涨知识！用逻辑规则进行机器学习

Skope-rules使用树模型生成规则候选项。首先建立一些决策树，并将从根节点到内部节点或叶子节点的路径视为规则候选项。然后通过一些预定义的标准（如精确度和召回率）对这些候选规则进行过滤。只有那些精确度和召回率高于其阈值的才会被保留。最后，应用相似性过滤来选择具有足够多样性的规则。一般情况下，应用Skope-rules来学习每个根本原因的潜在规则。

项目地址：https://github.com/scikit-learn-contrib/skope-rules

• Skope-rules是一个建立在scikit-learn之上的Python机器学习模块，在3条款BSD许可下发布。
• Skope-rules旨在学习逻辑的、可解释的规则，用于 "界定 "目标类别，即高精度地检测该类别的实例。
• Skope-rules是决策树的可解释性和随机森林的建模能力之间的一种权衡。

schema

安装

可以使用 pip 获取最新资源：

pip install skope-rules

快速开始

SkopeRules 可用于描述具有逻辑规则的类：

from sklearn.datasets import load_iris
from skrules import SkopeRules

dataset = load_iris()
feature_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
clf = SkopeRules(max_depth_duplicatinotallow=2,
 n_estimators=30,
 precision_min=0.3,
 recall_min=0.1,
 feature_names=feature_names)

for idx, species in enumerate(dataset.target_names):
X, y = dataset.data, dataset.target
clf.fit(X, y == idx)
rules = clf.rules_[0:3]
print("Rules for iris", species)
for rule in rules:
print(rule)
print()
print(20*'=')
print()

注意：

如果出现如下错误：

解决方案:

关于 Python 导入错误 : cannot import name 'six' from 'sklearn.externals' ，云朵君在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/61867945/

解决方案如下

import six
import sys
sys.modules['sklearn.externals.six'] = six
import mlrose

亲测有效！

如果使用“score_top_rules”方法，SkopeRules 也可以用作预测器：

from sklearn.datasets import load_boston
from sklearn.metrics import precision_recall_curve
from matplotlib import pyplot as plt
from skrules import SkopeRules

dataset = load_boston()
clf = SkopeRules(max_depth_duplicatinotallow=None,
 n_estimators=30,
 precision_min=0.2,
 recall_min=0.01,
 feature_names=dataset.feature_names)

X, y = dataset.data, dataset.target > 25
X_train, y_train = X[:len(y)//2], y[:len(y)//2]
X_test, y_test = X[len(y)//2:], y[len(y)//2:]
clf.fit(X_train, y_train)
y_score = clf.score_top_rules(X_test) # Get a risk score for each test example
precision, recall, _ = precision_recall_curve(y_test, y_score)
plt.plot(recall, precision)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision Recall curve')
plt.show()

实战案例

本案例展示了在著名的泰坦尼克号数据集上使用skope-rules。

skope-rules适用情况：

• 解决二分类问题
• 提取可解释的决策规则

本案例分为5个部分

• 导入相关库
• 数据准备
• 模型训练（使用ScopeRules().score_top_rules()方法）
• 解释 "生存规则"（使用SkopeRules().rules_属性）。
• 性能分析（使用SkopeRules.predict_top_rules()方法）。

导入相关库

# Import skope-rules
from skrules import SkopeRules

# Import librairies
import pandas as pd
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, precision_recall_curve
from matplotlib import cm
import numpy as np
from sklearn.metrics import confusion_matrix
from IPython.display import display

# Import Titanic data
data = pd.read_csv('../data/titanic-train.csv')

数据准备

# 删除年龄缺失的行
data = data.query('Age == Age')
# 为变量Sex创建编码值
data['isFemale'] = (data['Sex'] == 'female') * 1
# 未变量Embarked创建编码值
data = pd.concat(
[data,
pd.get_dummies(data.loc[:,'Embarked'], 
 dummy_na=False, 
 prefix='Embarked', 
 prefix_sep='_')],
axis=1
)
# 删除没有使用的变量
data = data.drop(['Name', 'Ticket', 'Cabin', 
'PassengerId', 'Sex', 'Embarked'], 
 axis = 1)
# 创建训练及测试集
X_train, X_test, y_train, y_test = train_test_split(
data.drop(['Survived'], axis=1), 
data['Survived'], 
test_size=0.25, random_state=42)
feature_names = X_train.columns

print('Column names are: ' + ' '.join(feature_names.tolist())+'.')
print('Shape of training set is: ' + str(X_train.shape) + '.')

