Merge'

(

.env.example

-# Environment variables go here, can be read by `python-dotenv` package:
-#
-#   `src/script.py`
-#   ----------------------------------------------------------------
-#    import dotenv
-#
-#    project_dir = os.path.join(os.path.dirname(__file__), os.pardir)
-#    dotenv_path = os.path.join(project_dir, '.env')
-#    dotenv.load_dotenv(dotenv_path)
-#   ----------------------------------------------------------------
-
-project_dir = "."
\ No newline at end of file

.gitignore

@@ -371,3 +371,6 @@ TSWLatexianTemp*
 *.lpz
 
 reports/*.pdf
+
+# Image
+*.png

TODO.md

-* Trouver des jeux de données pertinents
-* Entraîner et tester des forêts de différentes tailles
-* Entraîner et tester en regression et classification
-* Entraîner et tester sur différentes modalités (pas seulement des datasets d'images)
-* Entraîner avec différents hyperparamètres (d, profondeur, epsilon)
-* Appliquer OMP avec différentes valeurs de k (notamment un petit k)
-* Faire des figures
-* Implémenter et comparer les systèmes concurrents
\ No newline at end of file
+* Fix pickle loading of ModelRawResults, because saving the model_object leads import issues.
+* Fix ModelFactory.load function.
+* Fix model results loading in compute_results.py.
+* Check that omp multiclasses classifier is working as expected.
+* In the bayesian search computation, output a different file name depending on the task of the trained model.
+* Check the best params scores of the regressors (neg_mean_squared_error leads to huge negative values).
+* Prepare the json experiment files to run.
\ No newline at end of file

code/bolsonaro/data/__init__.py

+import os
+
+LOG_PATH = os.path.abspath(os.path.dirname(__file__) + os.sep + '..' + os.sep + '..' + os.sep + 'log')

code/bolsonaro/data/dataset.py

 class Dataset(object):
 
-    def __init__(self, task, dataset_parameters, X_train, X_dev, X_test, y_train,
+    def __init__(self, task, X_train, X_dev, X_test, y_train,
         y_dev, y_test):
         self._task = task
-        self._dataset_parameters = dataset_parameters
         self._X_train = X_train
         self._X_dev = X_dev
         self._X_test = X_test

code/bolsonaro/data/dataset_loader.py

 from bolsonaro.data.dataset import Dataset
 from bolsonaro.data.task import Task
+from bolsonaro.utils import change_binary_func_load
 
-from sklearn.datasets import load_boston, load_iris, load_diabetes, load_digits, load_linnerud, load_wine, load_breast_cancer
+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
@@ -19,45 +21,46 @@ class DatasetLoader(object):
             task = Task.REGRESSION
         elif name == 'iris':
             dataset_loading_func = load_iris
-            task = Task.CLASSIFICATION
+            task = Task.MULTICLASSIFICATION
         elif name == 'diabetes':
             dataset_loading_func = load_diabetes
             task = Task.REGRESSION
         elif name == 'digits':
             dataset_loading_func = load_digits
-            task = Task.CLASSIFICATION
+            task = Task.MULTICLASSIFICATION
         elif name == 'linnerud':
             dataset_loading_func = load_linnerud
             task = Task.REGRESSION
         elif name == 'wine':
             dataset_loading_func = load_wine
-            task = Task.CLASSIFICATION
+            task = Task.MULTICLASSIFICATION
         elif name == 'breast_cancer':
-            dataset_loading_func = load_breast_cancer
-            task = Task.CLASSIFICATION
-        elif name == 'olivetti_faces':
+            dataset_loading_func = change_binary_func_load(load_breast_cancer)
+            task = Task.BINARYCLASSIFICATION
+        elif name == 'olivetti_faces':  # bug (no return X_y)
             dataset_loading_func = fetch_olivetti_faces
-            task = Task.CLASSIFICATION
-        elif name == '20newsgroups':
+            task = Task.MULTICLASSIFICATION
+        elif name == '20newsgroups':  # bug (no return X_y)
             dataset_loading_func = fetch_20newsgroups
-            task = Task.CLASSIFICATION
+            task = Task.MULTICLASSIFICATION
         elif name == '20newsgroups_vectorized':
             dataset_loading_func = fetch_20newsgroups_vectorized
-            task = Task.CLASSIFICATION
-        elif name == 'lfw_people':
+            task = Task.MULTICLASSIFICATION
+        elif name == 'lfw_people':  # needs PIL (image dataset)
             dataset_loading_func = fetch_lfw_people
-            task = Task.CLASSIFICATION
+            task = Task.MULTICLASSIFICATION
         elif name == 'lfw_pairs':
             dataset_loading_func = fetch_lfw_pairs
+            task = Task.MULTICLASSIFICATION
         elif name == 'covtype':
             dataset_loading_func = fetch_covtype
-            task = Task.CLASSIFICATION
+            task = Task.MULTICLASSIFICATION
         elif name == 'rcv1':
             dataset_loading_func = fetch_rcv1
-            task = Task.CLASSIFICATION
+            task = Task.MULTICLASSIFICATION
         elif name == 'kddcup99':
             dataset_loading_func = fetch_kddcup99
-            task = Task.CLASSIFICATION
+            task = Task.MULTICLASSIFICATION
         elif name == 'california_housing':
             dataset_loading_func = fetch_california_housing
             task = Task.REGRESSION
@@ -87,5 +90,5 @@ class DatasetLoader(object):
             X_dev = scaler.transform(X_dev)
             X_test = scaler.transform(X_test)
 
