diff --git a/summit/multiview_platform/exec_classif.py b/summit/multiview_platform/exec_classif.py
index efaae89152deabd28d2071fc8055e134b346451e..ca5e8978aa62e334e20760625ce89fab733503fd 100644
--- a/summit/multiview_platform/exec_classif.py
+++ b/summit/multiview_platform/exec_classif.py
@@ -300,7 +300,7 @@ def init_kwargs(args, classifiers_names, framework="monoview"):
For example, for Adaboost, the KWARGS will be `{"n_estimators":<value>, "base_estimator":<value>}`"""
- logging.debug("Start:\t Initializing monoview classifiers arguments")
+ logging.info("Start:\t Initializing monoview classifiers arguments")
kwargs = {}
for classifiers_name in classifiers_names:
try:
@@ -316,7 +316,7 @@ def init_kwargs(args, classifiers_names, framework="monoview"):
kwargs[classifiers_name] = args[classifiers_name]
else:
kwargs[classifiers_name] = {}
- logging.debug("Done:\t Initializing monoview classifiers arguments")
+ logging.info("Done:\t Initializing monoview classifiers arguments")
return kwargs
@@ -402,7 +402,7 @@ def benchmark_init(directory, classification_indices, labels, labels_dictionary,
-------
"""
- logging.debug("Start:\t Benchmark initialization")
+ logging.info("Start:\t Benchmark initialization")
secure_file_path(os.path.join(directory, "train_labels.csv"))
train_indices = classification_indices[0]
train_labels = dataset_var.get_labels(sample_indices=train_indices)
@@ -421,7 +421,7 @@ def benchmark_init(directory, classification_indices, labels, labels_dictionary,
np.savetxt(file_name, train_labels[test_cv_indices[:min_fold_len]],
delimiter=",")
labels_names = list(labels_dictionary.values())
- logging.debug("Done:\t Benchmark initialization")
+ logging.info("Done:\t Benchmark initialization")
return results_monoview, labels_names
@@ -550,7 +550,7 @@ def exec_one_benchmark_mono_core(dataset_var=None, labels_dictionary=None,
labels_dictionary, k_folds,
dataset_var)
logging.getLogger('matplotlib.font_manager').disabled = True
- logging.debug("Start:\t monoview benchmark")
+ logging.info("Start:\t monoview benchmark")
traceback_outputs = {}
for arguments in argument_dictionaries["monoview"]:
try:
@@ -571,9 +571,9 @@ def exec_one_benchmark_mono_core(dataset_var=None, labels_dictionary=None,
else:
raise
- logging.debug("Done:\t monoview benchmark")
+ logging.info("Done:\t monoview benchmark")
- logging.debug("Start:\t multiview arguments initialization")
+ logging.info("Start:\t multiview arguments initialization")
# argument_dictionaries = initMultiviewArguments(args, benchmark, views,
# views_indices,
@@ -581,9 +581,9 @@ def exec_one_benchmark_mono_core(dataset_var=None, labels_dictionary=None,
# random_state, directory,
# resultsMonoview,
# classification_indices)
- logging.debug("Done:\t multiview arguments initialization")
+ logging.info("Done:\t multiview arguments initialization")
- logging.debug("Start:\t multiview benchmark")
+ logging.info("Start:\t multiview benchmark")
results_multiview = []
for arguments in argument_dictionaries["multiview"]:
try:
@@ -602,7 +602,7 @@ def exec_one_benchmark_mono_core(dataset_var=None, labels_dictionary=None,
arguments["classifier_name"]] = traceback.format_exc()
else:
raise
- logging.debug("Done:\t multiview benchmark")
+ logging.info("Done:\t multiview benchmark")
return [flag, results_monoview + results_multiview, traceback_outputs]
@@ -653,7 +653,7 @@ def exec_benchmark(nb_cores, stats_iter,
results : list of lists
The results of the benchmark.
