28c3d8747ab945a10ba0d89ecc17f29700827ad5 to 1db36b5d8d77b35b78984c19d13b7ab879e43d38 · Luc Giffon / bolsonaro

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code/bolsonaro/data/dataset_loader.py

+25
−4

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from bolsonaro.data.dataset import Dataset

from bolsonaro.data.dataset_parameters import DatasetParameters

from bolsonaro.data.task import Task

from bolsonaro.utils import change_binary_func_load

from bolsonaro.utils import change_binary_func_load, change_binary_func_openml

from sklearn.datasets import load_boston, load_iris, load_diabetes, \

    load_digits, load_linnerud, load_wine, load_breast_cancer

from sklearn.datasets import fetch_olivetti_faces, fetch_20newsgroups, \

    fetch_20newsgroups_vectorized, fetch_lfw_people, fetch_lfw_pairs, \

    fetch_covtype, fetch_rcv1, fetch_kddcup99, fetch_california_housing

    fetch_covtype, fetch_rcv1, fetch_kddcup99, fetch_california_housing, \

    fetch_openml

from sklearn.model_selection import train_test_split

from sklearn import preprocessing

import random

@@ -30,13 +31,15 @@ class DatasetLoader(object):

    dataset_names = ['boston', 'iris', 'diabetes', 'digits', 'linnerud', 'wine',

        'breast_cancer', 'olivetti_faces', '20newsgroups_vectorized', 'lfw_people',

        'lfw_pairs', 'covtype', 'rcv1', 'california_housing', 'diamonds']

        'lfw_pairs', 'covtype', 'rcv1', 'california_housing', 'diamonds', 'steel-plates',

        'kr-vs-kp', 'kin8nm', 'spambase', 'musk', 'gamma']

    dataset_seed_numbers = {'boston':15, 'iris':15, 'diabetes':15, 'digits':5,

        'linnerud':15, 'wine':15, 'breast_cancer':15, 'olivetti_faces':15,

        '20newsgroups_vectorized':3, 'lfw_people':3,

        'lfw_pairs':3, 'covtype':3, 'rcv1':3, 'california_housing':3,

        'diamonds': 15}

        'diamonds': 15, 'steel-plates': 15, 'kr-vs-kp': 15, 'kin8nm': 15,

        'spambase': 15, 'musk': 15, 'gamma': 15}

    @staticmethod

    def load(dataset_parameters):

@@ -103,6 +106,24 @@ class DatasetLoader(object):

            df['clarity'] = label_clarity.fit_transform(df['clarity'])

            X, y = df.drop(['price'], axis=1), df['price']

            task = Task.REGRESSION

        elif name == 'steel-plates':

            dataset_loading_func = change_binary_func_openml('steel-plates-fault')

            task = Task.BINARYCLASSIFICATION

        elif name == 'kr-vs-kp':

            dataset_loading_func = change_binary_func_openml('kr-vs-kp')

            task = Task.BINARYCLASSIFICATION

        elif name == 'kin8nm':

            X, y = fetch_openml('kin8nm', return_X_y=True)

            task = Task.REGRESSION

        elif name == 'spambase':

            dataset_loading_func = change_binary_func_openml('spambase')

            task = Task.BINARYCLASSIFICATION

        elif name == 'musk':

            dataset_loading_func = change_binary_func_openml('musk')

            task = Task.BINARYCLASSIFICATION

        elif name == 'gamma':

            dataset_loading_func = change_binary_func_openml('MagicTelescope')

            task = Task.BINARYCLASSIFICATION

        else:

            raise ValueError("Unsupported dataset '{}'".format(name))

code/bolsonaro/models/kmeans_forest_regressor.py0 → 100644

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from bolsonaro.utils import tqdm_joblib

from sklearn.ensemble import RandomForestRegressor

from sklearn.metrics import mean_squared_error

from sklearn.base import BaseEstimator

from sklearn.cluster import KMeans

from abc import abstractmethod, ABCMeta

import numpy as np

from scipy.stats import mode

from joblib import Parallel, delayed

from tqdm import tqdm

class KMeansForestRegressor(BaseEstimator, metaclass=ABCMeta):

    """

    On extreme pruning of random forest ensembles for ral-time predictive applications', by Khaled Fawagreh, Mohamed Medhat Gaber and Eyad Elyan.

    """

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

        self._models_parameters = models_parameters

        self._estimator = RandomForestRegressor(**self._models_parameters.hyperparameters,

            random_state=self._models_parameters.seed, n_jobs=-1)

        self._extracted_forest_size = self._models_parameters.extracted_forest_size

        self._score_metric = score_metric

    @property

    def models_parameters(self):

        return self._models_parameters

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

        self._estimator.fit(X_train, y_train)

        predictions = list()

        for tree in self._estimator.estimators_:

            predictions.append(tree.predict(X_train))

        predictions = np.array(predictions)

        kmeans = KMeans(n_clusters=self._extracted_forest_size, random_state=self._models_parameters.seed).fit(predictions)

        labels = np.array(kmeans.labels_)

        # For each cluster select the best tree on the validation set

        extracted_forest_sizes = list(range(self._extracted_forest_size))

        with tqdm_joblib(tqdm(total=self._extracted_forest_size, disable=True)) as prune_forest_job_pb:

            pruned_forest = Parallel(n_jobs=-1)(delayed(self._prune_forest_job)(prune_forest_job_pb,

                extracted_forest_sizes[i], labels, X_val, y_val, self._score_metric)

                for i in range(self._extracted_forest_size))

        self._estimator.estimators_ = pruned_forest

    def _prune_forest_job(self, prune_forest_job_pb, c, labels, X_val, y_val, score_metric):

        index = np.where(labels == c)[0]

        with tqdm_joblib(tqdm(total=len(index), disable=True)) as cluster_job_pb:

            cluster = Parallel(n_jobs=-1)(delayed(self._cluster_job)(cluster_job_pb, index[i], X_val, 

                y_val, score_metric) for i in range(len(index)))

        best_tree_index = np.argmax(cluster)

        prune_forest_job_pb.update()

        return self._estimator.estimators_[index[best_tree_index]]

    def _cluster_job(self, cluster_job_pb, i, X_val, y_val, score_metric):

        y_val_pred = self._estimator.estimators_[i].predict(X_val)

        tree_pred = score_metric(y_val, y_val_pred)

        cluster_job_pb.update()

        return tree_pred

    def predict(self, X):

        return self._estimator.predict(X)

    def score(self, X, y):

        predictions = list()

        for tree in self._estimator.estimators_:

            predictions.append(tree.predict(X))

        predictions = np.array(predictions)

