# ! # * Copyright (c) FLAML authors. All rights reserved. # * Licensed under the MIT License. See LICENSE file in the # * project root for license information. import time import os from typing import Callable, Optional, List, Union, Any import inspect from functools import partial import numpy as np from scipy.sparse import issparse from sklearn.model_selection import ( train_test_split, RepeatedStratifiedKFold, RepeatedKFold, GroupKFold, TimeSeriesSplit, GroupShuffleSplit, ) from sklearn.utils import shuffle from sklearn.base import BaseEstimator import pandas as pd import logging import json from .ml import ( compute_estimator, train_estimator, get_estimator_class, get_classification_objective, ) from .config import ( MIN_SAMPLE_TRAIN, MEM_THRES, RANDOM_SEED, SMALL_LARGE_THRES, CV_HOLDOUT_THRESHOLD, SPLIT_RATIO, N_SPLITS, SAMPLE_MULTIPLY_FACTOR, ) from .data import ( concat, CLASSIFICATION, TOKENCLASSIFICATION, TS_FORECAST, TS_FORECASTREGRESSION, REGRESSION, _is_nlp_task, NLG_TASKS, ) from . import tune from .training_log import training_log_reader, training_log_writer from flaml.default.suggest import suggest_learner logger = logging.getLogger(__name__) logger_formatter = logging.Formatter( "[%(name)s: %(asctime)s] {%(lineno)d} %(levelname)s - %(message)s", "%m-%d %H:%M:%S" ) try: import mlflow except ImportError: mlflow = None class SearchState: @property def search_space(self): return self._search_space_domain @property def estimated_cost4improvement(self): return max( self.time_best_found - self.time_best_found_old, self.total_time_used - self.time_best_found, ) def valid_starting_point_one_dim(self, value_one_dim, domain_one_dim): from .tune.space import sample """ For each hp in the starting point, check the following 3 conditions: (1) If the type of the starting point does not match the required type in search space, return false (2) If the starting point is not in the required search space, return false (3) If the search space is a value instead of domain, and the value is not equal to the starting point Notice (2) include the case starting point not in user specified search space custom_hp """ if isinstance(domain_one_dim, sample.Domain): renamed_type = list( inspect.signature(domain_one_dim.is_valid).parameters.values() )[0].annotation type_match = renamed_type == Any or isinstance(value_one_dim, renamed_type) if not (type_match and domain_one_dim.is_valid(value_one_dim)): return False elif value_one_dim != domain_one_dim: return False return True def valid_starting_point(self, starting_point, search_space): return any( [ self.valid_starting_point_one_dim( value, search_space[name].get("domain") ) for name, value in starting_point.items() ] ) def __init__( self, learner_class, data_size, task, starting_point=None, period=None, custom_hp=None, max_iter=None, ): self.init_eci = learner_class.cost_relative2lgbm() self._search_space_domain = {} self.init_config = {} self.low_cost_partial_config = {} self.cat_hp_cost = {} self.data_size = data_size self.ls_ever_converged = False self.learner_class = learner_class if task in TS_FORECAST: search_space = learner_class.search_space( data_size=data_size, task=task, pred_horizon=period ) else: search_space = learner_class.search_space(data_size=data_size, task=task) if custom_hp is not None: search_space.update(custom_hp) if ( isinstance(starting_point, dict) and max_iter > 1 # If the number of starting point is larger than max iter, avoid the checking and not self.valid_starting_point(starting_point, search_space) ): logger.warning( "Starting point {} removed because it is outside of the search space".format( starting_point ) ) starting_point = None elif isinstance(starting_point, list) and max_iter > len( starting_point ): # If the number of starting point is larger than max iter, avoid the checking starting_point_len = len(starting_point) starting_point = [ x for x in starting_point if self.valid_starting_point(x, search_space) ] if starting_point_len > len(starting_point): logger.warning( "Starting points outside of the search space are removed. " f"Remaining starting points: {starting_point}" ) starting_point = starting_point or None for name, space in search_space.items(): assert ( "domain" in space ), f"{name}'s domain is missing in the search space spec {space}" self._search_space_domain[name] = space["domain"] if "low_cost_init_value" in space: self.low_cost_partial_config[name] = space["low_cost_init_value"] if "cat_hp_cost" in space: self.cat_hp_cost[name] = space["cat_hp_cost"] # if a starting point is provided, set the init config to be # the starting point provided if ( isinstance(starting_point, dict) and starting_point.get(name) is not None ): self.init_config[name] = starting_point[name] elif ( not isinstance(starting_point, list) and "init_value" in space and self.valid_starting_point_one_dim( space["init_value"], space["domain"] ) ): # If starting point is list, no need to check the validity of self.init_config w.r.t search space self.init_config[name] = space[ "init_value" ] # If starting_point is list, no need to assign value to self.init_config here if isinstance(starting_point, list): self.init_config = starting_point self._hp_names = list(self._search_space_domain.keys()) self.search_alg = None self.best_config = None self.best_result = None self.best_loss = self.best_loss_old = np.inf self.total_time_used = 0 self.total_iter = 0 self.base_eci = None self.time_best_found = self.time_best_found_old = 0 self.time2eval_best = 0 self.time2eval_best_old = 0 self.trained_estimator = None self.sample_size = None self.trial_time = 0 def update(self, result, time_used): if result: config = result["config"] if config and "FLAML_sample_size" in config: self.sample_size = config["FLAML_sample_size"] else: self.sample_size = self.data_size[0] obj = result["val_loss"] metric_for_logging = result["metric_for_logging"] time2eval = result["time_total_s"] trained_estimator = result["trained_estimator"] del result["trained_estimator"] # free up RAM n_iter = ( trained_estimator and hasattr(trained_estimator, "ITER_HP") and trained_estimator.params[trained_estimator.ITER_HP] ) if n_iter: config[trained_estimator.ITER_HP] = n_iter else: obj, time2eval, trained_estimator = np.inf, 0.0, None metric_for_logging = config = None self.trial_time = time2eval self.total_time_used += time_used self.total_iter += 1 if self.base_eci is None: self.base_eci = time_used if (obj is not None) and (self.best_loss is None or obj < self.best_loss): self.best_loss_old = self.best_loss if self.best_loss < np.inf else 2 * obj self.best_loss = obj self.best_result = result self.time_best_found_old = self.time_best_found self.time_best_found = self.total_time_used self.iter_best_found = self.total_iter self.best_config = config self.best_config_sample_size = self.sample_size self.best_config_train_time = time_used if time2eval: self.time2eval_best_old = self.time2eval_best self.time2eval_best = time2eval if ( self.trained_estimator and trained_estimator and self.trained_estimator != trained_estimator ): self.trained_estimator.cleanup() if trained_estimator: self.trained_estimator = trained_estimator elif trained_estimator: trained_estimator.cleanup() self.metric_for_logging = metric_for_logging self.val_loss, self.config = obj, config def get_hist_config_sig(self, sample_size, config): config_values = tuple([config[k] for k in self._hp_names]) config_sig = str(sample_size) + "_" + str(config_values) return config_sig def est_retrain_time(self, retrain_sample_size): assert ( self.best_config_sample_size is not None ), "need to first get best_config_sample_size" return self.time2eval_best * retrain_sample_size / self.best_config_sample_size class AutoMLState: def _prepare_sample_train_data(self, sample_size): sampled_weight = groups = None if sample_size <= self.data_size[0]: if isinstance(self.X_train, pd.DataFrame): sampled_X_train = self.X_train.iloc[:sample_size] else: sampled_X_train = self.X_train[:sample_size] sampled_y_train = self.y_train[:sample_size] weight = self.fit_kwargs.get( "sample_weight" ) # NOTE: _prepare_sample_train_data is before if weight is not None: sampled_weight = weight[:sample_size] if self.groups is not None: groups = self.groups[:sample_size] else: sampled_X_train = self.X_train_all sampled_y_train = self.y_train_all if ( "sample_weight" in self.fit_kwargs ): # NOTE: _prepare_sample_train_data is before sampled_weight = self.sample_weight_all if self.groups is not None: groups = self.groups_all return sampled_X_train, sampled_y_train, sampled_weight, groups @staticmethod def _compute_with_config_base(config_w_resource, state, estimator): if "FLAML_sample_size" in config_w_resource: sample_size = int(config_w_resource["FLAML_sample_size"]) else: sample_size = state.data_size[0] this_estimator_kwargs = state.fit_kwargs_by_estimator.get( estimator ).copy() # NOTE: _compute_with_config_base is after ( sampled_X_train, sampled_y_train, sampled_weight, groups, ) = state._prepare_sample_train_data(sample_size) if sampled_weight is not None: weight = this_estimator_kwargs["sample_weight"] this_estimator_kwargs["sample_weight"] = sampled_weight if groups is not None: this_estimator_kwargs["groups"] = groups config = config_w_resource.copy() if "FLAML_sample_size" in config: del config["FLAML_sample_size"] budget = ( None if state.time_budget is None else state.time_budget - state.time_from_start if sample_size == state.data_size[0] else (state.time_budget - state.time_from_start) / 2 * sample_size / state.data_size[0] ) ( trained_estimator, val_loss, metric_for_logging, _, pred_time, ) = compute_estimator( sampled_X_train, sampled_y_train, state.X_val, state.y_val, state.weight_val, state.groups_val, state.train_time_limit if budget is None else min(budget, state.train_time_limit), state.kf, config, state.task, estimator, state.eval_method, state.metric, state.best_loss, state.n_jobs, state.learner_classes.get(estimator), state.log_training_metric, this_estimator_kwargs, ) if state.retrain_final and not state.model_history: trained_estimator.cleanup() result = { "pred_time": pred_time, "wall_clock_time": time.time() - state._start_time_flag, "metric_for_logging": metric_for_logging, "val_loss": val_loss, "trained_estimator": trained_estimator, } if sampled_weight is not None: this_estimator_kwargs["sample_weight"] = weight tune.report(**result) return result def _train_with_config( self, estimator, config_w_resource, sample_size=None, ): if not sample_size: sample_size = config_w_resource.get( "FLAML_sample_size", len(self.y_train_all) ) config = config_w_resource.get("ml", config_w_resource).copy() if "FLAML_sample_size" in config: del config["FLAML_sample_size"] if "learner" in config: del config["learner"] this_estimator_kwargs = self.fit_kwargs_by_estimator.get( estimator ).copy() # NOTE: _train_with_config is after ( sampled_X_train, sampled_y_train, sampled_weight, groups, ) = self._prepare_sample_train_data(sample_size) if sampled_weight is not None: weight = this_estimator_kwargs[ "sample_weight" ] # NOTE: _train_with_config is after this_estimator_kwargs[ "sample_weight" ] = sampled_weight # NOTE: _train_with_config is after if groups is not None: this_estimator_kwargs[ "groups" ] = groups # NOTE: _train_with_config is after budget = ( None if self.time_budget is None else self.time_budget - self.time_from_start ) estimator, train_time = train_estimator( X_train=sampled_X_train, y_train=sampled_y_train, config_dic=config, task=self.task, estimator_name=estimator, n_jobs=self.n_jobs, estimator_class=self.learner_classes.get(estimator), budget=budget, fit_kwargs=this_estimator_kwargs, # NOTE: _train_with_config is after eval_metric=self.metric if hasattr(self, "metric") else "train_time", ) if sampled_weight is not None: this_estimator_kwargs[ "sample_weight" ] = weight # NOTE: _train_with_config is after return estimator, train_time def size(state: AutoMLState, config: dict) -> float: """Size function. Returns: The mem size in bytes for a config. """ config = config.get("ml", config) estimator = config["learner"] learner_class = state.learner_classes.get(estimator) return learner_class.size(config) class AutoML(BaseEstimator): """The AutoML class. Example: ```python automl = AutoML() automl_settings = { "time_budget": 60, "metric": 'accuracy', "task": 'classification', "log_file_name": 'mylog.log', } automl.fit(X_train = X_train, y_train = y_train, **automl_settings) ``` """ from .version import __version__ def __init__(self, **settings): """Constructor. Many settings in fit() can be passed to the constructor