前向逐步变量选择

Python 预测分析入门

Nele Verbiest, Ph.D

Data Scientist @PythonPredictions

前向逐步变量选择流程

空集
找到最优变量 v1
在与 v1 组合下找到最优变量 v2
在与 v1、v2 组合下找到最优变量 v3
...

（直到加入所有变量，或达到预设数量）

Python 中的函数

def function_sum(a,b):

    s = a + b
    return(s)

print(function_sum(1,2))

前向逐步流程的实现

auc 函数：给定一组变量计算 AUC
best_next 函数：结合当前变量返回下一个最优变量
循环直至达到所需变量数

AUC 函数的实现

from sklearn import linear_model
from sklearn.metrics import roc_auc_score

def auc(variables, target, basetable):

    X = basetable[variables]
    y = basetable[target]

    logreg = linear_model.LogisticRegression()
    logreg.fit(X, y)

    predictions = logreg.predict_proba(X)[:,1]
    auc = roc_auc_score(y, predictions)
    return(auc)

auc = auc(["age","gender_F"],["target"],basetable)
print(round(auc,2))

0.54

计算下一个最优变量

def next_best(current_variables,candidate_variables, target, basetable):

    best_auc = -1
    best_variable = None

    for v in candidate_variables:
        auc_v = auc(current_variables + [v], target, basetable)

        if auc_v >= best_auc:
            best_auc = auc_v
            best_variable = v

    return best_variable

current_variables = ["age","gender_F"]
candidate_variables = ["min_gift","max_gift","mean_gift"]
next_variable = next_best(current_variables, candidate_variables, basetable)
print(next_variable)

min_gift

前向逐步变量选择流程

candidate_variables = ["mean_gift","min_gift","max_gift",
"age","gender_F","country_USA","income_low"]
current_variables = []
target = ["target"]

max_number_variables = 5
number_iterations = min(max_number_variables, len(candidate_variables))
for i in range(0,number_iterations):

    next_var = next_best(current_variables,candidate_variables,target,basetable)

    current_variables = current_variables + [next_variable]
    candidate_variables.remove(next_variable)

print(current_variables)

['max_gift', 'mean_gift', 'min_gift', 'age', 'gender_F']

Passons à la pratique !

Python 预测分析入门