Add 1.5.0 release notes

docs/release-notes/release-1.5.0.md

@@ -0,0 +1,101 @@
+# [NimbusML](https://docs.microsoft.com/en-us/nimbusml/overview) 1.5.0
+
+## **New Features**
+
+- **Initial implementation of `csr_matrix` output support.**
+
+    [PR#250](https://github.com/microsoft/NimbusML/pull/250)
+    Add support for data output in `scipy.sparse.csr_matrix` format.
+
+    ```python
+    xf = OneHotVectorizer(columns={'c0':'c0', 'c1':'c1'})
+    xf.fit(train_df)
+    result = xf.transform(train_df, as_csr=True)
+    ```
+
+- **Permutation Feature Importance for model interpretibility.**
+
+    [PR#279](https://github.com/microsoft/NimbusML/pull/279)
+    Adds `permutation_feature_importance()` method to `Pipeline` and
+    predictor estimators, enabling evaluation of model-wide feature
+    importances on any dataset with same schema as the dataset used
+    to fit the `Pipeline`.
+
+    ```python
+    pipe = Pipeline([
+        LogisticRegressionBinaryClassifier(label='label', feature=['feature'])
+    ])
+    pipe.fit(data)
+    pipe.permutation_feature_importance(data)
+    ```
+
+- **Initial implementation of DateTime input and output column support.**
+
+    [PR#290](https://github.com/microsoft/NimbusML/pull/290)
+    Add initial support for input and output of Pandas DateTime columns.
+
+- **Initial implementation of LpScaler.**
+
+    [PR#253](https://github.com/microsoft/NimbusML/pull/253)
+    Normalize vectors (rows) individually by rescaling them to unit norm (L2, L1 or LInf).
+    Performs the following operation on a vector X: Y = (X - M) / D, where M is mean and D
+    is either L2 norm, L1 norm or LInf norm.
+
+- **Add support for variable length vector output.**
+
+    [PR#267](https://github.com/microsoft/NimbusML/pull/267)
+    Support output of columns returned from ML.Net which contain variable length vectors.
+
+- **Save `predictor_model` when pickling a `Pipeline`.**
+
+    [PR#295](https://github.com/microsoft/NimbusML/pull/295)
+
+- **Initial implementation of the WordTokenizer transform.**
+
+    [PR#296](https://github.com/microsoft/NimbusML/pull/296)
+
+- **Add support for summary output from tree based predictors.**
+
+    [PR#298](https://github.com/microsoft/NimbusML/pull/298)
+
+## **Bug Fixes**
+
+- **Fixed `Pipeline.transform()` in transform only `Pipeline` fails if y column is provided **
+
+    [PR#294](https://github.com/microsoft/NimbusML/pull/294)
+    Enable calling `.transform()` on a `Pipeline` containing only transforms when the y column is provided 
+
+- **Fix issue when using `predict_proba` or `decision_function` with combined models.**
+
+    [PR#272](https://github.com/microsoft/NimbusML/pull/272)
+
+- **Fix `Pipeline._extract_classes_from_headers` was not checking for valid steps.**
+
+    [PR#292](https://github.com/microsoft/NimbusML/pull/292)
+
+- **Fix BinaryDataStream was not valid as input for transformer.**
+
+    [PR#307](https://github.com/microsoft/NimbusML/pull/307)
+
+- **Fix casing for the installPythonPackages build.sh argument.**
+
+    [PR#256](https://github.com/microsoft/NimbusML/pull/256)
+
+## **Breaking Changes**
+
+- **Removed `y` parameter from `Pipeline.transform()`**
+
+    [PR#294](https://github.com/microsoft/NimbusML/pull/294)
+    Removed `y` parameter from `Pipeline.transform()` as it is not needed nor used for transforming data with a fitted `Pipeline`.
+
+## **Enhancements**
+
+None.
+
+## **Documentation and Samples**
+
+None. 
+
+## **Remarks**
+
+None.

release-next.md

@@ -2,91 +2,11 @@
 
 ## **New Features**
 
-- **Initial implementation of `csr_matrix` output support.**
-
-    [PR#250](https://github.com/microsoft/NimbusML/pull/250)
-    Add support for data output in `scipy.sparse.csr_matrix` format.
-
-    ```python
-    xf = OneHotVectorizer(columns={'c0':'c0', 'c1':'c1'})
-    xf.fit(train_df)
-    result = xf.transform(train_df, as_csr=True)
-    ```
-
-- **Permutation Feature Importance for model interpretibility.**
-
-    [PR#279](https://github.com/microsoft/NimbusML/pull/279)
-    Adds `permutation_feature_importance()` method to `Pipeline` and
-    predictor estimators, enabling evaluation of model-wide feature
-    importances on any dataset with same schema as the dataset used
-    to fit the `Pipeline`.
-
-    ```python
-    pipe = Pipeline([
-        LogisticRegressionBinaryClassifier(label='label', feature=['feature'])
-    ])
-    pipe.fit(data)
-    pipe.permutation_feature_importance(data)
-    ```
-
-- **Initial implementation of DateTime input and output column support.**
-
-    [PR#290](https://github.com/microsoft/NimbusML/pull/290)
-    Add initial support for input and output of Pandas DateTime columns.
-
-- **Initial implementation of LpScaler.**
-
-    [PR#253](https://github.com/microsoft/NimbusML/pull/253)
-    Normalize vectors (rows) individually by rescaling them to unit norm (L2, L1 or LInf).
-    Performs the following operation on a vector X: Y = (X - M) / D, where M is mean and D
-    is either L2 norm, L1 norm or LInf norm.
-
-- **Add support for variable length vector output.**
-
-    [PR#267](https://github.com/microsoft/NimbusML/pull/267)
-    Support output of columns returned from ML.Net which contain variable length vectors.
-
-- **Save `predictor_model` when pickling a `Pipeline`.**
-
-    [PR#295](https://github.com/microsoft/NimbusML/pull/295)
-
-- **Initial implementation of the WordTokenizer transform.**
-
-    [PR#296](https://github.com/microsoft/NimbusML/pull/296)
-
-- **Add support for summary output from tree based predictors.**
-
-    [PR#298](https://github.com/microsoft/NimbusML/pull/298)
+None.
 
 ## **Bug Fixes**
 
-- **Fixed `Pipeline.transform()` in transform only `Pipeline` fails if y column is provided **
-
-    [PR#294](https://github.com/microsoft/NimbusML/pull/294)
-    Enable calling `.transform()` on a `Pipeline` containing only transforms when the y column is provided 
-
-- **Fix issue when using `predict_proba` or `decision_function` with combined models.**
-
-    [PR#272](https://github.com/microsoft/NimbusML/pull/272)
-
-- **Fix `Pipeline._extract_classes_from_headers` was not checking for valid steps.**
-
-    [PR#292](https://github.com/microsoft/NimbusML/pull/292)
-
-- **Fix BinaryDataStream was not valid as input for transformer.**
-
-    [PR#307](https://github.com/microsoft/NimbusML/pull/307)
-
-- **Fix casing for the installPythonPackages build.sh argument.**
-
-    [PR#256](https://github.com/microsoft/NimbusML/pull/256)
-
-## **Breaking Changes**
-
-- **Removed `y` parameter from `Pipeline.transform()`**
-
-    [PR#294](https://github.com/microsoft/NimbusML/pull/294)
-    Removed `y` parameter from `Pipeline.transform()` as it is not needed nor used for transforming data with a fitted `Pipeline`.
+None.
 
 ## **Enhancements**