b[0m Trial 34 finished with value: 1906233781.152753 and parameters: {'bagging_fraction': 0.9945003931775, 'bagging_freq': 1}. Best is trial 34 with value: 1906233781.152753.\u001b[0m\n", + "bagging, val_score: 1863498501.613274: 90%|######### | 9/10 [00:53<00:05, 5.85s/it]\u001b[32m[I 2021-09-29 23:17:03,142]\u001b[0m Trial 35 finished with value: 1977288663.8965576 and parameters: {'bagging_fraction': 0.7683884472431496, 'bagging_freq': 5}. Best is trial 34 with value: 1906233781.152753.\u001b[0m\n", + "bagging, val_score: 1863498501.613274: 100%|##########| 10/10 [00:59<00:00, 5.92s/it]\u001b[32m[I 2021-09-29 23:17:09,223]\u001b[0m Trial 36 finished with value: 2070275360.3552403 and parameters: {'bagging_fraction': 0.6452285190521252, 'bagging_freq': 2}. Best is trial 34 with value: 1906233781.152753.\u001b[0m\n", + "bagging, val_score: 1863498501.613274: 100%|##########| 10/10 [00:59<00:00, 5.99s/it]\n", + "feature_fraction_stage2, val_score: 1863498501.613274: 17%|#6 | 1/6 [00:04<00:22, 4.59s/it]\u001b[32m[I 2021-09-29 23:17:13,824]\u001b[0m Trial 37 finished with value: 1863498501.6132743 and parameters: {'feature_fraction': 0.852}. Best is trial 37 with value: 1863498501.6132743.\u001b[0m\n", + "feature_fraction_stage2, val_score: 1863498501.613274: 33%|###3 | 2/6 [00:09<00:18, 4.72s/it]\u001b[32m[I 2021-09-29 23:17:18,825]\u001b[0m Trial 38 finished with value: 1863498501.6132743 and parameters: {'feature_fraction': 0.8839999999999999}. Best is trial 37 with value: 1863498501.6132743.\u001b[0m\n", + "feature_fraction_stage2, val_score: 1863498501.613274: 50%|##### | 3/6 [00:14<00:14, 4.69s/it]\u001b[32m[I 2021-09-29 23:17:23,441]\u001b[0m Trial 39 finished with value: 1863498501.6132743 and parameters: {'feature_fraction': 0.9159999999999999}. Best is trial 37 with value: 1863498501.6132743.\u001b[0m\n", + "feature_fraction_stage2, val_score: 1863498501.613274: 67%|######6 | 4/6 [00:18<00:09, 4.69s/it]\u001b[32m[I 2021-09-29 23:17:28,139]\u001b[0m Trial 40 finished with value: 1950431559.883238 and parameters: {'feature_fraction': 0.948}. Best is trial 37 with value: 1863498501.6132743.\u001b[0m\n", + "feature_fraction_stage2, val_score: 1863498501.613274: 83%|########3 | 5/6 [00:23<00:04, 4.74s/it]\u001b[32m[I 2021-09-29 23:17:33,002]\u001b[0m Trial 41 finished with value: 1863498501.6132743 and parameters: {'feature_fraction': 0.82}. Best is trial 37 with value: 1863498501.6132743.\u001b[0m\n", + "feature_fraction_stage2, val_score: 1863498501.613274: 100%|##########| 6/6 [00:28<00:00, 4.73s/it]\u001b[32m[I 2021-09-29 23:17:37,694]\u001b[0m Trial 42 finished with value: 1950431559.883238 and parameters: {'feature_fraction': 0.9799999999999999}. Best is trial 37 with value: 1863498501.6132743.\u001b[0m\n", + "feature_fraction_stage2, val_score: 1863498501.613274: 100%|##########| 6/6 [00:28<00:00, 4.74s/it]\n", + "regularization_factors, val_score: 1863498501.613274: 5%|5 | 1/20 [00:04<01:30, 4.76s/it]\u001b[32m[I 2021-09-29 23:17:42,464]\u001b[0m Trial 43 finished with value: 1900519550.3985364 and parameters: {'lambda_l1': 1.724493294436389e-07, 'lambda_l2': 0.0011802180675328437}. Best is trial 43 with value: 1900519550.3985364.\u001b[0m\n", + "regularization_factors, val_score: 1863498501.613274: 10%|# | 2/20 [00:09<01:25, 4.76s/it]\u001b[32m[I 2021-09-29 23:17:47,207]\u001b[0m Trial 44 finished with value: 1897124810.0132961 and parameters: {'lambda_l1': 0.015497205121979793, 'lambda_l2': 0.00015637359721725182}. Best is trial 44 with value: 1897124810.0132961.\u001b[0m\n", + "regularization_factors, val_score: 1863498501.613274: 15%|#5 | 3/20 [00:14<01:21, 4.77s/it]\u001b[32m[I 2021-09-29 23:17:51,995]\u001b[0m Trial 45 finished with value: 1891252495.3075037 and parameters: {'lambda_l1': 9.16513906590219e-07, 'lambda_l2': 0.026000627128814197}. Best is trial 45 with value: 1891252495.3075037.\u001b[0m\n", + "regularization_factors, val_score: 1863498500.800182: 20%|## | 4/20 [00:19<01:16, 4.77s/it]\u001b[32m[I 2021-09-29 23:17:56,768]\u001b[0m Trial 46 finished with value: 1863498500.8001816 and parameters: {'lambda_l1': 0.0011712453303529063, 'lambda_l2': 6.256511928312648e-08}. Best is trial 46 with value: 1863498500.8001816.\u001b[0m\n", + "regularization_factors, val_score: 1863498470.597524: 25%|##5 | 5/20 [00:24<01:12, 4.84s/it]\u001b[32m[I 2021-09-29 23:18:01,762]\u001b[0m Trial 47 finished with value: 1863498470.5975237 and parameters: {'lambda_l1': 0.053923251257381184, 'lambda_l2': 6.016404526705668e-08}. Best is trial 47 with value: 1863498470.5975237.\u001b[0m\n", + "regularization_factors, val_score: 1863498470.597524: 30%|### | 6/20 [00:28<01:07, 4.82s/it]\u001b[32m[I 2021-09-29 23:18:06,535]\u001b[0m Trial 48 finished with value: 1863498500.6828027 and parameters: {'lambda_l1': 6.008912045555043e-05, 'lambda_l2': 3.5014865430650066e-07}. Best is trial 47 with value: 1863498470.5975237.\u001b[0m\n", + "regularization_factors, val_score: 1863498144.156982: 35%|###5 | 7/20 [00:34<01:04, 4.96s/it]\u001b[32m[I 2021-09-29 23:18:11,824]\u001b[0m Trial 49 finished with value: 1863498144.1569824 and parameters: {'lambda_l1': 0.2873876088430125, 'lambda_l2': 6.275345656705353e-05}. Best is trial 49 with value: 1863498144.1569824.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 40%|#### | 8/20 [00:39<00:59, 4.96s/it]\u001b[32m[I 2021-09-29 23:18:16,784]\u001b[0m Trial 50 finished with value: 1829411923.1271203 and parameters: {'lambda_l1': 0.014530100017980814, 'lambda_l2': 0.013764223496447852}. Best is trial 50 with value: 1829411923.1271203.