def test_regression(self):
data, target = load_boston(True)
x_train, x_test, y_train, y_test = train_test_split(data,
target,
test_size=0.2,
random_state=42)
ngb = NGBoost(Base=default_tree_learner,
Dist=Normal,
Score=MLE,
natural_gradient=True,
verbose=False)
ngb.fit(x_train, y_train)
preds = ngb.predict(x_test)
score = mean_squared_error(y_test, preds)
assert score <= 8.0
def fold_run(self,
src_dir : str,
X_train : pd.DataFrame,
y_train,
X_test : pd.DataFrame,
n_folds : int,
col : str,
parameters = None,
categorical_features = None):
"""
# Arguments:
: src_dir - str - main dir for saving model,history and fold runs
: X_train,y_train - training dataset with labels
: X_test,y_test - test dataset with labels
: n_folds - number of folds to split dataset
: col - if self.stratify is True, you need to specified a col that consist of
binary of multilabel classes because StratifiedKFold does not support continous values
: parameters - run_parameters that are necessary to run the Tree-Based models,
for better understading of these parameters, you should go and read LightGBM,
XGBoost,CatBoost API
: categorical_features - list - list of categorical features in dataset,
necessary for LightGBM and CatBoost
# Returns:
valid_predictions,test_predictions - predictions made by model
"""
if isinstance(y_train, pd.DataFrame):
y_train = y_train.values
if src_dir:
if os.path.isdir(src_dir):
pass
else:
print(f"Making dir:{src_dir}")
os.makedirs(src_dir)
try:
print("X_train_shape",X_train.shape)
if X_test is not None:
print("X_test_shape",X_test.shape)
except ValueError:
print("Shape does not fit")
if self.stratify:
print(f"Make {n_folds} stratified folds")
kf = StratifiedKFold(n_splits=n_folds,shuffle=True,random_state = self.seed)
elif self.time_series:
kf = TimeSeriesSplit(n_splits=n_folds,random_state = self.seed)
else:
kf = KFold(n_splits=n_folds,random_state = self.seed)
valid_predictions = np.zeros((X_train.shape[0],n_folds))
print("Vaild_predict",valid_predictions.shape[0])
if X_test is not None:
test_predictions = np.zeros((X_test.shape[0],n_folds))
print("Test_predict",test_predictions.shape[0])
i = 0
for train_index, val_index in kf.split(X_train,y_train) if self.stratify is False else kf.split(X_train,X_train[col]):
if self.train_gbm:
print("Train LightGBM")
train_X = X_train.iloc[train_index]
val_X = X_train.iloc[val_index]
if isinstance(y_train,pd.DataFrame):
train_y = y_train.iloc[train_index]
val_y = y_train.iloc[val_index]
else:
train_y = y_train[train_index]
val_y = y_train[val_index]
lgb_train = lgb.Dataset(train_X,train_y,categorical_feature = categorical_features)
lgb_val = lgb.Dataset(val_X,val_y,categorical_feature = categorical_features,reference = lgb_train)
gbm = lgb.train(params = parameters,
train_set=lgb_train,
num_boost_round=parameters['num_boost_round'],
valid_sets=[lgb_train,lgb_val],
early_stopping_rounds=parameters['early_stopping_rounds'],
evals_result = self.history,
verbose_eval = parameters['verbose_eval'],
feval = self.eval_metric)
valid_predictions[val_index,i] = gbm.predict(val_X,num_iteration=gbm.best_iteration)
valid_predictions[val_index,i] = np.clip(valid_predictions[val_index,i],a_min=0,a_max=None)
r2= r2_score(np.nan_to_num(val_y[val_index]),np.nan_to_num(valid_predictions[val_index,i]))
