def test_sagemaker_docker_model_scoring_with_default_conda_env(
spark_model_iris, model_path):
sparkm.save_model(spark_model_iris.model, path=model_path, conda_env=None)
scoring_response = score_model_in_sagemaker_docker_container(
model_uri=model_path,
data=spark_model_iris.pandas_df,
content_type=pyfunc_scoring_server.CONTENT_TYPE_JSON,
flavor=mlflow.pyfunc.FLAVOR_NAME)
deployed_model_preds = np.array(json.loads(scoring_response.content))
np.testing.assert_array_almost_equal(deployed_model_preds,
spark_model_iris.predictions,
decimal=4)
def test_estimator_model_export(spark_model_estimator, model_path,
spark_custom_env):
sparkm.save_model(spark_model_estimator.model,
path=model_path,
conda_env=spark_custom_env)
# score and compare the reloaded sparkml model
reloaded_model = sparkm.load_model(model_uri=model_path)
preds_df = reloaded_model.transform(spark_model_estimator.spark_df)
preds = [x.prediction for x in preds_df.select("prediction").collect()]
assert spark_model_estimator.predictions == preds
# 2. score and compare reloaded pyfunc
m = pyfunc.load_pyfunc(model_path)
preds2 = m.predict(spark_model_estimator.spark_df.toPandas())
assert spark_model_estimator.predictions == preds2
def test_spark_module_model_save_with_mleap_and_unsupported_transformer_raises_exception(
spark_model_iris, model_path
):
class CustomTransformer(JavaModel):
def _transform(self, dataset):
return dataset
unsupported_pipeline = Pipeline(stages=[CustomTransformer()])
unsupported_model = unsupported_pipeline.fit(spark_model_iris.spark_df)
with pytest.raises(ValueError, match="CustomTransformer"):
sparkm.save_model(
spark_model=unsupported_model, path=model_path, sample_input=spark_model_iris.spark_df
)
def test_save_with_sample_input_containing_unsupported_data_type_raises_serialization_exception(
spark_context, model_path):
sql_context = SQLContext(spark_context)
unsupported_df = sql_context.createDataFrame([(1, "2016-09-30"),
(2, "2017-02-27")])
unsupported_df = unsupported_df.withColumn(
"_2", unsupported_df._2.cast(DateType()))
pipeline = Pipeline(stages=[])
model = pipeline.fit(unsupported_df)
# The Spark `DateType` is not supported by MLeap, so we expect serialization to fail.
with pytest.raises(mleap.MLeapSerializationException):
sparkm.save_model(spark_model=model,
path=model_path,
sample_input=unsupported_df)
def test_sparkml_model_save_accepts_conda_env_as_dict(spark_model_iris, model_path):
conda_env = dict(mlflow.spark.get_default_conda_env())
conda_env["dependencies"].append("pytest")
sparkm.save_model(spark_model=spark_model_iris.model,
path=model_path,
conda_env=conda_env)
pyfunc_conf = _get_flavor_configuration(model_path=model_path, flavor_name=pyfunc.FLAVOR_NAME)
saved_conda_env_path = os.path.join(model_path, pyfunc_conf[pyfunc.ENV])
assert os.path.exists(saved_conda_env_path)
with open(saved_conda_env_path, "r") as f:
saved_conda_env_parsed = yaml.safe_load(f)
assert saved_conda_env_parsed == conda_env
def test_spark_module_model_save_with_relative_path_and_valid_sample_input_produces_mleap_flavor(
spark_model_iris):
with TempDir(chdr=True) as tmp:
model_path = os.path.basename(tmp.path("model"))
mlflow_model = Model()
sparkm.save_model(spark_model=spark_model_iris.model,
path=model_path,
sample_input=spark_model_iris.spark_df,
mlflow_model=mlflow_model)
assert mleap.FLAVOR_NAME in mlflow_model.flavors
config_path = os.path.join(model_path, "MLmodel")
assert os.path.exists(config_path)
config = Model.load(config_path)
assert mleap.FLAVOR_NAME in config.flavors
def test_model_export(spark_model_iris, model_path, spark_custom_env):
sparkm.save_model(spark_model_iris.model, path=model_path, conda_env=spark_custom_env)
# 1. score and compare reloaded sparkml model
reloaded_model = sparkm.load_model(model_uri=model_path)
preds_df = reloaded_model.transform(spark_model_iris.spark_df)
preds1 = [x.prediction for x in preds_df.select("prediction").collect()]
assert spark_model_iris.predictions == preds1
m = pyfunc.load_pyfunc(model_path)
# 2. score and compare reloaded pyfunc
preds2 = m.predict(spark_model_iris.pandas_df)
assert spark_model_iris.predictions == preds2
# 3. score and compare reloaded pyfunc Spark udf
preds3 = score_model_as_udf(model_uri=model_path, pandas_df=spark_model_iris.pandas_df)
assert spark_model_iris.predictions == preds3
