一、建模与调参
在本节心电图预测中，是关于时间序列的学习，我们在之前了解数据模型之后，了解到这是一个关于回归问题的学习，我们要拟合出回归的效果
回归模型我们一直是机器学习中很重到的一部分，所以我们要大概的了解回归模型：
在学习地址中能看到回归模型简单学习，而且对集成学习，调参都是有很好的说明的。在这里就不在讲述了，我只添加了一些代码注释。
代码部分：
#导入相关的库 import pandas as pd import numpy as np from sklearn.metrics import f1_score #sklarn中混淆矩阵F1score分数 import os import seaborn as sns import matplotlib.pyplot as plt import warnings warnings.filterwarnings("ignore")
节约资源函数啊，能节省一点是一点
# 这是一个减少内存开销的函数，每一组数据的类型确定后，，将一些不必要的内存给去掉，比如INT64的整形， #我们决定没有必要，就可以将其中的数据使用astype变成int32 def reduce_mem_usage(df): start_mem = df.memory_usage().sum() / 1024**2 print('Memory usage of dataframe is {:.2f} MB'.format(start_mem)) for col in df.columns: col_type = df[col].dtype if col_type != object: c_min = df[col].min() c_max = df[col].max() if str(col_type)[:3] == 'int': if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max: df[col] = df[col].astype(np.int8) elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max: df[col] = df[col].astype(np.int16) elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max: df[col] = df[col].astype(np.int32) elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max: df[col] = df[col].astype(np.int64) else: if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max: df[col] = df[col].astype(np.float16) elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max: df[col] = df[col].astype(np.float32) else: df[col] = df[col].astype(np.float64) else: df[col] = df[col].astype('category') end_mem = df.memory_usage().sum() / 1024**2 print('Memory usage after optimization is: {:.2f} MB'.format(end_mem)) print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem)) return df '运行 运行
# 读取数据 data = pd.read_csv('train.csv') # 简单预处理 data_list = [] for items in data.values: data_list.append([items[0]] + [float(i) for i in items[1].split(',')] + [items[2]]) data = pd.DataFrame(np.array(data_list)) data.columns = ['id'] + ['s_'+str(i) for i in range(len(data_list[0])-2)] + ['label'] data = reduce_mem_usage(data)
Memory usage of dataframe is 157.93 MB Memory usage after optimization is: 39.67 MB Decreased by 74.9%
使用函数节省了74.9%的内存
from sklearn.model_selection import KFold # 分离数据集，方便进行交叉验证 X_train = data.drop(['id','label'], axis=1)#删除了ID与Label y_train = data['label'] # 5折交叉验证 folds = 5 seed = 2021 kf = KFold(n_splits=folds, shuffle=True, random_state=seed)
自定义了f1score分数
F1分数（F1-score）是分类问题的一个衡量指标。一些多分类问题的机器学习竞赛，常常将F1-score作为最终测评的方法。它是精确率和召回率的调和平均数，最大为1，最小为0。
def f1_score_vali(preds, data_vali): labels = data_vali.get_label() preds = np.argmax(preds.reshape(4, -1), axis=0) score_vali = f1_score(y_true=labels, y_pred=preds, average='macro') return 'f1_score', score_vali, True '运行 运行
"""对训练集数据进行划分，分成训练集和验证集， 并进行相应的操作 test_size=0.2将数据切分成二八两份，作为验证集与训练集 """ from sklearn.model_selection import train_test_split import lightgbm as lgb # 数据集划分 X_train_split, X_val, y_train_split, y_val = train_test_split(X_train, y_train, test_size=0.2) train_matrix = lgb.Dataset(X_train_split, label=y_train_split) valid_matrix = lgb.Dataset(X_val, label=y_val) params = { "learning_rate": 0.1,#学习率 "boosting": 'gbdt', "lambda_l2": 0.1, "max_depth": -1,#最大树深度 "num_leaves": 128,#最大树 "bagging_fraction": 0.8, "feature_fraction": 0.8, "metric": None, "objective": "multiclass", "num_class": 4, "nthread": 10, "verbose": -1, } """使用训练集数据进行模型训练""" model = lgb.train(params, train_set=train_matrix, valid_sets=valid_matrix, num_boost_round=2000, verbose_eval=50, early_stopping_rounds=200, feval=f1_score_vali)