"""
- logging.debug("Start:\t Executing all the needed benchmarks")
+ logging.info("Start:\t Executing all the needed benchmarks")
results = []
# if nb_cores > 1:
# if stats_iter > 1 or nb_multiclass > 1:
@@ -681,17 +681,17 @@ def exec_benchmark(nb_cores, stats_iter,
metrics, sample_ids=dataset_var.sample_ids,
labels=dataset_var.get_labels())
results += [benchmark_results]
- logging.debug("Done:\t Executing all the needed benchmarks")
+ logging.info("Done:\t Executing all the needed benchmarks")
# Do everything with flagging
- logging.debug("Start:\t Analyzing predictions")
+ logging.info("Start:\t Analyzing predictions")
results_mean_stds = analyze(results, stats_iter,
benchmark_arguments_dictionaries,
metrics,
directory,
dataset_var.sample_ids,
dataset_var.get_labels())
- logging.debug("Done:\t Analyzing predictions")
+ logging.info("Done:\t Analyzing predictions")
delete(benchmark_arguments_dictionaries, nb_cores, dataset_var)
return results_mean_stds
diff --git a/summit/multiview_platform/monoview/exec_classif_mono_view.py b/summit/multiview_platform/monoview/exec_classif_mono_view.py
index 324fdc38c65ca7348e9867596bb0e4a709aa4488..59c6b1458eb5f9ca7ee88426af00b6704b43cabb 100644
--- a/summit/multiview_platform/monoview/exec_classif_mono_view.py
+++ b/summit/multiview_platform/monoview/exec_classif_mono_view.py
@@ -55,7 +55,7 @@ def exec_monoview(directory, X, Y, database_name, labels_names,
random_state, hyper_param_search="Random",
metrics={"accuracy_score*": {}}, n_iter=30, view_name="",
hps_kwargs={}, **args):
- logging.debug("Start:\t Loading data")
+ logging.info("Start:\t Loading data")
kwargs, \
t_start, \
view_name, \
@@ -68,9 +68,9 @@ def exec_monoview(directory, X, Y, database_name, labels_names,
base_file_name = init_constants(args, X, classification_indices,
labels_names,
database_name, directory, view_name, )
- logging.debug("Done:\t Loading data")
+ logging.info("Done:\t Loading data")
- logging.debug(
+ logging.info(
"Info:\t Classification - Database:" + str(
database_name) + " View:" + str(
view_name) + " train ratio:"
@@ -78,17 +78,17 @@ def exec_monoview(directory, X, Y, database_name, labels_names,
k_folds.n_splits) + ", cores:"
+ str(nb_cores) + ", algorithm : " + classifier_name)
- logging.debug("Start:\t Determine Train/Test split")
+ logging.info("Start:\t Determine Train/Test split")
X_train, y_train, X_test, y_test = init_train_test(X, Y,
classification_indices)
- logging.debug("Info:\t Shape X_train:" + str(
+ logging.info("Info:\t Shape X_train:" + str(
X_train.shape) + ", Length of y_train:" + str(len(y_train)))
- logging.debug("Info:\t Shape X_test:" + str(
+ logging.info("Info:\t Shape X_test:" + str(
X_test.shape) + ", Length of y_test:" + str(len(y_test)))
- logging.debug("Done:\t Determine Train/Test split")
+ logging.info("Done:\t Determine Train/Test split")
- logging.debug("Start:\t Generate classifier args")
+ logging.info("Start:\t Generate classifier args")
classifier_module = getattr(monoview_classifiers, classifier_name)
classifier_class_name = classifier_module.classifier_class_name
hyper_param_beg = time.monotonic()
@@ -100,9 +100,9 @@ def exec_monoview(directory, X, Y, database_name, labels_names,
