diff --git a/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py b/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
index 719501d394bdae7ea36433052e98abd02e37cf6b..0e1cd183b0e19ce4bf5b1d0dd015cce845376388 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py
@@ -63,7 +63,9 @@ def exec_monoview(directory, X, Y, database_name, labels_names, classification_i
X, \
learningRate, \
labelsString, \
- output_file_name = init_constants(args, X, classification_indices,
+ output_file_name,\
+ directory,\
+ base_file_name = init_constants(args, X, classification_indices,
labels_names,
database_name, directory, view_name, )
logging.debug("Done:\t Loading data")
@@ -143,7 +145,8 @@ def exec_monoview(directory, X, Y, database_name, labels_names, classification_i
class_label_names=labels_names,
train_pred=train_pred,
test_pred=test_pred,
- directory=output_file_name,
+ directory=directory,
+ base_file_name=base_file_name,
labels=Y,
database_name=database_name,
nb_cores=nb_cores,
@@ -175,11 +178,11 @@ def init_constants(args, X, classification_indices, labels_names,
len(classification_indices[0]) + len(classification_indices[1]))
labels_string = "-".join(labels_names)
cl_type_string = cl_type
- output_file_name = os.path.join(directory, cl_type_string, view_name,
- cl_type_string + '-' + name + "-" +
- view_name + "-")
+ directory = os.path.join(directory, cl_type_string, view_name,)
+ base_file_name = cl_type_string + '-' + name + "-" + view_name + "-"
+ output_file_name = os.path.join(directory, base_file_name)
secure_file_path(output_file_name)
- return kwargs, t_start, view_name, cl_type, X, learning_rate, labels_string, output_file_name
+ return kwargs, t_start, view_name, cl_type, X, learning_rate, labels_string, output_file_name, directory, base_file_name
def init_train_test(X, Y, classification_indices):
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py b/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
index 4af2ee3cbaeaf6443b1ce0d7349465c82b49c4f8..84540ab38d984bdafba615f235477e690d0a1ce4 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview/monoview_utils.py
@@ -1,4 +1,5 @@
import pickle
+import os
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import FuncFormatter
@@ -113,7 +114,7 @@ class CustomUniform:
class BaseMonoviewClassifier(BaseClassifier):
- def get_feature_importance(self, directory, nb_considered_feats=50):
+ def get_feature_importance(self, directory, base_file_name, nb_considered_feats=50):
"""Used to generate a graph and a pickle dictionary representing
feature importances"""
feature_importances = self.feature_importances_
@@ -127,7 +128,8 @@ class BaseMonoviewClassifier(BaseClassifier):
ax.yaxis.set_major_formatter(formatter)
plt.bar(x, feature_importances_sorted)
plt.title("Importance depending on feature")
- fig.savefig(directory + "feature_importances.png", transparent=True)
+ fig.savefig(os.path.join(directory, base_file_name + "feature_importances.png")
+ , transparent=True)
plt.close()
features_importances_dict = dict((featureIndex, featureImportance)
for featureIndex, featureImportance in
@@ -207,12 +209,12 @@ class MonoviewResultAnalyzer(ResultAnalyser):
def __init__(self, view_name, classifier_name, shape, classifier,
classification_indices, k_folds, hps_method, metrics_list,
n_iter, class_label_names, train_pred, test_pred,
- directory, labels, database_name, nb_cores, duration):
+ directory, base_file_name, labels, database_name, nb_cores, duration):
ResultAnalyser.__init__(self, classifier, classification_indices,
k_folds, hps_method, metrics_list, n_iter,
class_label_names, train_pred, test_pred,
- directory, labels, database_name, nb_cores,
- duration)
+ directory, base_file_name, labels,
+ database_name, nb_cores, duration)
self.view_name = view_name
self.classifier_name = classifier_name
self.shape = shape
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
index 9d36b968ad0a93ba7dd881a51192a928ef5b7e04..8f5e55a376582a90b784354408b43d5e38553f30 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/adaboost.py
@@ -1,4 +1,5 @@
import time
+import os
import numpy as np