log_error = np.sqrt(mean_squared_log_error(np.nan_to_num(val_y[val_index]),np.nan_to_num(valid_predictions[val_index,i])))
print(f"R2 Score for current validation set:{r2}")
print(f"RMSLE for current val set:{log_error}")
if self.save_model:
print("Saving model")
gbm.save_model(f'{src_dir}/fold_{i}_{self.name}_eval_history.txt')
if self.save_history:
print("Saving Hisotry")
pd.to_pickle(self.history,f'{src_dir}/fold_{i}_{self.name}_pickle_eval_history.pkl')
if self.test_predict:
test_predictions[:,i] = self.predict_test(X_test,i,src_dir)
test_predictions[:,i] = np.clip(test_predictions[:,i],a_min=0,a_max=None)
if self.importance:
self.visualize_importance(i,src_dir)
if self.show_metric_results:
self.show_results(i)
elif self.train_xg:
print("Train XGBooost")
train_X = np.nan_to_num(X_train.iloc[train_index])
val_X = np.nan_to_num(X_train.iloc[val_index])
if isinstance(y_train,pd.DataFrame):
train_y = np.nan_to_num(y_train.iloc[train_index])
val_y = np.nan_to_num(y_train.iloc[val_index])
else:
train_y = np.nan_to_num(y_train[train_index])
val_y = np.nan_to_num(y_train[val_index])
xg_train = xgb.DMatrix(train_X,label=train_y,feature_names=X_train.columns)
xg_val = xgb.DMatrix(val_X,label=val_y,feature_names=X_train.columns)
eval_list = [(xg_train,'train'),(xg_val,'val')]
xgboost_train = xgb.train(parameters,xg_train,evals=eval_list,
evals_result=self.history,
num_boost_round=parameters['boost_round'],
early_stopping_rounds=parameters['early_stopping'],
verbose_eval = parameters['verbose_eval'])
valid_predictions[val_index,i] = xgboost_train.predict(xg_val,
ntree_limit = xgboost_train.best_ntree_limit)
valid_predictions[val_index,i] = np.clip(valid_predictions[val_index,i],a_min=0,a_max=None)
r2= r2_score(np.nan_to_num(val_y[val_index]),np.nan_to_num(valid_predictions[val_index,i]))
log_error = np.sqrt(mean_squared_log_error(np.nan_to_num(val_y[val_index]),np.nan_to_num(valid_predictions[val_index,i])))
print(f"R2 Score for current validation set:{r2}")
print(f"RMSLE for current val set:{log_error}")
if self.save_model:
print("Saving model")
xgboost_train.save_model(f'{src_dir}/fold_{i}_{self.name}_eval_history.txt')
if self.save_history:
print("Saving Hisotry")
pd.to_pickle(self.history,f'{src_dir}/fold_{i}_{self.name}_pickle_eval_history.pkl')
if self.test_predict:
test_predictions[:,i] = self.predict_test(X_test,i,src_dir,xgboost_train)
test_predictions[:,i] = np.clip(test_predictions[:,i],a_min=0,a_max=None)
if self.importance:
self.visualize_importance(i,src_dir)
if self.show_metric_results:
self.show_results(i)
elif self.train_cat:
print("Training CatBoost")
train_X = np.array(X_train.iloc[train_index],dtype=np.float32)
val_X = np.array(X_train.iloc[val_index],dtype=np.float32)
if isinstance(y_train,pd.DataFrame):
train_y = np.array(y_train.iloc[train_index],dtype=np.float32)
val_y = np.array(y_train.iloc[val_index],dtype=np.float32)
else:
train_y = np.array(y_train[train_index],dtype=np.float32)
val_y = np.array(y_train[val_index],dtype=np.float32)
cat_train = catboost.Pool(train_X,label=train_y)
cat_test = catboost.Pool(val_X,label=val_y)
self.cat = catboost.CatBoostRegressor(**parameters).fit(cat_train,use_best_model=True,
eval_set=cat_test,verbose_eval=True)
self.history = self.cat.get_evals_result()
#Index Error after first epoch, need to fix it
valid_predictions[val_index,i] = self.cat.predict(cat_test)
