0.81.0

CHANGELOG.md

@@ -1,6 +1,10 @@
 Changelog
 =========
 
+Version 0.81.0
+--------------
+* added test selection to test module
+
 Version 0.80.4
 --------------
 * added test-file folder to demo file lists

nkululeko/constants.py

@@ -1,2 +1,2 @@
-VERSION="0.80.4"
+VERSION="0.81.0"
 SAMPLING_RATE = 16000

nkululeko/data/dataset.py

@@ -541,18 +541,18 @@ def prepare_labels(self):
         only_tests = eval(self.util.config_val("DATA", "tests", "False"))
         module = glob_conf.module
         if only_tests and module == "test":
-            self.df = self.map_labels(self.df)
+            self.df_test = self.map_labels(self.df_test)
             # Bin target values if they are continuous but a classification experiment should be done
-            self.map_continuous_classification(self.df)
-            self.df = self._add_labels(self.df)
+            self.map_continuous_classification(self.df_test)
+            self.df_test = self._add_labels(self.df_test)
             if self.util.config_val_data(self.name, "value_counts", False):
                 if not self.got_gender or not self.got_speaker:
                     self.util.error(
                         "can't plot value counts if no speaker or gender is" " given"
                     )
                 else:
                     self.plot.describe_df(
-                        self.name, self.df, self.target, f"{self.name}_distplot"
+                        self.name, self.df_test, self.target, f"{self.name}_distplot"
                     )
             return
         self.df_train = self.map_labels(self.df_train)

nkululeko/experiment.py

@@ -153,16 +153,17 @@ def fill_tests(self):
                     self.got_age = True
                 if data.got_speaker:
                     self.got_speaker = True
+                data.split()
                 data.prepare_labels()
                 self.df_test = pd.concat(
-                    [self.df_test, self.util.make_segmented_index(data.df)]
+                    [self.df_test, self.util.make_segmented_index(data.df_test)]
                 )
                 self.df_test.is_labeled = data.is_labeled
             self.df_test.got_gender = self.got_gender
             self.df_test.got_speaker = self.got_speaker
             # self.util.set_config_val('FEATS', 'needs_features_extraction', 'True')
             # self.util.set_config_val('FEATS', 'no_reuse', 'True')
-            self.df_test["class_labels"] = self.df_test[self.target]
+            self.df_test["class_label"] = self.df_test[self.target]
             self.df_test[self.target] = self.label_encoder.transform(
                 self.df_test[self.target]
             )

nkululeko/test.py

@@ -35,7 +35,7 @@ def main(src_dir):
     expr = Experiment(config)
     module = "test"
     expr.set_module(module)
-    util = Util(module)
+    util = Util(module, has_config=True)
     util.debug(
         f"running {expr.name} from config {config_file}, nkululeko version"
         f" {VERSION}"

nkululeko/test_predictor.py

@@ -2,6 +2,7 @@
     Predict targets from a model and save as csv file.
 
 """
+
 import nkululeko.glob_conf as glob_conf
 from nkululeko.utils.util import Util
 import pandas as pd
@@ -50,9 +51,14 @@ def predict_and_store(self):
         else:
             predictions = self.model.get_predictions()
             # print(predictions)
-            df = pd.DataFrame(index=self.orig_df.index)
-            df["speaker"] = self.orig_df["speaker"]
-            df["gender"] = self.orig_df["gender"]
-            df[self.target] = self.label_encoder.inverse_transform(predictions)
-            # df[self.target] = predictions
+            # df = pd.DataFrame(index=self.orig_df.index)
+            # df["speaker"] = self.orig_df["speaker"]
+            # df["gender"] = self.orig_df["gender"]
+            # df[self.target] = self.orig_df[self.target]
+            df = self.orig_df.copy()
+            df["predictions"] = self.label_encoder.inverse_transform(predictions)
+            target = self.util.config_val("DATA", "target", "emotion")
+            if "class_label" in df.columns:
+                df = df.drop(columns=[target])
+                df = df.rename(columns={"class_label": target})
             df.to_csv(self.name)

