def evaluate_no_cv(self, train_index, test_index):
x_train = self._x[train_index]
y_train = self._y[train_index]
x_test = self._x[test_index]
y_test = self._y[test_index]
best_parameter = dict()
best_parameter['n_estimators'] = self._algorithm_params['n_estimators_range']
best_parameter['max_depth'] = self._algorithm_params['max_depth_range']
best_parameter['min_samples_split'] = self._algorithm_params['min_samples_split_range']
best_parameter['max_features'] = self._algorithm_params['max_features_range']
_, y_hat, auc, y_hat_train = self._launch_random_forest(x_train, x_test, y_train, y_test,
self._algorithm_params['n_estimators_range'],
self._algorithm_params['max_depth_range'],
self._algorithm_params['min_samples_split_range'],
self._algorithm_params['max_features_range'])
result = dict()
result['best_parameter'] = best_parameter
result['evaluation'] = utils.evaluate_prediction(y_test, y_hat)
best_parameter['balanced_accuracy'] = result['evaluation']['balanced_accuracy']
result['evaluation_train'] = utils.evaluate_prediction(y_train, y_hat_train)
result['y_hat'] = y_hat
result['y_hat_train'] = y_hat_train
result['y'] = y_test
result['y_train'] = y_train
result['y_index'] = test_index
result['x_index'] = train_index
result['auc'] = auc
return result
def evaluate_no_cv(self, train_index, test_index):
x_train = self._x[train_index]
y_train = self._y[train_index]
x_test = self._x[test_index]
y_test = self._y[test_index]
best_parameter = dict()
best_parameter['max_depth'] = self._max_depth_range
best_parameter['learning_rate'] = self._learning_rate_range
best_parameter['n_estimators'] = self._n_estimators_range
best_parameter['colsample_bytree'] = self._colsample_bytree_range
_, y_hat, auc, y_hat_train = self._launch_xgboost(
x_train, x_test, y_train, y_test, self._max_depth_range,
self._learning_rate_range, self._n_estimators_range,
self._colsample_bytree_range)
result = dict()
result['best_parameter'] = best_parameter
result['evaluation'] = utils.evaluate_prediction(y_test, y_hat)
best_parameter['balanced_accuracy'] = result['evaluation'][
'balanced_accuracy']
result['evaluation_train'] = utils.evaluate_prediction(
y_train, y_hat_train)
result['y_hat'] = y_hat
result['y_hat_train'] = y_hat_train
result['y'] = y_test
result['y_train'] = y_train
result['y_index'] = test_index
result['x_index'] = train_index
result['auc'] = auc
return result
def evaluate(self, train_index, test_index):
inner_pool = ThreadPool(self._algorithm_params['n_threads'])
async_result = {}
for i in range(self._algorithm_params['grid_search_folds']):
async_result[i] = {}
x_train = self._x[train_index]
y_train = self._y[train_index]
skf = StratifiedKFold(
n_splits=self._algorithm_params['grid_search_folds'], shuffle=True)
inner_cv = list(skf.split(np.zeros(len(y_train)), y_train))
parameters_combinations = list(
itertools.product(
self._algorithm_params['max_depth_range'],
self._algorithm_params['learning_rate_range'],
self._algorithm_params['n_estimators_range'],
self._algorithm_params['colsample_bytree_range']))
for i in range(len(inner_cv)):
inner_train_index, inner_test_index = inner_cv[i]
x_train_inner = x_train[inner_train_index]
x_test_inner = x_train[inner_test_index]
y_train_inner = y_train[inner_train_index]
y_test_inner = y_train[inner_test_index]
for parameters in parameters_combinations:
async_result[i][parameters] = inner_pool.apply_async(
self._grid_search,
(x_train_inner, x_test_inner, y_train_inner, y_test_inner,
parameters[0], parameters[1], parameters[2],
parameters[3]))
inner_pool.close()
inner_pool.join()
best_parameter = self._select_best_parameter(async_result)
x_test = self._x[test_index]
y_test = self._y[test_index]
_, y_hat, auc, y_hat_train = self._launch_xgboost(
x_train, x_test, y_train, y_test, best_parameter['max_depth'],
best_parameter['learning_rate'], best_parameter['n_estimators'],
best_parameter['colsample_bytree'])
result = dict()
result['best_parameter'] = best_parameter
result['evaluation'] = utils.evaluate_prediction(y_test, y_hat)
