LightGBM

from sklearn.model_selection import train_test_split
col = [i for i in train_notnull.columns if i not in ['sales', 'date','id']]
y = 'sales'
train_x, test_x, train_y, test_y = train_test_split(train_notnull[col],train_notnull[y], test_size=0.2, random_state=2018)

import lightgbm as lgb
from bayes_opt import BayesianOptimization
# select the best parameter of LGBM
def bayes_parameter_opt_lgb(X, y, init_round=15, opt_round=25, n_folds=5, random_seed=6, n_estimators=10000, learning_rate=0.02, output_process=False):
    # prepare data
    train_data = lgb.Dataset(data=X, label=y)
    # parameters
    def lgb_eval(num_leaves, feature_fraction, bagging_fraction, max_depth, lambda_l1, lambda_l2, min_split_gain, min_child_weight):
        params = {'application':'regression_l1','num_iterations': n_estimators, 'learning_rate':learning_rate, 'early_stopping_round':100, 'metric':'auc'}
        params["num_leaves"] = int(round(num_leaves))
        params['feature_fraction'] = max(min(feature_fraction, 1), 0)
        params['bagging_fraction'] = max(min(bagging_fraction, 1), 0)
        params['max_depth'] = int(round(max_depth))
        params['lambda_l1'] = max(lambda_l1, 0)
        params['lambda_l2'] = max(lambda_l2, 0)
        params['min_split_gain'] = min_split_gain
        params['min_child_weight'] = min_child_weight
        cv_result = lgb.cv(params, train_data, nfold=n_folds, seed=random_seed, stratified=True, verbose_eval =200, metrics=['auc'])
        return max(cv_result['auc-mean'])
    # range 
    lgbBO = BayesianOptimization(lgb_eval, {'num_leaves': (24, 45),
                                            'feature_fraction': (0.1, 0.9),
                                            'bagging_fraction': (0.8, 1),
                                            'max_depth': (5, 8.99),
                                            'lambda_l1': (0, 5),
                                            'lambda_l2': (0, 3),
                                            'min_split_gain': (0.001, 0.1),
                                            'min_child_weight': (5, 50)}, random_state=0)
    # optimize
    lgbBO.maximize(init_points=init_round, n_iter=opt_round)
    
    # output optimization process
    if output_process==True: lgbBO.points_to_csv("bayes_opt_result.csv")
    
    # return best parameters
    return lgbBO.res['max']['max_params']

opt_params = bayes_parameter_opt_lgb(train_x, train_y, init_round=5, opt_round=10, n_folds=3, random_seed=6, n_estimators=100, learning_rate=0.02)
print(opt_params)

def model(train_x,train_y,test_x,test_y,col):
    params = {
        'nthread': 10,
#       'max_depth': 5.347645905809148,
        'max_depth': 9,
        'task': 'train',
        'boosting_type': 'gbdt',
        'objective': 'regression_l1',
        'metric': 'mape', # this is abs(a-e)/max(1,a)
        'num_leaves': 26,
#        'num_leaves': 64,
        'learning_rate': 0.2,
#        'feature_fraction': 0.9,
        'feature_fraction': 0.12341870437127458,
        'bagging_fraction': 0.8292882963367367,
#        'bagging_fraction': 0.8,
        'bagging_freq': 5,
        'lambda_l1': 4.214312104137376,
        'lambda_l2': 2.84705737213127,
#       'lambda_l1': 0.06,
#       'lambda_l2': 0.1,
        'verbose': -1,
        'min_child_weight': 5.489438096050738,
        'min_split_gain': 0.014593823181544105,
        }
    
    lgb_train = lgb.Dataset(train_x,train_y)
    lgb_valid = lgb.Dataset(test_x,test_y)
    model = lgb.train(params, lgb_train, 3000, valid_sets=[lgb_train, lgb_valid],early_stopping_rounds=50, verbose_eval=50)
    return model

model = model(train_x,train_y,test_x,test_y,col)

y_pred = model.predict(test[col])
result = pd.DataFrame({'id':sample_submission['id'].as_matrix(), 'sales':y_pred.astype(np.int32)})
result.to_csv('submission.csv', index=False)

