# ! # * Copyright (c) FLAML authors. All rights reserved. # * Licensed under the MIT License. See LICENSE file in the # * project root for license information. from contextlib import contextmanager from functools import partial import signal import os from typing import Callable, List import numpy as np import time from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier from sklearn.ensemble import ExtraTreesRegressor, ExtraTreesClassifier from sklearn.linear_model import LogisticRegression from sklearn.dummy import DummyClassifier, DummyRegressor from scipy.sparse import issparse import logging import shutil from pandas import DataFrame, Series, to_datetime import sys import math from . import tune from .data import ( group_counts, CLASSIFICATION, TS_FORECASTREGRESSION, TS_TIMESTAMP_COL, TS_VALUE_COL, SEQCLASSIFICATION, SEQREGRESSION, TOKENCLASSIFICATION, SUMMARIZATION, NLG_TASKS, MULTICHOICECLASSIFICATION, ) try: import psutil except ImportError: psutil = None try: import resource except ImportError: resource = None logger = logging.getLogger("flaml.automl") FREE_MEM_RATIO = 0.2 def TimeoutHandler(sig, frame): raise TimeoutError(sig, frame) @contextmanager def limit_resource(memory_limit, time_limit): if memory_limit > 0: soft, hard = resource.getrlimit(resource.RLIMIT_AS) if soft < 0 and (hard < 0 or memory_limit <= hard) or memory_limit < soft: resource.setrlimit(resource.RLIMIT_AS, (memory_limit, hard)) main_thread = False if time_limit is not None: try: signal.signal(signal.SIGALRM, TimeoutHandler) signal.alarm(int(time_limit) or 1) main_thread = True except ValueError: pass try: yield finally: if main_thread: signal.alarm(0) if memory_limit > 0: resource.setrlimit(resource.RLIMIT_AS, (soft, hard)) class BaseEstimator: """The abstract class for all learners. Typical examples: * XGBoostEstimator: for regression. * XGBoostSklearnEstimator: for classification. * LGBMEstimator, RandomForestEstimator, LRL1Classifier, LRL2Classifier: for both regression and classification. """ def __init__(self, task="binary", **config): """Constructor. Args: task: A string of the task type, one of 'binary', 'multiclass', 'regression', 'rank', 'seq-classification', 'seq-regression', 'token-classification', 'multichoice-classification', 'summarization', 'ts_forecast', 'ts_forecast_classification'. config: A dictionary containing the hyperparameter names, 'n_jobs' as keys. n_jobs is the number of parallel threads. """ self._task = task self.params = self.config2params(config) self.estimator_class = self._model = None if "_estimator_type" in config: self._estimator_type = self.params.pop("_estimator_type") else: self._estimator_type = ( "classifier" if task in CLASSIFICATION else "regressor" ) def get_params(self, deep=False): params = self.params.copy() params["task"] = self._task if hasattr(self, "_estimator_type"): params["_estimator_type"] = self._estimator_type return params @property def classes_(self): return self._model.classes_ @property def n_features_in_(self): return self._model.n_features_in_ @property def model(self): """Trained model after fit() is called, or None before fit() is called.""" return self._model @property def estimator(self): """Trained model after fit() is called, or None before fit() is called.""" return self._model def _preprocess(self, X): return X def _fit(self, X_train, y_train, **kwargs): current_time = time.time() if "groups" in kwargs: kwargs = kwargs.copy() groups = kwargs.pop("groups") if self._task == "rank": kwargs["group"] = group_counts(groups) # groups_val = kwargs.get('groups_val') # if groups_val is not None: # kwargs['eval_group'] = [group_counts(groups_val)] # kwargs['eval_set'] = [ # (kwargs['X_val'], kwargs['y_val'])] # kwargs['verbose'] = False # del kwargs['groups_val'], kwargs['X_val'], kwargs['y_val'] 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 def fit(self, X_train, y_train, budget=None, **kwargs): """Train the model from given training data. Args: X_train: A numpy array or a dataframe of training data in shape n*m. y_train: A numpy array or a series of labels in shape n*1. budget: A float of the time budget in seconds. Returns: train_time: A float of the training time in seconds. """ if ( getattr(self, "limit_resource", None) and resource is not None and (budget is not None or psutil is not None) ): start_time = time.time() mem = psutil.virtual_memory() if psutil is not None else None try: with limit_resource( mem.available * (1 - FREE_MEM_RATIO) + psutil.Process(os.getpid()).memory_info().rss if mem is not None else -1, budget, ): train_time = self._fit(X_train, y_train, **kwargs) except (MemoryError, TimeoutError) as e: logger.warning(f"{e.__class__} {e}") if self._task in CLASSIFICATION: model = DummyClassifier() else: model = DummyRegressor() X_train = self._preprocess(X_train) model.fit(X_train, y_train) self._model = model train_time = time.time() - start_time else: train_time = self._fit(X_train, y_train, **kwargs) return train_time def predict(self, X, **kwargs): """Predict label from features. Args: X: A numpy array or a dataframe of featurized instances, shape n*m. Returns: A numpy array of shape n*1. Each element is the label for a instance. """ if self._model is not None: X = self._preprocess(X) return self._model.predict(X) else: logger.warning( "Estimator is not fit yet. Please run fit() before predict()." ) return np.ones(X.shape[0]) def predict_proba(self, X, **kwargs): """Predict the probability of each class from features. Only works for classification problems Args: X: A numpy array of featurized instances, shape n*m. Returns: A numpy array of shape n*c. c is the # classes. Each element at (i,j) is the probability for instance i to be in class j. """ assert self._task in CLASSIFICATION, "predict_proba() only for classification." X = self._preprocess(X) return self._model.predict_proba(X) def score(self, X_val: DataFrame, y_val: Series, **kwargs): """Report the evaluation score of a trained estimator. Args: X_val: A pandas dataframe of the validation input data. y_val: A pandas series of the validation label. kwargs: keyword argument of the evaluation function, for example: - metric: A string of the metric name or a function e.g., 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'f1', 'micro_f1', 'macro_f1', 'log_loss', 'mae', 'mse', 'r2', 'mape'. Default is 'auto'. If metric is given, the score will report the user specified metric. If metric is not given, the metric is set to accuracy for classification and r2 for regression. You can also pass a customized metric function, for examples on how to pass a customized metric function, please check [test/nlp/test_autohf_custom_metric.py](https://github.com/microsoft/FLAML/blob/main/test/nlp/test_autohf_custom_metric.py) and [test/automl/test_multiclass.py](https://github.com/microsoft/FLAML/blob/main/test/automl/test_multiclass.py). Returns: The evaluation score on the validation dataset. """ from .ml import metric_loss_score from .ml import is_min_metric if self._model is not None: if self._task == "rank": raise NotImplementedError( "AutoML.score() is not implemented for ranking" ) else: X_val = self._preprocess(X_val) metric = kwargs.get("metric", None) if metric: y_pred = self.predict(X_val, **kwargs) if is_min_metric(metric): return metric_loss_score(metric, y_pred, y_val) else: return 1.0 - metric_loss_score(metric, y_pred, y_val) else: return self._model.score(X_val, y_val, **kwargs) else: logger.warning( "Estimator is not fit yet. Please run fit() before predict()." ) return 0.0 def cleanup(self): del self._model self._model = None @classmethod def search_space(cls, data_size, task, **params): """[required method] search space. Args: data_size: A tuple of two integers, number of rows and columns. task: A str of the task type, e.g., "binary", "multiclass", "regression". Returns: A dictionary of the search space. Each key is the name of a hyperparameter, and value is a dict with 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}```. """ return {} @classmethod def size(cls, config: dict) -> float: """[optional method] memory size of the estimator in bytes. Args: config: A dict of the hyperparameter config. Returns: A float of the memory size required by the estimator to train the given config. """ return 1.0 @classmethod def cost_relative2lgbm(cls) -> float: """[optional method] relative cost compared to lightgbm.""" return 1.0 @classmethod def init(cls): """[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. """ params = config.copy() if "FLAML_sample_size" in params: params.pop("FLAML_sample_size") return params class TransformersEstimator(BaseEstimator): """The class for fine-tuning language models, using huggingface transformers API.""" ITER_HP = "global_max_steps" def __init__(self, task="seq-classification", **config): super().__init__(task, **config) import uuid self.trial_id = str(uuid.uuid1().hex)[:8] if task not in NLG_TASKS: # TODO: not in NLG_TASKS from .nlp.huggingface.training_args import ( TrainingArgumentsForAuto as TrainingArguments, ) else: from .nlp.huggingface.training_args import ( Seq2SeqTrainingArgumentsForAuto as TrainingArguments, ) self._TrainingArguments = TrainingArguments @staticmethod def _join(X_train, y_train, task): y_train = DataFrame(y_train, index=X_train.index) y_train.columns = ["label"] if task != TOKENCLASSIFICATION else ["labels"] train_df = X_train.join(y_train) return train_df @classmethod def search_space(cls, data_size, task, **params): search_space_dict = { "learning_rate": { "domain": tune.loguniform(lower=1e-6, upper=1e-3), "init_value": 1e-5, }, "num_train_epochs": { "domain": tune.loguniform(lower=0.1, upper=10.0), "init_value": 3.0, # to be consistent with roberta }, "per_device_train_batch_size": { "domain": tune.choice([4, 8, 16, 32]), "init_value": 32, }, "warmup_ratio": { "domain": tune.uniform(lower=0.0, upper=0.3), "init_value": 0.0, }, "weight_decay": { "domain": tune.uniform(lower=0.0, upper=0.3), "init_value": 0.0, }, "adam_epsilon": { "domain": tune.loguniform(lower=1e-8, upper=1e-6), "init_value": 1e-6, }, "seed": {"domain": tune.choice(list(range(40, 45))), "init_value": 42}, "global_max_steps": { "domain": sys.maxsize, "init_value": sys.maxsize, }, } return search_space_dict @property def checkpoint_freq(self): return ( int( min(self._training_args.num_train_epochs, 1) * len(self._X_train) / self._training_args.per_device_train_batch_size / self._training_args.ckpt_per_epoch ) + 1 ) @property def fp16(self): return self._kwargs.get("gpu_per_trial") and self._training_args.fp16 @property def no_cuda(self): return not self._kwargs.get("gpu_per_trial") def _set_training_args(self, **kwargs): from .nlp.utils import date_str, Counter for (key, val) in kwargs.items(): assert key not in self.params, ( "Since {} is in the search space, it cannot exist in 'custom_fit_kwargs' at the same time." "If you need to fix the value of {} to {}, the only way is to add a single-value domain in the search " "space by adding:\n '{}': {{ 'domain': {} }} to 'custom_hp'. For example:" 'automl_settings["custom_hp"] = {{ "transformer": {{ "model_path": {{ "domain" : ' '"google/electra-small-discriminator" }} }} }}'.format( key, key, val, key, val ) ) """ If use has specified any custom args for TrainingArguments, update these arguments """ self._training_args = self._TrainingArguments(**kwargs) """ Update the attributes in TrainingArguments with self.params values """ for key, val in self.params.items(): if hasattr(self._training_args, key): setattr(self._training_args, key, val) """ Update the attributes in TrainingArguments that depends on the values of self.params """ local_dir = os.path.join( self._training_args.output_dir, "train_{}".format(date_str()) ) if self._use_ray is True: import ray self._training_args.output_dir = ray.tune.get_trial_dir() else: self._training_args.output_dir = Counter.get_trial_fold_name( local_dir, self.params, self.trial_id ) self._training_args.eval_steps = ( self._training_args.logging_steps ) = self._training_args.saving_steps = self.checkpoint_freq self._training_args.fp16 = self.fp16 self._training_args.no_cuda = self.no_cuda def _preprocess(self, X, y=None, **kwargs): from .nlp.utils import tokenize_text, is_a_list_of_str is_str = str(X.dtypes[0]) in ("string", "str") is_list_of_str = is_a_list_of_str(X[list(X.keys())[0]].to_list()[0]) if is_str or is_list_of_str: return tokenize_text( X=X, Y=y, task=self._task, hf_args=self._training_args, tokenizer=self.tokenizer, ) else: return X, None def _model_init(self): from .nlp.utils import load_model this_model = load_model( checkpoint_path=self._training_args.model_path, task=self._task, num_labels=self.num_labels, ) return this_model def preprocess_data(self, X, y): from datasets import Dataset if (self._task not in NLG_TASKS) and (self._task != TOKENCLASSIFICATION): processed_X, _ = self._preprocess(X=X, **self._kwargs) processed_y = y else: processed_X, processed_y = self._preprocess(X=X, y=y, **self._kwargs) processed_dataset = Dataset.from_pandas( TransformersEstimator._join(processed_X, processed_y, self._task) ) return processed_dataset, processed_X, processed_y @property def num_labels(self): from .data import SEQCLASSIFICATION, SEQREGRESSION, TOKENCLASSIFICATION if self._task == SEQREGRESSION: return 1 elif self._task == SEQCLASSIFICATION: return len(set(self._y_train)) elif self._task == TOKENCLASSIFICATION: return len(set([a for b in self._y_train.tolist() for a in b])) else: return None @property def tokenizer(self): from transformers import AutoTokenizer if self._task == SUMMARIZATION: return AutoTokenizer.from_pretrained( pretrained_model_name_or_path=self._training_args.model_path, cache_dir=None, use_fast=True, revision="main", use_auth_token=None, ) else: return AutoTokenizer.from_pretrained( self._training_args.model_path, use_fast=True ) @property def data_collator(self): from .nlp.huggingface.data_collator import task_to_datacollator_class return ( task_to_datacollator_class[self._task]( tokenizer=self.tokenizer, pad_to_multiple_of=8, # if self._training_args.fp16 else None, ) if self._task in (MULTICHOICECLASSIFICATION, TOKENCLASSIFICATION) else None ) def fit( self, X_train: DataFrame, y_train: Series, budget=None, X_val=None, y_val=None, gpu_per_trial=None, metric=None, **kwargs, ): import transformers transformers.logging.set_verbosity_error() from transformers import TrainerCallback from transformers.trainer_utils import set_seed from .nlp.huggingface.trainer import TrainerForAuto try: from ray.tune import is_session_enabled self._use_ray = is_session_enabled() except ImportError: self._use_ray = False this_params = self.params self._kwargs = kwargs self._X_train, self._y_train = X_train, y_train self._set_training_args(**kwargs) train_dataset, self._X_train, self._y_train = self.preprocess_data( X_train, y_train ) if X_val is not None: eval_dataset, self._X_val, self._y_val = self.preprocess_data(X_val, y_val) else: eval_dataset, self._X_val, self._y_val = None, None, None set_seed(self.params.get("seed", self._training_args.seed)) self._metric = metric class EarlyStoppingCallbackForAuto(TrainerCallback): def on_train_begin(self, args, state, control, **callback_kwargs): self.train_begin_time = time.time() def on_step_begin(self, args, state, control, **callback_kwargs): self.step_begin_time = time.time() def on_step_end(self, args, state, control, **callback_kwargs): if state.global_step == 1: self.time_per_iter = time.time() - self.step_begin_time if ( budget and ( time.time() + self.time_per_iter > self.train_begin_time + budget ) or state.global_step >= this_params[TransformersEstimator.ITER_HP] ): control.should_training_stop = True control.should_save = True control.should_evaluate = True return control def on_epoch_end(self, args, state, control, **callback_kwargs): if ( control.should_training_stop or state.epoch + 1 >= args.num_train_epochs ): control.should_save = True control.should_evaluate = True self._trainer = TrainerForAuto( args=self._training_args, model_init=self._model_init, train_dataset=train_dataset, eval_dataset=eval_dataset, tokenizer=self.tokenizer, data_collator=self.data_collator, compute_metrics=self._compute_metrics_by_dataset_name, callbacks=[EarlyStoppingCallbackForAuto], ) if self._task in NLG_TASKS: setattr(self._trainer, "_is_seq2seq", True) """ When not using ray for tuning, set the limit of CUDA_VISIBLE_DEVICES to math.ceil(gpu_per_trial), so each estimator does not see all the GPUs """ if gpu_per_trial is not None: tmp_cuda_visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "") self._trainer.args._n_gpu = gpu_per_trial # if gpu_per_trial == 0: # os.environ["CUDA_VISIBLE_DEVICES"] = "" if tmp_cuda_visible_devices.count(",") != math.ceil(gpu_per_trial) - 1: os.environ["CUDA_VISIBLE_DEVICES"] = ",".join( [str(x) for x in range(math.ceil(gpu_per_trial))] ) import time start_time = time.time() self._trainer.train() if gpu_per_trial is not None: os.environ["CUDA_VISIBLE_DEVICES"] = tmp_cuda_visible_devices self.params[self.ITER_HP] = self._trainer.state.global_step self._checkpoint_path = self._select_checkpoint(self._trainer) self._ckpt_remains = list(self._trainer.ckpt_to_metric.keys()) if hasattr(self._trainer, "intermediate_results"): self.intermediate_results = [ x[1] for x in sorted( self._trainer.intermediate_results.items(), key=lambda x: x[0] ) ] self._trainer = None return time.time() - start_time def _delete_one_ckpt(self, ckpt_location): if self._use_ray is False: try: shutil.rmtree(ckpt_location) except FileNotFoundError: logger.warning("checkpoint {} not found".format(ckpt_location)) def cleanup(self): super().cleanup() if hasattr(self, "_ckpt_remains"): for each_ckpt in self._ckpt_remains: self._delete_one_ckpt(each_ckpt) def _select_checkpoint(self, trainer): from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR if trainer.ckpt_to_metric: best_ckpt, _ = min( trainer.ckpt_to_metric.items(), key=lambda x: x[1]["eval_loss"] ) best_ckpt_global_step = trainer.ckpt_to_global_step[best_ckpt] for each_ckpt in list(trainer.ckpt_to_metric): if each_ckpt != best_ckpt: del trainer.ckpt_to_metric[each_ckpt] del trainer.ckpt_to_global_step[each_ckpt] self._delete_one_ckpt(each_ckpt) else: best_ckpt_global_step = trainer.state.global_step best_ckpt = os.path.join( trainer.args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{best_ckpt_global_step}", ) self.params[self.ITER_HP] = best_ckpt_global_step logger.debug(trainer.state.global_step) logger.debug(trainer.ckpt_to_global_step) return best_ckpt def _compute_metrics_by_dataset_name(self, eval_pred): if isinstance(self._metric, str): from .ml import metric_loss_score from .nlp.utils import postprocess_text predictions, labels = eval_pred if self._task in NLG_TASKS: if isinstance(predictions, tuple): predictions = np.argmax(predictions[0], axis=2) decoded_preds = self.tokenizer.batch_decode( predictions, skip_special_tokens=True ) labels = np.where(labels != -100, labels, self.tokenizer.pad_token_id) decoded_labels = self.tokenizer.batch_decode( labels, skip_special_tokens=True ) predictions, labels = postprocess_text(decoded_preds, decoded_labels) else: predictions = ( np.squeeze(predictions) if self._task == SEQREGRESSION else np.argmax(predictions, axis=2) if self._task == TOKENCLASSIFICATION else np.argmax(predictions, axis=1) ) metric_dict = { "automl_metric": metric_loss_score( metric_name=self._metric, y_predict=predictions, y_true=labels, labels=self._training_args.label_list, ) } else: loss, metric_dict = self._metric( X_test=self._X_val, y_test=self._y_val, estimator=self, labels=None, X_train=self._X_train, y_train=self._y_train, ) metric_dict["automl_metric"] = loss return metric_dict def _init_model_for_predict(self): from .nlp.huggingface.trainer import TrainerForAuto """ Need to reinit training_args because of a bug in deepspeed: if not reinit, the deepspeed config will be inconsistent with HF config https://github.com/huggingface/transformers/blob/main/src/transformers/training_args.py#L947 """ training_args = self._TrainingArguments( local_rank=-1, model_path=self._checkpoint_path, fp16=self.fp16 ) for key, val in self._training_args.