a59c05b1ff23415ee8c7ca5089a6cea05a240af2 to 4ad6e72501a1cc68436d5e5b0eb0e48556ea43d8 · Luc Giffon / bolsonaro

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TODO.md

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* Fix model results loading in compute_results.py.

* Check that omp multiclasses classifier is working as expected.

* Check that omp multiclasses classifier is working as expected.

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* Fix the dataset error of fetcher when job_number > 1.

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code/bolsonaro/models/ensemble_selection_forest_regressor.py0 → 100644

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from sklearn.metrics import mean_squared_error

from sklearn.base import BaseEstimator

from sklearn.tree import DecisionTreeRegressor

from abc import abstractmethod, ABCMeta

import numpy as np

from tqdm import tqdm

class EnsembleSelectionForestRegressor(BaseEstimator, metaclass=ABCMeta):

    """

    'Ensemble selection from libraries of models' by Rich Caruana et al

    """

    def __init__(self, models_parameters, library, score_metric=mean_squared_error):

        self._models_parameters = models_parameters

        self._library = library

        self._extracted_forest_size = self._models_parameters.extracted_forest_size

        self._score_metric = score_metric

    @property

    def models_parameters(self):

        return self._models_parameters

    @property

    def library(self):

        return self._library

    def fit(self, X_train, y_train, X_val, y_val):

        scores_list = list()

        for estimator in self._library:

            val_score = self._score_metric(estimator.predict(X_val), y_val)

            scores_list.append(val_score)

        class_list = list(self._library)

        m = np.argmax(np.asarray(scores_list))

        self._ensemble_selected = [class_list[m]]

        temp_pred = class_list[m].predict(X_val)

        del class_list[m]

        for k in range(self._extracted_forest_size - 1):

            candidate_index = 0

            best_score = 100000

            for j in range(len(class_list)):

                temp_pred = np.vstack((temp_pred, class_list[j].predict(X_val)))

                temp_mean = np.mean(temp_pred, axis=0)

                temp_score = self._score_metric(temp_mean, y_val)

                if (temp_score < best_score):

                    candidate_index = j

                    best_score = temp_score

                temp_pred = np.delete(temp_pred, -1, 0)

            self._ensemble_selected.append(class_list[candidate_index])

            temp_pred = np.vstack((temp_pred, class_list[candidate_index].predict(X_val)))

            del class_list[candidate_index]

    def score(self, X, y):

        predictions = self._predict_base_estimator(X)

        return self._score_metric(predictions, y)

    def predict_base_estimator(self, X):

        predictions = list()

        for tree in self._ensemble_selected:

            predictions.append(tree.predict(X))

        mean_predictions = np.mean(np.array(predictions), axis=0)

        return mean_predictions

    @staticmethod

    def generate_library(X_train, y_train, random_state=None):

        criterion_arr = ["mse"]#, "friedman_mse", "mae"]

        splitter_arr = ["best"]#, "random"]

        depth_arr = [i for i in range(5, 20, 1)]

        min_samples_split_arr = [i for i in range(2, 20, 1)]

        min_samples_leaf_arr = [i for i in range(2, 20, 1)]

        max_features_arr = ["sqrt"]#["auto", "sqrt", "log2"]

        library = list()

        with tqdm(total=len(criterion_arr) * len(splitter_arr) * \

            len(depth_arr) * len(min_samples_split_arr) * len(min_samples_leaf_arr) * \

            len(max_features_arr)) as bar:

            bar.set_description('Generating library')

            for criterion in criterion_arr:

                for splitter in splitter_arr:

                    for depth in depth_arr:

                        for min_samples_split in min_samples_split_arr:

                            for min_samples_leaf in min_samples_leaf_arr:

                                for max_features in max_features_arr:

                                    t = DecisionTreeRegressor(criterion=criterion, splitter=splitter, max_depth=depth, min_samples_split=min_samples_split,

                                        min_samples_leaf=min_samples_leaf, max_features=max_features, random_state=random_state)

                                    t.fit(X_train, y_train)

                                    library.append(t)

                                    bar.update(1)

        return library

code/bolsonaro/models/model_factory.py

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@@ -3,6 +3,7 @@ from bolsonaro.models.omp_forest_regressor import OmpForestRegressor

from bolsonaro.models.model_parameters import ModelParameters

from bolsonaro.models.model_parameters import ModelParameters

from bolsonaro.models.similarity_forest_regressor import SimilarityForestRegressor

from bolsonaro.models.similarity_forest_regressor import SimilarityForestRegressor

from bolsonaro.models.kmeans_forest_regressor import KMeansForestRegressor

from bolsonaro.models.kmeans_forest_regressor import KMeansForestRegressor

from bolsonaro.models.ensemble_selection_forest_regressor import EnsembleSelectionForestRegressor

from bolsonaro.data.task import Task

from bolsonaro.data.task import Task

from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier

from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier

@@ -13,7 +14,7 @@ import pickle

class ModelFactory(object):

class ModelFactory(object):

    @staticmethod

    @staticmethod

    def build(task, model_parameters):

    def build(task, model_parameters, library=None):

        if task not in [Task.BINARYCLASSIFICATION, Task.REGRESSION, Task.MULTICLASSIFICATION]:

        if task not in [Task.BINARYCLASSIFICATION, Task.REGRESSION, Task.MULTICLASSIFICATION]:

            raise ValueError("Unsupported task '{}'".format(task))

            raise ValueError("Unsupported task '{}'".format(task))

@@ -21,10 +22,10 @@ class ModelFactory(object):

            if model_parameters.extraction_strategy == 'omp':

            if model_parameters.extraction_strategy == 'omp':

                return OmpForestBinaryClassifier(model_parameters)

                return OmpForestBinaryClassifier(model_parameters)

            elif model_parameters.extraction_strategy == 'random':

            elif model_parameters.extraction_strategy == 'random':

                return RandomForestClassifier(n_estimators=model_parameters.extracted_forest_size,

                return RandomForestClassifier(**model_parameters.hyperparameters,

                    random_state=model_parameters.seed)

                    random_state=model_parameters.seed)

            elif model_parameters.extraction_strategy == 'none':

            elif model_parameters.extraction_strategy == 'none':

                return RandomForestClassifier(n_estimators=model_parameters.hyperparameters['n_estimators'],

                return RandomForestClassifier(**model_parameters.hyperparameters,

                    random_state=model_parameters.seed)

                    random_state=model_parameters.seed)

            else:

            else:

                raise ValueError('Invalid extraction strategy')

                raise ValueError('Invalid extraction strategy')

@@ -32,14 +33,16 @@ class ModelFactory(object):

            if model_parameters.extraction_strategy == 'omp':

            if model_parameters.extraction_strategy == 'omp':

                return OmpForestRegressor(model_parameters)

                return OmpForestRegressor(model_parameters)

            elif model_parameters.extraction_strategy == 'random':

            elif model_parameters.extraction_strategy == 'random':

                return RandomForestRegressor(n_estimators=model_parameters.extracted_forest_size,

                return RandomForestRegressor(**model_parameters.hyperparameters,

                    random_state=model_parameters.seed)

                    random_state=model_parameters.seed)

            elif model_parameters.extraction_strategy == 'similarity':

            elif model_parameters.extraction_strategy == 'similarity':

                return SimilarityForestRegressor(model_parameters)

                return SimilarityForestRegressor(model_parameters)

            elif model_parameters.extraction_strategy == 'kmeans':

            elif model_parameters.extraction_strategy == 'kmeans':

                return KMeansForestRegressor(model_parameters)

                return KMeansForestRegressor(model_parameters)

            elif model_parameters.extraction_strategy == 'ensemble':

                return EnsembleSelectionForestRegressor(model_parameters, library=library)

            elif model_parameters.extraction_strategy == 'none':

            elif model_parameters.extraction_strategy == 'none':

                return RandomForestRegressor(n_estimators=model_parameters.hyperparameters['n_estimators'],

                return RandomForestRegressor(**model_parameters.hyperparameters,

                    random_state=model_parameters.seed)

                    random_state=model_parameters.seed)

            else:

            else:

                raise ValueError('Invalid extraction strategy')

                raise ValueError('Invalid extraction strategy')

