From 9f65481c38eb433d45316013e58bc09f1ca03b36 Mon Sep 17 00:00:00 2001 From: Pinchuk Maya <57305249+maypink@users.noreply.github.com> Date: Tue, 12 Sep 2023 12:48:22 +0300 Subject: [PATCH] Universal ExperimentAnalyzer (#181) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add convergence analyze * unify iteration through paths * add metrics analyze * add mean, folders_to_ignore * add analyze structural complexity * add logging info * add stat test analysis * add confidence interval * add example of usage * add title for results plots * lend title arg * fix conditional expression * add title to plots * minor * add test data for analyzer * fixes after review * fixes after review * minor * archive data and results * extend docstrings * зуз8 * зуз8 #2 --- examples/experiment_analyzer/__init__.py | 0 examples/experiment_analyzer/data.tar.gz | Bin 0 -> 741113 bytes .../experiment_analyzer.py | 78 +++++ .../result_analysis.tar.gz | Bin 0 -> 56930 bytes experiments/experiment_analyzer.py | 322 ++++++++++++++++++ golem/core/dag/graph.py | 3 + golem/visualisation/graph_viz.py | 6 +- .../visualisation/opt_history/fitness_line.py | 4 +- 8 files changed, 410 insertions(+), 3 deletions(-) create mode 100644 examples/experiment_analyzer/__init__.py create mode 100644 examples/experiment_analyzer/data.tar.gz create mode 100644 examples/experiment_analyzer/experiment_analyzer.py create mode 100644 examples/experiment_analyzer/result_analysis.tar.gz create mode 100644 experiments/experiment_analyzer.py diff --git a/examples/experiment_analyzer/__init__.py b/examples/experiment_analyzer/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/experiment_analyzer/data.tar.gz b/examples/experiment_analyzer/data.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..556275ffb75e101c3f626495fa3eb08c31d00c88 GIT binary patch literal 741113 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literal 0 HcmV?d00001 diff --git a/examples/experiment_analyzer/experiment_analyzer.py b/examples/experiment_analyzer/experiment_analyzer.py new file mode 100644 index 00000000..6dca2af2 --- /dev/null +++ b/examples/experiment_analyzer/experiment_analyzer.py @@ -0,0 +1,78 @@ +import os +import tarfile + +import matplotlib.pyplot as plt +from scipy.stats import mannwhitneyu, kruskal, ttest_ind + +from experiments.experiment_analyzer import ExperimentAnalyzer +from golem.core.paths import project_root + + +if __name__ == '__main__': + """ The result of analysis can be seen without running the script in + '~/GOLEM/examples/experiment_analyzer/result_analysis.tar.gz'. """ + + path_to_root = os.path.join(project_root(), 'examples', 'experiment_analyzer') + + # extract data if there is an archive + if 'data.tar.gz' in os.listdir(path_to_root): + tar = tarfile.open(os.path.join(path_to_root, 'data.tar.gz'), "r:gz") + tar.extractall() + tar.close() + + path_to_experiment_data = os.path.join(path_to_root, 'data') + path_to_save = os.path.join(path_to_root, 'result_analysis') + + analyzer = ExperimentAnalyzer(path_to_root=path_to_experiment_data, folders_to_ignore=['result_analysis', + 'Thumbs.db']) + + # to get convergence table with mean values + path_to_save_convergence = os.path.join(path_to_save, 'convergence') + + convergence_mean = analyzer.analyze_convergence(history_folder='histories', is_raise=False, + path_to_save=path_to_save_convergence, + is_mean=True) + + # to get convergence boxplots + convergence = analyzer.analyze_convergence(history_folder='histories', is_raise=False) + path_to_save_convergence_boxplots = os.path.join(path_to_save_convergence, 'convergence_boxplots') + + for dataset in convergence[list(convergence.keys())[0]].keys(): + to_compare = dict() + for setup in convergence.keys(): + to_compare[setup] = [i for i in convergence[setup][dataset]] + plt.boxplot(list(to_compare.values()), labels=list(to_compare.keys())) + plt.title(f'Convergence on {dataset}') + os.makedirs(path_to_save_convergence_boxplots, exist_ok=True) + plt.savefig(os.path.join(path_to_save_convergence_boxplots, f'convergence_{dataset}.png')) + plt.close() + + # to