__dict__.items(): if key not in ("local_rank", "model_path", "fp16"): setattr(training_args, key, val) self._training_args = training_args new_trainer = TrainerForAuto( model=self._model_init(), args=self._training_args, data_collator=self.data_collator, compute_metrics=self._compute_metrics_by_dataset_name, ) if self._task in NLG_TASKS: setattr(new_trainer, "_is_seq2seq", True) return new_trainer def predict_proba(self, X, **pred_kwargs): from datasets import Dataset if pred_kwargs: for key, val in pred_kwargs.items(): setattr(self._training_args, key, val) assert ( self._task in CLASSIFICATION ), "predict_proba() only for classification tasks." X_test, _ = self._preprocess(X, **self._kwargs) test_dataset = Dataset.from_pandas(X_test) new_trainer = self._init_model_for_predict() predictions = new_trainer.predict(test_dataset) return predictions.predictions def score(self, X_val: DataFrame, y_val: Series, **kwargs): import transformers transformers.logging.set_verbosity_error() self._metric = kwargs["metric"] eval_dataset, X_val, y_val = self.preprocess_data(X_val, y_val) new_trainer = self._init_model_for_predict() return new_trainer.evaluate(eval_dataset) def predict(self, X, **pred_kwargs): import transformers from datasets import Dataset transformers.logging.set_verbosity_error() if pred_kwargs: for key, val in pred_kwargs.items(): setattr(self._training_args, key, val) X_test, _ = self._preprocess(X, **self._kwargs) test_dataset = Dataset.from_pandas(X_test) new_trainer = self._init_model_for_predict() if self._task not in NLG_TASKS: predictions = new_trainer.predict(test_dataset) else: predictions = new_trainer.predict( test_dataset, metric_key_prefix="predict", ) if self._task == SEQCLASSIFICATION: return np.argmax(predictions.predictions, axis=1) elif self._task == SEQREGRESSION: return predictions.predictions.reshape((len(predictions.predictions),)) elif self._task == TOKENCLASSIFICATION: return np.argmax(predictions.predictions, axis=2) elif self._task == SUMMARIZATION: decoded_preds = self.tokenizer.batch_decode( predictions.predictions, skip_special_tokens=True ) return decoded_preds elif self._task == MULTICHOICECLASSIFICATION: return np.argmax(predictions.predictions, axis=1) def config2params(self, config: dict) -> dict: params = super().config2params(config) params[TransformersEstimator.ITER_HP] = params.get( TransformersEstimator.ITER_HP, sys.maxsize ) return params class TransformersEstimatorModelSelection(TransformersEstimator): def __init__(self, task="seq-classification", **config): super().__init__(task, **config) @classmethod def search_space(cls, data_size, task, **params): search_space_dict = TransformersEstimator.search_space( data_size, task, **params ) """ For model selection, use the same search space regardless of memory constraint If OOM, user should change the search space themselves """ search_space_dict["model_path"] = { "domain": tune.choice( [ "google/electra-base-discriminator", "bert-base-uncased", "roberta-base", "facebook/muppet-roberta-base", "google/electra-small-discriminator", ] ), "init_value": "facebook/muppet-roberta-base", } return search_space_dict class SKLearnEstimator(BaseEstimator): """The base class for tuning scikit-learn estimators.""" def __init__(self, task="binary", **config): super().__init__(task, **config) def _preprocess(self, X): if isinstance(X, DataFrame): cat_columns = X.select_dtypes(include=["category"]).columns if not cat_columns.empty: X = X.copy() X[cat_columns] = X[cat_columns].apply(lambda x: x.cat.codes) elif isinstance(X, np.ndarray) and X.dtype.kind not in "buif": # numpy array is not of numeric dtype X = DataFrame(X) for col in X.columns: if isinstance(X[col][0], str): X[col] = X[col].astype("category").cat.codes X = X.to_numpy() return X class LGBMEstimator(BaseEstimator): """The class for tuning LGBM, using sklearn API.""" ITER_HP = "n_estimators" HAS_CALLBACK = True DEFAULT_ITER = 100 @classmethod def search_space(cls, data_size, **params): upper = max(5, min(32768, int(data_size[0]))) # upper must be larger than lower return { "n_estimators": { "domain": tune.lograndint(lower=4, upper=upper), "init_value": 4, "low_cost_init_value": 4, }, "num_leaves": { "domain": tune.lograndint(lower=4, upper=upper), "init_value": 4, "low_cost_init_value": 4, }, "min_child_samples": { "domain": tune.lograndint(lower=2, upper=2 ** 7 + 1), "init_value": 20, }, "learning_rate": { "domain": tune.loguniform(lower=1 / 1024, upper=1.0), "init_value": 0.1, }, # 'subsample': { # 'domain': tune.uniform(lower=0.1, upper=1.0), # 'init_value': 1.0, # }, "log_max_bin": { # log transformed with base 2 "domain": tune.lograndint(lower=3, upper=11), "init_value": 8, }, "colsample_bytree": { "domain": tune.uniform(lower=0.01, upper=1.0), "init_value": 1.0, }, "reg_alpha": { "domain": tune.loguniform(lower=1 / 1024, upper=1024), "init_value": 1 / 1024, }, "reg_lambda": { "domain": tune.loguniform(lower=1 / 1024, upper=1024), "init_value": 1.0, }, } def config2params(self, config: dict) -> dict: params = super().config2params(config) 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.get("max_leaves") or 1 << config.get("max_depth", 16) ) ) 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", **config): super().__init__(task, **config) if "verbose" not in self.params: self.params["verbose"] = -1 if "regression" == task: from lightgbm import LGBMRegressor self.estimator_class = LGBMRegressor elif "rank" == task: from lightgbm import LGBMRanker self.estimator_class = LGBMRanker else: from lightgbm import LGBMClassifier self.estimator_class = LGBMClassifier self._time_per_iter = None self._train_size = 0 self._mem_per_iter = -1 self.HAS_CALLBACK = self.HAS_CALLBACK and self._callbacks(0, 0) is not None def _preprocess(self, X): if ( not isinstance(X, DataFrame) and issparse(X) and np.issubdtype(X.dtype, np.integer) ): X = X.astype(float) elif isinstance(X, np.ndarray) and X.dtype.kind not in "buif": # numpy array is not of numeric dtype X = DataFrame(X) for col in X.columns: if isinstance(X[col][0], str): X[col] = X[col].astype("category").cat.codes X = X.to_numpy() return X def fit(self, X_train, y_train, budget=None, **kwargs): start_time = time.time() deadline = start_time + budget if budget else np.inf n_iter = self.params.get(self.ITER_HP, self.DEFAULT_ITER) trained = False if not self.HAS_CALLBACK: mem0 = psutil.virtual_memory().available if psutil is not None else 1 if ( ( not self._time_per_iter