@@ -47,10 +50,10 @@ class ModelFactory(object):

            if model_parameters.extraction_strategy == 'omp':

            if model_parameters.extraction_strategy == 'omp':

                return OmpForestMulticlassClassifier(model_parameters)

                return OmpForestMulticlassClassifier(model_parameters)

            elif model_parameters.extraction_strategy == 'random':

            elif model_parameters.extraction_strategy == 'random':

                return RandomForestClassifier(n_estimators=model_parameters.extracted_forest_size,

                return RandomForestClassifier(**model_parameters.hyperparameters,

                    random_state=model_parameters.seed)

                    random_state=model_parameters.seed)

            elif model_parameters.extraction_strategy == 'none':

            elif model_parameters.extraction_strategy == 'none':

                return RandomForestClassifier(n_estimators=model_parameters.hyperparameters['n_estimators'],

                return RandomForestClassifier(**model_parameters.hyperparameters,

                    random_state=model_parameters.seed)

                    random_state=model_parameters.seed)

            else:

            else:

                raise ValueError('Invalid extraction strategy')

                raise ValueError('Invalid extraction strategy')

code/bolsonaro/models/omp_forest.py

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@@ -123,7 +123,9 @@ class SingleOmpForest(OmpForest):

        forest_predictions = self._base_estimator_predictions(X)

        forest_predictions = self._base_estimator_predictions(X)

        if self._models_parameters.normalize_D:

        if self._models_parameters.normalize_D:

            forest_predictions = forest_predictions.T

            forest_predictions /= self._forest_norms

            forest_predictions /= self._forest_norms

            forest_predictions = forest_predictions.T

        return self._make_omp_weighted_prediction(forest_predictions, self._omp, self._models_parameters.normalize_weights)

        return self._make_omp_weighted_prediction(forest_predictions, self._omp, self._models_parameters.normalize_weights)

@@ -134,16 +136,15 @@ class SingleOmpForest(OmpForest):

        Make all the base tree predictions

        Make all the base tree predictions

        :param X: a Forest

        :param X: a Forest

        :return: a np.array of the predictions of the entire forest

        :return: a np.array of the predictions of the trees selected by OMP without applyong the weight

        """

        """

        forest_predictions = self._base_estimator_predictions(X).T

        forest_predictions = np.array([tree.predict(X) for tree in self._base_forest_estimator.estimators_])

        if self._models_parameters.normalize_D:

        if self._models_parameters.normalize_D:

            forest_predictions = forest_predictions.T

            forest_predictions /= self._forest_norms

            forest_predictions /= self._forest_norms

            forest_predictions = forest_predictions.T

        weights = self._omp.coef_

        weights = self._omp.coef_

        omp_trees_indices = np.nonzero(weights)[0]

        select_trees = np.mean(forest_predictions[weights != 0], axis=0)

        select_trees = np.mean(forest_predictions[omp_trees_indices], axis=0)

        print(len(omp_trees_indices))

        return select_trees

        return select_trees

code/bolsonaro/models/omp_forest_classifier.py

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@@ -37,12 +37,12 @@ class OmpForestBinaryClassifier(SingleOmpForest):

        forest_predictions = np.array([tree.predict_proba(X) for tree in self._base_forest_estimator.estimators_])

        forest_predictions = np.array([tree.predict_proba(X) for tree in self._base_forest_estimator.estimators_])

        if self._models_parameters.normalize_D:

        if self._models_parameters.normalize_D:

            forest_predictions = forest_predictions.T

            forest_predictions /= self._forest_norms

            forest_predictions /= self._forest_norms

            forest_predictions = forest_predictions.T

        weights = self._omp.coef_

        weights = self._omp.coef_

        omp_trees_indices = np.nonzero(weights)

        omp_trees_predictions = forest_predictions[weights != 0].T[1]

        omp_trees_predictions = forest_predictions[omp_trees_indices].T[1]

        # Here forest_pred is the probability of being class 1.

        # Here forest_pred is the probability of being class 1.

@@ -119,7 +119,9 @@ class OmpForestMulticlassClassifier(OmpForest):

        forest_predictions = np.array([tree.predict_proba(X) for tree in self._base_forest_estimator.estimators_]).T

        forest_predictions = np.array([tree.predict_proba(X) for tree in self._base_forest_estimator.estimators_]).T

        if self._models_parameters.normalize_D:

        if self._models_parameters.normalize_D:

            forest_predictions = forest_predictions.T

            forest_predictions /= self._forest_norms

            forest_predictions /= self._forest_norms

            forest_predictions = forest_predictions.T

        label_names = []

        label_names = []

        preds = []

        preds = []

code/bolsonaro/trainer.py

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@@ -2,6 +2,8 @@ from bolsonaro.models.model_raw_results import ModelRawResults

from bolsonaro.models.omp_forest_regressor import OmpForestRegressor

from bolsonaro.models.omp_forest_regressor import OmpForestRegressor

from bolsonaro.models.omp_forest_classifier import OmpForestBinaryClassifier, OmpForestMulticlassClassifier

from bolsonaro.models.omp_forest_classifier import OmpForestBinaryClassifier, OmpForestMulticlassClassifier

from bolsonaro.models.similarity_forest_regressor import SimilarityForestRegressor

from bolsonaro.models.similarity_forest_regressor import SimilarityForestRegressor

from bolsonaro.models.kmeans_forest_regressor import KMeansForestRegressor

from bolsonaro.models.ensemble_selection_forest_regressor import EnsembleSelectionForestRegressor

from bolsonaro.error_handling.logger_factory import LoggerFactory

from bolsonaro.error_handling.logger_factory import LoggerFactory

from bolsonaro.data.task import Task

from bolsonaro.data.task import Task

from . import LOG_PATH

from . import LOG_PATH

@@ -72,16 +74,21 @@ class Trainer(object):

        else:

        else:

            raise ValueError("Unknown specified subsets_used parameter '{}'".format(model.models_parameters.subsets_used))

            raise ValueError("Unknown specified subsets_used parameter '{}'".format(model.models_parameters.subsets_used))

    def train(self, model):

    def train(self, model, extracted_forest_size=None):

        """

        """

        :param model: An instance of either RandomForestRegressor, RandomForestClassifier, OmpForestRegressor,

        :param model: An instance of either RandomForestRegressor, RandomForestClassifier, OmpForestRegressor,

            OmpForestBinaryClassifier, OmpForestMulticlassClassifier.

            OmpForestBinaryClassifier, OmpForestMulticlassClassifier.

        :return:

        :return:

        """

        """

        self._logger.debug('Training model using train set...')

        self._logger.debug('Training model using train set...')

        self._begin_time = time.time()

        self._begin_time = time.time()

        if type(model) in [RandomForestRegressor, RandomForestClassifier]:

        if type(model) in [RandomForestRegressor, RandomForestClassifier]:

            if extracted_forest_size is not None:

                estimators_index = np.arange(len(model.estimators_))

                np.random.shuffle(estimators_index)

                choosen_estimators = estimators_index[:extracted_forest_size]

                model.estimators_ = np.array(model.estimators_)[choosen_estimators]

            else:

                model.fit(

                model.fit(

                    X=self._X_forest,

                    X=self._X_forest,

                    y=self._y_forest

                    y=self._y_forest

@@ -96,7 +103,7 @@ class Trainer(object):

        self._end_time = time.time()

        self._end_time = time.time()

    def __score_func(self, model, X, y_true, weights=True):

    def __score_func(self, model, X, y_true, weights=True):

        if type(model) in [OmpForestRegressor, RandomForestRegressor, SimilarityForestRegressor]:

        if type(model) in [OmpForestRegressor, RandomForestRegressor]:

            if weights:

            if weights:

                y_pred = model.predict(X)

                y_pred = model.predict(X)

            else:

            else:

@@ -111,10 +118,12 @@ class Trainer(object):

                y_pred = np.sign(y_pred)

                y_pred = np.sign(y_pred)

                y_pred = np.where(y_pred == 0, 1, y_pred)

                y_pred = np.where(y_pred == 0, 1, y_pred)

            result = self._classification_score_metric(y_true, y_pred)

            result = self._classification_score_metric(y_true, y_pred)

        elif type(model) in [SimilarityForestRegressor, KMeansForestRegressor, EnsembleSelectionForestRegressor]:

            result = model.score(X, y_true)

        return result

        return result

    def __score_func_base(self, model, X, y_true):

    def __score_func_base(self, model, X, y_true):

        if type(model) == OmpForestRegressor:

        if type(model) in [OmpForestRegressor, SimilarityForestRegressor, KMeansForestRegressor, EnsembleSelectionForestRegressor]:

            y_pred = model.predict_base_estimator(X)

            y_pred = model.predict_base_estimator(X)

            result = self._base_regression_score_metric(y_true, y_pred)

            result = self._base_regression_score_metric(y_true, y_pred)

        elif type(model) in [OmpForestBinaryClassifier, OmpForestMulticlassClassifier]:

        elif type(model) in [OmpForestBinaryClassifier, OmpForestMulticlassClassifier]:

