Moved model.meta.property to model.property

giskard/ml_worker/testing/utils.py

@@ -1,9 +1,9 @@
-from typing import Optional
-
 import numbers
 from enum import Enum
 from functools import wraps
 
+from typing import Optional
+
 from giskard.core.core import SupportedModelTypes
 from giskard.datasets.base import Dataset
 
@@ -27,7 +27,7 @@ def wrapper(*args, **kwargs):
         if (
             classification_label is not None
             and model is not None
-            and isinstance(model.meta.classification_labels[0], numbers.Number)
+            and isinstance(model.classification_labels[0], numbers.Number)
         ):
             try:
                 classification_label = int(classification_label)
@@ -39,14 +39,14 @@ def wrapper(*args, **kwargs):
         if target and classification_label and model:
             assert classification_label != target, (
                 'By "classification_label", we refer to one of the values: '
-                f'{model.meta.classification_labels} and not the target: "{target}". '
+                f'{model.classification_labels} and not the target: "{target}". '
                 "Please re-assign this argument."
             )
 
         assert (
-            model.meta.model_type != SupportedModelTypes.CLASSIFICATION
-            or classification_label in model.meta.classification_labels
-        ), f'"{classification_label}" is not part of model labels: {model.meta.classification_labels}'
+            model.model_type != SupportedModelTypes.CLASSIFICATION
+            or classification_label in model.classification_labels
+        ), f'"{classification_label}" is not part of model labels: {model.classification_labels}'
         return func(*args, **kwargs)
 
     wrapper.test_fn = func

giskard/models/model_explanation.py

@@ -1,10 +1,9 @@
-from typing import Any, Callable, Dict, List
-
 import logging
 import warnings
 
 import numpy as np
 import pandas as pd
+from typing import Any, Callable, Dict, List
 
 from giskard.core.errors import GiskardImportError
 from giskard.datasets.base import Dataset
@@ -69,7 +68,7 @@ def _get_columns_original_order(prepared_dataset: Dataset, model: BaseModel, dat
     list
         A list of column names in the order that the model was trained with.
     """
-    features_names = model.meta.feature_names
+    features_names = model.feature_names
     return features_names if features_names else [c for c in dataset.df.columns if c in prepared_dataset.df.columns]
 
 
@@ -199,11 +198,11 @@ def explain_with_shap(model: BaseModel, dataset: Dataset, only_highest_proba: bo
         shap_values = _get_highest_proba_shap(shap_values, model, dataset)
 
     # Put SHAP values to the Explanation object for a convenience.
-    feature_names = model.meta.feature_names or list(dataset.df.columns.drop(dataset.target, errors="ignore"))
+    feature_names = model.feature_names or list(dataset.df.columns.drop(dataset.target, errors="ignore"))
     shap_explanations = Explanation(shap_values, data=dataset.df[feature_names], feature_names=feature_names)
 
     feature_types = {key: dataset.column_types[key] for key in feature_names}
-    return ShapResult(shap_explanations, feature_types, model.meta.model_type, only_highest_proba)
+    return ShapResult(shap_explanations, feature_types, model.model_type, only_highest_proba)
 
 
 def _calculate_sample_shap_values(model: BaseModel, dataset: Dataset, input_data: Dict) -> np.ndarray:
@@ -228,18 +227,18 @@ def predict_array(array):
 @timer()
 def explain(model: BaseModel, dataset: Dataset, input_data: Dict):
     shap_values = _calculate_sample_shap_values(model, dataset, input_data)
-    feature_names = model.meta.feature_names or list(dataset.df.columns.drop(dataset.target, errors="ignore"))
+    feature_names = model.feature_names or list(dataset.df.columns.drop(dataset.target, errors="ignore"))
 
     if model.is_regression:
         explanation_chart_data = summary_shap_regression(shap_values=shap_values, feature_names=feature_names)
     elif model.is_classification:
         explanation_chart_data = summary_shap_classification(
             shap_values=shap_values,
             feature_names=feature_names,
-            class_names=model.meta.classification_labels,
+            class_names=model.classification_labels,
         )
     else:
-        raise ValueError(f"Prediction task is not supported: {model.meta.model_type}")
+        raise ValueError(f"Prediction task is not supported: {model.model_type}")
     return explanation_chart_data
 
