import copy import time import warnings from collections import OrderedDict import cloudpickle import numpy as np import pandas as pd from sklearn.model_selection import ( BaseCrossValidator, KFold, StratifiedKFold, train_test_split ) from .pipeline_search_plots import PipelineSearchPlots from evalml.automl.automl_algorithm import IterativeAlgorithm from evalml.automl.data_splitters import TrainingValidationSplit from evalml.data_checks import DataChecks, DefaultDataChecks, EmptyDataChecks from evalml.data_checks.data_check_message_type import DataCheckMessageType from evalml.exceptions import PipelineNotFoundError from evalml.objectives import get_objective, get_objectives from evalml.pipelines import ( BinaryClassificationPipeline, MeanBaselineRegressionPipeline, ModeBaselineBinaryPipeline, ModeBaselineMulticlassPipeline ) from evalml.pipelines.utils import get_estimators, make_pipeline from evalml.problem_types import ProblemTypes, handle_problem_types from evalml.tuners import SKOptTuner from evalml.utils import convert_to_seconds, get_random_state from evalml.utils.logger import ( get_logger, log_subtitle, log_title, time_elapsed, update_pipeline ) logger = get_logger(__file__) [docs]class AutoMLSearch: """Automated Pipeline search.""" _MAX_NAME_LEN = 40 _LARGE_DATA_ROW_THRESHOLD = int(1e5) # Necessary for "Plotting" documentation, since Sphinx does not work well with instance attributes. plot = PipelineSearchPlots _DEFAULT_OBJECTIVES = {'binary': 'log_loss_binary', 'multiclass': 'log_loss_multi', 'regression': 'r2'} [docs] def __init__(self, problem_type=None, objective='auto', max_pipelines=None, max_time=None, patience=None, tolerance=None, data_split=None, allowed_pipelines=None, allowed_model_families=None, start_iteration_callback=None, add_result_callback=None, additional_objectives=None, random_state=0, n_jobs=-1, tuner_class=None, verbose=True, optimize_thresholds=False): """Automated pipeline search Arguments: problem_type (str or ProblemTypes): Choice of 'regression', 'binary', or 'multiclass', depending on the desired problem type. objective (str, ObjectiveBase): The objective to optimize for. When set to auto, chooses: LogLossBinary for binary classification problems, LogLossMulticlass for multiclass classification problems, and R2 for regression problems. max_pipelines (int): Maximum number of pipelines to search. If max_pipelines and max_time is not set, then max_pipelines will default to max_pipelines of 5. max_time (int, str): Maximum time to search for pipelines. This will not start a new pipeline search after the duration has elapsed. If it is an integer, then the time will be in seconds. For strings, time can be specified as seconds, minutes, or hours. patience (int): Number of iterations without improvement to stop search early. Must be positive. If None, early stopping is disabled. Defaults to None. tolerance (float): Minimum percentage difference to qualify as score improvement for early stopping. Only applicable if patience is not None. Defaults to None. allowed_pipelines (list(class)): A list of PipelineBase subclasses indicating the pipelines allowed in the search. The default of None indicates all pipelines for this problem type are allowed. Setting this field will cause allowed_model_families to be ignored. allowed_model_families (list(str, ModelFamily)): The model families to search. The default of None searches over all model families. Run evalml.list_model_families("binary") to see options. Change `binary` to `multiclass` or `regression` depending on the problem type. Note that if allowed_pipelines is provided, this parameter will be ignored. data_split (sklearn.model_selection.BaseCrossValidator): data splitting method to use. Defaults to StratifiedKFold. tuner_class: the tuner class to use. Defaults to scikit-optimize tuner start_iteration_callback (callable): function called before each pipeline training iteration. Passed two parameters: pipeline_class, parameters. add_result_callback (callable): function called after each pipeline training iteration. Passed two parameters: results, trained_pipeline. additional_objectives (list): Custom set of objectives to score on. Will override default objectives for problem type if not empty. random_state (int, np.random.RandomState): The random seed/state. Defaults to 0. n_jobs (int or None): Non-negative integer describing level of parallelism used for pipelines. None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. verbose (boolean): If True, turn verbosity on. Defaults to True """ try: self.problem_type = handle_problem_types(problem_type) except ValueError: raise ValueError('choose one of (binary, multiclass, regression) as problem_type') self.tuner_class = tuner_class or SKOptTuner self.start_iteration_callback = start_iteration_callback self.add_result_callback = add_result_callback self.data_split = data_split self.verbose = verbose self.optimize_thresholds = optimize_thresholds if objective == 'auto': objective = self._DEFAULT_OBJECTIVES[self.problem_type.value] self.objective = get_objective(objective) if self.data_split is not None and not issubclass(self.data_split.__class__, BaseCrossValidator): raise ValueError("Not a valid data splitter") if self.problem_type != self.objective.problem_type: raise ValueError("Given objective {} is not compatible with a {} problem.".format(self.objective.name, self.problem_type.value)) if additional_objectives is None: additional_objectives = get_objectives(self.problem_type) # if our main objective is part of default set of objectives for problem_type, remove it existing_main_objective = next((obj for obj in additional_objectives if obj.name == self.objective.name), None) if existing_main_objective is not None: additional_objectives.remove(existing_main_objective) else: additional_objectives = [get_objective(o) for o in additional_objectives] self.additional_objectives = additional_objectives if max_time is None or isinstance(max_time, (int, float)): self.max_time = max_time elif isinstance(max_time, str): self.max_time = convert_to_seconds(max_time) else: raise TypeError("max_time must be a float, int, or string. Received a {}.".format(type(max_time))) self.max_pipelines = max_pipelines if self.max_pipelines is None and self.max_time is None: self.max_pipelines = 5 logger.info("Using default limit of max_pipelines=5.\n") if patience and (not isinstance(patience, int) or patience < 0): raise ValueError("patience value must be a positive integer. Received {} instead".format(patience)) if tolerance and (tolerance > 1.0 or tolerance < 0.0): raise ValueError("tolerance value must be a float between 0.0 and 1.0 inclusive. Received {} instead".format(tolerance)) self.patience = patience self.tolerance = tolerance or 0.0 self._results = { 'pipeline_results': {}, 'search_order': [] } self.random_state = get_random_state(random_state) self.n_jobs = n_jobs self.plot = None try: self.plot = PipelineSearchPlots(self) except ImportError: logger.warning("Unable to import plotly; skipping pipeline search plotting\n") self._data_check_results = None self.allowed_pipelines = allowed_pipelines self.allowed_model_families = allowed_model_families self._automl_algorithm = None self._start = None self._validate_problem_type() @property def data_check_results(self): return self._data_check_results def __str__(self): def _print_list(obj_list): lines = ['\t{}'.format(o.name) for o in obj_list] return '\n'.join(lines) def _get_funct_name(function): if callable(function): return function.__name__ else: return None search_desc = ( f"{handle_problem_types(self.problem_type).name} Search\n\n" f"Parameters: \n{'='*20}\n" f"Objective: {get_objective(self.objective).name}\n" f"Max Time: {self.max_time}\n" f"Max Pipelines: {self.max_pipelines}\n" f"Allowed Pipelines: \n{_print_list(self.allowed_pipelines or [])}\n" f"Patience: {self.patience}\n" f"Tolerance: {self.tolerance}\n" f"Data Splitting: {self.data_split}\n" f"Tuner: {self.tuner_class.