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

        score = self._score_metric(mean_predictions, y)

        return score

    def predict_base_estimator(self, X):

        return self._estimator.predict(X)

code/bolsonaro/models/model_factory.py

+12
−3

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@@ -2,6 +2,7 @@ from bolsonaro.models.omp_forest_classifier import OmpForestBinaryClassifier, Om

from bolsonaro.models.omp_forest_regressor import OmpForestRegressor

from bolsonaro.models.model_parameters import ModelParameters

from bolsonaro.models.similarity_forest_regressor import SimilarityForestRegressor

from bolsonaro.models.kmeans_forest_regressor import KMeansForestRegressor

from bolsonaro.data.task import Task

from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier

@@ -22,9 +23,11 @@ class ModelFactory(object):

            elif model_parameters.extraction_strategy == 'random':

                return RandomForestClassifier(n_estimators=model_parameters.extracted_forest_size,

                    random_state=model_parameters.seed)

            else:

            elif model_parameters.extraction_strategy == 'none':

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

                    random_state=model_parameters.seed)

            else:

                raise ValueError('Invalid extraction strategy')

        elif task == Task.REGRESSION:

            if model_parameters.extraction_strategy == 'omp':

                return OmpForestRegressor(model_parameters)

@@ -33,15 +36,21 @@ class ModelFactory(object):

                    random_state=model_parameters.seed)

            elif model_parameters.extraction_strategy == 'similarity':

                return SimilarityForestRegressor(model_parameters)

            else:

            elif model_parameters.extraction_strategy == 'kmeans':

                return KMeansForestRegressor(model_parameters)

            elif model_parameters.extraction_strategy == 'none':

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

                    random_state=model_parameters.seed)

            else:

                raise ValueError('Invalid extraction strategy')

        elif task == Task.MULTICLASSIFICATION:

            if model_parameters.extraction_strategy == 'omp':

                return OmpForestMulticlassClassifier(model_parameters)

            elif model_parameters.extraction_strategy == 'random':

                return RandomForestClassifier(n_estimators=model_parameters.extracted_forest_size,

                    random_state=model_parameters.seed)

            else:

            elif model_parameters.extraction_strategy == 'none':

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

                    random_state=model_parameters.seed)

            else:

                raise ValueError('Invalid extraction strategy')

code/bolsonaro/models/model_raw_results.py

+8
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@@ -6,12 +6,12 @@ import datetime

class ModelRawResults(object):

    def __init__(self, model_object, training_time,

    def __init__(self, model_weights, training_time,

        datetime, train_score, dev_score, test_score,

        train_score_base, dev_score_base,

        test_score_base, score_metric, base_score_metric):

        self._model_object = model_object

        self._model_weights = model_weights

        self._training_time = training_time

        self._datetime = datetime

        self._train_score = train_score

@@ -24,8 +24,8 @@ class ModelRawResults(object):

        self._base_score_metric = base_score_metric

    @property

    def model_object(self):

        return self.model_object

    def model_weights(self):

        return self.model_weights

    @property

    def training_time(self):

@@ -68,6 +68,8 @@ class ModelRawResults(object):

        return self._base_score_metric

    def save(self, models_dir):

        if not os.path.exists(models_dir):

            os.mkdir(models_dir)

        save_obj_to_pickle(models_dir + os.sep + 'model_raw_results.pickle',

            self.__dict__)

code/bolsonaro/models/omp_forest.py

+25
−1

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@@ -8,6 +8,7 @@ from sklearn.base import BaseEstimator

class OmpForest(BaseEstimator, metaclass=ABCMeta):

    def __init__(self, models_parameters, base_forest_estimator):

        self._base_forest_estimator = base_forest_estimator

        self._models_parameters = models_parameters

@@ -24,7 +25,6 @@ class OmpForest(BaseEstimator, metaclass=ABCMeta):

        return self._base_forest_estimator.score(X, y)

    def _base_estimator_predictions(self, X):

        # We need to use predict_proba to get the probabilities of each class

        return np.array([tree.predict(X) for tree in self._base_forest_estimator.estimators_]).T

    @property

@@ -33,6 +33,8 @@ class OmpForest(BaseEstimator, metaclass=ABCMeta):

    # sklearn baseestimator api methods

    def fit(self, X_forest, y_forest, X_omp, y_omp):

        # print(y_forest.shape)

        # print(set([type(y) for y in y_forest]))

        self._base_forest_estimator.fit(X_forest, y_forest)

        self._extract_subforest(X_omp, y_omp) # type: OrthogonalMatchingPursuit

        return self

@@ -96,6 +98,7 @@ class OmpForest(BaseEstimator, metaclass=ABCMeta):

        pass

class SingleOmpForest(OmpForest):

    def __init__(self, models_parameters, base_forest_estimator):

        # fit_intercept shouldn't be set to False as the data isn't necessarily centered here

        # normalization is handled outsite OMP

@@ -123,3 +126,24 @@ class SingleOmpForest(OmpForest):

            forest_predictions /= self._forest_norms

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

    def predict_no_weights(self, X):

        """

        Apply the SingleOmpForest to X without using the weights.

        Make all the base tree predictions

        :param X: a Forest

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

        """

        forest_predictions = self._base_estimator_predictions(X).T

        if self._models_parameters.normalize_D:

            forest_predictions /= self._forest_norms

        weights = self._omp.coef_

        omp_trees_indices = np.nonzero(weights)[0]

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

        print(len(omp_trees_indices))

        return select_trees

code/bolsonaro/models/omp_forest_classifier.py

+58
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@@ -24,6 +24,34 @@ class OmpForestBinaryClassifier(SingleOmpForest):

        return super().fit(X_forest, y_forest, X_omp, y_omp)

    def predict_no_weights(self, X):

        """

        Apply the SingleOmpForest to X without using the weights.