-        return Dataset(task, dataset_parameters, X_train,
+        return Dataset(task, X_train,
             X_dev, X_test, y_train, y_dev, y_test)

code/bolsonaro/data/dataset_parameters.py

@@ -15,11 +15,11 @@ class DatasetParameters(object):
     @property
     def name(self):
         return self._name
-    
+
     @property
     def test_size(self):
         return self._test_size
-    
+
     @property
     def dev_size(self):
         return self._dev_size

code/bolsonaro/data/task.py

@@ -2,5 +2,6 @@ from enum import Enum
 
 
 class Task(Enum):
-    CLASSIFICATION = 1
+    BINARYCLASSIFICATION = 1
     REGRESSION = 2
+    MULTICLASSIFICATION = 3

code/bolsonaro/hyperparameter_searcher.py

+'''
+This module is used to find the best hyperparameters for a given dataset.
+'''
+
+from bolsonaro.data.dataset_parameters import DatasetParameters
+from bolsonaro.data.dataset_loader import DatasetLoader
+from bolsonaro.data.task import Task
+from bolsonaro.error_handling.logger_factory import LoggerFactory
+from . import LOG_PATH
+
+from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
+from skopt import BayesSearchCV
+
+
+class HyperparameterSearcher(object):
+
+    def __init__(self):
+        self._logger = LoggerFactory.create(LOG_PATH, __name__)
+
+    def search(self, dataset, hyperparameter_space, n_iter, cv,
+               random_seed, scorer, verbose=False):
+        '''
+        For a given dataset and the space of hyperparameters, does a
+        bayesian hyperparameters search.
+        :input dataset: a Dataset object
+        :input hyperparameter_space: a dictionnary, keys are hyperparameters,
+        value their spaces defined with skopt
+        :input n_iter: the number of iterations of the bayesian search
+        :input cv: the size of the cross validation
+        :input random_seed: int, the seed for the bayesian search
+        :input scorer: str, the name of the scorer
+        :input verbose: bool, print state of the research
+        :return: a skopt.searchcv.BayesSearchCV object
+        '''
+
+        if dataset.task == Task.REGRESSION:
+            estimator = RandomForestRegressor(n_jobs=-1, random_state=random_seed)
+        else:
+            estimator = RandomForestClassifier(n_jobs=-1, random_state=random_seed)
+
+        opt = BayesSearchCV(estimator, hyperparameter_space, n_iter=n_iter,
+                            cv=cv, n_jobs=-1, random_state=random_seed,
+                            scoring=scorer, verbose=verbose)
+
+        opt.fit(dataset.X_train, dataset.y_train)
+
+        return opt

code/bolsonaro/models/__init__.py

+import os
+
+LOG_PATH = os.path.abspath(os.path.dirname(__file__) + os.sep + '..' + os.sep + '..' + os.sep + 'log')

code/bolsonaro/models/model_factory.py

-from bolsonaro.models.omp_forest_classifier import OmpForestClassifier
+from bolsonaro.models.omp_forest_classifier import OmpForestBinaryClassifier, OmpForestMulticlassClassifier
 from bolsonaro.models.omp_forest_regressor import OmpForestRegressor
 from bolsonaro.data.task import Task
 from bolsonaro.models.model_parameters import ModelParameters
@@ -11,18 +11,22 @@ class ModelFactory(object):
 
     @staticmethod
     def build(task, model_parameters):
-        if task == Task.CLASSIFICATION:
-            model_func = OmpForestClassifier
+        if task == Task.BINARYCLASSIFICATION:
+            model_func = OmpForestBinaryClassifier
         elif task == Task.REGRESSION:
             model_func = OmpForestRegressor
+        elif task == Task.MULTICLASSIFICATION:
+            model_func = OmpForestMulticlassClassifier
         else:
             raise ValueError("Unsupported task '{}'".format(task))
         return model_func(model_parameters)
 