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 45%|####5 | 9/20 [00:44<00:54, 4.95s/it]\u001b[32m[I 2021-09-29 23:18:21,714]\u001b[0m Trial 51 finished with value: 1908686079.6273117 and parameters: {'lambda_l1': 4.049940611761022e-08, 'lambda_l2': 0.00040034467394794076}. Best is trial 50 with value: 1829411923.1271203.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 50%|##### | 10/20 [00:49<00:49, 4.99s/it]\u001b[32m[I 2021-09-29 23:18:26,790]\u001b[0m Trial 52 finished with value: 1885012218.0004046 and parameters: {'lambda_l1': 0.0002200189403526506, 'lambda_l2': 0.20302349889094043}. Best is trial 50 with value: 1829411923.1271203.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 55%|#####5 | 11/20 [00:54<00:45, 5.03s/it]\u001b[32m[I 2021-09-29 23:18:31,926]\u001b[0m Trial 53 finished with value: 1906625061.3932586 and parameters: {'lambda_l1': 5.510584147431707, 'lambda_l2': 1.5865265278948395}. Best is trial 50 with value: 1829411923.1271203.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 60%|###### | 12/20 [00:59<00:41, 5.13s/it]\u001b[32m[I 2021-09-29 23:18:37,269]\u001b[0m Trial 54 finished with value: 1913356940.3398488 and parameters: {'lambda_l1': 7.919177639141472, 'lambda_l2': 7.0464807271647355e-06}. Best is trial 50 with value: 1829411923.1271203.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 65%|######5 | 13/20 [01:04<00:36, 5.20s/it]\u001b[32m[I 2021-09-29 23:18:42,634]\u001b[0m Trial 55 finished with value: 1943493473.7805178 and parameters: {'lambda_l1': 0.2709128839307005, 'lambda_l2': 0.008152038140390653}. Best is trial 50 with value: 1829411923.1271203.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 70%|####### | 14/20 [01:10<00:31, 5.18s/it]\u001b[32m[I 2021-09-29 23:18:47,759]\u001b[0m Trial 56 finished with value: 1869993775.185964 and parameters: {'lambda_l1': 0.6252702501891693, 'lambda_l2': 1.1976465197068111e-05}. Best is trial 50 with value: 1829411923.1271203.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 75%|#######5 | 15/20 [01:14<00:25, 5.04s/it]\u001b[32m[I 2021-09-29 23:18:52,482]\u001b[0m Trial 57 finished with value: 1863498474.3189669 and parameters: {'lambda_l1': 0.0031261290560461548, 'lambda_l2': 8.158381653124474e-06}. Best is trial 50 with value: 1829411923.1271203.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 80%|######## | 16/20 [01:19<00:19, 4.95s/it]\u001b[32m[I 2021-09-29 23:18:57,236]\u001b[0m Trial 58 finished with value: 1906922013.1929636 and parameters: {'lambda_l1': 5.0437093419934566e-06, 'lambda_l2': 8.510701285476182}. Best is trial 50 with value: 1829411923.1271203.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 85%|########5 | 17/20 [01:25<00:15, 5.17s/it]\u001b[32m[I 2021-09-29 23:19:02,913]\u001b[0m Trial 59 finished with value: 1941548787.4363914 and parameters: {'lambda_l1': 0.489782747632979, 'lambda_l2': 0.010063061940333582}. Best is trial 50 with value: 1829411923.1271203.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 90%|######### | 18/20 [01:30<00:10, 5.17s/it]\u001b[32m[I 2021-09-29 23:19:08,065]\u001b[0m Trial 60 finished with value: 1922251238.1017878 and parameters: {'lambda_l1': 0.014630309366687438, 'lambda_l2': 0.20905649983873378}. Best is trial 50 with value: 1829411923.1271203.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 95%|#########5| 19/20 [01:35<00:05, 5.09s/it]\u001b[32m[I 2021-09-29 23:19:12,966]\u001b[0m Trial 61 finished with value: 1907187734.8434992 and parameters: {'lambda_l1': 2.908849929024249, 'lambda_l2': 6.175780052432868e-05}. Best is trial 50 with value: 1829411923.1271203.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 100%|##########| 20/20 [01:40<00:00, 5.08s/it]\u001b[32m[I 2021-09-29 23:19:18,016]\u001b[0m Trial 62 finished with value: 1887887228.6365638 and parameters: {'lambda_l1': 0.08176402124056348, 'lambda_l2': 0.0022386651346889396}. Best is trial 50 with value: 1829411923.1271203.\u001b[0m\n", + "regularization_factors, val_score: 1829411923.127120: 100%|##########| 20/20 [01:40<00:00, 5.02s/it]\n", + "min_data_in_leaf, val_score: 1829411923.127120: 20%|## | 1/5 [00:05<00:21, 5.27s/it]\u001b[32m[I 2021-09-29 23:19:23,298]\u001b[0m Trial 63 finished with value: 1887743370.2363958 and parameters: {'min_child_samples': 25}. Best is trial 63 with value: 1887743370.2363958.\u001b[0m\n", + "min_data_in_leaf, val_score: 1829411923.127120: 40%|#### | 2/5 [00:11<00:16, 5.60s/it]\u001b[32m[I 2021-09-29 23:19:29,669]\u001b[0m Trial 64 finished with value: 1988738364.2495584 and parameters: {'min_child_samples': 100}. Best is trial 63 with value: 1887743370.2363958.\u001b[0m\n", + "min_data_in_leaf, val_score: 1829411923.127120: 60%|###### | 3/5 [00:16<00:10, 5.32s/it]\u001b[32m[I 2021-09-29 23:19:34,327]\u001b[0m Trial 65 finished with value: 1872759368.4778924 and parameters: {'min_child_samples': 10}. Best is trial 65 with value: 1872759368.4778924.\u001b[0m\n", + "min_data_in_leaf, val_score: 1829411923.127120: 80%|######## | 4/5 [00:20<00:04, 4.98s/it]\u001b[32m[I 2021-09-29 23:19:38,517]\u001b[0m Trial 66 finished with value: 1866723989.0731173 and parameters: {'min_child_samples': 5}. Best is trial 66 with value: 1866723989.0731173.\u001b[0m\n", + "min_data_in_leaf, val_score: 1829411923.127120: 100%|##########| 5/5 [00:27<00:00, 5.62s/it]\u001b[32m[I 2021-09-29 23:19:45,625]\u001b[0m Trial 67 finished with value: 1965123941.88074 and parameters: {'min_child_samples': 50}. Best is trial 66 with value: 1866723989.0731173.