wehub-resource-sync
474 lines
17 KiB
Python
474 lines
17 KiB
Python
import json
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import os
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from pathlib import Path
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from unittest import mock
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import numpy as np
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import pandas as pd
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import pytest
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import yaml
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import mlflow.pyfunc.scoring_server as pyfunc_scoring_server
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import mlflow.statsmodels
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from mlflow import pyfunc
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from mlflow.models import Model
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from mlflow.models.utils import _read_example, load_serving_example
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from mlflow.store.artifact.s3_artifact_repo import S3ArtifactRepository
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from mlflow.tracking.artifact_utils import _download_artifact_from_uri
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from mlflow.utils.environment import _mlflow_conda_env
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from mlflow.utils.file_utils import TempDir
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from mlflow.utils.model_utils import _get_flavor_configuration
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from tests.helper_functions import (
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_assert_pip_requirements,
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_compare_conda_env_requirements,
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_compare_logged_code_paths,
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_is_available_on_pypi,
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_mlflow_major_version_string,
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assert_register_model_called_with_local_model_path,
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pyfunc_serve_and_score_model,
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)
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from tests.statsmodels.model_fixtures import (
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arma_model,
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gee_model,
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glm_model,
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gls_model,
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glsar_model,
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ols_model,
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ols_model_signature,
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recursivels_model,
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rolling_ols_model,
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rolling_wls_model,
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wls_model,
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)
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EXTRA_PYFUNC_SERVING_TEST_ARGS = (
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[] if _is_available_on_pypi("statsmodels") else ["--env-manager", "local"]
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)
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# The code in this file has been adapted from the test cases of the lightgbm flavor.
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def _get_dates_from_df(df):
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start_date = df["start"][0]
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end_date = df["end"][0]
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return start_date, end_date
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@pytest.fixture
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def model_path(tmp_path, subdir="model"):
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return os.path.join(tmp_path, subdir)
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@pytest.fixture
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def statsmodels_custom_env(tmp_path):
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conda_env = os.path.join(tmp_path, "conda_env.yml")
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_mlflow_conda_env(conda_env, additional_pip_deps=["pytest", "statsmodels"])
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return conda_env
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def _test_models_list(tmp_path, func_to_apply):
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from statsmodels.tsa.base.tsa_model import TimeSeriesModel
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fixtures = [
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ols_model,
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arma_model,
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glsar_model,
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gee_model,
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glm_model,
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gls_model,
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recursivels_model,
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rolling_ols_model,
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rolling_wls_model,
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wls_model,
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]
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for algorithm in fixtures:
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name = algorithm.__name__
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path = os.path.join(tmp_path, name)
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model = algorithm()
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if isinstance(model.alg, TimeSeriesModel):
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start_date, end_date = _get_dates_from_df(model.inference_dataframe)
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func_to_apply(model, path, start_date, end_date)
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else:
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func_to_apply(model, path, model.inference_dataframe)
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def _test_model_save_load(statsmodels_model, model_path, *predict_args):
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mlflow.statsmodels.save_model(statsmodels_model=statsmodels_model.model, path=model_path)
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reloaded_model = mlflow.statsmodels.load_model(model_uri=model_path)
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reloaded_pyfunc = pyfunc.load_model(model_uri=model_path)
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if hasattr(statsmodels_model.model, "predict"):
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np.testing.assert_array_almost_equal(
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statsmodels_model.model.predict(*predict_args),
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reloaded_model.predict(*predict_args),
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)
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np.testing.assert_array_almost_equal(
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reloaded_model.predict(*predict_args),
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reloaded_pyfunc.predict(statsmodels_model.inference_dataframe),
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)
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def _test_model_log(statsmodels_model, model_path, *predict_args):
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model = statsmodels_model.model
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with TempDir(chdr=True, remove_on_exit=True) as tmp:
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try:
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artifact_path = "model"
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conda_env = os.path.join(tmp.path(), "conda_env.yaml")
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_mlflow_conda_env(conda_env, additional_pip_deps=["statsmodels"])
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model_info = mlflow.statsmodels.log_model(
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model, name=artifact_path, conda_env=conda_env
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)