Column names are: Pclass Age SibSp Parch Fare
isFemale Embarked_C Embarked_Q Embarked_S.
Shape of training set is: (535, 9).

模型训练

# 训练一个梯度提升分类器，用于基准测试
gradient_boost_clf = GradientBoostingClassifier(random_state=42, n_estimators=30, max_depth = 5)
gradient_boost_clf.fit(X_train, y_train)

# 训练一个随机森林分类器，用于基准测试
random_forest_clf = RandomForestClassifier(random_state=42, n_estimators=30, max_depth = 5)
random_forest_clf.fit(X_train, y_train)

# 训练一个决策树分类器，用于基准测试
decision_tree_clf = DecisionTreeClassifier(random_state=42, max_depth = 5)
decision_tree_clf.fit(X_train, y_train)

# 训练一个 skope-rules-boosting 分类器
skope_rules_clf = SkopeRules(feature_names=feature_names, random_state=42, n_estimators=30,
 recall_min=0.05, precision_min=0.9,
 max_samples=0.7,
 max_depth_duplicatinotallow= 4, max_depth = 5)
skope_rules_clf.fit(X_train, y_train)


# 计算预测分数
gradient_boost_scoring = gradient_boost_clf.predict_proba(X_test)[:, 1]
random_forest_scoring = random_forest_clf.predict_proba(X_test)[:, 1]
decision_tree_scoring = decision_tree_clf.predict_proba(X_test)[:, 1]

skope_rules_scoring = skope_rules_clf.score_top_rules(X_test)

"生存规则" 的提取

# 获得创建的生存规则的数量
print("用SkopeRules建立了" + str(len(skope_rules_clf.rules_)) + "条规则n")

# 打印这些规则
rules_explanations = [
 "3岁以下和37岁以下，在头等舱或二等舱的女性。"
"3岁以上乘坐头等舱或二等舱，支付超过26欧元的女性。"
 "坐一等舱或二等舱，支付超过29欧元的女性。"
 "年龄在39岁以上，在头等舱或二等舱的女性。"
]
print('其中表现最好的4条 "泰坦尼克号生存规则" 如下所示：/n')
for i_rule, rule in enumerate(skope_rules_clf.rules_[:4])
print(rule[0])
print('->'+rules_explanations[i_rule]+ 'n')

用SkopeRules建立了9条规则。

其中表现最好的4条 "泰坦尼克号生存规则" 如下所示：

Age <= 37.0 and Age > 2.5 
and Pclass <= 2.5 and isFemale > 0.5
-> 3岁以下和37岁以下，在头等舱或二等舱的女性。

Age > 2.5 and Fare > 26.125 
and Pclass <= 2.5 and isFemale > 0.5
-> 3岁以上乘坐头等舱或二等舱，支付超过26欧元的女性。

Fare > 29.356250762939453 
and Pclass <= 2.5 and isFemale > 0.5
-> 坐一等舱或二等舱，支付超过29欧元的女性。

Age > 38.5 and Pclass <= 2.5 
and isFemale > 0.5
-> 年龄在39岁以上，在头等舱或二等舱的女性。

def compute_y_pred_from_query(X, rule):
score = np.zeros(X.shape[0])
X = X.reset_index(drop=True)
score[list(X.query(rule).index)] = 1
return(score)

def compute_performances_from_y_pred(y_true, y_pred, index_name='default_index'):
df = pd.DataFrame(data=
{
'precision':[sum(y_true * y_pred)/sum(y_pred)],
'recall':[sum(y_true * y_pred)/sum(y_true)]
},
index=[index_name],
columns=['precision', 'recall']
)
return(df)

def compute_train_test_query_performances(X_train, y_train, X_test, y_test, rule):

y_train_pred = compute_y_pred_from_query(X_train, rule)
y_test_pred = compute_y_pred_from_query(X_test, rule)

