前置文章：
將一維機械振動信號構造為訓練集和測試集（Python）
https://mp.weixin.qq.com/s/DTKjBo6_WAQ7bUPZEdB1TA
旋轉機械振動信號特征提取（Python）
https://mp.weixin.qq.com/s/VwvzTzE-pacxqb9rs8hEVw
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import ListedColormap
import matplotlib.patches as mpatches
import lightgbm as lgb
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrixdf_train = pd.read_csv("statistics_10_train.csv" , sep = ',')
df_test = pd.read_csv("statistics_10_test.csv" , sep = ',')X_train = df_train[['Kurtosis', 'Impulse factor', 'RMS', 'Margin factor', 'Skewness','Shape factor', 'Peak to peak', 'Crest factor']].values
y_train = df_train['Tipo'].values
X_test = df_test[['Kurtosis', 'Impulse factor', 'RMS', 'Margin factor', 'Skewness','Shape factor', 'Peak to peak', 'Crest factor']].values
y_test = df_test['Tipo'].valuesfrom hyperopt import fmin, atpe, tpe, STATUS_OK, STATUS_FAIL, Trials
from hyperopt import hp
from hyperopt import space_eval
class HPOpt(object):def __init__(self, x_train, x_test, y_train, y_test):self.x_train = x_trainself.x_test = x_testself.y_train = y_trainself.y_test = y_testdef process(self, fn_name, space, trials, algo, max_evals):fn = getattr(self, fn_name)try:result = fmin(fn=fn, space=space, algo=algo, max_evals=max_evals, trials=trials)except Exception as e:return {'status': STATUS_FAIL,'exception': str(e)}return result, trialsdef lgb_clas(self, para):clf = lgb.LGBMClassifier(**para['clas_params'])return self.train_clf(clf, para)def train_clf(self, clf, para):clf.fit(self.x_train, self.y_train,eval_set=[(self.x_train, self.y_train), (self.x_test, self.y_test)], verbose = False, early_stopping_rounds = 20)pred = clf.predict(self.x_test)loss = para['loss_func'](self.y_test, pred)return {'loss': loss, 'status': STATUS_OK}from sklearn.metrics import accuracy_score
lgb_clas_params = {'learning_rate': hp.choice('learning_rate', np.arange(0.001, 0.5, 0.001)),'max_depth': hp.choice('max_depth', np.arange(5, 10, 1, dtype=int)),'min_child_weight': hp.choice('min_child_weight', np.arange(0, 10, 1)),'min_data_in_leaf': hp.choice('min_data_in_leaf', np.arange(0, 10, 1)),'subsample': hp.choice('subsample', np.arange(0.1, 1, 0.05)),'n_estimators': hp.choice('n_estimators', np.arange(10, 200, 10, dtype=int)),'num_leaves': hp.choice('num_leaves', np.arange(5, 51, 1, dtype=int)),}lgb_para = dict()
lgb_para['clas_params'] = lgb_clas_params
lgb_para['loss_func' ] = lambda y, pred: accuracy_score(y, pred)# squared = False)
lgb_para["max_evals"] = 100# Optimización
obj = HPOpt(X_train, X_test, y_train, y_test)lgb_opt = obj.process(fn_name='lgb_clas', space=lgb_para, trials=Trials(), algo=tpe.suggest, max_evals=lgb_para["max_evals"])
parametros = space_eval(lgb_clas_params, lgb_opt[0])clf = lgb.LGBMClassifier()
clf.set_params(**parametros)
clf.fit(X_train, y_train)LGBMClassifier(learning_rate=0.342, max_depth=9, min_child_weight=0,min_data_in_leaf=7, n_estimators=90, num_leaves=33,subsample=0.15000000000000002)from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
pred = clf.predict(X_test)
print(confusion_matrix(y_test, pred))
print(classification_report(y_test, pred))[[27 3 0][ 0 30 0][ 0 1 29]]precision recall f1-score supportInner 1.00 0.90 0.95 30Outer 0.88 1.00 0.94 30Sano 1.00 0.97 0.98 30accuracy 0.96 90macro avg 0.96 0.96 0.96 90
weighted avg 0.96 0.96 0.96 90clf = lgb.LGBMClassifier(n_estimators = 100, learning_rate = 0.01, min_data_in_leaf = 0)
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
target_names = ['Inner', 'Outer', 'Healthy']
print(confusion_matrix(y_test, pred))
print(classification_report(y_test, pred, target_names = target_names))[[29 1 0][ 0 30 0][ 0 3 27]]precision recall f1-score supportInner 1.00 0.97 0.98 30Outer 0.88 1.00 0.94 30Healthy 1.00 0.90 0.95 30accuracy 0.96 90macro avg 0.96 0.96 0.96 90
weighted avg 0.96 0.96 0.96 90pred_train = clf.predict(X_train)
print(confusion_matrix(y_train, pred_train))
print(classification_report(y_train, pred_train, target_names = target_names))知乎學術咨詢：
https://www.zhihu.com/consult/people/792359672131756032?isMe=1工學博士，擔任《Mechanical System and Signal Processing》《中國電機工程學報》《控制與決策》等期刊審稿專家，擅長領域：現代信號處理，機器學習，深度學習，數字孿生，時間序列分析，設備缺陷檢測、設備異常檢測、設備智能故障診斷與健康管理PHM等。
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