Training until validation scores don't improve for 200 rounds [50] valid_0's multi_logloss: 0.0494395 valid_0's f1_score: 0.958305 [100] valid_0's multi_logloss: 0.0449485 valid_0's f1_score: 0.965319 [150] valid_0's multi_logloss: 0.0464121 valid_0's f1_score: 0.96852 [200] valid_0's multi_logloss: 0.0481486 valid_0's f1_score: 0.969897 [250] valid_0's multi_logloss: 0.0494213 valid_0's f1_score: 0.969932 Early stopping, best iteration is: [85] valid_0's multi_logloss: 0.044785 valid_0's f1_score: 0.963145
val_pre_lgb = model.predict(X_val, num_iteration=model.best_iteration) preds = np.argmax(val_pre_lgb, axis=1) score = f1_score(y_true=y_val, y_pred=preds, average='macro') print('未调参前lightgbm单模型在验证集上的f1：{}'.format(score))
未调参前lightgbm单模型在验证集上的f1：0.9631447591337778
"""使用lightgbm 5折交叉验证进行建模预测""" cv_scores = [] for i, (train_index, valid_index) in enumerate(kf.split(X_train, y_train)): print('************************************ {} ************************************'.format(str(i+1))) X_train_split, y_train_split, X_val, y_val = X_train.iloc[train_index], y_train[train_index], X_train.iloc[valid_index], y_train[valid_index] train_matrix = lgb.Dataset(X_train_split, label=y_train_split) valid_matrix = lgb.Dataset(X_val, label=y_val) params = { "learning_rate": 0.1, "boosting": 'gbdt', "lambda_l2": 0.1, "max_depth": -1, "num_leaves": 128, "bagging_fraction": 0.8, "feature_fraction": 0.8, "metric": None, "objective": "multiclass", "num_class": 4, "nthread": 10, "verbose": -1, } model = lgb.train(params, train_set=train_matrix, valid_sets=valid_matrix, num_boost_round=2000, verbose_eval=100, early_stopping_rounds=200, feval=f1_score_vali) val_pred = model.predict(X_val, num_iteration=model.best_iteration) val_pred = np.argmax(val_pred, axis=1) cv_scores.append(f1_score(y_true=y_val, y_pred=val_pred, average='macro')) print(cv_scores) print("lgb_scotrainre_list:{}".format(cv_scores)) print("lgb_score_mean:{}".format(np.mean(cv_scores))) print("lgb_score_std:{}".format(np.std(cv_scores)))
************************************ 1 ************************************ Training until validation scores don't improve for 200 rounds [100] valid_0's multi_logloss: 0.0408155 valid_0's f1_score: 0.966797 [200] valid_0's multi_logloss: 0.0437957 valid_0's f1_score: 0.971239 Early stopping, best iteration is: [96] valid_0's multi_logloss: 0.0406453 valid_0's f1_score: 0.967452 [0.9674515729721614] ************************************ 2 ************************************ Training until validation scores don't improve for 200 rounds [100] valid_0's multi_logloss: 0.0472933 valid_0's f1_score: 0.965828 [200] valid_0's multi_logloss: 0.0514952 valid_0's f1_score: 0.968138 Early stopping, best iteration is: [87] valid_0's multi_logloss: 0.0467472 valid_0's f1_score: 0.96567 [0.9674515729721614, 0.9656700872844327] ************************************ 3 ************************************ Training until validation scores don't improve for 200 rounds [100] valid_0's multi_logloss: 0.0378154 valid_0's f1_score: 0.971004 [200] valid_0's multi_logloss: 0.0405053 valid_0's f1_score: 0.973736 Early stopping, best iteration is: [93] valid_0's multi_logloss: 0.037734 valid_0's f1_score: 0.970004 [0.9674515729721614, 0.9656700872844327, 0.9700043639844769] ************************************ 4 ************************************ Training until validation scores don't improve for 200 rounds [100] valid_0's multi_logloss: 0.0495142 valid_0's f1_score: 0.967106 [200] valid_0's multi_logloss: 0.0542324 valid_0's f1_score: 0.969746 Early stopping, best iteration is: [84] valid_0's multi_logloss: 0.0490886 valid_0's