k_folds, nb_cores, metrics, kwargs,
**hps_kwargs)
hyper_param_duration = time.monotonic() - hyper_param_beg
- logging.debug("Done:\t Generate classifier args")
+ logging.info("Done:\t Generate classifier args")
- logging.debug("Start:\t Training")
+ logging.info("Start:\t Training")
classifier = get_mc_estim(getattr(classifier_module,
classifier_class_name)
@@ -112,9 +112,9 @@ def exec_monoview(directory, X, Y, database_name, labels_names,
fit_beg = time.monotonic()
classifier.fit(X_train, y_train) # NB_CORES=nbCores,
fit_duration = time.monotonic() - fit_beg
- logging.debug("Done:\t Training")
+ logging.info("Done:\t Training")
- logging.debug("Start:\t Predicting")
+ logging.info("Start:\t Predicting")
train_pred = classifier.predict(X_train)
pred_beg = time.monotonic()
test_pred = classifier.predict(X_test)
@@ -127,14 +127,14 @@ def exec_monoview(directory, X, Y, database_name, labels_names,
for testIndex, index in enumerate(classification_indices[1]):
full_pred[index] = test_pred[testIndex]
- logging.debug("Done:\t Predicting")
+ logging.info("Done:\t Predicting")
whole_duration = time.monotonic() - t_start
- logging.debug(
+ logging.info(
"Info:\t Duration for training and predicting: " + str(
whole_duration) + "[s]")
- logging.debug("Start:\t Getting results")
+ logging.info("Start:\t Getting results")
result_analyzer = MonoviewResultAnalyzer(view_name=view_name,
classifier_name=classifier_name,
shape=X.shape,
@@ -154,9 +154,9 @@ def exec_monoview(directory, X, Y, database_name, labels_names,
duration=whole_duration)
string_analysis, images_analysis, metrics_scores, class_metrics_scores, \
confusion_matrix = result_analyzer.analyze()
- logging.debug("Done:\t Getting results")
+ logging.info("Done:\t Getting results")
- logging.debug("Start:\t Saving preds")
+ logging.info("Start:\t Saving preds")
save_results(string_analysis, output_file_name, full_pred, train_pred,
y_train, images_analysis, y_test, confusion_matrix)
logging.info("Done:\t Saving results")
@@ -203,7 +203,7 @@ def get_hyper_params(classifier_module, search_method, classifier_module_name,
output_file_name, k_folds, nb_cores, metrics, kwargs,
**hps_kwargs):
if search_method != "None":
- logging.debug(
+ logging.info(
"Start:\t " + search_method + " best settings for " + classifier_module_name)
classifier_hp_search = getattr(hyper_parameter_search, search_method)
estimator = getattr(classifier_module, classifier_class_name)(
@@ -218,7 +218,7 @@ def get_hyper_params(classifier_module, search_method, classifier_module_name,
hps.fit(X_train, y_train, **kwargs[classifier_module_name])
cl_kwargs = hps.get_best_params()
hps.gen_report(output_file_name)
- logging.debug("Done:\t " + search_method + " best settings")
+ logging.info("Done:\t " + search_method + " best settings")
else:
cl_kwargs = kwargs[classifier_module_name]
return cl_kwargs
diff --git a/summit/multiview_platform/multiview/exec_multiview.py b/summit/multiview_platform/multiview/exec_multiview.py
index bed8317a1656137838ecc093e42fb088fca668d6..fc203e4ef6adfca6ae759cb168d79af9210ace53 100644
--- a/summit/multiview_platform/multiview/exec_multiview.py
+++ b/summit/multiview_platform/multiview/exec_multiview.py