or abs(self._train_size - X_train.shape[0]) > 4 ) and budget is not None or self._mem_per_iter < 0 and psutil is not None ) and n_iter > 1: self.params[self.ITER_HP] = 1 self._t1 = self._fit(X_train, y_train, **kwargs) if budget is not None and self._t1 >= budget or n_iter == 1: # self.params[self.ITER_HP] = n_iter return self._t1 mem1 = psutil.virtual_memory().available if psutil is not None else 1 self._mem1 = mem0 - mem1 self.params[self.ITER_HP] = min(n_iter, 4) self._t2 = self._fit(X_train, y_train, **kwargs) mem2 = psutil.virtual_memory().available if psutil is not None else 1 self._mem2 = max(mem0 - mem2, self._mem1) # if self._mem1 <= 0: # self._mem_per_iter = self._mem2 / (self.params[self.ITER_HP] + 1) # elif self._mem2 <= 0: # self._mem_per_iter = self._mem1 # else: self._mem_per_iter = min( self._mem1, self._mem2 / self.params[self.ITER_HP] ) # if self._mem_per_iter <= 1 and psutil is not None: # n_iter = self.params[self.ITER_HP] self._time_per_iter = ( (self._t2 - self._t1) / (self.params[self.ITER_HP] - 1) if self._t2 > self._t1 else self._t1 if self._t1 else 0.001 ) self._train_size = X_train.shape[0] if ( budget is not None and self._t1 + self._t2 >= budget or n_iter == self.params[self.ITER_HP] ): # self.params[self.ITER_HP] = n_iter return time.time() - start_time trained = True # logger.debug(mem0) # logger.debug(self._mem_per_iter) if n_iter > 1: max_iter = min( n_iter, int( (budget - time.time() + start_time - self._t1) / self._time_per_iter + 1 ) if budget is not None else n_iter, int((1 - FREE_MEM_RATIO) * mem0 / self._mem_per_iter) if psutil is not None and self._mem_per_iter > 0 else n_iter, ) if trained and max_iter <= self.params[self.ITER_HP]: return time.time() - start_time # when not trained, train at least one iter self.params[self.ITER_HP] = max(max_iter, 1) if self.HAS_CALLBACK: kwargs_callbacks = kwargs.get("callbacks") if kwargs_callbacks: callbacks = kwargs_callbacks + self._callbacks(start_time, deadline) kwargs.pop("callbacks") else: callbacks = self._callbacks(start_time, deadline) self._fit( X_train, y_train, callbacks=callbacks, **kwargs, ) best_iteration = ( self._model.get_booster().best_iteration if isinstance(self, XGBoostSklearnEstimator) else self._model.best_iteration_ ) if best_iteration is not None: self._model.set_params(n_estimators=best_iteration + 1) else: self._fit(X_train, y_train, **kwargs) train_time = time.time() - start_time return train_time def _callbacks(self, start_time, deadline) -> List[Callable]: return [partial(self._callback, start_time, deadline)] def _callback(self, start_time, deadline, env) -> None: from lightgbm.callback import EarlyStopException now = time.time() if env.iteration == 0: self._time_per_iter = now - start_time if now + self._time_per_iter > deadline: raise EarlyStopException(env.iteration, env.evaluation_result_list) if psutil is not None: mem = psutil.virtual_memory() if mem.available / mem.total < FREE_MEM_RATIO: raise EarlyStopException(env.iteration, env.evaluation_result_list) class XGBoostEstimator(SKLearnEstimator): """The class for tuning XGBoost regressor, not using sklearn API.""" DEFAULT_ITER = 10 @classmethod def search_space(cls, data_size, **params): upper = max(5, min(32768, int(data_size[0]))) # upper must be larger than lower return { "n_estimators": { "domain": tune.lograndint(lower=4, upper=upper), "init_value": 4, "low_cost_init_value": 4, }, "max_leaves": { "domain": tune.lograndint(lower=4, upper=upper), "init_value": 4, "low_cost_init_value": 4, }, "max_depth": { "domain": tune.choice([0, 6, 12]), "init_value": 0, }, "min_child_weight": { "domain": tune.loguniform(lower=0.001, upper=128), "init_value": 1, }, "learning_rate": { "domain": tune.loguniform(lower=1 / 1024, upper=1.0), "init_value": 0.1, }, "subsample": { "domain": tune.uniform(lower=0.1, upper=1.0), "init_value": 1.0, }, "colsample_bylevel": { "domain": tune.uniform(lower=0.01, upper=1.0), "init_value": 1.0, }, "colsample_bytree": { "domain": tune.uniform(lower=0.01, upper=1.0), "init_value": 1.0, }, "reg_alpha": { "domain": tune.loguniform(lower=1 / 1024, upper=1024), "init_value": 1 / 1024, }, "reg_lambda": { "domain": tune.loguniform(lower=1 / 1024, upper=1024), "init_value": 1.0, }, } @classmethod def size(cls, config): return LGBMEstimator.size(config) @classmethod def cost_relative2lgbm(cls): return 1.6 def config2params(self, config: dict) -> dict: params = super().config2params(config) max_depth = params["max_depth"] = params.get("max_depth", 0) if max_depth == 0: params["grow_policy"] = params.get("grow_policy", "lossguide") params["tree_method"] = params.get("tree_method", "hist") # params["booster"] = params.get("booster", "gbtree") params["use_label_encoder"] = params.get("use_label_encoder", False) if "n_jobs" in config: params["nthread"] = params.pop("n_jobs") return params def __init__( self, task="regression", **config, ): super().__init__(task, **config) self.params["verbosity"] = 0 def fit(self, X_train, y_train, budget=None, **kwargs): import xgboost as xgb start_time = time.time() deadline = start_time + budget if budget else np.inf 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"]) else: dtrain = xgb.DMatrix(X_train, label=y_train) objective = self.params.get("objective") if isinstance(objective, str): obj = None else: obj = objective if "objective" in self.params: del self.params["objective"] _n_estimators = self.params.pop("n_estimators") callbacks = XGBoostEstimator._callbacks(start_time, deadline) if callbacks: self._model = xgb.train( self.params, dtrain, _n_estimators, obj=obj, callbacks=callbacks, ) self.params["n_estimators"] = self._model.best_iteration + 1 else: self._model = xgb.train(self.params, dtrain, _n_estimators, obj=obj) self.params["n_estimators"] = _n_estimators self.params["objective"] = objective del dtrain train_time = time.time() - start_time return train_time def predict(self, X, **kwargs): import xgboost as xgb if not issparse(X): X = self._preprocess(X) dtest = xgb.DMatrix(X) return super().predict(dtest) @classmethod def _callbacks(cls, start_time, deadline): try: from xgboost.callback import TrainingCallback except ImportError: # for xgboost<1.3 return None class ResourceLimit(TrainingCallback): def after_iteration(self, model, epoch, evals_log) -> bool: now = time.time() if epoch == 0: self._time_per_iter = now - start_time if now + self._time_per_iter > deadline: return True if psutil is not None: mem = psutil.virtual_memory() if mem.available / mem.total < FREE_MEM_RATIO: return True return False return [ResourceLimit()] class XGBoostSklearnEstimator(SKLearnEstimator, LGBMEstimator): """The class for tuning XGBoost with unlimited depth, using sklearn API.""" DEFAULT_ITER = 10 @classmethod def search_space(cls, data_size, **params): space = XGBoostEstimator.search_space(data_size) space.pop("max_depth") return space @classmethod def cost_relative2lgbm(cls): return XGBoostEstimator.cost_relative2lgbm() def config2params(self, config: dict) -> dict: params = super().config2params(config) max_depth = params["max_depth"] = params.get("max_depth", 0) if max_depth == 0: params["grow_policy"] = params.get("grow_policy", "lossguide") params["tree_method"] = params.get("tree_method", "hist") params["use_label_encoder"] = params.get("use_label_encoder", False) return params def __init__( self, task="binary", **config, ): super().