        Make all the base tree predictions

        :param X: a Forest

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

        """

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

        if self._models_parameters.normalize_D:

            forest_predictions /= self._forest_norms

        weights = self._omp.coef_

        omp_trees_indices = np.nonzero(weights)

        omp_trees_predictions = forest_predictions[omp_trees_indices].T[1]

        # Here forest_pred is the probability of being class 1.

        result_omp = np.mean(omp_trees_predictions, axis=1)

        result_omp = (result_omp - 0.5) * 2

        return result_omp

    def score(self, X, y, metric=DEFAULT_SCORE_METRIC):

        """

        Evaluate OMPForestClassifer on (`X`, `y`) using `metric`

@@ -106,6 +134,36 @@ class OmpForestMulticlassClassifier(OmpForest):

        max_preds = np.argmax(preds, axis=1)

        return np.array(label_names)[max_preds]

    def predict_no_weights(self, X):

        """

        Apply the SingleOmpForest to X without using the weights.

        Make all the base tree predictions

        :param X: a Forest

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

        """

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

        if self._models_parameters.normalize_D:

            forest_predictions /= self._forest_norms

        label_names = []

        preds = []

        num_class = 0

        for class_label, omp_class in self._dct_class_omp.items():

            weights = omp_class.coef_

            omp_trees_indices = np.nonzero(weights)

            label_names.append(class_label)

            atoms_binary = (forest_predictions[num_class].T - 0.5) * 2 # centré réduit de 0/1 à -1/1

            preds.append(np.sum(atoms_binary[omp_trees_indices], axis=0)/len(omp_trees_indices))

            num_class += 1

        preds = np.array(preds).T

        max_preds = np.argmax(preds, axis=1)

        return np.array(label_names)[max_preds]

    def score(self, X, y, metric=DEFAULT_SCORE_METRIC):

        predictions = self.predict(X)

code/bolsonaro/models/similarity_forest_regressor.py

+47
−33

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@@ -3,6 +3,7 @@ from sklearn.metrics import mean_squared_error

from sklearn.base import BaseEstimator

from abc import abstractmethod, ABCMeta

import numpy as np

from tqdm import tqdm

class SimilarityForestRegressor(BaseEstimator, metaclass=ABCMeta):

@@ -10,56 +11,69 @@ class SimilarityForestRegressor(BaseEstimator, metaclass=ABCMeta):

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2822360/

    """

    def __init__(self, models_parameters):

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

        self._models_parameters = models_parameters

        self._regressor = RandomForestRegressor(n_estimators=self._models_parameters.hyperparameters['n_estimators'],

            random_state=models_parameters.seed)

        self._estimator = RandomForestRegressor(**self._models_parameters.hyperparameters,

            random_state=self._models_parameters.seed, n_jobs=-1)

        self._extracted_forest_size = self._models_parameters.extracted_forest_size

        self._score_metric = score_metric

    @property

    def models_parameters(self):

        return self._models_parameters

    def fit(self, X_train, y_train, X_val, y_val, score_metric=mean_squared_error):

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

        self._estimator.fit(X_train, y_train)

        self._regressor.fit(X_train, y_train)

        y_val_pred = self._regressor.predict(X_val)

        forest_pred = score_metric(y_val, y_val_pred)

        forest = self._regressor.estimators_

        y_val_pred = self._estimator.predict(X_val)

        forest_pred = self._score_metric(y_val, y_val_pred)

        forest = self._estimator.estimators_

        selected_trees = list()

        tree_list = list(self._regressor.estimators_)

        tree_list = list(self._estimator.estimators_)

        val_scores = list()

        with tqdm(tree_list) as tree_pred_bar:

            tree_pred_bar.set_description('[Initial tree predictions]')

            for tree in tree_pred_bar:

                val_scores.append(tree.predict(X_val))

            tree_pred_bar.update(1)

        for _ in range(self._extracted_forest_size):

        with tqdm(range(self._extracted_forest_size), disable=True) as pruning_forest_bar:

            pruning_forest_bar.set_description(f'[Pruning forest s={self._extracted_forest_size}]')

            for i in pruning_forest_bar:

                best_similarity = 100000

                found_index = 0

            for i in range(len(tree_list)):

                lonely_tree = tree_list[i]

                del tree_list[i]

                val_list = list()

                for tree in tree_list:

                    val_pred = tree.predict(X_val)

                    val_list.append(val_pred)

                val_list = np.array(val_list)

                val_mean = np.mean(val_list, axis=0)

                val_score = score_metric(val_mean, y_val)

                with tqdm(range(len(tree_list)), disable=True) as tree_list_bar:

                    tree_list_bar.set_description(f'[Tree selection s={self._extracted_forest_size} #{i}]')

                    for j in tree_list_bar:

                        lonely_tree = tree_list[j]

                        del tree_list[j]

                        val_mean = np.mean(np.asarray(val_scores), axis=0)

                        val_score = self._score_metric(val_mean, y_val)

                        temp_similarity = abs(forest_pred - val_score)

                        if (temp_similarity < best_similarity):

                    found_index = i

                            found_index = j

                            best_similarity = temp_similarity

                tree_list.insert(i, lonely_tree)

                        tree_list.insert(j, lonely_tree)

                        val_scores.insert(j, lonely_tree.predict(X_val))

