Support IsolationForest (#693)

close iterative/mlem.ai#353
See also
#423 (comment)

.pre-commit-config.yaml

@@ -44,7 +44,7 @@ repos:
           - types-requests
           - types-six
           - types-PyYAML
-          - pydantic
+          - pydantic>=1.9.0,<2
           - types-filelock
           - types-emoji
   - repo: local

mlem/contrib/sklearn.py

@@ -6,7 +6,7 @@
 from typing import Any, ClassVar, Dict, List, Optional, Union
 
 import sklearn
-from sklearn.base import ClassifierMixin, RegressorMixin
+from sklearn.base import ClassifierMixin, OutlierMixin, RegressorMixin
 from sklearn.feature_extraction.text import TransformerMixin, _VectorizerMixin
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing._encoders import _BaseEncoder
@@ -28,7 +28,7 @@ class SklearnModel(ModelType, ModelHook, IsInstanceHookMixin):
     """ModelType implementation for `scikit-learn` models"""
 
     type: ClassVar[str] = "sklearn"
-    valid_types: ClassVar = (RegressorMixin, ClassifierMixin)
+    valid_types: ClassVar = (RegressorMixin, ClassifierMixin, OutlierMixin)
 
     io: ModelIO = SimplePickleIO()
     """IO"""

tests/contrib/test_sklearn.py

@@ -3,6 +3,7 @@
 import lightgbm as lgb
 import numpy as np
 import pytest
+from sklearn.ensemble import IsolationForest
 from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
 from sklearn.linear_model import LinearRegression, LogisticRegression
 from sklearn.pipeline import Pipeline
@@ -54,6 +55,13 @@ def regressor(inp_data, out_data):
     return lr
 
 
+@pytest.fixture
+def outlier(inp_data):
+    model = IsolationForest()
+    model.fit(inp_data)
+    return model
+
+
 @pytest.fixture
 def count_vectorizer(text_inp_data):
     vectorizer = CountVectorizer()
@@ -195,7 +203,9 @@ def test_hook_lgb(lgbm_model, inp_data):
     assert signature.returns == returns
 
 
-@pytest.mark.parametrize("model", ["classifier", "regressor", "pipeline"])
+@pytest.mark.parametrize(
+    "model", ["classifier", "regressor", "pipeline", "outlier"]
+)
 def test_model_type__predict(model, inp_data, request):
     model = request.getfixturevalue(model)
     model_type = ModelAnalyzer.analyze(model, sample_data=inp_data)
@@ -221,7 +231,14 @@ def test_model_type__reg_predict_proba(regressor, inp_data):
         model_type.call_method("predict_proba", inp_data)
 
 
-@pytest.mark.parametrize("model", ["classifier", "regressor"])
+def test_model_type__outlier_predict_proba(outlier, inp_data):
+    model_type = ModelAnalyzer.analyze(outlier, sample_data=inp_data)
+
+    with pytest.raises(ValueError):
+        model_type.call_method("predict_proba", inp_data)
+
+
+@pytest.mark.parametrize("model", ["classifier", "regressor", "outlier"])
 def test_model_type__dump_load(tmpdir, model, inp_data, request):
     model = request.getfixturevalue(model)
     model_type = ModelAnalyzer.analyze(model, sample_data=inp_data)