get metrics table with mean values + path_to_save_metrics = os.path.join(path_to_save, 'metrics') + metric_names = ['roc_auc', 'f1'] + metrics_dict_mean = analyzer.analyze_metrics(metric_names=metric_names, file_name='evaluation_results.csv', + is_raise=False, path_to_save=path_to_save_metrics, + is_mean=True) + + # to get metrics boxplots + metrics_dict = analyzer.analyze_metrics(metric_names=metric_names, file_name='evaluation_results.csv', + is_raise=False) + path_to_save_metrics_boxplots = os.path.join(path_to_save_metrics, 'metrics_boxplot') + + for metric in metric_names: + for dataset in metrics_dict[metric][list(metrics_dict[metric].keys())[0]].keys(): + to_compare = dict() + for setup in metrics_dict[metric].keys(): + to_compare[setup] = [-1 * i for i in metrics_dict[metric][setup][dataset]] + plt.boxplot(list(to_compare.values()), labels=list(to_compare.keys())) + plt.title(f'{metric} on {dataset}') + cur_path_to_save = os.path.join(path_to_save_metrics_boxplots, metric) + os.makedirs(cur_path_to_save, exist_ok=True) + plt.savefig(os.path.join(cur_path_to_save, f'{metric}_{dataset}.png')) + plt.close() + + # to get stat test results table + path_to_save_stat = os.path.join(path_to_save, 'statistic') + stat_dict = analyzer.analyze_statistical_significance(data_to_analyze=metrics_dict['roc_auc'], + stat_tests=[mannwhitneyu, kruskal, ttest_ind], + path_to_save=path_to_save_stat) diff --git 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00000000..c15549b1 --- /dev/null +++ b/experiments/experiment_analyzer.py @@ -0,0 +1,322 @@ +import os +from statistics import mean + +from typing import Dict, List, Tuple, Any, Callable, Union + +import pandas as pd + +from golem.core.log import default_log +from golem.core.optimisers.opt_history_objects.opt_history import OptHistory +from golem.visualisation.opt_history.fitness_line import MultipleFitnessLines + + +class ExperimentAnalyzer: + """ Class to analyze results of experiment. + The example of usage can be found here: ~/GOLEM/examples/experiment_analyzer/ + To use this class folder hierarchy must be organized as following: + + setup (e.g. configuration of framework) + \ + dataset + \ + launch + \ + all collected data (metrics.csv, history, saved_pipeline, etc) + + :param path_to_root: path to dir with experiment setups. + :param folders_to_ignore: folders without experiment data to ignore. + """ + + def __init__(self, path_to_root: str, folders_to_ignore: List[str] = []): + self.path_to_root = path_to_root + self._folders_to_ignore = folders_to_ignore + self._log = default_log('ExperimentAnalyzer') + + def analyze_convergence(self, history_folder: str = 'history', is_mean: bool = False, + path_to_save: str = None, is_raise: bool = False) \ + -> Dict[str, Dict[str, Union[List[float], float]]]: + """ Method to analyze convergence with the use of histories. + + :param history_folder: name of the history folder in experiment result folder (e.g. 'history', 'histories') + :param is_mean: bool flag to specify just storing all the results or calculating mean values + :param path_to_save: path to save results. + :param is_raise: bool specifying if exception must be raised if there is no history folder + """ + if path_to_save: + os.makedirs(path_to_save, exist_ok=True) + + convergence = dict() + for setup, dataset, path_to_launch in self._get_path_to_launch(): + convergence = self._extend_result_dict(result_dict=convergence, setup=setup, dataset=dataset) + + if not self._check_if_file_or_folder_present(path=path_to_launch, folder_or_file_name=history_folder, + is_raise=is_raise): + continue + + path_to_history_folder = os.path.join(path_to_launch, history_folder) + history_files = [file for file in os.listdir(path_to_history_folder) if file.endswith('.json')] + + # if there is no history + if len(history_files) == 0: + continue + + # load the first history in the folder + history = OptHistory.load(os.path.join(path_to_history_folder, history_files[0])) + convergence[setup][dataset].append(self._analyze_convergence(history=history)) + + if