@@ -123,7 +132,7 @@ class Trainer(object):

        elif type(model) == RandomForestClassifier:

        elif type(model) == RandomForestClassifier:

            y_pred = model.predict(X)

            y_pred = model.predict(X)

            result = self._base_classification_score_metric(y_true, y_pred)

            result = self._base_classification_score_metric(y_true, y_pred)

        elif type(model) in [RandomForestRegressor, SimilarityForestRegressor]:

        elif type(model) is RandomForestRegressor:

            y_pred = model.predict(X)

            y_pred = model.predict(X)

            result = self._base_regression_score_metric(y_true, y_pred)

            result = self._base_regression_score_metric(y_true, y_pred)

        return result

        return result

code/compute_results.py

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@@ -133,10 +133,11 @@ if __name__ == "__main__":

    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)

    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)

    parser.add_argument('--stage', nargs='?', type=int, required=True, help='Specify the stage number among [1, 5].')

    parser.add_argument('--stage', nargs='?', type=int, required=True, help='Specify the stage number among [1, 5].')

    parser.add_argument('--experiment_ids', nargs='+', type=int, required=True, help='Compute the results of the specified experiment id(s).' + \

    parser.add_argument('--experiment_ids', nargs='+', type=str, required=True, help='Compute the results of the specified experiment id(s).' + \

        'stage=1: {{base_with_params}} {{random_with_params}} {{omp_with_params}} {{base_wo_params}} {{random_wo_params}} {{omp_wo_params}}' + \

        'stage=1: {{base_with_params}} {{random_with_params}} {{omp_with_params}} {{base_wo_params}} {{random_wo_params}} {{omp_wo_params}}' + \

        'stage=2: {{no_normalization}} {{normalize_D}} {{normalize_weights}} {{normalize_D_and_weights}}' + \

        'stage=2: {{no_normalization}} {{normalize_D}} {{normalize_weights}} {{normalize_D_and_weights}}' + \

        'stage=3: {{train-dev_subset}} {{train-dev_train-dev_subset}} {{train-train-dev_subset}}')

        'stage=3: {{train-dev_subset}} {{train-dev_train-dev_subset}} {{train-train-dev_subset}}' + \

        'stage=5: {{base_with_params}} {{random_with_params}} {{omp_with_params}} [ensemble={{id}}] [similarity={{id}}] [kmean={{id}}]')

    parser.add_argument('--dataset_name', nargs='?', type=str, required=True, help='Specify the dataset name. TODO: read it from models dir directly.')

    parser.add_argument('--dataset_name', nargs='?', type=str, required=True, help='Specify the dataset name. TODO: read it from models dir directly.')

    parser.add_argument('--results_dir', nargs='?', type=str, default=DEFAULT_RESULTS_DIR, help='The output directory of the results.')

    parser.add_argument('--results_dir', nargs='?', type=str, default=DEFAULT_RESULTS_DIR, help='The output directory of the results.')

    parser.add_argument('--models_dir', nargs='?', type=str, default=DEFAULT_MODELS_DIR, help='The output directory of the trained models.')

    parser.add_argument('--models_dir', nargs='?', type=str, default=DEFAULT_MODELS_DIR, help='The output directory of the trained models.')

@@ -159,7 +160,7 @@ if __name__ == "__main__":

            raise ValueError('In the case of stage 1, the number of specified experiment ids must be 6.')

            raise ValueError('In the case of stage 1, the number of specified experiment ids must be 6.')

        # Retreive the extracted forest sizes number used in order to have a base forest axis as long as necessary

        # Retreive the extracted forest sizes number used in order to have a base forest axis as long as necessary

        extracted_forest_sizes_number = retreive_extracted_forest_sizes_number(args.models_dir, args.experiment_ids[1])

        extracted_forest_sizes_number = retreive_extracted_forest_sizes_number(args.models_dir, int(args.experiment_ids[1]))

        # Experiments that used the best hyperparameters found for this dataset

        # Experiments that used the best hyperparameters found for this dataset

@@ -167,18 +168,18 @@ if __name__ == "__main__":

        logger.info('Loading base_with_params experiment scores...')

        logger.info('Loading base_with_params experiment scores...')

        base_with_params_train_scores, base_with_params_dev_scores, base_with_params_test_scores, \

        base_with_params_train_scores, base_with_params_dev_scores, base_with_params_test_scores, \

            base_with_params_experiment_score_metric = \

            base_with_params_experiment_score_metric = \

            extract_scores_across_seeds_and_forest_size(args.models_dir, args.results_dir, args.experiment_ids[0],

            extract_scores_across_seeds_and_forest_size(args.models_dir, args.results_dir, int(args.experiment_ids[0]),

            extracted_forest_sizes_number)

            extracted_forest_sizes_number)

        # random_with_params

        # random_with_params

        logger.info('Loading random_with_params experiment scores...')

        logger.info('Loading random_with_params experiment scores...')

        random_with_params_train_scores, random_with_params_dev_scores, random_with_params_test_scores, \

        random_with_params_train_scores, random_with_params_dev_scores, random_with_params_test_scores, \

            with_params_extracted_forest_sizes, random_with_params_experiment_score_metric = \

            with_params_extracted_forest_sizes, random_with_params_experiment_score_metric = \

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir, args.experiment_ids[1])

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir, int(args.experiment_ids[1]))

        # omp_with_params

        # omp_with_params

        logger.info('Loading omp_with_params experiment scores...')

        logger.info('Loading omp_with_params experiment scores...')

        omp_with_params_train_scores, omp_with_params_dev_scores, omp_with_params_test_scores, _, \

        omp_with_params_train_scores, omp_with_params_dev_scores, omp_with_params_test_scores, _, \

            omp_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

            omp_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

                args.models_dir, args.results_dir, args.experiment_ids[2])

                args.models_dir, args.results_dir, int(args.experiment_ids[2]))

        # Experiments that didn't use the best hyperparameters found for this dataset

        # Experiments that didn't use the best hyperparameters found for this dataset

@@ -186,19 +187,19 @@ if __name__ == "__main__":

        logger.info('Loading base_wo_params experiment scores...')

        logger.info('Loading base_wo_params experiment scores...')

        base_wo_params_train_scores, base_wo_params_dev_scores, base_wo_params_test_scores, \

        base_wo_params_train_scores, base_wo_params_dev_scores, base_wo_params_test_scores, \

            base_wo_params_experiment_score_metric = extract_scores_across_seeds_and_forest_size(

            base_wo_params_experiment_score_metric = extract_scores_across_seeds_and_forest_size(

                args.models_dir, args.results_dir, args.experiment_ids[3],

                args.models_dir, args.results_dir, int(args.experiment_ids[3]),

            extracted_forest_sizes_number)

            extracted_forest_sizes_number)

        # random_wo_params

        # random_wo_params

        logger.info('Loading random_wo_params experiment scores...')

        logger.info('Loading random_wo_params experiment scores...')

        random_wo_params_train_scores, random_wo_params_dev_scores, random_wo_params_test_scores, \

        random_wo_params_train_scores, random_wo_params_dev_scores, random_wo_params_test_scores, \

            wo_params_extracted_forest_sizes, random_wo_params_experiment_score_metric = \

            wo_params_extracted_forest_sizes, random_wo_params_experiment_score_metric = \

                extract_scores_across_seeds_and_extracted_forest_sizes(

                extract_scores_across_seeds_and_extracted_forest_sizes(

                args.models_dir, args.results_dir, args.experiment_ids[4])

                args.models_dir, args.results_dir, int(args.experiment_ids[4]))

        # base_wo_params

        # base_wo_params

        logger.info('Loading base_wo_params experiment scores...')

        logger.info('Loading base_wo_params experiment scores...')

        omp_wo_params_train_scores, omp_wo_params_dev_scores, omp_wo_params_test_scores, _, \

        omp_wo_params_train_scores, omp_wo_params_dev_scores, omp_wo_params_test_scores, _, \

            omp_wo_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

            omp_wo_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

                args.models_dir, args.results_dir, args.experiment_ids[5])

                args.models_dir, args.results_dir, int(args.experiment_ids[5]))

        # Sanity check on the metrics retreived

        # Sanity check on the metrics retreived

        if not (base_with_params_experiment_score_metric == random_with_params_experiment_score_metric ==

        if not (base_with_params_experiment_score_metric == random_with_params_experiment_score_metric ==

@@ -243,25 +244,25 @@ if __name__ == "__main__":

        logger.info('Loading no_normalization experiment scores...')

        logger.info('Loading no_normalization experiment scores...')

