def transform_regression(self, c_config, d_config):
#step1
test_continuous_df = self.test_df[self.continuous_df_col]
test_discrete_df = self.test_df[self.discrete_df_col]
ag = AutoGenerator(test_continuous_df, None, c_config, None,
self.clf_obj, self.metric)
new_add = {}
for col, transform_method in self.c_transform_methods.items():
new_add[col] = ag.restore_ind(transform_method, test_continuous_df)
for col, new_add_value in new_add.items():
test_continuous_df[col] = new_add_value
#step2
for col, bagging_method in self.bagging_methods.items():
test_discrete_df["bagging{}".format(col)] = bagging_method(
test_continuous_df[col].values)
ag = AutoGenerator(test_discrete_df, None, d_config, None,
self.clf_obj, self.metric)
new_add = {}
for col, transform_method in self.d_transform_methods.items():
new_add[col] = ag.restore_ind(transform_method, test_discrete_df)
for col, new_add_value in new_add.items():
test_discrete_df[col] = new_add_value
test_df = pd.concat([test_discrete_df, test_continuous_df], axis=1)
test_df = get_dummy(test_df, self.dummy_candidate_col)
test_df = remove_same(test_df)
test_df.to_csv("middle2.csv", index=False)
#step3
new_test_df = remove_same(test_df)
return new_test_df
def __transform_classification(self, c_config, d_config, df):
target = pd.DataFrame()
if self.target_label in df.columns:
target = pd.DataFrame(df[self.target_label])
df = df.drop(self.target_label, axis=1)
#step1
test_continuous_df = df[self.continuous_df_col]
test_discrete_df = df[self.discrete_df_col]
ag = AutoGenerator(test_continuous_df, None, c_config, None,
self.clf_obj, self.metric)
new_add = {}
for col, transform_method in self.c_transform_methods.items():
new_add[col] = ag.restore_ind(transform_method, test_continuous_df)
for col, new_add_value in new_add.items():
test_continuous_df[col] = new_add_value
#step2
for col, bagging_method in self.bagging_methods.items():
test_discrete_df["bagging{}".format(col)] = bagging_method(
test_continuous_df[col].values)
ag = AutoGenerator(test_discrete_df, None, d_config, None,
self.clf_obj, self.metric)
new_add = {}
for col, transform_method in self.d_transform_methods.items():
new_add[col] = ag.restore_ind(transform_method, test_discrete_df)
for col, new_add_value in new_add.items():
test_discrete_df[col] = new_add_value
test_df = pd.concat([test_discrete_df, test_continuous_df], axis=1)
test_df = get_dummy(test_df, self.dummy_candidate_col)
test_df = remove_same(test_df)
test_df.to_csv("middle2.csv", index=False)
#step3
#new_test_df = pd.DataFrame()
#for col in self.select_col:
# if col in test_df.columns:
# new_test_df[col] = test_df[col].values
new_test_df = remove_same(test_df)
if len(target) != 0:
new_test_df = pd.concat([new_test_df, target], axis=1)
return new_test_df
def pre_select2(self, train_df, test_df, target_label):
fs = FeatureSelection()
train_df, select_col = fs.run(train_df, target_label, 150)
new_test_df = pd.DataFrame()
for col in select_col:
if col in test_df.columns:
new_test_df[col] = test_df[col].values
new_test_df = remove_same(new_test_df)
return train_df, new_test_df
def fit_classification(self, c_config, d_config):
target = self.train_df[self.target_label]
feature = self.train_df.drop(self.target_label, axis=1)
#step1
discrete_df, continuous_df = ColType(feature).run()
print("cshape")
print(continuous_df.shape)
print(discrete_df.shape)
self.continuous_df_col = continuous_df.columns
self.discrete_df_col = discrete_df.columns
#step2
print("begin generate")
continuous_df = pd.concat([continuous_df, target], axis=1)
ag = AutoGenerator(continuous_df, self.target_label, c_config, feature,
self.clf_obj, self.metric)
new_add_cols, transform_methods = ag.run(popsize=50,
matepb=0.7,
mutpb=0.2,
gensize=20,
selectsize=100,
kbest=50,
direction=self.direction)
for i in range(len(new_add_cols)):
col_name = "new{}".format(i)
continuous_df[col_name] = new_add_cols[i]
self.c_transform_methods[col_name] = transform_methods[i]
#step3
lb = LoopBagging(continuous_df, self.target_label)
new_add_bagging = lb.run()
print("new add baggung")
print(new_add_bagging)
for col, value in new_add_bagging.items():
col_name = "bagging{}".format(col)
discrete_df[col_name] = value[2]
self.bagging_methods[col] = value[0]
discrete_df = pd.concat([discrete_df, target], axis=1)
ag = AutoGenerator(discrete_df, self.target_label, d_config, feature,
self.clf_obj, self.metric)
new_add_cols, transform_methods = ag.run(popsize=50,
matepb=0.7,
mutpb=0.2,
gensize=20,
selectsize=100,
kbest=50,
direction=self.direction)
for i in range(len(new_add_cols)):
col_name = "newd{}".format(i)
discrete_df[col_name] = new_add_cols[i]
self.d_transform_methods[col_name] = transform_methods[i]
#step4
train_df = pd.concat([discrete_df, continuous_df], axis=1)
train_df = get_dummy(train_df, self.dummy_candidate_col)
train_df = remove_same(train_df)
train_df.to_csv("/tmp/middle.csv", index=False)
# fs = FeatureSelection()
# train_df, self.select_col = fs.run(train_df, self.target_label, self.final_feature_number)
# train_df_without_target = train_df.drop(target_label,axis=1)
# train_df_without_target_with_dummy, self.dummy_col = get_dummy(train_df_without_target)
# train_df = pd.concat([train_df_without_target_with_dummy, target],axis=1)
#step
# train_df = pre_select(train_df, target_label)
#
# #step5
# myas = AutoSelection(train_df, target_label)
# myas.run(pop_num=100, cxpb=0.6, mutpb=0.2, gen_num=10)
# train_df, select_col=myas.get_best()
train_df = remove_same(train_df)
return train_df