valid_predictions[val_index,i] = np.clip(valid_predictions[val_index,i],a_min=0,a_max=None)
r2= r2_score(np.nan_to_num(val_y[val_index]),np.nan_to_num(valid_predictions[val_index,i]))
log_error = np.sqrt(mean_squared_log_error(np.nan_to_num(val_y[val_index]),np.nan_to_num(valid_predictions[val_index,i])))
print(f"R2 Score for current validation set:{r2}")
print(f"RMSLE for current val set:{log_error}")
if self.save_model:
print("Saving model")
self.cat.save_model(f'{src_dir}/fold_{i}_{self.name}_eval_history',format='json')
if self.test_predict:
test_predictions[:,i] = self.predict_test(X_test,i,src_dir)
test_predictions[:,i] = np.clip(test_predictions[:,i],a_min=0,a_max=None)
elif self.train_ng:
print("Train NGBooost")
train_X = np.nan_to_num(X_train.iloc[train_index])
val_X = np.nan_to_num(X_train.iloc[val_index])
if isinstance(y_train,pd.DataFrame):
train_y = np.nan_to_num(y_train.iloc[train_index])
val_y = np.nan_to_num(y_train.iloc[val_index])
else:
train_y = np.nan_to_num(y_train[train_index])
val_y = np.nan_to_num(y_train[val_index])
ng = NGBoost(Dist=Normal,Score=MLE,
Base=default_tree_learner,natural_gradient=True,
n_estimators = 150,learning_rate = 0.01,verbose=True,
verbose_eval=50).fit(train_X,train_y)
valid_predictions[val_index,i] = ng.predict(val_X)
valid_predictions[val_index,i] = np.clip(valid_predictions[val_index,i],a_min=0,a_max=None)
rmse = np.sqrt(mean_squared_error(np.nan_to_num(val_y[val_index]),np.nan_to_num(valid_predictions[val_index,i])))
r2= r2_score(np.nan_to_num(val_y[val_index]),np.nan_to_num(valid_predictions[val_index,i]))
log_error = np.sqrt(mean_squared_log_error(np.nan_to_num(val_y[val_index]),np.nan_to_num(valid_predictions[val_index,i])))
print(f"RMSE for current fold:{rmse}")
print(f"R2 Score for current fold:{r2}")
print(f"RMSLE for current val set:{log_error}")
test_predictions[:,i] = np.clip(ng.predict(X_test),a_min=0,a_max=None)
i += 1
if self.jsonize:
print("Saving model parameters to json")
if os.path.isdir('parameters'):
pass
else:
print("Making Dir: parameters")
os.makedirs('parameters')
model_dict = {"model":f"{src_dir}_{i}_folds",
"parameters":parameters}
with open(f"./parameters/{src_dir}_{i}_fold.json",'w+') as model_param:
json.dump(model_dict,model_param)
if self.prepare_submission:
self.output_submission(self,test_predictions,i)
return valid_predictions,test_predictions
'number_of_particles_in_this_jet', 'jet_px', 'jet_py', 'jet_pz',
'jet_energy', 'jet_mass'
]]
# 随机切分数据集
X_train, X_test, Y_train, Y_test = train_test_split(features.values,
train_data.label.values,
test_size=0.2)
ngb = NGBoost(Base=default_tree_learner,
Dist=Normal,
Score=MLE(),
natural_gradient=True,
verbose=False)
# 拟合
ngb.fit(X_train, Y_train)
# 预测
Y_preds = ngb.predict(X_test)
test_data = pd.read_csv("jet_simple_data/simple_test_R04_jet.csv")
features = test_data[[
'number_of_particles_in_this_jet', 'jet_px', 'jet_py', 'jet_pz',
'jet_energy', 'jet_mass'
]]
Y_test_data = ngb.predict(features)
with open("submmission.csv", "") as f:
f.write("id,label\n")
for jet_id, label in zip(test_data['jet_id'], Y_test_data):
f.write(jet_id + "," + label + "\n")
Y_dists = ngb.pred_dist(X_test)
# 检验均方误差 test Mean Squared Error
test_MSE = mean_squared_error(Y_preds, Y_test)
print('Test MSE', test_MSE)
# 检验负对数似然test Negative Log Likelihood
