diff --git a/.env.example b/.env.example
deleted file mode 100644
index 9ca543b382b4889be1d93ed2065ef234b789153c..0000000000000000000000000000000000000000
--- a/.env.example
+++ /dev/null
@@ -1,12 +0,0 @@
-# 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
diff --git a/.gitignore b/.gitignore
index 5d8638d3bfb7c3a28756fe87a9cc84aed2cdb5e7..ed07278aa03dbf293f143b22d927fa9f08876edb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -371,3 +371,6 @@ TSWLatexianTemp*
*.lpz
reports/*.pdf
+
+# Image
+*.png
diff --git a/TODO.md b/TODO.md
index bfb32e8a131b5147b36c9ccba729a6e13e04e5b7..5ea6cc5cf2c933eed2e7ffbf2567d4fe812412cf 100644
--- a/TODO.md
+++ b/TODO.md
@@ -1,8 +1,7 @@
-* 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
diff --git a/code/bolsonaro/data/__init__.py b/code/bolsonaro/data/__init__.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..ce8e424d0cc66e3f349fcafd744216d818bacaa5 100644
--- a/code/bolsonaro/data/__init__.py
+++ b/code/bolsonaro/data/__init__.py
@@ -0,0 +1,3 @@
+import os
+
+LOG_PATH = os.path.abspath(os.path.dirname(__file__) + os.sep + '..' + os.sep + '..' + os.sep + 'log')
diff --git a/code/bolsonaro/data/dataset.py b/code/bolsonaro/data/dataset.py
index b2f67c489be94791397649ba292a49f65dd604d8..7108eb5781e1ef2b69926e9fd2239deaa81f44e2 100644
--- a/code/bolsonaro/data/dataset.py
+++ b/code/bolsonaro/data/dataset.py
@@ -1,9 +1,8 @@
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
diff --git a/code/bolsonaro/data/dataset_loader.py b/code/bolsonaro/data/dataset_loader.py
index d706b7a07751f715b6398b1f451ec9f337d00f60..b22ecaa83f6e69610c82796e068e91db23e83646 100644
--- a/code/bolsonaro/data/dataset_loader.py
+++ b/code/bolsonaro/data/dataset_loader.py
@@ -1,7 +1,9 @@
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)
diff --git a/code/bolsonaro/data/dataset_parameters.py b/code/bolsonaro/data/dataset_parameters.py
index 9854a75eb27ec83990b8ae032c58e7dec52a5e8c..88054257d4241ae0426c16ebaca1cb4985c3b65f 100644
--- a/code/bolsonaro/data/dataset_parameters.py
+++ b/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
diff --git a/code/bolsonaro/data/task.py b/code/bolsonaro/data/task.py
index 2f47fa22f472f769c075f40e1c25a7bf3de45f0d..f1214a64a27873e49f5dbbcb853e4f65f9b07f68 100644
--- a/code/bolsonaro/data/task.py
+++ b/code/bolsonaro/data/task.py
@@ -2,5 +2,6 @@ from enum import Enum
class Task(Enum):
- CLASSIFICATION = 1
+ BINARYCLASSIFICATION = 1
REGRESSION = 2
+ MULTICLASSIFICATION = 3
diff --git a/code/bolsonaro/hyperparameter_searcher.py b/code/bolsonaro/hyperparameter_searcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..7884d2d4271203e9ebee1e804baa7c1e94a76770
--- /dev/null
+++ b/code/bolsonaro/hyperparameter_searcher.py
@@ -0,0 +1,47 @@
+'''
+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
diff --git a/code/bolsonaro/models/__init__.py b/code/bolsonaro/models/__init__.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..ce8e424d0cc66e3f349fcafd744216d818bacaa5 100644
--- a/code/bolsonaro/models/__init__.py
+++ b/code/bolsonaro/models/__init__.py
@@ -0,0 +1,3 @@
+import os
+
+LOG_PATH = os.path.abspath(os.path.dirname(__file__) + os.sep + '..' + os.sep + '..' + os.sep + 'log')
diff --git a/code/bolsonaro/models/model_factory.py b/code/bolsonaro/models/model_factory.py
index fb6b32cb26727d2221367f208598f04e1a19dfb1..a93e6090e253dc9bdb3aacfc53e1c99a1f9ef120 100644
--- a/code/bolsonaro/models/model_factory.py
+++ b/code/bolsonaro/models/model_factory.py
@@ -1,4 +1,4 @@
-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
diff --git a/code/bolsonaro/models/model_parameters.py b/code/bolsonaro/models/model_parameters.py
index 450d97bc01fe8f02ccd399d2c47a0c7397b4cb13..31a451b70578835fe4663508de9e15f99bf6cc19 100644
--- a/code/bolsonaro/models/model_parameters.py