from sklearn.ensemble import AdaBoostClassifier
@@ -129,9 +130,9 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
[step_pred for step_pred in self.staged_predict(X)])
return pred
- def get_interpretation(self, directory, y_test, multi_class=False):
+ def get_interpretation(self, directory, base_file_name, y_test, multi_class=False):
interpretString = ""
- interpretString += self.get_feature_importance(directory)
+ interpretString += self.get_feature_importance(directory, base_file_name)
interpretString += "\n\n Estimator error | Estimator weight\n"
interpretString += "\n".join(
[str(error) + " | " + str(weight / sum(self.estimator_weights_)) for
@@ -141,11 +142,13 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
[self.plotted_metric.score(y_test, step_pred) for step_pred in
self.step_predictions])
get_accuracy_graph(step_test_metrics, "Adaboost",
- directory + "test_metrics.png",
+ os.path.join(directory, base_file_name +"test_metrics.png"),
self.plotted_metric_name, set="test")
- np.savetxt(directory + "test_metrics.csv", step_test_metrics,
+ np.savetxt(os.path.join(directory, base_file_name + "test_metrics.csv"),
+ step_test_metrics,
delimiter=',')
- np.savetxt(directory + "train_metrics.csv", self.metrics, delimiter=',')
- np.savetxt(directory + "times.csv",
+ np.savetxt(os.path.join(directory, base_file_name + "train_metrics.csv"),
+ self.metrics, delimiter=',')
+ np.savetxt(os.path.join(directory, base_file_name + "times.csv"),
np.array([self.train_time, self.pred_time]), delimiter=',')
return interpretString
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/decision_tree.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/decision_tree.py
index 7510971311b0c3162f4888e718552962984d0434..be9f5e24716345660437eda3eb44476a982af04d 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/decision_tree.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/decision_tree.py
@@ -27,9 +27,10 @@ class DecisionTree(DecisionTreeClassifier, BaseMonoviewClassifier):
["best", "random"], [random_state]]
self.weird_strings = {}
- def get_interpretation(self, directory, y_test):
+ def get_interpretation(self, directory, base_file_name, y_test,
+ multiclass=False):
interpretString = "First featrue : \n\t{} <= {}\n".format(
self.tree_.feature[0],
self.tree_.threshold[0])
- interpretString += self.get_feature_importance(directory)
+ interpretString += self.get_feature_importance(directory, base_file_name)
return interpretString
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py
index 4a3cae43e94f06ef4bf82d4ec4eeebfe3fbddb36..fc1f7c37b774debe568a15b6bab99e4f9089e354 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/gradient_boosting.py
@@ -1,4 +1,5 @@
import time
+import os
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
@@ -70,12 +71,12 @@ class GradientBoosting(GradientBoostingClassifier, BaseMonoviewClassifier):
[step_pred for step_pred in self.staged_predict(X)])
return pred
- def get_interpretation(self, directory, y_test, multi_class=False):
+ def get_interpretation(self, directory, base_file_name, y_test, multi_class=False):
interpretString = ""
if multi_class:
return interpretString
else:
- interpretString += self.get_feature_importance(directory)
+ interpretString += self.get_feature_importance(directory, base_file_name)
step_test_metrics = np.array(
[self.plotted_metric.score(y_test, step_pred) for step_pred in
self.step_predictions])
@@ -85,11 +86,11 @@ class GradientBoosting(GradientBoostingClassifier, BaseMonoviewClassifier):
get_accuracy_graph(self.metrics, "AdaboostClassic",
directory + "metrics.png",
self.plotted_metric_name)
- np.savetxt(directory + "test_metrics.csv", step_test_metrics,
+ np.savetxt(os.path.join(directory, base_file_name + "test_metrics.csv"), step_test_metrics,
delimiter=',')
- np.savetxt(directory + "train_metrics.csv", self.metrics,
+ np.savetxt(os.path.join(directory, base_file_name + "train_metrics.csv"), self.metrics,
delimiter=',')
- np.savetxt(directory + "times.csv",
+ np.savetxt(os.path.join(directory, base_file_name + "times.csv"),
np.array([self.train_time, self.pred_time]),