@@ -237,7 +237,7 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
``MultiviewResult``
"""
- logging.debug("Start:\t Initialize constants")
+ logging.info("Start:\t Initialize constants")
cl_type, \
t_start, \
views_indices, \
@@ -250,16 +250,16 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
base_file_name, \
metrics = init_constants(kwargs, classification_indices, metrics, name,
nb_cores, k_folds, dataset_var, directory)
- logging.debug("Done:\t Initialize constants")
+ logging.info("Done:\t Initialize constants")
extraction_time = time.time() - t_start
logging.info("Info:\t Extraction duration " + str(extraction_time) + "s")
- logging.debug("Start:\t Getting train/test split")
+ logging.info("Start:\t Getting train/test split")
learning_indices, validation_indices = classification_indices
- logging.debug("Done:\t Getting train/test split")
+ logging.info("Done:\t Getting train/test split")
- logging.debug("Start:\t Getting classifiers modules")
+ logging.info("Start:\t Getting classifiers modules")
# classifierPackage = getattr(multiview_classifiers,
# CL_type) # Permet d'appeler un module avec une string
classifier_module = getattr(multiview_classifiers, cl_type)
diff --git a/summit/multiview_platform/result_analysis/error_analysis.py b/summit/multiview_platform/result_analysis/error_analysis.py
index 12f018072c6ffbd099f304bb0a17c9ba7d6fadf7..f26671a37071bf0de5e844b5c55a9f0ab84b807d 100644
--- a/summit/multiview_platform/result_analysis/error_analysis.py
+++ b/summit/multiview_platform/result_analysis/error_analysis.py
@@ -47,7 +47,7 @@ def get_sample_errors(groud_truth, results):
def publish_sample_errors(sample_errors, directory, databaseName,
labels_names, sample_ids, labels): # pragma: no cover
- logging.debug("Start:\t Label analysis figure generation")
+ logging.info("Start:\t Label analysis figure generation")
base_file_name = os.path.join(directory, databaseName + "-")
@@ -64,13 +64,13 @@ def publish_sample_errors(sample_errors, directory, databaseName,
plot_errors_bar(error_on_samples, nb_samples,
base_file_name, sample_ids=sample_ids)
- logging.debug("Done:\t Label analysis figures generation")
+ logging.info("Done:\t Label analysis figures generation")
def publish_all_sample_errors(iter_results, directory,
stats_iter,
sample_ids, labels): # pragma: no cover
- logging.debug(
+ logging.info(
"Start:\t Global label analysis figure generation")
nb_samples, nb_classifiers, data, \
@@ -87,7 +87,7 @@ def publish_all_sample_errors(iter_results, directory,
plot_errors_bar(error_on_samples, nb_samples, os.path.join(directory, ""),
sample_ids=sample_ids)
- logging.debug(
+ logging.info(
"Done:\t Global label analysis figures generation")
diff --git a/summit/multiview_platform/result_analysis/execution.py b/summit/multiview_platform/result_analysis/execution.py
index 7d3c9c6fe80db4b9cb51f62683840019fcb46882..16ec3fe0eaf9b19b337dd897b88e2df4c266b01c 100644
--- a/summit/multiview_platform/result_analysis/execution.py
+++ b/summit/multiview_platform/result_analysis/execution.py
@@ -65,7 +65,7 @@ def analyze_iterations(results, benchmark_argument_dictionaries, stats_iter,
label combination, regrouping the scores for each metrics and the
information useful to plot errors on samples.