assert os.path.exists(sparkm.DFS_TMP)
def test_spark_module_model_save_with_sample_input_produces_sparkml_and_mleap_flavors(
spark_model_iris, model_path):
mlflow_model = Model()
sparkm.save_model(spark_model=spark_model_iris.model,
path=model_path,
sample_input=spark_model_iris.training_df,
mlflow_model=mlflow_model)
assert sparkm.FLAVOR_NAME in mlflow_model.flavors
assert mleap.FLAVOR_NAME in mlflow_model.flavors
config_path = os.path.join(model_path, "MLmodel")
assert os.path.exists(config_path)
config = Model.load(config_path)
assert sparkm.FLAVOR_NAME in config.flavors
assert mleap.FLAVOR_NAME in config.flavors
def test_sparkml_model_load_from_remote_uri_succeeds(spark_model_iris,
model_path,
mock_s3_bucket):
sparkm.save_model(spark_model=spark_model_iris.model, path=model_path)
artifact_root = "s3://{bucket_name}".format(bucket_name=mock_s3_bucket)
artifact_path = "model"
artifact_repo = S3ArtifactRepository(artifact_root)
artifact_repo.log_artifacts(model_path, artifact_path=artifact_path)
model_uri = artifact_root + "/" + artifact_path
reloaded_model = sparkm.load_model(model_uri=model_uri)
preds_df = reloaded_model.transform(spark_model_iris.spark_df)
preds = [x.prediction for x in preds_df.select("prediction").collect()]
assert spark_model_iris.predictions == preds
def test_model_deployment(spark_model_iris, model_path, spark_custom_env):
sparkm.save_model(
spark_model_iris.model,
path=model_path,
conda_env=spark_custom_env,
)
scoring_response = score_model_in_sagemaker_docker_container(
model_uri=model_path,
data=spark_model_iris.pandas_df,
content_type=pyfunc_scoring_server.CONTENT_TYPE_JSON_SPLIT_ORIENTED,
flavor=mlflow.pyfunc.FLAVOR_NAME,
)
np.testing.assert_array_almost_equal(
spark_model_iris.predictions, np.array(json.loads(scoring_response.content)), decimal=4
)
def test_sparkml_model_save_persists_specified_conda_env_in_mlflow_model_directory(
spark_model_iris, model_path, spark_custom_env):
sparkm.save_model(spark_model=spark_model_iris.model,
path=model_path,
conda_env=spark_custom_env)
pyfunc_conf = _get_flavor_configuration(model_path=model_path, flavor_name=pyfunc.FLAVOR_NAME)
saved_conda_env_path = os.path.join(model_path, pyfunc_conf[pyfunc.ENV])
assert os.path.exists(saved_conda_env_path)
assert saved_conda_env_path != spark_custom_env
with open(spark_custom_env, "r") as f:
spark_custom_env_parsed = yaml.safe_load(f)
with open(saved_conda_env_path, "r") as f:
saved_conda_env_parsed = yaml.safe_load(f)
assert saved_conda_env_parsed == spark_custom_env_parsed
def test_model_deployment(spark_model_iris, model_path, spark_conda_env):
sparkm.save_model(spark_model_iris.model, path=model_path,
conda_env=spark_conda_env,
# Test both spark ml and mleap
sample_input=spark_model_iris.spark_df)
# 1. score and compare pyfunc deployed in Sagemaker docker container
preds1 = score_model_in_sagemaker_docker_container(model_path=model_path,
data=spark_model_iris.pandas_df,
flavor=mlflow.pyfunc.FLAVOR_NAME)
assert spark_model_iris.predictions == preds1
# 2. score and compare mleap deployed in Sagemaker docker container
preds2 = score_model_in_sagemaker_docker_container(model_path=model_path,
data=spark_model_iris.pandas_df,
flavor=mlflow.mleap.FLAVOR_NAME)
assert spark_model_iris.predictions == preds2
def test__NgramSet(numbers_dataframe):
# Create the transformer
tokenizer = ct.NLTKWordPunctTokenizer(inputCol="text", outputCol="tokens")
# Filter to go words
goWords = ['two', 'three', 'four', 'five']
gofilt = ct.GoWordFilter(inputCol="tokens",
outputCol="go_word_filtered_tokens",
goWords=goWords)
# Create the transformer
ngrams = ct.NgramSet(inputCol="go_word_filtered_tokens",
outputCol="ngram_set",
maxN=5)
# Create a pipeline from the transformer
pipeline = Pipeline(stages=[tokenizer, gofilt, ngrams])
# fit the test data (which also builds the pipeline)
model = pipeline.fit(numbers_dataframe)
# Test the pipeline
df_original_transformed = model.transform(numbers_dataframe)
# Delete any previously save model (if it exists)
# (There may be a more elegant way to do this)
if os.path.exists("unit_test_model"):
os.system("rm -rf unit_test_model")
# Log the model and performance
save_model(model, "unit_test_model")
retrieved_model = load_model("unit_test_model")
df_retreived_transformed = retrieved_model.transform(numbers_dataframe)