delimiter=',')
return interpretString
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/knn.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/knn.py
index 01dfb1bb379a6850f0e2751cd8e2dab6ebc874f3..f3631bf6b7f281b65b0318028cbbebab89604a10 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/knn.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/knn.py
@@ -40,7 +40,3 @@ class KNN(KNeighborsClassifier, BaseMonoviewClassifier):
[random_state]]
self.weird_strings = {}
self.random_state = random_state
-
- def get_interpretation(self, directory, y_test, multiclass=False):
- interpretString = ""
- return interpretString
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/lasso.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/lasso.py
index 1b93ec2c7185b2ab60bf4400e568b0b8253c5397..c91d2355759867e18375b38d500b79439e23adc2 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/lasso.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/lasso.py
@@ -71,21 +71,4 @@ class Lasso(LassoSK, BaseMonoviewClassifier):
prediction = LassoSK.predict(self, X)
signed = np.sign(prediction)
signed[np.where(signed == -1)] = 0
- return signed
-
- def get_interpretation(self, directory, y_test, multiclass=False):
- """
- return the interpreted string
-
- Parameters
- ----------
- directory :
-
- y_test :
-
- Returns
- -------
- interpreted string, str interpret_string
- """
- interpret_string = ""
- return interpret_string
+ return signed
\ No newline at end of file
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py
index 06bb25af3a61617e529ab43bb745165b7303e877..18e2bc4c3577156b002fec24bfcbf70594ae0aa0 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/random_forest.py
@@ -65,7 +65,7 @@ class RandomForest(RandomForestClassifier, BaseMonoviewClassifier):
["gini", "entropy"], [random_state]]
self.weird_strings = {}
- def get_interpretation(self, directory, y_test, multiclass=False):
+ def get_interpretation(self, directory, base_file_name, y_test, multiclass=False):
"""
Parameters
@@ -78,5 +78,5 @@ class RandomForest(RandomForestClassifier, BaseMonoviewClassifier):
string for interpretation interpret_string
"""
interpret_string = ""
- interpret_string += self.get_feature_importance(directory)
+ interpret_string += self.get_feature_importance(directory, base_file_name)
return interpret_string
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/sgd.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/sgd.py
index 7318f9677e63ceaa266b7f18188a119693b25f89..1cbcec740b2d83cf1d57f2d4b09fa19f87c9f990 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/sgd.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/sgd.py
@@ -54,20 +54,3 @@ class SGD(SGDClassifier, BaseMonoviewClassifier):
["l1", "l2", "elasticnet"],
CustomUniform(loc=0, state=1), [random_state]]
self.weird_strings = {}
-
- def get_interpretation(self, directory, y_test, multiclass=False):
- """
-
- Parameters
- ----------
- directory
-
- y_test
-
- Returns
- -------
- interpret_string str to interpreted
- """
- interpret_string = ""
- # self.feature_importances_ = (self.coef_/np.sum(self.coef_)).reshape(self.coef_.shape[1])
- return interpret_string
diff --git a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_linear.py b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_linear.py
index 1c188b2bd60323de56f558501e77d6100e60903a..d7900a7f092705863c33b9aaa5280d4db7dd57c9 100644
--- a/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_linear.py
+++ b/multiview_platform/mono_multi_view_classifiers/monoview_classifiers/svm_linear.py
@@ -34,8 +34,3 @@ class SVMLinear(SVCClassifier, BaseMonoviewClassifier):
)
self.param_names = ["C", "random_state"]
self.distribs = [CustomUniform(loc=0, state=1), [random_state]]
-
- def get_interpretation(self, directory, y_test):
- interpret_string = ""
- # self.feature_importances_ = (self.coef_/np.sum(self.coef_)).reshape((self.coef_.shape[1],))
- return interpret_string
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py b/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