        _, _, no_normalization_test_scores, extracted_forest_sizes, no_normalization_experiment_score_metric = \

        _, _, no_normalization_test_scores, extracted_forest_sizes, no_normalization_experiment_score_metric = \

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            args.experiment_ids[0])

            int(args.experiment_ids[0]))

        # normalize_D

        # normalize_D

        logger.info('Loading normalize_D experiment scores...')

        logger.info('Loading normalize_D experiment scores...')

        _, _, normalize_D_test_scores, _, normalize_D_experiment_score_metric = \

        _, _, normalize_D_test_scores, _, normalize_D_experiment_score_metric = \

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            args.experiment_ids[1])

            int(args.experiment_ids[1]))

        # normalize_weights

        # normalize_weights

        logger.info('Loading normalize_weights experiment scores...')

        logger.info('Loading normalize_weights experiment scores...')

        _, _, normalize_weights_test_scores, _, normalize_weights_experiment_score_metric = \

        _, _, normalize_weights_test_scores, _, normalize_weights_experiment_score_metric = \

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            args.experiment_ids[2])

            int(args.experiment_ids[2]))

        # normalize_D_and_weights

        # normalize_D_and_weights

        logger.info('Loading normalize_D_and_weights experiment scores...')

        logger.info('Loading normalize_D_and_weights experiment scores...')

        _, _, normalize_D_and_weights_test_scores, _, normalize_D_and_weights_experiment_score_metric = \

        _, _, normalize_D_and_weights_test_scores, _, normalize_D_and_weights_experiment_score_metric = \

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            args.experiment_ids[3])

            int(args.experiment_ids[3]))

        # Sanity check on the metrics retreived

        # Sanity check on the metrics retreived

        if not (no_normalization_experiment_score_metric == normalize_D_experiment_score_metric

        if not (no_normalization_experiment_score_metric == normalize_D_experiment_score_metric

@@ -290,21 +291,21 @@ if __name__ == "__main__":

        train_dev_subset_train_scores, train_dev_subset_dev_scores, train_dev_subset_test_scores, \

        train_dev_subset_train_scores, train_dev_subset_dev_scores, train_dev_subset_test_scores, \

            extracted_forest_sizes, train_dev_subset_experiment_score_metric = \

            extracted_forest_sizes, train_dev_subset_experiment_score_metric = \

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            args.experiment_ids[0])

            int(args.experiment_ids[0]))

        # train-dev_train-dev_subset

        # train-dev_train-dev_subset

        logger.info('Loading train-dev_train-dev_subset experiment scores...')

        logger.info('Loading train-dev_train-dev_subset experiment scores...')

        train_dev_train_dev_subset_train_scores, train_dev_train_dev_subset_dev_scores, train_dev_train_dev_subset_test_scores, \

        train_dev_train_dev_subset_train_scores, train_dev_train_dev_subset_dev_scores, train_dev_train_dev_subset_test_scores, \

            _, train_dev_train_dev_subset_experiment_score_metric = \

            _, train_dev_train_dev_subset_experiment_score_metric = \

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            args.experiment_ids[1])

            int(args.experiment_ids[1]))

        # train-train-dev_subset

        # train-train-dev_subset

        logger.info('Loading train-train-dev_subset experiment scores...')

        logger.info('Loading train-train-dev_subset experiment scores...')

        train_train_dev_subset_train_scores, train_train_dev_subset_dev_scores, train_train_dev_subset_test_scores, \

        train_train_dev_subset_train_scores, train_train_dev_subset_dev_scores, train_train_dev_subset_test_scores, \

            _, train_train_dev_subset_experiment_score_metric = \

            _, train_train_dev_subset_experiment_score_metric = \

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir,

            args.experiment_ids[2])

            int(args.experiment_ids[2]))

        # Sanity check on the metrics retreived

        # Sanity check on the metrics retreived

        if not (train_dev_subset_experiment_score_metric == train_dev_train_dev_subset_experiment_score_metric

        if not (train_dev_subset_experiment_score_metric == train_dev_train_dev_subset_experiment_score_metric

@@ -349,13 +350,13 @@ if __name__ == "__main__":

        logger.info('Loading base_with_params experiment scores...')

        logger.info('Loading base_with_params experiment scores...')

        base_with_params_train_scores, base_with_params_dev_scores, base_with_params_test_scores, \

        base_with_params_train_scores, base_with_params_dev_scores, base_with_params_test_scores, \

            base_with_params_experiment_score_metric = \

            base_with_params_experiment_score_metric = \

            extract_scores_across_seeds_and_forest_size(args.models_dir, args.results_dir, args.experiment_ids[0],

            extract_scores_across_seeds_and_forest_size(args.models_dir, args.results_dir, int(args.experiment_ids[0]),

            extracted_forest_sizes_number)

            extracted_forest_sizes_number)

        # random_with_params

        # random_with_params

        logger.info('Loading random_with_params experiment scores...')

        logger.info('Loading random_with_params experiment scores...')

        random_with_params_train_scores, random_with_params_dev_scores, random_with_params_test_scores, \

        random_with_params_train_scores, random_with_params_dev_scores, random_with_params_test_scores, \

            with_params_extracted_forest_sizes, random_with_params_experiment_score_metric = \

            with_params_extracted_forest_sizes, random_with_params_experiment_score_metric = \

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir, args.experiment_ids[1])

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir, int(args.experiment_ids[1]))

        # omp_with_params

        # omp_with_params

        logger.info('Loading omp_with_params experiment scores...')

        logger.info('Loading omp_with_params experiment scores...')

        """omp_with_params_train_scores, omp_with_params_dev_scores, omp_with_params_test_scores, _, \

        """omp_with_params_train_scores, omp_with_params_dev_scores, omp_with_params_test_scores, _, \

@@ -363,12 +364,12 @@ if __name__ == "__main__":

                args.models_dir, args.results_dir, args.experiment_ids[2])"""

                args.models_dir, args.results_dir, args.experiment_ids[2])"""

        omp_with_params_train_scores, omp_with_params_dev_scores, omp_with_params_test_scores, _, \

        omp_with_params_train_scores, omp_with_params_dev_scores, omp_with_params_test_scores, _, \

            omp_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

            omp_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

                args.models_dir, args.results_dir, args.experiment_ids[2])

                args.models_dir, args.results_dir, int(args.experiment_ids[2]))

        #omp_with_params_without_weights

        #omp_with_params_without_weights

        logger.info('Loading omp_with_params experiment scores...')

        logger.info('Loading omp_no_weights experiment scores...')

        omp_with_params_without_weights_train_scores, omp_with_params_without_weights_dev_scores, omp_with_params_without_weights_test_scores, _, \

        omp_with_params_without_weights_train_scores, omp_with_params_without_weights_dev_scores, omp_with_params_without_weights_test_scores, _, \

            omp_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

            omp_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

                args.models_dir, args.results_dir, args.experiment_ids[2], weights=False)

                args.models_dir, args.results_dir, int(args.experiment_ids[2]), weights=False)

        """# base_with_params

        """# base_with_params

        logger.info('Loading base_with_params experiment scores 2...')

        logger.info('Loading base_with_params experiment scores 2...')