# Assert the retrieved model give the same results as the saved model
rows_in_common = df_original_transformed.intersect(
df_retreived_transformed).count()
assert (df_original_transformed.count() == rows_in_common)
# Print results for visual inspection
print("\n")
print("test__NgramSet: should see a set of 1-5 ngram set")
df_retreived_transformed.show(truncate=False)
# If we make it this far without crashing we pass (plus I'm visually reviewing results)
assert True
def test_model_export_with_signature_and_examples(iris_df, spark_model_iris):
_, _, iris_spark_df = iris_df
signature_ = infer_signature(iris_spark_df)
example_ = iris_spark_df.toPandas().head(3)
for signature in (None, signature_):
for example in (None, example_):
with TempDir() as tmp:
path = tmp.path("model")
sparkm.save_model(
spark_model_iris.model, path=path, signature=signature, input_example=example
)
mlflow_model = Model.load(path)
assert signature == mlflow_model.signature
if example is None:
assert mlflow_model.saved_input_example_info is None
else:
assert all((_read_example(mlflow_model, path) == example).all())
def test__LevenshteinSubstituter(numbers_dataframe):
# Create the transformer
tokenizer = ct.NLTKWordPunctTokenizer(inputCol="text", outputCol="tokens")
# Create the transformer
tokenMatchers = ['two1', 'four2', 'nineee']
toksub = ct.LevenshteinSubstituter(inputCol="tokens",
outputCol="swapped_tokens",
tokenMatchers=tokenMatchers,
levenshteinThresh=1)
# Create a pipeline from the transformer
pipeline = Pipeline(stages=[tokenizer, toksub])
# fit the test data (which also builds the pipeline)
model = pipeline.fit(numbers_dataframe)
# Test the pipeline
df_original_transformed = model.transform(numbers_dataframe)
# Delete any previously save model (if it exists)
# (There may be a more elegant way to do this)
if os.path.exists("unit_test_model"):
os.system("rm -rf unit_test_model")
# Log the model and performance
save_model(model, "unit_test_model")
retrieved_model = load_model("unit_test_model")
df_retreived_transformed = retrieved_model.transform(numbers_dataframe)
# Assert the retrieved model give the same results as the saved model
rows_in_common = df_original_transformed.intersect(
df_retreived_transformed).count()
assert (df_original_transformed.count() == rows_in_common)
# Print results for visual inspection
print("\n")
print(
"test__LevenshteinSubstituter: two and four shold be substituted and nine should not"
)
df_retreived_transformed.show(truncate=False)
# If we make it this far without crashing we pass (plus I'm visually reviewing results)
assert True
def test__RegexSubstituter(simple_test_dataframe):
# Create the transformer
regexMatchers = ['(?<=[a-zA-Z])\.(?=[A-Z])', '<BR>', '<br>']
substitutions = ['. ', '. ', '. ']
transformer = ct.RegexSubstituter(inputCol="text",
outputCol="regexcorrected",
regexMatchers=regexMatchers,
substitutions=substitutions)
# Create a pipeline from the transformer
pipeline = Pipeline(stages=[transformer])
# fit the test data (which also builds the pipeline)
model = pipeline.fit(simple_test_dataframe)
# Test the pipeline
df_original_transformed = model.transform(simple_test_dataframe)
# Delete any previously save model (if it exists)
# (There may be a more elegant way to do this)
if os.path.exists("unit_test_model"):
os.system("rm -rf unit_test_model")
# Log the model and performance
save_model(model, "unit_test_model")
retrieved_model = load_model("unit_test_model")
df_retreived_transformed = retrieved_model.transform(simple_test_dataframe)
# Assert the retrieved model give the same results as the saved model
rows_in_common = df_original_transformed.intersect(
df_retreived_transformed).count()
assert (df_original_transformed.count() == rows_in_common)
# Print results for visual inspection
print("\n")
print(
"test__RegexSubstituter: The following should show sentences broken into words"
)
df_retreived_transformed.show(truncate=False)
# If we make it this far without crashing we pass (plus I'm visually reviewing results)
assert True
def test_model_export(tmpdir):
conda_env = os.path.join(str(tmpdir), "conda_env.yml")
_mlflow_conda_env(
conda_env, additional_pip_deps=["pyspark=={}".format(pyspark_version)])
iris = datasets.load_iris()
X = iris.data # we only take the first two features.