is_mean: + for setup in convergence.keys(): + for dataset in convergence[setup].keys(): + convergence[setup][dataset] = mean(convergence[setup][dataset]) + + # save results per metric + if path_to_save: + df = pd.DataFrame(convergence) + path_to_save = os.path.join(path_to_save, 'convergence_results.csv') + df.to_csv(path_to_save) + self._log.info(f"Convergence table was saved to {path_to_save}") + return convergence + + @staticmethod + def _analyze_convergence(history: OptHistory) -> float: + """ Method to get time in what individual with the best fitness was firstly obtained. """ + best_fitness = history.final_choices.data[0].fitness + first_gen_with_best_fitness = history.generations_count + for i, gen_fitnesses in enumerate(history.historical_fitness): + if best_fitness in gen_fitnesses: + first_gen_with_best_fitness = i + break + total_time_to_get_best_fitness = 0 + for i, gen in enumerate(history.individuals): + if i == first_gen_with_best_fitness: + break + for ind in gen.data: + total_time_to_get_best_fitness += ind.metadata['computation_time_in_seconds'] + return total_time_to_get_best_fitness + + def analyze_metrics(self, metric_names: List[str], file_name: str, is_mean: bool = False, + path_to_save: str = None, is_raise: bool = False) \ + -> Dict[str, Dict[str, Dict[str, Union[List[float], float]]]]: + """ Method to analyze specified metrics. + :param metric_names: names of metrics to analyze. e.g. ['f1', 'inference_time'] + :param file_name: name of the file with metrics (e.g. 'metrics.csv'). + The file with metrics must have metric names in columns. + :param is_mean: bool flag to specify just storing all the results or calculating mean values + :param path_to_save: path to save results + :param is_raise: bool specifying if exception must be raised if there is no history folder + """ + if path_to_save: + os.makedirs(path_to_save, exist_ok=True) + + dict_with_metrics = dict() + for setup, dataset, path_to_launch in self._get_path_to_launch(): + + df_metrics = self._get_metrics_df_from_path(path=path_to_launch, file_name=file_name, is_raise=is_raise) + if df_metrics.empty: + continue + + for metric in metric_names: + dict_with_metrics.setdefault(metric, dict()) + dict_with_metrics[metric] = self._extend_result_dict(result_dict=dict_with_metrics[metric], + setup=setup, dataset=dataset) + if metric not in df_metrics.columns: + self._log.warning(f"There is no column in {file_name} with {metric}") + dict_with_metrics[metric][setup][dataset].append(df_metrics[metric][0]) + + if is_mean: + for metric in metric_names: + for setup in dict_with_metrics[metric].keys(): + for dataset in dict_with_metrics[metric][setup].keys(): + dict_with_metrics[metric][setup][dataset] = mean(dict_with_metrics[metric][setup][dataset]) + + # save results per metric + if path_to_save: + for metric in dict_with_metrics.keys(): + df = pd.DataFrame(dict_with_metrics[metric]) + cur_path_to_save = os.path.join(path_to_save, f'{metric}_results.csv') + df.to_csv(cur_path_to_save) + self._log.info(f"Metric table was saved to {cur_path_to_save}") + return dict_with_metrics + + def plot_convergence(self, path_to_save: str, with_confidence: bool = True, + history_folder: str = 'history', is_raise: bool = False): + """ Method to analyze convergence with the use of histories. + + :param path_to_save: path to save the results. + :param with_confidence: bool param specifying to use confidence interval or not. + :param history_folder: name of the history folder in experiment result folder (e.g. 