     @staticmethod
     def load(task, directory_path, experiment_id, model_raw_results):
+        raise NotImplementedError
         model_parameters = ModelParameters.load(directory_path, experiment_id)
         model = ModelFactory.build(task, model_parameters)
-        model.set_forest(model_raw_results.forest)
-        model.set_weights(model_raw_results.weights)
+        # todo faire ce qu'il faut ici pour rétablir correctement le modèle
+        model.set_forest(model_raw_results.model_object.forest)
+        model.set_weights(model_raw_results.model_object.weights)
         return model

code/bolsonaro/models/model_parameters.py

@@ -5,16 +5,13 @@ import os
 
 class ModelParameters(object):
 
-    def __init__(self, forest_size, extracted_forest_size, normalize_D, use_dev_subset, seed=None):
-        self._forest_size = forest_size
+    def __init__(self, extracted_forest_size, normalize_D, subsets_used, normalize_weights, seed, hyperparameters):
         self._extracted_forest_size = extracted_forest_size
         self._normalize_D = normalize_D
-        self._use_dev_subset = use_dev_subset
+        self._subsets_used = subsets_used
+        self._normalize_weights = normalize_weights
         self._seed = seed
-
-    @property
-    def forest_size(self):
-        return self._forest_size
+        self._hyperparameters = hyperparameters
 
     @property
     def extracted_forest_size(self):
@@ -25,13 +22,21 @@ class ModelParameters(object):
         return self._normalize_D
 
     @property
-    def use_dev_subset(self):
-        return self._use_dev_subset
+    def subsets_used(self):
+        return self._subsets_used
+
+    @property
+    def normalize_weights(self):
+        return self._normalize_weights
 
     @property
     def seed(self):
         return self._seed
 
+    @property
+    def hyperparameters(self):
+        return self._hyperparameters
+
     def save(self, directory_path, experiment_id):
         save_obj_to_json(directory_path + os.sep + 'model_parameters_{}.json'.format(experiment_id),
             self.__dict__)

code/bolsonaro/models/model_raw_results.py

@@ -6,13 +6,12 @@ import datetime
 
 class ModelRawResults(object):
 
-    def __init__(self, forest, weights, training_time,
+    def __init__(self, model_object, training_time,
         datetime, train_score, dev_score, test_score,
         score_metric, train_score_regressor, dev_score_regressor,
         test_score_regressor):
 
-        self._forest = forest
-        self._weights = weights
+        self._model_object = model_object
         self._training_time = training_time
         self._datetime = datetime
         self._train_score = train_score
@@ -24,12 +23,8 @@ class ModelRawResults(object):
         self._test_score_regressor = test_score_regressor
     
     @property
-    def forest(self):
-        return self._forest
-
-    @property
-    def weights(self):
-        return self._weights
+    def model_object(self):
+        return self.model_object
 
     @property
     def training_time(self):