performances = None
performances = pd.concat([
performances,
compute_performances_from_y_pred(y_train, y_train_pred, 'train_set')],
axis=0)
performances = pd.concat([
performances,
compute_performances_from_y_pred(y_test, y_test_pred, 'test_set')],
axis=0)

return(performances)


print('Precision = 0.96 表示规则确定的96%的人是幸存者。')
print('Recall = 0.12 表示规则识别的幸存者占幸存者总数的12%n')

for i in range(4):
print('Rule '+str(i+1)+':')
display(compute_train_test_query_performances(X_train, y_train,
X_test, y_test,
skope_rules_clf.rules_[i][0])
 )

Precision = 0.96 表示规则确定的96%的人是幸存者。
Recall = 0.12 表示规则识别的幸存者占幸存者总数的12%。

模型性能检测

def plot_titanic_scores(y_true, scores_with_line=[], scores_with_points=[],
labels_with_line=['Gradient Boosting', 'Random Forest', 'Decision Tree'],
labels_with_points=['skope-rules']):
gradient = np.linspace(0, 1, 10)
color_list = [ cm.tab10(x) for x in gradient ]

fig, axes = plt.subplots(1, 2, figsize=(12, 5),
 sharex=True, sharey=True)
ax = axes[0]
n_line = 0
for i_score, score in enumerate(scores_with_line):
n_line = n_line + 1
fpr, tpr, _ = roc_curve(y_true, score)
ax.plot(fpr, tpr, linestyle='-.', c=color_list[i_score], lw=1, label=labels_with_line[i_score])
for i_score, score in enumerate(scores_with_points):
fpr, tpr, _ = roc_curve(y_true, score)
ax.scatter(fpr[:-1], tpr[:-1], c=color_list[n_line + i_score], s=10, label=labels_with_points[i_score])
ax.set_title("ROC", fnotallow=20)
ax.set_xlabel('False Positive Rate', fnotallow=18)
ax.set_ylabel('True Positive Rate (Recall)', fnotallow=18)
ax.legend(loc='lower center', fnotallow=8)

ax = axes[1]
n_line = 0
for i_score, score in enumerate(scores_with_line):
n_line = n_line + 1
precision, recall, _ = precision_recall_curve(y_true, score)
ax.step(recall, precision, linestyle='-.', c=color_list[i_score], lw=1, where='post', label=labels_with_line[i_score])
for i_score, score in enumerate(scores_with_points):
precision, recall, _ = precision_recall_curve(y_true, score)
ax.scatter(recall, precision, c=color_list[n_line + i_score], s=10, label=labels_with_points[i_score])
ax.set_title("Precision-Recall", fnotallow=20)
ax.set_xlabel('Recall (True Positive Rate)', fnotallow=18)
ax.set_ylabel('Precision', fnotallow=18)
ax.legend(loc='lower center', fnotallow=8)
plt.show()

plot_titanic_scores(y_test,
scores_with_line=[gradient_boost_scoring, random_forest_scoring, decision_tree_scoring],
scores_with_points=[skope_rules_scoring]
 )

在ROC曲线上，每个红点对应于激活的规则（来自skope-rules）的数量。例如，最低点是1个规则（最好的）的结果点。第二低点是2条规则结果点，等等。

在准确率-召回率曲线上，同样的点是用不同的坐标轴绘制的。警告：左边的第一个红点（0%召回率，100%精度）对应于0条规则。左边的第二个点是第一个规则，等等。

从这个例子可以得出一些结论。

• skope-rules的表现比决策树好。
• skope-rules的性能与随机森林/梯度提升相似（在这个例子中）。
• 使用4个规则可以获得很好的性能（61%的召回率，94%的精确度）（在这个例子中）。

n_rule_chosen = 4
y_pred = skope_rules_clf.predict_top_rules(X_test, n_rule_chosen)

print('The performances reached with '+str(n_rule_chosen)+' discovered rules are the following:')
compute_performances_from_y_pred(y_test, y_pred, 'test_set')

predict_top_rules(new_data, n_r)方法用来计算对new_data的预测，其中有前n_r条skope-rules规则。