moved mlflow-flavor

build.gradle

@@ -156,7 +156,7 @@ ext {
     pythonProjects = [
       project(':h2o-py'),
       project(':h2o-py-cloud-extensions'),
-      project(':h2o-py-mlflow-flavors')
+      project(':h2o-py-mlflow-flavor')
     ]
 
     // The project which need to be run under CI only

h2o-py-mlflow-flavor/DESCRIPTION.rst

@@ -0,0 +1,4 @@
+H2O-3 MLFlow Flavor
+===================
+
+A tiny library containing MLFlow flavor for working with H2O-3 MOJO and POJO models. 

h2o-py-mlflow-flavor/README.md

@@ -0,0 +1,3 @@
+# H2O-3 MLFlow Flavor
+
+A tiny library containing [MLFlow](https://mlflow.org/) flavors for working with H2O-3 MOJO and POJO models.

h2o-py-mlflow-flavors/build.gradle → h2o-py-mlflow-flavor/build.gradle

@@ -1,4 +1,4 @@
-description = "H2O-3 MLFlow Flavors"
+description = "H2O-3 MLFlow Flavor"
 
 dependencies {}
 
@@ -20,11 +20,11 @@ ext {
 //
 task createVersionFiles() {
     doLast {
-        file("${buildDir}/h2o_mlflow_flavors/").mkdirs()
-        File version_file = new File("${buildDir}/h2o_mlflow_flavors/", "version.txt")
+        file("${buildDir}/h2o_mlflow_flavor/").mkdirs()
+        File version_file = new File("${buildDir}/h2o_mlflow_flavor/", "version.txt")
         version_file.write(PROJECT_VERSION)
 
-        File build_file = new File("${buildDir}/h2o_mlflow_flavors/", "buildinfo.txt")
+        File build_file = new File("${buildDir}/h2o_mlflow_flavor/", "buildinfo.txt")
         build_file.write(buildVersion.toString())
     }
 }
@@ -34,7 +34,7 @@ task copySrcFiles(type: Copy) {
     from ("${projectDir}") {
         include "setup.py"
         include "setup.cfg"
-        include "h2o_mlflow_flavors/**"
+        include "h2o_mlflow_flavor/**"
         include "README.md"
         include "DESCRIPTION.rst"
     }
@@ -45,7 +45,7 @@ task buildDist(type: Exec, dependsOn: [createVersionFiles, copySrcFiles]) {
     workingDir buildDir
     doFirst {
         file("${buildDir}/tmp").mkdirs()
-        standardOutput = new FileOutputStream(file("${buildDir}/tmp/h2o_mlflow_flavors_buildDist.out"))
+        standardOutput = new FileOutputStream(file("${buildDir}/tmp/h2o_mlflow_flavor_buildDist.out"))
     }
     commandLine getOsSpecificCommandLine([pythonexe, "setup.py", "bdist_wheel"])
 }

h2o-py-mlflow-flavors/h2o_mlflow_flavors/h2o_gen_model.py → h2o-py-mlflow-flavor/h2o_mlflow_flavor/__init__.py

@@ -1,5 +1,5 @@
 """
-The `h2o_mlflow_flavors.h2o_gen_model` module provides an API for working with H2O MOJO and POJO models.
+The `h2o_mlflow_flavor` module provides an API for working with H2O MOJO and POJO models.
 """
 
 import logging
@@ -16,8 +16,8 @@
 from mlflow.models import Model
 from mlflow.models.model import MLMODEL_FILE_NAME
 from mlflow.models.utils import _save_example
-from mlflow.models.signature import ModelSignature
-from mlflow.models.utils import ModelInputExample
+from mlflow.models import ModelSignature, ModelInputExample
+from mlflow.types.schema import ColSpec, ParamSchema, ParamSpec, Schema, DataType
 from mlflow.utils.environment import (
     _CONDA_ENV_FILE_NAME,
     _CONSTRAINTS_FILE_NAME,
@@ -31,14 +31,12 @@
 )
 from mlflow.utils.file_utils import write_to
 from mlflow.utils.model_utils import (
-    _add_code_from_conf_to_system_path,
     _get_flavor_configuration,
     _validate_and_copy_code_paths,
     _validate_and_prepare_target_save_path,
 )
-from mlflow.utils.requirements_utils import _get_pinned_requirement
 from mlflow.tracking.artifact_utils import _download_artifact_from_uri
-from mlflow.models.signature import _infer_signature_from_input_example
+from mlflow.types.utils import _infer_pandas_column
 
 _logger = logging.getLogger(__name__)
 
@@ -68,11 +66,11 @@ def get_params(h2o_model):
     
     :param h2o_model: An H2O binary model.
     :return: A dictionary of parameters that were used for training the model. 
-    """    
-    def is_valid(key): 
+    """
+    def is_valid(key):
         return key != "model_id" and \
-               not key.endswith("_frame") and \
-               not key.startswith("keep_cross_validation_")
+            not key.endswith("_frame") and \
+            not key.startswith("keep_cross_validation_")
 
     return {key: val for key, val in h2o_model.actual_params.items() if is_valid(key)}
 
@@ -123,20 +121,39 @@ def get_input_example(h2o_model, number_of_records=5, relevant_columns_only=True
     :param number_of_records: A number of records that will be extracted from the training dataset.
     :param relevant_columns_only: A flag indicating whether the output dataset should contain 
                                   only columns required by the model.  Defaults to ``True``.
-    :return: 
+    :return: Pandas dataset made from the training dataset of H2O binary model
     """
-    
+
     import h2o
     frame = h2o.get_frame(h2o_model.actual_params["training_frame"]).head(number_of_records)
     result = frame.as_data_frame()
     if relevant_columns_only:
         relevant_columns = h2o_model.varimp(use_pandas=True)["variable"].values.tolist()
         input_columns = [col for col in frame.col_names if col in relevant_columns]
         return result[input_columns]
-    else: 
+    else:
         return result
 
