user_preference_predict/test.py

import datetime
import numpy as np
import pandas as pd
import lightgbm as lgb
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score


def printlog(info):
    nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    print("\n" + "==========" * 8 + "%s" % nowtime)
    print(info + '...\n\n')


# ================================================================================
# 一，读取数据
# ================================================================================
printlog("step1: reading data...")

# 读取dftrain,dftest
breast = datasets.load_breast_cancer()
df = pd.DataFrame(breast.data, columns=[x.replace(' ', '_') for x in breast.feature_names])
df['label'] = breast.target
df['mean_radius'] = df['mean_radius'].apply(lambda x: int(x))
df['mean_texture'] = df['mean_texture'].apply(lambda x: int(x))
dftrain, dftest = train_test_split(df)

categorical_features = ['mean_radius', 'mean_texture']
lgb_train = lgb.Dataset(dftrain.drop(['label'], axis=1), label=dftrain['label'],
                        categorical_feature=categorical_features)

lgb_valid = lgb.Dataset(dftest.drop(['label'], axis=1), label=dftest['label'],
                        categorical_feature=categorical_features,
                        reference=lgb_train)

# ================================================================================
# 二，设置参数
# ================================================================================
printlog("step2: setting parameters...")

boost_round = 50
early_stop_rounds = 10

params = {
    'boosting_type': 'gbdt',
    'objective': 'binary',
    'metric': ['auc'],
    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9,
    'bagging_fraction': 0.8,
    'bagging_freq': 5,
    'verbose': 0
}

# ================================================================================
# 三，训练模型
# ================================================================================
printlog("step3: training model...")

results = {}
gbm = lgb.train(params,
                lgb_train,
                num_boost_round=boost_round,
                valid_sets=(lgb_valid, lgb_train),
                valid_names=('validate', 'train'),
                early_stopping_rounds=early_stop_rounds,
                evals_result=results)

# ================================================================================
# 四，评估模型
# ================================================================================
printlog("step4: evaluating model ...")

y_pred_train = gbm.predict(dftrain.drop('label', axis=1), num_iteration=gbm.best_iteration)
y_pred_test = gbm.predict(dftest.drop('label', axis=1), num_iteration=gbm.best_iteration)

print('train accuracy: {:.5} '.format(accuracy_score(dftrain['label'], y_pred_train > 0.5)))
print('valid accuracy: {:.5} \n'.format(accuracy_score(dftest['label'], y_pred_test > 0.5)))

lgb.plot_metric(results)
lgb.plot_importance(gbm, importance_type="gain")

# ================================================================================
# 五，保存模型
# ================================================================================
printlog("step5: saving model ...")

model_dir = "data/gbm.model"
print("model_dir: %s" % model_dir)
gbm.save_model("data/gbm.model")
printlog("task end...")

###
##
#


lgb.plot_metric(results, metric="auc")

lgb.plot_importance(gbm, importance_type="gain")