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reloaded_model = mlflow.statsmodels.load_model(model_uri=model_info.model_uri)
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if hasattr(model, "predict"):
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np.testing.assert_array_almost_equal(
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model.predict(*predict_args), reloaded_model.predict(*predict_args)
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)
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model_path = _download_artifact_from_uri(artifact_uri=model_info.model_uri)
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model_config = Model.load(os.path.join(model_path, "MLmodel"))
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assert pyfunc.FLAVOR_NAME in model_config.flavors
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assert pyfunc.ENV in model_config.flavors[pyfunc.FLAVOR_NAME]
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env_path = model_config.flavors[pyfunc.FLAVOR_NAME][pyfunc.ENV]["conda"]
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assert os.path.exists(os.path.join(model_path, env_path))
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finally:
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mlflow.end_run()
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def test_models_save_load(tmp_path):
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_test_models_list(tmp_path, _test_model_save_load)
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def test_models_log(tmp_path):
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_test_models_list(tmp_path, _test_model_log)
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def test_signature_and_examples_are_saved_correctly():
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model, _, X = ols_model()
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signature_ = ols_model_signature()
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example_ = X[0:3, :]
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for signature in (None, signature_):
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for example in (None, example_):
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with TempDir() as tmp:
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path = tmp.path("model")
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mlflow.statsmodels.save_model(
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model, path=path, signature=signature, input_example=example
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)
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mlflow_model = Model.load(path)
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if signature is None and example is None:
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assert mlflow_model.signature is None
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else:
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assert mlflow_model.signature == signature_
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if example is None:
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assert mlflow_model.saved_input_example_info is None
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else:
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np.testing.assert_array_equal(_read_example(mlflow_model, path), example)
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def test_model_load_from_remote_uri_succeeds(model_path, mock_s3_bucket):
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model, _, inference_dataframe = arma_model()
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mlflow.statsmodels.save_model(statsmodels_model=model, path=model_path)
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artifact_root = f"s3://{mock_s3_bucket}"
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artifact_path = "model"
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artifact_repo = S3ArtifactRepository(artifact_root)
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artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
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model_uri = artifact_root + "/" + artifact_path
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reloaded_model = mlflow.statsmodels.load_model(model_uri=model_uri)
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start_date, end_date = _get_dates_from_df(inference_dataframe)
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np.testing.assert_array_almost_equal(
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model.predict(start=start_date, end=end_date),
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reloaded_model.predict(start=start_date, end=end_date),
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)
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def test_log_model_calls_register_model():
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# Adapted from lightgbm tests
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ols = ols_model()
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artifact_path = "model"
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register_model_patch = mock.patch("mlflow.tracking._model_registry.fluent._register_model")
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with mlflow.start_run(), register_model_patch, TempDir(chdr=True, remove_on_exit=True) as tmp:
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conda_env = os.path.join(tmp.path(), "conda_env.yaml")
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_mlflow_conda_env(conda_env, additional_pip_deps=["statsmodels"])
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model_info = mlflow.statsmodels.log_model(
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ols.model,
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name=artifact_path,
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conda_env=conda_env,
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registered_model_name="OLSModel1",
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)
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assert_register_model_called_with_local_model_path(
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register_model_mock=mlflow.tracking._model_registry.fluent._register_model,
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model_uri=model_info.model_uri,
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registered_model_name="OLSModel1",
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)
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def test_log_model_no_registered_model_name():
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ols = ols_model()
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artifact_path = "model"
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register_model_patch = mock.patch("mlflow.tracking._model_registry.fluent._register_model")
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with mlflow.start_run(), register_model_patch, TempDir(chdr=True, remove_on_exit=True) as tmp:
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conda_env = os.path.join(tmp.path(), "conda_env.yaml")
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_mlflow_conda_env(conda_env, additional_pip_deps=["statsmodels"])
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mlflow.statsmodels.log_model(ols.model, name=artifact_path, conda_env=conda_env)
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mlflow.tracking._model_registry.fluent._register_model.assert_not_called()
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def test_model_save_persists_specified_conda_env_in_mlflow_model_directory(
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model_path, statsmodels_custom_env
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):
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ols = ols_model()
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mlflow.statsmodels.save_model(
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statsmodels_model=ols.model, path=model_path, conda_env=statsmodels_custom_env
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)
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pyfunc_conf = _get_flavor_configuration(model_path=model_path, flavor_name=pyfunc.FLAVOR_NAME)
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saved_conda_env_path = os.path.join(model_path, pyfunc_conf[pyfunc.ENV]["conda"])
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assert os.path.exists(saved_conda_env_path)
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assert saved_conda_env_path != statsmodels_custom_env
