lgb model quickstart

model_v1

%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import lightgbm as lgb
import shap
from sklearn.model_selection import train_test_split

shap.initjs()

# enter input file info here #
input_file = ''

dataset = pd.read_csv(input_file)

X_train, X_test, y_train, y_test = train_test_split(
     dataset[[c for c in dataset if 'target' not in c]], dataset['target'], test_size=0.33, random_state=42)
     
lgb_train = lgb.Dataset(X_train, label=y_train)
lgb_test = lgb.Dataset(X_test, label=y_test, reference=lgb_train)

params = {        'objective':'binary',
        'boost_from_average': False,
        'learning_rate': 0.05,
        'num_leaves': 31,
        'lambda_l2': 0.1,
        'lambda_l1': 0.1,
        'bagging_fraction': 0.8,
        'bagging_freq': 1,
        'min_child_weight': 2,
        'min_split_gain': 0.5,        
        'feature_fraction': 0.25,
        'metric': ['binary_logloss'],
        'max_bin': 63    
         }

params['metric'] = 'binary_error'

num_round = 1000
lgb_model = lgb.train(params, lgb_train, num_round, valid_sets=[lgb_test], early_stopping_rounds=250, verbose_eval=250)

lgb.plot_importance(lgb_model, importance_type='gain', max_num_features=20)
lgb.plot_importance(lgb_model, importance_type='split', max_num_features=20)

features = list(X_train)
X = X_train.apply(pd.to_numeric).values
shap_values = lgb_model.predict(X, pred_contrib=True)

shap.summary_plot(shap_values, X_train.values, X_train.columns, plot_type='violin')

def get_top_feats(shap_values, num_feats):
    feature_order = np.argsort(np.sum(np.abs(shap_values), axis=0)[:-1])
    feature_order = list(feature_order[-min(num_feats,len(feature_order)):])
    feature_order = feature_order[::-1]
    return list(mds['X_test'].iloc[:,feature_order])

top_feats = get_top_feats(shap_values, 40)
print(top_feats)