too. If an argument in fit() is provided, it will override the setting passed to the constructor. If an argument in fit() is not provided but provided in the constructor, the value passed to the constructor will be used. Args: 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 passing a customized metric function, the function needs to have the follwing signature: ```python def custom_metric( X_test, y_test, estimator, labels, X_train, y_train, weight_test=None, weight_train=None, config=None, groups_test=None, groups_train=None, ): return metric_to_minimize, metrics_to_log ``` which returns a float number as the minimization objective, and a dictionary as the metrics to log. E.g., ```python def custom_metric( X_val, y_val, estimator, labels, X_train, y_train, weight_val=None, weight_train=None, *args, ): from sklearn.metrics import log_loss import time start = time.time() y_pred = estimator.predict_proba(X_val) pred_time = (time.time() - start) / len(X_val) val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val) y_pred = estimator.predict_proba(X_train) train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train) alpha = 0.5 return val_loss * (1 + alpha) - alpha * train_loss, { "val_loss": val_loss, "train_loss": train_loss, "pred_time": pred_time, } ``` task: A string of the task type, e.g., 'classification', 'regression', 'ts_forecast', 'rank', 'seq-classification', 'seq-regression', 'summarization'. n_jobs: An integer of the number of threads for training | default=-1. Use all available resources when n_jobs == -1. log_file_name: A string of the log file name | default="". To disable logging, set it to be an empty string "". estimator_list: A list of strings for estimator names, or 'auto' e.g., ```['lgbm', 'xgboost', 'xgb_limitdepth', 'catboost', 'rf', 'extra_tree']``` time_budget: A float number of the time budget in seconds. Use -1 if no time limit. max_iter: An integer of the maximal number of iterations. sample: A boolean of whether to sample the training data during search. ensemble: boolean or dict | default=False. Whether to perform ensemble after search. Can be a dict with keys 'passthrough' and 'final_estimator' to specify the passthrough and final_estimator in the stacker. eval_method: A string of resampling strategy, one of ['auto', 'cv', 'holdout']. split_ratio: A float of the valiation data percentage for holdout. n_splits: An integer of the number of folds for cross - validation. log_type: A string of the log type, one of ['better', 'all']. 'better' only logs configs with better loss than previos iters 'all' logs all the tried configs. model_history: A boolean of whether to keep the best model per estimator. Make sure memory is large enough if setting to True. log_training_metric: A boolean of whether to log the training metric for each model. mem_thres: A float of the memory size constraint in bytes. pred_time_limit: A float of the prediction latency constraint in seconds. It refers to the average prediction time per row in validation data. train_time_limit: A float of the training time constraint in seconds. 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. True - retrain only after search finishes; False - no retraining; 'budget' - do best effort to retrain without violating the time budget. split_type: str or splitter object, default="auto" | the data split type. * A valid splitter object is an instance of a derived class of scikit-learn [KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold) and have ``split`` and ``get_n_splits`` methods with the same signatures. Set eval_method to "cv" to use the splitter object. * Valid str options depend on different tasks. For classification tasks, valid choices are ["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified. For regression tasks, valid choices are ["auto", 'uniform', 'time']. "auto" -> uniform. For ts_forecast tasks, must be "auto" or 'time'. For ranking task, must be "auto" or 'group'. hpo_method: str, default="auto" | The hyperparameter optimization method. By default, CFO is used for sequential search and BlendSearch is used for parallel search. No need to set when using flaml's default search space or using a simple customized search space. When set to 'bs', BlendSearch is used. BlendSearch can be tried when the search space is complex, for example, containing multiple disjoint, discontinuous subspaces. When set to 'random', random search is used. starting_points: A dictionary or a str to specify the starting hyperparameter config for the estimators | default="static". If str: - if "data", use data-dependent defaults; - if "data:path" use data-dependent defaults which are stored at path; - if "static", use data-independent defaults. If dict, keys are the name of the estimators, and values are the starting hyperparamter configurations for the corresponding estimators. The value can be a single hyperparamter configuration dict or a list of hyperparamter configuration dicts. In the following code example, we get starting_points from the `automl` object and use them in the `new_automl` object. e.g., ```python from flaml import AutoML automl = AutoML() X_train, y_train = load_iris(return_X_y=True) automl.fit(X_train, y_train) starting_points = automl.best_config_per_estimator new_automl = AutoML() new_automl.fit(X_train, y_train, starting_points=starting_points) ``` seed: int or None, default=None | The random seed for hpo. n_concurrent_trials: [Experimental] int, default=1 | The number of concurrent trials. When n_concurrent_trials > 1, flaml performes [parallel tuning](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning) and installation of ray is required: `pip install flaml[ray]`. keep_search_state: boolean, default=False | Whether to keep data needed for model search after fit(). By default the state is deleted for space 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 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. use_ray: boolean, default=False | Whether to use ray to run the training in separate processes. This can be used to prevent OOM for large datasets, but will incur more overhead in time. Only use it if you run into OOM failures. metric_constraints: list, default=[] | The list of metric constraints. Each element in this list is a 3-tuple, which shall be expressed in the following format: the first element of the 3-tuple is the name of the metric, the second element is the inequality sign chosen from ">=" and "<=", and the third element is the constraint value. E.g., `('val_loss', '<=', 0.1)`. Note that all the metric names in metric_constraints need to be reported via the metrics_to_log dictionary returned by a customized metric function. The customized metric function shall be provided via the `metric` key word argument of the fit() function or the automl constructor. Find an example in the 4th constraint type in this [doc](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#constraint). If `pred_time_limit` is provided as one of keyword arguments to fit() function or the automl constructor, flaml will automatically (and under the hood) add it as an additional element in the metric_constraints. Essentially 'pred_time_limit' specifies a constraint about the prediction latency constraint in seconds. custom_hp: dict, default=None | The custom search space specified by user Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the domain of the custom search space can either be a value of a sample.Domain object. e.g., ```python custom_hp = { "transformer_ms": { "model_path": { "domain": "albert-base-v2", }, "learning_rate": { "domain": tune.choice([1e-4, 1e-5]), } } } ``` fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name. e.g., ```python fit_kwargs_by_estimator = { "transformer": { "output_dir": "test/data/output/", "ckpt_per_epoch": 1, "fp16": False, } } ``` """ self._track_iter = 0 self._state = AutoMLState() self._state.learner_classes = {} self._settings = settings settings["time_budget"] = settings.get("time_budget", 60) settings["task"] = settings.get("task", "classification") settings["n_jobs"] = settings.get("n_jobs", -1) settings["eval_method"] = settings.get("eval_method", "auto") settings["split_ratio"] = settings.get("split_ratio", SPLIT_RATIO) settings["n_splits"] = settings.get("n_splits", N_SPLITS) settings["auto_augment"] = settings.get("auto_augment", True) settings["metric"] = settings.get("metric", "auto") settings["estimator_list"] = settings.get("estimator_list", "auto") settings["log_file_name"] = settings.get("log_file_name", "") settings["max_iter"] = settings.get("max_iter", 1000000) settings["sample"] = settings.get("sample", True) settings["ensemble"] = settings.get("ensemble", False) settings["log_type"] = settings.get("log_type", "better") settings["model_history"] = settings.get("model_history", False) settings["log_training_metric"] = settings.get("log_training_metric", False) settings["mem_thres"] = settings.get("mem_thres", MEM_THRES) settings["pred_time_limit"] = settings.get("pred_time_limit", np.inf) settings["train_time_limit"] = settings.get("train_time_limit", np.inf) settings["verbose"] = settings.get("verbose", 3) settings["retrain_full"] = settings.get("retrain_full", True) settings["split_type"] = settings.get("split_type", "auto") settings["hpo_method"] = settings.get("hpo_method", "auto") settings["learner_selector"] = settings.get("learner_selector", "sample") settings["starting_points"] = settings.get("starting_points", "static") settings["n_concurrent_trials"] = settings.get("n_concurrent_trials", 1) settings["keep_search_state"] = settings.get("keep_search_state", False) settings["early_stop"] = settings.get("early_stop", False) settings["append_log"] = settings.get("append_log", False) settings["min_sample_size"] = settings.get("min_sample_size", MIN_SAMPLE_TRAIN) settings["use_ray"] = settings.get("use_ray", False) settings["metric_constraints"] = settings.get("metric_constraints", []) settings["fit_kwargs_by_estimator"] = settings.get( "fit_kwargs_by_estimator", {} ) settings["custom_hp"] = settings.get("custom_hp", {}) self._estimator_type = ( "classifier" if settings["task"] in CLASSIFICATION else "regressor" ) def get_params(self, deep=False): return self._settings.copy() @property def config_history(self): """A dictionary of iter->(estimator, config, time), storing the best estimator, config, and the time when the best model is updated each time. """ return self._config_history @property def model(self): """An object with `predict()` and `predict_proba()` method (for classification), storing the best trained model. """ return self.__dict__.get("_trained_estimator") def best_model_for_estimator(self, estimator_name): """Return the best model found for a particular estimator. Args: estimator_name: a str of the estimator's name. Returns: An object storing the best model for estimator_name. If `model_history` was set to False during fit(), then the returned model is untrained unless estimator_name is the best estimator. If `model_history` was set to True, then the returned model is trained. """ state = self._search_states.get(estimator_name) return state and getattr(state, "trained_estimator", None) @property def best_estimator(self): """A string indicating the best estimator found.""" return self._best_estimator @property def best_iteration(self): """An integer of the iteration number where the best config is found.""" return self._best_iteration @property def best_config(self): """A dictionary of the best configuration.""" state = self._search_states.get(self._best_estimator) return state and getattr(state, "best_config", None) @property def best_config_per_estimator(self): """A dictionary of all estimators' best configuration.""" return { e: e_search_state.best_config for e, e_search_state in self._search_states.items() } @property def best_loss_per_estimator(self): """A dictionary of all estimators' best loss.""" return { e: e_search_state.best_loss for e, e_search_state in self._search_states.items() } @property def best_loss(self): """A float of the best loss found.""" return self._state.best_loss @property def best_result(self): """Result dictionary for model trained with the best config.""" state = self._search_states.get(self._best_estimator) return state and getattr(state, "best_result", None) @property def metrics_for_best_config(self): """Returns a float of the best loss, and a dictionary of the auxiliary metrics to log associated with the best config. These two objects correspond to the returned objects by the customized metric function for the config with the best loss.""" state = self._search_states.get(self._best_estimator) return self._state.best_loss, state and getattr(state, "best_result", {}).get( "metric_for_logging" ) @property def best_config_train_time(self): """A float of the seconds taken by training the best config.""" return getattr( self._search_states[self._best_estimator], "best_config_train_time", None ) def save_best_config(self, filename): best = { "class": self.best_estimator, "hyperparameters": self.best_config, } os.makedirs(os.path.dirname(filename), exist_ok=True) with open(filename, "w") as f: json.dump(best, f) @property def classes_(self): """A numpy array of shape (n_classes,) for class labels.""" attr = getattr(self, "_label_transformer", None) if attr: return attr.classes_ attr = getattr(self, "_trained_estimator", None) if attr: return attr.classes_ return None @property def n_features_in_(self): return self._trained_estimator.n_features_in_ @property def time_to_find_best_model(self) -> float: """Time taken to find best model in seconds.""" return self.