\u001b[0m\n", + "min_data_in_leaf, val_score: 1829411923.127120: 100%|##########| 5/5 [00:27<00:00, 5.52s/it]" ] }, { "output_type": "stream", "name": "stdout", "text": [ - "CPU times: user 5min 24s, sys: 17.3 s, total: 5min 41s\n", - "Wall time: 5min 42s\n" + "CPU times: user 5min 14s, sys: 16.8 s, total: 5min 31s\n", + "Wall time: 5min 32s\n" ] }, { @@ -759,7 +719,7 @@ "output_type": "stream", "name": "stdout", "text": [ - "Optuna LightGBM Tuner r2 = 0.8428464421292586\n" + "Optuna LightGBM Tuner r2 = 0.8444445782478855\n" ] } ], @@ -786,7 +746,6 @@ "cell_type": "code", "execution_count": 21, "source": [ - "\n", "import numpy as np \n", "\n", "''' define your customized objective function '''\n", @@ -817,8 +776,8 @@ " '''LGBMEstimator with my_loss_obj as the objective function\n", " '''\n", "\n", - " def __init__(self, **params):\n", - " super().__init__(objective=my_loss_obj, **params)" + " def __init__(self, **config):\n", + " super().__init__(objective=my_loss_obj, **config)" ], "outputs": [], "metadata": {} @@ -850,106 +809,99 @@ "output_type": "stream", "name": "stderr", "text": [ - "[flaml.automl: 08-22 21:19:04] {1130} INFO - Evaluation method: cv\n", - "[flaml.automl: 08-22 21:19:04] {634} INFO - Using RepeatedKFold\n", - "[flaml.automl: 08-22 21:19:04] {1155} INFO - Minimizing error metric: 1-r2\n", - "[flaml.automl: 08-22 21:19:04] {1175} INFO - List of ML learners in AutoML Run: ['my_lgbm']\n", - "[flaml.automl: 08-22 21:19:04] {1358} INFO - iteration 0, current learner my_lgbm\n", - "[flaml.automl: 08-22 21:19:04] {1515} INFO - at 0.2s,\tbest my_lgbm's error=2.9888,\tbest my_lgbm's error=2.9888\n", - "[flaml.automl: 08-22 21:19:04] {1358} INFO - iteration 1, current learner my_lgbm\n", - "[flaml.automl: 08-22 21:19:04] {1515} INFO - at 0.3s,\tbest my_lgbm's error=2.9888,\tbest my_lgbm's error=2.9888\n", - "[flaml.automl: 08-22 21:19:04] {1358} INFO - iteration 2, current learner my_lgbm\n", - "[flaml.automl: 08-22 21:19:04] {1515} INFO - at 0.5s,\tbest my_lgbm's error=1.7087,\tbest my_lgbm's error=1.7087\n", - "[flaml.automl: 08-22 21:19:04] {1358} INFO - iteration 3, current learner my_lgbm\n", - 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selected model: LGBMRegressor(colsample_bytree=0.8422311526890249,\n", + " learning_rate=0.4130805075333343, max_bin=1023,\n", + " min_child_samples=10, n_estimators=95, num_leaves=221,\n", + " objective=,\n", + " reg_alpha=0.007704104902643932, reg_lambda=0.003151767359549649,\n", " verbose=-1)\n", - "[flaml.automl: 08-22 21:21:39] {1633} INFO - retrain my_lgbm for 1.6s\n", - "[flaml.automl: 08-22 21:21:39] {1636} INFO - retrained model: LGBMRegressor(colsample_bytree=0.8251774147208681,\n", - " learning_rate=0.21049408131691624, max_bin=512,\n", - " min_child_samples=19, n_estimators=196, num_leaves=195,\n", - " objective=,\n", - " reg_alpha=0.0009765625, reg_lambda=0.0117923889609937,\n", + "[flaml.automl: 09-29 23:22:20] {2122} INFO - retrain my_lgbm for 1.6s\n", + "[flaml.automl: 09-29 23:22:20] {2128} INFO - retrained model: LGBMRegressor(colsample_bytree=0.8422311526890249,\n", + " learning_rate=0.4130805075333343, max_bin=1023,\n", + " min_child_samples=10, n_estimators=95, num_leaves=221,\n", + " objective=,\n", + " reg_alpha=0.007704104902643932, reg_lambda=0.003151767359549649,\n", " verbose=-1)\n", - "[flaml.automl: 08-22 21:21:39] {1199} INFO - fit succeeded\n", - "[flaml.automl: 08-22 21:21:39] {1200} INFO - Time taken to find the best model: 111.22549629211426\n", - "[flaml.automl: 08-22 21:21:39] {1205} WARNING - Time taken to find the best model is 74% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n" + "[flaml.automl: 09-29 23:22:20] {1557} INFO - fit succeeded\n", + "[flaml.automl: 09-29 23:22:20] {1558} INFO - Time taken to find the best model: 121.97463893890381\n", + "[flaml.automl: 09-29 23:22:20] {1569} WARNING - Time taken to find the best model is 81% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n" ] } ], @@ -979,11 +931,11 @@ "output_type": "stream", "name": "stdout", "text": [ - "Best hyperparmeter config: {'n_estimators': 196, 'num_leaves': 195, 'min_child_samples': 19, 'learning_rate': 0.21049408131691624, 'log_max_bin': 10, 'colsample_bytree': 0.8251774147208681, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0117923889609937}\n", - "Best r2 on validation data: 0.8403\n", - "Training duration of best run: 8.28 s\n", - "Predicted labels [137336.50894266 249721.8950541 155077.11127769 ... 191822.32898046\n", - " 197332.92376977 286448.29599298]\n", + "Best hyperparmeter config: {'n_estimators': 95, 'num_leaves': 221, 'min_child_samples': 10, 'learning_rate': 0.4130805075333343, 'log_max_bin': 10, 'colsample_bytree': 0.8422311526890249, 'reg_alpha': 0.007704104902643932, 'reg_lambda': 0.003151767359549649}\n", + "Best r2 on validation data: 0.8368\n", + "Training duration of best run: 6.404 s\n", + "Predicted labels [161485.59767093 248585.87889042 157837.93378105 ... 