xgboost

调参流程：

1. 设置初始值，如最重要的learning_rate和estimator，由于后续grid search时间较长，所以learning_rate要较大，一般可以取值为0.1；
2. 保持learning rate和其他booster相关的参数不变，用户cv的方法调节estimators的参数；
3. 保持estimator和learning rate不变，调节booster相关的参数，可以从影响最大的max_depth和min_child_weight开始，然后是gamma, subsample, colsample_bytree和Regularization Parameters等，逐步调节所有可能有影响的booster参数，调节时，可以先粗粒度，确定粗粒度范围后，再细粒度调节，在调节过程中，每得到一组最佳参数后，可以尝试不断用cv的方法调节estimator参数；
4. 最后缩小learning rate，此时estimator的数量会增加，训练时间也会较长，得到最佳的learning rate和estimator的值；
5. 得到一组较好的参数。

import xgboost as xgb
from xgboost import plot_importance
from sklearn.model_selection import train_test_split
# train_xgb = train.filter(regex='sales|year|item_.*|month_.*|weekday_.*|quarter_.*|store_.*|monthavg|weekdayavg')
# test_xgb = test.filter(regex='year|item_.*|month_.*|weekday_.*|quarter_.*|store_.*|monthavg|weekdayavg')
train_xgb = train.dropna(axis=0, how='any')
test_xgb = test

x_train,x_test,y_train,y_test = train_test_split(train_xgb.drop('sales',axis=1),train_xgb.pop('sales'),random_state=123,test_size=0.2)

def XGBmodel(x_train,x_test,y_train,y_test):
    matrix_train = xgb.DMatrix(x_train,label=y_train)
    matrix_test = xgb.DMatrix(x_test,label=y_test)
    model=xgb.train(params={'objective':'reg:linear','eval_metric':'mae'}
                    ,dtrain=matrix_train,num_boost_round=500, 
                    early_stopping_rounds=20,evals=[(matrix_test,'test')],)
    return model
m_xgb = XGBmodel(x_train, x_test, y_train, y_test)
## the importance of features
plot_importance(m_xgb)

## xgb : test data processor
y_pred = m_xgb.predict(xgb.DMatrix(test_xgb), ntree_limit = m_xgb.best_ntree_limit)

result = pd.DataFrame({'id':sample_submission['id'].as_matrix(), 'sales':y_pred.astype(np.int32)})
result.to_csv('submission.csv', index=False)

Learning Curves

from sklearn.learning_curve import learning_curve

# 用sklearn的learning_curve得到training_score和cv_score，使用matplotlib画出learning curve
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=1, 
                        train_sizes=np.linspace(.05, 1., 20), verbose=0, plot=True):
    """
    画出data在某模型上的learning curve.
    参数解释
    ----------
    estimator : 你用的分类器。
    title : 表格的标题。
    X : 输入的feature，numpy类型
    y : 输入的target vector
    ylim : tuple格式的(ymin, ymax), 设定图像中纵坐标的最低点和最高点
    cv : 做cross-validation的时候，数据分成的份数，其中一份作为cv集，其余n-1份作为training(默认为3份)
    n_jobs : 并行的的任务数(默认1)
    """
    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, verbose=verbose)

    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)

    if plot:
        plt.figure()
        plt.title(title)
        if ylim is not None:
            plt.ylim(*ylim)
        plt.xlabel("num of train")
        plt.ylabel("scores")
        plt.gca().invert_yaxis()
        plt.grid()

        plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, 
                         alpha=0.1, color="b")
        plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, 
                         alpha=0.1, color="r")
        plt.plot(train_sizes, train_scores_mean, 'o-', color="b", label=u"scores of train")
        plt.plot(train_sizes, test_scores_mean, 'o-', color="r", label=u"scores of cross validation")

        plt.legend(loc="best")

        plt.gca().invert_yaxis()
        plt.draw()
        plt.show()
        

    midpoint = ((train_scores_mean[-1] + train_scores_std[-1]) + (test_scores_mean[-1] - test_scores_std[-1])) / 2
    diff = (train_scores_mean[-1] + train_scores_std[-1]) - (test_scores_mean[-1] - test_scores_std[-1])
    return midpoint, diff
    
plot_learning_curve(m_xgb, "Learning Curves", x_train.as_matrix(), y_train.as_matrix())

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