test_runs.sh

@@ -1,29 +1,38 @@
+# test explore module
 python -m nkululeko.explore --config tests/exp_emodb_explore_data.ini
 python -m nkululeko.explore --config tests/exp_emodb_explore_featimportance.ini
 python -m nkululeko.explore --config tests/exp_emodb_explore_scatter.ini
 python -m nkululeko.explore --config tests/exp_emodb_explore_features.ini
 python -m nkululeko.explore --config tests/exp_androids_explore.ini
+python -m nkululeko.explore --config tests/exp_agedb_explore_data.ini
+# test basic nkululeko
 python -m nkululeko.nkululeko --config tests/exp_emodb_os_praat_xgb.ini
 python -m nkululeko.nkululeko --config tests/exp_emodb_featimport_xgb.ini
 python -m nkululeko.nkululeko --config tests/exp_emodb_cnn.ini
 python -m nkululeko.nkululeko --config tests/exp_emodb_balancing.ini
 python -m nkululeko.nkululeko --config tests/exp_emodb_audmodel_xgb.ini
 python -m nkululeko.nkululeko --config tests/exp_emodb_split.ini
-#python -m nkululeko.nkululeko --config tests/exp_emodb_clap_mlp.ini
 python -m nkululeko.nkululeko --config tests/exp_emodb_os_mlp.ini
 python -m nkululeko.nkululeko --config tests/exp_ravdess_os_xgb.ini
 python -m nkululeko.nkululeko --config tests/exp_agedb_os_xgr.ini 
 python -m nkululeko.nkululeko --config tests/exp_agedb_os_mlp.ini 
 python -m nkululeko.nkululeko --config tests/exp_agedb_class_os_xgb.ini 
-python -m nkululeko.explore --config tests/exp_agedb_explore_data.ini
+# test augmentation
 python -m nkululeko.augment --config tests/exp_emodb_augment_os_xgb.ini
 python -m nkululeko.nkululeko --config tests/exp_emodb-aug_os_xgb.ini
 python -m nkululeko.augment --config tests/exp_emodb_random_splice_os_xgb.ini
 python -m nkululeko.nkululeko --config tests/exp_emodb_rs_os_xgb.ini
 python -m nkululeko.aug_train --config tests/emodb_aug_train.ini
 python -m nkululeko.nkululeko --config tests/exp_androids_os_svm.ini
+# test prediction
 python -m nkululeko.predict --config tests/exp_emodb_predict.ini
 python -m nkululeko.nkululeko --config tests/emodb_demo.ini
+# test demo
 python -m nkululeko.demo --config tests/emodb_demo.ini --list data/test/samples.csv
+# test test module
+python -m nkululeko.nkululeko --config exp_emodb_os_xgb_test.ini
+python -m nkululeko.test --config exp_emodb_os_xgb_test.ini
+# test multidb
 python -m nkululeko.multidb --config tests/exp_multidb.ini
+# test spotlight
 python -m nkululeko.explore --config tests/exp_explore.ini

tests/data_roots.ini

@@ -6,5 +6,5 @@ emodb.train_tables = ['emotion.categories.train.gold_standard']
 emodb.mapping = {'anger':'angry', 'happiness':'happy', 'sadness':'sad', 'neutral':'neutral'}
 polish = ./data/polish_emo
 polish.mapping = {'anger':'angry', 'joy':'happy', 'sadness':'sad', 'neutral':'neutral'}
-polish.split_strategy = speaker_split
+polish.split_strategy = test
 polish.test_size = 30

tests/exp_emodb_os_xgb_test.ini

@@ -0,0 +1,17 @@
+[EXP]
+root = ./tests/results/
+name = exp_testmodule
+save = True
+[DATA]
+databases = ['emodb']
+root_folders = tests/data_roots.ini
+target = emotion
+tests = ['polish']
+polish.split_strategy = speaker_split
+labels = ['angry', 'happy', 'neutral', 'sad']
+no_reuse = True
+[FEATS]
+type = ['os']
+[MODEL]
+type = xgb
+save = True