                        tree_list_bar.update(1)

                selected_trees.append(tree_list[found_index])

                del tree_list[found_index]

                del val_scores[found_index]

                pruning_forest_bar.update(1)

        pruned_forest = list(set(forest) - set(selected_trees))

        self._regressor.estimators_ = pruned_forest

        self._estimator.estimators_ = pruned_forest

    def score(self, X, y):

        test_list = list()

        for mod in self._regressor.estimators_:

        for mod in self._estimator.estimators_:

            test_pred = mod.predict(X)

            test_list.append(test_pred)

        test_list = np.array(test_list)

        test_mean = np.mean(test_list, axis=0)

        score = mean_squared_error(test_mean, y)

        score = self._score_metric(test_mean, y)

        return score

    def predict_base_estimator(self, X):

        return self._estimator.predict(X)

code/bolsonaro/trainer.py

+190
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@@ -95,14 +95,21 @@ class Trainer(object):

            )

        self._end_time = time.time()

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

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

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

            if weights:

                y_pred = model.predict(X)

            else:

                y_pred = model.predict_no_weights(X)

            result = self._regression_score_metric(y_true, y_pred)

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

            if weights:

                y_pred = model.predict(X)

            else:

                y_pred = model.predict_no_weights(X)

            if type(model) is OmpForestBinaryClassifier:

                y_pred = y_pred.round()

                y_pred = np.sign(y_pred)

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

            result = self._classification_score_metric(y_true, y_pred)

        return result

@@ -126,8 +133,17 @@ class Trainer(object):

        :param model: Object with

        :param models_dir: Where the results will be saved

        """

        model_weights = ''

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

            model_weights = model._omp.coef_

        elif type(model) == OmpForestMulticlassClassifier:

            model_weights = model._dct_class_omp

        elif type(model) == OmpForestBinaryClassifier:

            model_weights = model._omp

        results = ModelRawResults(

            model_object='',

            model_weights=model_weights,

            training_time=self._end_time - self._begin_time,

            datetime=datetime.datetime.now(),

            train_score=self.__score_func(model, self._dataset.X_train, self._dataset.y_train),

@@ -148,3 +164,27 @@ class Trainer(object):

        self._logger.info("Base performance on dev: {}".format(results.dev_score_base))

        self._logger.info("Performance on dev: {}".format(results.dev_score))

        if type(model) not in [RandomForestRegressor, RandomForestClassifier]:

            results = ModelRawResults(

                model_weights='',

                training_time=self._end_time - self._begin_time,

                datetime=datetime.datetime.now(),

                train_score=self.__score_func(model, self._dataset.X_train, self._dataset.y_train, False),

                dev_score=self.__score_func(model, self._dataset.X_dev, self._dataset.y_dev, False),

                test_score=self.__score_func(model, self._dataset.X_test, self._dataset.y_test, False),

                train_score_base=self.__score_func_base(model, self._dataset.X_train, self._dataset.y_train),

                dev_score_base=self.__score_func_base(model, self._dataset.X_dev, self._dataset.y_dev),

                test_score_base=self.__score_func_base(model, self._dataset.X_test, self._dataset.y_test),

                score_metric=self._score_metric_name,

                base_score_metric=self._base_score_metric_name

            )

            results.save(models_dir+'_no_weights')

            self._logger.info("Base performance on test without weights: {}".format(results.test_score_base))

            self._logger.info("Performance on test: {}".format(results.test_score))

            self._logger.info("Base performance on train without weights: {}".format(results.train_score_base))

            self._logger.info("Performance on train: {}".format(results.train_score))

            self._logger.info("Base performance on dev without weights: {}".format(results.dev_score_base))

            self._logger.info("Performance on dev: {}".format(results.dev_score))

code/bolsonaro/utils.py

+12
−0

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@@ -5,6 +5,8 @@ from copy import deepcopy

import contextlib

import joblib

from sklearn.datasets import fetch_openml

def resolve_experiment_id(models_dir):

    """

@@ -78,6 +80,16 @@ def change_binary_func_load(base_load_function):

        return X, y

    return func_load

def change_binary_func_openml(dataset_name):

    def func_load(return_X_y=True, random_state=None):

        X, y = fetch_openml(dataset_name, return_X_y=return_X_y)

        possible_classes = sorted(set(y))

        assert len(possible_classes) == 2, "Function change binary_func_load only work for binary classfication"

        y = binarize_class_data(y, possible_classes[-1])

        y = y.astype('int')

        return X, y

    return func_load

@contextlib.contextmanager

def tqdm_joblib(tqdm_object):

    """Context manager to patch joblib to report into tqdm progress bar given as argument"""

code/bolsonaro/visualization/plotter.py

+4
−4

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Diff line number
Diff line

@@ -109,16 +109,16 @@ class Plotter(object):

        fig, ax = plt.subplots()

        n = len(all_experiment_scores)

        nb_experiments = len(all_experiment_scores)

        """

        Get as many different colors from the specified cmap (here nipy_spectral)

        as there are curve to plot.

        """

        colors = Plotter.get_colors_from_cmap(n)

        colors = Plotter.get_colors_from_cmap(nb_experiments)

        # For each curve to plot

        for i in range(n):

        for i in range(nb_experiments):

            # Retreive the scores in a list for each seed

            experiment_scores = list(all_experiment_scores[i].values())

            # Compute the mean and the std for the CI

code/compute_results.py

+70
−19

Original line number
Diff line number
Diff line

@@ -17,7 +17,7 @@ def retreive_extracted_forest_sizes_number(models_dir, experiment_id):

    extracted_forest_sizes_root_path = experiment_seed_path + os.sep + 'extracted_forest_sizes'

    return len(os.listdir(extracted_forest_sizes_root_path))

def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_dir, experiment_id):

def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_dir, experiment_id, weights=True):

    experiment_id_path = models_dir + os.sep + str(experiment_id) # models/{experiment_id}

    experiment_seed_root_path = experiment_id_path + os.sep + 'seeds' # models/{experiment_id}/seeds

@@ -28,6 +28,7 @@ def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_d

    experiment_train_scores = dict()

    experiment_dev_scores = dict()

    experiment_test_scores = dict()

    experiment_weights = dict()

    all_extracted_forest_sizes = list()

    # Used to check if all losses were computed using the same metric (it should be the case)

@@ -44,14 +45,19 @@ def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_d

        experiment_train_scores[seed] = list()

        experiment_dev_scores[seed] = list()

        experiment_test_scores[seed] = list()

        experiment_weights[seed] = list()

