init

Q3/code.py

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+# code.py
+# Please use env.yaml to build corresponding conda env if you want to run this script
+#
+# author  : deng
+# date    : 20230919
+# platform: MacBook Pro 14 2021
+
+import pandas
+from sklearn import metrics
+import matplotlib.pyplot as plt
+
+
+if __name__ == '__main__':
+
+    csv_path = 'psudo_result.csv'
+    df = pandas.read_csv(csv_path)
+    df = df[:-1]
+
+    # Quick glance
+    print('Ground truth')
+    print(df['Ground truth'].value_counts())
+    print('Gender')
+    print(df['Gender'].value_counts())
+    print('Age')
+    plt.title('All patients')
+    plt.xlabel('age')
+    plt.ylabel('count')
+    df['Age'].hist(bins=20)
+    plt.show()
+    plt.title('Sick patients')
+    plt.xlabel('age')
+    plt.ylabel('count')
+    df[df['Ground truth'] == 'Sick']['Age'].hist(bins=20)
+    plt.show()
+
+    # Confusion Matrix
+    # threshold = 0.5
+    # gt = df['Ground truth']
+    # pred = ['Sick' if val >= threshold else 'No Sick'
+    #         for val in df['AI pred']]
+    # print(metrics.classification_report(gt, pred))
+
+    # ROC curve
+    gt = [1 if val == 'Sick' else 0
+          for val in df['Ground truth']]
+    pred = df['AI pred']
+    fpr, tpr, thresholds = metrics.roc_curve(gt, pred, pos_label=1)
+    plt.title('ROC curve')
+    plt.plot(fpr, tpr)
+    plt.ylabel('True Positive Rate')
+    plt.xlabel('False Positive Rate')
+    plt.show()

Q3/env.yaml

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+name: Q3
+channels:
+  - conda-forge
+dependencies:
+  - brotli=1.1.0
+  - brotli-bin=1.1.0
+  - bzip2=1.0.8
+  - ca-certificates=2023.7.22
+  - certifi=2023.7.22
+  - contourpy=1.1.1
+  - cycler=0.11.0
+  - fonttools=4.42.1
+  - freetype=2.12.1
+  - joblib=1.3.2
+  - kiwisolver=1.4.5
+  - lcms2=2.15
+  - lerc=4.0.0
+  - libblas=3.9.0
+  - libbrotlicommon=1.1.0
+  - libbrotlidec=1.1.0
+  - libbrotlienc=1.1.0
+  - libcblas=3.9.0
+  - libcxx=16.0.6
+  - libdeflate=1.19
+  - libffi=3.4.2
+  - libgfortran=5.0.0
+  - libgfortran5=13.2.0
+  - libjpeg-turbo=2.1.5.1
+  - liblapack=3.9.0
+  - libopenblas=0.3.24
+  - libpng=1.6.39
+  - libsqlite=3.43.0
+  - libtiff=4.6.0
+  - libwebp-base=1.3.2
+  - libxcb=1.15
+  - libzlib=1.2.13
+  - llvm-openmp=16.0.6
+  - matplotlib=3.8.0
+  - matplotlib-base=3.8.0
+  - munkres=1.1.4
+  - ncurses=6.4
+  - numpy=1.26.0
+  - openjpeg=2.5.0
+  - openssl=3.1.2
+  - packaging=23.1
+  - pandas=2.1.0
+  - pillow=10.0.1
+  - pip=23.2.1
+  - pthread-stubs=0.4
+  - pyparsing=3.1.1
+  - python=3.10.12
+  - python-dateutil=2.8.2
+  - python-tzdata=2023.3
+  - python_abi=3.10
+  - pytz=2023.3.post1
+  - readline=8.2
+  - scikit-learn=1.3.0
+  - scipy=1.11.2
+  - setuptools=68.2.2
+  - six=1.16.0
+  - threadpoolctl=3.2.0
+  - tk=8.6.12
+  - tornado=6.3.3
+  - tzdata=2023c
+  - unicodedata2=15.0.0
+  - wheel=0.41.2
+  - xorg-libxau=1.0.11
+  - xorg-libxdmcp=1.1.3
+  - xz=5.2.6
+  - zstd=1.5.5
+prefix: /Users/xiao_deng/miniforge3/envs/Q3

