mirror of
https://github.com/hanzo-ml/kserve.git
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0cc7b33d97
Signed-off-by: ZabinskiMichal <[email protected]> Signed-off-by: Michał Żabiński <[email protected]> Signed-off-by: Spolti <[email protected]> Co-authored-by: Spolti <[email protected]>
94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
# Copyright 2026 The KServe Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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from unittest.mock import MagicMock
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from autogluonserver.predictor_factory import (
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AutoGluonDetectedModel,
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create_autogluon_model,
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)
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from autogluonserver.tabular_model import AutoGluonTabularModel
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from autogluonserver.timeseries_model import AutoGluonTimeSeriesModel
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def test_factory_returns_detected_model(tmp_path):
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m = create_autogluon_model("n", str(tmp_path))
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assert isinstance(m, AutoGluonDetectedModel)
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def test_load_delegates_to_tabular(monkeypatch, tmp_path):
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fake = MagicMock()
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monkeypatch.setattr(
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"autogluonserver.predictor_factory.detect_and_load_predictor",
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lambda _: ("tabular", fake),
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)
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monkeypatch.setattr(
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"autogluonserver.predictor_factory.Storage.download", lambda _: str(tmp_path)
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)
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m = create_autogluon_model("n", str(tmp_path))
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assert m.load()
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assert isinstance(m._impl, AutoGluonTabularModel)
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assert m._impl._predictor is fake
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def test_load_delegates_to_timeseries_without_metadata_json(monkeypatch, tmp_path):
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fake = MagicMock()
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fake.target = "y"
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fake.prediction_length = 1
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fake.known_covariates_names = []
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monkeypatch.setattr(
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"autogluonserver.predictor_factory.detect_and_load_predictor",
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lambda _: ("timeseries", fake),
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)
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monkeypatch.setattr(
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"autogluonserver.predictor_factory.Storage.download", lambda _: str(tmp_path)
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)
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m = create_autogluon_model("n", str(tmp_path))
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assert m.load()
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assert isinstance(m._impl, AutoGluonTimeSeriesModel)
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assert m._impl._predictor is fake
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assert m._impl._metadata.target == "y"
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assert m._impl._metadata.id_column == "item_id"
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assert m._impl._metadata.timestamp_column == "timestamp"
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def test_load_delegates_to_timeseries(monkeypatch, tmp_path):
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fake = MagicMock()
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fake.target = "y"
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fake.prediction_length = 1
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fake.known_covariates_names = []
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(tmp_path / "predictor_metadata.json").write_text(
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json.dumps(
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{
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"target": "y",
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"id_column": "item_id",
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"timestamp_column": "timestamp",
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}
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),
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encoding="utf-8",
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)
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monkeypatch.setattr(
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"autogluonserver.predictor_factory.detect_and_load_predictor",
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lambda _: ("timeseries", fake),
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)
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monkeypatch.setattr(
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"autogluonserver.predictor_factory.Storage.download", lambda _: str(tmp_path)
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)
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m = create_autogluon_model("n", str(tmp_path))
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assert m.load()
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assert isinstance(m._impl, AutoGluonTimeSeriesModel)
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assert m._impl._predictor is fake
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assert m._impl._metadata.target == "y"
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