reducing strength for antagonic prior

BayesianKDEy/TODO.txt

@@ -4,11 +4,17 @@
 - analyze across shift
 - add Bayesian EM:
     - https://github.com/ChangkunYe/MAPLS/blob/main/label_shift/mapls.py
-    - take this opportunity to add RLLS: https://github.com/ChangkunYe/MAPLS/blob/main/label_shift/rlls.py
+    - take this opportunity to add RLLS:
+        https://github.com/Angie-Liu/labelshift
+        https://github.com/ChangkunYe/MAPLS/blob/main/label_shift/rlls.py
 - add CIFAR10 and MNIST? Maybe consider also previously tested types of shift (tweak-one-out, etc.)? from RLLS paper
     - https://github.com/Angie-Liu/labelshift/tree/master
     - https://github.com/Angie-Liu/labelshift/blob/master/cifar10_for_labelshift.py
     - Note: MNIST is downloadable from https://archive.ics.uci.edu/dataset/683/mnist+database+of+handwritten+digits
+    - Seem to be some pretrained models in:
+        https://github.com/geifmany/cifar-vgg
+        https://github.com/EN10/KerasMNIST
+        https://github.com/tohinz/SVHN-Classifier
 - consider prior knowledge in experiments:
     - One scenario in which our prior is uninformative (i.e., uniform)
     - One scenario in which our prior is wrong (e.g., alpha-prior = (3,2,1), protocol-prior=(1,1,5))

BayesianKDEy/prior_effect.py

@@ -39,6 +39,9 @@ def methods():
     yield f'BaKDE-Ait-T2', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0,
                                                                                   engine='numpyro', temperature=2.,
                                                                                   prior='uniform', **hyper)
+    yield f'BaKDE-Ait-T1', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0,
+                                                                                  engine='numpyro', temperature=1.,
+                                                                                  prior='uniform', **hyper)
 
 
 def run_test(test, alpha_test, alpha_train, concentration, prior_type, bay_quant, train_prev, dataset_name, method_name, results):
@@ -104,7 +107,7 @@ def experiment(dataset: Dataset,
             run_test(test, alpha_test_informative, alpha_train, concentration, prior_type, bay_quant, train_prev, dataset_name, method_name, results)
 
             # informative prior
-            alpha_test_wrong = antagonistic_prevalence(train_prev, strength=1) * concentration
+            alpha_test_wrong = antagonistic_prevalence(train_prev, strength=0.5) * concentration
             prior_type = 'wrong'
             run_test(test, alpha_test_wrong, alpha_train, concentration, prior_type, bay_quant, train_prev, dataset_name, method_name, results)
 
@@ -120,7 +123,7 @@ def concat_reports(reports):
     return df
 
 
-def error_vs_concentration_plot(df, err='ae'):
+def error_vs_concentration_plot(df, err='ae', save_path=None):
     import seaborn as sns
     import matplotlib.pyplot as plt
 
@@ -147,10 +150,14 @@ def error_vs_concentration_plot(df, err='ae'):
         ax.set_ylabel('M'+err.upper())
 
     plt.tight_layout()
-    plt.show()
+    if save_path is None:
+        plt.show()
+    else:
+        os.makedirs(Path(save_path).parent, exist_ok=True)
+        plt.savefig(save_path)
 
 
-def coverage_vs_concentration_plot(df):
+def coverage_vs_concentration_plot(df, save_path=None):
     import seaborn as sns
     import matplotlib.pyplot as plt
 
@@ -176,10 +183,14 @@ def coverage_vs_concentration_plot(df):
         ax.set_ylabel('Coverage')
 
     plt.tight_layout()
-    plt.show()
+    if save_path is None:
+        plt.show()
+    else:
+        os.makedirs(Path(save_path).parent, exist_ok=True)
+        plt.savefig(save_path)
 
 
-def amplitude_vs_concentration_plot(df):
+def amplitude_vs_concentration_plot(df, save_path=None):
     import seaborn as sns
     import matplotlib.pyplot as plt
 
@@ -204,10 +215,14 @@ def amplitude_vs_concentration_plot(df):
         ax.set_ylabel('Amplitude')
 
     plt.tight_layout()
-    plt.show()
+    if save_path is None:
+        plt.show()
+    else:
+        os.makedirs(Path(save_path).parent, exist_ok=True)
+        plt.savefig(save_path)
 
 
-def coverage_vs_amplitude_plot(df):
+def coverage_vs_amplitude_plot(df, save_path=None):
     import seaborn as sns
     import matplotlib.pyplot as plt
 
@@ -243,7 +258,11 @@ def coverage_vs_amplitude_plot(df):
         ax.axhline(0.95, linestyle='--', color='gray', alpha=0.7)
 
     plt.tight_layout()
-    plt.show()
+    if save_path is None:
+        plt.show()
+    else:
+        os.makedirs(Path(save_path).parent, exist_ok=True)
+        plt.savefig(save_path)
 
 
 if __name__ == '__main__':
@@ -267,11 +286,12 @@ if __name__ == '__main__':
     # concat all reports as a dataframe
     df = concat_reports(reports)
 
-    for data_name in selected:
+    # for data_name in selected:
-        print(data_name)
+    #     print(data_name)
-        df_ = df[df['dataset']==data_name]
+    #     df_ = df[df['dataset']==data_name]
-        error_vs_concentration_plot(df_)
+    df_ = df
-        coverage_vs_concentration_plot(df_)
+    error_vs_concentration_plot(df_, save_path='./plots/prior_effect/error_vs_concentration.pdf')
-        amplitude_vs_concentration_plot(df_)
+    coverage_vs_concentration_plot(df_, save_path='./plots/prior_effect/coverage_vs_concentration.pdf')
-        coverage_vs_amplitude_plot(df_)
+    amplitude_vs_concentration_plot(df_, save_path='./plots/prior_effect/amplitude_vs_concentration.pdf')
+    coverage_vs_amplitude_plot(df_, save_path='./plots/prior_effect/coverage_vs_amplitude.pdf')