Refactoring in progress

reportportal · Nov 21, 2023 · ae2056f · ae2056f
1 parent 7a94a40
commit ae2056f
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Showing 4 changed files with 12 additions and 14 deletions.
diff --git a/app/machine_learning/models/__init__.py b/app/machine_learning/models/__init__.py
@@ -38,7 +38,7 @@ def __init__(self, folder: str, tags: str, *, object_saver: ObjectSaver = None,
             if app_config:
                 self.object_saver = ObjectSaver(app_config)
             else:
-                self.object_saver = ObjectSaver({CONFIG_KEY: 'filesystem', 'filesystemDefaultPath': folder})
+                self.object_saver = ObjectSaver({CONFIG_KEY: 'filesystem', 'filesystemDefaultPath': ''})
 
     def load_models(self, model_files: list[str]) -> list[Any]:
         result = []

diff --git a/app/machine_learning/models/boosting_decision_maker.py b/app/machine_learning/models/boosting_decision_maker.py
@@ -31,8 +31,8 @@
 
 class BoostingDecisionMaker(MlModel):
 
-    def __init__(self, folder: str, n_estimators: int = 75, max_depth: int = 5, monotonous_features: str = '',
-                 tags: str = 'global boosting model', object_saver: ObjectSaver = None,
+    def __init__(self, folder: str, tags: str = 'global boosting model', *, n_estimators: int = 75, max_depth: int = 5,
+                 monotonous_features: str = '', object_saver: ObjectSaver = None,
                  app_config: dict[str, Any] = None) -> None:
         super().__init__(folder, tags, object_saver=object_saver, app_config=app_config)
         self.n_estimators = n_estimators

diff --git a/app/machine_learning/models/custom_boosting_decision_maker.py b/app/machine_learning/models/custom_boosting_decision_maker.py
@@ -20,6 +20,6 @@
 
 class CustomBoostingDecisionMaker(BoostingDecisionMaker):
 
-    def __init__(self, folder: str, app_config: dict[str, Any], project_id: int | str):
-        super().__init__(folder=folder, tags='custom boosting model', app_config=app_config,
-                         object_saver=ObjectSaver(app_config, project_id))
+    def __init__(self, folder: str, *, app_config: dict[str, Any], project_id: int | str):
+        super().__init__(folder, 'custom boosting model',
+                         object_saver=ObjectSaver(app_config, project_id), app_config=app_config)
diff --git a/app/machine_learning/models/weighted_similarity_calculator.py b/app/machine_learning/models/weighted_similarity_calculator.py
@@ -13,13 +13,14 @@
 #  limitations under the License.
 
 import math
+from typing import Any
 
 import numpy as np
 
 from app.machine_learning.models import MlModel
 from app.utils import text_processing
 
-MODEL_FILES: list[str] = ['weights.pickle', 'config.pickle']
+MODEL_FILES: list[str] = ['weights.pickle']
 
 
 class WeightedSimilarityCalculator(MlModel):
@@ -28,19 +29,16 @@ def __init__(self, folder, block_to_split=10, min_log_number_in_block=1):
         super().__init__(folder, 'global similarity model')
         self.block_to_split = block_to_split
         self.min_log_number_in_block = min_log_number_in_block
-        weights, self.config = self.load_model()
-        self.block_to_split, self.min_log_number_in_block, self.weights, self.softmax_weights = weights
+        weights = self.load_model()
+        self.block_to_split, self.min_log_number_in_block, self.weights, self.softmax_weights = tuple(*weights)
 
-    def load_model(self):
+    def load_model(self) -> list[Any]:
         return self.load_models(MODEL_FILES)
 
-    def add_config_info(self, config):
-        self.config = config
-
     def save_model(self):
         self.save_models(zip(
             MODEL_FILES,
-            [[self.block_to_split, self.min_log_number_in_block, self.weights, self.softmax_weights], self.config]))
+            [[self.block_to_split, self.min_log_number_in_block, self.weights, self.softmax_weights]]))
 
     def message_to_array(self, detected_message_res, stacktrace_res):
         all_lines = [" ".join(text_processing.split_words(detected_message_res))]