@@ -388,7 +389,7 @@ if __name__ == "__main__":

            raise ValueError('Score metrics of all experiments must be the same.')

            raise ValueError('Score metrics of all experiments must be the same.')

        experiments_score_metric = base_with_params_experiment_score_metric

        experiments_score_metric = base_with_params_experiment_score_metric

        output_path = os.path.join(args.results_dir, args.dataset_name, 'stage4_fix')

        output_path = os.path.join(args.results_dir, args.dataset_name, 'stage4')

        pathlib.Path(output_path).mkdir(parents=True, exist_ok=True)

        pathlib.Path(output_path).mkdir(parents=True, exist_ok=True)

        Plotter.plot_stage2_losses(

        Plotter.plot_stage2_losses(

@@ -402,47 +403,63 @@ if __name__ == "__main__":

            title='Loss values of {}\nusing best params of previous stages'.format(args.dataset_name))

            title='Loss values of {}\nusing best params of previous stages'.format(args.dataset_name))

    elif args.stage == 5:

    elif args.stage == 5:

        # Retreive the extracted forest sizes number used in order to have a base forest axis as long as necessary

        # Retreive the extracted forest sizes number used in order to have a base forest axis as long as necessary

        extracted_forest_sizes_number = retreive_extracted_forest_sizes_number(args.models_dir, args.experiment_ids[1])

        extracted_forest_sizes_number = retreive_extracted_forest_sizes_number(args.models_dir, int(args.experiment_ids[1]))

        all_labels = list()

        all_scores = list()

        # base_with_params

        # base_with_params

        logger.info('Loading base_with_params experiment scores...')

        logger.info('Loading base_with_params experiment scores...')

        base_with_params_train_scores, base_with_params_dev_scores, base_with_params_test_scores, \

        base_with_params_train_scores, base_with_params_dev_scores, base_with_params_test_scores, \

            base_with_params_experiment_score_metric = \

            base_with_params_experiment_score_metric = \

            extract_scores_across_seeds_and_forest_size(args.models_dir, args.results_dir, args.experiment_ids[0],

            extract_scores_across_seeds_and_forest_size(args.models_dir, args.results_dir, int(args.experiment_ids[0]),

            extracted_forest_sizes_number)

            extracted_forest_sizes_number)

        # random_with_params

        # random_with_params

        logger.info('Loading random_with_params experiment scores...')

        logger.info('Loading random_with_params experiment scores...')

        random_with_params_train_scores, random_with_params_dev_scores, random_with_params_test_scores, \

        random_with_params_train_scores, random_with_params_dev_scores, random_with_params_test_scores, \

            with_params_extracted_forest_sizes, random_with_params_experiment_score_metric = \

            with_params_extracted_forest_sizes, random_with_params_experiment_score_metric = \

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir, args.experiment_ids[1])

            extract_scores_across_seeds_and_extracted_forest_sizes(args.models_dir, args.results_dir, int(args.experiment_ids[1]))

        # omp_with_params

        # omp_with_params

        logger.info('Loading omp_with_params experiment scores...')

        logger.info('Loading omp_with_params experiment scores...')

        omp_with_params_train_scores, omp_with_params_dev_scores, omp_with_params_test_scores, _, \

        omp_with_params_train_scores, omp_with_params_dev_scores, omp_with_params_test_scores, _, \

            omp_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

            omp_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

                args.models_dir, args.results_dir, args.experiment_ids[2])

                args.models_dir, args.results_dir, int(args.experiment_ids[2]))

        # omp_with_params

        #omp_with_params_without_weights

        logger.info('Loading kmeans_with_params experiment scores...')

        logger.info('Loading omp_with_params experiment scores...')

        kmeans_with_params_train_scores, kmeans_with_params_dev_scores, kmeans_with_params_test_scores, _, \

        omp_with_params_without_weights_train_scores, omp_with_params_without_weights_dev_scores, omp_with_params_without_weights_test_scores, _, \

            kmeans_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

            omp_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

                args.models_dir, args.results_dir, args.experiment_ids[3])

                args.models_dir, args.results_dir, int(args.experiment_ids[2]), weights=False)

        all_labels = ['base', 'random', 'omp', 'omp_without_weights']

        all_scores = [base_with_params_test_scores, random_with_params_test_scores, omp_with_params_test_scores,

            omp_with_params_without_weights_test_scores]

        for i in range(3, len(args.experiment_ids)):

            if 'kmeans' in args.experiment_ids[i]:

                label = 'kmeans'

            elif 'similarity' in args.experiment_ids[i]:

                label = 'similarity'

            elif 'ensemble' in args.experiment_ids[i]:

                label = 'ensemble'

            else:

                logger.error('Invalid value encountered')

                continue

        # Sanity check on the metrics retreived

            logger.info(f'Loading {label} experiment scores...')

        if not (base_with_params_experiment_score_metric == random_with_params_experiment_score_metric

            _, _, current_test_scores, _, _ = extract_scores_across_seeds_and_extracted_forest_sizes(

            == omp_with_params_experiment_score_metric == kmeans_with_params_experiment_score_metric):

                args.models_dir, args.results_dir, int(args.experiment_ids[i].split('=')[1]))

            raise ValueError('Score metrics of all experiments must be the same.')

            all_labels.append(label)

        experiments_score_metric = base_with_params_experiment_score_metric

            all_scores.append(current_test_scores)

        output_path = os.path.join(args.results_dir, args.dataset_name, 'stage5_kmeans')

        output_path = os.path.join(args.results_dir, args.dataset_name, 'stage5')

        pathlib.Path(output_path).mkdir(parents=True, exist_ok=True)

        pathlib.Path(output_path).mkdir(parents=True, exist_ok=True)

        Plotter.plot_stage2_losses(

        Plotter.plot_stage2_losses(

            file_path=output_path + os.sep + 'losses.png',

            file_path=output_path + os.sep + f"losses_{'-'.join(all_labels)}.png",

            all_experiment_scores=[base_with_params_test_scores, random_with_params_test_scores, omp_with_params_test_scores,

            all_experiment_scores=all_scores,

                kmeans_with_params_test_scores],

            all_labels=all_labels,

            all_labels=['base', 'random', 'omp', 'kmeans'],

            x_value=with_params_extracted_forest_sizes,

            x_value=with_params_extracted_forest_sizes,

            xlabel='Number of trees extracted',

            xlabel='Number of trees extracted',

            ylabel=experiments_score_metric,

            ylabel=base_with_params_experiment_score_metric,

            title='Loss values of {}\nusing best params of previous stages'.format(args.dataset_name))

            title='Loss values of {}\nusing best params of previous stages'.format(args.dataset_name))

    else:

    else:

        raise ValueError('This stage number is not supported yet, but it will be!')

        raise ValueError('This stage number is not supported yet, but it will be!')

code/train.py

+51
−18

Original line number
Original line
Diff line number
Diff line

@@ -2,6 +2,7 @@ from bolsonaro.data.dataset_parameters import DatasetParameters

from bolsonaro.data.dataset_loader import DatasetLoader

from bolsonaro.data.dataset_loader import DatasetLoader

from bolsonaro.models.model_factory import ModelFactory

from bolsonaro.models.model_factory import ModelFactory

from bolsonaro.models.model_parameters import ModelParameters

from bolsonaro.models.model_parameters import ModelParameters

from bolsonaro.models.ensemble_selection_forest_regressor import EnsembleSelectionForestRegressor

from bolsonaro.trainer import Trainer

from bolsonaro.trainer import Trainer

from bolsonaro.utils import resolve_experiment_id, tqdm_joblib

from bolsonaro.utils import resolve_experiment_id, tqdm_joblib

from bolsonaro import LOG_PATH

from bolsonaro import LOG_PATH

@@ -9,6 +10,7 @@ from bolsonaro.error_handling.logger_factory import LoggerFactory

from dotenv import find_dotenv, load_dotenv

from dotenv import find_dotenv, load_dotenv

import argparse

import argparse

import copy

import json

import json

import pathlib

import pathlib

import random

import random

@@ -53,10 +55,37 @@ def seed_job(seed_job_pb, seed, parameters, experiment_id, hyperparameters, verb

    trainer = Trainer(dataset)

    trainer = Trainer(dataset)

    if parameters['extraction_strategy'] == 'ensemble':

        library = EnsembleSelectionForestRegressor.generate_library(dataset.X_train, dataset.y_train, random_state=seed)

    else:

        library = None

    if parameters['extraction_strategy'] == 'random':

        pretrained_model_parameters = ModelParameters(

            extracted_forest_size=parameters['forest_size'],

            normalize_D=parameters['normalize_D'],

            subsets_used=parameters['subsets_used'],

            normalize_weights=parameters['normalize_weights'],

            seed=seed,

            hyperparameters=hyperparameters,

            extraction_strategy=parameters['extraction_strategy']

        )

        pretrained_estimator = ModelFactory.build(dataset.task, pretrained_model_parameters, library=library)

        pretraned_trainer = Trainer(dataset)

        pretraned_trainer.init(pretrained_estimator, subsets_used=parameters['subsets_used'])

        pretrained_estimator.fit(

            X=pretraned_trainer._X_forest,

            y=pretraned_trainer._y_forest

        )

    else:

        pretrained_estimator = None

        pretrained_model_parameters = None

    if parameters['extraction_strategy'] != 'none':

    if parameters['extraction_strategy'] != 'none':

        with tqdm_joblib(tqdm(total=len(parameters['extracted_forest_size']), disable=not verbose)) as extracted_forest_size_job_pb:

        with tqdm_joblib(tqdm(total=len(parameters['extracted_forest_size']), disable=not verbose)) as extracted_forest_size_job_pb:

            Parallel(n_jobs=-1)(delayed(extracted_forest_size_job)(extracted_forest_size_job_pb, parameters['extracted_forest_size'][i],

            Parallel(n_jobs=-1)(delayed(extracted_forest_size_job)(extracted_forest_size_job_pb, parameters['extracted_forest_size'][i],

                models_dir, seed, parameters, dataset, hyperparameters, experiment_id, trainer)

                models_dir, seed, parameters, dataset, hyperparameters, experiment_id, trainer, library,

                pretrained_estimator=pretrained_estimator, pretrained_model_parameters=pretrained_model_parameters)

                for i in range(len(parameters['extracted_forest_size'])))

                for i in range(len(parameters['extracted_forest_size'])))

    else:

    else:

        forest_size = hyperparameters['n_estimators']

        forest_size = hyperparameters['n_estimators']

@@ -88,7 +117,7 @@ def seed_job(seed_job_pb, seed, parameters, experiment_id, hyperparameters, verb

            )

            )

            model_parameters.save(sub_models_dir, experiment_id)

            model_parameters.save(sub_models_dir, experiment_id)

            model = ModelFactory.build(dataset.task, model_parameters)

            model = ModelFactory.build(dataset.task, model_parameters, library=library)

            trainer.init(model, subsets_used=parameters['subsets_used'])

            trainer.init(model, subsets_used=parameters['subsets_used'])

            trainer.train(model)

            trainer.train(model)

@@ -97,7 +126,8 @@ def seed_job(seed_job_pb, seed, parameters, experiment_id, hyperparameters, verb

    seed_job_pb.update(1)

    seed_job_pb.update(1)

def extracted_forest_size_job(extracted_forest_size_job_pb, extracted_forest_size, models_dir,

def extracted_forest_size_job(extracted_forest_size_job_pb, extracted_forest_size, models_dir,

    seed, parameters, dataset, hyperparameters, experiment_id, trainer):

    seed, parameters, dataset, hyperparameters, experiment_id, trainer, library,

    pretrained_estimator=None, pretrained_model_parameters=None):

    logger = LoggerFactory.create(LOG_PATH, 'training_seed{}_extracted_forest_size{}_ti{}'.format(

    logger = LoggerFactory.create(LOG_PATH, 'training_seed{}_extracted_forest_size{}_ti{}'.format(

        seed, extracted_forest_size, threading.get_ident()))

        seed, extracted_forest_size, threading.get_ident()))

@@ -121,6 +151,7 @@ def extracted_forest_size_job(extracted_forest_size_job_pb, extracted_forest_siz

    pathlib.Path(sub_models_dir).mkdir(parents=True, exist_ok=True)

    pathlib.Path(sub_models_dir).mkdir(parents=True, exist_ok=True)

    if not pretrained_estimator:

        model_parameters = ModelParameters(

        model_parameters = ModelParameters(

            extracted_forest_size=extracted_forest_size,

            extracted_forest_size=extracted_forest_size,

            normalize_D=parameters['normalize_D'],

            normalize_D=parameters['normalize_D'],

@@ -131,11 +162,13 @@ def extracted_forest_size_job(extracted_forest_size_job_pb, extracted_forest_siz

            extraction_strategy=parameters['extraction_strategy']

            extraction_strategy=parameters['extraction_strategy']

        )

        )

        model_parameters.save(sub_models_dir, experiment_id)

        model_parameters.save(sub_models_dir, experiment_id)

        model = ModelFactory.build(dataset.task, model_parameters, library=library)

    model = ModelFactory.build(dataset.task, model_parameters)

    else:

        model = copy.deepcopy(pretrained_estimator)

        pretrained_model_parameters.save(sub_models_dir, experiment_id)

    trainer.init(model, subsets_used=parameters['subsets_used'])

    trainer.init(model, subsets_used=parameters['subsets_used'])

    trainer.train(model)

    trainer.train(model, extracted_forest_size=extracted_forest_size)

    trainer.compute_results(model, sub_models_dir)

    trainer.compute_results(model, sub_models_dir)

"""

"""

@@ -202,7 +235,7 @@ if __name__ == "__main__":

    parser.add_argument('--skip_best_hyperparams', action='store_true', default=DEFAULT_SKIP_BEST_HYPERPARAMS, help='Do not use the best hyperparameters if there exist.')

    parser.add_argument('--skip_best_hyperparams', action='store_true', default=DEFAULT_SKIP_BEST_HYPERPARAMS, help='Do not use the best hyperparameters if there exist.')

    parser.add_argument('--save_experiment_configuration', nargs='+', default=None, help='Save the experiment parameters specified in the command line in a file. Args: {{stage_num}} {{name}}')

    parser.add_argument('--save_experiment_configuration', nargs='+', default=None, help='Save the experiment parameters specified in the command line in a file. Args: {{stage_num}} {{name}}')

    parser.add_argument('--job_number', nargs='?', type=int, default=DEFAULT_JOB_NUMBER, help='Specify the number of job used during the parallelisation across seeds.')

    parser.add_argument('--job_number', nargs='?', type=int, default=DEFAULT_JOB_NUMBER, help='Specify the number of job used during the parallelisation across seeds.')

    parser.add_argument('--extraction_strategy', nargs='?', type=str, default=DEFAULT_EXTRACTION_STRATEGY, help='Specify the strategy to apply to extract the trees from the forest. Either omp, random, none, similarity, kmeans.')

    parser.add_argument('--extraction_strategy', nargs='?', type=str, default=DEFAULT_EXTRACTION_STRATEGY, help='Specify the strategy to apply to extract the trees from the forest. Either omp, random, none, similarity, kmeans, ensemble.')

    parser.add_argument('--overwrite', action='store_true', default=DEFAULT_OVERWRITE, help='Overwrite the experiment id')

    parser.add_argument('--overwrite', action='store_true', default=DEFAULT_OVERWRITE, help='Overwrite the experiment id')

    args = parser.parse_args()

    args = parser.parse_args()

@@ -213,7 +246,7 @@ if __name__ == "__main__":

    else:

    else:

        parameters = args.__dict__

        parameters = args.__dict__

    if parameters['extraction_strategy'] not in ['omp', 'random', 'none', 'similarity', 'kmeans']:

    if parameters['extraction_strategy'] not in ['omp', 'random', 'none', 'similarity', 'kmeans', 'ensemble']:

        raise ValueError('Specified extraction strategy {} is not supported.'.format(parameters.extraction_strategy))

        raise ValueError('Specified extraction strategy {} is not supported.'.format(parameters.extraction_strategy))

    pathlib.Path(parameters['models_dir']).mkdir(parents=True, exist_ok=True)

    pathlib.Path(parameters['models_dir']).mkdir(parents=True, exist_ok=True)

experiments/boston/stage1/none_with_params.json

+38
−14

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Diff line number
Diff line

@@ -6,15 +6,18 @@

    "normalize_D": false,

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "dataset_normalizer": "standard",

    "forest_size": null,

    "forest_size": null,

    "extracted_forest_size_samples": 10,

    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 0.4,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/boston/stage1",

    "models_dir": "models/boston/stage1",

    "dev_size": 0.2,

    "dev_size": 0.2,

    "test_size": 0.2,

    "test_size": 0.2,

    "random_seed_number": 1,

    "random_seed_number": 1,

    "seeds": [

    "seeds": [

        2078,

        1,

        90

        2,

        3,

        4,

        5

    ],

    ],

    "subsets_used": "train,dev",

    "subsets_used": "train,dev",

    "normalize_weights": false,

    "normalize_weights": false,

@@ -26,16 +29,37 @@

    ],

    ],

    "job_number": -1,

    "job_number": -1,

    "extraction_strategy": "none",

    "extraction_strategy": "none",

    "overwrite": false,

    "extracted_forest_size": [

    "extracted_forest_size": [

        36,

        33,

        73,

        67,

        109,

        100,

        145,

        133,

        182,

        167,

        218,

        200,

        255,

        233,

        291,

        267,

        327,

        300,

        364

        333,

        367,

        400,

        433,

        467,

        500,

        533,

        567,

        600,

        633,

        667,

        700,

        733,

        767,

        800,

        833,

        867,

        900,

        933,

        967,

        1000

    ]