__dict__.get("_time_taken_best_iter") def score(self, X: pd.DataFrame, y: pd.Series, **kwargs): estimator = getattr(self, "_trained_estimator", None) if estimator is None: logger.warning( "No estimator is trained. Please run fit with enough budget." ) return None X = self._preprocess(X) return estimator.score(X, y, **kwargs) def predict( self, X: Union[np.array, pd.DataFrame, List[str], List[List[str]]], **pred_kwargs, ): """Predict label from features. Args: X: A numpy array of featurized instances, shape n * m, or for ts_forecast tasks: a pandas dataframe with the first column containing timestamp values (datetime type) or an integer n for the predict steps (only valid when the estimator is arima or sarimax). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). **pred_kwargs: Other key word arguments to pass to predict() function of the searched learners, such as per_device_eval_batch_size. ```python multivariate_X_test = pd.DataFrame({ 'timeStamp': pd.date_range(start='1/1/2022', end='1/07/2022'), 'categorical_col': ['yes', 'yes', 'no', 'no', 'yes', 'no', 'yes'], 'continuous_col': [105, 107, 120, 118, 110, 112, 115] }) model.predict(multivariate_X_test) ``` Returns: A array-like of shape n * 1: each element is a predicted label for an instance. """ estimator = getattr(self, "_trained_estimator", None) if estimator is None: logger.warning( "No estimator is trained. Please run fit with enough budget." ) return None X = self._preprocess(X) y_pred = estimator.predict(X, **pred_kwargs) if ( isinstance(y_pred, np.ndarray) and y_pred.ndim > 1 and isinstance(y_pred, np.ndarray) ): y_pred = y_pred.flatten() if self._label_transformer: return self._label_transformer.inverse_transform( pd.Series(y_pred.astype(int)) ) else: return y_pred def predict_proba(self, X, **pred_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. **pred_kwargs: Other key word arguments to pass to predict_proba() function of the searched learners, such as per_device_eval_batch_size. 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. """ estimator = getattr(self, "_trained_estimator", None) if estimator is None: logger.warning( "No estimator is trained. Please run fit with enough budget." ) return None X = self._preprocess(X) proba = self._trained_estimator.predict_proba(X, **pred_kwargs) return proba def _preprocess(self, X): if isinstance(X, List): try: if isinstance(X[0], List): X = [x for x in zip(*X)] X = pd.DataFrame( dict( [ (self._transformer._str_columns[idx], X[idx]) if isinstance(X[0], List) else (self._transformer._str_columns[idx], [X[idx]]) for idx in range(len(X)) ] ) ) except IndexError: raise IndexError( "Test data contains more columns than training data, exiting" ) elif isinstance(X, int): return X elif issparse(X): X = X.tocsr() if self._state.task in TS_FORECAST: X = pd.DataFrame(X) if self._transformer: X = self._transformer.transform(X) return X def _validate_ts_data( self, dataframe, y_train_all=None, ): assert ( dataframe[dataframe.columns[0]].dtype.name == "datetime64[ns]" ), f"For '{TS_FORECAST}' task, the first column must contain timestamp values." if y_train_all is not None: y_df = ( pd.DataFrame(y_train_all) if isinstance(y_train_all, pd.Series) else pd.DataFrame(y_train_all, columns=["labels"]) ) dataframe = dataframe.join(y_df) duplicates = dataframe.duplicated() if any(duplicates): logger.warning( "Duplicate timestamp values found in timestamp column. " f"\n{dataframe.loc[duplicates, dataframe][dataframe.columns[0]]}" ) dataframe = dataframe.drop_duplicates() logger.warning("Removed duplicate rows based on all columns") assert ( dataframe[[dataframe.columns[0]]].duplicated() is None ), "Duplicate timestamp values with different values for other columns." ts_series = pd.to_datetime(dataframe[dataframe.columns[0]]) inferred_freq = pd.infer_freq(ts_series) if inferred_freq is None: logger.warning( "Missing timestamps detected. To avoid error with estimators, set estimator list to ['prophet']. " ) if y_train_all is not None: return dataframe.iloc[:, :-1], dataframe.iloc[:, -1] return dataframe def _validate_data( self, X_train_all, y_train_all, dataframe, label, X_val=None, y_val=None, groups_val=None, groups=None, ): if X_train_all is not None and y_train_all is not None: assert ( isinstance(X_train_all, np.ndarray) or issparse(X_train_all) or isinstance(X_train_all, pd.DataFrame) ), ( "X_train_all must be a numpy array, a pandas dataframe, " "or Scipy sparse matrix." ) assert isinstance(y_train_all, np.ndarray) or isinstance( y_train_all, pd.Series ), "y_train_all must be a numpy array or a pandas series." assert ( X_train_all.size != 0 and y_train_all.size != 0 ), "Input data must not be empty." if isinstance(X_train_all, np.ndarray) and len(X_train_all.shape) == 1: X_train_all = np.reshape(X_train_all, (X_train_all.size, 1)) if isinstance(y_train_all, np.ndarray): y_train_all = y_train_all.flatten() assert ( X_train_all.shape[0] == y_train_all.shape[0] ), "# rows in X_train must match length of y_train." self._df = isinstance(X_train_all, pd.DataFrame) self._nrow, self._ndim = X_train_all.shape if self._state.task in TS_FORECAST: X_train_all = pd.DataFrame(X_train_all) X_train_all, y_train_all = self._validate_ts_data( X_train_all, y_train_all ) X, y = X_train_all, y_train_all elif dataframe is not None and label is not None: assert isinstance( dataframe, pd.DataFrame ), "dataframe must be a pandas DataFrame" assert label in dataframe.columns, "label must a column name in dataframe" self._df = True if self._state.task in TS_FORECAST: dataframe = self._validate_ts_data(dataframe) X = dataframe.drop(columns=label) self._nrow, self._ndim = X.shape y = dataframe[label] else: raise ValueError("either X_train+y_train or dataframe+label are required") # check the validity of input dimensions for NLP tasks, so need to check _is_nlp_task not estimator if _is_nlp_task(self._state.task): from .nlp.utils import is_a_list_of_str is_all_str = True is_all_list = True for column in X.columns: assert X[column].dtype.name in ( "object", "string", ), "If the task is an NLP task, X can only contain text columns" for each_cell in X[column]: if each_cell is not None: is_str = isinstance(each_cell, str) is_list_of_int = isinstance(each_cell, list) and all( isinstance(x, int) for x in each_cell ) is_list_of_str = is_a_list_of_str(each_cell) if self._state.task == TOKENCLASSIFICATION: assert is_list_of_str, ( "For the token-classification task, the input column needs to be a list of string," "instead of string, e.g., ['EU', 'rejects','German', 'call','to','boycott','British','lamb','.',].", "For more examples, please refer to test/nlp/test_autohf_tokenclassification.py", ) else: assert is_str or is_list_of_int, ( "Each column of the input must either be str (untokenized) " "or a list of integers (tokenized)" ) is_all_str &= is_str is_all_list &= is_list_of_int or is_list_of_str assert is_all_str or is_all_list, ( "Currently FLAML only supports two modes for NLP: either all columns of X are string (non-tokenized), " "or all columns of X are integer ids (tokenized)" ) if issparse(X_train_all): self._transformer = self._label_transformer = False self._X_train_all, self._y_train_all = X, y else: from .data import DataTransformer self._transformer = DataTransformer() self._X_train_all, self._y_train_all = self._transformer.fit_transform( X, y, self._state.task ) self._label_transformer = self._transformer.label_transformer self._sample_weight_full = self._state.fit_kwargs.get( "sample_weight" ) # NOTE: _validate_data is before, if X_val is not None and y_val is not None: assert ( isinstance(X_val, np.ndarray) or issparse(X_val) or isinstance(X_val, pd.DataFrame) ), ( "X_val must be None, a numpy array, a pandas dataframe, " "or Scipy sparse matrix." ) assert isinstance(y_val, np.ndarray) or isinstance( y_val, pd.Series ), "y_val must be None, a numpy array or a pandas series." assert X_val.size != 0 and y_val.size != 0, ( "Validation data are expected to be nonempty. " "Use None for X_val and y_val if no validation data." ) if isinstance(y_val, np.ndarray): y_val = y_val.flatten() assert ( X_val.shape[0] == y_val.shape[0] ), "# rows in X_val must match length of y_val." if self._transformer: self._state.X_val = self._transformer.transform(X_val) else: self._state.X_val = X_val # If it's NLG_TASKS, y_val is a pandas series containing the output sequence tokens, # so we cannot use label_transformer.transform to process it if self._label_transformer: self._state.y_val = self._label_transformer.transform(y_val) else: self._state.y_val = y_val else: self._state.X_val = self._state.y_val = None if groups is not None and len(groups) != self._nrow: # groups is given as group counts self._state.groups = np.concatenate([[i] * c for i, c in enumerate(groups)]) assert ( len(self._state.groups) == self._nrow ), "the sum of group counts must match the number of examples" self._state.groups_val = ( np.concatenate([[i] * c for i, c in enumerate(groups_val)]) if groups_val is not None else None ) else: self._state.groups_val = groups_val self._state.groups = groups def _prepare_data(self, eval_method, split_ratio, n_splits): X_val, y_val = self._state.X_val, self._state.y_val if issparse(X_val): X_val = X_val.tocsr() X_train_all, y_train_all = self._X_train_all, self._y_train_all if issparse(X_train_all): X_train_all = X_train_all.tocsr() if ( self._state.task in CLASSIFICATION and self._auto_augment and self._state.fit_kwargs.get("sample_weight") is None # NOTE: _prepare_data is before and self._split_type in ["stratified", "uniform"] and self._state.task != TOKENCLASSIFICATION ): # logger.info(f"label {pd.unique(y_train_all)}") label_set, counts = np.unique(y_train_all, return_counts=True) # augment rare classes rare_threshld = 20 rare = counts < rare_threshld rare_label, rare_counts = label_set[rare], counts[rare] for i, label in enumerate(rare_label): count = rare_count = rare_counts[i] rare_index = y_train_all == label n = len(y_train_all) while count < rare_threshld: if self._df: X_train_all = concat( X_train_all, X_train_all.iloc[:n].loc[rare_index] ) else: X_train_all = concat( X_train_all, X_train_all[:n][rare_index, :] ) if isinstance(y_train_all, pd.Series): y_train_all = concat( y_train_all, y_train_all.iloc[:n].loc[rare_index] ) else: y_train_all = np.concatenate( [y_train_all, y_train_all[:n][rare_index]] ) count += rare_count logger.info(f"class {label} augmented from {rare_count} to {count}") SHUFFLE_SPLIT_TYPES = ["uniform", "stratified"] if self._split_type in SHUFFLE_SPLIT_TYPES: if self._sample_weight_full is not None: X_train_all, y_train_all, self._state.sample_weight_all = shuffle( X_train_all, y_train_all, self._sample_weight_full, random_state=RANDOM_SEED, ) self._state.fit_kwargs[ "sample_weight" ] = self._state.sample_weight_all # NOTE: _prepare_data is before else: X_train_all, y_train_all = shuffle( X_train_all, y_train_all, random_state=RANDOM_SEED ) if self._df: X_train_all.reset_index(drop=True, inplace=True) if isinstance(y_train_all, pd.Series): y_train_all.reset_index(drop=True, inplace=True) X_train, y_train = X_train_all, y_train_all self._state.groups_all = self._state.groups if X_val is None and eval_method == "holdout": # if eval_method = holdout, make holdout data if self._split_type == "time": if self._state.task in TS_FORECAST: num_samples = X_train_all.shape[0] period = self._state.fit_kwargs[ "period" ] # NOTE: _prepare_data is before assert ( period < num_samples ), f"period={period}>#examples={num_samples}" split_idx = num_samples - period