184356.07034452\n", + " 223247.80995858 259281.61167122]\n", "True labels 14740 136900.0\n", "10101 241300.0\n", "20566 200700.0\n", @@ -996,9 +948,9 @@ "8522 227300.0\n", "16798 265600.0\n", "Name: median_house_value, Length: 5160, dtype: float64\n", - "r2 = 0.8498843855121221\n", - "mse = 1984304232.0760334\n", - "mae = 29465.919207148785\n" + "r2 = 0.8429833151406843\n", + "mse = 2075526075.9236276\n", + "mae = 30102.910560642205\n" ] } ], @@ -1009,7 +961,7 @@ ], "metadata": { "interpreter": { - "hash": "ea9f131eb1b7663628f6445553ba215a834e2f0b4d18774746f0f47938ce4671" + "hash": "0cfea3304185a9579d09e0953576b57c8581e46e6ebc6dfeb681bc5a511f7544" }, "kernelspec": { "name": "python3", diff --git a/notebook/flaml_xgboost.ipynb b/notebook/flaml_xgboost.ipynb index 7257bea0a..0cfd0f6e8 100644 --- a/notebook/flaml_xgboost.ipynb +++ b/notebook/flaml_xgboost.ipynb @@ -3,7 +3,7 @@ { "cell_type": "markdown", "source": [ - "Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved. \n", + "Copyright (c) Microsoft Corporation. All rights reserved. \n", "\n", "Licensed under the MIT License.\n", "\n", @@ -140,70 +140,71 @@ "output_type": "stream", "name": "stderr", "text": [ - "[flaml.automl: 08-22 21:23:40] {1130} INFO - Evaluation method: cv\n", - "[flaml.automl: 08-22 21:23:40] {634} INFO - Using RepeatedKFold\n", - "[flaml.automl: 08-22 21:23:40] {1155} INFO - Minimizing error metric: 1-r2\n", - "[flaml.automl: 08-22 21:23:40] {1175} INFO - List of ML learners in AutoML Run: ['xgboost']\n", - "[flaml.automl: 08-22 21:23:40] {1358} INFO - iteration 0, current learner xgboost\n", - "[flaml.automl: 08-22 21:23:41] {1515} INFO - at 0.4s,\tbest xgboost's error=2.1267,\tbest xgboost's error=2.1267\n", - "[flaml.automl: 08-22 21:23:41] {1358} INFO - iteration 1, current learner xgboost\n", - "[flaml.automl: 08-22 21:23:41] {1515} INFO - at 0.6s,\tbest xgboost's error=2.1267,\tbest xgboost's error=2.1267\n", - "[flaml.automl: 08-22 21:23:41] {1358} INFO - iteration 2, current learner xgboost\n", - "[flaml.automl: 08-22 21:23:41] {1515} INFO - 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"[flaml.automl: 08-22 21:24:39] {1592} INFO - selected model: \n", - "[flaml.automl: 08-22 21:24:43] {1633} INFO - retrain xgboost for 4.4s\n", - "[flaml.automl: 08-22 21:24:43] {1636} INFO - retrained model: \n", - "[flaml.automl: 08-22 21:24:43] {1199} INFO - fit succeeded\n", - "[flaml.automl: 08-22 21:24:43] {1200} INFO - Time taken to find the best model: 58.49340343475342\n", - "[flaml.automl: 08-22 21:24:43] {1205} WARNING - Time taken to find the best model is 97% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n" + "[flaml.automl: 09-29 23:06:46] {1446} INFO - Data split method: uniform\n", + "[flaml.automl: 09-29 23:06:46] {1450} INFO - Evaluation method: cv\n", + "[flaml.automl: 09-29 23:06:46] {1496} INFO - Minimizing error metric: 1-r2\n", + "[flaml.automl: 09-29 23:06:46] {1533} INFO - List of ML learners in AutoML Run: ['xgboost']\n", + "[flaml.automl: 09-29 23:06:46] {1763} INFO - iteration 0, current learner xgboost\n", + "[flaml.automl: 09-29 23:06:47] {1880} INFO - Estimated sufficient time budget=2621s. 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Consider increasing the time budget.\n" ] } ], @@ -240,8 +241,8 @@ "name": "stdout", "text": [ "Best hyperparmeter config: {'n_estimators': 776, 'max_leaves': 160, 'min_child_weight': 32.57408640781376, 'learning_rate': 0.03478685333241491, 'subsample': 0.9152991332236934, 'colsample_bylevel': 0.5656764254642628, 'colsample_bytree': 0.7313266091895249, 'reg_alpha': 0.005771390107656191, 'reg_lambda': 1.4912667278658753}\n", - "Best r2 on validation data: 0.834\n", - "Training duration of best run: 23 s\n" + "Best r2 on validation data: 0.8337\n", + "Training duration of best run: 20.25 s\n" ] } ], @@ -263,7 +264,7 @@ "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -382,8 +383,7 @@ "{'Current Learner': 'xgboost', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 58, 'max_leaves': 8, 'min_child_weight': 51.84874392377363, 'learning_rate': 0.23511987355535005, 'subsample': 1.0, 'colsample_bylevel': 0.8182737361783602, 'colsample_bytree': 0.8031986460435498, 'reg_alpha': 0.00400039941928546, 'reg_lambda': 0.3870252968100477}, 'Best Learner': 'xgboost', 'Best Hyper-parameters': {'n_estimators': 58, 'max_leaves': 8, 'min_child_weight': 51.84874392377363, 'learning_rate': 0.23511987355535005, 'subsample': 1.0, 'colsample_bylevel': 0.8182737361783602, 'colsample_bytree': 0.8031986460435498, 'reg_alpha': 0.00400039941928546, 'reg_lambda': 0.3870252968100477}}\n", "{'Current Learner': 'xgboost', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 101, 'max_leaves': 14, 'min_child_weight': 7.444058088783045, 'learning_rate': 0.39220715578198356, 'subsample': 1.0, 'colsample_bylevel': 0.6274332478496758, 'colsample_bytree': 0.7190251742957809, 'reg_alpha': 0.007212902167942765, 'reg_lambda': 0.20172056689658158}, 'Best Learner': 'xgboost', 'Best Hyper-parameters': {'n_estimators': 101, 'max_leaves': 14, 'min_child_weight': 7.444058088783045, 'learning_rate': 0.39220715578198356, 'subsample': 1.0, 'colsample_bylevel': 0.6274332478496758, 'colsample_bytree': 0.7190251742957809, 'reg_alpha': 0.007212902167942765, 'reg_lambda': 0.20172056689658158}}\n", "{'Current Learner': 'xgboost', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 205, 'max_leaves': 30, 'min_child_weight': 5.450621032615104, 'learning_rate': 0.12229148765139466, 'subsample': 0.8895588746662894, 'colsample_bylevel': 0.47518959001130784, 'colsample_bytree': 0.6845612830806885, 'reg_alpha': 0.01126059820390593, 'reg_lambda': 0.08170816686602438}, 'Best Learner': 'xgboost', 'Best Hyper-parameters': {'n_estimators': 205, 'max_leaves': 30, 'min_child_weight': 5.450621032615104, 'learning_rate': 0.12229148765139466, 'subsample': 0.8895588746662894, 'colsample_bylevel': 