'history', 'histories') + :param is_raise: bool specifying if exception must be raised if there is no history folder + """ + if path_to_save: + os.makedirs(path_to_save, exist_ok=True) + + histories = dict() + for setup, dataset, path_to_launch in self._get_path_to_launch(): + histories = self._extend_result_dict(result_dict=histories, setup=setup, dataset=dataset) + + if not self._check_if_file_or_folder_present(path=path_to_launch, folder_or_file_name=history_folder, + is_raise=is_raise): + continue + + path_to_history_folder = os.path.join(path_to_launch, history_folder) + history_files = [file for file in os.listdir(path_to_history_folder) if file.endswith('.json')] + + # if there is no history + if len(history_files) == 0: + continue + + # load the first history in the folder + history = OptHistory.load(os.path.join(path_to_history_folder, history_files[0])) + histories[setup][dataset].append(history) + + histories_to_compare = dict() + # plot convergence pics + for dataset in histories[list(histories.keys())[0]]: + for setup in histories.keys(): + histories_to_compare[setup] = histories[setup][dataset] if dataset in histories[setup].keys() else [] + multiple_fitness_plot = MultipleFitnessLines(histories_to_compare=histories_to_compare) + file_name = f'{dataset}_convergence_with_confidence' if with_confidence \ + else f'{dataset}_convergence_without_confidence' + cur_path_to_save = os.path.join(path_to_save, file_name) + multiple_fitness_plot.visualize(save_path=cur_path_to_save, with_confidence=with_confidence) + self._log.info(f"Convergence plot for {dataset} dataset was saved to {cur_path_to_save}") + + def analyze_statistical_significance(self, data_to_analyze: Dict[str, Dict[str, List[float]]], + stat_tests: List[Callable], path_to_save: str = None, + test_format: List[str] = None) -> Dict[str, Dict[str, Dict[str, float]]]: + """ Method to perform statistical analysis of data. Metric data obtained with 'analyze_metrics' and + convergence data obtained with 'analyze_convergence' can be simply analyzed, for example. + :param data_to_analyze: data to analyze. + NB! data must have the specified format Dict[str, Dict[str, float]]: + first key -- framework/setup name, second -- dataset name and then list of metric values + :param stat_tests: list of functions of statistical tests to perform. E.g. scipy.stats.kruskal + :param path_to_save: path to save results + :param test_format: argument to specify what every test function must return. default: ['statistic', 'pvalue'] + """ + if path_to_save: + os.makedirs(path_to_save, exist_ok=True) + + if not test_format: + test_format = ['statistic', 'pvalue'] + + stat_dict = dict.fromkeys(test_format, None) + datasets = list(data_to_analyze[list(data_to_analyze.keys())[0]].keys()) + for dataset in datasets: + values_to_compare = [] + for setup in data_to_analyze.keys(): + values_to_compare.append(data_to_analyze[setup][dataset]) + for test in stat_tests: + try: + cur_test_result = test(*values_to_compare) + except Exception as e: + self._log.critical(f"Statistical test ({test}) failed with exception: {e}") + cur_test_result = [None] * len(test_format) + for i, arg in enumerate(test_format): + if not stat_dict[arg]: + stat_dict[arg] = dict.fromkeys([t.__name__ for t in stat_tests], None) + if not stat_dict[arg][test.__name__]: + stat_dict[arg][test.__name__] = dict.fromkeys(datasets, None) + stat_dict[arg][test.__name__][dataset] = cur_test_result[i] + + # save results for all tests + if path_to_save: + for arg in test_format: + df = pd.DataFrame(stat_dict[arg]) + cur_path_to_save = os.path.join(path_to_save, f'stat_{arg}_results.csv') + df.to_csv(cur_path_to_save) + self._log.info(f"Stat test table for {arg} was saved to {cur_path_to_save}") + return stat_dict + + def analyze_structural_complexity(self, path_to_save: str, dir_name: str, class_to_load: Any = None, + load_func: Callable = None, is_raise: bool = False, + file_name: str = None, metrics_to_display: List[str] = None): + """ Method to save pictures of final graphs in directories to compare it visually. + :param path_to_save: root path to pictures per setup. + :param dir_name: name of directory in which final graph is saved. + :param class_to_load: class of objects to load. + :param load_func: function that load object. Can be used in case method 'load' is not defined for the object. + :param is_raise: bool specifying if exception must be raised if there is no history folder. + :param file_name: name of the file with metrics (e.g. 