y = iris.target
pandas_df = pd.DataFrame(X, columns=iris.feature_names)
pandas_df['label'] = pd.Series(y)
spark_session = pyspark.sql.SparkSession.builder \
.config(key="spark_session.python.worker.reuse", value=True) \
.master("local-cluster[2, 1, 1024]") \
.getOrCreate()
spark_df = spark_session.createDataFrame(pandas_df)
model_path = tmpdir.mkdir("model")
assembler = VectorAssembler(inputCols=iris.feature_names,
outputCol="features")
lr = LogisticRegression(maxIter=50, regParam=0.1, elasticNetParam=0.8)
pipeline = Pipeline(stages=[assembler, lr])
# Fit the model
model = pipeline.fit(spark_df)
# Print the coefficients and intercept for multinomial logistic regression
preds_df = model.transform(spark_df)
preds1 = [x.prediction for x in preds_df.select("prediction").collect()]
sparkm.save_model(model, path=str(model_path), conda_env=conda_env)
reloaded_model = sparkm.load_model(path=str(model_path))
preds_df_1 = reloaded_model.transform(spark_df)
preds1_1 = [
x.prediction for x in preds_df_1.select("prediction").collect()
]
assert preds1 == preds1_1
m = pyfunc.load_pyfunc(str(model_path))
preds2 = m.predict(pandas_df)
assert preds1 == preds2
preds3 = score_model_in_sagemaker_docker_container(
model_path=str(model_path), data=pandas_df)
assert preds1 == preds3
assert os.path.exists(sparkm.DFS_TMP)
print(os.listdir(sparkm.DFS_TMP))
assert not os.listdir(sparkm.DFS_TMP)
def test_model_export(spark_model_iris, model_path, spark_conda_env):
preds_df = spark_model_iris.model.transform(spark_model_iris.training_df)
preds1 = [x.prediction for x in preds_df.select("prediction").collect()]
sparkm.save_model(spark_model_iris.model,
path=model_path,
conda_env=spark_conda_env)
reloaded_model = sparkm.load_model(path=model_path)
preds_df_1 = reloaded_model.transform(spark_model_iris.training_df)
preds1_1 = [
x.prediction for x in preds_df_1.select("prediction").collect()
]
assert preds1 == preds1_1
m = pyfunc.load_pyfunc(model_path)
preds2 = m.predict(spark_model_iris.inference_df)
assert preds1 == preds2
preds3 = score_model_in_sagemaker_docker_container(
model_path=model_path, data=spark_model_iris.inference_df)
assert preds1 == preds3
assert os.path.exists(sparkm.DFS_TMP)
print(os.listdir(sparkm.DFS_TMP))
# We expect not to delete the DFS tempdir.
assert os.listdir(sparkm.DFS_TMP)
def test_model_export(spark_model_iris, model_path, spark_conda_env):
sparkm.save_model(spark_model_iris.model,
path=model_path,
conda_env=spark_conda_env)
# 1. score and compare reloaded sparkml model
reloaded_model = sparkm.load_model(path=model_path)
preds_df = reloaded_model.transform(spark_model_iris.spark_df)
preds1 = [x.prediction for x in preds_df.select("prediction").collect()]
assert spark_model_iris.predictions == preds1
m = pyfunc.load_pyfunc(model_path)
# 2. score and compare reloaded pyfunc
preds2 = m.predict(spark_model_iris.pandas_df)
assert spark_model_iris.predictions == preds2
# 3. score and compare reloaded pyfunc Spark udf
preds3 = score_model_as_udf(model_path,
run_id=None,
pandas_df=spark_model_iris.pandas_df)
assert spark_model_iris.predictions == preds3
assert os.path.exists(sparkm.DFS_TMP)
print(os.listdir(sparkm.DFS_TMP))