X_train = X_train_all[:split_idx] y_train = y_train_all[:split_idx] X_val = X_train_all[split_idx:] y_val = y_train_all[split_idx:] else: if ( "sample_weight" in self._state.fit_kwargs ): # NOTE: _prepare_data is before ( X_train, X_val, y_train, y_val, self._state.fit_kwargs[ "sample_weight" ], # NOTE: _prepare_data is before self._state.weight_val, ) = train_test_split( X_train_all, y_train_all, self._state.fit_kwargs[ "sample_weight" ], # NOTE: _prepare_data is before test_size=split_ratio, shuffle=False, ) else: X_train, X_val, y_train, y_val = train_test_split( X_train_all, y_train_all, test_size=split_ratio, shuffle=False, ) elif self._split_type == "group": gss = GroupShuffleSplit( n_splits=1, test_size=split_ratio, random_state=RANDOM_SEED ) for train_idx, val_idx in gss.split( X_train_all, y_train_all, self._state.groups_all ): if self._df: X_train = X_train_all.iloc[train_idx] X_val = X_train_all.iloc[val_idx] else: X_train, X_val = X_train_all[train_idx], X_train_all[val_idx] 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 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) rest = [] last = 0 first.sort() for i in range(len(first)): rest.extend(range(last, first[i])) last = first[i] + 1 rest.extend(range(last, len(y_train_all))) X_first = X_train_all.iloc[first] if self._df else X_train_all[first] X_rest = X_train_all.iloc[rest] if self._df else X_train_all[rest] y_rest = y_train_all[rest] stratify = y_rest if self._split_type == "stratified" else None if ( "sample_weight" in self._state.fit_kwargs ): # NOTE: _prepare_data is before ( X_train, X_val, y_train, y_val, weight_train, weight_val, ) = train_test_split( X_rest, y_rest, self._state.fit_kwargs["sample_weight"][ rest ], # NOTE: _prepare_data is before test_size=split_ratio, random_state=RANDOM_SEED, ) weight1 = self._state.fit_kwargs["sample_weight"][ first ] # NOTE: _prepare_data is before self._state.weight_val = concat(weight1, weight_val) self._state.fit_kwargs[ "sample_weight" ] = concat( # NOTE: _prepare_data is before weight1, weight_train ) else: X_train, X_val, y_train, y_val = train_test_split( X_rest, y_rest, test_size=split_ratio, stratify=stratify, random_state=RANDOM_SEED, ) X_train = concat(X_first, X_train) y_train = ( concat(label_set, y_train) if self._df else np.concatenate([label_set, y_train]) ) X_val = concat(X_first, X_val) y_val = ( concat(label_set, y_val) if self._df else np.concatenate([label_set, y_val]) ) elif self._state.task in REGRESSION: if ( "sample_weight" in self._state.fit_kwargs ): # NOTE: _prepare_data is before ( X_train, X_val, y_train, y_val, self._state.fit_kwargs[ "sample_weight" ], # NOTE: _prepare_data is before self._state.weight_val, ) = train_test_split( X_train_all, y_train_all, self._state.fit_kwargs[ "sample_weight" ], # NOTE: _prepare_data is before test_size=split_ratio, random_state=RANDOM_SEED, ) else: X_train, X_val, y_train, y_val = train_test_split( X_train_all, y_train_all, test_size=split_ratio, random_state=RANDOM_SEED, ) self._state.data_size = X_train.shape self.data_size_full = len(y_train_all) self._state.X_train, self._state.y_train = X_train, y_train self._state.X_val, self._state.y_val = X_val, y_val self._state.X_train_all = X_train_all self._state.y_train_all = y_train_all if eval_method == "holdout": self._state.kf = None return if self._split_type == "group": # logger.info("Using GroupKFold") assert ( len(self._state.groups_all) == y_train_all.size ), "the length of groups must match the number of examples" assert ( len(np.unique(self._state.groups_all)) >= n_splits ), "the number of groups must be equal or larger than n_splits" self._state.kf = GroupKFold(n_splits) self._state.kf.groups = self._state.groups_all elif self._split_type == "stratified": # logger.info("Using StratifiedKFold") assert y_train_all.size >= n_splits, ( f"{n_splits}-fold cross validation" f" requires input data with at least {n_splits} examples." ) assert y_train_all.size >= 2 * n_splits, ( f"{n_splits}-fold cross validation with metric=r2 " f"requires input data with at least {n_splits*2} examples." ) self._state.kf = RepeatedStratifiedKFold( n_splits=n_splits, n_repeats=1, random_state=RANDOM_SEED ) elif self._split_type == "time": # logger.info("Using TimeSeriesSplit") if self._state.task in TS_FORECAST: period = self._state.fit_kwargs[ "period" ] # NOTE: _prepare_data is before if period * (n_splits + 1) > y_train_all.size: n_splits = int(y_train_all.size / period - 1) assert n_splits >= 2, ( f"cross validation for forecasting period={period}" f" requires input data with at least {3 * period} examples." ) logger.info(f"Using nsplits={n_splits} due to data size limit.") self._state.kf = TimeSeriesSplit(n_splits=n_splits, test_size=period) else: self._state.kf = TimeSeriesSplit(n_splits=n_splits) elif isinstance(self._split_type, str): # logger.info("Using RepeatedKFold") self._state.kf = RepeatedKFold( n_splits=n_splits, n_repeats=1, random_state=RANDOM_SEED ) else: # logger.info("Using splitter object") self._state.kf = self._split_type def add_learner(self, learner_name, learner_class): """Add a customized learner. Args: learner_name: A string of the learner's name. learner_class: A subclass of flaml.model.BaseEstimator. """ self._state.learner_classes[learner_name] = learner_class def get_estimator_from_log(self, log_file_name, record_id, task): """Get the estimator from log file. Args: log_file_name: A string of the log file name. record_id: An integer of the record ID in the file, 0 corresponds to the first trial. task: A string of the task type, 'binary', 'multiclass', 'regression', 'ts_forecast', 'rank'. Returns: An estimator object for the given configuration. """ with training_log_reader(log_file_name) as reader: record = reader.get_record(record_id) estimator = record.learner config = record.config estimator, _ = train_estimator( X_train=None, y_train=None, config_dic=config, task=task, estimator_name=estimator, estimator_class=self._state.learner_classes.get(estimator), eval_metric="train_time", ) return estimator def retrain_from_log( self, log_file_name, X_train=None, y_train=None, dataframe=None, label=None, time_budget=np.inf, task=None, eval_method=None, split_ratio=None, n_splits=None, split_type=None, groups=None, n_jobs=-1, # gpu_per_trial=0, train_best=True, train_full=False, record_id=-1, auto_augment=None, custom_hp=None, fit_kwargs_by_estimator=None, **fit_kwargs, ): """Retrain from log file. Args: log_file_name: A string of the log file name. X_train: A numpy array or dataframe of training data in shape n*m. For ts_forecast tasks, the first column of X_train must be the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). y_train: A numpy array or series of labels in shape n*1. dataframe: A dataframe of training data including label column. For ts_forecast tasks, dataframe must be specified and should have at least two columns: timestamp and label, where the first column is the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). label: A str of the label column name, e.g., 'label'; Note: If X_train and y_train are provided, dataframe and label are ignored; If not, dataframe and label must be provided. time_budget: A float number of the time budget in seconds. task: A string of the task type, e.g., 'classification', 'regression', 'ts_forecast', 'rank', 'seq-classification', 'seq-regression', 'summarization'. eval_method: A string of resampling strategy, one of ['auto', 'cv', 'holdout']. split_ratio: A float of the validation data percentage for holdout. n_splits: An integer of the number of folds for cross-validation. split_type: str or splitter object, default="auto" | the data split type. * A valid splitter object is an instance of a derived class of scikit-learn [KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold) and have ``split`` and ``get_n_splits`` methods with the same signatures. Set eval_method to "cv" to use the splitter object. * Valid str options depend on different tasks. For classification tasks, valid choices are ["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified. For regression tasks, valid choices are ["auto", 'uniform', 'time']. "auto" -> uniform. For ts_forecast tasks, must be "auto" or 'time'. For ranking task, must be "auto" or 'group'. 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 | default=-1. 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, eval_method and sample_size in the log file will be ignored. record_id: the ID of the training log record from which the model will 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. custom_hp: dict, default=None | The custom search space specified by user Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the domain of the custom search space can either be a value or a sample.Domain object. ```python custom_hp = { "transformer_ms": { "model_path": { "domain": "albert-base-v2", }, "learning_rate": { "domain": tune.choice([1e-4, 1e-5]), } } } fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name. e.g., ```python fit_kwargs_by_estimator = { "transformer": { "output_dir": "test/data/output/", "ckpt_per_epoch": 1, "fp16": False, } } ``` **fit_kwargs: Other key word arguments to pass to fit() function of the searched learners, such as sample_weight. Include: period: int | forecast horizon for ts_forecast tasks. gpu_per_trial: float, default = 0 | A float of the number of gpus per trial, only used by TransformersEstimator and XGBoostSklearnEstimator. """ task = task or self._settings.get("task") eval_method = eval_method or self._settings.get("eval_method") split_ratio = split_ratio or self._settings.get("split_ratio") n_splits = n_splits or self._settings.get("n_splits") split_type = split_type or self._settings.get("split_type") auto_augment = ( self._settings.get("auto_augment") if auto_augment is None else auto_augment ) self._state.task = task self._estimator_type = "classifier" if task in CLASSIFICATION else "regressor" self._state.fit_kwargs = fit_kwargs self._state.custom_hp = custom_hp or self._settings.get("custom_hp") self._state.fit_kwargs_by_estimator = ( fit_kwargs_by_estimator or self._settings.get("fit_kwargs_by_estimator") ) self._validate_data(X_train, y_train, dataframe, label, groups=groups) logger.info("log file name {}".format(log_file_name)) best_config = None best_val_loss = float("+inf") best_estimator = None sample_size = None time_used = 0.0 training_duration = 0 best = None with training_log_reader(log_file_name) as reader: if record_id >= 0: best = reader.get_record(record_id) else: for record in reader.records(): time_used = record.wall_clock_time if time_used > time_budget: break training_duration = time_used val_loss = record.validation_loss if val_loss <= best_val_loss or not train_best: if val_loss == best_val_loss and train_best: size = record.sample_size if size > sample_size: best = record best_val_loss = val_loss sample_size = size else: best = record size = record.sample_size best_val_loss = val_loss sample_size = size if not training_duration: logger.warning( f"No estimator found within time_budget={time_budget}" ) from .model import BaseEstimator as Estimator self._trained_estimator = Estimator() return training_duration if not best: return best_estimator = best.learner best_config = best.config sample_size = len(self._y_train_all) if train_full else best.sample_size this_estimator_kwargs = self._state.fit_kwargs_by_estimator.get(best_estimator) if this_estimator_kwargs: this_estimator_kwargs = ( this_estimator_kwargs.copy() ) # make another shallow copy of the value (a dict obj), so user's fit_kwargs_by_estimator won't be updated this_estimator_kwargs.update(self._state.fit_kwargs) self._state.fit_kwargs_by_estimator[best_estimator] = this_estimator_kwargs else: self._state.fit_kwargs_by_estimator[best_estimator] = self._state.fit_kwargs logger.info( "estimator = {}, config = {}, #training instances = {}".format( best_estimator, best_config, sample_size ) ) # Partially copied from fit() function # Initilize some attributes required for retrain_from_log self._decide_split_type(split_type) if record_id >= 0: eval_method = "cv" 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 import os self._state.resources_per_trial = ( {"cpu": os.cpu_count(), "gpu": fit_kwargs.get("gpu_per_trial", 0)} if self._state.n_jobs < 0 else {"cpu": self._state.n_jobs, "gpu": fit_kwargs.get("gpu_per_trial", 0)} ) self._trained_estimator = self._state._train_with_config( best_estimator, best_config, sample_size=sample_size, )[0] logger.info("retrain from log succeeded") return training_duration def _decide_split_type(self, split_type): if self._state.task == "classification": self._state.task = get_classification_objective( len(np.unique(self._y_train_all)) ) if not isinstance(split_type, str): assert hasattr(split_type, "split") and hasattr( split_type, "get_n_splits" ), "split_type must be a string or a splitter object with split and get_n_splits methods." self._split_type = split_type elif self._state.task in CLASSIFICATION: assert split_type in ["auto", "stratified", "uniform", "time", "group"] self._split_type = ( split_type if split_type != "auto" else self._state.groups is None and "stratified" or "group" ) elif self._state.task in REGRESSION: assert split_type in ["auto", "uniform", "time", "group"] self._split_type = split_type if split_type != "auto" else "uniform" elif self._state.task in TS_FORECAST: assert split_type in ["auto", "time"] self._split_type = "time" assert isinstance( self._state.fit_kwargs.get("period"), int, # NOTE: _decide_split_type is before ), f"missing a required integer 'period' for '{TS_FORECAST}' task." elif self._state.task == "rank": assert ( self._state.groups is not None ), "groups must be specified for ranking task." assert split_type in ["auto", "group"] self._split_type = "group" elif self._state.task in NLG_TASKS: assert split_type in ["auto", "uniform", "time", "group"] self._split_type = split_type if split_type != "auto" else "uniform" def _decide_eval_method(self, time_budget): if self._state.X_val is not None: return "holdout" nrow, dim = self._nrow, self._ndim if ( time_budget is None or nrow * dim / 0.9 < SMALL_LARGE_THRES * (time_budget / 3600) and nrow < CV_HOLDOUT_THRESHOLD ): # time allows or sampling can be used and cv is necessary return "cv" else: return "holdout" @property def search_space(self) -> dict: """Search space. Must be called after fit(...) (use max_iter=0 and retrain_final=False to prevent actual fitting). Returns: A dict of the search space. """ estimator_list = self.estimator_list if len(estimator_list) == 1: estimator = estimator_list[0] space = self._search_states[estimator].search_space.copy() space["learner"] = estimator return space choices = [] for estimator in estimator_list: space = self._search_states[estimator].search_space.copy() space["learner"] = estimator choices.append(space) return {"ml": tune.choice(choices)} @property def low_cost_partial_config(self) -> dict: """Low cost partial config. Returns: A dict. (a) if there is only one estimator in estimator_list, each key is a hyperparameter name. (b) otherwise, it is a nested dict with 'ml' as the key, and a list of the low_cost_partial_configs as the value, corresponding to each learner's low_cost_partial_config; the estimator index as an integer corresponding to the cheapest learner is appended to the list at the end. """ if len(self.estimator_list) == 1: estimator = self.estimator_list[0] c = self._search_states[estimator].low_cost_partial_config return c else: configs = [] for estimator in self.estimator_list: c = self._search_states[estimator].low_cost_partial_config configs.append(c) configs.append( np.argmin( [ self._state.learner_classes.get(estimator).cost_relative2lgbm() for estimator in self.estimator_list ] ) ) config = {"ml": configs} return config @property def cat_hp_cost(self) -> dict: """Categorical hyperparameter cost Returns: A dict. (a) if there is only one estimator in estimator_list, each key is a hyperparameter name. (b) otherwise, it is a nested dict with 'ml' as the key, and a list of the cat_hp_cost's as the value, corresponding to each learner's cat_hp_cost; the cost relative to lgbm for each learner (as a list itself) is appended to the list at the end. """ if len(self.estimator_list) == 1: estimator = self.estimator_list[0] c = self._search_states[estimator].cat_hp_cost return c else: configs = [] for estimator in self.estimator_list: c = self._search_states[estimator].cat_hp_cost configs.append(c) configs.append( [ self._state.learner_classes.get(estimator).cost_relative2lgbm() for estimator in self.estimator_list ] ) config = {"ml": configs} return config @property def points_to_evaluate(self) -> dict: """Initial points to evaluate. Returns: A list of dicts. Each dict is the initial point for each learner. """ points = [] for estimator in self.estimator_list: if isinstance(self._search_states[estimator].init_config, list): configs = self._search_states[estimator].init_config else: configs = [self._search_states[estimator].init_config] for config in configs: config["learner"] = estimator if len(self.estimator_list) > 1: points.append({"ml": config}) else: points.append(config) return points @property def resource_attr(self) -> Optional[str]: """Attribute of the resource dimension. Returns: A string for the sample size attribute (the resource attribute in AutoML) or None. """ return "FLAML_sample_size" if self._sample else None @property def min_resource(self) -> Optional[float]: """Attribute for pruning. Returns: A float for the minimal sample size or None. """ return self._min_sample_size if self._sample else None @property def max_resource(self) -> Optional[float]: """Attribute for pruning. Returns: A float for the maximal sample size or None. """ return self._state.data_size[0] if self._sample else None def pickle(self, output_file_name): import pickle estimator_to_training_function = {} for estimator in self.estimator_list: search_state = self._search_states[estimator] if hasattr(search_state, "training_function"): estimator_to_training_function[ estimator ] = search_state.training_function del search_state.training_function with open(output_file_name, "wb") as f: pickle.dump(self, f, pickle.HIGHEST_PROTOCOL) @property def trainable(self) -> Callable[[dict], Optional[float]]: """Training function. Returns: A function that evaluates each config and returns the loss. """ self._state.time_from_start = 0 for estimator in self.estimator_list: search_state = self._search_states[estimator] if not hasattr(search_state, "training_function"): if self._use_ray is not False: from ray.tune import with_parameters search_state.training_function = with_parameters( AutoMLState._compute_with_config_base, state=self._state, estimator=estimator, ) else: search_state.training_function = partial( AutoMLState._compute_with_config_base, state=self._state, estimator=estimator, ) states = self._search_states mem_res = self._mem_thres def train(config: dict): sample_size = config.get("FLAML_sample_size") config = config.get("ml", config).copy() if sample_size: config["FLAML_sample_size"] = sample_size estimator = config["learner"] # check memory constraints before training if states[estimator].learner_class.size(config) <= mem_res: del config["learner"] result = states[estimator].training_function(config) return result else: return { "pred_time": 0, "wall_clock_time": None, "metric_for_logging": np.inf, "val_loss": np.inf, "trained_estimator": None, } return train @property def metric_constraints(self) -> list: """Metric constraints. Returns: A list of the metric constraints. """ return self._metric_constraints def fit( self, X_train=None, y_train=None, dataframe=None, label=None, metric=None, task=None, n_jobs=None, # gpu_per_trial=0, log_file_name=None, estimator_list=None, time_budget=None, max_iter=None, sample=None, ensemble=None, eval_method=None, log_type=None, model_history=None, split_ratio=None, n_splits=None, log_training_metric=None, mem_thres=None, pred_time_limit=None, train_time_limit=None, X_val=None, y_val=None, sample_weight_val=None, groups_val=None, groups=None, verbose=None, retrain_full=None, split_type=None, learner_selector=None, hpo_method=None, starting_points=None, seed=None, n_concurrent_trials=None, keep_search_state=None, early_stop=None, append_log=None, auto_augment=None, min_sample_size=None, use_ray=None, metric_constraints=None, custom_hp=None, fit_kwargs_by_estimator=None, **fit_kwargs, ): """Find a model for a given task. Args: X_train: A numpy array or a pandas dataframe of training data in shape (n, m). For ts_forecast tasks, the first column of X_train must be the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). When using ray, X_train can be a ray.ObjectRef. y_train: A numpy array or a pandas series of labels in shape (n, ). dataframe: A dataframe of training data including label column. For ts_forecast tasks, dataframe must be specified and must have at least two columns, timestamp and label, where the first column is the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). When using ray, dataframe can be a ray.ObjectRef. label: A str of the label column name for, e.g., 'label'; Note: If X_train and y_train are provided, dataframe and label are ignored; If not, dataframe and label must be provided. 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 passing a customized metric function, the function needs to have the following signature: ```python def custom_metric( X_test, y_test, estimator, labels, X_train, y_train, weight_test=None, weight_train=None, config=None, groups_test=None, groups_train=None, ): return metric_to_minimize, metrics_to_log ``` which returns a float number as the minimization objective, and a dictionary as the metrics to log. E.g., ```python def custom_metric( X_val, y_val, estimator, labels, X_train, y_train, weight_val=None, weight_train=None, *args, ): from sklearn.metrics import log_loss import time start = time.time() y_pred = estimator.predict_proba(X_val) pred_time = (time.time() - start) / len(X_val) val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val) y_pred = estimator.predict_proba(X_train) train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train) alpha = 0.5 return val_loss * (1 + alpha) - alpha * train_loss, { "val_loss": val_loss, "train_loss": train_loss, "pred_time": pred_time, } ``` task: A string of the task type, e.g., 'classification', 'regression', 'ts_forecast_regression', 'ts_forecast_classification', 'rank', 'seq-classification', 'seq-regression', 'summarization' n_jobs: An integer of the number of threads for training | default=-1. Use all available resources when n_jobs == -1. log_file_name: A string of the log file name | default="". To disable logging, set it to be an empty string "". estimator_list: A list of strings for estimator names, or 'auto' e.g., ```['lgbm', 'xgboost', 'xgb_limitdepth', 'catboost', 'rf', 'extra_tree']``` time_budget: A float number of the time budget in seconds. Use -1 if no time limit. max_iter: An integer of the maximal number of iterations. sample: A boolean of whether to sample the training data during search. ensemble: boolean or dict | default=False. Whether to perform ensemble after search. Can be a dict with keys 'passthrough' and 'final_estimator' to specify the passthrough and final_estimator in the stacker. eval_method: A string of resampling strategy, one of ['auto', 'cv', 'holdout']. split_ratio: A float of the valiation data percentage for holdout. n_splits: An integer of the number of folds for cross - validation. log_type: A string of the log type, one of ['better', 'all']. 'better' only logs configs with better loss than previos iters 'all' logs all the tried configs. model_history: A boolean of whether to keep the trained best model per estimator. Make sure memory is large enough if setting to True. Default value is False: best_model_for_estimator would return a untrained model for non-best learner. log_training_metric: A boolean of whether to log the training metric for each model. mem_thres: A float of the memory size constraint in bytes. pred_time_limit: A float of the prediction latency constraint in seconds. It refers to the average prediction time per row in validation data. train_time_limit: A float of the training time constraint in seconds. X_val: None or a numpy array or a pandas dataframe of validation data. y_val: None or a numpy array or a pandas series of validation labels. sample_weight_val: None or a numpy array of the sample weight of validation data of the same shape as y_val. groups_val: None or array-like | group labels (with matching length to y_val) or group counts (with sum equal to length of y_val) for validation data. Need to be consistent with groups. 