'metrics.csv') + :param metrics_to_display: list of metrics to display in the title of the picture with result. + """ + if path_to_save: + os.makedirs(path_to_save, exist_ok=True) + + for setup, dataset, path_to_launch in self._get_path_to_launch(): + if not self._check_if_file_or_folder_present(path=path_to_launch, folder_or_file_name=dir_name, + is_raise=is_raise): + continue + + path_to_json = None + for address, dirs, files in os.walk(path_to_launch): + for name in files: + if '.json' in name: + path_to_json = os.path.join(address, name) + break + + # final result was not saved in this launch + if not path_to_json: + continue + + result = class_to_load.load(path_to_json) if class_to_load is not None \ + else load_func(path_to_json) if load_func else None + + # load metrics for title if specified + df_metrics = self._get_metrics_df_from_path(path=path_to_launch, file_name=file_name, is_raise=is_raise) + title = '' + for metric in metrics_to_display: + if metric not in df_metrics.columns: + self._log.warning(f"There is no column in {file_name} with {metric}") + else: + title += f'{metric}={df_metrics[metric][0]} ' + title = 'Best metrics for launch: ' + title + cur_path_to_save = os.path.join(path_to_save, setup) + + if not os.path.exists(cur_path_to_save): + os.makedirs(cur_path_to_save) + saved_results = [int(cur_name.split("_")[0]) for cur_name in os.listdir(cur_path_to_save) + if cur_name not in self._folders_to_ignore] + max_saved_num = max(saved_results) if saved_results else 0 + cur_path_to_save = os.path.join(cur_path_to_save, f'{max_saved_num+1}_result.png') + result.show(cur_path_to_save, title=title) + self._log.info(f"Resulting graph was saved to {cur_path_to_save}") + + def _get_path_to_launch(self) -> Tuple[str, str, str]: + """ Yields setup name, dataset name and paths to dirs with experiment results. + If experiment saving configuration/files structure somehow differs from the structure implied in this class + this method can be used externally to get paths to launches. + """ + for setup in os.listdir(self.path_to_root): + if setup in self._folders_to_ignore: + continue + path_to_setup = os.path.join(self.path_to_root, setup) + for dataset in os.listdir(path_to_setup): + if dataset in self._folders_to_ignore: + continue + path_to_dataset = os.path.join(path_to_setup, dataset) + for launch in os.listdir(path_to_dataset): + path_to_launch = os.path.join(path_to_dataset, launch) + yield setup, dataset, path_to_launch + + @staticmethod + def _extend_result_dict(result_dict: dict, setup: str, dataset: str) -> Dict[str, Dict[str, list]]: + """ Extends result dictionary with new setup and dataset name. """ + result_dict.setdefault(setup, dict()) + result_dict[setup].setdefault(dataset, list()) + return result_dict + + def _get_metrics_df_from_path(self, path: str, file_name: str, is_raise: bool) -> pd.DataFrame: + if not self._check_if_file_or_folder_present(path, file_name, is_raise): + return pd.DataFrame() + + df_metrics = pd.read_csv(os.path.join(path, file_name)) + return df_metrics + + @staticmethod + def _check_if_file_or_folder_present(path: str, folder_or_file_name: str, is_raise: bool) -> bool: + if folder_or_file_name not in os.listdir(path): + if is_raise: + raise ValueError(f"There is no folder/file with name {folder_or_file_name}") + else: + return False + return True diff --git a/golem/core/dag/graph.py b/golem/core/dag/graph.py index 19a13773..8c64280b 100644 --- a/golem/core/dag/graph.py +++ b/golem/core/dag/graph.py @@ -206,6 +206,7 @@ def show(self, save_path: Optional[Union[PathLike, str]] = None, engine: Optiona node_color: Optional[NodeColorType] = None, dpi: Optional[int] = None, node_size_scale: Optional[float] = None, font_size_scale: Optional[float] = None, edge_curvature_scale: Optional[float] = None, + title: Optional[str] = None, nodes_labels: Dict[int, str] = None, edges_labels: Dict[int, str] = None): """Visualizes graph or saves its picture to the specified ``path`` @@ -217,6 +218,7 @@ def show(self, save_path: Optional[Union[PathLike, str]] = None, engine: Optiona