f1_score: 0.965566 [0.9674515729721614, 0.9656700872844327, 0.9700043639844769, 0.9655663272378014] ************************************ 5 ************************************ Training until validation scores don't improve for 200 rounds [100] valid_0's multi_logloss: 0.0412544 valid_0's f1_score: 0.964054 [200] valid_0's multi_logloss: 0.0443025 valid_0's f1_score: 0.965507 Early stopping, best iteration is: [96] valid_0's multi_logloss: 0.0411855 valid_0's f1_score: 0.963114 [0.9674515729721614, 0.9656700872844327, 0.9700043639844769, 0.9655663272378014, 0.9631137190307674] lgb_scotrainre_list:[0.9674515729721614, 0.9656700872844327, 0.9700043639844769, 0.9655663272378014, 0.9631137190307674] lgb_score_mean:0.9663612141019279 lgb_score_std:0.0022854824074775683
二、调参
2.1、贪心调参
先使用当前对模型影响最大的参数进行调优，达到当前参数下的模型最优化，再使用对模型影响次之的参数进行调优，如此下去，直到所有的参数调整完毕。
这个方法的缺点就是可能会调到局部最优而不是全局最优，但是只需要一步一步的进行参数最优化调试即可，容易理解。
from sklearn.model_selection import cross_val_score from lightgbm.sklearn import LGBMRegressor# 调objective best_obj = dict() objective=["regression"] for obj in objective: model = LGBMRegressor(objective=obj) """预测并计算roc的相关指标""" score = cross_val_score(model, X_train, y_train, cv=5, scoring='f1').mean() best_obj[obj] = score # num_leaves best_leaves = dict() for leaves in num_leaves: model = LGBMRegressor(objective=min(best_obj.items(), key=lambda x:x[1])[0], num_leaves=leaves) """预测并计算roc的相关指标""" score = cross_val_score(model, X_train, y_train, cv=5, scoring='f1').mean() best_leaves[leaves] = score # max_depth best_depth = dict() for depth in max_depth: model = LGBMRegressor(objective=min(best_obj.items(), key=lambda x:x[1])[0], num_leaves=min(best_leaves.items(), key=lambda x:x[1])[0], max_depth=depth) """预测并计算roc的相关指标""" score = cross_val_score(model, X_train, y_train, cv=5, scoring='f1').mean() best_depth[depth] = score """ 可依次将模型的参数通过上面的方式进行调整优化，并且通过可视化观察在每一个最优参数下模型的得分情况 """
2.2、 网格搜索
sklearn 提供GridSearchCV用于进行网格搜索，只需要把模型的参数输进去，就能给出最优化的结果和参数。相比起贪心调参，网格搜索的结果会更优，但是网格搜索只适合于小数据集，一旦数据的量级上去了，很难得出结果。(比较暴力，浪费时间)
网格搜索的思想非常简单，比如你有2个超参数需要去选择，那你就把所有的超参数选择列出来分别做排列组合。然后针对每组超参数分别建立一个模型，然后选择测试误差最小的那组超参数。换句话说，我们需要从超参数空间中寻找最优的超参数，很像一个网格中找到一个最优的节点，因此叫网格搜索。
"""通过网格搜索确定最优参数""" from sklearn.model_selection import GridSearchCV def get_best_cv_params(learning_rate=0.1, n_estimators=581, num_leaves=31, max_depth=-1, bagging_fraction=1.0, feature_fraction=1.0, bagging_freq=0, min_data_in_leaf=20, min_child_weight=0.001, min_split_gain=0, reg_lambda=0, reg_alpha=0, param_grid=None): # 设置5折交叉验证 cv_fold = KFold(n_splits=5, shuffle=True, random_state=2021) model_lgb = lgb.LGBMClassifier(learning_rate=learning_rate, n_estimators=n_estimators, num_leaves=num_leaves, max_depth=max_depth, bagging_fraction=bagging_fraction, feature_fraction=feature_fraction, bagging_freq=bagging_freq, min_data_in_leaf=min_data_in_leaf, min_child_weight=min_child_weight, min_split_gain=min_split_gain, reg_lambda=reg_lambda, reg_alpha=reg_alpha, n_jobs= 8 ) f1 = make_scorer(f1_score, average='micro') grid_search = GridSearchCV(estimator=model_lgb, cv=cv_fold, param_grid=param_grid, scoring=f1 ) grid_search.fit(X_train, y_train) print('模型当前最优参数为:{}'.format(grid_search.best_params_)) print('模型当前最优得分为:{}'.format(grid_search.best_score_))
2.3、贝叶斯调参
贝叶斯调参的主要思想是：给定优化的目标函数(广义的函数，只需指定输入和输出即可，无需知道内部结构以及数学性质)，通过不断地添加样本点来更新目标函数的后验分布(高斯过程,直到后验分布基本贴合于真实分布）。简单的说，就是考虑了上一次参数的信息，从而更好的调整当前的参数。
安装贝叶斯调参
pip install bayesian-optimization • 1
具体代码就不说了。未运行出来。
这是调参对整体模型的性能影响：
参数对random forest影响：
参数对Gradient Tree Boosting模型参数影响：
有的时候调参结果并未发生太多变化，需要回去特征处理，这样可能 会有意想不到结果