+++ b/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__)
diff --git a/code/bolsonaro/models/model_raw_results.py b/code/bolsonaro/models/model_raw_results.py
index 673cb0fc65b7378e95c03b186d246cb70b384a07..df8b2ec0b10704a8a8c397b9012298e8b901e14b 100644
--- a/code/bolsonaro/models/model_raw_results.py
+++ b/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):
diff --git a/code/bolsonaro/models/omp_forest.py b/code/bolsonaro/models/omp_forest.py
new file mode 100644
index 0000000000000000000000000000000000000000..2da0beab64ef5361efbde6d6197f957fe627886c
--- /dev/null
+++ b/code/bolsonaro/models/omp_forest.py
@@ -0,0 +1,121 @@
+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)
diff --git a/code/bolsonaro/models/omp_forest_classifier.py b/code/bolsonaro/models/omp_forest_classifier.py
index 12cc23fab69fc0b79ff40b1d6957db5532a8c452..128347aa61caf79dc908397ef0588d646d8b0dee 100644
--- a/code/bolsonaro/models/omp_forest_classifier.py
+++ b/code/bolsonaro/models/omp_forest_classifier.py
@@ -1,11 +1,106 @@
-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
diff --git a/code/bolsonaro/models/omp_forest_regressor.py b/code/bolsonaro/models/omp_forest_regressor.py
index 50754246abc13e0282e4cbd6aa1e917a0e3544ed..a0c8b4708d52336bf39544ffd0b66c527466620a 100644
--- a/code/bolsonaro/models/omp_forest_regressor.py
+++ b/code/bolsonaro/models/omp_forest_regressor.py
@@ -1,64 +1,18 @@
-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
diff --git a/code/bolsonaro/trainer.py b/code/bolsonaro/trainer.py
index 01d0a036c8058cb55f947c0d80645b5619da0cdb..4a32bffb8c7a2e129bf6f010c8a2c3a339a53a4b 100644
--- a/code/bolsonaro/trainer.py
+++ b/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))
diff --git a/code/bolsonaro/utils.py b/code/bolsonaro/utils.py
index 82e501878ba06320914230096213d2d28548e4dc..d7509ad9e85cde3cc0c649f85cfb5b60ead9a854 100644
--- a/code/bolsonaro/utils.py
+++ b/code/bolsonaro/utils.py
@@ -1,6 +1,7 @@
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
diff --git a/code/bolsonaro/visualization/plotter.py b/code/bolsonaro/visualization/plotter.py
index 9d7058732970fb5981ef04ce7a56e022ee68d5a9..0d5706bc27cb0745fe065456231b7e3023707ac9 100644
--- a/code/bolsonaro/visualization/plotter.py
+++ b/code/bolsonaro/visualization/plotter.py
@@ -1,26 +1,51 @@
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)
diff --git a/code/compute_hyperparameters.py b/code/compute_hyperparameters.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f7aa3a666d61618a3a5d50b1de8e996c235034c
--- /dev/null
+++ b/code/compute_hyperparameters.py
@@ -0,0 +1,97 @@
+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)
diff --git a/code/compute_results.py b/code/compute_results.py
index fb09e42d3fc6829358b622ad4291bd064a9c65a3..64124af70954cc6af6a923f03f5a122a75f453fb 100644
--- a/code/compute_results.py
+++ b/code/compute_results.py
@@ -12,7 +12,7 @@ import os
if __name__ == "__main__":
# get environment variables in .env
- load_dotenv(find_dotenv('.env.example'))
+ load_dotenv(find_dotenv('.env'))
DEFAULT_RESULTS_DIR = os.environ["project_dir"] + os.sep + 'results'
DEFAULT_MODELS_DIR = os.environ["project_dir"] + os.sep + 'models'
@@ -59,6 +59,8 @@ if __name__ == "__main__":
experiment_dev_scores = dict()
experiment_test_scores = dict()
+ experiment_weights = dict()
+
# Used to check if all losses were computed using the same metric (it should be the case)
experiment_score_metrics = list()
@@ -74,6 +76,8 @@ if __name__ == "__main__":
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_size
extracted_forest_sizes = os.listdir(extracted_forest_size_root_path)
for extracted_forest_size in extracted_forest_sizes:
@@ -84,9 +88,13 @@ if __name__ == "__main__":