font_size_scale: use to make font size bigger or lesser. Supported only for the engine 'matplotlib'. edge_curvature_scale: use to make edges more or less curved. Supported only for the engine 'matplotlib'. dpi: DPI of the output image. Not supported for the engine 'pyvis'. + title: title for plot nodes_labels: labels to display near nodes edges_labels: labels to display near edges """ @@ -224,6 +226,7 @@ def show(self, save_path: Optional[Union[PathLike, str]] = None, engine: Optiona .visualise(save_path=save_path, engine=engine, node_color=node_color, dpi=dpi, node_size_scale=node_size_scale, font_size_scale=font_size_scale, edge_curvature_scale=edge_curvature_scale, + title=title, nodes_labels=nodes_labels, edges_labels=edges_labels) @property diff --git a/golem/visualisation/graph_viz.py b/golem/visualisation/graph_viz.py index aa4a1f99..fac8bd78 100644 --- a/golem/visualisation/graph_viz.py +++ b/golem/visualisation/graph_viz.py @@ -57,6 +57,7 @@ def visualise(self, save_path: Optional[PathType] = None, engine: Optional[str] node_color: Optional[NodeColorType] = None, dpi: Optional[int] = None, node_size_scale: Optional[float] = None, font_size_scale: Optional[float] = None, edge_curvature_scale: Optional[float] = None, + title: Optional[str] = None, nodes_labels: Dict[int, str] = None, edges_labels: Dict[int, str] = None): engine = engine or self.get_predefined_value('engine') @@ -67,7 +68,7 @@ def visualise(self, save_path: Optional[PathType] = None, engine: Optional[str] self.__draw_with_networkx(save_path=save_path, node_color=node_color, dpi=dpi, node_size_scale=node_size_scale, font_size_scale=font_size_scale, edge_curvature_scale=edge_curvature_scale, - nodes_labels=nodes_labels, edges_labels=edges_labels) + title=title, nodes_labels=nodes_labels, edges_labels=edges_labels) elif engine == 'pyvis': self.__draw_with_pyvis(save_path, node_color) elif engine == 'graphviz': @@ -166,7 +167,7 @@ def __draw_with_networkx(self, save_path: Optional[PathType] = None, node_color: Optional[NodeColorType] = None, dpi: Optional[int] = None, node_size_scale: Optional[float] = None, font_size_scale: Optional[float] = None, edge_curvature_scale: Optional[float] = None, - graph_to_nx_convert_func: Optional[Callable] = None, + graph_to_nx_convert_func: Optional[Callable] = None, title: Optional[str] = None, nodes_labels: Dict[int, str] = None, edges_labels: Dict[int, str] = None): save_path = save_path or self.get_predefined_value('save_path') node_color = node_color or self.get_predefined_value('node_color') @@ -180,6 +181,7 @@ def __draw_with_networkx(self, save_path: Optional[PathType] = None, fig, ax = plt.subplots(figsize=(7, 7)) fig.set_dpi(dpi) + plt.title(title) self.draw_nx_dag(ax, node_color, node_size_scale, font_size_scale, edge_curvature_scale, graph_to_nx_convert_func, nodes_labels, edges_labels) if not save_path: diff --git a/golem/visualisation/opt_history/fitness_line.py b/golem/visualisation/opt_history/fitness_line.py index 7b82f792..44ca12ed 100644 --- a/golem/visualisation/opt_history/fitness_line.py +++ b/golem/visualisation/opt_history/fitness_line.py @@ -281,11 +281,13 @@ def __init__(self, def visualize(self, save_path: Optional[Union[os.PathLike, str]] = None, + with_confidence: bool = True, metric_id: int = 0, dpi: Optional[int] = None): """ Visualizes the best fitness values during the evolution in the form of line. :param save_path: path to save the visualization. If set, then the image will be saved, and if not, it will be displayed. + :param with_confidence: bool param specifying to use confidence interval or not. :param metric_id: numeric index of the metric to visualize (for multi-objective opt-n). :param dpi: DPI of the output figure. """ @@ -294,7 +296,7 @@ def visualize(self, fig, ax = plt.subplots(figsize=(6.4, 4.8), facecolor='w') xlabel = 'Generation' - self.plot_multiple_fitness_lines(ax, metric_id) + self.plot_multiple_fitness_lines(ax, metric_id, with_confidence) setup_fitness_plot(ax, xlabel) plt.legend() show_or_save_figure(fig, save_path, dpi)