        # List the forest sizes in models/{experiment_id}/seeds/{seed}/extracted_forest_sizes

        extracted_forest_sizes = os.listdir(extracted_forest_sizes_root_path)

        extracted_forest_sizes = [nb_tree for nb_tree in extracted_forest_sizes if not 'no_weights' in nb_tree ]

        extracted_forest_sizes.sort(key=int)

        all_extracted_forest_sizes.append(list(map(int, extracted_forest_sizes)))

        for extracted_forest_size in extracted_forest_sizes:

            # models/{experiment_id}/seeds/{seed}/extracted_forest_sizes/{extracted_forest_size}

            if weights:

                extracted_forest_size_path = extracted_forest_sizes_root_path + os.sep + extracted_forest_size

            else:

                extracted_forest_size_path = extracted_forest_sizes_root_path + os.sep + extracted_forest_size + '_no_weights'

            # Load models/{experiment_id}/seeds/{seed}/extracted_forest_sizes/{extracted_forest_size}/model_raw_results.pickle file

            model_raw_results = ModelRawResults.load(extracted_forest_size_path)

            # Save the scores

@@ -60,6 +66,8 @@ def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_d

            experiment_test_scores[seed].append(model_raw_results.test_score)

            # Save the metric

            experiment_score_metrics.append(model_raw_results.score_metric)

            # Save the weights

            #experiment_weights[seed].append(model_raw_results.model_weights)

    # Sanity checks

    if len(set(experiment_score_metrics)) > 1:

@@ -67,7 +75,8 @@ def extract_scores_across_seeds_and_extracted_forest_sizes(models_dir, results_d

    if len(set([sum(extracted_forest_sizes) for extracted_forest_sizes in all_extracted_forest_sizes])) != 1:

        raise ValueError("The extracted forest sizes aren't the sames across seeds.")

    return experiment_train_scores, experiment_dev_scores, experiment_test_scores, all_extracted_forest_sizes[0], experiment_score_metrics[0]

    return experiment_train_scores, experiment_dev_scores, experiment_test_scores, \

        all_extracted_forest_sizes[0], experiment_score_metrics[0]#, experiment_weights

def extract_scores_across_seeds_and_forest_size(models_dir, results_dir, experiment_id, extracted_forest_sizes_number):

    experiment_id_path = models_dir + os.sep + str(experiment_id) # models/{experiment_id}

@@ -120,6 +129,7 @@ if __name__ == "__main__":

    DEFAULT_RESULTS_DIR = os.environ["project_dir"] + os.sep + 'results'

    DEFAULT_MODELS_DIR = os.environ["project_dir"] + os.sep + 'models'

    DEFAULT_PLOT_WEIGHT_DENSITY = False

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

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

@@ -130,6 +140,7 @@ if __name__ == "__main__":

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

    parser.add_argument('--plot_weight_density', action='store_true', default=DEFAULT_PLOT_WEIGHT_DENSITY, help='Plot the weight density. Only working for regressor models for now.')

    args = parser.parse_args()

    if args.stage not in list(range(1, 6)):

@@ -347,9 +358,17 @@ if __name__ == "__main__":

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

        # omp_with_params

        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_experiment_score_metric, experiment_weights = extract_scores_across_seeds_and_extracted_forest_sizes(

                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_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

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

        #omp_with_params_without_weights

        logger.info('Loading omp_with_params 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_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

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

        """# base_with_params

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

@@ -369,13 +388,58 @@ if __name__ == "__main__":

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

        experiments_score_metric = base_with_params_experiment_score_metric

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

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

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

        Plotter.plot_stage2_losses(

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

            all_experiment_scores=[base_with_params_test_scores, random_with_params_test_scores, omp_with_params_test_scores],

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

            all_experiment_scores=[base_with_params_test_scores, random_with_params_test_scores, omp_with_params_test_scores,

                                   omp_with_params_without_weights_test_scores],

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

            x_value=with_params_extracted_forest_sizes,

            xlabel='Number of trees extracted',

            ylabel=experiments_score_metric,

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

    elif args.stage == 5:

        # 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])

        # base_with_params

        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_experiment_score_metric = \

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

            extracted_forest_sizes_number)

        # random_with_params

        logger.info('Loading random_with_params experiment 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 = \

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

        # omp_with_params

        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_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

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

        # omp_with_params

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

        kmeans_with_params_train_scores, kmeans_with_params_dev_scores, kmeans_with_params_test_scores, _, \

            kmeans_with_params_experiment_score_metric = extract_scores_across_seeds_and_extracted_forest_sizes(

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

        # Sanity check on the metrics retreived

        if not (base_with_params_experiment_score_metric == random_with_params_experiment_score_metric

            == omp_with_params_experiment_score_metric == kmeans_with_params_experiment_score_metric):

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

        experiments_score_metric = base_with_params_experiment_score_metric

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

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

        Plotter.plot_stage2_losses(

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

            all_experiment_scores=[base_with_params_test_scores, random_with_params_test_scores, omp_with_params_test_scores,

                kmeans_with_params_test_scores],

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

            x_value=with_params_extracted_forest_sizes,

            xlabel='Number of trees extracted',

            ylabel=experiments_score_metric,

@@ -384,16 +448,3 @@ if __name__ == "__main__":

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

    logger.info('Done.')

    """

    TODO:

    For each dataset:

    Stage 1) [DONE for california_housing] A figure for the selection of the best base forest model hyperparameters (best vs default/random hyperparams)

    Stage 2) [DONE for california_housing] A figure for the selection of the best combination of normalization: D normalization vs weights normalization (4 combinations)

    Stage 3) [DONE for california_housing] A figure for the selection of the most relevant subsets combination: train,dev vs train+dev,train+dev vs train,train+dev

    Stage 4) A figure to finally compare the perf of our approach using the previous selected

        parameters vs the baseline vs other papers using different extracted forest size

        (percentage of the tree size found previously in best hyperparams search) on the abscissa.

    IMPORTANT: Compare experiments that used the same seeds among them (except for stage 1).

