Added Python code for MVAdiscbh

QID-1208-MVAdiscbh/MVAdiscbh.py

@@ -0,0 +1,70 @@
+import pandas as pd
+import numpy as np
+from scipy.cluster import hierarchy
+import random
+import matplotlib.pyplot as plt
+
+data = pd.read_csv("bostonh.dat", sep = "\s+", header=None)
+
+# transform data
+
+xt = data
+for i in [0, 2, 4, 5, 7, 8, 9, 13]:
+    xt.iloc[:, i] = np.log(data.iloc[:, i])
+
+xt.iloc[:, 1] = data.iloc[:, 1]/10
+xt.iloc[:, 6] = (data.iloc[:, 6]**(2.5))/10000
+xt.iloc[:, 10] = np.exp(0.4 * data.iloc[:, 10])/1000
+xt.iloc[:, 11] = data.iloc[:, 11]/100
+xt.iloc[:, 12] = np.sqrt(data.iloc[:, 12])
+data = xt.drop(3, axis = 1)
+
+da = (data - np.mean(data))/np.std(data, ddof = 1)
+d = np.zeros([len(da),len(da)])
+
+
+for i in range(0, len(da)):
+    for j in range(0, len(da)):
+        d[i, j] = np.linalg.norm(da.iloc[i, :] - da.iloc[j, :])
+
+ddd  = d[1:, :-1][:, 0]
+for i in range(1, len(da)-1):
+    ddd = np.concatenate((ddd, d[1:, :-1][i:, i]))
+
+w = hierarchy.linkage(ddd, 'ward')
+tree = hierarchy.cut_tree(w, n_clusters = 2)
+
+da["tree"] = tree
+
+t1 = da[da["tree"] == 0].iloc[:, :-1]
+t2 = da[da["tree"] == 1].iloc[:, :-1]
+
+m1 = t1.mean() # mean of first cluster
+m2 = t2.mean() # mean of second cluster
+m = (m1 + m2)/2 
+s = ((len(t1) - 1) * t1.cov() + (len(t2) - 1) * t2.cov())/(len(xt) - 2)
+alpha = np.linalg.inv(s) @ (m1 - m2)
+
+mis1  = ((t1 - m) @ alpha < 0).sum()
+mis2  = ((t2 - m) @ alpha > 0).sum()
+
+corr1  = ((t1 - m) @ alpha > 0).sum()
+corr2  = ((t2 - m) @ alpha < 0).sum()
+
+aper  = (mis1 + mis2)/len(xt)
+alph  = (da.iloc[:, :-1] - np.tile(np.array(m), (len(da), 1))) @ alpha
+
+random.seed(1)
+
+p = tree.flatten() + (0.05*np.random.normal(size = len(tree)))
+
+dav = pd.DataFrame(data = {"tree": tree.flatten(), "p": p, "alph": alph})
+
+fig, ax = plt.subplots(figsize = (10, 10))
+ax.scatter(dav[dav["tree"] == 0]["alph"], dav[dav["tree"] == 0]["p"], c = "r")
+ax.scatter(dav[dav["tree"] == 1]["alph"], dav[dav["tree"] == 1]["p"], c = "k", 
+           marker = "^")
+ax.axvline(0, c = "k")
+plt.title("Discrimination scores", fontsize = 16)
+plt.show()
+

QID-1208-MVAdiscbh/Metainfo.txt

@@ -10,9 +10,11 @@ See also: MVAaer, MVAaper, MVAaerbh, MVAdisfbank, MVAdisnorm
 
 Author: Zografia Anastasiadou
 Author[SAS]: Svetlana Bykovskaya
+Author[Python]: 'Matthias Fengler, Liudmila Gorkun-Voevoda'
 
 Submitted: Thu, August 04 2011 by Awdesch Melzer
 Submitted[SAS]: Wen, April 6 2016 by Svetlana Bykovskaya
+Submitted[Python]: Tue, February 22 2022 by Liudmila Gorkun-Voevoda
 
 Datafile: bostonh.dat