0.47518959001130784, 'colsample_bytree': 0.6845612830806885, 'reg_alpha': 0.01126059820390593, 'reg_lambda': 0.08170816686602438}}\n", - "{'Current Learner': 'xgboost', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 222, 'max_leaves': 62, 'min_child_weight': 7.5054716192185795, 'learning_rate': 0.04623175582706431, 'subsample': 0.8756054034199897, 'colsample_bylevel': 0.44768367042684304, 'colsample_bytree': 0.7352307811741962, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.6207832675443758}, 'Best Learner': 'xgboost', 'Best Hyper-parameters': {'n_estimators': 222, 'max_leaves': 62, 'min_child_weight': 7.5054716192185795, 'learning_rate': 0.04623175582706431, 'subsample': 0.8756054034199897, 'colsample_bylevel': 0.44768367042684304, 'colsample_bytree': 0.7352307811741962, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.6207832675443758}}\n", - "{'Current Learner': 'xgboost', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 776, 'max_leaves': 160, 'min_child_weight': 32.57408640781376, 'learning_rate': 0.03478685333241491, 'subsample': 0.9152991332236934, 'colsample_bylevel': 0.5656764254642628, 'colsample_bytree': 0.7313266091895249, 'reg_alpha': 0.005771390107656191, 'reg_lambda': 1.4912667278658753}, 'Best Learner': 'xgboost', 'Best Hyper-parameters': {'n_estimators': 776, 'max_leaves': 160, 'min_child_weight': 32.57408640781376, 'learning_rate': 0.03478685333241491, 'subsample': 0.9152991332236934, 'colsample_bylevel': 0.5656764254642628, 'colsample_bytree': 0.7313266091895249, 'reg_alpha': 0.005771390107656191, 'reg_lambda': 1.4912667278658753}}\n" + "{'Current Learner': 'xgboost', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 222, 'max_leaves': 62, 'min_child_weight': 7.5054716192185795, 'learning_rate': 0.04623175582706431, 'subsample': 0.8756054034199897, 'colsample_bylevel': 0.44768367042684304, 'colsample_bytree': 0.7352307811741962, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.6207832675443758}, 'Best Learner': 'xgboost', 'Best Hyper-parameters': {'n_estimators': 222, 'max_leaves': 62, 'min_child_weight': 7.5054716192185795, 'learning_rate': 0.04623175582706431, 'subsample': 0.8756054034199897, 'colsample_bylevel': 0.44768367042684304, 'colsample_bytree': 0.7352307811741962, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.6207832675443758}}\n" ] } ], @@ -412,11 +412,10 @@ { "output_type": "display_data", "data": { + "image/png": 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" + ] }, "metadata": { "needs_background": "light" @@ -552,16 +551,16 @@ " '''XGBoostEstimator with the logregobj function as the objective function\n", " '''\n", "\n", - " def __init__(self, **params):\n", - " super().__init__(objective=logregobj, **params) \n", + " def __init__(self, **config):\n", + " super().__init__(objective=logregobj, **config) \n", "\n", "\n", "class MyXGB2(XGBoostEstimator):\n", " '''XGBoostEstimator with 'reg:squarederror' as the objective function\n", " '''\n", "\n", - " def __init__(self, **params):\n", - " super().__init__(objective='reg:gamma', **params)\n", + " def __init__(self, **config):\n", + " super().__init__(objective='reg:gamma', **config)\n", "\n", "\n", "from flaml import AutoML\n", @@ -582,244 +581,195 @@ "output_type": "stream", "name": "stderr", "text": [ - "[flaml.automl: 08-22 21:24:46] {1130} INFO - Evaluation method: holdout\n", - "[flaml.automl: 08-22 21:24:46] {634} INFO - Using RepeatedKFold\n", - "[flaml.automl: 08-22 21:24:46] {1155} INFO - Minimizing error metric: 1-r2\n", - "[flaml.automl: 08-22 21:24:46] {1175} INFO - List of ML learners in AutoML Run: ['my_xgb1', 'my_xgb2']\n", - "[flaml.automl: 08-22 21:24:46] {1358} INFO - iteration 0, current learner my_xgb1\n", - "[flaml.automl: 08-22 21:24:46] {1515} INFO - at 0.1s,\tbest my_xgb1's error=53750617.1059,\tbest my_xgb1's error=53750617.1059\n", - "[flaml.automl: 08-22 21:24:46] {1358} INFO - iteration 1, current learner my_xgb1\n", - "[flaml.automl: 08-22 21:24:46] {1515} INFO - at 0.1s,\tbest my_xgb1's error=260718.5183,\tbest my_xgb1's error=260718.5183\n", - "[flaml.automl: 08-22 21:24:46] {1358} INFO - iteration 2, current learner my_xgb2\n", - "[flaml.automl: 08-22 21:24:46] {1515} INFO - at 0.1s,\tbest my_xgb2's error=4.1611,\tbest my_xgb2's error=4.1611\n", - "[flaml.automl: 08-22 21:24:46] {1358} INFO - iteration 3, current learner my_xgb2\n", - "[flaml.automl: 08-22 21:24:46] {1515} INFO - at 0.2s,\tbest my_xgb2's error=4.1611,\tbest my_xgb2's error=4.1611\n", - "[flaml.automl: 08-22 21:24:46] {1358} INFO - iteration 4, current learner my_xgb1\n", - "[flaml.automl: 08-22 21:24:46] {1515} INFO - at 0.2s,\tbest my_xgb1's error=260718.5183,\tbest my_xgb2's error=4.1611\n", - "[flaml.automl: 08-22 21:24:46] {1358} INFO - iteration 5, current learner my_xgb1\n", - "[flaml.automl: 08-22 21:24:46] {1515} INFO - at 0.2s,\tbest my_xgb1's error=260718.5183,\tbest my_xgb2's error=4.1611\n", - "[flaml.automl: 08-22 21:24:46] {1358} INFO - iteration 6, current learner my_xgb1\n", - "[flaml.automl: 08-22 21:24:46] {1515} INFO - at 0.3s,\tbest my_xgb1's error=40726.5769,\tbest my_xgb2's error=4.1611\n", - "[flaml.automl: 08-22 21:24:46] {1358} INFO - iteration 7, current learner my_xgb1\n", - "[flaml.automl: 08-22 21:24:46] {1515} INFO - at 0.3s,\tbest my_xgb1's error=1918.9637,\tbest my_xgb2's error=4.1611\n", - "[flaml.automl: 08-22 21:24:46] {1358} INFO - iteration 8, current learner my_xgb1\n", - "[flaml.automl: 08-22 21:24:46] {1515} 