Add stats

call_experiment_stats.py

 from new_experiment.new_dependency_provider import get_experiment_repository
+from new_experiment.utils.get_spacy_model_name import get_spacy_model_name
+from new_experiment.utils.property_helper import PropertyHelper
 
 
 def get_stats_for(dataset_name: str, property_name: str) -> float:
     repo = get_experiment_repository(dataset_name)
     vals = [repo.get_property_for_key(it, property_name) for it in repo.get_all_record_ids_for_property(property_name)]
-    print(vals)
     vals = [it for it in vals if isinstance(it, float)]
     ret = 0.0
     if len(vals) == 0:
@@ -12,6 +13,7 @@ def get_stats_for(dataset_name: str, property_name: str) -> float:
     else:
         ret = sum(vals) / len(vals)
     print(dataset_name, property_name, ret)
+    return ret
 
 
 def get_stats_for_classic_wer(dataset_name: str, property_name: str) -> float:
@@ -19,6 +21,12 @@ def get_stats_for_classic_wer(dataset_name: str, property_name: str) -> float:
     vals = [repo.get_property_for_key(it, property_name) for it in repo.get_all_record_ids_for_property(property_name)]
     vals = [it['classic_wer'] for it in vals if 'classic_wer' in it]
     vals = [it for it in vals if isinstance(it, float)]
+    ret = 0.0
+    if len(vals) == 0:
+        ret = -1
+    else:
+        ret = sum(vals) / len(vals)
+    print(dataset_name, property_name, ret)
     return sum(vals) / len(vals)
 
 
@@ -27,6 +35,12 @@ def get_stats_for_soft_wer(dataset_name: str, property_name: str) -> float:
     vals = [repo.get_property_for_key(it, property_name) for it in repo.get_all_record_ids_for_property(property_name)]
     vals = [it['soft_wer'] for it in vals if 'soft_wer' in it]
     vals = [it for it in vals if isinstance(it, float)]
+    ret = 0.0
+    if len(vals) == 0:
+        ret = -1
+    else:
+        ret = sum(vals) / len(vals)
+    print(dataset_name, property_name, ret)
     return sum(vals) / len(vals)
 
 
@@ -36,3 +50,41 @@ def get_stats_for_embedding_wer(dataset_name: str, property_name: str) -> float:
     vals = [it['embedding_wer'] for it in vals if 'embedding_wer' in it]
     vals = [it for it in vals if isinstance(it, float)]
     return sum(vals) / len(vals)
+
+
+if __name__ == '__main__':
+    COMMANDS = ['run_word_wer_classic_pipeline', 'run_word_wer_embedding_pipeline', 'run_spacy_dep_tag_wer_pipeline',
+                'run_spacy_ner_wer_pipeline', 'run_spacy_pos_wer_pipeline']
+    LANGUAGES = ['nl', 'fr', 'de', 'it', 'pl', 'es', 'en']
+    WHISPER_ASR_MODEL = ['tiny', 'base', 'small', 'medium', 'large-v2']
+    DATASETS = ['google_fleurs', 'minds14', 'voxpopuli']
+    FULL_DATASET_NAMES = []
+    for itt in LANGUAGES:
+        for it in DATASETS:
+            FULL_DATASET_NAMES.append(f'{itt}_{it}')
+
+    FULL_LANGUAGE_MODELS = [f'whisper_{it}' for it in WHISPER_ASR_MODEL]
+
+    for dataset in FULL_DATASET_NAMES:
+        for model in FULL_LANGUAGE_MODELS:
+            get_stats_for(dataset, PropertyHelper.ner_metrics(model, get_spacy_model_name(dataset[:2])))
+
+    for dataset in FULL_DATASET_NAMES:
+        for model in FULL_LANGUAGE_MODELS:
+            get_stats_for(dataset, PropertyHelper.pos_metrics(model, get_spacy_model_name(dataset[:2])))
+
+    for dataset in FULL_DATASET_NAMES:
+        for model in FULL_LANGUAGE_MODELS:
+            get_stats_for(dataset, PropertyHelper.dep_tag_metrics(model, get_spacy_model_name(dataset[:2])))
+
+    for dataset in FULL_DATASET_NAMES:
+        for model in FULL_LANGUAGE_MODELS:
+            get_stats_for_classic_wer(dataset, PropertyHelper.word_wer_classic_metrics(model))
+
+    for dataset in FULL_DATASET_NAMES:
+        for model in FULL_LANGUAGE_MODELS:
+            get_stats_for_soft_wer(dataset, PropertyHelper.word_wer_embeddings_metrics(model))
+
+    for dataset in FULL_DATASET_NAMES:
+        for model in FULL_LANGUAGE_MODELS:
+            get_stats_for_embedding_wer(dataset, PropertyHelper.word_wer_embeddings_metrics(model))