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wilcoxon_test.py
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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.stats import wilcoxon
def build_df(cnn, cgan, jit, flip, smote, perm):
return pd.DataFrame({"CNN (No Augmentation)": cnn,
"CNN (Orig. + Jitter)": jit,
"CNN (Orig. + Flip)": flip,
"CNN (Orig. + Permute)": perm,
"CNN (Orig. + AVG_TS_SMOTE)": smote,
"CNN (Orig + C-GAN)": cgan})
def boxplot(df, title=""):
title = "Balanced Accuracy Score (" + title + ")"
sns.boxplot(data=df, orient="h", palette="pastel")
plt.title(title)
plt.tight_layout()
plt.show()
def test(x, y):
"""
Tests whether the distribution of the differences x - y is symmetric about zero
:param x: 1d np.array or list, potential improvement
:param y: 1d np.array or list, baseline
:return: None
"""
print("x median: ", np.sort(np.array(x))[4:6].mean())
print("x mean: ", np.array(x).mean())
print("x std: ", np.array(x).std())
print("y median: ", np.sort(np.array(y))[4:6].mean())
print("y mean: ", np.array(y).mean())
print("y std: ", np.array(y).std())
# Two-sided test:
# Null hypothesis: the median of the differences is zero
# Alternative hypothesis: the median is different from zero
print("Two-sided test")
stat, p = wilcoxon(x, y, alternative="two-sided")
print(p)
if p < 0.05:
print("We reject the null hypothesis at a confidence level of 0.05%")
else:
print("We cannot reject the null hypothesis")
# One-sided test:
# Null hypothesis: the median is negative
# Alternative hypothesis: the median is positive
print("One-sided test")
stat, p = wilcoxon(x, y, alternative="greater")
print(p)
if p < 0.05:
print("We reject the null hypothesis at a confidence level of 0.05%")
else:
print("We cannot reject the null hypothesis")
if __name__ == '__main__':
# balanced accuracy
# BEEF --> DONE
# beef_cnn = [0.7797619047619048, 0.7683333333333333,
# 0.7892857142857143, 0.8800000000000001,
# 0.7005555555555556, 0.7666666666666667,
# 0.7636363636363637, 0.8333333333333334,
# 0.7980952380952381, 0.7357142857142857]
# beef_cgan = [0.8, 0.8300000000000001,
# 0.8466666666666667, 0.8405555555555555,
# 0.8454545454545455, 0.8550000000000001,
# 0.9444444444444444, 0.8228571428571427,
# 0.8466666666666667, 0.7849999999999999]
# beef_jit = [0.7807142857142857, 0.9133333333333333,
# 0.7619047619047619, 0.8,
# 0.7899999999999999, 0.8595238095238095,
# 0.7842857142857144, 0.8133333333333332,
# 0.89, 0.7733333333333333]
# beef_flip = [0.6977777777777778, 0.7057142857142857,
# 0.8933333333333333, 0.75,
# 0.6805555555555556, 0.6266666666666667,
# 0.7157575757575757, 0.7083333333333334,
# 0.7880952380952382, 0.73]
# beef_perm = [0.9166666666666667, 0.7466666666666667,
# 0.7225641025641025, 0.85,
# 0.7542857142857142, 0.7771428571428571,
# 0.7733333333333333, 0.835,
# 0.775, 0.8311111111111111]
# beef_smote = [0.7264285714285714, 0.7516666666666667,
# 0.7257142857142856, 0.8555555555555555,
# 0.7476190476190476, 0.9416666666666668,
# 0.8678571428571429, 0.8333333333333334,
# 0.7404761904761904, 0.9047619047619048]
# beef = build_df(cnn=beef_cnn, jit=beef_jit, flip=beef_flip,
# perm=beef_perm, smote=beef_smote, cgan=beef_cgan)
# boxplot(beef, title="Beef")
# x = Aug, y = Orig
# test(x=beef_cgan, y=beef_cnn)
# test(x=beef_jit, y=beef_cnn)
# test(x=beef_flip, y=beef_cnn)
# test(x=beef_perm, y=beef_cnn)
# test(x=beef_smote, y=beef_cnn)
# COFFEE --> DONE
# coffee_cnn = [1.0, 