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=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. True - retrain only after search finishes; False - no retraining; 'budget' - do best effort to retrain without violating the time budget. split_type: str or splitter object, default="auto" | the data split type. * A valid splitter object is an instance of a derived class of scikit-learn [KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold) and have ``split`` and ``get_n_splits`` methods with the same signatures. Set eval_method to "cv" to use the splitter object. * Valid str options depend on different tasks. For classification tasks, valid choices are ["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified. For regression tasks, valid choices are ["auto", 'uniform', 'time']. "auto" -> uniform. For ts_forecast tasks, must be "auto" or 'time'. For ranking task, must be "auto" or 'group'. hpo_method: str, default="auto" | The hyperparameter optimization method. By default, CFO is used for sequential search and BlendSearch is used for parallel search. No need to set when using flaml's default search space or using a simple customized search space. When set to 'bs', BlendSearch is used. BlendSearch can be tried when the search space is complex, for example, containing multiple disjoint, discontinuous subspaces. When set to 'random', random search is used. starting_points: A dictionary or a str to specify the starting hyperparameter config for the estimators | default="data". If str: - if "data", use data-dependent defaults; - if "data:path" use data-dependent defaults which are stored at path; - if "static", use data-independent defaults. If dict, keys are the name of the estimators, and values are the starting hyperparamter configurations for the corresponding estimators. The value can be a single hyperparamter configuration dict or a list of hyperparamter configuration dicts. In the following code example, we get starting_points from the `automl` object and use them in the `new_automl` object. e.g., ```python from flaml import AutoML automl = AutoML() X_train, y_train = load_iris(return_X_y=True) automl.fit(X_train, y_train) starting_points = automl.best_config_per_estimator new_automl = AutoML() new_automl.fit(X_train, y_train, starting_points=starting_points) ``` seed: int or None, default=None | The random seed for hpo. n_concurrent_trials: [Experimental] int, default=1 | The number of concurrent trials. When n_concurrent_trials > 1, flaml performes [parallel tuning](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning) and installation of ray is required: `pip install flaml[ray]`. keep_search_state: boolean, default=False | Whether to keep data needed for model search after fit(). By default the state is deleted for space 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 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. use_ray: boolean or dict If boolean: default=False | Whether to use ray to run the training in separate processes. This can be used to prevent OOM for large datasets, but will incur more overhead in time. metric_constraints: list, default=[] | The list of metric constraints. Each element in this list is a 3-tuple, which shall be expressed in the following format: the first element of the 3-tuple is the name of the metric, the second element is the inequality sign chosen from ">=" and "<=", and the third element is the constraint value. E.g., `('precision', '>=', 0.9)`. Note that all the metric names in metric_constraints need to be reported via the metrics_to_log dictionary returned by a customized metric function. The customized metric function shall be provided via the `metric` key word argument of the fit() function or the automl constructor. Find examples in this [test](https://github.com/microsoft/FLAML/tree/main/test/automl/test_constraints.py). If `pred_time_limit` is provided as one of keyword arguments to fit() function or the automl constructor, flaml will automatically (and under the hood) add it as an additional element in the metric_constraints. Essentially 'pred_time_limit' specifies a constraint about the prediction latency constraint in seconds. custom_hp: dict, default=None | The custom search space specified by user Each key is the estimator name, each value is a dict of the custom search space for that estimator. Notice the domain of the custom search space can either be a value of a sample.Domain object. ```python custom_hp = { "transformer_ms": { "model_path": { "domain": "albert-base-v2", }, "learning_rate": { "domain": tune.choice([1e-4, 1e-5]), } } } fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name. For TransformersEstimator, available fit_kwargs can be found from [flaml/nlp/training_args.py:TrainingArgumentsForAuto](https://microsoft.github.io/FLAML/docs/reference/nlp/huggingface/training_args). e.g., ```python fit_kwargs_by_estimator = { "transformer": { "output_dir": "test/data/output/", "ckpt_per_epoch": 1, "fp16": False, } } ``` **fit_kwargs: Other key word arguments to pass to fit() function of the searched learners, such as sample_weight. Include: period: int | forecast horizon for ts_forecast tasks. gpu_per_trial: float, default = 0 | A float of the number of gpus per trial, only used by TransformersEstimator and XGBoostSklearnEstimator. """ self._state._start_time_flag = self._start_time_flag = time.time() task = task or self._settings.get("task") self._estimator_type = "classifier" if task in CLASSIFICATION else "regressor" time_budget = time_budget or self._settings.get("time_budget") n_jobs = n_jobs or self._settings.get("n_jobs") gpu_per_trial = fit_kwargs.get("gpu_per_trial", 0) eval_method = eval_method or self._settings.get("eval_method") split_ratio = split_ratio or self._settings.get("split_ratio") n_splits = n_splits or self._settings.get("n_splits") auto_augment = ( self._settings.get("auto_augment") if auto_augment is None else auto_augment ) metric = metric or self._settings.get("metric") estimator_list = estimator_list or self._settings.get("estimator_list") log_file_name = ( self._settings.get("log_file_name") if log_file_name is None else log_file_name ) max_iter = self._settings.get("max_iter") if max_iter is None else max_iter sample = self._settings.get("sample") if sample is None else sample ensemble = self._settings.get("ensemble") if ensemble is None else ensemble log_type = log_type or self._settings.get("log_type") model_history = ( self._settings.get("model_history") if model_history is None else model_history ) log_training_metric = ( self._settings.get("log_training_metric") if log_training_metric is None else log_training_metric ) mem_thres = mem_thres or self._settings.get("mem_thres") pred_time_limit = pred_time_limit or self._settings.get("pred_time_limit") train_time_limit = train_time_limit or self._settings.get("train_time_limit") self._metric_constraints = metric_constraints or self._settings.get( "metric_constraints" ) if np.isfinite(pred_time_limit): self._metric_constraints.append(("pred_time", "<=", pred_time_limit)) verbose = self._settings.get("verbose") if verbose is None else verbose retrain_full = ( self._settings.get("retrain_full") if retrain_full is None else retrain_full ) split_type = split_type or self._settings.get("split_type") hpo_method = hpo_method or self._settings.get("hpo_method") learner_selector = learner_selector or self._settings.get("learner_selector") starting_points = ( self._settings.get("starting_points") if starting_points is None else starting_points ) n_concurrent_trials = n_concurrent_trials or self._settings.get( "n_concurrent_trials" ) keep_search_state = ( self._settings.get("keep_search_state") if keep_search_state is None else keep_search_state ) early_stop = ( self._settings.get("early_stop") if early_stop is None else early_stop ) append_log = ( self._settings.get("append_log") if append_log is None else append_log ) min_sample_size = min_sample_size or self._settings.get("min_sample_size") use_ray = self._settings.get("use_ray") if use_ray is None else use_ray self._state.n_jobs = n_jobs self._n_concurrent_trials = n_concurrent_trials self._early_stop = early_stop self._use_ray = use_ray or n_concurrent_trials > 1 # use the following condition if we have an estimation of average_trial_time and average_trial_overhead # self._use_ray = use_ray or n_concurrent_trials > ( average_trail_time + average_trial_overhead) / (average_trial_time) if self._use_ray is not False: import ray n_cpus = use_ray and ray.available_resources()["CPU"] or os.cpu_count() self._state.resources_per_trial = ( # when using gpu, default cpu is 1 per job; otherwise, default cpu is n_cpus / n_concurrent_trials ( { "cpu": max(int((n_cpus - 2) / 2 / n_concurrent_trials), 1), "gpu": gpu_per_trial, } if gpu_per_trial == 0 else {"cpu": 1, "gpu": gpu_per_trial} ) if n_jobs < 0 else {"cpu": n_jobs, "gpu": gpu_per_trial} ) if isinstance(X_train, ray.ObjectRef): X_train = ray.get(X_train) elif isinstance(dataframe, ray.ObjectRef): dataframe = ray.get(dataframe) self._state.task = task self._state.log_training_metric = log_training_metric self._state.fit_kwargs = fit_kwargs custom_hp = custom_hp or self._settings.get("custom_hp") fit_kwargs_by_estimator = fit_kwargs_by_estimator or self._settings.get( "fit_kwargs_by_estimator" ) self._state.fit_kwargs_by_estimator = ( fit_kwargs_by_estimator.copy() ) # shallow copy of fit_kwargs_by_estimator self._state.weight_val = sample_weight_val self._validate_data( X_train, y_train, dataframe, label, X_val, y_val, groups_val, groups ) self._search_states = {} # key: estimator name; value: SearchState self._random = np.random.RandomState(RANDOM_SEED) self._seed = seed if seed is not None else 20 self._learner_selector = learner_selector old_level = logger.getEffectiveLevel() self.verbose = verbose logger.setLevel(50 - verbose * 10) if not logger.handlers: # Add the console handler. _ch = logging.StreamHandler() _ch.setFormatter(logger_formatter) logger.addHandler(_ch) logger.info(f"task = {task}") self._decide_split_type(split_type) logger.info(f"Data split method: {self._split_type}") if eval_method == "auto" or self._state.X_val is not None: eval_method = self._decide_eval_method(time_budget) self._state.eval_method = eval_method logger.info("Evaluation method: {}".format(eval_method)) self._retrain_in_budget = retrain_full == "budget" and ( eval_method == "holdout" and self._state.X_val is None ) self._state.retrain_final = ( retrain_full is True and eval_method == "holdout" and (self._state.X_val is None or self._use_ray is not False) or eval_method == "cv" and (max_iter > 0 or retrain_full is True) or max_iter == 1 ) 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 ( self._min_sample_size * SAMPLE_MULTIPLY_FACTOR < self._state.data_size[0] ) ) if "auto" == metric: if _is_nlp_task(self._state.task): from .nlp.utils import load_default_huggingface_metric_for_task metric = load_default_huggingface_metric_for_task(self._state.task) elif "binary" in self._state.task: metric = "roc_auc" elif "multiclass" in self._state.task: metric = "log_loss" elif self._state.task in TS_FORECAST: metric = "mape" elif self._state.task == "rank": metric = "ndcg" else: metric = "r2" self._state.metric = metric def is_to_reverse_metric(metric, task): if metric.startswith("ndcg"): return True, f"1-{metric}" if metric in [ "r2", "accuracy", "roc_auc", "roc_auc_ovr", "roc_auc_ovo", "f1", "ap", "micro_f1", "macro_f1", ]: return True, f"1-{metric}" if _is_nlp_task(task): from .ml import huggingface_metric_to_mode if ( metric in huggingface_metric_to_mode and huggingface_metric_to_mode[metric] == "max" ): return True, f"-{metric}" return False, None if isinstance(metric, str): is_reverse, reverse_metric = is_to_reverse_metric(metric, task) if is_reverse: error_metric = reverse_metric else: error_metric = metric else: error_metric = "customized metric" logger.info(f"Minimizing error metric: {error_metric}") if "auto" == estimator_list: if self._state.task == "rank": estimator_list = ["lgbm", "xgboost", "xgb_limitdepth"] elif _is_nlp_task(self._state.task): estimator_list = ["transformer"] else: try: import catboost estimator_list = [ "lgbm", "rf", "catboost", "xgboost", "extra_tree", "xgb_limitdepth", ] except ImportError: estimator_list = [ "lgbm", "rf", "xgboost", "extra_tree", "xgb_limitdepth", ] if self._state.task in TS_FORECAST: # catboost is removed because it has a `name` parameter, making it incompatible with hcrystalball if "catboost" in estimator_list: estimator_list.remove("catboost") if self._state.task in TS_FORECASTREGRESSION: try: import prophet estimator_list += ["prophet", "arima", "sarimax"] except ImportError: estimator_list += ["arima", "sarimax"] elif "regression" != self._state.task: estimator_list += ["lrl1"] for estimator_name in estimator_list: if estimator_name not in self._state.learner_classes: self.add_learner( estimator_name, get_estimator_class(self._state.task, estimator_name), ) # set