# coding: utf-8
from pathlib import Path

import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV

import lightgbm as lgb

print("Loading data...")
# load or create your dataset
regression_example_dir = Path(__file__).absolute().parents[1] / "regression"
df_train = pd.read_csv(str(regression_example_dir / "regression.train"), header=None, sep="\t")
df_test = pd.read_csv(str(regression_example_dir / "regression.test"), header=None, sep="\t")

y_train = df_train[0]
y_test = df_test[0]
X_train = df_train.drop(0, axis=1)
X_test = df_test.drop(0, axis=1)

print("Starting training...")
# train
gbm = lgb.LGBMRegressor(num_leaves=31, learning_rate=0.05, n_estimators=20)
gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], eval_metric="l1", callbacks=[lgb.early_stopping(5)])

print("Starting predicting...")
# predict
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)
# eval
rmse_test = mean_squared_error(y_test, y_pred) ** 0.5
print(f"The RMSE of prediction is: {rmse_test}")

# feature importances
print(f"Feature importances: {list(gbm.feature_importances_)}")


# self-defined eval metric
# f(y_true: array, y_pred: array) -> name: str, eval_result: float, is_higher_better: bool
# Root Mean Squared Logarithmic Error (RMSLE)
def rmsle(y_true, y_pred):
    return "RMSLE", np.sqrt(np.mean(np.power(np.log1p(y_pred) - np.log1p(y_true), 2))), False


print("Starting training with custom eval function...")
# train
gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], eval_metric=rmsle, callbacks=[lgb.early_stopping(5)])


# another self-defined eval metric
# f(y_true: array, y_pred: array) -> name: str, eval_result: float, is_higher_better: bool
# Relative Absolute Error (RAE)
def rae(y_true, y_pred):
    return "RAE", np.sum(np.abs(y_pred - y_true)) / np.sum(np.abs(np.mean(y_true) - y_true)), False


print("Starting training with multiple custom eval functions...")
# train
gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], eval_metric=[rmsle, rae], callbacks=[lgb.early_stopping(5)])

print("Starting predicting...")
# predict
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)
# eval
rmsle_test = rmsle(y_test, y_pred)[1]
rae_test = rae(y_test, y_pred)[1]
print(f"The RMSLE of prediction is: {rmsle_test}")
print(f"The RAE of prediction is: {rae_test}")

# other scikit-learn modules
estimator = lgb.LGBMRegressor(num_leaves=31)

param_grid = {"learning_rate": [0.01, 0.1, 1], "n_estimators": [20, 40]}

gbm = GridSearchCV(estimator, param_grid, cv=3)
gbm.fit(X_train, y_train)

print(f"Best parameters found by grid search are: {gbm.best_params_}")

# coding: utf-8
from pathlib import Path

import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV

import lightgbm as lgb

print('Loading data...')
# load or create your dataset
regression_example_dir = Path(__file__).absolute().parents[1] / 'regression'
df_train = pd.read_csv(str(regression_example_dir / 'regression.train'), header=None, sep='\t')
df_test = pd.read_csv(str(regression_example_dir / 'regression.test'), header=None, sep='\t')

y_train = df_train[0]
y_test = df_test[0]
X_train = df_train.drop(0, axis=1)
X_test = df_test.drop(0, axis=1)

print('Starting training...')
# train
gbm = lgb.LGBMRegressor(num_leaves=31,
                        learning_rate=0.05,
                        n_estimators=20)
gbm.fit(X_train, y_train,
        eval_set=[(X_test, y_test)],
        eval_metric='l1',
        callbacks=[lgb.early_stopping(5)])

print('Starting predicting...')
# predict
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)
# eval
rmse_test = mean_squared_error(y_test, y_pred) ** 0.5
print(f'The RMSE of prediction is: {rmse_test}')

# feature importances
print(f'Feature importances: {list(gbm.feature_importances_)}')


# self-defined eval metric
# f(y_true: array, y_pred: array) -> name: str, eval_result: float, is_higher_better: bool
# Root Mean Squared Logarithmic Error (RMSLE)
def rmsle(y_true, y_pred):
    return 'RMSLE', np.sqrt(np.mean(np.power(np.log1p(y_pred) - np.log1p(y_true), 2))), False


print('Starting training with custom eval function...')
# train
gbm.fit(X_train, y_train,
        eval_set=[(X_test, y_test)],
        eval_metric=rmsle,
        callbacks=[lgb.early_stopping(5)])


# another self-defined eval metric
# f(y_true: array, y_pred: array) -> name: str, eval_result: float, is_higher_better: bool
# Relative Absolute Error (RAE)
def rae(y_true, y_pred):
    return 'RAE', np.sum(np.abs(y_pred - y_true)) / np.sum(np.abs(np.mean(y_true) - y_true)), False


print('Starting training with multiple custom eval functions...')
# train
gbm.fit(X_train, y_train,
        eval_set=[(X_test, y_test)],
        eval_metric=[rmsle, rae],
        callbacks=[lgb.early_stopping(5)])

print('Starting predicting...')
# predict
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)
# eval
rmsle_test = rmsle(y_test, y_pred)[1]
rae_test = rae(y_test, y_pred)[1]
print(f'The RMSLE of prediction is: {rmsle_test}')
print(f'The RAE of prediction is: {rae_test}')

# other scikit-learn modules
estimator = lgb.LGBMRegressor(num_leaves=31)

param_grid = {
    'learning_rate': [0.01, 0.1, 1],
    'n_estimators': [20, 40]
}

gbm = GridSearchCV(estimator, param_grid, cv=3)
gbm.fit(X_train, y_train)

print(f'Best parameters found by grid search are: {gbm.best_params_}')