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with open(statsmodels_custom_env) as f:
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statsmodels_custom_env_parsed = yaml.safe_load(f)
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with open(saved_conda_env_path) as f:
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saved_conda_env_parsed = yaml.safe_load(f)
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assert saved_conda_env_parsed == statsmodels_custom_env_parsed
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def test_model_save_persists_requirements_in_mlflow_model_directory(
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model_path, statsmodels_custom_env
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):
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ols = ols_model()
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mlflow.statsmodels.save_model(
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statsmodels_model=ols.model, path=model_path, conda_env=statsmodels_custom_env
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)
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saved_pip_req_path = os.path.join(model_path, "requirements.txt")
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_compare_conda_env_requirements(statsmodels_custom_env, saved_pip_req_path)
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def test_log_model_with_pip_requirements(tmp_path):
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expected_mlflow_version = _mlflow_major_version_string()
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ols = ols_model()
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# Path to a requirements file
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req_file = tmp_path.joinpath("requirements.txt")
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req_file.write_text("a")
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with mlflow.start_run():
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model_info = mlflow.statsmodels.log_model(
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ols.model, name="model", pip_requirements=str(req_file)
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)
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_assert_pip_requirements(model_info.model_uri, [expected_mlflow_version, "a"], strict=True)
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# List of requirements
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with mlflow.start_run():
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model_info = mlflow.statsmodels.log_model(
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ols.model, name="model", pip_requirements=[f"-r {req_file}", "b"]
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)
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_assert_pip_requirements(
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model_info.model_uri, [expected_mlflow_version, "a", "b"], strict=True
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)
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# Constraints file
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with mlflow.start_run():
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model_info = mlflow.statsmodels.log_model(
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ols.model, name="model", pip_requirements=[f"-c {req_file}", "b"]
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)
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_assert_pip_requirements(
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model_info.model_uri,
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[expected_mlflow_version, "b", "-c constraints.txt"],
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["a"],
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strict=True,
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)
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def test_log_model_with_extra_pip_requirements(tmp_path):
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expected_mlflow_version = _mlflow_major_version_string()
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ols = ols_model()
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default_reqs = mlflow.statsmodels.get_default_pip_requirements()
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# Path to a requirements file
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req_file = tmp_path.joinpath("requirements.txt")
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req_file.write_text("a")
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with mlflow.start_run():
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model_info = mlflow.statsmodels.log_model(
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ols.model, name="model", extra_pip_requirements=str(req_file)
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)
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_assert_pip_requirements(
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model_info.model_uri, [expected_mlflow_version, *default_reqs, "a"]
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)
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# List of requirements
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with mlflow.start_run():
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model_info = mlflow.statsmodels.log_model(
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ols.model, name="model", extra_pip_requirements=[f"-r {req_file}", "b"]
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)
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_assert_pip_requirements(
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model_info.model_uri, [expected_mlflow_version, *default_reqs, "a", "b"]
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)
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# Constraints file
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with mlflow.start_run():
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model_info = mlflow.statsmodels.log_model(
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ols.model, name="model", extra_pip_requirements=[f"-c {req_file}", "b"]
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)
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_assert_pip_requirements(
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model_info.model_uri,
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[expected_mlflow_version, *default_reqs, "b", "-c constraints.txt"],
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["a"],
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)
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def test_model_save_accepts_conda_env_as_dict(model_path):
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ols = ols_model()
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conda_env = dict(mlflow.statsmodels.get_default_conda_env())
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conda_env["dependencies"].append("pytest")
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mlflow.statsmodels.save_model(statsmodels_model=ols.model, path=model_path, conda_env=conda_env)
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pyfunc_conf = _get_flavor_configuration(model_path=model_path, flavor_name=pyfunc.FLAVOR_NAME)
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saved_conda_env_path = os.path.join(model_path, pyfunc_conf[pyfunc.ENV]["conda"])
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assert os.path.exists(saved_conda_env_path)
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with open(saved_conda_env_path) as f:
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saved_conda_env_parsed = yaml.safe_load(f)
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assert saved_conda_env_parsed == conda_env
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def test_model_log_persists_specified_conda_env_in_mlflow_model_directory(statsmodels_custom_env):
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ols = ols_model()
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with mlflow.start_run():
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model_info = mlflow.statsmodels.log_model(
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ols.model,
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name="model",
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conda_env=statsmodels_custom_env,
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)
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model_path = _download_artifact_from_uri(artifact_uri=model_info.model_uri)