code/bolsonaro/models/omp_forest.py

+from bolsonaro import LOG_PATH
+from bolsonaro.error_handling.logger_factory import LoggerFactory
+
+from abc import abstractmethod, ABCMeta
+import numpy as np
+from sklearn.linear_model import OrthogonalMatchingPursuit
+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
+        self._logger = LoggerFactory.create(LOG_PATH, __name__)
+
+    @property
+    def models_parameters(self):
+        return self._models_parameters
+
+    def score_base_estimator(self, X, y):
+        return self._base_forest_estimator.score(X, y)
+
+    def _base_estimator_predictions(self, X):
+        return np.array([tree.predict(X) for tree in self._base_forest_estimator.estimators_]).T
+
+    @property
+    def forest(self):
+        return self._base_forest_estimator.estimators_
+
+    # sklearn baseestimator api methods
+    def fit(self, X_forest, y_forest, X_omp, y_omp):
+        self._base_forest_estimator.fit(X_forest, y_forest)
+        self._extract_subforest(X_omp, y_omp)  # type: OrthogonalMatchingPursuit
+        return self
+
+    def _extract_subforest(self, X, y):
+        """
+        Given an already estimated regressor: apply OMP to get the weight of each tree.
+
+        The X data is used for interrogation of every tree in the forest. The y data
+        is used for finding the weights in OMP.
+
+        :param X: (n_sample, n_features) array
+        :param y: (n_sample,) array
+        :return:
+        """
+        self._logger.debug("Forest make prediction on X")
+        D = self._base_estimator_predictions(X)
+
+        if self._models_parameters.normalize_D:
+            # question: maybe consider other kinds of normalization.. centering?
+            self._logger.debug("Compute norm of predicted vectors on X")
+            self._forest_norms = np.linalg.norm(D, axis=0)
+            D /= self._forest_norms
+
+        self._logger.debug("Apply orthogonal maching pursuit on forest for {} extracted trees."
+                           .format(self._models_parameters.extracted_forest_size))
+
+        self.fit_omp(D, y)
+
+    @staticmethod
+    def _make_omp_weighted_prediction(base_predictions, omp_obj, normalize_weights=False):
+        if normalize_weights:
+            # we can normalize weights (by their sum) so that they sum to 1
+            # and they can be interpreted as impact percentages for interpretability.
+            # this necessits to remove the (-) in weights, e.g. move it to the predictions (use unsigned_coef)
+
+            # question: je comprend pas le truc avec nonszero?
+            # predictions = self._omp.predict(forest_predictions) * (1 / (np.sum(self._omp.coef_) / len(np.nonzero(self._omp.coef_))))
+            coef_signs = np.sign(omp_obj.coef_)[np.newaxis, :]  # add axis to make sure it will be broadcasted line-wise (there might be a confusion when forest_prediction is square)
+            unsigned_coef = (coef_signs * omp_obj.coef_).squeeze()
+            intercept = omp_obj.intercept_
+
+            adjusted_forest_predictions = base_predictions * coef_signs
+            predictions = adjusted_forest_predictions.dot(unsigned_coef) + intercept
+
+        else:
+            predictions = omp_obj.predict(base_predictions)
+
+        return predictions
+
+    @abstractmethod
+    def fit_omp(self, atoms, objective):
+        pass
+
+    @abstractmethod
+    def predict(self, X):
+        pass
+
+    @abstractmethod
+    def score(self, X, y):
+        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
+        self._omp = OrthogonalMatchingPursuit(
+            n_nonzero_coefs=models_parameters.extracted_forest_size,
+            fit_intercept=True, normalize=False)
+
+        super().__init__(models_parameters, base_forest_estimator)
+
+    def fit_omp(self, atoms, objective):
+        self._omp.fit(atoms, objective)
+
+    def predict(self, X):
+        """
+        Apply the SingleOmpForest to X.
+
+        Make all the base tree predictions then apply the OMP weights for pruning.
+
+        :param X:
+        :return:
+        """
+        forest_predictions = self._base_estimator_predictions(X)
+
+        if self._models_parameters.normalize_D:
+            forest_predictions /= self._forest_norms
+
+        return self._make_omp_weighted_prediction(forest_predictions, self._omp, self._models_parameters.normalize_weights)

code/bolsonaro/models/omp_forest_classifier.py

-from sklearn.base import BaseEstimator
+from bolsonaro.models.omp_forest import OmpForest, SingleOmpForest
+from bolsonaro.utils import binarize_class_data
+
+import numpy as np
 from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import OrthogonalMatchingPursuit
+
+
+class OmpForestBinaryClassifier(SingleOmpForest):
+
+    DEFAULT_SCORE_METRIC = 'indicator'
+
+    def __init__(self, models_parameters):
+        estimator = RandomForestClassifier(**models_parameters.hyperparameters,
+                                           random_state=models_parameters.seed, n_jobs=-1)
+        super().__init__(models_parameters, estimator)
+
+    def _check_classes(self, y):
+        assert len(set(y).difference({-1, 1})) == 0, "Classes for binary classifier must be {-1, +1}"
+
+    def fit(self, X_forest, y_forest, X_omp, y_omp):
+        self._check_classes(y_forest)
+        self._check_classes(y_omp)
+
+        return super().fit(X_forest, y_forest, X_omp, y_omp)
+
+    def score(self, X, y, metric=DEFAULT_SCORE_METRIC):
+        """
+        Evaluate OMPForestClassifer on (`X`, `y`) using `metric`
+
+        :param X:
+        :param y:
+        :param metric: might be "indicator"
+        :return:
+        """
+        predictions = self.predict(X)
+
+        if metric == 'indicator':
+            evaluation = np.abs(np.mean(np.abs(np.sign(predictions) - y) - 1))
+        else:
+            raise ValueError("Unsupported metric '{}'.".format(metric))
+
+        return evaluation
+
+
+class OmpForestMulticlassClassifier(OmpForest):
+
+    DEFAULT_SCORE_METRIC = 'indicator'
+
+    def __init__(self, models_parameters):
+        estimator = RandomForestClassifier(**models_parameters.hyperparameters,
+                                           random_state=models_parameters.seed, n_jobs=-1)
+        super().__init__(models_parameters, estimator)
+        # question: peut-être initialiser les omps dans le __init__? comme pour le SingleOmpForest
+        self._dct_class_omp = {}
+
+    def fit_omp(self, atoms, objective):
+        assert len(self._dct_class_omp) == 0, "fit_omp can be called only once on {}".format(self.__class__.__name__)
+        possible_classes = sorted(set(objective))
+        for class_label in possible_classes:
+            atoms_binary = binarize_class_data(atoms, class_label, inplace=False)
+            objective_binary = binarize_class_data(objective, class_label, inplace=False)
+            # todo peut etre considérer que la taille de forêt est globale et donc seulement une fraction est disponible pour chaque OMP...
+            omp_class = OrthogonalMatchingPursuit(
+                n_nonzero_coefs=self.models_parameters.extracted_forest_size,
+                fit_intercept=True, normalize=False)
+            omp_class.fit(atoms_binary, objective_binary)
+            self._dct_class_omp[class_label] = omp_class
+        return self._dct_class_omp
+
+    def predict(self, X):
+        forest_predictions = self._base_estimator_predictions(X)
+
+        if self._models_parameters.normalize_D:
+            forest_predictions /= self._forest_norms
+
+        label_names = []
+        preds = []
+        for class_label, omp_class in self._dct_class_omp.items():
+            label_names.append(class_label)
+            atoms_binary = binarize_class_data(forest_predictions, class_label, inplace=False)
+            preds.append(self._make_omp_weighted_prediction(atoms_binary, omp_class, self._models_parameters.normalize_weights))
+
+        # todo verifier que ce n'est pas bugué ici
+
+        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)
 
