Updated constants and usage of those

Author: Nirmal-nibo

nyoka/base/constants.py

@@ -17,6 +17,12 @@
    limitations under the License.
  """
 
+class HEADER_INFO:
+    COPYRIGHT = "Copyright (c) 2021 Software AG"
+    APPLICATION_NAME = "Nyoka"
+    APPLICATION_VERSION = "5.0.0"
+    DEFAULT_DESCRIPTION = "Default description"
+
 class MISSING_VALUE_TREATMENT_METHOD:
     AS_IS = "asIs"
     AS_MEAN = "asMean"

nyoka/custom/trendminer.py

@@ -34,8 +34,6 @@
 _JSON = ".json"
 _MAX_VALUE = "maxValue"
 _MIN_VALUE = "minValue"
-_APPLICATION_NAME = "Nyoka"
-_APPLICATION_VERSION = "4.2.1"
 
 
 class FingerprintToPmml:
@@ -107,8 +105,8 @@ def _generate_pmml(self):
         def get_header():
             header = pml.Header(
                 Application=pml.Application(
-                    name=_APPLICATION_NAME,
-                    version=_APPLICATION_VERSION
+                    name=HEADER_INFO.APPLICATION_NAME,
+                    version=HEADER_INFO.APPLICATION_VERSION
                 ),
                 description=self._fingerprint_description
             )

nyoka/skl/skl_to_pmml.py

@@ -26,7 +26,6 @@
 from skl import pre_process as pp
 from datetime import datetime
 import math
-import metadata
 from base.constants import *
 
 def skl_to_pmml(pipeline, col_names, target_name='target', pmml_f_name='from_sklearn.pmml', model_name=None, description=None):
@@ -2218,9 +2217,6 @@ def super_cls_names(cls):
     super_cls_names(cls)
     return parents
 
-
-from nyoka import metadata
-
 def get_header(description):
 
     """
@@ -2232,10 +2228,10 @@ def get_header(description):
          Returns Nyoka's Header object.
 
      """
-    copyryt = "Copyright (c) 2018 Software AG"
-    description = description if description else "Default Description"
+    copyryt = HEADER_INFO.COPYRIGHT
+    description = description if description else HEADER_INFO.DEFAULT_DESCRIPTION
     timestamp = pml.Timestamp(datetime.now())
-    application=pml.Application(name="Nyoka",version=metadata.__version__)
+    application=pml.Application(name=HEADER_INFO.APPLICATION_NAME,version=HEADER_INFO.APPLICATION_VERSION)
     header = pml.Header(copyright=copyryt, description=description, Timestamp=timestamp, Application=application)
     return header
 

nyoka/statsmodels/exponential_smoothing.py

@@ -168,11 +168,12 @@ def get_mining_field_objs():
         else:
             season_obj = None
         pmml = PMML(
-            version='4.4',
+            version=PMML_SCHEMA.VERSION,
             Header=Header(
-                copyright="Copyright (c) 2018 Software AG", description=description if description else "Exponential Smoothing Model",
+                copyright=HEADER_INFO.COPYRIGHT,
+                description=description if description else HEADER_INFO.DEFAULT_DESCRIPTION,
                 Timestamp=Timestamp(datetime.now()),
-                Application=Application(name="Nyoka",version=metadata.__version__)
+                Application=Application(name=HEADER_INFO.APPLICATION_NAME,version=HEADER_INFO.APPLICATION_VERSION)
             ),
             DataDictionary=DataDictionary(numberOfFields=n_columns, DataField=get_data_field_objs()),
             TimeSeriesModel=[TimeSeriesModel(

nyoka/statsmodels/statsmodels_to_pmml.py

@@ -26,7 +26,6 @@
 from pprint import pprint
 from PMML44 import *
 from datetime import datetime
-import metadata
 import warnings
 import math
 from base.constants import *
@@ -69,10 +68,10 @@ def export_pmml(self):
         pmml = PMML(
             version=PMML_SCHEMA.VERSION,
             Header=Header(
-                copyright = "Copyright (c) 2018 Software AG",
-                description = self.description,
+                copyright = HEADER_INFO.COPYRIGHT,
+                description = self.description if self.description else HEADER_INFO.DEFAULT_DESCRIPTION,
                 Timestamp = Timestamp(datetime.now()),
-                Application=Application(name="Nyoka",version=metadata.__version__)
+                Application=Application(name=HEADER_INFO.APPLICATION_NAME,version=HEADER_INFO.APPLICATION_VERSION)
             ),
             DataDictionary=self.data_dictionary,
             TimeSeriesModel=[self.ts_model]