基于逻辑回归的数据生成函数用于提升分类问题

此数据生成函数使用逻辑回归作为基础的数据生成模型。该函数能够更好地控制特征模式：特征如何与结果基线和治疗效果相关联。它支持6种不同的模式：线性、二次、三次、Relu、正弦和余弦。

本笔记本展示了如何使用此数据生成函数来生成数据，并可视化特征模式。

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

%matplotlib inline

导入数据生成函数

from causalml.dataset import make_uplift_classification_logistic

The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.

生成数据

df, feature_name = make_uplift_classification_logistic( n_samples=100000,
                                                        treatment_name=['control', 'treatment1', 'treatment2', 'treatment3'],
                                                        y_name='conversion',
                                                        n_classification_features=10,
                                                        n_classification_informative=5,
                                                        n_classification_redundant=0,
                                                        n_classification_repeated=0,
                                                        n_uplift_dict={'treatment1': 2, 'treatment2': 2, 'treatment3': 3},
                                                        n_mix_informative_uplift_dict={'treatment1': 1, 'treatment2': 1, 'treatment3': 0},
                                                        delta_uplift_dict={'treatment1': 0.05, 'treatment2': 0.02, 'treatment3': -0.05},
                                                       feature_association_list = ['linear','quadratic','cubic','relu','sin','cos'],
                                                       random_select_association = False,
                                                       random_seed=20200416

                                                      )

df.head()

	treatment_group_key	x1_informative	x1_informative_transformed	x2_informative	x2_informative_transformed	x3_informative	x3_informative_transformed	x4_informative	x4_informative_transformed	x5_informative	...	conversion_prob	control_conversion_prob	control_true_effect	treatment1_conversion_prob	treatment1_true_effect	treatment2_conversion_prob	treatment2_true_effect	treatment3_conversion_prob	treatment3_true_effect	conversion
0	treatment1	-0.194205	-0.192043	1.791408	1.572609	0.678028	0.080696	-0.169306	-0.683035	-1.837155	...	0.126770	0.076138	0.0	0.126770	0.050632	0.087545	0.011407	0.029396	-0.046742	0
1	treatment1	-0.898070	-0.894462	0.252125	-0.663393	-0.842844	-0.156004	-0.047769	-0.683035	-0.251752	...	0.064278	0.070799	0.0	0.064278	-0.006522	0.101076	0.030277	0.050778	-0.020021	0
2	treatment1	0.701002	0.701325	0.239320	-0.667867	1.700766	1.278676	-0.734568	-0.683035	-1.130113	...	0.018480	0.014947	0.0	0.018480	0.003534	0.018055	0.003109	0.019327	0.004380	0
3	control	-1.653684	-1.648524	-0.119123	-0.698492	-0.037645	-0.000355	0.687429	0.495943	-1.427400	...	0.102799	0.102799	0.0	0.101410	-0.001390	0.040230	-0.062569	0.030753	-0.072046	0
4	treatment3	1.057909	1.057498	-2.019523	2.190564	-0.950180	-0.223370	-1.505741	-0.683035	-0.399457	...	0.012964	0.106241	0.0	0.171309	0.065068	0.114526	0.008285	0.012964	-0.093277	0

5 行 × 47 列

feature_name

['x1_informative', 'x2_informative', 'x3_informative', 'x4_informative', 'x5_informative', 'x6_irrelevant', 'x7_irrelevant', 'x8_irrelevant', 'x9_irrelevant', 'x10_irrelevant', 'x11_uplift', 'x12_uplift', 'x13_uplift', 'x14_uplift', 'x15_uplift', 'x16_uplift', 'x17_uplift', 'x18_mix', 'x19_mix']

实验组平均值

df.groupby(['treatment_group_key'])['conversion'].mean()

treatment_group_key
control       0.09896
treatment1    0.15088
treatment2    0.12042
treatment3    0.04972
Name: conversion, dtype: float64

可视化特征模式

# Extract control and treatment1 for illustration
treatment_group_keys = ['control','treatment1']
y_name='conversion'
df1 = df[df['treatment_group_key'].isin(treatment_group_keys)].reset_index(drop=True)
df1.groupby(['treatment_group_key'])['conversion'].mean()

treatment_group_key
control       0.09896
treatment1    0.15088
Name: conversion, dtype: float64

color_dict = {'control':'#2471a3','treatment1':'#FF5733','treatment2':'#5D6D7E'
             ,'treatment3':'#34495E','treatment4':'#283747'}

hatch_dict = {'control':'','treatment1':'//'}

x_name_plot = ['x11_uplift', 'x12_uplift', 'x2_informative', 'x5_informative']

x_new_name_plot = ['Uplift Feature 1', 'Uplift Feature 2', 'Classification Feature 1','Classification Feature 2']
opacity = 0.8

plt.figure(figsize=(20, 3))
subplot_list = [141,142,143,144]
counter = 0
bar_width = 0.9/len(treatment_group_keys)
for x_name_i in x_name_plot:
    bins = np.percentile(df1[x_name_i].values, np.linspace(0, 100, 11))[:-1]
    df1['x_bin'] = np.digitize(df1[x_name_i].values, bins)
    df_gb = df1.groupby(['treatment_group_key','x_bin'],as_index=False)[y_name].mean()
    plt.subplot(subplot_list[counter])
    for ti in range(len(treatment_group_keys)):
        x_index = [ti * bar_width - len(treatment_group_keys)/2*bar_width + xi for xi in range(10)]
        plt.bar(x_index,
                df_gb[df_gb['treatment_group_key']==treatment_group_keys[ti]][y_name].values,
                bar_width,
                alpha=opacity,
                color=color_dict[treatment_group_keys[ti]],
                hatch = hatch_dict[treatment_group_keys[ti]],
                label=treatment_group_keys[ti]
               )
    plt.xticks(range(10), [int(xi+10) for xi in np.linspace(0, 100, 11)[:-1]])
    plt.xlabel(x_new_name_plot[counter],fontsize=16)
    plt.ylabel('Conversion',fontsize=16)
    #plt.title(x_name_i)
    if counter == 0:
        plt.legend(treatment_group_keys, loc=2,fontsize=16)
    plt.ylim([0.,0.3])
    counter+=1

在上图中，Uplift Feature 1 对处理效果呈现线性模式，Uplift Feature 2 对处理效果呈现二次模式，Classification Feature 1 对处理和对照的基线呈现二次模式，而 Classification Feature 2 对处理和对照的基线呈现正弦模式。