index 9de4e48deb2733476e50b12c1ad937426df5db9a..f1b23d9a5ceb3e6bf26505e05841b943d43d61f1 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py
@@ -63,10 +63,12 @@ def init_constants(kwargs, classification_indices, metrics,
logging.info("Info:\t Shape of " + str(view_name) + " :" + str(
dataset_var.get_shape()))
labels = dataset_var.get_labels()
- output_file_name = os.path.join(directory, classifier_name,
- classifier_name+"-"+dataset_var.get_name()+"-")
+ directory = os.path.join(directory, classifier_name)
+ base_file_name = classifier_name+"-"+dataset_var.get_name()+"-"
+ output_file_name = os.path.join(directory, base_file_name)
return classifier_name, t_start, views_indices, \
- classifier_config, views, learning_rate, labels, output_file_name
+ classifier_config, views, learning_rate, labels, output_file_name,\
+ directory, base_file_name
def save_results(string_analysis, images_analysis, output_file_name):
@@ -239,7 +241,9 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
views, \
learning_rate, \
labels, \
- output_file_name = init_constants(kwargs, classification_indices, metrics, name,
+ output_file_name,\
+ directory,\
+ base_file_name = init_constants(kwargs, classification_indices, metrics, name,
nb_cores, k_folds, dataset_var, directory)
logging.debug("Done:\t Initialize constants")
@@ -330,7 +334,8 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
class_label_names=list(labels_dictionary.values()),
train_pred=train_pred,
test_pred=test_pred,
- output_file_name=output_file_name,
+ directory=directory,
+ base_file_name=base_file_name,
labels=labels,
database_name=dataset_var.get_name(),
nb_cores=nb_cores,
diff --git a/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py b/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
index 7503bbe4c499e0a06ebdb0f6c16f26ef9c50c05d..b51389ca07f270e9c8c8f5be89167f579014a9f8 100644
--- a/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
+++ b/multiview_platform/mono_multi_view_classifiers/multiview/multiview_utils.py
@@ -177,13 +177,14 @@ class MultiviewResultAnalyzer(ResultAnalyser):
def __init__(self, view_names, classifier, classification_indices, k_folds,
hps_method, metrics_list, n_iter, class_label_names,
- train_pred, test_pred, output_file_name, labels, database_name,
- nb_cores, duration):
+ train_pred, test_pred, directory, base_file_name, labels,
+ database_name, nb_cores, duration):
if hps_method.endswith("equiv"):
n_iter = n_iter*len(view_names)
ResultAnalyser.__init__(self, classifier, classification_indices, k_folds,
hps_method, metrics_list, n_iter, class_label_names,
- train_pred, test_pred, output_file_name, labels, database_name,
+ train_pred, test_pred, directory,
+ base_file_name, labels, database_name,
nb_cores, duration)
self.classifier_name = classifier.short_name
self.view_names = view_names
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/base.py b/multiview_platform/mono_multi_view_classifiers/utils/base.py
index 0f786270c130414ca3e427ed1989a1310bb3670e..6530eeac96f599c3d55e9643ace5b07d56b2642b 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/base.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/base.py
@@ -96,7 +96,8 @@ class BaseClassifier(BaseEstimator, ):
else:
return str(self.get_params()[param_name])
- def get_interpretation(self, directory, y_test, multi_class=False):
+ def get_interpretation(self, directory, base_file_name, y_test,
+ multi_class=False):
"""
Base method that returns an empty string if there is not interpretation
method in the classifier's module
@@ -160,8 +161,8 @@ class ResultAnalyser():
def __init__(self, classifier, classification_indices, k_folds,
hps_method, metrics_list, n_iter, class_label_names,
- train_pred, test_pred, directory, labels, database_name,
- nb_cores, duration):
+ train_pred, test_pred, directory, base_file_name, labels,
+ database_name, nb_cores, duration):
"""
Parameters
@@ -204,6 +205,7 @@ class ResultAnalyser():
self.train_pred = train_pred
self.test_pred = test_pred
self.directory = directory
+ self.base_file_name = base_file_name
self.labels = labels
self.string_analysis = ""
self.database_name = database_name
@@ -336,7 +338,7 @@ class ResultAnalyser():
