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我正在研究广告活动的倾向建模问题。我的数据集由历史上点击过广告的用户和未点击过的用户组成。

为了衡量我的模型的性能，我使用 sklearn 绘制了累积增益和提升图。下面是相同的代码：

import matplotlib.pyplot as plt
import scikitplot as skplt

Y_test_pred_ = model.predict_proba(X_test_df)[:]

skplt.metrics.plot_cumulative_gain(Y_test, Y_test_pred_)
plt.show()

skplt.metrics.plot_lift_curve(Y_test, Y_test_pred_)
plt.show()

我得到的图显示了 0 类用户和 1 类用户的图表样本累积增益曲线示例提升图

我只需要根据基线曲线绘制 1 类曲线。有没有办法我可以做到这一点？

4

3 回答 3

3

You can use the kds package for the same.

For Cummulative Gains Plot:

# pip install kds
import kds
kds.metrics.plot_cumulative_gain(y_test, y_prob)

For Lift Chart:

import kds
kds.metrics.plot_lift(y_test, y_prob)

Example

# REPRODUCABLE EXAMPLE
# Load Dataset and train-test split
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn import tree

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, 
test_size=0.33,random_state=3)
clf = tree.DecisionTreeClassifier(max_depth=1,random_state=3)
clf = clf.fit(X_train, y_train)
y_prob = clf.predict_proba(X_test)


# CUMMULATIVE GAIN PLOT
import kds
kds.metrics.plot_cumulative_gain(y_test, y_prob[:,1])

# LIFT PLOT
kds.metrics.plot_lift(y_test, y_prob[:,1])

Cummulative Gains Plot Python Lift Plot Python

于 2021-02-25T10:07:50.920 回答
2

如果需要，我可以解释代码：

参数：

df : 包含一个分数列和一个目标列的数据框

score : 包含分数列名称的字符串

target : 包含目标列名称的字符串

title : 包含将要生成的图形名称的字符串

def get_cum_gains(df, score, target, title):
    df1 = df[[score,target]].dropna()
    fpr, tpr, thresholds = roc_curve(df1[target], df1[score])
    ppr=(tpr*df[target].sum()+fpr*(df[target].count()- 
    df[target].sum()))/df[target].count()
    plt.figure(figsize=(12,4))
    plt.subplot(1,2,1)

    plt.plot(ppr, tpr, label='')
    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.grid(b=True, which='both', color='0.65',linestyle='-')
    plt.xlabel('%Population')
    plt.ylabel('%Target')
    plt.title(title+'Cumulative Gains Chart')
    plt.legend(loc="lower right")
    plt.subplot(1,2,2)
    plt.plot(ppr, tpr/ppr, label='')
    plt.plot([0, 1], [1, 1], 'k--')
    plt.grid(b=True, which='both', color='0.65',linestyle='-')
    plt.xlabel('%Population')
    plt.ylabel('Lift')
    plt.title(title+'Lift Curve')
于 2019-12-16T10:06:37.813 回答
0

这有点hacky，但它可以满足您的需求。关键是可以访问axmatplotlib 创建的变量。然后对其进行操作以删除不需要的情节。

# Some dummy data to work with
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
X, y = load_breast_cancer(return_X_y=True)


# ploting
import scikitplot as skplt
import matplotlib.pyplot as plt

# classify
clf = LogisticRegression(solver='liblinear', random_state=42).fit(X, y)


# classifier's output probabilities for the two classes
y_preds_probas = clf.predict_proba(X)


# get access to the figure and axes
fig, ax = plt.subplots()
# ax=ax creates the plot on the same ax we just initialized.
skplt.metrics.plot_lift_curve(y, y_preds_probas, ax=ax)  


## Now the solution to your problem.
del ax.lines[0]                 # delete the desired class plot
ax.legend().set_visible(False)  # hide the legend
ax.legend().get_texts()[0].set_text("Cancer")  # turn the legend back on
plt.show()

当然，您可能不得不乱用ax.lines[1]etc 来删除您想要的内容。

于 2021-03-26T19:28:52.100 回答