__name__}\n" f"Start Iteration Callback: {_get_funct_name(self.start_iteration_callback)}\n" f"Add Result Callback: {_get_funct_name(self.add_result_callback)}\n" f"Additional Objectives: {_print_list(self.additional_objectives or [])}\n" f"Random State: {self.random_state}\n" f"n_jobs: {self.n_jobs}\n" f"Verbose: {self.verbose}\n" f"Optimize Thresholds: {self.optimize_thresholds}\n" ) rankings_desc = "" if not self.rankings.empty: rankings_str = self.rankings.drop(['parameters'], axis='columns').to_string() rankings_desc = f"\nSearch Results: \n{'='*20}\n{rankings_str}" return search_desc + rankings_desc @staticmethod def _validate_data_checks(data_checks): """Validate data_checks parameter. Arguments: data_checks (DataChecks, list(Datacheck), str, None): Input to validate. If not of the right type, raise an exception. Returns: An instance of DataChecks used to perform checks before search. """ if isinstance(data_checks, DataChecks): return data_checks elif isinstance(data_checks, list): return DataChecks(data_checks) elif isinstance(data_checks, str): if data_checks == "auto": return DefaultDataChecks() elif data_checks == "disabled": return EmptyDataChecks() else: raise ValueError("If data_checks is a string, it must be either 'auto' or 'disabled'. " f"Received '{data_checks}'.") elif data_checks is None: return EmptyDataChecks() else: return DataChecks(data_checks) [docs] def search(self, X, y, data_checks="auto", feature_types=None, raise_errors=True, show_iteration_plot=True): """Find best classifier Arguments: X (pd.DataFrame): the input training data of shape [n_samples, n_features] y (pd.Series): the target training labels of length [n_samples] feature_types (list, optional): list of feature types, either numerical or categorical. Categorical features will automatically be encoded raise_errors (boolean): If True, raise errors and exit search if a pipeline errors during fitting. If False, set scores for the errored pipeline to NaN and continue search. Defaults to True. show_iteration_plot (boolean, True): Shows an iteration vs. score plot in Jupyter notebook. Disabled by default in non-Jupyter enviroments. data_checks (DataChecks, list(Datacheck), str, None): A collection of data checks to run before automl search. If data checks produce any errors, an exception will be thrown before the search begins. If "disabled" or None, no data checks will be done. If set to "auto", DefaultDataChecks will be done. Default value is set to "auto". Returns: self """ # don't show iteration plot outside of a jupyter notebook if show_iteration_plot: try: get_ipython except NameError: show_iteration_plot = False # make everything pandas objects if not isinstance(X, pd.DataFrame): X = pd.DataFrame(X) if not isinstance(y, pd.Series): y = pd.Series(y) # Set the default data splitter if self.problem_type == ProblemTypes.REGRESSION: default_data_split = KFold(n_splits=3, random_state=self.random_state) elif self.problem_type in [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]: default_data_split = StratifiedKFold(n_splits=3, random_state=self.random_state) if X.shape[0] > self._LARGE_DATA_ROW_THRESHOLD: default_data_split = TrainingValidationSplit(test_size=0.25) self.data_split = self.data_split or default_data_split data_checks = self._validate_data_checks(data_checks) data_check_results = data_checks.validate(X, y) if len(data_check_results) > 0: self._data_check_results = data_check_results for message in self._data_check_results: if message.message_type == DataCheckMessageType.WARNING: logger.warning(message) elif message.message_type == DataCheckMessageType.ERROR: logger.error(message) if any([message.message_type == DataCheckMessageType.ERROR for message in self._data_check_results]): raise ValueError("Data checks raised some warnings and/or errors. Please see `self.data_check_results` for more information or pass data_checks=EmptyDataChecks() to search() to disable data checking.") if self.allowed_pipelines is None: logger.info("Generating pipelines to search over...") allowed_estimators = get_estimators(self.problem_type, self.allowed_model_families) logger.debug(f"allowed_estimators set to {[estimator.name for estimator in allowed_estimators]}") self.allowed_pipelines = [make_pipeline(X, y, estimator, self.problem_type) for