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learner my_xgb2\n", - "[flaml.automl: 08-22 21:24:46] {1515} INFO - at 0.5s,\tbest my_xgb2's error=4.1603,\tbest my_xgb2's error=4.1603\n", - "[flaml.automl: 08-22 21:24:46] {1358} INFO - iteration 14, current learner my_xgb1\n", - "[flaml.automl: 08-22 21:24:46] {1515} INFO - at 0.5s,\tbest my_xgb1's error=1918.9637,\tbest my_xgb2's error=4.1603\n", - "[flaml.automl: 08-22 21:24:46] {1358} INFO - iteration 15, current learner my_xgb2\n", - "[flaml.automl: 08-22 21:24:46] {1515} INFO - at 0.6s,\tbest my_xgb2's error=3.8476,\tbest my_xgb2's error=3.8476\n", - "[flaml.automl: 08-22 21:24:46] {1358} INFO - iteration 16, current learner my_xgb1\n", - "[flaml.automl: 08-22 21:24:46] {1515} INFO - at 0.6s,\tbest my_xgb1's error=93.9115,\tbest my_xgb2's error=3.8476\n", - "[flaml.automl: 08-22 21:24:46] {1358} INFO - iteration 17, current learner my_xgb2\n", - "[flaml.automl: 08-22 21:24:46] {1515} INFO - at 0.7s,\tbest my_xgb2's error=0.3645,\tbest my_xgb2's error=0.3645\n", - 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retrained model: \n", + "[flaml.automl: 09-29 23:08:35] {1557} INFO - fit succeeded\n", + "[flaml.automl: 09-29 23:08:35] {1558} INFO - Time taken to find the best model: 28.05234169960022\n", + "[flaml.automl: 09-29 23:08:35] {1569} WARNING - Time taken to find the best model is 94% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n" ] } ], @@ -851,7 +801,7 @@ "text": [ "Best hyperparmeter config: {'n_estimators': 810, 'max_leaves': 148, 'min_child_weight': 30.65305732414229, 'learning_rate': 0.05793074143079172, 'subsample': 0.9452642648281835, 'colsample_bylevel': 0.8662229421401874, 'colsample_bytree': 0.7851677398738949, 'reg_alpha': 0.00738292823760415, 'reg_lambda': 1.2202619267865558}\n", "Best r2 on validation data: 0.8315\n", - "Training duration of best run: 4.888 s\n", + "Training duration of best run: 5.028 s\n", "Predicted labels [146309.06 253975.23 148795.17 ... 192561.88 182641.44 270495.53]\n", "True labels 14740 136900.0\n", "10101 241300.0\n", diff --git a/setup.py b/setup.py index f17e675e0..4ae501fbe 100644 --- a/setup.py +++ b/setup.py @@ -18,7 +18,7 @@ install_requires = [ "lightgbm>=2.3.1", "xgboost>=0.90,<=1.3.3", "scipy>=1.4.1", - "catboost>=0.23", + # "catboost>=0.23", # making optional for conda "scikit-learn>=0.24", ] @@ -47,6 +47,7 @@ setuptools.setup( "coverage>=5.3", "pre-commit", "xgboost<1.3", + "catboost>=0.23", "rgf-python", "optuna==2.8.0", "vowpalwabbit", @@ -58,6 +59,7 @@ setuptools.setup( "azure-storage-blob", "statsmodels>=0.12.2", ], + "catboost": ["catboost>=0.23"], "blendsearch": ["optuna==2.8.0"], "ray": [ "ray[tune]==1.6.0", diff --git a/test/test_automl.py b/test/test_automl.py index adbbb8f38..ae2814f64 100644 --- a/test/test_automl.py +++ b/test/test_automl.py @@ -2,7 +2,12 @@ import unittest import numpy as np import scipy.sparse -from sklearn.datasets import load_boston, load_iris, load_wine, load_breast_cancer +from sklearn.datasets import ( + fetch_california_housing, + load_iris, + load_wine, + load_breast_cancer, +) import pandas as pd from datetime import datetime @@ -17,59 +22,37 @@ from flaml.training_log import training_log_reader class MyRegularizedGreedyForest(SKLearnEstimator): - def __init__( - self, - task="binary", - n_jobs=1, - max_leaf=4, - n_iter=1, - n_tree_search=1, - opt_interval=1, - learning_rate=1.0, - min_samples_leaf=1, - **params - ): + def __init__(self, task="binary", **config): - super().__init__(task, **params) + super().__init__(task, **config) - if "regression" in task: - self.estimator_class = RGFRegressor - else: + if task in ("binary", "multi"): self.estimator_class = RGFClassifier - - # round integer hyperparameters - self.params = { - "n_jobs": n_jobs, - "max_leaf": int(round(max_leaf)), - "n_iter": int(round(n_iter)), - "n_tree_search": int(round(n_tree_search)), - "opt_interval": int(round(opt_interval)), - "learning_rate": learning_rate, - "min_samples_leaf": int(round(min_samples_leaf)), - } + else: + self.estimator_class = RGFRegressor @classmethod def search_space(cls, data_size, task): space = { "max_leaf": { - "domain": tune.qloguniform(lower=4, upper=data_size, q=1), + "domain": tune.lograndint(lower=4, upper=data_size), "init_value": 4, }, "n_iter": { - "domain": tune.qloguniform(lower=1, upper=data_size, q=1), + "domain": tune.lograndint(lower=1, upper=data_size), "init_value": 1, }, "n_tree_search": { - "domain": tune.qloguniform(lower=1, upper=32768, q=1), + "domain": tune.lograndint(lower=1, upper=32768), "init_value": 1, }, "opt_interval": { - "domain": tune.qloguniform(lower=1, upper=10000, q=1), + "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.qloguniform(lower=1, upper=20, q=1), + "domain": tune.lograndint(lower=1, upper=20), "init_value": 20, }, } @@ -97,15 +80,15 @@ def logregobj(preds, dtrain): class MyXGB1(XGBoostEstimator): """XGBoostEstimator with logregobj as the objective function""" - def __init__(self, **params): - super().__init__(objective=logregobj, **params) + def __init__(self, **config): + super().__init__(objective=logregobj, **config) class MyXGB2(XGBoostEstimator): """XGBoostEstimator with 'reg:squarederror' as the objective function""" - def __init__(self, **params): - super().__init__(objective="reg:squarederror", **params) + def __init__(self, **config): + super().