string_analysis += "\n\n Classification took {}".format(hms(seconds=int(self.duration)))
string_analysis += "\n\n Classifier Interpretation : \n"
string_analysis += self.classifier.get_interpretation(
- self.directory,
+ self.directory, self.base_file_name,
self.labels[self.test_indices])
image_analysis = {}
return string_analysis, image_analysis, self.metric_scores
diff --git a/multiview_platform/mono_multi_view_classifiers/utils/multiclass.py b/multiview_platform/mono_multi_view_classifiers/utils/multiclass.py
index dbd323500f0499545eeb53a16f12e906662176b6..bf5e1f8ea567621176a5ef43cff68f8400790e13 100644
--- a/multiview_platform/mono_multi_view_classifiers/utils/multiclass.py
+++ b/multiview_platform/mono_multi_view_classifiers/utils/multiclass.py
@@ -166,7 +166,7 @@ class MultiClassWrapper:
def get_config(self):
return self.estimator.get_config()
- def get_interpretation(self, output_file_name=None, y_test=None):
+ def get_interpretation(self, directory, base_file_name, y_test=None):
# return self.estimator.get_interpretation(output_file_name, y_test,
# multi_class=True)
# TODO : Multiclass interpretation
diff --git a/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py b/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py
index 055fa1cb80ef74d01ab3679cf2c3f95031517abe..c717411688e4f70fc0c17f995b39ff598215b59f 100644
--- a/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py
+++ b/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py
@@ -41,7 +41,9 @@ class Test_initConstants(unittest.TestCase):
X, \
learningRate, \
labelsString, \
- outputFileName = exec_classif_mono_view.init_constants(cls.args,
+ output_file_name,\
+ directory,\
+ base_file_name = exec_classif_mono_view.init_constants(cls.args,
cls.X,
cls.classification_indices,
cls.labels_names,
@@ -54,7 +56,7 @@ class Test_initConstants(unittest.TestCase):
np.testing.assert_array_equal(X, cls.X_value)
cls.assertEqual(learningRate, 0.5)
cls.assertEqual(labelsString, "test_true-test_false")
- # cls.assertEqual(outputFileName, "Code/tests/temp_tests/test_dir/test_clf/test_dataset/results-test_clf-test_true-test_false-learnRate0.5-test-test_dataset-")
+ # cls.assertEqual(output_file_name, "Code/tests/temp_tests/test_dir/test_clf/test_dataset/results-test_clf-test_true-test_false-learnRate0.5-test-test_dataset-")
@classmethod
def tearDownClass(cls):
diff --git a/multiview_platform/tests/test_utils/test_base.py b/multiview_platform/tests/test_utils/test_base.py
index b09d50e936fa8d7ba3c8f78c30dc418ffa2faede..41186fcd58c8b2cd9a20ed75ddce7c444637d098 100644
--- a/multiview_platform/tests/test_utils/test_base.py
+++ b/multiview_platform/tests/test_utils/test_base.py
@@ -41,6 +41,7 @@ class Test_ResultAnalyzer(unittest.TestCase):
cls.test_pred = np.random.randint(0, cls.n_classes,
size=cls.test_length)
cls.directory = "fake_directory"
+ cls.base_file_name = "fake_file"
cls.labels = np.random.randint(0, cls.n_classes,
size=cls.n_examples)
cls.database_name = "test_database"
@@ -60,8 +61,9 @@ class Test_ResultAnalyzer(unittest.TestCase):
self.k_folds, self.hps_method, self.metrics_list,
self.n_iter, self.class_label_names,
self.train_pred, self.test_pred, self.directory,
- self.labels, self.database_name,
- self.nb_cores, self.duration)
+ self.base_file_name, self.labels,
+ self.database_name, self.nb_cores,
+ self.duration)
def test_get_metric_scores(self):
RA = base.ResultAnalyser(self.classifier, self.classification_indices,
@@ -69,7 +71,7 @@ class Test_ResultAnalyzer(unittest.TestCase):
self.metrics_list,
self.n_iter, self.class_label_names,
self.train_pred, self.test_pred,
- self.directory,
+ self.directory, self.base_file_name,
self.labels, self.database_name,
self.nb_cores, self.duration)
train_score, test_score = RA.get_metric_scores("accuracy_score", {})
@@ -82,7 +84,7 @@ class Test_ResultAnalyzer(unittest.TestCase):
self.metrics_list,
self.n_iter, self.class_label_names,
self.train_pred, self.test_pred,
- self.directory,
+ self.directory, self.base_file_name,
self.labels, self.database_name,
self.nb_cores, self.duration)
RA.get_all_metrics_scores()