 

giskard/utils/analytics_collector.py

@@ -1,6 +1,3 @@
-from types import TracebackType
-from typing import Dict, Optional, Type
-
 import getpass
 import hashlib
 import os
@@ -11,9 +8,11 @@
 from functools import wraps
 from threading import ExceptHookArgs, Lock
 from traceback import TracebackException
+from types import TracebackType
 
 import requests
 from mixpanel import Mixpanel
+from typing import Dict, Optional, Type
 
 from giskard.client.dtos import ServerInfo
 from giskard.settings import settings
@@ -55,11 +54,11 @@ def get_model_properties(model):
         return {}
 
     inner_model_class = fullname(model.model) if isinstance(model, WrapperModel) else None
-    feature_names = [anonymize(n) for n in model.meta.feature_names] if model.meta.feature_names else None
+    feature_names = [anonymize(n) for n in model.feature_names] if model.feature_names else None
 
     return {
         "model_id": str(model.id),
-        "model_type": model.meta.model_type.value,
+        "model_type": model.model_type.value,
         "model_class": fullname(model),
         "model_inner_class": inner_model_class,
         "model_feature_names": feature_names,

tests/communications/test_websocket_actor.py

@@ -406,7 +406,7 @@ def test_websocket_actor_explain_text_ws_classification(internal, request):
         reply = listener.explain_text_ws(client=None if internal else client, params=params)
         assert isinstance(reply, websocket.ExplainText)
         # Classification labels
-        for label in model.meta.classification_labels:
+        for label in model.classification_labels:
             assert label in reply.weights.keys()
 
 

tests/scan/llm/test_prompt_injection_detector.py

@@ -1,12 +1,12 @@
+import ast
 from unittest.mock import Mock, patch
 
-import ast
 import pandas as pd
 
 from giskard.datasets.base import Dataset
+from giskard.llm.evaluators.string_matcher import StringMatcherConfig
 from giskard.scanner.llm.llm_prompt_injection_detector import LLMPromptInjectionDetector
 from giskard.testing.tests.llm.injections import _test_llm_output_against_strings
-from giskard.llm.evaluators.string_matcher import StringMatcherConfig
 
 
 def test_prompt_injection_data_loader_properties():
@@ -95,7 +95,7 @@ def test_detector(PromptInjectionDataLoader):  # noqa
     detector = LLMPromptInjectionDetector()
 
     # First run
-    issues = detector.run(model, dataset, model.meta.feature_names)
+    issues = detector.run(model, dataset, model.feature_names)
     assert len(issues) == 1
     assert issues[0].is_major
 

tests/scan/test_performance_bias_detector.py

@@ -15,7 +15,7 @@ def test_performance_bias_detector_skips_small_datasets(german_credit_model, ger
     small_dataset = german_credit_data.slice(lambda df: df.sample(50), row_level=False)
     detector = PerformanceBiasDetector()
     with caplog.at_level(logging.WARNING):
-        issues = detector.run(german_credit_model, small_dataset, features=german_credit_model.meta.feature_names)
+        issues = detector.run(german_credit_model, small_dataset, features=german_credit_model.feature_names)
     record = caplog.records[-1]
 
     assert len(issues) == 0
@@ -30,7 +30,7 @@ def test_performance_bias_detector_trims_large_dataset(german_credit_model, germ
 
     detector = PerformanceBiasDetector()
     try:
-        detector.run(german_credit_model, large_dataset, features=german_credit_model.meta.feature_names)
+        detector.run(german_credit_model, large_dataset, features=german_credit_model.feature_names)
     except (ValueError, TypeError):
         pass
     assert large_dataset.slice.called
@@ -41,7 +41,7 @@ def test_performance_bias_detector_trims_large_dataset(german_credit_model, germ
 
     detector = PerformanceBiasDetector()
     try:
-        detector.run(german_credit_model, normal_dataset, features=german_credit_model.meta.feature_names)
+        detector.run(german_credit_model, normal_dataset, features=german_credit_model.feature_names)
     except (ValueError, TypeError):
         pass
 