__init__(task, **config) del self.params["verbose"] self.params["verbosity"] = 0 import xgboost as xgb self.estimator_class = xgb.XGBRegressor if "rank" == task: self.estimator_class = xgb.XGBRanker elif task in CLASSIFICATION: self.estimator_class = xgb.XGBClassifier def fit(self, X_train, y_train, budget=None, **kwargs): if issparse(X_train): self.params["tree_method"] = "auto" if kwargs.get("gpu_per_trial"): self.params["tree_method"] = "gpu_hist" kwargs.pop("gpu_per_trial") return super().fit(X_train, y_train, budget, **kwargs) def _callbacks(self, start_time, deadline) -> List[Callable]: return XGBoostEstimator._callbacks(start_time, deadline) class XGBoostLimitDepthEstimator(XGBoostSklearnEstimator): """The class for tuning XGBoost with limited depth, using sklearn API.""" @classmethod def search_space(cls, data_size, **params): space = XGBoostEstimator.search_space(data_size) space.pop("max_leaves") upper = max(6, int(np.log2(data_size[0]))) space["max_depth"] = { "domain": tune.randint(lower=1, upper=min(upper, 16)), "init_value": 6, "low_cost_init_value": 1, } space["learning_rate"]["init_value"] = 0.3 space["n_estimators"]["init_value"] = 10 return space @classmethod def cost_relative2lgbm(cls): return 64 class RandomForestEstimator(SKLearnEstimator, LGBMEstimator): """The class for tuning Random Forest.""" HAS_CALLBACK = False nrows = 101 @classmethod def search_space(cls, data_size, task, **params): RandomForestEstimator.nrows = int(data_size[0]) upper = min(2048, RandomForestEstimator.nrows) init = 1 / np.sqrt(data_size[1]) if task in CLASSIFICATION else 1 lower = min(0.1, init) space = { "n_estimators": { "domain": tune.lograndint(lower=4, upper=max(5, upper)), "init_value": 4, "low_cost_init_value": 4, }, "max_features": { "domain": tune.loguniform(lower=lower, upper=1.0), "init_value": init, }, "max_leaves": { "domain": tune.lograndint( lower=4, upper=max(5, min(32768, RandomForestEstimator.nrows >> 1)), # ), "init_value": 4, "low_cost_init_value": 4, }, } if task in CLASSIFICATION: space["criterion"] = { "domain": tune.choice(["gini", "entropy"]), # "init_value": "gini", } return space @classmethod def cost_relative2lgbm(cls): return 2 def config2params(self, config: dict) -> dict: params = super().config2params(config) if "max_leaves" in params: params["max_leaf_nodes"] = params.get( "max_leaf_nodes", params.pop("max_leaves") ) if self._task not in CLASSIFICATION and "criterion" in config: params.pop("criterion") return params def __init__( self, task="binary", **params, ): super().__init__(task, **params) self.params["verbose"] = 0 self.estimator_class = RandomForestRegressor if task in CLASSIFICATION: self.estimator_class = RandomForestClassifier class ExtraTreesEstimator(RandomForestEstimator): """The class for tuning Extra Trees.""" @classmethod def cost_relative2lgbm(cls): return 1.9 def __init__(self, task="binary", **params): super().__init__(task, **params) if "regression" in task: self.estimator_class = ExtraTreesRegressor else: self.estimator_class = ExtraTreesClassifier class LRL1Classifier(SKLearnEstimator): """The class for tuning Logistic Regression with L1 regularization.""" @classmethod def search_space(cls, **params): return { "C": { "domain": tune.loguniform(lower=0.03125, upper=32768.0), "init_value": 1.0, }, } @classmethod def cost_relative2lgbm(cls): return 160 def config2params(self, config: dict) -> dict: params = super().config2params(config) 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 class LRL2Classifier(SKLearnEstimator): """The class for tuning Logistic Regression with L2 regularization.""" limit_resource = True @classmethod def search_space(cls, **params): return LRL1Classifier.search_space(**params) @classmethod def cost_relative2lgbm(cls): return 25 def config2params(self, config: dict) -> dict: params = super().config2params(config) 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 class CatBoostEstimator(BaseEstimator): """The class for tuning CatBoost.""" ITER_HP = "n_estimators" DEFAULT_ITER = 1000 @classmethod def search_space(cls, data_size, **params): upper = max(min(round(1500000 / data_size[0]), 150), 12) return { "early_stopping_rounds": { "domain": tune.lograndint(lower=10, upper=upper), "init_value": 10, "low_cost_init_value": 10, }, "learning_rate": { "domain": tune.loguniform(lower=0.005, upper=0.2), "init_value": 0.1, }, "n_estimators": { "domain": 8192, "init_value": 8192, }, } @classmethod def size(cls, config): n_estimators = config.get("n_estimators", 8192) max_leaves = 64 return (max_leaves * 3 + (max_leaves - 1) * 4 + 1.0) * n_estimators * 8 @classmethod def cost_relative2lgbm(cls): return 15 def _preprocess(self, X): if isinstance(X, DataFrame): cat_columns = X.select_dtypes(include=["category"]).columns if not cat_columns.empty: X = X.copy() X[cat_columns] = X[cat_columns].apply( lambda x: x.cat.rename_categories( [ str(c) if isinstance(c, float) else c for c in x.cat.categories ] ) ) elif isinstance(X, np.ndarray) and X.dtype.kind not in "buif": # numpy array is not of numeric dtype X = DataFrame(X) for col in X.columns: if isinstance(X[col][0], str): X[col] = X[col].astype("category").cat.codes X = X.to_numpy() return X def config2params(self, config: dict) -> dict: params = super().config2params(config) 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", **config, ): super().