    """

code/train.py

+78
−37

Original line number
Diff line number
Diff line

@@ -21,7 +21,7 @@ import numpy as np

import shutil

def process_job(seed, parameters, experiment_id, hyperparameters):

def seed_job(seed_job_pb, seed, parameters, experiment_id, hyperparameters, verbose):

    """

    Experiment function.

@@ -34,7 +34,6 @@ def process_job(seed, parameters, experiment_id, hyperparameters):

    """

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

        seed, threading.get_ident()))

    logger.info('seed={}'.format(seed))

    seed_str = str(seed)

    experiment_id_str = str(experiment_id)

@@ -55,13 +54,31 @@ def process_job(seed, parameters, experiment_id, hyperparameters):

    trainer = Trainer(dataset)

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

        for extracted_forest_size in parameters['extracted_forest_size']:

            logger.info('extracted_forest_size={}'.format(extracted_forest_size))

            sub_models_dir = models_dir + os.sep + 'extracted_forest_sizes' + os.sep + str(extracted_forest_size)

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

        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],

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

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

    else:

        forest_size = hyperparameters['n_estimators']

        logger.info('Base forest training with fixed forest size of {}'.format(forest_size))

        sub_models_dir = models_dir + os.sep + 'forest_size' + os.sep + str(forest_size)

        # Check if the result file already exists

        already_exists = False

        if os.path.isdir(sub_models_dir):

            sub_models_dir_files = os.listdir(sub_models_dir)

            for file_name in sub_models_dir_files:

                if '.pickle' != os.path.splitext(file_name)[1]:

                    continue

                else:

                    already_exists = os.path.getsize(os.path.join(sub_models_dir, file_name)) > 0

                    break

        if already_exists:

            logger.info('Base forest result already exists. Skipping...')

        else:

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

            model_parameters = ModelParameters(

                extracted_forest_size=extracted_forest_size,

                extracted_forest_size=forest_size,

                normalize_D=parameters['normalize_D'],

                subsets_used=parameters['subsets_used'],

                normalize_weights=parameters['normalize_weights'],

@@ -76,14 +93,36 @@ def process_job(seed, parameters, experiment_id, hyperparameters):

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

            trainer.train(model)

            trainer.compute_results(model, sub_models_dir)

    logger.info(f'Training done for seed {seed_str}')

    seed_job_pb.update(1)

def extracted_forest_size_job(extracted_forest_size_job_pb, extracted_forest_size, models_dir,

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

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

        seed, extracted_forest_size, threading.get_ident()))

    logger.info('extracted_forest_size={}'.format(extracted_forest_size))

    sub_models_dir = models_dir + os.sep + 'extracted_forest_sizes' + os.sep + str(extracted_forest_size)

    # Check if the result file already exists

    already_exists = False

    if os.path.isdir(sub_models_dir):

        sub_models_dir_files = os.listdir(sub_models_dir)

        for file_name in sub_models_dir_files:

            if '.pickle' != os.path.splitext(file_name)[1]:

                return

            else:

        forest_size = hyperparameters['n_estimators']

        logger.info('Base forest training with fixed forest size of {}'.format(forest_size))

        sub_models_dir = models_dir + os.sep + 'forest_size' + os.sep + str(forest_size)

                already_exists = os.path.getsize(os.path.join(sub_models_dir, file_name)) > 0

                break

    if already_exists:

        logger.info(f'Extracted forest {extracted_forest_size} result already exists. Skipping...')

        return

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

    model_parameters = ModelParameters(

            extracted_forest_size=forest_size,

        extracted_forest_size=extracted_forest_size,

        normalize_D=parameters['normalize_D'],

        subsets_used=parameters['subsets_used'],

        normalize_weights=parameters['normalize_weights'],

@@ -98,7 +137,6 @@ def process_job(seed, parameters, experiment_id, hyperparameters):

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

    trainer.train(model)

    trainer.compute_results(model, sub_models_dir)

    logger.info('Training done')

"""

Command lines example for stage 1:

@@ -138,6 +176,7 @@ if __name__ == "__main__":

    DEFAULT_SKIP_BEST_HYPERPARAMS = False

    DEFAULT_JOB_NUMBER = -1

    DEFAULT_EXTRACTION_STRATEGY = 'omp'

    DEFAULT_OVERWRITE = False

    begin_random_seed_range = 1

    end_random_seed_range = 2000

@@ -163,7 +202,8 @@ 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('--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('--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 or similarity.')

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

    args = parser.parse_args()

    if args.experiment_configuration:

@@ -173,7 +213,7 @@ if __name__ == "__main__":

    else:

        parameters = args.__dict__

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

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

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

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

@@ -208,7 +248,7 @@ if __name__ == "__main__":

    parameters['extracted_forest_size'] = np.unique(np.around(hyperparameters['n_estimators'] *

        np.linspace(0, args.extracted_forest_size_stop,

        parameters['extracted_forest_size_samples'] + 1,

        endpoint=False)[1:]).astype(np.int)).tolist()

        endpoint=True)[1:]).astype(np.int)).tolist()

    if parameters['seeds'] != None and parameters['random_seed_number'] > 1:

        logger.warning('seeds and random_seed_number parameters are both specified. Seeds will be used.')    

