def test_base():
base = BaseMachineLearning(
configuration_file=
r'D:\My_Codes\virtualenv_eslearn\Lib\site-packages\eslearn\machine_learning\classification\tests/clf_configuration.json'
)
data_loader = DataLoader(
configuration_file=
r'D:\My_Codes\virtualenv_eslearn\Lib\site-packages\eslearn\machine_learning\classification\tests/clf_configuration.json'
)
data_loader.load_data()
base.get_all_inputs()
print(base.method_feature_preprocessing_)
print(base.param_feature_preprocessing_)
print(base.method_dim_reduction_)
print(base.param_dim_reduction_)
print(base.method_feature_selection_)
print(base.param_feature_selection_)
print(base.method_unbalance_treatment_)
print(base.param_unbalance_treatment_)
print(base.method_machine_learning_)
print(base.param_machine_learning_)
print(base.method_model_evaluation_)
print(base.param_model_evaluation_)
print(base.method_statistical_analysis_)
print(base.param_statistical_analysis_)
def test_base():
base = BaseMachineLearning(
configuration_file=
r'D:\work\lichao\nanjingshifandaxue\code\EEG-Classification-master\data1\eeg.json'
)
data_loader = DataLoader(
configuration_file=
r'D:\work\lichao\nanjingshifandaxue\code\EEG-Classification-master\data1\eeg.json'
)
data_loader.load_data()
base.get_all_inputs()
print(base.method_feature_preprocessing_)
print(base.param_feature_preprocessing_)
print(base.method_dim_reduction_)
print(base.param_dim_reduction_)
print(base.method_feature_selection_)
print(base.param_feature_selection_)
print(base.method_unbalance_treatment_)
print(base.param_unbalance_treatment_)
print(base.method_machine_learning_)
print(base.param_machine_learning_)
print(base.method_model_evaluation_)
print(base.param_model_evaluation_)
print(base.method_statistical_analysis_)
print(base.param_statistical_analysis_)
def run(self):
"""
"""
# Load model
output = pickle.load(open(self.model_file, "rb"))
preprocessor = output["preprocessor"]
best_model = output["model"]
if hasattr(best_model, "best_estimator_"):
best_model = best_model.best_estimator_
# Get data
data_loader = DataLoader(configuration_file=self.data_file)
data_loader.load_data()
feature = data_loader.features_
target = data_loader.targets_
# Predict
if not isinstance(best_model, list): # Force the model and preprocessor is a list
best_model = [best_model, best_model]
if not isinstance(preprocessor, list): # Force the model and preprocessor is a list
preprocessor = [preprocessor, preprocessor]
predict_label = []
pred_prob = []
for prep, model_ in zip(preprocessor, best_model):
# Feature Preprocessing
feature = prep.transform(feature)
# Predict
predict_label.append(model_.predict(feature))
if hasattr(model_, 'predict_proba'):
pred_prob.append(model_.predict_proba(feature))
elif hasattr(model_, 'decision_function'):
prob_pos = model_.decision_function(feature)
prob_pos = \
(prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())
pred_prob.append(prob_pos)
else:
pred_prob = predict_label
# Get voted predict label
final_label = self.vote(predict_label)
final_prob = np.mean(pred_prob,0)
# Evaluation
acc, sens, spec, _, _ = ModelEvaluator().binary_evaluator(
target, final_label, final_prob,
verbose=1, is_showfig=False, is_savefig=False
)
return acc, sens, spec
def test_dataLoading():
data_loader = DataLoader(configuration_file='./dataLoadingTest.json')
data_loader.load_data()