estimator in allowed_estimators] if self.allowed_pipelines == []: raise ValueError("No allowed pipelines to search") self.allowed_model_families = list(set([p.model_family for p in (self.allowed_pipelines)])) logger.debug(f"allowed_pipelines set to {[pipeline.name for pipeline in self.allowed_pipelines]}") logger.debug(f"allowed_model_families set to {self.allowed_model_families}") self._automl_algorithm = IterativeAlgorithm( max_pipelines=self.max_pipelines, allowed_pipelines=self.allowed_pipelines, tuner_class=self.tuner_class, random_state=self.random_state, n_jobs=self.n_jobs, number_features=X.shape[1] ) log_title(logger, "Beginning pipeline search") logger.info("Optimizing for %s. " % self.objective.name) logger.info("{} score is better.\n".format('Greater' if self.objective.greater_is_better else 'Lower')) if self.max_pipelines is not None: logger.info("Searching up to %s pipelines. " % self.max_pipelines) if self.max_time is not None: logger.info("Will stop searching for new pipelines after %d seconds.\n" % self.max_time) logger.info("Allowed model families: %s\n" % ", ".join([model.value for model in self.allowed_model_families])) search_iteration_plot = None if self.plot: search_iteration_plot = self.plot.search_iteration_plot(interactive_plot=show_iteration_plot) self._start = time.time() self._add_baseline_pipelines(X, y, raise_errors=raise_errors) current_batch_pipelines = [] while self._check_stopping_condition(self._start): if len(current_batch_pipelines) == 0: try: current_batch_pipelines = self._automl_algorithm.next_batch() except StopIteration: logger.info('AutoML Algorithm out of recommendations, ending') break pipeline = current_batch_pipelines.pop(0) parameters = pipeline.parameters logger.debug('Evaluating pipeline {}'.format(pipeline.name)) logger.debug('Pipeline parameters: {}'.format(parameters)) if self.start_iteration_callback: self.start_iteration_callback(pipeline.__class__, parameters) desc = f"{pipeline.name}" if len(desc) > self._MAX_NAME_LEN: desc = desc[:self._MAX_NAME_LEN - 3] + "..." desc = desc.ljust(self._MAX_NAME_LEN) update_pipeline(logger, desc, len(self._results['pipeline_results']) + 1, self.max_pipelines, self._start) evaluation_results = self._evaluate(pipeline, X, y, raise_errors=raise_errors) score = evaluation_results['cv_score_mean'] score_to_minimize = -score if self.objective.greater_is_better else score self._automl_algorithm.add_result(score_to_minimize, pipeline) if search_iteration_plot: search_iteration_plot.update() elapsed_time = time_elapsed(self._start) desc = f"\nSearch finished after {elapsed_time}" desc = desc.ljust(self._MAX_NAME_LEN) logger.info(desc) best_pipeline = self.rankings.iloc[0] best_pipeline_name = best_pipeline["pipeline_name"] logger.info(f"Best pipeline: {best_pipeline_name}") logger.info(f"Best pipeline {self.objective.name}: {best_pipeline['score']:3f}") def _check_stopping_condition(self, start): should_continue = True num_pipelines = len(self._results['pipeline_results']) if num_pipelines == 0: return True # check max_time and max_pipelines elapsed = time.time() - start if self.max_time and elapsed >= self.max_time: return False elif self.max_pipelines and num_pipelines >= self.max_pipelines: return False # check for early stopping if self.patience is None: return True first_id = self._results['search_order'][0] best_score = self._results['pipeline_results'][first_id]['score'] num_without_improvement = 0 for id in self._results['search_order'][1:]: curr_score = self._results['pipeline_results'][id]['score'] significant_change = abs((curr_score - best_score) / best_score) > self.tolerance score_improved = curr_score > best_score if self.objective.greater_is_better else curr_score < best_score if score_improved and significant_change: best_score = curr_score num_without_improvement = 0 else: num_without_improvement += 1 if num_without_improvement >= self.patience: logger.info("\n\n{} iterations without improvement. Stopping search early...".format(self.patience)) return False return should_continue def _validate_problem_type(self): for