前置文章：
将一维机械振动信号构造为训练集和测试集（Python）
https://mp.weixin.qq.com/s/DTKjBo6_WAQ7bUPZEdB1TA
旋转机械振动信号特征提取（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))知乎学术咨询：
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