数据预处理
# 导入数据
import pandas as pd
import numpy as np
import plotly_express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme(style="whitegrid")
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
df = pd.read_excel("faults.xlsx")
df.head()
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# 数据分割
df1 = df.loc[:,"Pastry":]
df2 = df.loc[:,:"SigmoidOfAreas"]
df1.head()
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# 下面是27个特征的数据
df2.head()
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# 分类标签生成
columns = df1.columns.tolist()
columns
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for i in range(len(df1)):
for col in columns:
if df1.loc[i, col] == 1:
df1.loc[i, "Label"] = col
df1.head()
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# 类型编码
dic = {}
for i, v in enumerate(columns):
dic[v] = i
dic
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df1["Label"] = df1["Label"].map(dic)
df1.head()
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# 数据合并
df2["Label"] = df1["Label"]
df2.head()
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EDA
# 数据的基本统计信息
# 缺失值
df2.isnull().sum()
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# 单个特征分布
parameters = df2.columns[:-1].tolist()
sns.boxplot(data=df2, y="Steel_Plate_Thickness")
plt.show()
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从箱型图中能够观察到单个特征的取值分布情况。下面绘制全部参数的取值分布箱型图
fig = make_subplots(rows=7, cols=4)
for i, v in enumerate(parameters):
r = i // 4 + 1
c = (i + 1) % 4
if c == 0:
fig.add_trace(go.Box(y=df2[v].tolist(), name=v), row=r, col=4)
else:
fig.add_trace(go.Box(y=df2[v].tolist(), name=v), row=r, col=c)
fig.update_layout(width=1000, height=900)
fig.show()
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1.特征之间的取值范围不同，从负数到10M
2.部分特征的取值中存在异常值
3.有些特征的取值只存在0和1
样本不均衡
# 每种类别数量
df2["Label"].value_counts()
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可以看到第6类的样本有673条，但是第4类的样本只有55条。明显地不均衡
# SMOTE解决
X = df2.drop("Label", axis=1)
y = df2[["Label"]]
from imblearn.over_sampling import SMOTE
smo = SMOTE(random_state=42)
X_smo, y_smo = smo.fit_resample(X, y)
y_smo
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# 实施上采样后的结果
y_smo["Label"].value_counts()
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统计一下每个分类变量的数量：现在我们发现每个类别下的样本是一样的，克服了样本不均衡问题
建模
# 随机打乱数据
from sklearn.utils import shuffle
df3 = pd.concat([X_smo, y_smo], axis=1)
df3 = shuffle(df3)
# 数据集划分
from sklearn import preprocessing
X = df3.drop("Label", axis=1)
X = preprocessing.scale(X)
y = df3[["Label"]]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)
# 建模评价
from sklearn.model_selection import cross_val_score
from sklearn import metrics
def build_model(model, X_test, y_test):
model.fit(X_train, y_train)
# 预测概率
y_proba = model_LR.predict_proba(X_test)
# 找出概率值最大的所在索引，作为预测的分类结果
y_pred = np.argmax(y_proba, axis=1)
y_test = np.array(y_test).reshape(943)
print(f"{model}模型得分：")
print("召回率: ", metrics.recall_score(y_test, y_pred, average="macro"))
print("精准率: ", metrics.precision_score(y_test, y_pred, average="macro"))
# 逻辑回归
from sklearn.linear_model import LogisticRegression
model_LR = LogisticRegression()
build_model(model_LR, X_test, y_test)
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逻辑回归
# 建模
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn import metrics
model_LR = LogisticRegression()
model_LR.fit(X_train, y_train)
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# 预测概率
y_proba = model_LR.predict_proba(X_test)
y_proba[:3]
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# 找出概率值最大的所在索引，作为预测的分类结果
y_pred = np.argmax(y_proba, axis=1)
y_pred[:3]
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# 评价
# 混淆矩阵
confusion_matrix = metrics.confusion_matrix(y_test, y_pred)
confusion_matrix
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y_pred.shape
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y_test = np.array(y_test).reshape(943)
print("召回率: ", metrics.recall_score(y_test, y_pred, average="macro"))
print("精准率: ", metrics.precision_score(y_test, y_pred, average="macro"))
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随机森林回归
from sklearn.ensemble import RandomForestClassifier
model_RR = RandomForestClassifier()
model_RR.fit(X_train, y_train)
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# 预测概率
y_proba = model_RR.predict_proba(X_test)
# 最大概率的索引
y_pred = np.argmax(y_proba, axis=1)
print("召回率: ", metrics.recall_score(y_test, y_pred, average="macro"))
print("精准率: ", metrics.precision_score(y_test, y_pred, average="macro"))
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SVR
from sklearn.svm import SVC
svm_model = SVC(probability=True)
svm_model.fit(X_train, y_train)
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# 预测概率
y_proba = svm_model.predict_proba(X_test)
# 最大概率的索引
y_pred = np.argmax(y_proba, axis=1)
print("召回率: ", metrics.recall_score(y_test, y_pred, average="macro"))
print("精准率: ", metrics.precision_score(y_test, y_pred, average="macro"))
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决策树回归
from sklearn.tree import DecisionTreeClassifier
model_tree = DecisionTreeClassifier()
model_tree.fit(X_train, y_train)
# 预测概率
y_proba = model_tree.predict_proba(X_test)
# 最大概率的索引
y_pred = np.argmax(y_proba, axis=1)
print("召回率: ", metrics.recall_score(y_test, y_pred, average="macro"))
print("精准率: ", metrics.precision_score(y_test, y_pred, average="macro"))
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神经网络
from sklearn.neural_network import MLPClassifier
mlp = MLPClassifier()
mlp.fit(X_train, y_train)
# 预测概率
y_proba = mlp.predict_proba(X_test)
# 最大概率的索引
y_pred = np.argmax(y_proba, axis=1)
print("召回率: ", metrics.recall_score(y_test, y_pred, average="macro"))
print("精准率: ", metrics.precision_score(y_test, y_pred, average="macro"))
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GBDT
from sklearn.ensemble import GradientBoostingClassifier
gbdt = GradientBoostingClassifier(
# loss="deviance",
# learning_rate=1,
# n_estimators=5,
# subsample=1,
# min_samples_split=2,
# min_samples_leaf=1,
# max_depth=2,
# init=None,
# random_state=None,
# max_feature=None,
# verbose=0,
# max_leaf_nodes=None,
# warm_start=False
)
gbdt.fit(X_train, y_train)
# 预测概率
y_proba = gbdt.predict_proba(X_test)
# 最大概率的索引
y_pred = np.argmax(y_proba, axis=1)
print("召回率: ", metrics.recall_score(y_test, y_pred, average="macro"))
print("精准率: ", metrics.precision_score(y_test, y_pred, average="macro"))
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LightGBM
from lightgbm import LGBMClassifier
lgb = LGBMClassifier()
lgb.fit(X_train, y_train)
# 预测概率
y_proba = lgb.predict_proba(X_test)
# 最大概率的索引
y_pred = np.argmax(y_proba, axis=1)
print("召回率: ", metrics.recall_score(y_test, y_pred, average="macro"))
print("精准率: ", metrics.precision_score(y_test, y_pred, average="macro"))
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来源：尤而小屋