obj in self.additional_objectives: if obj.problem_type != self.problem_type: raise ValueError("Additional objective {} is not compatible with a {} problem.".format(obj.name, self.problem_type.value)) for pipeline in self.allowed_pipelines or []: if not pipeline.problem_type == self.problem_type: raise ValueError("Given pipeline {} is not compatible with problem_type {}.".format(pipeline.name, self.problem_type.value)) def _add_baseline_pipelines(self, X, y, raise_errors=True): if self.problem_type == ProblemTypes.BINARY: strategy_dict = {"strategy": "random_weighted"} baseline = ModeBaselineBinaryPipeline(parameters={"Baseline Classifier": strategy_dict}) elif self.problem_type == ProblemTypes.MULTICLASS: strategy_dict = {"strategy": "random_weighted"} baseline = ModeBaselineMulticlassPipeline(parameters={"Baseline Classifier": strategy_dict}) elif self.problem_type == ProblemTypes.REGRESSION: strategy_dict = {"strategy": "mean"} baseline = MeanBaselineRegressionPipeline(parameters={"Baseline Regressor": strategy_dict}) if self.start_iteration_callback: self.start_iteration_callback(baseline.__class__, baseline.parameters) desc = f"{baseline.name}" if len(desc) > self._MAX_NAME_LEN: desc = desc[:self._MAX_NAME_LEN - 3] + "..." desc = desc.ljust(self._MAX_NAME_LEN) update_pipeline(logger, desc, len(self._results['pipeline_results']) + 1, self.max_pipelines, self._start) baseline_results = self._compute_cv_scores(baseline, X, y, raise_errors=raise_errors) self._add_result(trained_pipeline=baseline, parameters=baseline.parameters, training_time=baseline_results['training_time'], cv_data=baseline_results['cv_data'], cv_scores=baseline_results['cv_scores']) def _compute_cv_scores(self, pipeline, X, y, raise_errors=True): start = time.time() cv_data = [] logger.info("\tStarting cross validation") for i, (train, test) in enumerate(self.data_split.split(X, y)): logger.debug(f"\t\tTraining and scoring on fold {i}") X_train, X_test = X.iloc[train], X.iloc[test] y_train, y_test = y.iloc[train], y.iloc[test] objectives_to_score = [self.objective] + self.additional_objectives try: X_threshold_tuning = None y_threshold_tuning = None if self.optimize_thresholds and self.objective.problem_type == ProblemTypes.BINARY and self.objective.can_optimize_threshold: X_train, X_threshold_tuning, y_train, y_threshold_tuning = train_test_split(X_train, y_train, test_size=0.2, random_state=self.random_state) cv_pipeline = pipeline.clone() logger.debug(f"\t\t\tFold {i}: starting training") cv_pipeline.fit(X_train, y_train) logger.debug(f"\t\t\tFold {i}: finished training") if self.objective.problem_type == ProblemTypes.BINARY: cv_pipeline.threshold = 0.5 if self.optimize_thresholds and self.objective.can_optimize_threshold: logger.debug(f"\t\t\tFold {i}: Optimizing threshold for {self.objective.name}") y_predict_proba = cv_pipeline.predict_proba(X_threshold_tuning) if isinstance(y_predict_proba, pd.DataFrame): y_predict_proba = y_predict_proba.iloc[:, 1] else: y_predict_proba = y_predict_proba[:, 1] cv_pipeline.threshold = self.objective.optimize_threshold(y_predict_proba, y_threshold_tuning, X=X_threshold_tuning) logger.debug(f"\t\t\tFold {i}: Optimal threshold found ({cv_pipeline.threshold:.3f})") logger.debug(f"\t\t\tFold {i}: Scoring trained pipeline") scores = cv_pipeline.score(X_test, y_test, objectives=objectives_to_score) logger.debug(f"\t\t\tFold {i}: {self.objective.name} score: {scores[self.objective.name]:.3f}") score = scores[self.objective.name] except Exception as e: logger.error("Exception during automl search: {}".format(str(e))) if raise_errors: raise e score = np.nan scores = OrderedDict(zip([n.name for n in self.additional_objectives], [np.nan] * len(self.additional_objectives))) ordered_scores = OrderedDict() ordered_scores.update({self.objective.name: score}) ordered_scores.update(scores) ordered_scores.update({"# Training": len(y_train)}) ordered_scores.update({"# Testing": len(y_test)}) evaluation_entry = {"all_objective_scores": ordered_scores, "score": score, 'binary_classification_threshold': None} if