    ]

}

}

 No newline at end of file

experiments/boston/stage1/none_wo_params.json0 → 100644

+65
−0

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{

    "experiment_id": 4,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "boston",

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/boston/stage1",

    "dev_size": 0.2,

    "test_size": 0.2,

    "random_seed_number": 1,

    "seeds": [

        1,

        2,

        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

    "normalize_weights": false,

    "verbose": false,

    "skip_best_hyperparams": true,

    "save_experiment_configuration": [

        "1",

        "none_wo_params"

    ],

    "job_number": -1,

    "extraction_strategy": "none",

    "overwrite": false,

    "extracted_forest_size": [

        33,

        67,

        100,

        133,

        167,

        200,

        233,

        267,

        300,

        333,

        367,

        400,

        433,

        467,

        500,

        533,

        567,

        600,

        633,

        667,

        700,

        733,

        767,

        800,

        833,

        867,

        900,

        933,

        967,

        1000

    ]

}

 No newline at end of file

experiments/boston/stage1/omp_with_params.json0 → 100644

+65
−0

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{

    "experiment_id": 3,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "boston",

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/boston/stage1",

    "dev_size": 0.2,

    "test_size": 0.2,

    "random_seed_number": 1,

    "seeds": [

        1,

        2,

        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

    "normalize_weights": false,

    "verbose": false,

    "skip_best_hyperparams": false,

    "save_experiment_configuration": [

        "1",

        "omp_with_params"

    ],

    "job_number": -1,

    "extraction_strategy": "omp",

    "overwrite": false,

    "extracted_forest_size": [

        33,

        67,

        100,

        133,

        167,

        200,

        233,

        267,

        300,

        333,

        367,

        400,

        433,

        467,

        500,

        533,

        567,

        600,

        633,

        667,

        700,

        733,

        767,

        800,

        833,

        867,

        900,

        933,

        967,

        1000

    ]

}

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experiments/boston/stage1/omp_wo_params.json0 → 100644

+65
−0

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Diff line number
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{

    "experiment_id": 6,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "boston",

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/boston/stage1",

    "dev_size": 0.2,

    "test_size": 0.2,

    "random_seed_number": 1,

    "seeds": [

        1,

        2,

        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

    "normalize_weights": false,

    "verbose": false,

    "skip_best_hyperparams": true,

    "save_experiment_configuration": [

        "1",

        "omp_wo_params"

    ],

    "job_number": -1,

    "extraction_strategy": "omp",

    "overwrite": false,

    "extracted_forest_size": [

        33,

        67,

        100,

        133,

        167,

        200,

        233,

        267,

        300,

        333,

        367,

        400,

        433,

        467,

        500,

        533,

        567,

        600,

        633,

        667,

        700,

        733,

        767,

        800,

        833,

        867,

        900,

        933,

        967,

        1000

    ]

}

 No newline at end of file

experiments/boston/stage1/random_with_params.json0 → 100644

+65
−0

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{

    "experiment_id": 2,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "boston",

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/boston/stage1",

    "dev_size": 0.2,

    "test_size": 0.2,

    "random_seed_number": 1,

    "seeds": [

        1,

        2,

        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

    "normalize_weights": false,

    "verbose": false,

    "skip_best_hyperparams": false,

    "save_experiment_configuration": [

        "1",

        "random_with_params"

    ],

    "job_number": -1,

    "extraction_strategy": "random",

    "overwrite": false,

    "extracted_forest_size": [

        33,

        67,

        100,

        133,

        167,

        200,

        233,

        267,

        300,

        333,

        367,

        400,

        433,

        467,

        500,

        533,

        567,

        600,

        633,

        667,

        700,

        733,

        767,

        800,

        833,

        867,

        900,

        933,

        967,

        1000

    ]

}

 No newline at end of file

experiments/boston/stage1/random_wo_params.json0 → 100644

+65
−0

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{

    "experiment_id": 5,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "boston",

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/boston/stage1",

    "dev_size": 0.2,

    "test_size": 0.2,

    "random_seed_number": 1,

    "seeds": [

        1,

        2,

        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

    "normalize_weights": false,

    "verbose": false,

    "skip_best_hyperparams": true,

    "save_experiment_configuration": [

        "1",

        "random_wo_params"

    ],

    "job_number": -1,

    "extraction_strategy": "random",

    "overwrite": false,

    "extracted_forest_size": [

        33,

        67,

        100,

        133,

        167,

        200,

        233,

        267,

        300,

        333,

        367,

        400,

        433,

        467,

        500,

        533,

        567,

        600,

        633,

        667,

        700,

        733,

        767,

        800,

        833,

        867,

        900,

        933,

        967,

        1000

    ]

}

 No newline at end of file

experiments/boston/stage2/no_normalization.json0 → 100644

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Diff line number
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{

    "experiment_id": 1,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "boston",

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/boston/stage2",

    "dev_size": 0.2,

    "test_size": 0.2,

    "random_seed_number": 1,

    "seeds": [

        1,

        2,

        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

    "normalize_weights": false,

    "verbose": false,

    "skip_best_hyperparams": false,

    "save_experiment_configuration": [

        "2",

        "no_normalization"

    ],

    "job_number": -1,

    "extraction_strategy": "omp",

    "overwrite": false,

    "extracted_forest_size": [

        33,

        67,

        100,

        133,

        167,

        200,

        233,

        267,

        300,

        333,

        367,

        400,

        433,

        467,

        500,

        533,

        567,

        600,

        633,

        667,

        700,

        733,

        767,

        800,

        833,

        867,

        900,

        933,

        967,

        1000

    ]

}

 No newline at end of file

experiments/boston/stage2/normalize_D.json0 → 100644

+65
−0

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{

    "experiment_id": 2,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "boston",

    "normalize_D": true,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/boston/stage2",

    "dev_size": 0.2,

    "test_size": 0.2,

    "random_seed_number": 1,

    "seeds": [

        1,

        2,

        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

    "normalize_weights": false,

    "verbose": false,

    "skip_best_hyperparams": false,

    "save_experiment_configuration": [

        "2",

        "normalize_D"

    ],

    "job_number": -1,

    "extraction_strategy": "omp",

    "overwrite": false,

    "extracted_forest_size": [

        33,

        67,

        100,

        133,

        167,

        200,

        233,

        267,

        300,

        333,

        367,

        400,

        433,

        467,

        500,

        533,

        567,

        600,

        633,

        667,

        700,

        733,

        767,

        800,

        833,

        867,

        900,

        933,

        967,

        1000

    ]

}

 No newline at end of file

experiments/boston/stage2/normalize_D_and_weights.json0 → 100644

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{

    "experiment_id": 4,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "boston",

    "normalize_D": true,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/boston/stage2",

    "dev_size": 0.2,

    "test_size": 0.2,

    "random_seed_number": 1,

    "seeds": [

        1,

        2,

        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

    "normalize_weights": true,

    "verbose": false,

    "skip_best_hyperparams": false,

    "save_experiment_configuration": [

        "2",

        "normalize_D_and_weights"

    ],

    "job_number": -1,

    "extraction_strategy": "omp",

    "overwrite": false,

    "extracted_forest_size": [

        33,

        67,

        100,

        133,

        167,

        200,

        233,

        267,

        300,

        333,

        367,

        400,

        433,

        467,

        500,

        533,

        567,

        600,

        633,

        667,

        700,

        733,

        767,

        800,

        833,

        867,

        900,

        933,

        967,

        1000

    ]

}

 No newline at end of file

experiments/boston/stage2/normalize_weights.json0 → 100644

+65
−0

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Diff line number
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{

    "experiment_id": 3,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "boston",

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/boston/stage2",

    "dev_size": 0.2,

    "test_size": 0.2,

    "random_seed_number": 1,

    "seeds": [

        1,

        2,

        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

    "normalize_weights": true,

    "verbose": false,

    "skip_best_hyperparams": false,

    "save_experiment_configuration": [

        "2",

        "normalize_weights"

    ],

    "job_number": -1,

    "extraction_strategy": "omp",

    "overwrite": false,

    "extracted_forest_size": [

        33,

        67,

        100,

        133,

        167,

        200,

        233,

        267,

        300,

        333,

        367,

        400,

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        467,

        500,

        533,

        567,

        600,

        633,

        667,

        700,

        733,

        767,

        800,

        833,

        867,

        900,

        933,

        967,

        1000

    ]