+        if metric == 'indicator':
+            evaluation = np.sum(np.ones_like(predictions)[predictions == y]) / X.shape[0]
+        else:
+            raise ValueError("Unsupported metric '{}'.".format(metric))
 
-class OmpForestClassifier(BaseEstimator):
+        return evaluation
 
-    def __init__(self):
-        raise ValueError('Classification tasks are not supported for now')
 
-    def fit(self, X, y):
-        pass
+if __name__ == "__main__":
+    forest = RandomForestClassifier(n_estimators=10)
+    X = np.random.rand(10, 5)
+    y = np.random.choice([-1, +1], 10)
+    forest.fit(X, y)
+    print(forest.predict(np.random.rand(10, 5)))
\ No newline at end of file

code/bolsonaro/models/omp_forest_regressor.py

-from bolsonaro import LOG_PATH
-from bolsonaro.error_handling.logger_factory import LoggerFactory
+from bolsonaro.models.omp_forest import SingleOmpForest
 
 from sklearn.ensemble import RandomForestRegressor
-from sklearn.linear_model import OrthogonalMatchingPursuit
-from sklearn.base import BaseEstimator
 import numpy as np
 
 
-class OmpForestRegressor(BaseEstimator):
+class OmpForestRegressor(SingleOmpForest):
 
     DEFAULT_SCORE_METRIC = 'mse'
 
     def __init__(self, models_parameters):
-        self._regressor = RandomForestRegressor(n_estimators=models_parameters.forest_size,
-            random_state=models_parameters.seed)
-        self._models_parameters = models_parameters
-        self._logger = LoggerFactory.create(LOG_PATH, __name__)
+        estimator = RandomForestRegressor(**models_parameters.hyperparameters,
+                                          random_state=models_parameters.seed, n_jobs=-1)
 
-    @property
-    def forest(self):
-        return self._forest
-
-    def set_forest(self, forest):
-        self._forest = forest
-        self._regressor.estimators_ = forest
-
-    @property
-    def weights(self):
-        return self._weights
-
-    def set_weights(self, weights):
-        self._weights = weights
-
-    @property
-    def models_parameters(self):
-        return self._models_parameters
-
-    def fit(self, X_forest, y_forest, X_omp, y_omp):
-        self._forest = self._train_forest(X_forest, y_forest)
-        self._weights = self._extract_subforest(X_omp, y_omp)
-        return self
-
-    def score_regressor(self, X, y):
-        return self._regressor.score(X, y)
-
-    def predict(self, X):
-        """
-        Apply the OMPForestRegressor to X.
-
-        :param X:
-        :return:
-        """
-        D = self._forest_prediction(X)
-
-        if self._models_parameters.normalize_D:
-            D /= self._forest_norms
-
-        predictions = D @ self.weights
-
-        return predictions
+        super().__init__(models_parameters, estimator)
 
     def score(self, X, y, metric=DEFAULT_SCORE_METRIC):
         """
@@ -77,41 +31,3 @@ class OmpForestRegressor(BaseEstimator):
             raise ValueError("Unsupported metric '{}'.".format(metric))
 
