diff --git a/config_files/config_test.yml b/config_files/config_test.yml
index 00ee0c77962ffa24ccb651ef1eb90a6621df10da..6cadcb608b3142c4f3329ef021f4424de73ada72 100644
--- a/config_files/config_test.yml
+++ b/config_files/config_test.yml
@@ -4,7 +4,7 @@ name: ["generated_dset",]
label: "_"
file_type: ".hdf5"
views:
-pathf: "/home/baptiste/Documents/Gitwork/multiview_generator/generator/"
+pathf: "/home/baptiste/Documents/Gitwork/multiview_generator/demo/"
nice: 0
random_state: 42
nb_cores: 1
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py
index fd18a3b02edc250479a688084ec4759c7dde6e5f..103dd50d2cc4d93a655bc63eb455127277acc867 100644
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py
+++ b/multiview_platform/mono_multi_view_classifiers/result_analysis/error_analysis.py
@@ -50,8 +50,7 @@ def publish_example_errors(example_errors, directory, databaseName,
labels_names, example_ids, labels):
logging.debug("Start:\t Biclass Label analysis figure generation")
- base_file_name = os.path.join(directory, databaseName + "-" + "_vs_".join(
- labels_names) + "-")
+ base_file_name = os.path.join(directory, databaseName + "-" )
nb_classifiers, nb_examples, classifiers_names, \
data_2d, error_on_examples = gen_error_data(example_errors)
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis/metric_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis/metric_analysis.py
index 9cb296f2dea29686416c36be6325e0a62102ec36..a0876f9ce7f2697f9d65b81aaf34a1f07dc63825 100644
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis/metric_analysis.py
+++ b/multiview_platform/mono_multi_view_classifiers/result_analysis/metric_analysis.py
@@ -7,6 +7,7 @@ import logging
from ..utils.organization import secure_file_path
+
def get_metrics_scores(metrics, results, label_names):
r"""Used to extract metrics scores in case of classification
@@ -41,6 +42,10 @@ def get_metrics_scores(metrics, results, label_names):
index=["train", "test"],
columns=classifier_names))
for metric in metrics)
+ class_metric_scores = dict((metric[0], pd.DataFrame(
+ index=pd.MultiIndex.from_product([["train", "test"], label_names]),
+ columns=classifier_names, dtype=float))
+ for metric in metrics)
for metric in metrics:
for classifier_result in results:
@@ -50,12 +55,6 @@ def get_metrics_scores(metrics, results, label_names):
metrics_scores[metric[0]].loc[
"test", classifier_result.get_classifier_name()] = \
classifier_result.metrics_scores[metric[0]][1]
-
- class_metric_scores = dict((metric[0], pd.DataFrame(index=pd.MultiIndex.from_product([["train", "test"], label_names]),
- columns=classifier_names, dtype=float))
- for metric in metrics)
- for metric in metrics:
- for classifier_result in results:
for label_index, label_name in enumerate(label_names):
class_metric_scores[metric[0]].loc[(
"train", label_name),classifier_result.get_classifier_name()] = \
@@ -95,10 +94,10 @@ def publish_metrics_graphs(metrics_scores, directory, database_name,
train_scores, test_scores, classifier_names, \
file_name, nb_results, results,\
class_test_scores = init_plot(results, metric_name,
- metrics_scores[metric_name],
- directory,
- database_name, labels_names,
- class_metric_scores[metric_name])
+ metrics_scores[metric_name],
+ directory,
+ database_name,
+ class_metric_scores[metric_name])
plot_metric_scores(train_scores, test_scores, classifier_names,
nb_results, metric_name, file_name,
@@ -148,32 +147,8 @@ def publish_all_metrics_scores(iter_results, class_iter_results, directory,
plot_class_metric_scores(test, file_name, label_names, classifier_names, metric_name, stds=test_std, tag="averaged")
return results
-# def publish_all_class_metrics_scores(iter_results, directory,
-# data_base_name, stats_iter,
-# min_size=10):
-# results = []
-# secure_file_path(os.path.join(directory, "a"))
-#
-# for metric_name, scores in iter_results.items():
-# train = np.array(scores["mean"].loc["train"])
-# test = np.array(scores["mean"].loc["test"])
-# names = np.array(scores["mean"].columns)
-# train_std = np.array(scores["std"].loc["train"])
-# test_std = np.array(scores["std"].loc["test"])
-#
-# file_name = os.path.join(directory, data_base_name + "-mean_on_" + str(
-# stats_iter) + "_iter-" + metric_name+"-class")
-#
-# plot_class_metric_scores(test, file_name, labels_names=names, file_name, tag=" averaged",
-# train_STDs=train_std, test_STDs=test_std)
-# results += [[classifier_name, metric_name, test_mean, test_std]
-# for classifier_name, test_mean, test_std
-# in zip(names, test, test_std)]
-# return results
-
-
def init_plot(results, metric_name, metric_dataframe,
- directory, database_name, labels_names, class_metric_scores):
+ directory, database_name, class_metric_scores):
train = np.array(metric_dataframe.loc["train"])
test = np.array(metric_dataframe.loc["test"])
class_test = np.array(class_metric_scores.loc["test"])
@@ -181,8 +156,7 @@ def init_plot(results, metric_name, metric_dataframe,
nb_results = metric_dataframe.shape[1]
- file_name = os.path.join(directory, database_name + "-" + "_vs_".join(
- labels_names) + "-" + metric_name)
+ file_name = os.path.join(directory, database_name + "-" + metric_name)
results += [[classifiers_name, metric_name, test_mean, test_std, class_mean]
for classifiers_name, test_mean, class_mean, test_std in