"""
- logging.debug("Start:\t Analyzing all results")
+ logging.info("Start:\t Analyzing all results")
iter_results = {"metrics_scores": [i for i in range(stats_iter)],
"class_metrics_scores": [i for i in range(stats_iter)],
"sample_errors": [i for i in range(stats_iter)],
@@ -105,7 +105,7 @@ def analyze_iterations(results, benchmark_argument_dictionaries, stats_iter,
iter_results["labels"] = labels
iter_results["durations"][iter_index] = durations
- logging.debug("Done:\t Analyzing all results")
+ logging.info("Done:\t Analyzing all results")
return res, iter_results, flagged_tracebacks_list, labels_names
diff --git a/summit/multiview_platform/result_analysis/metric_analysis.py b/summit/multiview_platform/result_analysis/metric_analysis.py
index c2db7b26d9d933f2981e809a2cf7ee5505c4ab7a..3948676b202355656e473e09b72125065bebeba5 100644
--- a/summit/multiview_platform/result_analysis/metric_analysis.py
+++ b/summit/multiview_platform/result_analysis/metric_analysis.py
@@ -95,7 +95,7 @@ def publish_metrics_graphs(metrics_scores, directory, database_name,
"""
results = []
for metric_name in metrics_scores.keys():
- logging.debug(
+ logging.info(
"Start:\t Score graph generation for " + metric_name)
train_scores, test_scores, classifier_names, \
file_name, nb_results, results, \
@@ -112,7 +112,7 @@ def publish_metrics_graphs(metrics_scores, directory, database_name,
class_file_name = file_name+"-class"
plot_class_metric_scores(class_test_scores, class_file_name,
labels_names, classifier_names, metric_name)
- logging.debug(
+ logging.info(
"Done:\t Score graph generation for " + metric_name)
return results
diff --git a/summit/multiview_platform/utils/dataset.py b/summit/multiview_platform/utils/dataset.py
index 25c396fa8fcb84fcb0e4e7152bd266fb824f7c77..07023b756fea909cc01e821de05f1f28febf61b8 100644
--- a/summit/multiview_platform/utils/dataset.py
+++ b/summit/multiview_platform/utils/dataset.py
@@ -693,14 +693,14 @@ def init_multiple_datasets(path_f, name, nb_cores): # pragma: no cover
"""
if nb_cores > 1:
if datasets_already_exist(path_f, name, nb_cores):
- logging.debug(
+ logging.info(
"Info:\t Enough copies of the dataset are already available")
pass
else:
if os.path.getsize(
os.path.join(path_f, name + ".hdf5")) * nb_cores / float(
1024) / 1000 / 1000 > 0.1:
- logging.debug("Start:\t Creating " + str(
+ logging.info("Start:\t Creating " + str(
nb_cores) + " temporary datasets for multiprocessing")
logging.warning(
r" WARNING : /!\ This may use a lot of HDD storage space : " +
@@ -715,7 +715,7 @@ def init_multiple_datasets(path_f, name, nb_cores): # pragma: no cover
else:
pass
dataset_files = copy_hdf5(path_f, name, nb_cores)
- logging.debug("Start:\t Creating datasets for multiprocessing")
+ logging.info("Start:\t Creating datasets for multiprocessing")
return dataset_files
@@ -732,10 +732,10 @@ def copy_hdf5(pathF, name, nbCores):
def delete_HDF5(benchmarkArgumentsDictionaries, nbCores, dataset):
"""Used to delete temporary copies at the end of the benchmark"""
if nbCores > 1:
- logging.debug("Start:\t Deleting " + str(
+ logging.info("Start:\t Deleting " + str(
nbCores) + " temporary datasets for multiprocessing")
args = benchmarkArgumentsDictionaries[0]["args"]
- logging.debug("Start:\t Deleting datasets for multiprocessing")
+ logging.info("Start:\t Deleting datasets for multiprocessing")
for coreIndex in range(nbCores):
os.remove(args["pathf"] + args["name"] + str(coreIndex) + ".hdf5")
diff --git a/summit/multiview_platform/utils/execution.py b/summit/multiview_platform/utils/execution.py
index 0ce9886406018fabe7c30f0264106009bac857a5..459335a099e4ed30d045bd9f2df9fdd85f2bf56d 100644
--- a/summit/multiview_platform/utils/execution.py
+++ b/summit/multiview_platform/utils/execution.py
@@ -171,7 +171,7 @@ def init_log_file(name, views, cl_type, log, debug, label,
log_file_path = os.path.join(result_directory, log_file_name)
os.makedirs(os.path.dirname(log_file_path))
logging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s',
- filename=log_file_path, level=logging.DEBUG,
+ filename=log_file_path, level=logging.INFO,
filemode='w')
if log:
logging.getLogger().addHandler(logging.StreamHandler())