@@ -220,6 +260,7 @@ if __name__ == "__main__":

    if args.experiment_id:

        experiment_id = args.experiment_id

        if args.overwrite:

            shutil.rmtree(os.path.join(parameters['models_dir'], str(experiment_id)), ignore_errors=True)

    else:

        # Resolve the next experiment id number (last id + 1)

@@ -255,6 +296,6 @@ if __name__ == "__main__":

            )

    # Run as much job as there are seeds

    with tqdm_joblib(tqdm(total=len(seeds), disable=not args.verbose)) as progress_bar:

        Parallel(n_jobs=args.job_number)(delayed(process_job)(seeds[i],

            parameters, experiment_id, hyperparameters) for i in range(len(seeds)))

    with tqdm_joblib(tqdm(total=len(seeds), disable=not args.verbose)) as seed_job_pb:

        Parallel(n_jobs=args.job_number)(delayed(seed_job)(seed_job_pb, seeds[i],

            parameters, experiment_id, hyperparameters, args.verbose) for i in range(len(seeds)))

experiments/.gitkeepdeleted100644 → 0

+0
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experiments/20newsgroups_vectorized/stage1/none_with_params.jsondeleted100644 → 0

+0
−39

Original line number
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{

    "experiment_id": 1,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "20newsgroups_vectorized",

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 5,

    "extracted_forest_size_stop": 0.05,

    "models_dir": "models/20newsgroups_vectorized/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",

        "none_with_params"

    ],

    "job_number": -1,

    "extraction_strategy": "none",

    "extracted_forest_size": [

        7,

        13,

        20,

        27,

        34

    ]

}

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experiments/20newsgroups_vectorized/stage1/none_wo_params.jsondeleted100644 → 0

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{

    "experiment_id": 4,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "20newsgroups_vectorized",

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 5,

    "extracted_forest_size_stop": 0.05,

    "models_dir": "models/20newsgroups_vectorized/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",

    "extracted_forest_size": [

        7,

        13,

        20,

        27,

        34

    ]

}

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experiments/20newsgroups_vectorized/stage1/omp_wo_params.jsondeleted100644 → 0

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{

    "experiment_id": 6,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "20newsgroups_vectorized",

    "normalize_D": false,

    "dataset_normalizer": "standard",

    "forest_size": null,

    "extracted_forest_size_samples": 5,

    "extracted_forest_size_stop": 0.05,

    "models_dir": "models/20newsgroups_vectorized/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",

    "extracted_forest_size": [

        7,

        13,

        20,

        27,

        34

    ]

}

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experiments/20newsgroups_vectorized/stage1/params.jsondeleted100644 → 0

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{

    "scorer": "accuracy",

    "best_score_train": 0.7953125,

    "best_score_test": 0.7909854175872735,

    "best_parameters": {

        "max_depth": 20,

        "max_features": "sqrt",

        "min_samples_leaf": 1,

        "n_estimators": 809

    },

    "random_seed": 1763

}

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experiments/20newsgroups_vectorized/stage1/random_with_params.jsondeleted100644 → 0

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{

    "experiment_id": 2,

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{

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{

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{

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experiments/boston/stage4/none_with_params.json

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@@ -7,7 +7,7 @@

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@@ -15,7 +15,11 @@

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        3

        3,

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@@ -28,35 +32,35 @@

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experiments/boston/stage4/omp_with_params.json

+36
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@@ -7,7 +7,7 @@

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@@ -15,7 +15,11 @@

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@@ -28,35 +32,35 @@

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 No newline at end of file

experiments/boston/stage4/random_with_params.json

+36
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@@ -7,7 +7,7 @@

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@@ -15,7 +15,11 @@

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@@ -28,35 +32,35 @@

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experiments/breast_cancer/stage1/none_wo_params.jsondeleted100644 → 0

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{

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experiments/breast_cancer/stage1/omp_wo_params.jsondeleted100644 → 0

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{

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experiments/breast_cancer/stage1/random_wo_params.jsondeleted100644 → 0

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{

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experiments/breast_cancer/stage2/no_normalization.jsondeleted100644 → 0

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{

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 No newline at end of file

experiments/breast_cancer/stage2/normalize_D.jsondeleted100644 → 0

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{

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}

 No newline at end of file

experiments/breast_cancer/stage2/normalize_D_and_weights.jsondeleted100644 → 0

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{

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    "experiment_configuration_path": "experiments",

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}

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experiments/breast_cancer/stage2/normalize_weights.jsondeleted100644 → 0

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{

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experiments/breast_cancer/stage3/train-dev_subset.jsondeleted100644 → 0

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{

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experiments/breast_cancer/stage3/train-dev_train-dev_subset.jsondeleted100644 → 0

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{

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}

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experiments/breast_cancer/stage3/train-train-dev_subset.jsondeleted100644 → 0

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{

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}

 No newline at end of file

experiments/breast_cancer/stage4/none_with_params.json

+36
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@@ -7,7 +7,7 @@

    "dataset_normalizer": "standard",

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    "extracted_forest_size_samples": 30,

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@@ -15,7 +15,11 @@

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@@ -28,35 +32,35 @@

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 No newline at end of file

experiments/breast_cancer/stage4/omp_with_params.json

+36
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@@ -7,7 +7,7 @@

    "dataset_normalizer": "standard",

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@@ -15,7 +15,11 @@

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@@ -28,35 +32,35 @@

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 No newline at end of file

experiments/breast_cancer/stage4/random_with_params.json

+36
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@@ -7,7 +7,7 @@

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@@ -15,7 +15,11 @@

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@@ -28,35 +32,35 @@

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 No newline at end of file

experiments/california_housing/stage1/none_with_params_1.json→experiments/california_housing/stage1/none_with_params_26.json

+2
−2

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@@ -7,7 +7,7 @@

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@@ -33,5 +33,5 @@

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}

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experiments/california_housing/stage1/none_wo_params.jsondeleted100644 → 0

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experiments/california_housing/stage4/none_with_params.json

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@@ -7,7 +7,7 @@

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@@ -15,7 +15,11 @@

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@@ -28,35 +32,35 @@

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experiments/california_housing/stage4/omp_with_params.json

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@@ -15,7 +15,11 @@

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@@ -28,35 +32,35 @@

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experiments/california_housing/stage4/random_with_params.json

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@@ -7,7 +7,7 @@

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@@ -15,7 +15,11 @@

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experiments/diabetes/stage1/none_with_params.jsondeleted100644 → 0

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{

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{

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experiments/diabetes/stage2/normalize_D.jsondeleted100644 → 0

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{

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experiments/diabetes/stage2/normalize_weights.jsondeleted100644 → 0

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{

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experiments/diabetes/stage3/train-dev_subset.jsondeleted100644 → 0

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{

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{

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experiments/diabetes/stage3/train-train-dev_subset.jsondeleted100644 → 0

