Refactoring

CHANGELOG.md

@@ -13,6 +13,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - `OVERVIEW` in `pymilo_param.py`
 - `get_sklearn_class` in `utils.util.py`
 ### Changed
+- `ML Streaming` testcases modified to use PyMilo CLI
 - `to_pymilo_issue` function in `PymiloException`
 - `valid_url_valid_file` testcase added in `test_exceptions.py`
 - `valid_url_valid_file` function in `import_exceptions.py`

pymilo/__main__.py

@@ -3,7 +3,13 @@
 import re
 import argparse
 from art import tprint
-from .pymilo_param import PYMILO_VERSION, URL_REGEX
+from .pymilo_param import (
+    PYMILO_VERSION,
+    URL_REGEX,
+    CLI_MORE_INFO,
+    CLI_UNKNOWN_MODEL,
+    CLI_ML_STREAMING_NOT_INSTALLED,
+)
 from .pymilo_func import print_supported_ml_models, pymilo_help
 from .pymilo_obj import Import
 from .utils.util import get_sklearn_class
@@ -70,10 +76,8 @@ def main():
         print(PYMILO_VERSION)
         return
     if not ml_streaming_support:
-        print("Error: ML Streaming is not installed.")
-        print("To install ML Streaming, run the following command:")
-        print("pip install pymilo[streaming]")
-        print("For more information, visit the PyMilo README at https://github.com/openscilab/pymilo")
+        print(CLI_ML_STREAMING_NOT_INSTALLED)
+        print(CLI_MORE_INFO)
         tprint("PyMilo")
         tprint("V:" + PYMILO_VERSION)
         pymilo_help()
@@ -88,19 +92,17 @@ def main():
         path = args.load
         run_ps = True
         _model = Import(url=path) if re.match(URL_REGEX, path) else Import(file_adr=path)
+        _model = _model.to_model()
     elif args.init:
         model_name = args.init
         model_class = get_sklearn_class(model_name)
         if model_class is None:
-            print(
-                "The given ML model name is neither valid nor supported, use the list below: \n{print_supported_ml_models}")
-            print_supported_ml_models()
+            print(f"{CLI_UNKNOWN_MODEL}\n{print_supported_ml_models()}")
             return
         run_ps = True
         _model = model_class()
     elif args.bare:
         run_ps = True
-        _model = model_class()
     if not run_ps:
         tprint("PyMilo")
         tprint("V:" + PYMILO_VERSION)
@@ -114,5 +116,6 @@ def main():
             communication_protocol=_communication_protocol,
         ).communicator.run()
 
+
 if __name__ == '__main__':
     main()

pymilo/chains/ensemble_chain.py

@@ -170,8 +170,10 @@ def deserialize(self, ensemble, is_inner_model=False):
             setattr(raw_model, item, data[item])
         return raw_model
 
+
 ensemble_chain = EnsembleModelChain(ENSEMBLE_CHAIN, SKLEARN_ENSEMBLE_TABLE)
 
+
 def get_transporter(model):
     """
     Get associated transporter for the given ML model.
@@ -188,6 +190,7 @@ def get_transporter(model):
     else:
         return get_concrete_transporter(model)
 
+
 def serialize_possible_ml_model(possible_ml_model):
     """
     Check whether the given object is a ML model and if it is, serialize it.
@@ -209,6 +212,7 @@ def serialize_possible_ml_model(possible_ml_model):
     else:
         return False, possible_ml_model
 
+
 def deserialize_possible_ml_model(possible_serialized_ml_model):
     """
     Check whether the given object is previously serialized ML model and if it is, deserialize it back to the associated ML model.
@@ -226,6 +230,7 @@ def deserialize_possible_ml_model(possible_serialized_ml_model):
     else:
         return False, possible_serialized_ml_model
 
+
 def serialize_models_in_ndarray(ndarray_instance):
     """
     Serialize the ml models inside the given ndarray.
@@ -268,6 +273,7 @@ def serialize_models_in_ndarray(ndarray_instance):
             'pymiloed-data-structure': 'numpy.ndarray'
         }
 
+
 def deserialize_models_in_ndarray(serialized_ndarray):
     """
     Deserializes possible ML models within the given ndarray instance.

pymilo/chains/linear_model_chain.py

@@ -78,8 +78,10 @@ def deserialize(self, linear_model, is_inner_model=False):
             setattr(raw_model, item, data[item])
         return raw_model
 
+
 linear_chain = LinearModelChain(LINEAR_MODEL_CHAIN, SKLEARN_LINEAR_MODEL_TABLE)
 
+
 def is_deserialized_linear_model(content):
     """
     Check if the given content is a previously serialized model by Pymilo's Export or not.

pymilo/pymilo_param.py

@@ -96,6 +96,12 @@
 INVALID_DOWNLOADED_MODEL = "The downloaded content is not a valid JSON file."
 BATCH_IMPORT_INVALID_DIRECTORY = "The given directory does not exist."
 