# Load [...]/model_parameters.json file and build the model using these parameters and the weights and forest from model_raw_results.pickle
model = ModelFactory.load(dataset.task, extracted_forest_size_path, experiment_id, model_raw_results)
# Save temporarly some raw results (TODO: to complete to retreive more results)
+ # Save the scores
experiment_train_scores[seed].append(model_raw_results.train_score)
experiment_dev_scores[seed].append(model_raw_results.dev_score)
experiment_test_scores[seed].append(model_raw_results.test_score)
+ # Save the weights
+ experiment_weights[seed].append(model_raw_results.weights)
+ # Save the metric
experiment_score_metrics.append(model_raw_results.score_metric)
if len(set(experiment_score_metrics)) > 1:
@@ -107,3 +115,48 @@ if __name__ == "__main__":
all_labels=['train', 'dev', 'test'],
title='Loss values of the trained model'
)
+
+ """
+ TODO:
+ For each dataset:
+ Stage 1) A figure for the selection of the best base forest model hyperparameters (best vs default/random hyperparams)
+ Stage 2) A figure for the selection of the best dataset normalization method
+ Stage 3) A figure for the selection of the best combination of dataset: normalization vs D normalization vs weights normalization
+ Stage 4) A figure for the selection of the most relevant subsets combination: train,dev vs train+dev,train+dev vs train,train+dev
+ Stage 5) A figure for the selection of the best extracted forest size?
+ Stage 6) A figure to finally compare the perf of our approach using the previous selected parameters vs the baseline vs other papers
+
+ Stage 3)
+ In all axis:
+ - untrained forest
+ - trained base forest (straight line cause it doesn't depend on the number of extracted trees)
+
+ Axis 1:
+ - test with forest on train+dev and OMP on train+dev
+ - test with forest on train+dev and OMP on train+dev with dataset normalization
+ - test with forest on train+dev and OMP on train+dev with dataset normalization + D normalization
+ - test with forest on train+dev and OMP on train+dev with dataset normalization + weights normalization
+ - test with forest on train+dev and OMP on train+dev with dataset normalization + D normalization + weights normalization
+
+ Axis 2:
+ - test with forest on train and OMP on dev
+ - test with forest on train and OMP on dev with dataset normalization
+ - test with forest on train and OMP on dev with dataset normalization + D normalization
+ - test with forest on train and OMP on dev with dataset normalization + weights normalization
+ - test with forest on train and OMP on dev with dataset normalization + D normalization + weights normalization
+
+ Axis 3:
+ - test with forest on train and OMP train+dev
+ - test with forest on train and OMP train+dev with dataset normalization
+ - test with forest on train and OMP train+dev with dataset normalization + D normalization
+ - test with forest on train and OMP train+dev with dataset normalization + weights normalization
+ - test with forest on train and OMP train+dev with dataset normalization + D normalization + weights normalization
+
+ IMPORTANT: Same seeds used in all axis.
+ """
+
+ # Plot the density of the weights
+ Plotter.weight_density(
+ file_path=args.results_dir + os.sep + experiment_id + os.sep + 'density_weight.png',
+ all_experiment_weights=experiment_weights
+ )
diff --git a/code/train.py b/code/train.py
index 5783fef4bd32d75f62f5dcf05c9812e82f9c0338..0d9713252b0e5e2345331952edaca6adfa5424c0 100644
--- a/code/train.py
+++ b/code/train.py
@@ -9,16 +9,75 @@ from bolsonaro.error_handling.logger_factory import LoggerFactory
from dotenv import find_dotenv, load_dotenv
import argparse
+import json
import pathlib
import random
import os
-from tqdm import tqdm
+from concurrent import futures
+import threading
+import json
+def process_job(seed, parameters, experiment_id, hyperparameters):
+ """
+ Experiment function.