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pyfunc_conf = _get_flavor_configuration(model_path=model_path, flavor_name=pyfunc.FLAVOR_NAME)
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saved_conda_env_path = os.path.join(model_path, pyfunc_conf[pyfunc.ENV]["conda"])
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assert os.path.exists(saved_conda_env_path)
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assert saved_conda_env_path != statsmodels_custom_env
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with open(statsmodels_custom_env) as f:
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statsmodels_custom_env_parsed = yaml.safe_load(f)
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with open(saved_conda_env_path) as f:
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saved_conda_env_parsed = yaml.safe_load(f)
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assert saved_conda_env_parsed == statsmodels_custom_env_parsed
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def test_model_log_persists_requirements_in_mlflow_model_directory(statsmodels_custom_env):
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ols = ols_model()
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artifact_path = "model"
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with mlflow.start_run():
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model_info = mlflow.statsmodels.log_model(
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ols.model,
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name=artifact_path,
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conda_env=statsmodels_custom_env,
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)
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model_path = _download_artifact_from_uri(artifact_uri=model_info.model_uri)
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saved_pip_req_path = os.path.join(model_path, "requirements.txt")
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_compare_conda_env_requirements(statsmodels_custom_env, saved_pip_req_path)
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def test_model_save_without_specified_conda_env_uses_default_env_with_expected_dependencies(
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model_path,
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):
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ols = ols_model()
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mlflow.statsmodels.save_model(statsmodels_model=ols.model, path=model_path)
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_assert_pip_requirements(model_path, mlflow.statsmodels.get_default_pip_requirements())
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def test_model_log_without_specified_conda_env_uses_default_env_with_expected_dependencies():
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ols = ols_model()
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artifact_path = "model"
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with mlflow.start_run():
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model_info = mlflow.statsmodels.log_model(ols.model, name=artifact_path)
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_assert_pip_requirements(
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model_info.model_uri, mlflow.statsmodels.get_default_pip_requirements()
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)
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def test_pyfunc_serve_and_score():
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model, _, inference_dataframe = ols_model()
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artifact_path = "model"
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with mlflow.start_run():
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model_info = mlflow.statsmodels.log_model(
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model, name=artifact_path, input_example=inference_dataframe
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)
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inference_payload = load_serving_example(model_info.model_uri)
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resp = pyfunc_serve_and_score_model(
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model_info.model_uri,
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data=inference_payload,
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content_type=pyfunc_scoring_server.CONTENT_TYPE_JSON,
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extra_args=EXTRA_PYFUNC_SERVING_TEST_ARGS,
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)
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scores = pd.DataFrame(
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data=json.loads(resp.content.decode("utf-8"))["predictions"]
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).values.squeeze()
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np.testing.assert_array_almost_equal(scores, model.predict(inference_dataframe))
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def test_log_model_with_code_paths():
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artifact_path = "model"
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ols = ols_model()
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with (
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mlflow.start_run(),
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mock.patch("mlflow.statsmodels._add_code_from_conf_to_system_path") as add_mock,
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):
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model_info = mlflow.statsmodels.log_model(
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ols.model, name=artifact_path, code_paths=[__file__]
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)
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_compare_logged_code_paths(__file__, model_info.model_uri, mlflow.statsmodels.FLAVOR_NAME)
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mlflow.statsmodels.load_model(model_info.model_uri)
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add_mock.assert_called()
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def test_virtualenv_subfield_points_to_correct_path(model_path):
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ols = ols_model()
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mlflow.statsmodels.save_model(ols.model, path=model_path)
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pyfunc_conf = _get_flavor_configuration(model_path=model_path, flavor_name=pyfunc.FLAVOR_NAME)
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python_env_path = Path(model_path, pyfunc_conf[pyfunc.ENV]["virtualenv"])
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assert python_env_path.exists()
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assert python_env_path.is_file()
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def test_model_save_load_with_metadata(model_path):
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ols = ols_model()
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mlflow.statsmodels.save_model(
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ols.model, path=model_path, metadata={"metadata_key": "metadata_value"}
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)
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reloaded_model = mlflow.pyfunc.load_model(model_uri=model_path)
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assert reloaded_model.metadata.metadata["metadata_key"] == "metadata_value"
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|
|
|
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|
def test_model_log_with_metadata():
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|
ols = ols_model()
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|
artifact_path = "model"
|
|
|
|
with mlflow.start_run():
|
|
model_info = mlflow.statsmodels.log_model(
|
|
ols.model, name=artifact_path, metadata={"metadata_key": "metadata_value"}
|
|
)
|
|
|
|
reloaded_model = mlflow.pyfunc.load_model(model_uri=model_info.model_uri)
|
|
assert reloaded_model.metadata.metadata["metadata_key"] == "metadata_value"
|
|
|
|
|
|
def test_model_log_with_signature_inference():
|
|
model, _, X = ols_model()
|
|
|
|
artifact_path = "model"
|
|
example = X[0:3, :]
|
|
|
|
with mlflow.start_run():
|
|
model_info = mlflow.statsmodels.log_model(model, name=artifact_path, input_example=example)
|
|
|
|
loaded_model = Model.load(model_info.model_uri)
|
|
assert loaded_model.signature == ols_model_signature()
|