LGBM - Hyper Param Search

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in

import numpy as np
import pandas as pd
import lightgbm
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer#
# Prepare the data
#
num_leaves_valid_values=[2,4,6,8]
max_depth_valid_values=[2,3,4,5]
min_data_in_leaf_valid_values=[3,5,8,13,21]

for min_data_in_leaf_value in min_data_in_leaf_valid_values:
    for max_depth_value in max_depth_valid_values:  
        for num_leaves_value in num_leaves_valid_values: 

            train = pd.read_csv('train.csv')

            # get the labels
            y = train.target.values
            train.drop(['id', 'target'], inplace=True, axis=1)

            x = train.values

            #
            # Create training and validation sets
            #
            x, x_test, y, y_test = train_test_split(x, y, test_size=.2, random_state=42, stratify=y)

            #
            # Create the LightGBM data containers
            #
            categorical_features = [c for c, col in enumerate(train.columns) if 'cat' in col]
            train_data = lightgbm.Dataset(x, label=y)
            #if you have categorical variables, use this:
            #train_data = lightgbm.Dataset(x, label=y, categorical_feature=categorical_features)

            test_data = lightgbm.Dataset(x_test, label=y_test)


            #
            # Train the model
            #
            myoutput={}

            parameters = {
                'application': 'binary',
                'objective': 'binary',
                'metric': 'auc',
                'is_unbalance': 'true',
                'boosting': 'gbdt',
                'feature_fraction': 0.5,
                'bagging_fraction': 0.5,
                'bagging_freq': 20,
                'learning_rate': 0.05,
                'verbose': -1,
                'verbosity':-1,

                'num_leaves': num_leaves_value,
                'max_depth':max_depth_value,
                'min_data_in_leaf':min_data_in_leaf_value
            }

            model = lightgbm.train(parameters,
                                   train_data,
                                   verbose_eval=False,
                                   evals_result=myoutput,
                                   valid_sets=test_data,
                                   num_boost_round=5000,
                                   early_stopping_rounds=100)
            #
            # Create a submission
            #

            submission = pd.read_csv('test.csv')
            ids = submission['id'].values
            submission.drop('id', inplace=True, axis=1)


            x = submission.values
            y = model.predict(x)
            binY = [round(i) for i in y]
            output = pd.DataFrame({'id': ids, 'target': binY})

            output.to_csv("submission.csv", index=False)
                          
            print(min_data_in_leaf_value,max_depth_value,num_leaves_value,max(myoutput['valid_0']['auc']))