diff --git a/config_files/config_test.yml b/config_files/config_test.yml
index 8135f0e500b8135f75f35fd0371b817374b22a02..f05f4cad771622de0cf3c4918d68553a765f6c8b 100644
--- a/config_files/config_test.yml
+++ b/config_files/config_test.yml
@@ -20,16 +20,16 @@ Classification:
multiclass_method: "oneVersusOne"
split: 0.4
nb_folds: 2
- nb_class: 2
+ nb_class: 3
classes:
- type: ["multiview", "monoview"]
- algos_monoview: ["adaboost","decision_tree"]
+ type: ["monoview"]
+ algos_monoview: ["decision_tree",]
algos_multiview: ["svm_jumbo_fusion"]
- stats_iter: 1
+ stats_iter: 2
metrics: ["accuracy_score", "f1_score"]
metric_princ: "f1_score"
hps_type: "randomized_search-equiv"
- hps_iter: 2
+ hps_iter: 1
#####################################
diff --git a/multiview_platform/mono_multi_view_classifiers/result_analysis.py b/multiview_platform/mono_multi_view_classifiers/result_analysis.py
index 606f6a8196de3067f0547d490bbde9a38eabe448..f56b49f57e50304e45ea5e82554c17a1ffd283aa 100644
--- a/multiview_platform/mono_multi_view_classifiers/result_analysis.py
+++ b/multiview_platform/mono_multi_view_classifiers/result_analysis.py
@@ -196,7 +196,6 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
### The following part is used to generate an interactive graph.
if use_plotly:
label_index_list = [np.where(labels==i)[0] for i in np.unique(labels)]
- print(label_index_list)
hover_text = [[example_ids[i] + " failed "+ str(stats_iter-data[i,j])+" time(s)"
for j in range(data.shape[1])]
for i in range(data.shape[0]) ]
@@ -498,9 +497,9 @@ def init_plot(results, metric_name, metric_dataframe,
nb_results = metric_dataframe.shape[1]
- file_name = directory + time.strftime(
+ file_name = os.path.join(directory, time.strftime(
"%Y_%m_%d-%H_%M_%S") + "-" + database_name + "-" + "_vs_".join(
- labels_names) + "-" + metric_name
+ labels_names) + "-" + metric_name)
results += [[classifiers_name, metric_name, testMean, testSTD]
for classifiers_name, testMean, testSTD in
@@ -548,18 +547,20 @@ def gen_error_data(example_errors):
data_2d = np.zeros((nb_examples, nb_classifiers))
for classifierIndex, (classifier_name, error_on_examples) in enumerate(
example_errors.items()):
- data_2d[:, classifierIndex] = error_on_examples
+ try:
+ data_2d[:, classifierIndex] = error_on_examples
+ except:
+ import pdb;pdb.set_trace()
error_on_examples = -1 * np.sum(data_2d, axis=1) / nb_classifiers
-
return nb_classifiers, nb_examples, classifiers_names, data_2d, error_on_examples
def publishExampleErrors(example_errors, directory, databaseName, labels_names, example_ids, labels):
logging.debug("Start:\t Biclass Label analysis figure generation")
- base_file_name = directory + time.strftime(
+ base_file_name = os.path.join(directory, time.strftime(
"%Y_%m_%d-%H_%M_%S") + "-" + databaseName + "-" + "_vs_".join(
- labels_names) + "-"
+ labels_names) + "-")
nb_classifiers, nb_examples, classifiers_names, \
data_2d, error_on_examples = gen_error_data(example_errors)
@@ -579,9 +580,9 @@ def publishExampleErrors(example_errors, directory, databaseName, labels_names,
def publish_feature_importances(feature_importances, directory, database_name, labels_names, feature_stds=None):
for view_name, feature_importance in feature_importances.items():
- file_name = directory + time.strftime(
+ file_name = os.path.join(directory, time.strftime(
"%Y_%m_%d-%H_%M_%S") + "-" + database_name + "-" + "_vs_".join(
- labels_names) + "-" + view_name + "-feature_importances"
+ labels_names) + "-" + view_name + "-feature_importances")
if feature_stds is not None:
feature_std = feature_stds[view_name]
feature_std.to_csv(file_name+"_dataframe_stds.csv")
@@ -814,9 +815,9 @@ def publishMulticlassScores(multiclass_results, metrics, stats_iter, direcories,
classifiers_names])
nbResults = classifiers_names.shape[0]
- fileName = directory + time.strftime(
+ fileName = os.path.join(directory , time.strftime(
"%Y_%m_%d-%H_%M_%S") + "-" + databaseName + "-" + metric[
- 0]
+ 0])
plot_metric_scores(train_scores, validationScores, classifiers_names,
nbResults, metric[0], fileName, tag=" multiclass")
@@ -828,22 +829,20 @@ def publishMulticlassScores(multiclass_results, metrics, stats_iter, direcories,
def publishMulticlassExmapleErrors(multiclass_results, directories,
- databaseName, example_ids):
- for iter_index, multiclassResult in enumerate(multiclass_results):
+ databaseName, example_ids, multiclass_labels):
+ for iter_index, multiclass_result in enumerate(multiclass_results):
directory = directories[iter_index]