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{

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experiments/diabetes/stage4/none_with_params.json

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@@ -7,7 +7,7 @@

    "dataset_normalizer": "standard",

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@@ -15,7 +15,11 @@

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@@ -28,35 +32,35 @@

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experiments/diabetes/stage4/omp_with_params.json

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@@ -7,7 +7,7 @@

    "dataset_normalizer": "standard",

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@@ -15,7 +15,11 @@

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@@ -28,35 +32,35 @@

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experiments/diabetes/stage4/random_with_params.json

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@@ -7,7 +7,7 @@

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@@ -15,7 +15,11 @@

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@@ -28,35 +32,35 @@

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experiments/diamonds/stage4/none_with_params.json

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@@ -15,7 +15,11 @@

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@@ -28,35 +32,35 @@

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experiments/diamonds/stage4/omp_with_params.json

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@@ -7,7 +7,7 @@

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@@ -15,7 +15,11 @@

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@@ -28,35 +32,35 @@

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experiments/diamonds/stage4/random_with_params.json

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@@ -7,7 +7,7 @@

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    "extracted_forest_size_samples": 30,

    "extracted_forest_size_stop": 0.4,

    "extracted_forest_size_stop": 1.0,

    "models_dir": "models/diamonds/stage4",

    "dev_size": 0.2,

    "test_size": 0.2,

@@ -15,7 +15,11 @@

    "seeds": [

        1,

        2,

        3

        3,

        4,

        5,

        6,

        7

    ],

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

    "normalize_weights": false,

@@ -28,35 +32,35 @@

    "job_number": -1,

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        6,

        11,

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        343,

        358,

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        386,

        400,

        415,

        429

    ]

}

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experiments/digits/stage1/none_wo_params.jsondeleted100644 → 0

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−39

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{

    "experiment_id": 4,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "digits",

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        5

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    "save_experiment_configuration": [

        "1",

        "none_wo_params"

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    "job_number": -1,

    "extraction_strategy": "none",

    "extracted_forest_size": [

        8,

        17,

        25,

        33,

        42

    ]

}

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experiments/digits/stage1/omp_with_params.jsondeleted100644 → 0

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{

    "experiment_id": 3,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

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    "save_experiment_configuration": [

        "1",

        "omp_with_params"

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    "job_number": -1,

    "extraction_strategy": "omp",

    "extracted_forest_size": [

        8,

        17,

        25,

        33,

        42

    ]

}

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experiments/digits/stage1/omp_wo_params.jsondeleted100644 → 0

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{

    "experiment_id": 6,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "digits",

    "normalize_D": false,

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        3,

        4,

        5

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    "subsets_used": "train,dev",

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    "verbose": false,

    "skip_best_hyperparams": true,

    "save_experiment_configuration": [

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        "omp_wo_params"

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    "job_number": -1,

    "extraction_strategy": "omp",

    "extracted_forest_size": [

        8,

        17,

        25,

        33,

        42

    ]

}

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experiments/digits/stage1/random_wo_params.jsondeleted100644 → 0

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−39

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{

    "experiment_id": 5,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "digits",

    "normalize_D": false,

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    "seeds": [

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        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

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    "verbose": false,

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    "save_experiment_configuration": [

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    "job_number": -1,

    "extraction_strategy": "random",

    "extracted_forest_size": [

        8,

        17,

        25,

        33,

        42

    ]

}

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experiments/digits/stage2/no_normalization.jsondeleted100644 → 0

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−39

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{

    "experiment_id": 1,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "digits",

    "normalize_D": false,

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    "seeds": [

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        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

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    "verbose": false,

    "skip_best_hyperparams": false,

    "save_experiment_configuration": [

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        "no_normalization"

    ],

    "job_number": -1,

    "extraction_strategy": "omp",

    "extracted_forest_size": [

        8,

        17,

        25,

        33,

        42

    ]

}

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experiments/digits/stage2/normalize_D.jsondeleted100644 → 0

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{

    "experiment_id": 2,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "digits",

    "normalize_D": true,

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    "seeds": [

        1,

        2,

        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

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    "verbose": false,

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    "save_experiment_configuration": [

        "2",

        "normalize_D"

    ],

    "job_number": -1,

    "extraction_strategy": "omp",

    "extracted_forest_size": [

        8,

        17,

        25,

        33,

        42

    ]

}

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experiments/digits/stage2/normalize_D_and_weights.jsondeleted100644 → 0

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{

    "experiment_id": 4,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "digits",

    "normalize_D": true,

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    "forest_size": null,

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    "seeds": [

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        2,

        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

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    "verbose": false,

    "skip_best_hyperparams": false,

    "save_experiment_configuration": [

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        "normalize_D_and_weights"

    ],

    "job_number": -1,

    "extraction_strategy": "omp",

    "extracted_forest_size": [

        8,

        17,

        25,

        33,

        42

    ]

}

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experiments/digits/stage2/normalize_weights.jsondeleted100644 → 0

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{

    "experiment_id": 3,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "digits",

    "normalize_D": false,

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    "models_dir": "models/digits/stage2",

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    "seeds": [

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        3,

        4,

        5

    ],

    "subsets_used": "train,dev",

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    "verbose": false,

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    "save_experiment_configuration": [

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    ],

    "job_number": -1,

    "extraction_strategy": "omp",

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        8,

        17,

        25,

        33,

        42

    ]

}

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experiments/digits/stage3/train-dev_subset.jsondeleted100644 → 0

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{

    "experiment_id": 1,

    "experiment_configuration": null,

    "experiment_configuration_path": "experiments",

    "dataset_name": "digits",

    "normalize_D": false,

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    "models_dir": "models/digits/stage3",

    "dev_size": 0.2,

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    "seeds": [

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        3,

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    ],

    "subsets_used": "train,dev",

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    "save_experiment_configuration": [

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        "train-dev_subset"

    ],

    "job_number": -1,

    "extraction_strategy": "omp",

    "extracted_forest_size": [

        8,

        17,

        25,

        33,

        42

    ]

}

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Source

Target

Files

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