isinstance(cv_pipeline, BinaryClassificationPipeline) and cv_pipeline.threshold is not None: evaluation_entry['binary_classification_threshold'] = cv_pipeline.threshold cv_data.append(evaluation_entry) training_time = time.time() - start cv_scores = pd.Series([fold['score'] for fold in cv_data]) cv_score_mean = cv_scores.mean() logger.info(f"\tFinished cross validation - mean {self.objective.name}: {cv_score_mean:.3f}") return {'cv_data': cv_data, 'training_time': training_time, 'cv_scores': cv_scores, 'cv_score_mean': cv_score_mean} def _add_result(self, trained_pipeline, parameters, training_time, cv_data, cv_scores): cv_score = cv_scores.mean() # calculate high_variance_cv # if the coefficient of variance is greater than .2 with warnings.catch_warnings(): warnings.simplefilter('ignore') high_variance_cv = (cv_scores.std() / cv_scores.mean()) > .2 pipeline_name = trained_pipeline.name pipeline_summary = trained_pipeline.summary pipeline_id = len(self._results['pipeline_results']) self._results['pipeline_results'][pipeline_id] = { "id": pipeline_id, "pipeline_name": pipeline_name, "pipeline_class": type(trained_pipeline), "pipeline_summary": pipeline_summary, "parameters": parameters, "score": cv_score, "high_variance_cv": high_variance_cv, "training_time": training_time, "cv_data": cv_data, } self._results['search_order'].append(pipeline_id) if self.add_result_callback: self.add_result_callback(self._results['pipeline_results'][pipeline_id], trained_pipeline) def _evaluate(self, pipeline, X, y, raise_errors=True): parameters = pipeline.parameters evaluation_results = self._compute_cv_scores(pipeline, X, y, raise_errors=raise_errors) logger.debug('Adding results for pipeline {}\nparameters {}\nevaluation_results {}'.format(pipeline.name, parameters, evaluation_results)) self._add_result(trained_pipeline=pipeline, parameters=parameters, training_time=evaluation_results['training_time'], cv_data=evaluation_results['cv_data'], cv_scores=evaluation_results['cv_scores']) logger.debug('Adding results complete') return evaluation_results [docs] def get_pipeline(self, pipeline_id, random_state=0): """Given the ID of a pipeline training result, returns an untrained instance of the specified pipeline initialized with the parameters used to train that pipeline during automl search. Arguments: pipeline_id (int): pipeline to retrieve random_state (int, np.random.RandomState): The random seed/state. Defaults to 0. Returns: PipelineBase: untrained pipeline instance associated with the provided ID """ pipeline_results = self.results['pipeline_results'].get(pipeline_id) if pipeline_results is None: raise PipelineNotFoundError("Pipeline not found in automl results") pipeline_class = pipeline_results.get('pipeline_class') parameters = pipeline_results.get('parameters') if pipeline_class is None or parameters is None: raise PipelineNotFoundError("Pipeline class or parameters not found in automl results") return pipeline_class(parameters, random_state=random_state) [docs] def describe_pipeline(self, pipeline_id, return_dict=False): """Describe a pipeline Arguments: pipeline_id (int): pipeline to describe return_dict (bool): If True, return dictionary of information about pipeline. Defaults to False. Returns: Description of specified pipeline. Includes information such as type of pipeline components, problem, training time, cross validation, etc. """ if pipeline_id not in self._results['pipeline_results']: raise PipelineNotFoundError("Pipeline not found") pipeline = self.get_pipeline(pipeline_id) pipeline_results = self._results['pipeline_results'][pipeline_id] pipeline.describe() log_subtitle(logger, "Training") logger.info("Training for {} problems.".format(pipeline.problem_type)) if self.optimize_thresholds and self.objective.problem_type == ProblemTypes.BINARY and self.objective.can_optimize_threshold: logger.info("Objective to optimize binary classification pipeline thresholds for: {}".format(self.objective)) logger.info("Total training time (including CV): %.1f seconds" % pipeline_results["training_time"]) log_subtitle(logger, "Cross