+CLI_ML_STREAMING_NOT_INSTALLED = """ML Streaming is not installed.
+To install ML Streaming, run the following command:
+pip install pymilo[streaming]"""
+CLI_MORE_INFO = "For more information, visit the PyMilo README at https://github.com/openscilab/pymilo"
+CLI_UNKNOWN_MODEL = "The provided ML model name is either invalid or unsupported."
+
 SKLEARN_LINEAR_MODEL_TABLE = {
     "DummyRegressor": dummy.DummyRegressor,
     "DummyClassifier": dummy.DummyClassifier,

pymilo/utils/util.py

@@ -179,5 +179,4 @@ def get_sklearn_class(model_name):
     for _, category_models in SKLEARN_SUPPORTED_CATEGORIES.items():
         if model_name in category_models:
             return category_models[model_name]
-    # todo raise exception
     return None

tests/test_ml_streaming/run_server.py

@@ -48,4 +48,4 @@ def main():
     communicator.run()
 
 if __name__ == '__main__':
-    main()
+    main()

tests/test_ml_streaming/test_streaming.py

@@ -13,6 +13,17 @@
     params=["NULL", "GZIP", "ZLIB", "LZMA", "BZ2"])
 def prepare_bare_server(request):
     compression_method = request.param
+    # Using PyMilo direct CLI
+    # server_proc = subprocess.Popen(
+    #     [
+    #         executable,
+    #         "-m", "pymilo",
+    #         "--compression", compression_method,
+    #         "--protocol", "REST",
+    #         "--port", "8000",
+    #         "--bare",
+    #     ],
+    #     )
     path = os.path.join(
         os.getcwd(),
         "tests",
@@ -28,7 +39,7 @@ def prepare_bare_server(request):
         ],
         )
     time.sleep(10)
-    yield (server_proc, compression_method, "REST")
+    yield (compression_method, "REST")
     server_proc.terminate()
 
 
@@ -38,7 +49,18 @@ def prepare_bare_server(request):
 def prepare_ml_server(request):
     communication_protocol = request.param
     compression_method = "ZLIB"
-    print(communication_protocol)
+    # Using PyMilo direct CLI
+    # server_proc = subprocess.Popen(
+    #     [
+    #         executable,
+    #         "-m", "pymilo",
+    #         "--compression", compression_method,
+    #         "--protocol", communication_protocol,
+    #         "--port", "9000",
+    #         "--load", os.path.join(os.getcwd(), "tests", "test_exceptions", "valid_jsons", "linear_regression.json")
+    #         # "--load", "https://raw.githubusercontent.com/openscilab/pymilo/main/tests/test_exceptions/valid_jsons/linear_regression.json",
+    #     ],
+    #     )
     path = os.path.join(
         os.getcwd(),
         "tests",
@@ -54,21 +76,21 @@ def prepare_ml_server(request):
             "--init",
         ],
         )
-    time.sleep(5)
-    yield (server_proc, compression_method, communication_protocol)
+    time.sleep(10)
+    yield (compression_method, communication_protocol)
     server_proc.terminate()
 
 
 def test1(prepare_bare_server):
-    _, compression_method, communication_protocol = prepare_bare_server
+    compression_method, communication_protocol = prepare_bare_server
     assert scenario1(compression_method, communication_protocol) == 0
 
 
 def test2(prepare_bare_server):
-    _, compression_method, communication_protocol = prepare_bare_server
+    compression_method, communication_protocol = prepare_bare_server
     assert scenario2(compression_method, communication_protocol) == 0
 
 
 def test3(prepare_ml_server):
-    _, compression_method, communication_protocol = prepare_ml_server
+    compression_method, communication_protocol = prepare_ml_server
     assert scenario3(compression_method, communication_protocol) == 0