+
+ Will be used as base function for worker in multithreaded application.
+
+ :param seed:
+ :param parameters:
+ :param experiment_id:
+ :return:
+ """
+ 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)
+ models_dir = parameters['models_dir'] + os.sep + experiment_id_str + os.sep + 'seeds' + \
+ os.sep + seed_str
+ pathlib.Path(models_dir).mkdir(parents=True, exist_ok=True)
+
+ dataset_parameters = DatasetParameters(
+ name=parameters['dataset_name'],
+ test_size=parameters['test_size'],
+ dev_size=parameters['dev_size'],
+ random_state=seed,
+ dataset_normalizer=parameters['dataset_normalizer']
+ )
+ dataset_parameters.save(models_dir, experiment_id_str)
+ dataset = DatasetLoader.load(dataset_parameters)
+
+ trainer = Trainer(dataset)
+
+ for extracted_forest_size in parameters['extracted_forest_size']:
+ # question if training is too long, one may also split experiments for different forest sizes into different workers
+ logger.info('extracted_forest_size={}'.format(extracted_forest_size))
+ sub_models_dir = models_dir + os.sep + 'extracted_forest_size' + os.sep + str(extracted_forest_size)
+ pathlib.Path(sub_models_dir).mkdir(parents=True, exist_ok=True)
+
+ model_parameters = ModelParameters(
+ extracted_forest_size=extracted_forest_size,
+ normalize_D=parameters['normalize_D'],
+ subsets_used=parameters['subsets_used'],
+ normalize_weights=parameters['normalize_weights'],
+ seed=seed,
+ hyperparameters=hyperparameters
+ )
+ model_parameters.save(sub_models_dir, experiment_id)
+
+ model = ModelFactory.build(dataset.task, model_parameters)
+
+ trainer.init(model)
+ trainer.train(model)
+ trainer.compute_results(model, sub_models_dir)
+ logger.info('Training done')
+
if __name__ == "__main__":
- # get environment variables in .env
- load_dotenv(find_dotenv('.env.example'))
+ load_dotenv(find_dotenv('.env'))
+ DEFAULT_EXPERIMENT_CONFIGURATION_PATH = 'experiments'
DEFAULT_DATASET_NAME = 'boston'
DEFAULT_NORMALIZE_D = False
DEFAULT_DATASET_NORMALIZER = None
@@ -29,13 +88,15 @@ if __name__ == "__main__":
DEFAULT_DEV_SIZE = 0.2
DEFAULT_TEST_SIZE = 0.2
DEFAULT_RANDOM_SEED_NUMBER = 1
- DEFAULT_USE_DEV_SUBSET = False
- DEFAULT_DISABLE_PROGRESS = False
+ DEFAULT_SUBSETS_USED = 'train,dev'
+ DEFAULT_NORMALIZE_WEIGHTS = False
begin_random_seed_range = 1
end_random_seed_range = 2000
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ parser.add_argument('--experiment_configuration', nargs='?', type=str, default=None, help='Specify an experiment configuration file name. Overload all other parameters.')
+ parser.add_argument('--experiment_configuration_path', nargs='?', type=str, default=DEFAULT_EXPERIMENT_CONFIGURATION_PATH, help='Specify the experiment configuration directory path.')
parser.add_argument('--dataset_name', nargs='?', type=str, default=DEFAULT_DATASET_NAME, help='Specify the dataset. Regression: boston, diabetes, linnerud, california_housing. Classification: iris, digits, wine, breast_cancer, olivetti_faces, 20newsgroups, 20newsgroups_vectorized, lfw_people, lfw_pairs, covtype, rcv1, kddcup99.')
parser.add_argument('--normalize_D', action='store_true', default=DEFAULT_NORMALIZE_D, help='Specify if we want to normalize the prediction of the forest by doing the L2 division of the pred vectors.')
parser.add_argument('--dataset_normalizer', nargs='?', type=str, default=DEFAULT_DATASET_NORMALIZER, help='Specify which dataset normalizer use (either standard, minmax, robust or normalizer).')