0.9545454545454546,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0]
# coffee_cgan = [1.0, 0.9545454545454546,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0]
# coffee_jit = [1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0]
# coffee_flip = [0.8444444444444444, 0.8888888888888888,
# 0.8512820512820514, 0.9615384615384616,
# 0.8663101604278074, 0.8636363636363636,
# 1.0, 0.7410256410256411,
# 0.78125, 0.9615384615384616]
# coffee_perm = [0.9411764705882353, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 0.96875]
# coffee_smote = [1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 0.9583333333333333,
# 1.0, 1.0]
# coffee = build_df(cnn=coffee_cnn, jit=coffee_jit, flip=coffee_flip,
# perm=coffee_perm, smote=coffee_smote, cgan=coffee_cgan)
# boxplot(coffee, title="Coffee")
# x = Aug, y = Orig
# test(x=coffee_cgan, y=coffee_cnn)
# test(x=coffee_jit, y=coffee_cnn)
# test(x=coffee_flip, y=coffee_cnn)
# test(x=coffee_perm, y=coffee_cnn)
# test(x=coffee_smote, y=coffee_cnn)
# FREEZER --> CGAN MISSING
freezer_cnn = [0.9866666666666667, 0.9868942080378251,
0.9590222861250899, 0.9830028328611897,
0.9835960591133004, 0.984561403508772,
0.9903632265454679, 0.9862668769094314,
0.984308753138471, 0.9874184288401051]
freezer_cgan = [0.9887719298245614, 0.9738330975954739,
0.9760101507834829, 0.9810171919770774,
0.984530310014301, 0.9910391996220393,
0.9803242456371721, 0.9749826268241835,
0.9793781865154385, 0.9875346260387812]
freezer_jit = [0.9905817882560832, 0.9862258640984114,
0.984199930661708, 0.9889334658161363,
0.9895104895104896, 0.9860945642521851,
0.9890894316950656, 0.9843314763231198,
0.9576315509538331, 0.9855989945909982]
freezer_flip = [0.9761575673807878, 0.9820931890886742,
0.9849246231155779, 0.9854388585731869,
0.9835419399827441, 0.9860485662955345,
0.985032074126871, 0.9771448663853728,
0.9800947068952609, 0.9827838827838828]
freezer_perm = [0.9599167908594138, 0.9518510893164633,
0.9614816390691299, 0.9527848255864346,
0.9530677493321986, 0.9628325568357324,
0.9730835602273752, 0.9585233745107686,
0.9544115409107496, 0.9635480968629794]
freezer_smote = [0.9875886524822695, 0.9886010140641925,
0.9861737327522636, 0.991534209133106,
0.9895199459093982, 0.9761570827489481,
0.9855144144015441, 0.984284262199494,
0.9825326079832208, 0.9849223686906095]
freezer = build_df(cnn=freezer_cnn, jit=freezer_jit, flip=freezer_flip,
perm=freezer_perm, smote=freezer_smote, cgan=freezer_cgan)
boxplot(freezer, title="Freezer")
# x = Aug, y = Orig
test(x=freezer_cgan, y=freezer_cnn)
# test(x=freezer_jit, y=freezer_cnn)
# test(x=freezer_flip, y=freezer_cnn)
# test(x=freezer_perm, y=freezer_cnn)
# test(x=freezer_smote, y=freezer_cnn)
# GUNPOINT --> DONE
# gunpoint_cnn = [0.9464793741109531, 0.9198079658605974,
# 0.9532361308677098, 0.9464793741109531,
# 0.9458013906222142, 0.9500379939209727,
# 0.9666666666666667, 0.9334415584415585,
# 0.9267857142857143, 0.96]
# gunpoint_cgan = [0.9783798576902025, 0.9586688137412775,
# 0.9366883116883117, 0.9538338373954812,
# 0.98, 0.9416666666666667,
# 0.9145299145299145, 0.9393669985775248,
# 0.9412238325281803, 0.9145299145299145]
# gunpoint_jit = [0.9668856048166393, 0.9660714285714286,
# 0.9587609970674487, 0.9802631578947368,
# 0.9665718349928876, 0.9545940170940171,
# 0.9583931133428981, 0.9401854162952398,
# 0.9794520547945205, 0.9517857142857142]
# gunpoint_flip = [0.9337606837606838, 0.9920634920634921,
# 0.9732572651096452, 