up learner search space if isinstance(starting_points, str) and starting_points.startswith("data"): from flaml.default import suggest_config location = starting_points[5:] starting_points = {} for estimator_name in estimator_list: try: configs = suggest_config( self._state.task, self._X_train_all, self._y_train_all, estimator_name, location, k=1, ) starting_points[estimator_name] = [ x["hyperparameters"] for x in configs ] except FileNotFoundError: pass try: learner = suggest_learner( self._state.task, self._X_train_all, self._y_train_all, estimator_list=estimator_list, location=location, ) if learner != estimator_list[0]: estimator_list.remove(learner) estimator_list.insert(0, learner) except FileNotFoundError: pass starting_points = {} if starting_points == "static" else starting_points for estimator_name in estimator_list: estimator_class = self._state.learner_classes[estimator_name] estimator_class.init() this_estimator_kwargs = self._state.fit_kwargs_by_estimator.get( estimator_name ) if this_estimator_kwargs: # make another shallow copy of the value (a dict obj), so user's fit_kwargs_by_estimator won't be updated this_estimator_kwargs = this_estimator_kwargs.copy() this_estimator_kwargs.update( self._state.fit_kwargs ) # update the shallow copy self._state.fit_kwargs_by_estimator[ estimator_name ] = this_estimator_kwargs # set self._state.fit_kwargs_by_estimator[estimator_name] to the update, so only self._state.fit_kwargs_by_estimator will be updated else: self._state.fit_kwargs_by_estimator[ estimator_name ] = self._state.fit_kwargs self._search_states[estimator_name] = SearchState( learner_class=estimator_class, data_size=self._state.data_size, task=self._state.task, starting_point=starting_points.get(estimator_name), period=self._state.fit_kwargs.get("period"), # NOTE: this is after custom_hp=custom_hp and custom_hp.get(estimator_name), max_iter=max_iter, ) logger.info("List of ML learners in AutoML Run: {}".format(estimator_list)) self.estimator_list = estimator_list self._state.time_budget = time_budget if time_budget > 0 else 1e10 self._active_estimators = estimator_list.copy() self._ensemble = ensemble self._max_iter = max_iter self._mem_thres = mem_thres self._pred_time_limit = pred_time_limit self._state.train_time_limit = train_time_limit self._log_type = log_type self.split_ratio = split_ratio self._state.model_history = model_history self._hpo_method = ( hpo_method if hpo_method != "auto" else ( "bs" if n_concurrent_trials > 1 or self._use_ray is not False and len(estimator_list) > 1 else "cfo" ) ) if log_file_name: with training_log_writer(log_file_name, append_log) as save_helper: self._training_log = save_helper self._search() else: self._training_log = None self._search() if self._best_estimator: logger.info("fit succeeded") logger.info( f"Time taken to find the best model: {self._time_taken_best_iter}" ) if ( self._hpo_method in ("cfo", "bs") and (self._time_taken_best_iter >= self._state.time_budget * 0.7) and not all( state.search_alg and state.search_alg.searcher.is_ls_ever_converged for state in self._search_states.values() ) ): logger.warning( "Time taken to find the best model is {0:.0f}% of the " "provided time budget and not all estimators' hyperparameter " "search converged. Consider increasing the time budget.".format( self._time_taken_best_iter / self._state.time_budget * 100 ) ) if not keep_search_state: # release space del self._X_train_all, self._y_train_all, self._state.kf del self._state.X_train, self._state.X_train_all, self._state.X_val del self._state.y_train, self._state.y_train_all, self._state.y_val del ( self._sample_weight_full, self._state.fit_kwargs_by_estimator, self._state.fit_kwargs, ) # NOTE: this is after del self._state.groups, self._state.groups_all, self._state.groups_val logger.setLevel(old_level) def _search_parallel(self): try: from ray import __version__ as ray_version assert ray_version >= "1.10.0" import ray from ray.tune.suggest import ConcurrencyLimiter except (ImportError, AssertionError): raise ImportError( "n_concurrent_trial>1 or use_ray=True requires installation of ray. " "Please run pip install flaml[ray]" ) if self._hpo_method in ("cfo", "grid"): from flaml import CFO as SearchAlgo elif "bs" == self._hpo_method: from flaml import BlendSearch as SearchAlgo elif "random" == self._hpo_method: from ray.tune.suggest import BasicVariantGenerator as SearchAlgo from ray.tune.sample import Domain elif "optuna" == self._hpo_method: try: from ray import __version__ as ray_version assert ray_version >= "1.10.0" from ray.tune.suggest.optuna import OptunaSearch as SearchAlgo except (ImportError, AssertionError): from .searcher.suggestion import OptunaSearch as SearchAlgo else: raise NotImplementedError( f"hpo_method={self._hpo_method} is not recognized. " "'auto', 'cfo' and 'bs' are supported." ) space = self.search_space if self._hpo_method == "random": # Any point in points_to_evaluate must consist of hyperparamters # that are tunable, which can be identified by checking whether # the corresponding value in the search space is an instance of # the 'Domain' class from flaml or ray.tune points_to_evaluate = self.points_to_evaluate.copy() to_del = [] for k, v in space.items(): if not isinstance(v, Domain): to_del.append(k) for k in to_del: for p in points_to_evaluate: if k in p: del p[k] search_alg = SearchAlgo( max_concurrent=self._n_concurrent_trials, 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 if self._hpo_method != "optuna": search_alg = SearchAlgo( metric="val_loss", space=space, low_cost_partial_config=self.low_cost_partial_config, points_to_evaluate=self.points_to_evaluate, cat_hp_cost=self.cat_hp_cost, resource_attr=self.resource_attr, min_resource=self.min_resource, max_resource=self.max_resource, config_constraints=[ (partial(size, self._state), "<=", self._mem_thres) ], metric_constraints=self.metric_constraints, seed=self._seed, time_budget_s=time_left, ) else: # if self._hpo_method is bo, sometimes the search space and the initial config dimension do not match # need to remove the extra keys from the search space to be consistent with the initial config converted_space = SearchAlgo.convert_search_space(space) removed_keys = set(space.keys()).difference(converted_space.keys()) new_points_to_evaluate = [] for idx in range(len(self.points_to_evaluate)): r = self.points_to_evaluate[idx].copy() for each_key in removed_keys: r.pop(each_key) new_points_to_evaluate.append(r) search_alg = SearchAlgo( metric="val_loss", mode="min", points_to_evaluate=[ p for p in new_points_to_evaluate if len(p) == len(converted_space) ], ) search_alg = ConcurrencyLimiter(search_alg, self._n_concurrent_trials) resources_per_trial = self._state.resources_per_trial analysis = ray.tune.run( self.trainable, search_alg=search_alg, config=space, metric="val_loss", mode="min", resources_per_trial=resources_per_trial, time_budget_s=self._state.time_budget, num_samples=self._max_iter, verbose=max(self.verbose - 2, 0), raise_on_failed_trial=False, keep_checkpoints_num=1, checkpoint_score_attr="min-val_loss", **self._use_ray if isinstance(self._use_ray, dict) else {}, ) # logger.info([trial.last_result for trial in analysis.trials]) trials = sorted( ( trial for trial in analysis.trials if trial.last_result and trial.last_result.get("wall_clock_time") is not None ), key=lambda x: x.last_result["wall_clock_time"], ) for self._track_iter, trial in enumerate(trials): result = trial.last_result better = False if result: config = result["config"] estimator = config.get("ml", config)["learner"] search_state = self._search_states[estimator] search_state.update(result, 0) wall_time = result.get("wall_clock_time") if wall_time is not None: self._state.time_from_start = wall_time self._iter_per_learner[estimator] += 1 if search_state.sample_size == self._state.data_size[0]: if not self._fullsize_reached: self._fullsize_reached = True if search_state.best_loss < self._state.best_loss: self._state.best_loss = search_state.best_loss self._best_estimator = estimator self._config_history[self._track_iter] = ( self._best_estimator, config, self._time_taken_best_iter, ) self._trained_estimator = search_state.trained_estimator self._best_iteration = self._track_iter self._time_taken_best_iter = self._state.time_from_start better = True self._search_states[estimator].best_config = config if better or self._log_type == "all": self._log_trial(search_state, estimator) def _log_trial(self, search_state, estimator): if self._training_log: self._training_log.append( self._iter_per_learner[estimator], search_state.metric_for_logging, search_state.trial_time, self._state.time_from_start, search_state.val_loss, search_state.config, estimator, search_state.sample_size, ) if mlflow is not None and mlflow.active_run(): with mlflow.start_run(nested=True): mlflow.log_metric("iter_counter", self._track_iter) if "intermediate_results" in search_state.metric_for_logging: for each_entry in search_state.metric_for_logging[ "intermediate_results" ]: with mlflow.start_run(nested=True): mlflow.log_metrics(each_entry) mlflow.log_metric( "iter_counter", self._iter_per_learner[estimator] ) del search_state.metric_for_logging["intermediate_results"] mlflow.log_metrics(search_state.metric_for_logging) mlflow.log_metric("trial_time", search_state.trial_time) mlflow.log_metric("wall_clock_time", self._state.time_from_start) mlflow.log_metric("validation_loss", search_state.val_loss) mlflow.log_param("config", search_state.config) mlflow.log_param("learner", estimator) mlflow.log_param("sample_size", search_state.sample_size) mlflow.log_metric("best_validation_loss", search_state.best_loss) mlflow.log_param("best_config", search_state.best_config) mlflow.log_param("best_learner", self._best_estimator) def _search_sequential(self): try: from ray import __version__ as ray_version assert ray_version >= "1.10.0" from ray.tune.suggest import ConcurrencyLimiter except (ImportError, AssertionError): from .searcher.suggestion import ConcurrencyLimiter if self._hpo_method in ("cfo", "grid"): from flaml import CFO as SearchAlgo elif "optuna" == self._hpo_method: try: from ray import __version__ as ray_version assert ray_version >= "1.10.0" from ray.tune.suggest.optuna import OptunaSearch as SearchAlgo except (ImportError, AssertionError): from .searcher.suggestion import OptunaSearch as SearchAlgo elif "bs" == self._hpo_method: from flaml import BlendSearch as SearchAlgo elif "random" == self._hpo_method: from flaml.searcher import RandomSearch as SearchAlgo elif "cfocat" == self._hpo_method: from flaml.searcher.cfo_cat import CFOCat as SearchAlgo else: raise NotImplementedError( f"hpo_method={self._hpo_method} is not recognized. " "'cfo' and 'bs' are supported." ) est_retrain_time = next_trial_time = 0 best_config_sig = None better = True # whether we find a better model in one trial for self._track_iter in range(self._max_iter): if self._estimator_index is None: estimator = self._active_estimators[0] else: estimator = self._select_estimator(self._active_estimators) if not estimator: break logger.info(f"iteration {self._track_iter}, current learner {estimator}") search_state = self._search_states[estimator] self._state.time_from_start = time.time() - self._start_time_flag time_left = self._state.time_budget - self._state.time_from_start budget_left = ( time_left if not self._retrain_in_budget or better or (not self.best_estimator) or self._search_states[self.best_estimator].sample_size < self._state.data_size[0] else time_left - est_retrain_time ) if not search_state.search_alg: search_state.training_function = partial( AutoMLState._compute_with_config_base, state=self._state, estimator=estimator, ) search_space = search_state.search_space if self._sample: resource_attr = "FLAML_sample_size" min_resource = self._min_sample_size max_resource = self._state.data_size[0] else: resource_attr = min_resource = max_resource = None learner_class = self._state.learner_classes.get(estimator) if "grid" == self._hpo_method: # for synthetic exp only points_to_evaluate = [] space = search_space keys = list(space.keys()) domain0, domain1 = space[keys[0]], space[keys[1]] for x1 in range(domain0.lower, domain0.upper + 1): for x2 in range(domain1.lower, domain1.upper + 1): points_to_evaluate.append( { keys[0]: x1, keys[1]: x2, } ) self._max_iter_per_learner = len(points_to_evaluate) low_cost_partial_config = None else: points_to_evaluate = ( search_state.init_config if isinstance(search_state.init_config, list) else [search_state.init_config] ) low_cost_partial_config = search_state.low_cost_partial_config