Validation", underline="-") if pipeline_results["high_variance_cv"]: logger.warning("High variance within cross validation scores. " + "Model may not perform as estimated on unseen data.") all_objective_scores = [fold["all_objective_scores"] for fold in pipeline_results["cv_data"]] all_objective_scores = pd.DataFrame(all_objective_scores) for c in all_objective_scores: if c in ["# Training", "# Testing"]: all_objective_scores[c] = all_objective_scores[c].astype("object") continue mean = all_objective_scores[c].mean(axis=0) std = all_objective_scores[c].std(axis=0) all_objective_scores.loc["mean", c] = mean all_objective_scores.loc["std", c] = std all_objective_scores.loc["coef of var", c] = std / mean if abs(mean) > 0 else np.inf all_objective_scores = all_objective_scores.fillna("-") with pd.option_context('display.float_format', '{:.3f}'.format, 'expand_frame_repr', False): logger.info(all_objective_scores) if return_dict: return pipeline_results [docs] def add_to_rankings(self, pipeline, X, y): """Fits and evaluates a given pipeline then adds the results to the automl rankings with the requirement that automl search has been run. Please use the same data as previous runs of automl search. If pipeline already exists in rankings this method will return `None`. Arguments: pipeline (PipelineBase): pipeline to train and evaluate. X (pd.DataFrame): the input training data of shape [n_samples, n_features]. y (pd.Series): the target training labels of length [n_samples]. """ if not isinstance(X, pd.DataFrame): X = pd.DataFrame(X) if not isinstance(y, pd.Series): y = pd.Series(y) if not self.has_searched: raise RuntimeError("Please run automl search before calling `add_to_rankings()`") pipeline_rows = self.full_rankings[self.full_rankings['pipeline_name'] == pipeline.name] for parameter in pipeline_rows['parameters']: if pipeline.parameters == parameter: return self._evaluate(pipeline, X, y, raise_errors=True) @property def results(self): """Class that allows access to a copy of the results from `automl_search`. Returns: dict containing `pipeline_results`: a dict with results from each pipeline, and `search_order`: a list describing the order the pipelines were searched. """ return copy.deepcopy(self._results) @property def has_searched(self): """Returns `True` if search has been ran and `False` if not""" searched = True if self._results['pipeline_results'] else False return searched @property def rankings(self): """Returns a pandas.DataFrame with scoring results from the highest-scoring set of parameters used with each pipeline.""" return self.full_rankings.drop_duplicates(subset="pipeline_name", keep="first") @property def full_rankings(self): """Returns a pandas.DataFrame with scoring results from all pipelines searched""" ascending = True if self.objective.greater_is_better: ascending = False full_rankings_cols = ["id", "pipeline_name", "score", "high_variance_cv", "parameters"] if not self.has_searched: return pd.DataFrame(columns=full_rankings_cols) rankings_df = pd.DataFrame(self._results['pipeline_results'].values()) rankings_df = rankings_df[full_rankings_cols] rankings_df.sort_values("score", ascending=ascending, inplace=True) rankings_df.reset_index(drop=True, inplace=True) return rankings_df @property def best_pipeline(self): """Returns an untrained instance of the best pipeline and parameters found during automl search. Returns: PipelineBase: untrained pipeline instance associated with the best automl search result. """ if not self.has_searched: raise PipelineNotFoundError("automl search must be run before selecting `best_pipeline`.") best = self.rankings.iloc[0] return self.get_pipeline(best["id"]) [docs] def save(self, file_path): """Saves AutoML object at file path Arguments: file_path (str) : location to save file Returns: None """ with open(file_path, 'wb') as f: cloudpickle.dump(self, f) [docs] @staticmethod def load(file_path): """Loads AutoML object at file path Arguments: file_path (str) : location to find file to load Returns: AutoSearchBase object """ with open(file_path, 'rb') as f: return cloudpickle.load(f)