0.9653679653679654,
# 0.9671766589574808, 0.9472276698163665,
# 0.926731078904992, 0.9725441255125691,
# 0.9338563023711892, 0.9866666666666667]
# gunpoint_perm = [0.9596157267390144, 0.9657534246575342,
# 0.9527664116705212, 0.9821428571428572,
# 0.993421052631579, 0.9804519368723099,
# 0.9878048780487805, 0.9484702093397746,
# 0.9733333333333334, 0.9823529411764707]
# gunpoint_smote = [0.941611695489392, 0.9664650418075076,
# 0.9473214285714286, 0.9466666666666667,
# 0.9165324745034891, 0.9134495641344956,
# 0.9487087517934003, 0.9563927351195828,
# 0.9069264069264069, 0.92]
# gunpoint = build_df(cnn=gunpoint_cnn, jit=gunpoint_jit, flip=gunpoint_flip,
# perm=gunpoint_perm, smote=gunpoint_smote, cgan=gunpoint_cgan)
# boxplot(gunpoint, title="Gunpoint")
# x = Aug, y = Orig
# test(x=gunpoint_cgan, y=gunpoint_cnn)
# test(x=gunpoint_jit, y=gunpoint_cnn)
# test(x=gunpoint_flip, y=gunpoint_cnn)
# test(x=gunpoint_perm, y=gunpoint_cnn)
# test(x=gunpoint_smote, y=gunpoint_cnn)
# ECG200 --> DONE
# ecg_cnn = [0.8194614443084456, 0.7491319444444444,
# 0.7708333333333333, 0.7664092664092663,
# 0.7925347222222222, 0.7666666666666666,
# 0.760438197602315, 0.7883310719131615,
# 0.7875, 0.764367816091954]
# ecg_cgan = [0.823076923076923, 0.8062211013030685,
# 0.8376736111111112, 0.8341346153846154,
# 0.7885572139303483, 0.8491632745364088,
# 0.8168094655242758, 0.8057309373482273,
# 0.8307620383356709, 0.840213695968917]
# ecg_jit = [0.8516042780748663, 0.8131868131868132,
# 0.8646010186757216, 0.7896213183730716,
# 0.817062818336163, 0.788783355947535,
# 0.7986886838271006, 0.7958404074702886,
# 0.8338999514327343, 0.7958333333333334]
# ecg_flip = [0.7932263814616756, 0.8833333333333333,
# 0.8179907524169819, 0.7583333333333333,
# 0.8041666666666667, 0.8570782451379466,
# 0.787001287001287, 0.8601458601458601,
# 0.7692307692307692, 0.872003618272275]
# ecg_perm = [0.8683409436834094, 0.8386958386958387,
# 0.8901098901098901, 0.8603565365025467,
# 0.8186341022161918, 0.8581112669471715,
# 0.8541666666666667, 0.8483516483516484,
# 0.8602611179110566, 0.8941655359565808]
# ecg_smote = [0.841055341055341, 0.821658615136876,
# 0.8547619047619048, 0.8390079865489701,
# 0.8785714285714286, 0.8676470588235294,
# 0.8726655348047538, 0.8214285714285714,
# 0.8542673107890499, 0.8715277777777778]
# ecg = build_df(cnn=ecg_cnn, jit=ecg_jit, flip=ecg_flip,
# perm=ecg_perm, smote=ecg_smote, cgan=ecg_cgan)
# boxplot(ecg, title="ECG200")
# x = Aug, y = Orig
# test(x=ecg_cgan, y=ecg_cnn)
# test(x=ecg_jit, y=ecg_cnn)
# test(x=ecg_flip, y=ecg_cnn)
# test(x=ecg_perm, y=ecg_cnn)
# test(x=ecg_smote, y=ecg_cnn)
# INSECTEPG --> DONE
# ins_cnn = [1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0]
# ins_cgan = [1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0]
# ins_jit = [1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0]
# ins_flip = [0.9971751412429378, 0.9972677595628415,
# 1.0, 1.0,
# 0.997245179063361, 0.9970501474926253,
# 1.0, 1.0,
# 1.0, 1.0]
# ins_perm = [1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0]
# ins_smote = [1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0,
# 1.0, 1.0]
# ins = build_df(cnn=ins_cnn, jit=ins_jit, flip=ins_flip,
# perm=ins_perm, smote=ins_smote, cgan=ins_cgan)
# boxplot(ins, title="InsectEPG")
# x = Aug, y = Orig
# test(x=ins_cgan, y=ins_cnn)
# test(x=ins_jit, y=ins_cnn)
# test(x=ins_flip, y=ins_cnn)
# test(x=ins_perm, y=ins_cnn)
# test(x=ins_smote, y=ins_cnn)