adding one plot, the error by drift plot... have to fix the legend though

quapy/benchmarking/_base.py

@@ -49,6 +49,8 @@ class Benchmark(ABC):
         makedirs(join(home_dir, 'tables'))
         makedirs(join(home_dir, 'plots'))
 
+        self.train_prevalence = {}
+
     def _run_id(self, method: MethodDescriptor, dataset: str):
         sep = Benchmark.ID_SEPARATOR
         assert sep not in method.id, \
@@ -104,10 +106,34 @@ class Benchmark(ABC):
         Table.LatexPDF(join(self.home_dir, 'tables', 'results.pdf'), list(tables.values()))
 
 
-    def gen_plots(self):
+    def gen_plots(self, results, metrics=None):
-        pass
+        import matplotlib.pyplot as plt
+        plt.rcParams.update({'font.size': 11})
 
-    def show_report(self, method, dataset, report: pd.DataFrame):
+        if metrics is None:
+            metrics = ['ae']
+
+        for metric in metrics:
+            method_names, true_prevs, estim_prevs, train_prevs = [], [], [], []
+            skip=False
+            for (method, dataset, result) in results:
+                method_names.append(method.name)
+                true_prevs.append(np.vstack(result['true-prev'].values))
+                estim_prevs.append(np.vstack(result['estim-prev'].values))
+                train_prevs.append(self.get_training_prevalence(dataset))
+            if not skip:
+                path = join(self.home_dir, 'plots', f'err_by_drift_{metric}.pdf')
+                qp.plot.error_by_drift(method_names, true_prevs, estim_prevs, train_prevs, error_name=metric, n_bins=20, savepath=path)
+
+
+
+
+
+
+
+
+
+    def _show_report(self, method, dataset, report: pd.DataFrame):
         id = method.id
         MAE = report['mae'].mean()
         mae_std = report['mae'].std()
@@ -146,19 +172,20 @@ class Benchmark(ABC):
                 seed=0,
                 asarray=False
             )
-            results += [(method, dataset, result) for (method, dataset), result in zip(pending_job_args, remaining_results)]
+            results += [
+                (method, dataset, result) for (method, dataset), result in zip(pending_job_args, remaining_results)
+            ]
 
         # print results
         for method, dataset, result in results:
-            self.show_report(method, dataset, result)
+            self._show_report(method, dataset, result)
 
         self.gen_tables(results)
-        self.gen_plots()
+        self.gen_plots(results)
-
-    # def gen_plots(self, methods=None):
-    #     if methods is None:
-
 
+    @abstractmethod
+    def get_training_prevalence(self, dataset: str):
+        ...
 
     def __add__(self, other: 'Benchmark'):
         return CombinedBenchmark(self, other, self.n_jobs)
@@ -192,6 +219,10 @@ class TypicalBenchmark(Benchmark):
     def get_sample_size(self)-> int:
         ...
 
+    @abstractmethod
+    def get_training(self, dataset:str)-> LabelledCollection:
+        ...
+
     @abstractmethod
     def get_trModsel_valprotModsel_trEval_teprotEval(self, dataset:str)->\
             (LabelledCollection, AbstractProtocol, LabelledCollection, AbstractProtocol):
@@ -212,7 +243,8 @@ class TypicalBenchmark(Benchmark):
 
         with qp.util.temp_seed(random_state):
             # data split
-            trModSel, valprotModSel, trEval, teprotEval =  self.get_trModsel_valprotModsel_trEval_teprotEval(dataset)
+            trModSel, valprotModSel, trEval, teprotEval = self.get_trModsel_valprotModsel_trEval_teprotEval(dataset)
+            self.train_prevalence[dataset] = trEval.prevalence()
 
             # model selection
             modsel = GridSearchQ(
@@ -247,6 +279,12 @@ class TypicalBenchmark(Benchmark):
 
         return report
 
+    def get_training_prevalence(self, dataset: str):
+        if not dataset in self.train_prevalence:
+            training = self.get_training(dataset)
+            self.train_prevalence[dataset] = training.prevalence()
+        return self.train_prevalence[dataset]
+
 
 class UCIBinaryBenchmark(TypicalBenchmark):
 
@@ -259,6 +297,9 @@ class UCIBinaryBenchmark(TypicalBenchmark):
         testprotModsel = APP(teEval, n_prevalences=21, repeats=100)
         return trModsel, valprotModsel, trEval, testprotModsel
 
+    def get_training(self, dataset:str) -> LabelledCollection:
+        return qp.datasets.fetch_UCIBinaryDataset(dataset).training
+
     def get_sample_size(self) -> int:
         return 100
 
@@ -284,6 +325,9 @@ class UCIMultiBenchmark(TypicalBenchmark):
         testprotModsel = UPP(teEval, repeats=1000)
         return trModsel, valprotModsel, trEval, testprotModsel
 
+    def get_training(self, dataset:str) -> LabelledCollection:
+        return qp.datasets.fetch_UCIMulticlassDataset(dataset).training
+
     def get_sample_size(self) -> int:
         return 500
 
@@ -291,6 +335,7 @@ class UCIMultiBenchmark(TypicalBenchmark):
         return 'mae'
 
 
+
 if __name__ == '__main__':
 
     from quapy.benchmarking.typical import *

quapy/plot.py

@@ -259,7 +259,7 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
     data = _join_data_by_drift(method_names, true_prevs, estim_prevs, tr_prevs, x_error, y_error, method_order)
 
     if method_order is None:
-        method_order = method_names
+        method_order = np.unique(method_names)
 
     _set_colors(ax, n_methods=len(method_order))
 
@@ -329,10 +329,9 @@ def error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
     
     
     if show_legend:
-        fig.legend(loc='lower center',
+        # fig.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=(len(method_order)//2)+1)
-                  bbox_to_anchor=(1, 0.5),
+        fig.legend(loc='upper right', bbox_to_anchor=(1, 0.6))
-                  ncol=(len(method_names)+1)//2)
+
-      
     _save_or_show(savepath)