result['evaluation_train'] = utils.evaluate_prediction(
y_train, y_hat_train)
result['y_hat'] = y_hat
result['y_hat_train'] = y_hat_train
result['y'] = y_test
result['y_train'] = y_train
result['y_index'] = test_index
result['x_index'] = train_index
result['auc'] = auc
return result
def evaluate(self, train_index, test_index):
inner_pool = ThreadPool(self._algorithm_params["n_threads"])
async_result = {}
for i in range(self._algorithm_params["grid_search_folds"]):
async_result[i] = {}
outer_kernel = self._kernel[train_index, :][:, train_index]
y_train = self._y[train_index]
skf = StratifiedKFold(
n_splits=self._algorithm_params["grid_search_folds"], shuffle=True)
inner_cv = list(skf.split(np.zeros(len(y_train)), y_train))
for i in range(len(inner_cv)):
inner_train_index, inner_test_index = inner_cv[i]
inner_kernel = outer_kernel[
inner_train_index, :][:, inner_train_index]
x_test_inner = outer_kernel[inner_test_index, :][:,
inner_train_index]
y_train_inner, y_test_inner = (
y_train[inner_train_index],
y_train[inner_test_index],
)
for c in self._algorithm_params["c_range"]:
async_result[i][c] = inner_pool.apply_async(
self._grid_search,
(inner_kernel, x_test_inner, y_train_inner, y_test_inner,
c),
)
inner_pool.close()
inner_pool.join()
best_parameter = self._select_best_parameter(async_result)
x_test = self._kernel[test_index, :][:, train_index]
y_train, y_test = self._y[train_index], self._y[test_index]
_, y_hat, auc, y_hat_train = self._launch_svc(outer_kernel, x_test,
y_train, y_test,
best_parameter["c"])
result = dict()
result["best_parameter"] = best_parameter
result["evaluation"] = utils.evaluate_prediction(y_test, y_hat)
result["evaluation_train"] = utils.evaluate_prediction(
y_train, y_hat_train)
result["y_hat"] = y_hat
result["y_hat_train"] = y_hat_train
result["y"] = y_test
result["y_train"] = y_train
result["y_index"] = test_index
result["x_index"] = train_index
result["auc"] = auc
return result
def evaluate(self, train_index, test_index):
inner_pool = ThreadPool(self._n_threads)
async_result = {}
for i in range(self._grid_search_folds):
async_result[i] = {}
outer_kernel = self._kernel[train_index, :][:, train_index]
y_train = self._y[train_index]
skf = StratifiedKFold(n_splits=self._grid_search_folds, shuffle=True)
inner_cv = list(skf.split(np.zeros(len(y_train)), y_train))
for i in range(len(inner_cv)):
inner_train_index, inner_test_index = inner_cv[i]
inner_kernel = outer_kernel[
inner_train_index, :][:, inner_train_index]
x_test_inner = outer_kernel[inner_test_index, :][:,
inner_train_index]
y_train_inner, y_test_inner = y_train[inner_train_index], y_train[
inner_test_index]
for c in self._c_range:
async_result[i][c] = inner_pool.apply_async(
self._grid_search, (inner_kernel, x_test_inner,
y_train_inner, y_test_inner, c))
inner_pool.close()
inner_pool.join()
best_parameter = self._select_best_parameter(async_result)
x_test = self._kernel[test_index, :][:, train_index]
y_train, y_test = self._y[train_index], self._y[test_index]
_, y_hat, auc, y_hat_train = self._launch_svc(outer_kernel, x_test,
y_train, y_test,
best_parameter['c'])
result = dict()
result['best_parameter'] = best_parameter
result['evaluation'] = utils.evaluate_prediction(y_test, y_hat)
result['evaluation_train'] = utils.evaluate_prediction(
y_train, y_hat_train)
result['y_hat'] = y_hat
result['y_hat_train'] = y_hat_train
result['y'] = y_test
result['y_train'] = y_train
result['y_index'] = test_index
result['x_index'] = train_index
result['auc'] = auc
return result
def _grid_search(self, kernel_train, x_test, y_train, y_test, c):
_, y_hat, _, _ = self._launch_svc(kernel_train, x_test, y_train,
y_test, c)
res = utils.evaluate_prediction(y_test, y_hat)
return res['balanced_accuracy']
def _grid_search(self, x_train, x_test, y_train, y_test, max_depth, learning_rate, n_estimators, colsample_bytree):
_, y_hat, _, _ = self._launch_xgboost(x_train, x_test, y_train, y_test, max_depth, learning_rate, n_estimators,
colsample_bytree)
res = utils.evaluate_prediction(y_test, y_hat)