logging.debug("Start:\t Multiclass Label analysis figure generation")
- base_file_name = directory + time.strftime(
- "%Y_%m_%d-%H_%M_%S") + "-" + databaseName + "-"
-
- nbClassifiers, nbExamples, nCopies, classifiers_names, data, error_on_examples = gen_error_data(
- multiclassResult,
- base_file_name)
+ base_file_name = os.path.join(directory, time.strftime(
+ "%Y_%m_%d-%H_%M_%S") + "-" + databaseName + "-")
+ nb_classifiers, nb_examples, classifiers_names, data, error_on_examples = gen_error_data(
+ dict((key, multiclass_result[key]['error_on_examples'])
+ for key in multiclass_result.keys()),)
+ plot_2d(data, classifiers_names, nb_classifiers, nb_examples,
+ base_file_name, example_ids=example_ids, labels=multiclass_labels)
- plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
- nCopies, base_file_name, example_ids=example_ids)
-
- plot_errors_bar(error_on_examples, nbClassifiers, nbExamples,
+ plot_errors_bar(error_on_examples, nb_classifiers, nb_examples,
base_file_name)
logging.debug("Done:\t Multiclass Label analysis figure generation")
@@ -903,7 +902,7 @@ def analyzeMulticlass(results, stats_iter, benchmark_argument_dictionaries,
benchmark_argument_dictionaries[0]["args"]["Base"]["name"])
publishMulticlassExmapleErrors(multiclass_results, directories,
benchmark_argument_dictionaries[0][
- "args"].name, example_ids)
+ "args"]["Base"]["name"], example_ids, multiclass_labels)
return results, multiclass_results
@@ -1001,9 +1000,9 @@ def publish_iter_multiclass_metrics_scores(iter_multiclass_results, classifiers_
nb_results = classifiers_names.shape[0]
- file_name = directory + time.strftime(
+ file_name = os.path.join(directory, time.strftime(
"%Y_%m_%d-%H_%M_%S") + "-" + data_base_name + "-Mean_on_" + str(
- stats_iter) + "_iter-" + metric_name + ".png"
+ stats_iter) + "_iter-" + metric_name + ".png")
plot_metric_scores(trainMeans, testMeans, classifiers_names, nb_results,
metric_name, file_name, tag=" averaged multiclass",
@@ -1014,16 +1013,19 @@ def publish_iter_multiclass_metrics_scores(iter_multiclass_results, classifiers_
def publish_iter_multiclass_example_errors(iter_multiclass_results, directory,
- classifiers_names, stats_iter, example_ids, min_size=10):
+ classifiers_names, stats_iter, example_ids, multiclass_labels, min_size=10):
logging.debug(
"Start:\t Global multiclass label analysis figures generation")
- base_file_name = directory + time.strftime("%Y_%m_%d-%H_%M_%S") + "-"
-
- nb_examples, nb_classifiers, data, error_on_examples = gen_error_data_glob(
- iter_multiclass_results, stats_iter, base_file_name)
+ base_file_name = os.path.join(directory, time.strftime("%Y_%m_%d-%H_%M_%S") + "-")
+ nb_examples, nb_classifiers, data, error_on_examples, classifiers_names = gen_error_data_glob(
+ dict((clf_name, combi_res)
+ for clf_name, combi_res
+ in zip(classifiers_names,
+ iter_multiclass_results["error_on_examples"])),
+ stats_iter)
- plot_2d(data, classifiers_names, nb_classifiers, nb_examples, 1,
- base_file_name, stats_iter=stats_iter, example_ids=example_ids)
+ plot_2d(data, classifiers_names, nb_classifiers, nb_examples,
+ base_file_name, stats_iter=stats_iter, example_ids=example_ids, labels=multiclass_labels)
plot_errors_bar(error_on_examples, nb_classifiers * stats_iter, nb_examples,
base_file_name)
@@ -1152,7 +1154,7 @@ def analyzebiclass_iter(biclass_results, stats_iter, directory,
return results
def analyze_iter_multiclass(multiclass_results, directory, stats_iter, metrics,
- data_base_name, nb_examples, example_ids):
+ data_base_name, nb_examples, example_ids, multiclass_labels):
"""Used to mean the multiclass results on the iterations executed with different random states"""
logging.debug("Start:\t Getting mean results for multiclass classification")
@@ -1189,7 +1191,7 @@ def analyze_iter_multiclass(multiclass_results, directory, stats_iter, metrics,
iter_multiclass_results, classifiers_names,
data_base_name, directory, stats_iter)
publish_iter_multiclass_example_errors(iter_multiclass_results, directory,
- classifiers_names, stats_iter, example_ids)
+ classifiers_names, stats_iter, example_ids, multiclass_labels)
return results
@@ -1226,5 +1228,5 @@ def get_results(results, stats_iter, nb_multiclass, benchmark_argument_dictionar
labels_dictionary, data_base_name, example_ids)
if nb_multiclass > 1:
results_means_std = analyze_iter_multiclass(multiclass_results, directory, stats_iter,
- metrics, data_base_name, nb_examples, example_ids)
+ metrics, data_base_name, nb_examples, example_ids, multiclass_labels)
return results_means_std