         return evaluation
-
-    def _train_forest(self, X, y):
-        self._regressor.fit(X, y)
-        forest = self._regressor.estimators_
-        return forest
-    
-    def _extract_subforest(self, X, y):
-        """
-        Given an already estimated regressor: apply OMP to get the weight of each tree.
-
-        The X data is used for interrogation of every tree in the forest. The y data
-        is used for finding the weights in OMP.
-
-        :param X: (n_sample, n_features) array
-        :param y: (n_sample,) array
-        :return:
-        """
-        self._logger.debug("Forest make prediction on X")
-        D = self._forest_prediction(X)
-
-        if self._models_parameters.normalize_D:
-            # question: maybe consider other kinds of normalization
-            self._logger.debug("Compute norm of predicted vectors on X")
-            self._forest_norms = np.linalg.norm(D, axis=0)
-            D /= self._forest_norms
-
-        omp = OrthogonalMatchingPursuit(
-            n_nonzero_coefs=self._models_parameters.extracted_forest_size,
-            fit_intercept=False, normalize=False)
-        self._logger.debug("Apply orthogonal maching pursuit on forest for {} extracted trees."
-                           .format(self._models_parameters.extracted_forest_size))
-        omp.fit(D, y)
-        weights = omp.coef_
-        # question: why not to use directly the omp estimator instead of bypassing it using the coefs?
-        return weights
-
-    def _forest_prediction(self, X):
-        return np.array([tree.predict(X) for tree in self._forest]).T

code/bolsonaro/trainer.py

@@ -8,46 +8,78 @@ import numpy as np
 
 
 class Trainer(object):
+    """
+    Class capable of fitting any model object to some prepared data then evaluate and save results through the `train` method.
+    """
 
     def __init__(self, dataset):
+        """
+
+        :param dataset: Object with X_train, y_train, X_dev, y_dev, X_test and Y_test attributes
+        """
         self._dataset = dataset
         self._logger = LoggerFactory.create(LOG_PATH, __name__)
 
-    def train(self, model, models_dir):
-        self._logger.debug('Training model using train set...')
-        begin_time = time.time()
-
-        if model.models_parameters.use_dev_subset:
-            X_forest = self._dataset.X_train
-            y_forest = self._dataset.y_train
-            X_omp = self._dataset.X_dev
-            y_omp = self._dataset.y_dev
+    def init(self, model):
+        if model.models_parameters.subsets_used == 'train,dev':
+            self._X_forest = self._dataset.X_train
+            self._y_forest = self._dataset.y_train
+            self._X_omp = self._dataset.X_dev
+            self._y_omp = self._dataset.y_dev
             self._logger.debug('Fitting the forest on train subset and OMP on dev subset.')
-        else:
-            X_forest = np.concatenate([self._dataset.X_train, self._dataset.X_dev])
-            X_omp = X_forest
-            y_forest = np.concatenate([self._dataset.y_train, self._dataset.y_dev])
-            y_omp = y_forest
+        elif model.models_parameters.subsets_used == 'train+dev,train+dev':
+            self._X_forest = np.concatenate([self._dataset.X_train, self._dataset.X_dev])
+            self._X_omp = self._X_forest
+            self._y_forest = np.concatenate([self._dataset.y_train, self._dataset.y_dev])
+            self._y_omp = self._y_forest
             self._logger.debug('Fitting both the forest and OMP on train+dev subsets.')
+        elif model.models_parameters.subsets_used == 'train,train+dev':
+            self._X_forest = self._dataset.X_train
+            self._y_forest = self._dataset.y_train
+            self._X_omp = np.concatenate([self._dataset.X_train, self._dataset.X_dev])
+            self._y_omp = np.concatenate([self._dataset.y_train, self._dataset.y_dev])
+        else:
+            raise ValueError("Unknown specified subsets_used parameter '{}'".format(model.models_parameters.subsets_used))
 
+    def train(self, model):
+        """
+        :param model: Object with
+        :return:
+        """
+        
+        self._logger.debug('Training model using train set...')
+        self._begin_time = time.time()
         model.fit(
-            X_forest=X_forest,
-            y_forest=y_forest,
-            X_omp=X_omp,
-            y_omp=y_omp
+            X_forest=self._X_forest,
+            y_forest=self._y_forest,
+            X_omp=self._X_omp,
+            y_omp=self._y_omp
         )
-        end_time = time.time()
+        self._end_time = time.time()
 