return res['balanced_accuracy']
def _grid_search(
self,
x_train,
x_test,
y_train,
y_test,
n_estimators,
max_depth,
min_samples_split,
max_features,
):
_, y_hat, _, _ = self._launch_random_forest(
x_train,
x_test,
y_train,
y_test,
n_estimators,
max_depth,
min_samples_split,
max_features,
)
res = utils.evaluate_prediction(y_test, y_hat)
return res["balanced_accuracy"]
def _grid_search(self, x_train, x_test, y_train, y_test, c):
_, y_hat, _, _ = self._launch_logistic_reg(x_train, x_test, y_train,
y_test, c)
res = utils.evaluate_prediction(y_test, y_hat)
return res["balanced_accuracy"]
def evaluate_no_cv(self, train_index, test_index):
x_train = self._x[train_index]
y_train = self._y[train_index]
x_test = self._x[test_index]
y_test = self._y[test_index]
best_parameter = dict()
best_parameter["max_depth"] = self._algorithm_params["max_depth_range"]
best_parameter["learning_rate"] = self._algorithm_params[
"learning_rate_range"]
best_parameter["n_estimators"] = self._algorithm_params[
"n_estimators_range"]
best_parameter["colsample_bytree"] = self._algorithm_params[
"colsample_bytree_range"]
_, y_hat, auc, y_hat_train = self._launch_xgboost(
x_train,
x_test,
y_train,
y_test,
self._algorithm_params["max_depth_range"],
self._algorithm_params["learning_rate_range"],
self._algorithm_params["n_estimators_range"],
self._algorithm_params["colsample_bytree_range"],
)
result = dict()
result["best_parameter"] = best_parameter
result["evaluation"] = utils.evaluate_prediction(y_test, y_hat)
best_parameter["balanced_accuracy"] = result["evaluation"][
"balanced_accuracy"]
result["evaluation_train"] = utils.evaluate_prediction(
y_train, y_hat_train)
result["y_hat"] = y_hat
result["y_hat_train"] = y_hat_train
result["y"] = y_test
result["y_train"] = y_train
result["y_index"] = test_index
result["x_index"] = train_index
result["auc"] = auc
return result
def _compute_average_test_accuracy(self, y_list, yhat_list):
from clinica.pipelines.machine_learning.ml_utils import evaluate_prediction
return evaluate_prediction(y_list, yhat_list)['balanced_accuracy']
def evaluate(self, train_index, test_index):
inner_pool = ThreadPool(self._algorithm_params["n_threads"])
async_result = {}
for i in range(self._algorithm_params["grid_search_folds"]):
async_result[i] = {}
x_train = self._x[train_index]
y_train = self._y[train_index]
skf = StratifiedKFold(
n_splits=self._algorithm_params["grid_search_folds"], shuffle=True)
inner_cv = list(skf.split(np.zeros(len(y_train)), y_train))
parameters_combinations = list(
itertools.product(
self._algorithm_params["n_estimators_range"],
self._algorithm_params["max_depth_range"],
self._algorithm_params["min_samples_split_range"],
self._algorithm_params["max_features_range"],
))
for i in range(len(inner_cv)):
inner_train_index, inner_test_index = inner_cv[i]
x_train_inner = x_train[inner_train_index]
x_test_inner = x_train[inner_test_index]
y_train_inner = y_train[inner_train_index]
y_test_inner = y_train[inner_test_index]
for parameters in parameters_combinations:
async_result[i][parameters] = inner_pool.apply_async(
self._grid_search,
(
x_train_inner,
x_test_inner,
y_train_inner,
y_test_inner,
parameters[0],
parameters[1],
parameters[2],
parameters[3],
),
)
inner_pool.close()
inner_pool.join()
best_parameter = self._select_best_parameter(async_result)
x_test = self._x[test_index]
y_test = self._y[test_index]
_, y_hat, auc, y_hat_train = self._launch_random_forest(
x_train,
x_test,
y_train,
y_test,
best_parameter["n_estimators"],
best_parameter["max_depth"],
best_parameter["min_samples_split"],
best_parameter["max_features"],
)
result = dict()
result["best_parameter"] = best_parameter
result["evaluation"] = utils.evaluate_prediction(y_test, y_hat)
result["evaluation_train"] = utils.evaluate_prediction(
y_train, y_hat_train)
result["y_hat"] = y_hat
result["y_hat_train"] = y_hat_train
result["y"] = y_test
result["y_train"] = y_train
result["y_index"] = test_index
result["x_index"] = train_index
result["auc"] = auc
return result