__init__(task, **config) self.params.update( { "verbose": config.get("verbose", False), "random_seed": config.get("random_seed", 10242048), } ) from catboost import CatBoostRegressor self.estimator_class = CatBoostRegressor if task in CLASSIFICATION: from catboost import CatBoostClassifier self.estimator_class = CatBoostClassifier def fit(self, X_train, y_train, budget=None, **kwargs): start_time = time.time() deadline = start_time + budget if budget else np.inf train_dir = f"catboost_{str(start_time)}" X_train = self._preprocess(X_train) if isinstance(X_train, DataFrame): cat_features = list(X_train.select_dtypes(include="category").columns) else: cat_features = [] n = max(int(len(y_train) * 0.9), len(y_train) - 1000) X_tr, y_tr = X_train[:n], y_train[:n] if "sample_weight" in kwargs: weight = kwargs["sample_weight"] if weight is not None: kwargs["sample_weight"] = weight[:n] else: weight = None from catboost import Pool, __version__ model = self.estimator_class(train_dir=train_dir, **self.params) if __version__ >= "0.26": model.fit( X_tr, y_tr, cat_features=cat_features, eval_set=Pool( data=X_train[n:], label=y_train[n:], cat_features=cat_features ), callbacks=CatBoostEstimator._callbacks(start_time, deadline), **kwargs, ) else: model.fit( X_tr, y_tr, cat_features=cat_features, eval_set=Pool( data=X_train[n:], label=y_train[n:], cat_features=cat_features ), **kwargs, ) shutil.rmtree(train_dir, ignore_errors=True) if weight is not None: kwargs["sample_weight"] = weight self._model = model self.params[self.ITER_HP] = self._model.tree_count_ train_time = time.time() - start_time return train_time @classmethod def _callbacks(cls, start_time, deadline): class ResourceLimit: def after_iteration(self, info) -> bool: now = time.time() if info.iteration == 1: self._time_per_iter = now - start_time if now + self._time_per_iter > deadline: return False if psutil is not None: mem = psutil.virtual_memory() if mem.available / mem.total < FREE_MEM_RATIO: return False return True # can continue return [ResourceLimit()] class KNeighborsEstimator(BaseEstimator): @classmethod def search_space(cls, data_size, **params): upper = min(512, int(data_size[0] / 2)) return { "n_neighbors": { "domain": tune.lograndint(lower=1, upper=max(2, upper)), "init_value": 5, "low_cost_init_value": 1, }, } @classmethod def cost_relative2lgbm(cls): return 30 def config2params(self, config: dict) -> dict: params = super().config2params(config) params["weights"] = params.get("weights", "distance") return params 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, DataFrame): cat_columns = X.select_dtypes(["category"]).columns if X.shape[1] == len(cat_columns): raise ValueError("kneighbor requires at least one numeric feature") X = X.drop(cat_columns, axis=1) elif isinstance(X, np.ndarray) and X.dtype.kind not in "buif": # drop categocial columns if any X = DataFrame(X) cat_columns = [] for col in X.columns: if isinstance(X[col][0], str): cat_columns.append(col) X = X.drop(cat_columns, axis=1) X = X.to_numpy() return X class Prophet(SKLearnEstimator): """The class for tuning Prophet.""" @classmethod def search_space(cls, **params): space = { "changepoint_prior_scale": { "domain": tune.loguniform(lower=0.001, upper=0.05), "init_value": 0.05, "low_cost_init_value": 0.001, }, "seasonality_prior_scale": { "domain": tune.loguniform(lower=0.01, upper=10), "init_value": 10, }, "holidays_prior_scale": { "domain": tune.loguniform(lower=0.01, upper=10), "init_value": 10, }, "seasonality_mode": { "domain": tune.choice(["additive", "multiplicative"]), "init_value": "multiplicative", }, } return space def __init__(self, task="ts_forecast", n_jobs=1, **params): super().__init__(task, **params) def _join(self, X_train, y_train): assert TS_TIMESTAMP_COL in X_train, ( "Dataframe for training ts_forecast model must have column" f' "{TS_TIMESTAMP_COL}" with the dates in X_train.' ) y_train = DataFrame(y_train, columns=[TS_VALUE_COL]) train_df = X_train.join(y_train) return train_df def fit(self, X_train, y_train, budget=None, **kwargs): from prophet import Prophet current_time = time.time() train_df = self._join(X_train, y_train) train_df = self._preprocess(train_df) cols = list(train_df) cols.remove(TS_TIMESTAMP_COL) cols.remove(TS_VALUE_COL) logging.getLogger("prophet").setLevel(logging.WARNING) model = Prophet(**self.params) for regressor in cols: model.add_regressor(regressor) with suppress_stdout_stderr(): model.fit(train_df) train_time = time.time() - current_time self._model = model return train_time def predict(self, X, **kwargs): if isinstance(X, int): raise ValueError( "predict() with steps is only supported for arima/sarimax." " For Prophet, pass a dataframe with the first column containing" " the timestamp values." ) if self._model is not None: X = self._preprocess(X) forecast = self._model.predict(X) return forecast["yhat"] else: logger.warning( "Estimator is not fit yet. Please run fit() before predict()." ) return np.ones(X.shape[0]) def score(self, X_val: DataFrame, y_val: Series, **kwargs): from sklearn.metrics import r2_score from .ml import metric_loss_score y_pred = self.predict(X_val) self._metric = kwargs.get("metric", None) if self._metric: return metric_loss_score(self._metric, y_pred, y_val) else: return r2_score(y_pred, y_val) class ARIMA(Prophet): """The class for tuning ARIMA.""" @classmethod def search_space(cls, **params): space = { "p": { "domain": tune.quniform(lower=0, upper=10, q=1), "init_value": 2, "low_cost_init_value": 0, }, "d": { "domain": tune.quniform(lower=0, upper=10, q=1), "init_value": 2, "low_cost_init_value": 0, }, "q": { "domain": tune.quniform(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, } return space def _join(self, X_train, y_train): train_df = super()._join(X_train, y_train) train_df.index = to_datetime(train_df[TS_TIMESTAMP_COL]) train_df = train_df.drop(TS_TIMESTAMP_COL, axis=1) return train_df def fit(self, X_train, y_train, budget=None, **kwargs): import warnings warnings.filterwarnings("ignore") from statsmodels.tsa.arima.model import ARIMA as ARIMA_estimator current_time = time.time() train_df = self._join(X_train, y_train) train_df = self._preprocess(train_df) regressors = list(train_df) regressors.remove(TS_VALUE_COL) if regressors: model = ARIMA_estimator( train_df[[TS_VALUE_COL]], exog=train_df[regressors], order=(self.params["p"], self.params["d"], self.params["q"]), enforce_stationarity=False, enforce_invertibility=False, ) else: model = ARIMA_estimator( train_df, order=(self.params["p"], self.params["d"], self.params["q"]), enforce_stationarity=False, enforce_invertibility=False, ) with suppress_stdout_stderr(): model = model.fit() train_time = time.time() - current_time self._model = model return train_time def predict(self, X, **kwargs): if self._model is not None: if isinstance(X, int): forecast = self._model.forecast(steps=X) elif isinstance(X, DataFrame): start = X[TS_TIMESTAMP_COL].iloc[0] end = X[TS_TIMESTAMP_COL].iloc[-1] if len(X.columns) > 1: X = self._preprocess(X.drop(columns=TS_TIMESTAMP_COL)) regressors = list(X) forecast = self._model.predict( start=start, end=end, exog=X[regressors] ) else: forecast = self._model.predict(start=start, end=end) else: raise ValueError( "X needs to be