# We expect not to delete the DFS tempdir.
assert os.listdir(sparkm.DFS_TMP)
def test_transformer_model_export(spark_model_transformer, model_path, spark_custom_env):
with pytest.raises(MlflowException, match="Cannot serialize this model"):
sparkm.save_model(
spark_model_transformer.model, path=model_path, conda_env=spark_custom_env
)
def model_selection_via_crossvalidation(num_features, reg_param, net_param,
cv_num_folds):
# hyper parameters for the cross validator
num_features = num_features
reg_param = reg_param
net_param = net_param
cv_num_folds = cv_num_folds
# Start a new MLflow run
with mlflow.start_run():
tokenizer, remover, counts, lr = build_ml_pipeline()
pipeline = Pipeline().setStages([tokenizer, remover, counts, lr])
evaluator = BinaryClassificationEvaluator(
rawPredictionCol="rawPrediction")
paramGrid = ParamGridBuilder() \
.addGrid(counts.numFeatures, num_features) \
.addGrid(lr.regParam, reg_param) \
.addGrid(lr.elasticNetParam, net_param) \
.build()
crossval = CrossValidator(estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=evaluator,
numFolds=cv_num_folds)
# Run cross-validation, and choose the best set of parameters.
training_df, validate_df, test_df = prepare_data()
logger.warn("training classifier")
cv_model = crossval.fit(training_df)
# cv_best_pipeline_model == Pipeline model, this holds the best pipeline model based out of cross validation run
cv_best_pipeline_model = cv_model.bestModel
logger.info("evaluate trained classifier")
prediction = cv_model.transform(validate_df)
prediction.show(n=10)
area_under_ROC = evaluator.evaluate(prediction)
logger.info(
"Area under the curve metric for the best model selected out of CV: "
+ str(area_under_ROC))
print("\n area_under_ROC: " + str(area_under_ROC))
accuracy = cv_best_pipeline_model.stages[-1].summary.accuracy
logger.info("Accuracy metric for the best model selected out of CV: " +
str(accuracy))
print("\n accuracy: " + str(accuracy))
# save trained model to a local directory, in this case under your local system /uap/nlp/
mlflow_spark.save_model(cv_best_pipeline_model,
path="pyfunc-cv-model",
conda_env=None)
# save trained model to a dbfs
mlflow_spark.log_model(
cv_best_pipeline_model,
artifact_path=
"/dbfs/tmp/dbconnect-demo/uap/reviews/pyfunc-cv-model",
conda_env=None)
# save model as spark flavor
logger.info(
"logging cv_best_pipeline_model as a spark flavor on hosted mlflow server"
)
spark_cv_model_path = "spark-cv-model"
mlflow_spark.log_model(cv_best_pipeline_model, spark_cv_model_path)
# save model as mleap flavor
# mleap_cv_model_path = "mleap-cv-model"
# mlflow.mleap.log_model(cv_best_pipeline_model, test_df, mleap_cv_model_path)
mlflow.log_param(
"max_iterations",
cv_best_pipeline_model.stages[-1]._java_obj.getMaxIter())
mlflow.log_param(
"reg_param",
cv_best_pipeline_model.stages[-1]._java_obj.getRegParam())
mlflow.log_metric("accuracy", accuracy)
mlflow.log_metric("area_under_ROC", area_under_ROC)
cv_runid = mlflow.active_run().info.run_uuid
cv_artifactUri = mlflow.get_artifact_uri()
logger.warn("\ncv_runid: " + str(cv_runid))
logger.warn("\ncv_artifactUri: " + str(cv_artifactUri))
return cv_runid, cv_artifactUri
def test_sparkml_model_save_without_specified_conda_env_uses_default_env_with_expected_dependencies(
spark_model_iris, model_path):
sparkm.save_model(spark_model=spark_model_iris.model, path=model_path)
_assert_pip_requirements(model_path, sparkm.get_default_pip_requirements())
def test_transformer_model_export(spark_model_transformer, model_path, spark_custom_env):
with pytest.raises(MlflowException) as e:
sparkm.save_model(
spark_model_transformer.model, path=model_path, conda_env=spark_custom_env
)
assert "Cannot serialize this model" in e.value.message