@@ -46,66 +107,64 @@ if __name__ == "__main__":
parser.add_argument('--test_size', nargs='?', type=float, default=DEFAULT_TEST_SIZE, help='Test subset ratio.')
parser.add_argument('--random_seed_number', nargs='?', type=int, default=DEFAULT_RANDOM_SEED_NUMBER, help='Number of random seeds used.')
parser.add_argument('--seeds', nargs='+', type=int, default=None, help='Specific a list of seeds instead of generate them randomly')
- parser.add_argument('--use_dev_subset', action='store_true', default=DEFAULT_USE_DEV_SUBSET, help='If specify the forest will be trained on train subset and OMP on dev subset. Otherwise both the forest and OMP will be trained on train+dev subsets.')
- parser.add_argument('--disable_progress', action='store_true', default=DEFAULT_DISABLE_PROGRESS, help='Disable the progress bars.')
+ parser.add_argument('--subsets_used', nargs='+', type=str, default=DEFAULT_SUBSETS_USED, help='train,dev: forest on train, OMP on dev. train+dev,train+dev: both forest and OMP on train+dev. train,train+dev: forest on train+dev and OMP on dev.')
+ parser.add_argument('--normalize_weights', action='store_true', default=DEFAULT_NORMALIZE_WEIGHTS, help='Divide the predictions by the weights sum.')
args = parser.parse_args()
- pathlib.Path(args.models_dir).mkdir(parents=True, exist_ok=True)
+ if args.experiment_configuration:
+ with open(args.experiment_configuration_path + os.sep + \
+ args.experiment_configuration + '.json', 'r') as input_file:
+ parameters = json.load(input_file)
+ else:
+ parameters = args.__dict__
+
+ pathlib.Path(parameters['models_dir']).mkdir(parents=True, exist_ok=True)
logger = LoggerFactory.create(LOG_PATH, os.path.basename(__file__))
- args.extracted_forest_size = args.extracted_forest_size \
- if type(args.extracted_forest_size) == list \
- else [args.extracted_forest_size]
+ # The number of tree to extract from forest (K)
+ parameters['extracted_forest_size'] = parameters['extracted_forest_size'] \
+ if type(parameters['extracted_forest_size']) == list \
+ else [parameters['extracted_forest_size']]
- if args.seeds != None and args.random_seed_number > 1:
- logger.warning('seeds and random_seed_number parameters are both specified. Seeds will be used.')
+ hyperparameters_path = os.path.join('experiments', args.dataset_name, 'stage1', 'params.json')
+ if os.path.exists(hyperparameters_path):
+ logger.info("Hyperparameters found for this dataset at '{}'".format(hyperparameters_path))
+ with open(hyperparameters_path, 'r+') as file_hyperparameter:
+ hyperparameters = json.load(file_hyperparameter)['best_parameters']
+ else:
+ hyperparameters = {}
- seeds = args.seeds if args.seeds is not None \
- else [random.randint(begin_random_seed_range, end_random_seed_range) \
- for i in range(args.random_seed_number)]
+ if parameters['forest_size'] is not None:
+ hyperparameters['n_estimators'] = parameters['forest_size']
- experiment_id = resolve_experiment_id(args.models_dir)
- experiment_id_str = str(experiment_id)
+ 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.')
- logger.info('Experiment id: {}'.format(experiment_id_str))
-
- with tqdm(seeds, disable=args.disable_progress) as seed_bar:
- for seed in seed_bar:
- seed_bar.set_description('seed={}'.format(seed))
- seed_str = str(seed)
- models_dir = args.models_dir + os.sep + experiment_id_str + os.sep + 'seeds' + \
- os.sep + seed_str
- pathlib.Path(models_dir).mkdir(parents=True, exist_ok=True)
-
- dataset_parameters = DatasetParameters(
- name=args.dataset_name,
- test_size=args.test_size,
- dev_size=args.dev_size,
- random_state=seed,
- dataset_normalizer=args.dataset_normalizer
+ # Seeds are either provided as parameters or generated at random
+ seeds = parameters['seeds'] if parameters['seeds'] is not None \
+ else [random.randint(begin_random_seed_range, end_random_seed_range) \
+ for i in range(parameters['random_seed_number'])]
+
+ # Resolve the next experiment id number (last id + 1)
+ experiment_id = resolve_experiment_id(parameters['models_dir'])
+ logger.info('Experiment id: {}'.format(experiment_id))
+
+ """
+ If the experiment configuration isn't coming from
+ an already existing file, save it to a json file to
+ keep trace of it.