either a pandas Dataframe with dates as the first column" " or an int number of periods for predict()." ) return forecast else: return np.ones(X if isinstance(X, int) else X.shape[0]) class SARIMAX(ARIMA): """The class for tuning SARIMA.""" @classmethod def search_space(cls, **params): space = { "p": { "domain": tune.quniform(lower=0, upper=10, q=1), "init_value": 2, "low_cost_init_value": 0, }, "d": { "domain": tune.quniform(lower=0, upper=10, q=1), "init_value": 2, "low_cost_init_value": 0, }, "q": { "domain": tune.quniform(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, "P": { "domain": tune.quniform(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, "D": { "domain": tune.quniform(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, "Q": { "domain": tune.quniform(lower=0, upper=10, q=1), "init_value": 1, "low_cost_init_value": 0, }, "s": { "domain": tune.choice([1, 4, 6, 12]), "init_value": 12, }, } return space def fit(self, X_train, y_train, budget=None, **kwargs): import warnings warnings.filterwarnings("ignore") from statsmodels.tsa.statespace.sarimax import SARIMAX as SARIMAX_estimator current_time = time.time() train_df = self._join(X_train, y_train) train_df = self._preprocess(train_df) regressors = list(train_df) regressors.remove(TS_VALUE_COL) if regressors: model = SARIMAX_estimator( train_df[[TS_VALUE_COL]], exog=train_df[regressors], order=(self.params["p"], self.params["d"], self.params["q"]), seasonality_order=( self.params["P"], self.params["D"], self.params["Q"], self.params["s"], ), enforce_stationarity=False, enforce_invertibility=False, ) else: model = SARIMAX_estimator( train_df, order=(self.params["p"], self.params["d"], self.params["q"]), seasonality_order=( self.params["P"], self.params["D"], self.params["Q"], self.params["s"], ), enforce_stationarity=False, enforce_invertibility=False, ) with suppress_stdout_stderr(): model = model.fit() train_time = time.time() - current_time self._model = model return train_time class TS_SKLearn(SKLearnEstimator): """The class for tuning SKLearn Regressors for time-series forecasting, using hcrystalball""" base_class = SKLearnEstimator @classmethod def search_space(cls, data_size, pred_horizon, **params): space = cls.base_class.search_space(data_size, **params) space.update( { "optimize_for_horizon": { "domain": tune.choice([True, False]), "init_value": False, "low_cost_init_value": False, }, "lags": { "domain": tune.randint( lower=1, upper=max(2, int(np.sqrt(data_size[0]))) ), "init_value": 3, }, } ) return space def __init__(self, task="ts_forecast", **params): super().__init__(task, **params) self.hcrystaball_model = None self.ts_task = ( "regression" if task in TS_FORECASTREGRESSION else "classification" ) def transform_X(self, X): cols = list(X) if len(cols) == 1: ds_col = cols[0] X = DataFrame(index=X[ds_col]) elif len(cols) > 1: ds_col = cols[0] exog_cols = cols[1:] X = X[exog_cols].set_index(X[ds_col]) return X def _fit(self, X_train, y_train, budget=None, **kwargs): from hcrystalball.wrappers import get_sklearn_wrapper X_train = self.transform_X(X_train) X_train = self._preprocess(X_train) params = self.params.copy() lags = params.pop("lags") optimize_for_horizon = params.pop("optimize_for_horizon") estimator = self.base_class(task=self.ts_task, **params) self.hcrystaball_model = get_sklearn_wrapper(estimator.estimator_class) self.hcrystaball_model.lags = int(lags) self.hcrystaball_model.fit(X_train, y_train) if optimize_for_horizon: # Direct Multi-step Forecast Strategy - fit a seperate model for each horizon model_list = [] for i in range(1, kwargs["period"] + 1): ( X_fit, y_fit, ) = self.hcrystaball_model._transform_data_to_tsmodel_input_format( X_train, y_train, i ) self.hcrystaball_model.model.set_params(**estimator.params) model = self.hcrystaball_model.model.fit(X_fit, y_fit) model_list.append(model) self._model = model_list else: ( X_fit, y_fit, ) = self.hcrystaball_model._transform_data_to_tsmodel_input_format( X_train, y_train, kwargs["period"] ) self.hcrystaball_model.model.set_params(**estimator.params) model = self.hcrystaball_model.model.fit(X_fit, y_fit) self._model = model def fit(self, X_train, y_train, budget=None, **kwargs): current_time = time.time() self._fit(X_train, y_train, budget=budget, **kwargs) train_time = time.time() - current_time return train_time def predict(self, X, **kwargs): if self._model is not None: X = self.transform_X(X) X = self._preprocess(X) if isinstance(self._model, list): assert len(self._model) == len( X ), "Model is optimized for horizon, length of X must be equal to `period`." preds = [] for i in range(1, len(self._model) + 1): ( X_pred, _, ) = self.hcrystaball_model._transform_data_to_tsmodel_input_format( X.iloc[:i, :] ) preds.append(self._model[i - 1].predict(X_pred)[-1]) forecast = DataFrame( data=np.asarray(preds).reshape(-1, 1), columns=[self.hcrystaball_model.name], index=X.index, ) else: ( X_pred, _, ) = self.hcrystaball_model._transform_data_to_tsmodel_input_format(X) forecast = self._model.predict(X_pred) return forecast else: logger.warning( "Estimator is not fit yet. Please run fit() before predict()." ) return np.ones(X.shape[0]) class LGBM_TS(TS_SKLearn): """The class for tuning LGBM Regressor for time-series forecasting""" base_class = LGBMEstimator class XGBoost_TS(TS_SKLearn): """The class for tuning XGBoost Regressor for time-series forecasting""" base_class = XGBoostSklearnEstimator # catboost regressor is invalid because it has a `name` parameter, making it incompatible with hcrystalball # class CatBoost_TS_Regressor(TS_Regressor): # base_class = CatBoostEstimator class RF_TS(TS_SKLearn): """The class for tuning Random Forest Regressor for time-series forecasting""" base_class = RandomForestEstimator class ExtraTrees_TS(TS_SKLearn): """The class for tuning Extra Trees Regressor for time-series forecasting""" base_class = ExtraTreesEstimator class XGBoostLimitDepth_TS(TS_SKLearn): """The class for tuning XGBoost Regressor with unlimited depth for time-series forecasting""" base_class = XGBoostLimitDepthEstimator class suppress_stdout_stderr(object): def __init__(self): # Open a pair of null files self.null_fds = [os.open(os.devnull, os.O_RDWR) for x in range(2)] # Save the actual stdout (1) and stderr (2) file descriptors. self.save_fds = (os.dup(1), os.dup(2)) def __enter__(self): # Assign the null pointers to stdout and stderr. os.dup2(self.null_fds[0], 1) os.dup2(self.null_fds[1], 2) def __exit__(self, *_): # Re-assign the real stdout/stderr back to (1) and (2) os.dup2(self.save_fds[0], 1) os.dup2(self.save_fds[1], 2) # Close the null files os.close(self.null_fds[0]) os.close(self.null_fds[1])