Regression -Based Simpson’s Paradox

#imports
import numpy as np
import pandas as pd
import seaborn as sns
import scipy.stats as stats
import matplotlib.pyplot as plt


import mlsim 
from mlsim.anomaly import sp_plot

The basic version of SP in the regression form is the clustering model based SP- we use a gaussian mixture model and control parameters of the shape

# setup
r_clusters = -.6  # correlation coefficient of clusters
cluster_spread = .8 # pearson correlation of means
p_sp_clusters = .5 # portion of clusters with SP 
k = 5 # number of clusters
cluster_size = [2,3]
domain_range = [0, 20, 0, 20]
N = 200 # number of points
p_clusters = [1.0/k]*k

We choose the portion of the clusters to have SP and then can draw samples

p_sp_clusters = .9
sp_df2 = mlsim.anomaly.geometric_2d_gmm_sp(r_clusters,cluster_size,cluster_spread,
                                    p_sp_clusters, domain_range,k,N,p_clusters)
sp_plot(sp_df2,'x1','x2','color')

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

We can change the parameters and see variation

# setup
r_clusters = -.4  # correlation coefficient of clusters
cluster_spread = .8 # pearson correlation of means
p_sp_clusters = .6 # portion of clusters with SP 
k = 5 # number of clusters
cluster_size = [4,4]
domain_range = [0, 20, 0, 20]
N = 200 # number of points
p_clusters = [.5, .2, .1, .1, .1]

sp_df3 = mlsim.anomaly.geometric_2d_gmm_sp(r_clusters,cluster_size,cluster_spread,
                                    p_sp_clusters, domain_range,k,N,p_clusters)
sp_plot(sp_df3,'x1','x2','color')

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

Multiple Views

The first extension is to add multiple independent views, we have a wrapper function for that

many_sp_df = mlsim.anomaly.geometric_indep_views_gmm_sp(2,r_clusters,cluster_size,cluster_spread,p_sp_clusters,
                domain_range,k,N,p_clusters)

sp_plot(many_sp_df,'x1','x2','A')
sp_plot(many_sp_df,'x3','x4','B')
many_sp_df.head()

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

	x1	x2	x3	x4	A	B
0	13.172482	14.230178	12.409012	10.026851	A3	B1
1	7.908730	11.563656	11.753394	10.979174	A2	B1
2	4.377354	8.629879	14.536545	15.306251	A0	B0
3	4.943237	7.607691	8.204048	7.204525	A0	B2
4	3.957393	9.682433	13.900453	15.426102	A0	B0

The views do not have to have the same parameters though. We can make each parameter a list of values with the length set to the number of views.

# setup
r_clusters = [.8, -.2]  # correlation coefficient of clusters
cluster_spread = [.8, .2] # pearson correlation of means
p_sp_clusters = [.6, 1] # portion of clusters with SP 
k = [5,3] # number of clusters
cluster_size = [4,4]
domain_range = [0, 20, 0, 20]
N = 200 # number of points
p_clusters = [[.5, .2, .1, .1, .1],[1.0/3]*3]


many_sp_df_diff = mlsim.anomaly.geometric_indep_views_gmm_sp(2,r_clusters,cluster_size,cluster_spread,p_sp_clusters,
                domain_range,k,N,p_clusters)

sp_plot(many_sp_df_diff,'x1','x2','A')
sp_plot(many_sp_df_diff,'x3','x4','B')
many_sp_df.head()

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

	x1	x2	x3	x4	A	B
0	13.172482	14.230178	12.409012	10.026851	A3	B1
1	7.908730	11.563656	11.753394	10.979174	A2	B1
2	4.377354	8.629879	14.536545	15.306251	A0	B0
3	4.943237	7.607691	8.204048	7.204525	A0	B2
4	3.957393	9.682433	13.900453	15.426102	A0	B0