-        ModelRawResults(
-            forest=model.forest,
-            weights=model.weights,
-            training_time=end_time - begin_time,
+    def compute_results(self, model, models_dir):
+        """
+        :param model: Object with
+        :param models_dir: Where the results will be saved
+        """
+        results = ModelRawResults(
+            model_object=model,
+            training_time=self._end_time - self._begin_time,
             datetime=datetime.datetime.now(),
             train_score=model.score(self._dataset.X_train, self._dataset.y_train),
             dev_score=model.score(self._dataset.X_dev, self._dataset.y_dev),
             test_score=model.score(self._dataset.X_test, self._dataset.y_test),
             score_metric=model.DEFAULT_SCORE_METRIC, # TODO: resolve the used metric in a proper way
-            train_score_regressor=model.score_regressor(self._dataset.X_train, self._dataset.y_train),
-            dev_score_regressor=model.score_regressor(self._dataset.X_dev, self._dataset.y_dev),
-            test_score_regressor=model.score_regressor(self._dataset.X_test, self._dataset.y_test)
-        ).save(models_dir)
+            train_score_regressor=model.score_base_estimator(self._dataset.X_train, self._dataset.y_train),
+            dev_score_regressor=model.score_base_estimator(self._dataset.X_dev, self._dataset.y_dev),
+            test_score_regressor=model.score_base_estimator(self._dataset.X_test, self._dataset.y_test)
+        )
+        results.save(models_dir)
+        self._logger.info("Base performance on test: {}".format(results.test_score_regressor))
+        self._logger.info("Performance on test: {}".format(results.test_score))
+
+        self._logger.info("Base performance on train: {}".format(results.train_score_regressor))
+        self._logger.info("Performance on train: {}".format(results.train_score))
+
+        self._logger.info("Base performance on dev: {}".format(results.dev_score_regressor))
+        self._logger.info("Performance on dev: {}".format(results.dev_score))

code/bolsonaro/utils.py

 import os
 import json
 import pickle
+from copy import deepcopy
 
 
 def resolve_experiment_id(models_dir):
@@ -45,3 +46,30 @@ def load_obj_from_pickle(file_path, constructor):
     with open(file_path, 'rb') as input_file:
         parameters = pickle.load(input_file)
     return constructor(**parameters)
+
+def binarize_class_data(data, class_pos, inplace=True):
+    """
+    Replace class_pos by +1 and ~class_pos by -1.
+
+    :param data: an array of classes
+    :param class_pos: the positive class to be replaced by +1
+    :param inplace: If True, modify data in place (still return it, also)
+    :return:
+    """
+    if not inplace:
+        data = deepcopy(data)
+
+    position_class_labels = (data == class_pos)
+    data[~(position_class_labels)] = -1
+    data[(position_class_labels)] = +1
+
+    return data
+
+def change_binary_func_load(base_load_function):
+    def func_load(return_X_y):
+        X, y = base_load_function(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])
+        return X, y
+    return func_load

code/bolsonaro/visualization/plotter.py

 import matplotlib.pyplot as plt
 import numpy as np
-from sklearn.neighbors.kde import KernelDensity
+import pandas as pd
 
 
 class Plotter(object):
 
     @staticmethod
-    def weight_density(weights, X, file_path):
-        X_plot = [np.exp(elem) for elem in weights]
-        fig, ax = plt.subplots()
+    def weight_density(all_experiment_weights, file_path):
+        """
+        Function that creates the figure with the density of the weights
+        :param all_experiment_weights: The weights for the different experiments
+        :param file path: str, path where the figure will be saved
+        TODO: colored by seed number or not?
+        TODO: represents both the seed AND the extracted tree information in the legend
+        """
 
-        for kernel in ['gaussian', 'tophat', 'epanechnikov']:
-            kde = KernelDensity(kernel=kernel, bandwidth=0.5).fit(X_plot)
-            log_dens = kde.score_samples(X_plot)
-            ax.plot(X_plot, np.exp(log_dens), '-',
-                    label="kernel = '{0}'".format(kernel))
+        """
+        Convert dictionnary of structure
+        {seed_1:[M x W]], seed_k:[M x W]}
+        to numpy.ndarray with dim [K x M x W]
+        where K is the seed number, M is the
+        number of extracted trees and W the
+        weight number.
+        """
+        all_experiment_weights = np.array(list(all_experiment_weights.values()))
+
+        n = len(all_experiment_weights)
 
-        ax.legend(loc='upper left')
-        ax.plot(X[:, 0], -0.005 - 0.01 * np.random.random(X.shape[0]), '+k')
+        """
+        Get as many different colors from the specified cmap (here nipy_spectral)
+        as there are seeds used.
+        """
+        colors = Plotter.get_colors_from_cmap(n)
+
+        fig, ax = plt.subplots()
+        # For each seed
+        for i in range(n):
+            # For each weight set of a given extracted tree number
+            for weights in all_experiment_weights[i]:
+                """
+                Plot the series of weights that aren't zero,
+                colored by seed number.
+                """
+                pd.Series(weights[np.nonzero(weights)]).plot.kde(
+                    figsize=(15, 10), ax=ax, color=colors[i])
 