Q3/psudo_result.csv

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+Sample ID,Gender,Age,AI pred,Ground truth
+S_0,Male,42.0,0.183843222651211,No Sick
+S_1,Male,54.0,0.032451971485353635,No Sick
+S_2,Male,27.0,0.1649091208860276,No Sick
+S_3,Female,51.0,0.9838273079563034,Sick
+S_4,Female,32.0,0.7008005948749091,No Sick
+S_5,Female,50.0,0.04646539805577132,No Sick
+S_6,Female,63.0,0.37605963432485756,No Sick
+S_7,Male,48.0,0.5221502126827561,Sick
+S_8,Female,43.0,0.6380703073440606,No Sick
+S_9,Male,50.0,0.16000187268400592,No Sick
+S_10,Female,33.0,0.8684964025015954,No Sick
+S_11,Female,52.0,0.6292457724763052,No Sick
+S_12,Male,49.0,0.4549646243576516,Sick
+S_13,Female,26.0,0.5097453569913539,No Sick
+S_14,Female,60.0,0.3337711914417254,No Sick
+S_15,Female,48.0,0.019190399941885428,No Sick
+S_16,Male,47.0,0.04086642173184299,No Sick
+S_17,Female,54.0,0.18241359146528532,No Sick
+S_18,Male,58.0,0.32089169346089924,No Sick
+S_19,Female,36.0,0.413237627716878,Sick
+S_20,Female,48.0,0.13062595790437023,No Sick
+S_21,Female,56.0,0.29018011048552517,No Sick
+S_22,Female,53.0,0.2555372095893874,No Sick
+S_23,Female,44.0,0.09580993903210833,No Sick
+S_24,Male,55.0,0.6877916599706746,Sick
+S_25,Male,56.0,0.40153291103181854,Sick
+S_26,Male,44.0,0.17088154472476824,No Sick
+S_27,Male,60.0,0.28903419437712474,No Sick
+S_28,Male,39.0,0.3059480568045402,No Sick
+S_29,Female,51.0,0.30293016203624257,No Sick
+S_30,Male,32.0,0.5986074572752382,Sick
+S_31,Male,61.0,0.1679725784069874,No Sick
+S_32,Female,20.0,0.19933363134841395,No Sick
+S_33,Female,75.0,0.2936272223993208,No Sick
+S_34,Female,60.0,0.38458266378064254,No Sick
+S_35,Female,36.0,0.5476216653350379,No Sick
+S_36,Female,22.0,0.2540285986407038,No Sick
+S_37,Female,41.0,0.30688487550042554,No Sick
+S_38,Male,58.0,0.010881451914966034,No Sick
+S_39,Female,57.0,0.3141640693118162,No Sick
+S_40,Female,57.0,0.19447009952931774,No Sick
+S_41,Male,49.0,0.33065208227575404,Sick
+S_42,Female,59.0,0.751020059639999,Sick
+S_43,Male,73.0,0.2874468176061555,No Sick
+S_44,Male,47.0,0.12089859681706382,No Sick
+S_45,Male,66.0,0.17528475949731379,No Sick
+S_46,Female,64.0,0.8819982427894725,No Sick
+S_47,Female,42.0,0.5005003573463266,No Sick
+S_48,Female,43.0,0.2745791003849961,No Sick
+S_49,Female,45.0,0.4161238121442544,No Sick
+S_50,Male,35.0,0.41528904708151504,No Sick
+S_51,Female,52.0,0.8146647348503631,No Sick
+S_52,Male,49.0,0.17604193457147554,No Sick
+S_53,Male,53.0,0.05322796458244725,No Sick
+S_54,Female,45.0,0.4299485265172557,No Sick
+S_55,Female,63.0,0.35375757738991287,No Sick
+S_56,Male,37.0,0.2164630863950512,No Sick
+S_57,Female,43.0,0.7542813473217973,No Sick
+S_58,Male,40.0,0.12277033509439539,No Sick
+S_59,Male,57.0,0.028993766542943746,No Sick
+S_60,Male,63.0,0.1840667841524664,No Sick
+S_61,Female,42.0,0.9182713866681889,No Sick
+S_62,Female,38.0,0.4997258327162861,No Sick
+S_63,Female,27.0,0.057822477359941504,No Sick
+S_64,Male,45.0,0.2869275808238832,No Sick
+S_65,Male,56.0,0.27075663892334423,No Sick
+S_66,Female,78.0,0.5147121232117046,Sick
+S_67,Male,57.0,0.4303055422658295,Sick
+S_68,Female,65.0,0.5790290855646982,No Sick
+S_69,Male,34.0,0.4488264948240428,No Sick
+S_70,Male,52.0,0.31922894933435064,No Sick
+S_71,Male,48.0,0.2704984074393081,No Sick
+S_72,Female,57.0,0.5318201811015301,No Sick
+S_73,Female,45.0,0.915191370314399,No Sick
+S_74,Male,33.0,0.16627710406505336,No Sick
+S_75,Male,57.0,0.9744135322178493,Sick
+S_76,Male,36.0,0.3890723811007448,No Sick
+S_77,Male,50.0,0.4782644098424499,No Sick
+S_78,Female,33.0,0.5124992859909004,Sick
+S_79,Male,36.0,0.11506051247073344,No Sick
+S_80,Male,62.0,0.3673796432900976,No Sick
+S_81,Male,56.0,0.061334487122536044,No Sick
+S_82,Female,50.0,0.5792648132889187,No Sick
+S_83,Female,55.0,0.33218576134014133,No Sick
+S_84,Male,39.0,0.32085392248494365,No Sick
+S_85,Male,34.0,0.04450511997589546,No Sick
+S_86,Male,67.0,0.15133055497828424,No Sick
+S_87,Female,50.0,0.18709959213671942,No Sick
+S_88,Male,53.0,0.10073701418285581,No Sick
+S_89,Male,35.0,0.5110592056032592,Sick
+S_90,Male,38.0,0.8793802677769047,Sick
+S_91,Female,47.0,0.1247705954358479,No Sick
+S_92,Female,51.0,0.23236933325127848,No Sick
+S_93,Female,64.0,0.5361768277573149,No Sick
+S_94,Male,39.0,0.7145348867998527,Sick
+S_95,Female,49.0,0.8949969712274917,No Sick
+S_96,Female,51.0,0.4008792149067575,No Sick
+S_97,Female,51.0,0.37491428399612803,No Sick
+S_98,Female,39.0,0.3545989884164421,No Sick
+S_99,Male,51.0,0.26829780510576695,No Sick
+,,,,