'''
验证曲线：模型性能 = f(超参数)----通过优化模型参数，达到优化模型预测结果，使得模型预测更加精准
验证曲线所需API：
train_scores, test_scores = ms.validation_curve(
model, # 模型
输入集, 输出集,
'n_estimators', #超参数名
np.arange(50, 550, 50), #超参数序列
cv=5 #折叠数
)
train_scores的结构:
超参数取值 第一次折叠 第二次折叠 第三次折叠 第四次折叠 第五次折叠
50 0.91823444 0.91968162 0.92619392 0.91244573 0.91040462
100 0.91968162 0.91823444 0.91244573 0.92619392 0.91244573
... ... ... ... ... ...
test_scores的结构与train_scores的结构相同。
案例：在小汽车评级案例中使用验证曲线选择较优参数。
'''
import numpy as np
import matplotlib.pyplot as mp
import sklearn.preprocessing as sp
import sklearn.ensemble as se
import sklearn.model_selection as ms
import sklearn.metrics as sm
import warnings
warnings.filterwarnings('ignore')
data =
[]
with open('./ml_data/car.txt',
'r') as f:
for line
in f.readlines():
sample = line[:-1].split(
',')
data.append(sample)
data =
np.array(data)
# print(data.shape)
# 整理好每一列的标签编码器encoders
# 整理好训练输入集与输出集
data =
data.T
# print(data.shape)
encoders =
[]
train_x, train_y =
[], []
for row
in range(len(data)):
encoder =
sp.LabelEncoder()
if row < len(data) - 1:
# 不是最后列
train_x.append(encoder.fit_transform(data[row]))
else:
# 是最后一列，作为输出集
train_y =
encoder.fit_transform(data[row])
encoders.append(encoder)
train_x =
np.array(train_x).T
# 训练随机森林分类器
model = se.RandomForestClassifier(max_depth=6, n_estimators=150, random_state=7
)
# 获取n_estimators的验证曲线
train_scores, test_scores = ms.validation_curve(model, train_x, train_y,
'n_estimators', np.arange(50, 550, 50), cv=5
)
# print('test_scores', test_scores, sep='\n')
# print(np.mean(test_scores, axis=1))
# 获取max_depth的验证曲线
train_scores1, test_scores1 = ms.validation_curve(model, train_x, train_y,
'max_depth', np.arange(1, 7), cv=5
)
# print('test_scores1', test_scores1, sep='\n')
# print(np.mean(test_scores1, axis=1))
# 训练之前进行交叉验证
cv = ms.cross_val_score(model, train_x, train_y, cv=4, scoring=
'f1_weighted')
# print(cv.mean())
model.fit(train_x, train_y)
# 自定义测试集，预测小汽车的等级
# 保证每个特征使用的标签编码器与训练时使用的标签编码器匹配
data =
[
['high',
'med',
'5more',
'4',
'big',
'low',
'unacc'],
['high',
'high',
'4',
'4',
'med',
'med',
'acc'],
['low',
'low',
'2',
'4',
'small',
'high',
'good'],
['low',
'med',
'3',
'4',
'med',
'high',
'vgood']]
data =
np.array(data).T
test_x, test_y =
[], []
for row
in range(len(data)):
encoder = encoders[row]
# 每列对应的标签编码器
if row < len(data) - 1
:
test_x.append(encoder.transform(data[row])) # 这里需要训练了，直接转换
else:
test_y =
encoder.transform(data[row])
test_x =
np.array(test_x).T
pred_test_y =
model.predict(test_x)
print(pred_test_y)
pred_test_y = encoders[-1
].inverse_transform(pred_test_y)
test_y = encoders[-1
].inverse_transform(test_y)
# print(pred_test_y)
# print(test_y)
# 画图显示验证曲线
mp.figure(
'Validation Curve', facecolor=
'lightgray')
mp.subplot(211
)
mp.title('N_estimators')
mp.xlabel('N_estimators')
mp.ylabel('f1 score')
mp.grid(linestyle=
":")
mp.plot(np.arange(50, 550, 50), np.mean(test_scores, axis=1), label=
'N_estimators VC')
mp.legend()
mp.subplot(212
)
mp.title('Max_depth')
mp.xlabel('Max_depth')
mp.ylabel('f1 score')
mp.grid(linestyle=
":")
mp.plot(np.arange(1, 7), np.mean(test_scores1, axis=1), label=
'Max_depth VC')
mp.legend()
mp.show()
输出结果：
[2 0 0 3
]
['unacc' 'acc' 'acc' 'vgood']
['unacc' 'acc' 'good' 'vgood']
转载于:https://www.cnblogs.com/yuxiangyang/p/11194151.html
