class LogisticRegressionExperiment(object):
def __init__(self):
self._data_set = get_pick_data("LogisticRegression")
self._num_features = self._data_set.dynamic_features.shape[1]
self._time_steps = 1
self._n_output = 1
self._model_format()
self._check_path()
def _model_format(self):
learning_rate, max_loss, max_pace, ridge, batch_size, hidden_size, epoch, dropout = lr_setup.all
self._model = LogisticRegression(
num_features=self._num_features,
time_steps=self._time_steps,
n_output=self._n_output,
batch_size=batch_size,
epochs=epoch,
output_n_epoch=ExperimentSetup.output_n_epochs,
learning_rate=learning_rate,
max_loss=max_loss,
dropout=dropout,
max_pace=max_pace,
ridge=ridge)
def _check_path(self):
if not os.path.exists("result_9_16_0"):
os.makedirs("result_9_16_0")
self._filename = "result_9_16_0" + "/" + self._model.name + " " + \
time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
def do_experiments(self):
n_output = 1
dynamic_features = self._data_set.dynamic_features
labels = self._data_set.labels
# tol_test_index = np.zeros(shape=0, dtype=np.int32)
tol_pred = np.zeros(shape=(0, n_output))
tol_label = np.zeros(shape=(0, n_output), dtype=np.int32)
train_dynamic_features, test_dynamic_features, train_labels, test_labels = \
split_logistic_data(dynamic_features,labels)
for i in range(5):
train_dynamic_res, train_labels_res = imbalance_preprocess(
train_dynamic_features[i], train_labels[i],
'LogisticRegression')
train_set = DataSet(train_dynamic_res, train_labels_res)
test_set = DataSet(test_dynamic_features[i].reshape(-1, 92),
test_labels[i].reshape(-1, 1))
self._model.fit(train_set, test_set)
y_score = self._model.predict(test_set)
tol_pred = np.vstack((tol_pred, y_score))
tol_label = np.vstack((tol_label, test_labels[i]))
print("Cross validation: {} of {}".format(i, 5),
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
tol_test_index = np.arange(labels.shape[0] * labels.shape[1])
evaluate(tol_test_index, tol_label, tol_pred, self._filename)
self._model.close()
class LogisticRegressionExperiment(object):
def __init__(self, event_type):
self._event_type = event_type
self._data_set = read_data(event_type)
self._num_features = self._data_set.dynamic_feature.shape[2]
self._time_steps = self._data_set.dynamic_feature.shape[1]
self._n_output = self._data_set.labels.shape[1]
print(event_type)
self._model_format()
self._check_path()
def _model_format(self):
if self._event_type == "qx":
learning_rate, max_loss, max_pace, lasso, ridge = lr_qx_setup.all
elif self._event_type == "cx":
learning_rate, max_loss, max_pace, lasso, ridge = lr_cx_setup.all
else:
learning_rate, max_loss, max_pace, lasso, ridge = lr_xycj_setup.all
self._model = LogisticRegression(
num_features=self._num_features,
time_steps=self._time_steps,
n_output=self._n_output,
batch_size=ExperimentSetup.batch_size,
epochs=ExperimentSetup.epochs,
output_n_epoch=ExperimentSetup.output_n_epochs,
learning_rate=learning_rate,
max_loss=max_loss,
max_pace=max_pace,
lasso=lasso,
ridge=ridge)
def _check_path(self):
if not os.path.exists("average_result_cx_TEST" + self._event_type):
os.makedirs("average_result_cx_TEST" + self._event_type)
self._filename = "average_result_cx_TEST" + self._event_type + "/" + self._model.name + " " + time.strftime(
"%Y-%m-%d-%H-%M-%S", time.localtime())
def do_experiments(self):
dynamic_feature = self._data_set.dynamic_feature
labels = self._data_set.labels
kf = sklearn.model_selection.StratifiedKFold(
n_splits=ExperimentSetup.kfold, shuffle=False)
n_output = labels.shape[1] # classes
tol_test_index = np.zeros(shape=0, dtype=np.int32)
tol_pred = np.zeros(shape=(0, n_output))
tol_label = np.zeros(shape=(0, n_output), dtype=np.int32)
i = 1
for train_idx, test_idx in kf.split(X=dynamic_feature,
y=labels.reshape(-1)): # 五折交叉
train_dynamic = dynamic_feature[train_idx]
train_y = labels[train_idx]
train_dynamic_res, train_y_res = imbalance_preprocess(
train_dynamic, train_y) # SMOTE过采样方法处理不平衡数据集
test_dynamic = dynamic_feature[test_idx]
test_y = labels[test_idx]
train_set = DataSet(train_dynamic_res, train_y_res)
test_set = DataSet(test_dynamic, test_y)
self._model.fit(train_set, test_set, self._event_type)
y_score = self._model.predict(test_set)
tol_test_index = np.concatenate((tol_test_index, test_idx))
tol_pred = np.vstack((tol_pred, y_score))
tol_label = np.vstack((tol_label, test_y))
print(
"Cross validation: {} of {}".format(i, ExperimentSetup.kfold),
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
i += 1
evaluate(tol_test_index, tol_label, tol_pred, self._filename)
self._model.close()