if self._hpo_method in ("bs", "cfo", "grid", "cfocat", "random"): algo = SearchAlgo( metric="val_loss", mode="min", space=search_space, points_to_evaluate=points_to_evaluate, low_cost_partial_config=low_cost_partial_config, cat_hp_cost=search_state.cat_hp_cost, resource_attr=resource_attr, min_resource=min_resource, max_resource=max_resource, config_constraints=[ (learner_class.size, "<=", self._mem_thres) ], metric_constraints=self.metric_constraints, seed=self._seed, ) else: # if self._hpo_method is bo, sometimes the search space and the initial config dimension do not match # need to remove the extra keys from the search space to be consistent with the initial config converted_space = SearchAlgo.convert_search_space(search_space) removed_keys = set(search_space.keys()).difference( converted_space.keys() ) new_points_to_evaluate = [] for idx in range(len(points_to_evaluate)): r = points_to_evaluate[idx].copy() for each_key in removed_keys: r.pop(each_key) new_points_to_evaluate.append(r) points_to_evaluate = new_points_to_evaluate algo = SearchAlgo( metric="val_loss", mode="min", space=search_space, points_to_evaluate=[ p for p in points_to_evaluate if len(p) == len(search_space) ], ) search_state.search_alg = ConcurrencyLimiter(algo, max_concurrent=1) # search_state.search_alg = algo else: search_space = None if self._hpo_method in ("bs", "cfo", "cfocat"): search_state.search_alg.searcher.set_search_properties( metric=None, mode=None, setting={ "metric_target": self._state.best_loss, }, ) start_run_time = time.time() analysis = tune.run( 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 - 3, 0), use_ray=False, ) time_used = time.time() - start_run_time better = False if analysis.trials: result = analysis.trials[-1].last_result search_state.update(result, time_used=time_used) if self._estimator_index is None: # update init eci estimate eci_base = search_state.init_eci self._eci.append(search_state.estimated_cost4improvement) for e in self.estimator_list[1:]: self._eci.append( self._search_states[e].init_eci / eci_base * self._eci[0] ) self._estimator_index = 0 min_budget = max(10 * self._eci[0], sum(self._eci)) max_budget = 10000 * self._eci[0] if search_state.sample_size: ratio = search_state.data_size[0] / search_state.sample_size min_budget *= ratio max_budget *= ratio logger.info( f"Estimated sufficient time budget={max_budget:.0f}s." f" Estimated necessary time budget={min_budget:.0f}s." ) wall_time = result.get("wall_clock_time") if wall_time is not None: self._state.time_from_start = wall_time # logger.info(f"{self._search_states[estimator].sample_size}, {data_size}") if search_state.sample_size == self._state.data_size[0]: self._iter_per_learner_fullsize[estimator] += 1 self._fullsize_reached = True self._iter_per_learner[estimator] += 1 if search_state.best_loss < self._state.best_loss: best_config_sig = estimator + search_state.get_hist_config_sig( self.data_size_full, search_state.best_config ) self._state.best_loss = search_state.best_loss self._best_estimator = estimator est_retrain_time = ( search_state.est_retrain_time(self.data_size_full) if (best_config_sig not in self._retrained_config) else 0 ) self._config_history[self._track_iter] = ( estimator, search_state.best_config, self._state.time_from_start, ) if self._trained_estimator: self._trained_estimator.cleanup() del self._trained_estimator self._trained_estimator = None if not self._state.retrain_final: self._trained_estimator = search_state.trained_estimator self._best_iteration = self._track_iter self._time_taken_best_iter = self._state.time_from_start better = True next_trial_time = search_state.time2eval_best if ( search_state.trained_estimator and not self._state.model_history and search_state.trained_estimator != self._trained_estimator ): search_state.trained_estimator.cleanup() if better or self._log_type == "all": self._log_trial(search_state, estimator) logger.info( " at {:.1f}s,\testimator {}'s best error={:.4f},\tbest estimator {}'s best error={:.4f}".format( self._state.time_from_start, estimator, search_state.best_loss, self._best_estimator, self._state.best_loss, ) ) if ( self._hpo_method in ("cfo", "bs") and all( state.search_alg and state.search_alg.searcher.is_ls_ever_converged for state in self._search_states.values() ) and ( self._state.time_from_start > self._warn_threshold * self._time_taken_best_iter ) ): logger.warning( "All estimator hyperparameters local search has " "converged at least once, and the total search time " f"exceeds {self._warn_threshold} times the time taken " "to find the best model." ) if self._early_stop: logger.warning("Stopping search as early_stop is set to True.") break self._warn_threshold *= 10 else: logger.info(f"stop trying learner {estimator}") if self._estimator_index is not None: self._active_estimators.remove(estimator) self._estimator_index -= 1 search_state.search_alg.searcher._is_ls_ever_converged = True if ( self._retrain_in_budget and best_config_sig and est_retrain_time and not better and self._search_states[self._best_estimator].sample_size == self._state.data_size[0] and ( est_retrain_time <= self._state.time_budget - self._state.time_from_start <= est_retrain_time + next_trial_time ) ): state = self._search_states[self._best_estimator] self._trained_estimator, retrain_time = self._state._train_with_config( self._best_estimator, state.best_config, self.data_size_full, ) logger.info( "retrain {} for {:.1f}s".format(self._best_estimator, retrain_time) ) self._retrained_config[ best_config_sig ] = state.best_config_train_time = retrain_time est_retrain_time = 0 self._state.time_from_start = time.time() - self._start_time_flag if ( self._state.time_from_start >= self._state.time_budget or not self._active_estimators ): break if self._ensemble and self._best_estimator: time_left = self._state.time_budget - self._state.time_from_start time_ensemble = self._search_states[self._best_estimator].time2eval_best if time_left < time_ensemble < 2 * time_left: break def _search(self): # initialize the search_states self._eci = [] self._state.best_loss = float("+inf") self._state.time_from_start = 0 self._estimator_index = None self._best_iteration = 0 self._time_taken_best_iter = 0 self._config_history = {} self._max_iter_per_learner = 10000 self._iter_per_learner = dict([(e, 0) for e in self.estimator_list]) self._iter_per_learner_fullsize = dict([(e, 0) for e in self.estimator_list]) self._fullsize_reached = False self._trained_estimator = None self._best_estimator = None self._retrained_config = {} self._warn_threshold = 10 self._selected = None self.modelcount = 0 if self._max_iter < 2 and self.estimator_list and self._state.retrain_final: # when max_iter is 1, no need to search # TODO: otherwise, need to make sure SearchStates.init_config is inside search space self.modelcount = self._max_iter self._max_iter = 0 self._best_estimator = estimator = self.estimator_list[0] self._selected = state = self._search_states[estimator] state.best_config_sample_size = self._state.data_size[0] state.best_config = ( state.init_config if isinstance(state.init_config, dict) else state.init_config[0] ) elif self._use_ray is False: self._search_sequential() else: self._search_parallel() # Add a checkpoint for the current best config to the log. if self._training_log: self._training_log.checkpoint() if self._best_estimator: self._selected = self._search_states[self._best_estimator] self.modelcount = sum( search_state.total_iter for search_state in self._search_states.values() ) if self._trained_estimator: logger.info(f"selected model: {self._trained_estimator.model}") estimators = [] if self._ensemble and self._state.task in ( "binary", "multiclass", "regression", ): search_states = list( x for x in self._search_states.items() if x[1].best_config ) search_states.sort(key=lambda x: x[1].best_loss) estimators = [ ( x[0], x[1].learner_class( task=self._state.task, n_jobs=self._state.n_jobs, **x[1].best_config, ), ) for x in search_states[:2] ] estimators += [ ( x[0], x[1].learner_class( task=self._state.task, n_jobs=self._state.n_jobs, **x[1].best_config, ), ) for x in search_states[2:] if x[1].best_loss < 4 * self._selected.best_loss ] logger.info(estimators) if len(estimators) > 1: if self._state.task in CLASSIFICATION: from sklearn.ensemble import StackingClassifier as Stacker else: from sklearn.ensemble import StackingRegressor as Stacker if isinstance(self._ensemble, dict): final_estimator = self._ensemble.get( "final_estimator", self._trained_estimator ) passthrough = self._ensemble.get("passthrough", True) else: final_estimator = self._trained_estimator passthrough = True stacker = Stacker( estimators, final_estimator, n_jobs=self._state.n_jobs, passthrough=passthrough, ) sample_weight_dict = ( (self._sample_weight_full is not None) and {"sample_weight": self._sample_weight_full} or {} ) for e in estimators: e[1].__class__.init() try: stacker.fit( self._X_train_all, self._y_train_all, **sample_weight_dict, # NOTE: _search is after ) logger.info(f"ensemble: {stacker}") self._trained_estimator = stacker self._trained_estimator.model = stacker except ValueError as e: if passthrough: logger.warning( "Using passthrough=False for ensemble because the data contain categorical features." ) stacker = Stacker( estimators, final_estimator, n_jobs=self._state.n_jobs, passthrough=False, ) stacker.fit( self._X_train_all, self._y_train_all, **sample_weight_dict, # NOTE: _search is after ) logger.info(f"ensemble: {stacker}") self._trained_estimator = stacker self._trained_estimator.model = stacker else: raise e elif self._state.retrain_final: # reset time budget for retraining if self._max_iter > 1: self._state.time_from_start -= self._state.time_budget if ( self._state.task in TS_FORECAST or self._trained_estimator is None or self._trained_estimator.model is None or ( self._state.time_budget - self._state.time_from_start > self._selected.est_retrain_time(self.data_size_full) and self._selected.best_config_sample_size == self._state.data_size[0] ) ): state = self._search_states[self._best_estimator] ( self._trained_estimator, retrain_time, ) = self._state._train_with_config( self._best_estimator, state.best_config, self.data_size_full, ) logger.info( "retrain {} for {:.1f}s".format( self._best_estimator, retrain_time ) ) state.best_config_train_time = retrain_time if self._trained_estimator: logger.info(f"retrained model: {self._trained_estimator.model}") else: logger.info("not retraining because the time budget is too small.") def __del__(self): if ( hasattr(self, "_trained_estimator") and self._trained_estimator and hasattr(self._trained_estimator, "cleanup") ): self._trained_estimator.cleanup() del self._trained_estimator def _select_estimator(self, estimator_list): if self._learner_selector == "roundrobin": self._estimator_index += 1 if self._estimator_index == len(estimator_list): self._estimator_index = 0 return estimator_list[self._estimator_index] min_estimated_cost, selected = np.Inf, None inv = [] untried_exists = False for i, estimator in enumerate(estimator_list): if estimator in self._search_states and ( self._search_states[estimator].sample_size ): # sample_size=None meaning no result search_state = self._search_states[estimator] if ( self._search_states[estimator].time2eval_best > self._state.time_budget - self._state.time_from_start or self._iter_per_learner_fullsize[estimator] >= self._max_iter_per_learner ): inv.append(0) continue estimated_cost = search_state.estimated_cost4improvement if search_state.sample_size < self._state.data_size[0]: estimated_cost = min( estimated_cost, search_state.time2eval_best * min( SAMPLE_MULTIPLY_FACTOR, self._state.data_size[0] / search_state.sample_size, ), ) gap = search_state.best_loss - self._state.best_loss if gap > 0 and not self._ensemble: delta_loss = ( search_state.best_loss_old - search_state.best_loss ) or search_state.best_loss delta_time = ( search_state.total_time_used - search_state.time_best_found_old ) or 1e-10 speed = delta_loss / delta_time if speed: estimated_cost = max(2 * gap / speed, estimated_cost) estimated_cost = estimated_cost or 1e-9 inv.append(1 / estimated_cost) else: estimated_cost = self._eci[i] inv.append(0) untried_exists = True if estimated_cost < min_estimated_cost: min_estimated_cost = estimated_cost selected = estimator if untried_exists or not selected: state = self._search_states.get(selected) if not (state and state.sample_size): return selected s = sum(inv) p = self._random.rand() q = 0 for i in range(len(inv)): if inv[i]: q += inv[i] / s if p < q: return estimator_list[i]