+ """
+ if args.experiment_configuration is None:
+ with open(args.experiment_configuration_path + os.sep + 'unnamed_{}.json'.format(
+ experiment_id), 'w') as output_file:
+ json.dump(
+ parameters,
+ output_file,
+ indent=4
)
- dataset_parameters.save(models_dir, experiment_id_str)
-
- dataset = DatasetLoader.load(dataset_parameters)
-
- trainer = Trainer(dataset)
-
- with tqdm(args.extracted_forest_size, disable=args.disable_progress) as extracted_forest_size_bar:
- for extracted_forest_size in extracted_forest_size_bar:
- extracted_forest_size_bar.set_description('extracted_forest_size={}'.format(extracted_forest_size))
- sub_models_dir = models_dir + os.sep + 'extracted_forest_size' + os.sep + str(extracted_forest_size)
- pathlib.Path(sub_models_dir).mkdir(parents=True, exist_ok=True)
-
- model_parameters = ModelParameters(
- forest_size=args.forest_size,
- extracted_forest_size=extracted_forest_size,
- normalize_D=args.normalize_D,
- use_dev_subset=args.use_dev_subset,
- seed=seed
- )
- model_parameters.save(sub_models_dir, experiment_id)
-
- model = ModelFactory.build(dataset.task, model_parameters)
- trainer.train(model, sub_models_dir)
+ # Train as much job as there are seeds
+ with futures.ProcessPoolExecutor(len(seeds)) as executor:
+ list(f.result() for f in futures.as_completed(executor.submit(process_job, seed,
+ parameters, experiment_id, hyperparameters) for seed in seeds))
diff --git a/experiments/.gitkeep b/experiments/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/experiments/boston/stage1/params.json b/experiments/boston/stage1/params.json
index 6a5a1e9a05d8f081af6abe38fa0aadfff1e736b8..ad9a695df71e3bf329b4d7b30094329daf6c6b4f 100644
--- a/experiments/boston/stage1/params.json
+++ b/experiments/boston/stage1/params.json
@@ -1,5 +1,6 @@
{
"scorer": "neg_mean_squared_error",
+<<<<<<< HEAD
"best_score_train": -11.238253315624897,
"best_score_test": -7.312532120669678,
"best_parameters": {
@@ -9,4 +10,15 @@
"n_estimators": 1000
},
"random_seed": 289
+=======
+ "best_score_train": -12.900217003727361,
+ "best_score_test": -12.682938620298733,
+ "best_parameters": {
+ "max_depth": 18,
+ "max_features": "sqrt",
+ "min_samples_leaf": 1,
+ "n_estimators": 1000
+ },
+ "random_seed": 883
+>>>>>>> a0f7c96f51b3e1575f6db1b704579b0cf1042c42
}
\ No newline at end of file
diff --git a/experiments/iris/stage1/params.json b/experiments/iris/stage1/params.json
new file mode 100644
index 0000000000000000000000000000000000000000..fd852cace9852ee492649374e915b639fe785b28
--- /dev/null
+++ b/experiments/iris/stage1/params.json
@@ -0,0 +1,12 @@
+{
+ "scorer": "accuracy",
+ "best_score_train": 0.9576271186440678,
+ "best_score_test": 1.0,
+ "best_parameters": {
+ "max_depth": 20,
+ "max_features": "log2",
+ "min_samples_leaf": 1,
+ "n_estimators": 1000
+ },
+ "random_seed": 883
+}
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 437c0178801cca15b39561318f6e244c15b34533..d585159632442f7293f858e1fad391031658d60a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,6 +9,7 @@ awscli
flake8
python-dotenv>=0.5.1
scikit-learn
+git+git://github.com/darenr/scikit-optimize@master
python-dotenv
-tqdm
-matplotlib
\ No newline at end of file
+matplotlib
+pandas
\ No newline at end of file