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Commit bbfd5353 authored by Baptiste Bauvin's avatar Baptiste Bauvin
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Slight mods

parent e0e15256
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......@@ -215,7 +215,7 @@ def get_hyper_params(classifier_module, search_method, classifier_module_name,
random_state=random_state,
framework="monoview", n_jobs=nb_cores,
**hps_kwargs)
hps.fit(X_train, y_train, **kwargs[classifier_module_name])
hps.fit(X_train, y_train)
cl_kwargs = hps.get_best_params()
hps.gen_report(output_file_name)
logging.info("Done:\t " + search_method + " best settings")
......
......@@ -265,9 +265,9 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
classifier_module = getattr(multiview_classifiers, cl_type)
classifier_name = classifier_module.classifier_class_name
# classifierClass = getattr(classifierModule, CL_type + "Class")
logging.debug("Done:\t Getting classifiers modules")
logging.info("Done:\t Getting classifiers modules")
logging.debug("Start:\t Optimizing hyperparameters")
logging.info("Start:\t Optimizing hyperparameters")
hps_beg = time.monotonic()
if hps_method != "None":
hps_method_class = getattr(hyper_parameter_search, hps_method)
......@@ -298,16 +298,16 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
**classifier_config),
random_state, multiview=True,
y=dataset_var.get_labels())
logging.debug("Done:\t Optimizing hyperparameters")
logging.debug("Start:\t Fitting classifier")
logging.info("Done:\t Optimizing hyperparameters")
logging.info("Start:\t Fitting classifier")
fit_beg = time.monotonic()
classifier.fit(dataset_var, dataset_var.get_labels(),
train_indices=learning_indices,
view_indices=views_indices)
fit_duration = time.monotonic() - fit_beg
logging.debug("Done:\t Fitting classifier")
logging.info("Done:\t Fitting classifier")
logging.debug("Start:\t Predicting")
logging.info("Start:\t Predicting")
train_pred = classifier.predict(dataset_var,
sample_indices=learning_indices,
view_indices=views_indices)
......@@ -349,10 +349,10 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
confusion_matrix = result_analyzer.analyze()
logging.info("Done:\t Result Analysis for " + cl_type)
logging.debug("Start:\t Saving preds")
logging.info("Start:\t Saving preds")
save_results(string_analysis, images_analysis, output_file_name,
confusion_matrix)
logging.debug("Start:\t Saving preds")
logging.info("Start:\t Saving preds")
return MultiviewResult(cl_type, classifier_config, metrics_scores,
full_pred, hps_duration, fit_duration,
......
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