Merge pull request #10 from godatadriven/dev

Dev

README.md

@@ -115,7 +115,7 @@ def piven_model(input_size, hidden_units):
 ```
 
 The most straightforward way of running your Model is to subclass the `PivenBaseModel` class. This requires you
-to define a `build_model()` method in which you can add preprocessing pipelines etc. 
+to define a `build()` method in which you can add preprocessing pipelines etc. 
 
 ```python
 from piven.models.base import PivenBaseModel
@@ -125,7 +125,7 @@ from sklearn.preprocessing import StandardScaler
 
 
 class MyPivenModel(PivenBaseModel):
-    def build_model(self, build_fn = piven_model):
+    def build(self, build_fn = piven_model):
         model = PivenKerasRegressor(build_fn=build_fn, **self.params)
         # Finally, normalize the output target
         self.model = PivenTransformedTargetRegressor(
@@ -146,7 +146,7 @@ MyPivenModel(
 Note that the inputs to `MyPivenModel` must match the inputs to the `piven_model` function.
 
 You can now call all methods defined as in the PivenBaseModel class. Check the 
-[PivenMlpModel class](https://gitlab.com/jasperginn/piven.py/-/blob/dev/src/piven/Models/mlp_regressor.py)
+[PivenMlpModel class](https://github.com/godatadriven/piven/blob/master/src/piven/models/mlp_regressor.py)
 for a more detailed example.
 
 ## Details: loss function

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.3.3
+current_version = 0.4.0
 commit = True
 tag = True
 

setup.py

@@ -2,7 +2,7 @@
 
 setuptools.setup(
     name="piven",
-    version="0.3.3",
+    version="0.4.0",
     description="Prediction Intervals with specific value prediction",
     url="https://gitlab.com/jasperginn/piven.py",
     author="Jasper Ginn",

src/piven/__init__.py

@@ -2,4 +2,4 @@
 
 logging.getLogger(__name__).addHandler(logging.NullHandler())
 
-__version__ = "0.3.3"
+__version__ = "0.4.0"