-        ax.set_xlim(-4, 9)
-        ax.set_ylim(-0.02, 0.4)
+        ax.set_title('Density weights of the OMP')
         fig.savefig(file_path, dpi=fig.dpi)
         plt.close(fig)
 

code/compute_hyperparameters.py

+from bolsonaro import LOG_PATH
+from bolsonaro.data.dataset_loader import DatasetLoader
+from bolsonaro.data.dataset_parameters import DatasetParameters
+from bolsonaro.data.task import Task
+from bolsonaro.error_handling.logger_factory import LoggerFactory
+from bolsonaro.hyperparameter_searcher import HyperparameterSearcher
+from bolsonaro.utils import save_obj_to_json
+
+import argparse
+import os
+import pathlib
+import pickle
+import random
+from dotenv import find_dotenv, load_dotenv
+
+"""
+I had to install skopt from this repository
+https://github.com/darenr/scikit-optimize that handles
+the issue described here https://github.com/scikit-optimize/scikit-optimize/issues/762.
+"""
+from skopt.space import Categorical, Integer, Real
+
+
+def clean_numpy_int_dict(dictionary):
+    return dict([a, int(x)] if type(x) == Integer else
+                [a, clean_numpy_int_dict(x)] if type(x) == dict else
+                [a, clean_numpy_int_list(x)] if type(x) == list else [a, (x)]
+                for a, x in dictionary.items())
+
+
+def clean_numpy_int_list(list_n):
+    return [int(elem) if type(elem) == Integer else
+            clean_numpy_int_dict(elem) if type(elem) == dict else
+            clean_numpy_int_list(elem) if type(elem) == list else elem
+            for elem in list_n]
+
+
+if __name__ == "__main__":
+    # get environment variables in .env
+    load_dotenv(find_dotenv('.env'))
+
+    DEFAULT_CV = 3
+    DEFAULT_N_ITER = 50
+    DICT_PARAM_SPACE = {'n_estimators': Integer(10, 1000),
+                        'min_samples_leaf': Integer(1, 1000),
+                        'max_depth': Integer(1, 20),
+                        'max_features': Categorical(['auto', 'sqrt', 'log2'], [0.5, 0.25, 0.25])}
+    DATASET_LIST = ['boston', 'iris', 'diabetes']
+    # , 'digits', 'linnerud', 'wine']
+
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument('--cv', nargs='?', type=int, default=DEFAULT_CV, help='Specify the size of the cross-validation.')
+    parser.add_argument('--n_iter', nargs='?', type=int, default=DEFAULT_N_ITER, help='Specify the number of iterations for the bayesian search.')
+    parser.add_argument('--seed', nargs='?', type=int, default=None, help='Specify a seed instead of generate it randomly.')
+    parser.add_argument('--datasets', nargs='+', type=str, default=DATASET_LIST, help='Specify the dataset used by the estimator.')
+    parser.add_argument('--verbose', action='store_true', default=False, help='Print information during the bayesian search.')
+
+    args = parser.parse_args()
+
+    logger = LoggerFactory.create(LOG_PATH, os.path.basename(__file__))
+
+    begin_random_seed_range = 1
+    end_random_seed_range = 2000
+
+    if args.seed is None:
+        random_seed = random.randint(begin_random_seed_range, end_random_seed_range)
+    else:
+        random_seed = args.seed
+
+    for dataset_name in args.datasets:
+
+        dataset_dir = os.path.join('experiments', dataset_name, 'stage1')
+
+        pathlib.Path(dataset_dir).mkdir(parents=True, exist_ok=True)
+
+        logger.info('Bayesian search on dataset {}'.format(dataset_name))
+
+        dataset_parameters = DatasetParameters(dataset_name, test_size=0.2, dev_size=0.01, random_state=random_seed, dataset_normalizer=None)
+        dataset = DatasetLoader.load(dataset_parameters)
+
+        if dataset.task == Task.REGRESSION:
+            scorer = 'neg_mean_squared_error'
+        else:
+            scorer = 'accuracy'
+
+        bayesian_searcher = HyperparameterSearcher()
+        opt = bayesian_searcher.search(dataset, DICT_PARAM_SPACE, args.n_iter,
+            args.cv, random_seed, scorer, args.verbose)
+
+        dict_results = {'_scorer': scorer,
+                        '_best_score_train': opt.best_score_,
+                        '_best_score_test': opt.score(dataset.X_test, dataset.y_test),
+                        '_best_parameters': clean_numpy_int_dict(opt.best_params_),
+                        '_random_seed': random_seed
+                        }
+
+        save_obj_to_json(os.path.join(dataset_dir, 'params.json'), dict_results)