__init__(objective="reg:squarederror", **config) class MyLargeLGBM(LGBMEstimator): @@ -266,7 +249,7 @@ class TestAutoML(unittest.TestCase): "n_splits": 3, "metric": "accuracy", "log_training_metric": True, - "verbose": 1, + "verbose": 4, "ensemble": True, } automl.fit(X, y, **automl_settings) @@ -281,7 +264,7 @@ class TestAutoML(unittest.TestCase): "n_splits": 3, "metric": "accuracy", "log_training_metric": True, - "verbose": 1, + "verbose": 4, "ensemble": True, } automl.fit(X, y, **automl_settings) @@ -296,7 +279,7 @@ class TestAutoML(unittest.TestCase): "n_splits": 3, "metric": "accuracy", "log_training_metric": True, - "verbose": 1, + "verbose": 4, "ensemble": True, } automl.fit(X, y, **automl_settings) @@ -311,7 +294,7 @@ class TestAutoML(unittest.TestCase): "n_splits": 3, "metric": "accuracy", "log_training_metric": True, - "verbose": 1, + "verbose": 4, "ensemble": True, } automl.fit(X, y, **automl_settings) @@ -525,7 +508,7 @@ class TestAutoML(unittest.TestCase): "n_jobs": 1, "model_history": True, } - X_train, y_train = load_boston(return_X_y=True) + X_train, y_train = fetch_california_housing(return_X_y=True) n = int(len(y_train) * 9 // 10) automl_experiment.fit( X_train=X_train[:n], @@ -648,7 +631,7 @@ class TestAutoML(unittest.TestCase): "n_concurrent_trials": 2, "hpo_method": hpo_method, } - X_train, y_train = load_boston(return_X_y=True) + X_train, y_train = fetch_california_housing(return_X_y=True) try: automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings) print(automl_experiment.predict(X_train)) @@ -861,8 +844,8 @@ class TestAutoML(unittest.TestCase): automl_experiment = AutoML() automl_settings = { "time_budget": 3, - "metric": 'accuracy', - "task": 'classification', + "metric": "accuracy", + "task": "classification", "log_file_name": "test/iris.log", "log_training_metric": True, "n_jobs": 1, @@ -873,16 +856,19 @@ class TestAutoML(unittest.TestCase): # test drop column X_train.columns = range(X_train.shape[1]) X_train[X_train.shape[1]] = np.zeros(len(y_train)) - automl_experiment.fit(X_train=X_train, y_train=y_train, - **automl_settings) + automl_experiment.fit(X_train=X_train, y_train=y_train, **automl_settings) automl_val_accuracy = 1.0 - automl_experiment.best_loss - print('Best ML leaner:', automl_experiment.best_estimator) - print('Best hyperparmeter config:', automl_experiment.best_config) - print('Best accuracy on validation data: {0:.4g}'.format(automl_val_accuracy)) - print('Training duration of best run: {0:.4g} s'.format(automl_experiment.best_config_train_time)) + print("Best ML leaner:", automl_experiment.best_estimator) + print("Best hyperparmeter config:", automl_experiment.best_config) + print("Best accuracy on validation data: {0:.4g}".format(automl_val_accuracy)) + print( + "Training duration of best run: {0:.4g} s".format( + automl_experiment.best_config_train_time + ) + ) starting_points = {} - log_file_name = automl_settings['log_file_name'] + log_file_name = automl_settings["log_file_name"] with training_log_reader(log_file_name) as reader: for record in reader.records(): config = record.config @@ -893,25 +879,28 @@ class TestAutoML(unittest.TestCase): max_iter = sum([len(s) for k, s in starting_points.items()]) automl_settings_resume = { "time_budget": 2, - "metric": 'accuracy', - "task": 'classification', + "metric": "accuracy", + "task": "classification", "log_file_name": "test/iris_resume_all.log", "log_training_metric": True, "n_jobs": 1, "max_iter": max_iter, "model_history": True, - "log_type": 'all', + "log_type": "all", "starting_points": starting_points, "append_log": True, } new_automl_experiment = AutoML() - new_automl_experiment.fit(X_train=X_train, y_train=y_train, - **automl_settings_resume) + new_automl_experiment.fit( + X_train=X_train, y_train=y_train, **automl_settings_resume + ) new_automl_val_accuracy = 1.0 - new_automl_experiment.best_loss # print('Best ML leaner:', new_automl_experiment.best_estimator) # print('Best hyperparmeter config:', new_automl_experiment.best_config) - print('Best accuracy on validation data: {0:.4g}'.format(new_automl_val_accuracy)) + print( + "Best accuracy on validation data: {0:.4g}".format(new_automl_val_accuracy) + ) # print('Training duration of best run: {0:.4g} s'.format(new_automl_experiment.best_config_train_time)) diff --git a/test/test_python_log.py b/test/test_python_log.py index b68ad18e8..370e7c393 100644 --- a/test/test_python_log.py +++ b/test/test_python_log.py @@ -1,6 +1,6 @@ from flaml.tune.space import unflatten_hierarchical from flaml import AutoML -from sklearn.datasets import load_boston +from sklearn.datasets import fetch_california_housing import os import unittest import logging @@ -9,7 +9,6 @@ import io class TestLogging(unittest.TestCase): - def test_logging_level(self): from flaml import logger, logger_formatter @@ -30,8 +29,8 @@ class TestLogging(unittest.TestCase): automl = AutoML() automl_settings = { "time_budget": 1, - "metric": 'rmse', - "task": 'regression', + "metric": "rmse", + "task": "regression", "log_file_name": training_log, "log_training_metric": True, "n_jobs": 1, @@ -39,35 +38,42 @@ class TestLogging(unittest.TestCase): "keep_search_state": True, "learner_selector": "roundrobin", } - X_train, y_train = load_boston(return_X_y=True) + X_train, y_train = fetch_california_housing(return_X_y=True) n = len(y_train) >> 1 print(automl.model, automl.classes_, automl.predict(X_train)) - automl.fit(X_train=X_train[:n], y_train=y_train[:n], - X_val=X_train[n:], y_val=y_train[n:], - **automl_settings) + automl.fit( + X_train=X_train[:n], + y_train=y_train[:n], + X_val=X_train[n:], + y_val=y_train[n:], + **automl_settings + ) logger.info(automl.search_space) logger.info(automl.low_cost_partial_config) logger.info(automl.points_to_evaluate) logger.info(automl.cat_hp_cost) import optuna as ot + study = ot.create_study() from flaml.tune.space import define_by_run_func, add_cost_to_space + sample = define_by_run_func(study.ask(), automl.search_space) logger.info(sample) logger.info(unflatten_hierarchical(sample, automl.search_space)) add_cost_to_space( - automl.search_space, automl.low_cost_partial_config, - automl.cat_hp_cost + automl.search_space, automl.low_cost_partial_config, automl.cat_hp_cost ) logger.info(automl.search_space["ml"].categories) config = automl.best_config.copy() - config['learner'] = automl.best_estimator + config["learner"] = automl.best_estimator automl.trainable({"ml": config}) from flaml import tune, BlendSearch from flaml.automl import size from functools import partial + search_alg = BlendSearch( - metric='val_loss', mode='min', + metric="val_loss", + mode="min", space=automl.search_space, low_cost_partial_config=automl.low_cost_partial_config, points_to_evaluate=automl.points_to_evaluate, @@ -75,19 +81,25 @@ class TestLogging(unittest.TestCase): prune_attr=automl.prune_attr, min_resource=automl.min_resource, max_resource=automl.max_resource, - config_constraints=[(partial(size, automl._state), '<=', automl._mem_thres)], - metric_constraints=automl.metric_constraints) + config_constraints=[ + (partial(size, automl._state), "<=", automl._mem_thres) + ], + metric_constraints=automl.metric_constraints, + ) analysis = tune.run( - automl.trainable, search_alg=search_alg, # verbose=2, - time_budget_s=1, num_samples=-1) - print(min(trial.last_result["val_loss"] - for trial in analysis.trials)) - config = analysis.trials[-1].last_result['config']['ml'] - automl._state._train_with_config(config['learner'], config) + automl.trainable, + search_alg=search_alg, # verbose=2, + time_budget_s=1, + num_samples=-1, + ) + print(min(trial.last_result["val_loss"] for trial in analysis.trials)) + config = analysis.trials[-1].last_result["config"]["ml"] + automl._state._train_with_config(config["learner"], config) # Check if the log buffer is populated. self.assertTrue(len(buf.getvalue()) > 0) import pickle - with open('automl.pkl', 'wb') as f: + + with open("automl.pkl", "wb") as f: pickle.dump(automl, f, pickle.HIGHEST_PROTOCOL) print(automl.__version__) diff --git a/test/test_training_log.py b/test/test_training_log.py index 40ac91ef0..73de45d25 100644 --- a/test/test_training_log.py +++ b/test/test_training_log.py @@ -2,15 +2,14 @@ import os import unittest from tempfile import TemporaryDirectory -from sklearn.datasets import load_boston +from sklearn.datasets import fetch_california_housing from flaml import AutoML from flaml.training_log import training_log_reader class TestTrainingLog(unittest.TestCase): - - def test_training_log(self, path='test_training_log.log'): + def test_training_log(self, path="test_training_log.log"): with TemporaryDirectory() as d: filename = os.path.join(d, path) @@ -19,8 +18,8 @@ class TestTrainingLog(unittest.TestCase): automl = AutoML() automl_settings = { "time_budget": 1, - "metric": 'mse', - "task": 'regression', + "metric": "mse", + "task": "regression", "log_file_name": filename, "log_training_metric": True, "mem_thres": 1024 * 1024, @@ -31,10 +30,9 @@ class TestTrainingLog(unittest.TestCase): "ensemble": True, "keep_search_state": True, } - X_train, y_train = load_boston(return_X_y=True) + X_train, y_train = fetch_california_housing(return_X_y=True) automl.fit(X_train=X_train, y_train=y_train, **automl_settings) - automl._state._train_with_config( - automl.best_estimator, automl.best_config) + automl._state._train_with_config(automl.best_estimator, automl.best_config) # Check if the training log file is populated. self.assertTrue(os.path.exists(filename)) @@ -49,11 +47,11 @@ class TestTrainingLog(unittest.TestCase): automl.fit(X_train=X_train, y_train=y_train, **automl_settings) automl._selected.update(None, 0) automl = AutoML() - automl.fit(X_train=X_train, y_train=y_train, max_iter=0) + automl.fit(X_train=X_train, y_train=y_train, max_iter=0, task="regression") def test_illfilename(self): try: - self.test_training_log('/') + self.test_training_log("/") except IsADirectoryError: print("IsADirectoryError happens as expected in linux.") except PermissionError: diff --git a/test/tune/test_searcher.py b/test/tune/test_searcher.py index 0a1421aef..ce07670ac 100644 --- a/test/tune/test_searcher.py +++ b/test/tune/test_searcher.py @@ -72,8 +72,9 @@ except (ImportError, AssertionError): searcher = BlendSearch( metric="m", global_search_alg=searcher, metric_constraints=[("c", "<", 1)] ) - searcher.set_search_properties(metric="m2", config=config) - searcher.set_search_properties(config={"time_budget_s": 0}) + searcher.set_search_properties( + metric="m2", config=config, setting={"time_budget_s": 0} + ) c = searcher.suggest("t1") searcher.on_trial_complete("t1", {"config": c}, True) c = searcher.suggest("t2") @@ -146,3 +147,11 @@ except (ImportError, AssertionError): print(searcher.suggest("t4")) searcher.on_trial_complete({"t1"}, {}) searcher.on_trial_result({"t2"}, {}) + np.random.seed(654321) + searcher = RandomSearch( + space=config, + points_to_evaluate=[{"a": 7, "b": 1e-3}, {"a": 6, "b": 3e-4}], + ) + print(searcher.suggest("t1")) + print(searcher.suggest("t2")) + print(searcher.suggest("t3"))