}

 No newline at end of file

experiments/boston/stage3/train-dev_subset.json0 → 100644

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{

    "experiment_id": 1,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "boston",

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/boston/stage3",

    "dev_size": 0.2,

    "test_size": 0.2,

    "random_seed_number": 1,

    "seeds": [

        1,

        2,

        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

    "normalize_weights": false,

    "verbose": false,

    "skip_best_hyperparams": false,

    "save_experiment_configuration": [

        "3",

        "train-dev_subset"

    ],

    "job_number": -1,

    "extraction_strategy": "omp",

    "overwrite": false,

    "extracted_forest_size": [

        33,

        67,

        100,

        133,

        167,

        200,

        233,

        267,

        300,

        333,

        367,

        400,

        433,

        467,

        500,

        533,

        567,

        600,

        633,

        667,

        700,

        733,

        767,

        800,

        833,

        867,

        900,

        933,

        967,

        1000

    ]

}

 No newline at end of file

experiments/boston/stage3/train-dev_train-dev_subset.json0 → 100644

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−0

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{

    "experiment_id": 2,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "boston",

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/boston/stage3",

    "dev_size": 0.2,

    "test_size": 0.2,

    "random_seed_number": 1,

    "seeds": [

        1,

        2,

        3,

        4,

        5

    ],

    "subsets_used": "train+dev,train+dev",

    "normalize_weights": false,

    "verbose": false,

    "skip_best_hyperparams": false,

    "save_experiment_configuration": [

        "3",

        "train-dev_train-dev_subset"

    ],

    "job_number": -1,

    "extraction_strategy": "omp",

    "overwrite": false,

    "extracted_forest_size": [

        33,

        67,

        100,

        133,

        167,

        200,

        233,

        267,

        300,

        333,

        367,

        400,

        433,

        467,

        500,

        533,

        567,

        600,

        633,

        667,

        700,

        733,

        767,

        800,

        833,

        867,

        900,

        933,

        967,

        1000

    ]

}

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experiments/boston/stage3/train-train-dev_subset.json0 → 100644

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{

    "experiment_id": 3,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "boston",

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/boston/stage3",

    "dev_size": 0.2,

    "test_size": 0.2,

    "random_seed_number": 1,

    "seeds": [

        1,

        2,

        3,

        4,

        5

    ],

    "subsets_used": "train,train+dev",

    "normalize_weights": false,

    "verbose": false,

    "skip_best_hyperparams": false,

    "save_experiment_configuration": [

        "3",

        "train-train-dev_subset"

    ],

    "job_number": -1,

    "extraction_strategy": "omp",

    "overwrite": false,

    "extracted_forest_size": [

        33,

        67,

        100,

        133,

        167,

        200,

        233,

        267,

        300,

        333,

        367,

        400,

        433,

        467,

        500,

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experiments/boston/stage4/none_with_params.json

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@@ -17,9 +17,7 @@

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@@ -31,6 +29,7 @@

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experiments/boston/stage4/omp_with_params.json

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@@ -17,9 +17,7 @@

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experiments/boston/stage4/random_with_params.json

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experiments/breast_cancer/stage1/none_with_params.json0 → 100644

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experiments/breast_cancer/stage1/none_wo_params.json0 → 100644

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experiments/breast_cancer/stage1/omp_with_params.json0 → 100644

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experiments/breast_cancer/stage1/omp_wo_params.json0 → 100644

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experiments/breast_cancer/stage1/random_with_params.json0 → 100644

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experiments/breast_cancer/stage1/random_wo_params.json0 → 100644

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experiments/breast_cancer/stage2/no_normalization.json0 → 100644

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experiments/breast_cancer/stage2/normalize_D.json0 → 100644

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experiments/breast_cancer/stage2/normalize_D_and_weights.json0 → 100644

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experiments/breast_cancer/stage2/normalize_weights.json0 → 100644

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experiments/breast_cancer/stage3/train-dev_subset.json0 → 100644

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experiments/breast_cancer/stage3/train-dev_train-dev_subset.json0 → 100644

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experiments/breast_cancer/stage3/train-train-dev_subset.json0 → 100644

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experiments/breast_cancer/stage4/none_with_params.json

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experiments/breast_cancer/stage4/omp_with_params.json

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experiments/breast_cancer/stage4/random_with_params.json

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experiments/california_housing/stage1/none_with_params.json0 → 100644

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experiments/california_housing/stage1/none_wo_params.json0 → 100644

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experiments/california_housing/stage1/omp_with_params.json0 → 100644

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experiments/california_housing/stage1/omp_wo_params.json0 → 100644

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experiments/california_housing/stage1/random_with_params.json0 → 100644

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experiments/california_housing/stage1/random_wo_params.json0 → 100644

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experiments/california_housing/stage2/no_normalization.json0 → 100644

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experiments/california_housing/stage2/normalize_D.json0 → 100644

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experiments/california_housing/stage2/normalize_D_and_weights.json0 → 100644

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experiments/california_housing/stage2/normalize_weights.json0 → 100644

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experiments/california_housing/stage3/train-dev_subset.json0 → 100644

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experiments/california_housing/stage3/train-train-dev_subset.json0 → 100644

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experiments/california_housing/stage4/none_with_params.json

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experiments/california_housing/stage4/omp_with_params.json

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experiments/california_housing/stage4/random_with_params.json

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experiments/diabetes/stage1/none_with_params.json0 → 100644

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experiments/diabetes/stage1/none_wo_params.json0 → 100644

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experiments/diabetes/stage1/omp_with_params.json0 → 100644

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experiments/diabetes/stage1/omp_wo_params.json0 → 100644

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experiments/diabetes/stage1/random_with_params.json0 → 100644

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experiments/diabetes/stage1/random_wo_params.json0 → 100644

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experiments/diabetes/stage2/no_normalization.json0 → 100644

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experiments/diabetes/stage2/normalize_D.json0 → 100644

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experiments/diabetes/stage2/normalize_D_and_weights.json0 → 100644

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experiments/diabetes/stage2/normalize_weights.json0 → 100644

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experiments/diabetes/stage3/train-dev_subset.json0 → 100644

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experiments/diabetes/stage3/train-dev_train-dev_subset.json0 → 100644

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experiments/diabetes/stage3/train-train-dev_subset.json0 → 100644

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experiments/diabetes/stage4/none_with_params.json

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experiments/diabetes/stage4/omp_with_params.json

+2
−3

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@@ -17,9 +17,7 @@

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@@ -31,6 +29,7 @@

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experiments/diabetes/stage4/random_with_params.json

+2
−3

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@@ -17,9 +17,7 @@

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@@ -31,6 +29,7 @@

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experiments/diamonds/stage1/none_with_params.json

+36
−13

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@@ -6,8 +6,8 @@

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@@ -15,7 +15,9 @@

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@@ -27,16 +29,37 @@

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 No newline at end of file

experiments/diamonds/stage1/none_wo_params.json

+36
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@@ -6,8 +6,8 @@

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@@ -15,7 +15,9 @@

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@@ -27,16 +29,37 @@

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 No newline at end of file

experiments/diamonds/stage1/omp_with_params.json

+37
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@@ -6,13 +6,17 @@

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@@ -25,16 +29,37 @@

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 No newline at end of file

experiments/diamonds/stage1/omp_wo_params.json

+36
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@@ -6,8 +6,8 @@

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@@ -15,7 +15,9 @@

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@@ -27,16 +29,37 @@

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 No newline at end of file

experiments/diamonds/stage1/random_with_params.json

+36
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@@ -6,8 +6,8 @@

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@@ -15,7 +15,9 @@

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@@ -27,16 +29,37 @@

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 No newline at end of file

experiments/diamonds/stage1/random_wo_params.json

+36
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@@ -6,8 +6,8 @@

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@@ -15,7 +15,9 @@

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@@ -27,16 +29,37 @@

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 No newline at end of file

experiments/diamonds/stage2/no_normalization.json

+36
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@@ -6,8 +6,8 @@

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@@ -15,7 +15,9 @@

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@@ -27,16 +29,37 @@

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 No newline at end of file

experiments/diamonds/stage2/normalize_D.json

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@@ -6,8 +6,8 @@

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@@ -15,7 +15,9 @@

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@@ -27,16 +29,37 @@

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 No newline at end of file

experiments/diamonds/stage2/normalize_D_and_weights.json

+36
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@@ -6,8 +6,8 @@

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@@ -15,7 +15,9 @@

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@@ -27,16 +29,37 @@

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Source

Target

Files

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