model.py

# Copyright (c) Microsoft Corporation
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# MIT License
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import numpy as np 
import pandas as pd 
import lightgbm as lgb 
import gc
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.metrics import roc_auc_score, roc_curve,log_loss


def get_fea_importance(clf, feature_name):
    gain = clf.feature_importance('gain')
    split_= clf.feature_importance('split')
    importance_df = pd.DataFrame({
        'feature_name':clf.feature_name(),
        'split': split_,
        'gain': gain, 
        'gain_percent':100 *gain / gain.sum(),
        'split_percent':100 *split_ / split_.sum(),
        })
    importance_df['feature_score'] =  0.3*  importance_df['gain_percent'] + 0.7 * importance_df['split_percent'] 
    importance_df.loc[importance_df['split'] ==0, 'feature_score'] = 0
    importance_df['feature_score'] = importance_df['feature_score'] / importance_df['feature_score'].sum()
    importance_df = importance_df.sort_values('feature_score',ascending=False)
    return importance_df


def train_test_split(X, y, test_size, random_state=2018):
    sss = list(StratifiedShuffleSplit(
        n_splits=1, test_size=test_size, random_state=random_state).split(X, y))
    X_train = np.take(X, sss[0][0], axis=0)
    X_test = np.take(X, sss[0][1], axis=0)
    y_train = np.take(y, sss[0][0], axis=0)
    y_test = np.take(y, sss[0][1], axis=0)
    return [X_train, X_test, y_train, y_test]


def lgb_model_train( df, _epoch=1000, target_name='Label', id_index='Id'):
    df = df.loc[df[target_name].isnull()==False]
    feature_name = [i for i in df.columns if i not in [target_name, id_index]]
    for i in feature_name:
        if df[i].dtypes == 'object':
            if df[i].fillna('na').nunique() < 12:
                df.loc[:,i] = df.loc[:,i].fillna('na').astype('category')
            else:
                df.loc[:,i] = LabelEncoder().fit_transform(df.loc[:,i].fillna('na').astype(str))
    params_lgb = {
            "objective": "binary", 
            "metric":"auc", 
            'verbose': -1, 
            "seed": 1024, 
            'num_threads': 4,
            'num_leaves':64, 
            'learning_rate': 0.05,
            'min_data': 200, 
            'bagging_fraction': 0.5,
            'feature_fraction': 0.5,
            'max_depth': -1 ,
    }
    X_train, X_val, y_train, y_val = train_test_split(df[feature_name], df[target_name].values, 0.15, 1024)
    del df
    gc.collect()

    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_val = lgb.Dataset(X_val, y_val, reference=lgb_train)

    #del X_train, X_val, y_train, y_val
    gc.collect()
    clf = lgb.train(
        params_lgb, lgb_train, valid_sets=lgb_val, valid_names='eval', 
        verbose_eval=50, early_stopping_rounds=100, num_boost_round=_epoch)

    fea_importance_now = get_fea_importance(clf, feature_name)
    val_auc = roc_auc_score(y_val,  clf.predict(X_val, num_iteration=clf.best_iteration))
    return fea_importance_now, val_auc