 
+def _infer_signature(h2o_model, wrapped_model, input_example):
+
+    input_schema = _get_input_schema(h2o_model)
+    prediction = wrapped_model.predict(input_example)
+    output_schema = Schema(
+        [ColSpec(type=_infer_pandas_column(prediction[col]), name=col) for col in prediction.columns]
+    )
+    return ModelSignature(inputs=input_schema, outputs=output_schema)
+
+
+def _get_input_schema(h2o_model):
+    import h2o
+    training_frame = h2o.get_frame(h2o_model.actual_params["training_frame"])
+    relevant_columns = h2o_model.varimp(use_pandas=True)["variable"].values.tolist()
+    input_columns = [ColSpec(name=key, type=DataType.string)
+                     for key, val in training_frame.types.items()
+                     if key in relevant_columns]
+    return Schema(input_columns) 
+
 def save_model(
     h2o_model,
     path,
@@ -170,8 +187,8 @@ def save_model(
                                   --setConvertInvalidNum - Converts invalid numbers to NA
                                   --predictContributions - Returns also Shapley values a long with the predictions 
                                   --predictCalibrated - Return also calibrated prediction values.     
-    """    
-    
+    """
+
     import h2o
     model_type_upper = model_type.upper()
     if model_type_upper != "MOJO" and model_type_upper != "POJO":
@@ -194,22 +211,20 @@ def save_model(
 
     if signature is None and input_example is not None:
         wrapped_model = _H2OModelWrapper(model_file, model_type, path, extra_prediction_args)
-        signature = _infer_signature_from_input_example(input_example, wrapped_model)
+        signature = _infer_signature(h2o_model, wrapped_model, input_example)
     elif signature is False:
         signature = None
-    
+
     if mlflow_model is None:
         mlflow_model = Model()
     if signature is not None:
         mlflow_model.signature = signature
     if input_example is not None:
         _save_example(mlflow_model, input_example, path)
-    if metadata is not None:
-        mlflow_model.metadata = metadata
 
     pyfunc.add_to_model(
         mlflow_model,
-        loader_module="h2o_mlflow_flavors.h2o_gen_model",
+        loader_module="h2o_mlflow_flavor",
         model_path=model_file,
         conda_env=_CONDA_ENV_FILE_NAME,
         python_env=_PYTHON_ENV_FILE_NAME,
@@ -292,10 +307,10 @@ def log_model(
     :return: A :py:class:`ModelInfo <mlflow.models.model.ModelInfo>` instance that contains the
              metadata of the logged model.
     """
-    import h2o_mlflow_flavors
+    import h2o_mlflow_flavor
     return Model.log(
         artifact_path=artifact_path,
-        flavor=h2o_mlflow_flavors.h2o_gen_model,
+        flavor=h2o_mlflow_flavor,
         registered_model_name=registered_model_name,
         h2o_model=h2o_model,
         conda_env=conda_env,
@@ -305,7 +320,6 @@ def log_model(
         pip_requirements=pip_requirements,
         extra_pip_requirements=extra_pip_requirements,
         model_type=model_type,
-        metadata=metadata,
         extra_prediction_args=extra_prediction_args,
         **kwargs,
     )

h2o-py-mlflow-flavors/setup.cfg → h2o-py-mlflow-flavor/setup.cfg

@@ -12,7 +12,7 @@
 #
 ignore = E241,E265,E302,E303,E701,D105
 max-line-length = 120
-application-import-names = h2o_mlflow_flavors
+application-import-names = h2o_mlflow_flavor
 import-order-style = smarkets
 inline-quotes = "
 

h2o-py-mlflow-flavors/setup.py → h2o-py-mlflow-flavor/setup.py

@@ -2,32 +2,30 @@
 from setuptools import setup, find_packages
 from codecs import open
 import os
-import sys
-import shutil
 
 here = os.path.abspath(os.path.dirname(__file__))
 
 # Get the long description from the relevant file
 with open(os.path.join(here, 'DESCRIPTION.rst'), encoding='utf-8') as f:
     long_description = f.read()
 
-version = "0.0.local"
+version = "0.1.0-SNAPSHOT"
 # Get the version from the relevant file
-with open(os.path.join(here, 'h2o_mlflow_flavors/version.txt'), encoding='utf-8') as f:
+with open(os.path.join(here, 'h2o_mlflow_flavor/version.txt'), encoding='utf-8') as f:
     version = f.read()
 
 packages = find_packages(exclude=["tests*"])
 print("Found packages: %r" % packages)
 
 setup(
-    name='h2o_mlflow_flavors',
+    name='h2o_mlflow_flavor',
 
     # Versions should comply with PEP440.  For a discussion on single-sourcing
     # the version across setup.py and the project code, see
     # https://packaging.python.org/en/latest/single_source_version.html
     version = version,
 
-    description='Collection of extensions for integration of H2O-3 with H2O.ai Cloud',
+    description='A mlflow flavor for working with H2O-3 MOJO and POJO models',
     long_description=long_description,
 
     # The project's main homepage.
@@ -68,7 +66,7 @@
         "Programming Language :: Python :: 3.11",
     ],
 
-    keywords='machine learning, data mining, statistical analysis, modeling, big data, distributed, parallel',
+    keywords='ML Flow, H2O-3',
 
     packages=packages,
     package_data={"h2o": [