Merge pull request #131 from molgenis/feat/update_libraries

feat: Updated libraries

scripts/balance_dataset.py

@@ -209,21 +209,22 @@ def split(dataset: pd.DataFrame):
         all_benign._is_copy = None
         v_benign_samples = all_benign.sample(frac=0.1, random_state=__random_state__)
         # A bit cryptic to remove the random samples from the benign dataset, but it works
-        all_benign = all_benign.append(v_benign_samples)
+        all_benign = pd.concat([all_benign, v_benign_samples], axis=0, ignore_index=True)
         all_benign.drop_duplicates(keep=False, inplace=True)
-        return_dataset = return_dataset.append(all_benign, ignore_index=True)
-        validation_dataset = validation_dataset.append(v_benign_samples, ignore_index=True)
+        return_dataset = pd.concat([return_dataset, all_benign], axis=0, ignore_index=True)
+        validation_dataset = pd.concat([validation_dataset, v_benign_samples], axis=0,
+                                       ignore_index=True)
 
         # Pathogenic
         all_pathogenic = dataset[dataset['binarized_label'] == 1]
         all_pathogenic._is_copy = None
         v_patho_samples = all_pathogenic.sample(frac=0.1, random_state=__random_state__)
         # Again a cryptic way to remove the randomly samples pathogenic samples
-        all_pathogenic = all_pathogenic.append(v_patho_samples)
+        all_pathogenic = pd.concat([all_pathogenic, v_patho_samples], axis=0, ignore_index=True)
         all_pathogenic.drop_duplicates(keep=False, inplace=True)
-        return_dataset = return_dataset.append(all_pathogenic, ignore_index=True)
-        validation_dataset = validation_dataset.append(v_patho_samples, ignore_index=True)
-
+        return_dataset = pd.concat([return_dataset, all_pathogenic], axis=0, ignore_index=True)
+        validation_dataset = pd.concat([validation_dataset, v_patho_samples], axis=0,
+                                       ignore_index=True)
         return validation_dataset, return_dataset
 
 
@@ -247,7 +248,12 @@ def balance(self, dataset: pd.DataFrame):
             processed_consequence = self._process_consequence(
                 pathogenic_dataset=selected_pathogenic, benign_dataset=selected_benign
             )
-            return_dataset = return_dataset.append(processed_consequence)
+            return_dataset = pd.concat(
+                [
+                    return_dataset,
+                    processed_consequence
+                ], axis=0, ignore_index=True
+            )
         return return_dataset
 
     def _process_consequence(self, pathogenic_dataset, benign_dataset):
@@ -267,10 +273,13 @@ def _process_consequence(self, pathogenic_dataset, benign_dataset):
             lower_bound = bins[ind]
             upper_bound = bins[ind + 1]
             sample_number = pathogenic_histogram[ind]
-            processed_bins = processed_bins.append(
-                self._process_bins(
-                    pathogenic_dataset, benign_dataset, upper_bound, lower_bound, sample_number
-                )
+            processed_bins = pd.concat(
+                [
+                    processed_bins,
+                    self._process_bins(
+                        pathogenic_dataset, benign_dataset, upper_bound, lower_bound, sample_number
+                    )
+                ], axis=0, ignore_index=True
             )
         return processed_bins
 
@@ -295,7 +304,9 @@ def _process_bins(
                 selected_benign.shape[0],
                 random_state=__random_state__
             )
-        return return_benign.append(return_pathogenic, ignore_index=True)
+        return pd.concat(
+            [return_benign, return_pathogenic], axis=0,  ignore_index=True
+        )
 
     @staticmethod
     def _get_variants_within_range(dataset, upper_bound, lower_bound):

setup.py

@@ -29,11 +29,11 @@
     ],
     python_requires='>=3.8',
     install_requires=[
-        'numpy==1.22.0',
-        'pandas==1.3.5',
-        'scipy==1.7.3',
-        'scikit-learn==1.0.2',
-        'xgboost==1.4.2'
+        'numpy==1.23.2',
+        'pandas==1.4.4',
+        'scipy==1.9.1',
+        'scikit-learn==1.1.2',
+        'xgboost==1.6.2'
     ],
     extras_require={
         'testing': [

src/molgenis/capice/__init__.py

@@ -1 +1 @@
-__version__ = '4.0.0-rc1'
+__version__ = '4.0.0-rc2'

src/molgenis/capice/main_train.py

@@ -179,6 +179,12 @@ def train(self, test_set: pd.DataFrame, train_set: pd.DataFrame):
             random_state=self.model_random_state,
             use_label_encoder=False
         )
+        model_estimator.set_params(
+            **{
+                'eval_metric': ["auc"],
+                'early_stopping_rounds': self.esr
+            }
+        )
         randomised_search_cv = RandomizedSearchCV(estimator=model_estimator,
                                                   param_distributions=param_dist,
                                                   scoring='roc_auc', n_jobs=8,
@@ -191,8 +197,6 @@ def train(self, test_set: pd.DataFrame, train_set: pd.DataFrame):
         self.log.info('Random search starting, please hold.')
         randomised_search_cv.fit(train_set[self.processed_features],
                                  train_set[TrainEnums.binarized_label.value],
-                                 early_stopping_rounds=self.esr,
-                                 eval_metric=["auc"],
                                  eval_set=eval_set,
                                  verbose=xgb_verbosity,
                                  sample_weight=train_set[TrainEnums.sample_weight.value])

tests/capice/test_main_train.py

@@ -55,6 +55,18 @@ def test_integration_training(self):
         best_model = str(model.__class__).split("'")[1]
         self.assertEqual('xgboost.sklearn.XGBClassifier', best_model)
 
+    def test_params(self):
+        """
+        Test to see if the >1.6.2 XGBoost parameter settings are applied correctly to the model
+        """
+        print('Test params')
+        self.main.run()
+        output_path = os.path.join(self.output_dir, self.output_filename)
+        with open(output_path, 'rb') as model_dat:
+            model = pickle.load(model_dat)
+        self.assertEqual(model.get_params()['early_stopping_rounds'], 1)
+        self.assertEqual(model.get_params()['eval_metric'], ['auc'])
+
     def test_unit_split(self):
         """
         Unit test to see if split works.