@@ -51,7 +51,7 @@ def test_performance_bias_detector_trims_large_dataset(german_credit_model, germ
 def test_performance_bias_detector_with_tabular(german_credit_model, german_credit_data):
     detector = PerformanceBiasDetector()
 
-    issues = detector.run(german_credit_model, german_credit_data, features=german_credit_model.meta.feature_names)
+    issues = detector.run(german_credit_model, german_credit_data, features=german_credit_model.feature_names)
     assert len(issues) > 0
     assert all([isinstance(issue, Issue) for issue in issues])
 
@@ -73,7 +73,7 @@ def test_performance_bias_detector_with_text_features(enron_model, enron_data):
     dataset = Dataset(df, target=enron_data.target, column_types=enron_data.column_types)
     detector = PerformanceBiasDetector()
 
-    issues = detector.run(enron_model, dataset, enron_model.meta.feature_names)
+    issues = detector.run(enron_model, dataset, enron_model.feature_names)
     assert len(issues) > 0
     assert all([isinstance(issue, Issue) for issue in issues])
 

tests/scan/test_scanner.py

@@ -50,7 +50,7 @@ def _test_scanner_returns_non_empty_scan_result(dataset_name, model_name, reques
     dataset = request.getfixturevalue(dataset_name)
     model = request.getfixturevalue(model_name)
 
-    result = scanner.analyze(model, dataset, features=model.meta.feature_names, raise_exceptions=True)
+    result = scanner.analyze(model, dataset, features=model.feature_names, raise_exceptions=True)
 
     assert isinstance(result, ScanReport)
     assert result.to_html()
@@ -67,7 +67,7 @@ def test_scanner_should_work_with_empty_model_feature_names(german_credit_data,
     scanner = Scanner()
     german_credit_model.meta.feature_names = None
     result = scanner.analyze(
-        german_credit_model, german_credit_data, features=german_credit_model.meta.feature_names, raise_exceptions=True
+        german_credit_model, german_credit_data, features=german_credit_model.feature_names, raise_exceptions=True
     )
 
     assert isinstance(result, ScanReport)
@@ -86,7 +86,7 @@ def test_scanner_works_if_dataset_has_no_target(titanic_model, titanic_dataset):
     scanner = Scanner()
     no_target_dataset = Dataset(titanic_dataset.df, target=None)
     result = scanner.analyze(
-        titanic_model, no_target_dataset, features=titanic_model.meta.feature_names, raise_exceptions=True
+        titanic_model, no_target_dataset, features=titanic_model.feature_names, raise_exceptions=True
     )
 
     assert isinstance(result, ScanReport)

tests/scan/test_spurious_correlation_detector.py

@@ -48,11 +48,11 @@ def random_classifier(df):
 @pytest.mark.memory_expensive
 def test_threshold(titanic_model, titanic_dataset):
     detector = SpuriousCorrelationDetector(threshold=0.6)
-    issues = detector.run(titanic_model, titanic_dataset, features=titanic_model.meta.feature_names)
+    issues = detector.run(titanic_model, titanic_dataset, features=titanic_model.feature_names)
     assert len(issues) > 0
 
     detector = SpuriousCorrelationDetector(threshold=0.9)
-    issues = detector.run(titanic_model, titanic_dataset, features=titanic_model.meta.feature_names)
+    issues = detector.run(titanic_model, titanic_dataset, features=titanic_model.feature_names)
     assert not issues
 
 

tests/scan/test_text_perturbation_detector.py

@@ -11,7 +11,7 @@
 
 def test_perturbation_classification(titanic_model, titanic_dataset):
     analyzer = TextPerturbationDetector(threshold=0.01)
-    res = analyzer.run(titanic_model, titanic_dataset, features=titanic_model.meta.feature_names)
+    res = analyzer.run(titanic_model, titanic_dataset, features=titanic_model.feature_names)
     assert res
 
 

tests/test_suite.py

@@ -45,7 +45,7 @@ def constant_pred(df):
         return np.stack((np.ones(len(df)), np.zeros(len(df)))).T
 
     bad_model = Model(
-        constant_pred, model_type="classification", classification_labels=german_credit_model.meta.classification_labels
+        constant_pred, model_type="classification", classification_labels=german_credit_model.classification_labels
     )
 
     # The test will not pass