rszper commented on code in PR #27709:
URL: https://github.com/apache/beam/pull/27709#discussion_r1280925325
##########
website/www/site/content/en/documentation/transforms/python/overview.md:
##########
@@ -27,6 +27,7 @@ limitations under the License.
<tr><td><a
href="/documentation/transforms/python/elementwise/keys">Keys</a></td><td>Extracts
the key from each element in a collection of key-value pairs.</td></tr>
<tr><td><a
href="/documentation/transforms/python/elementwise/kvswap">KvSwap</a></td><td>Swaps
the key and value of each element in a collection of key-value pairs.</td></tr>
<tr><td><a
href="/documentation/transforms/python/elementwise/map">Map</a></td><td>Applies
a function to every element in the input and outputs the result.</td></tr>
+ <tr><td><a
href="/documentation/transforms/python/elementwise/mltransform">MLTransform</a></td><td>Applies
Data processing transforms on the dataset.</td></tr>
Review Comment:
```suggestion
<tr><td><a
href="/documentation/transforms/python/elementwise/mltransform">MLTransform</a></td><td>Applies
data processing transforms to the dataset.</td></tr>
```
##########
website/www/site/layouts/partials/section-menu/en/documentation.html:
##########
@@ -291,6 +291,7 @@
<li><a
href="/documentation/transforms/python/elementwise/keys/">Keys</a></li>
<li><a
href="/documentation/transforms/python/elementwise/kvswap/">KvSwap</a></li>
<li><a
href="/documentation/transforms/python/elementwise/map/">Map</a></li>
+ <li><a
href="/documentation/transforms/python/elementwise/mltransform/">Map</a></li>
Review Comment:
```suggestion
<li><a
href="/documentation/transforms/python/elementwise/mltransform/">MLTransform</a></li>
```
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
Review Comment:
```suggestion
Use `MLTransform` to apply common machine learning (ML) processing tasks on
keyed data. Apache Beam provides ML data processing transformations that you
can use with `MLTransform`. For the full list of available data
```
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
Review Comment:
```suggestion
processing transformations, see the [tft.py
file](https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52)
in GitHub.
```
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
+
+Let's look at an example
Review Comment:
```suggestion
The following example demonstrates how to use `MLTransform` to normalize
your data between 0 and 1 by using the minimum and maximum values from your
entire dataset. `MLTransform` uses the `ScaleTo01` transformation.
```
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
+
+Let's look at an example
+
+```
+scale_to_z_score_transform = ScaleToZScore(columns=['x', 'y'])
+with beam.Pipeline() as p:
+ (data |
MLTransform(artifact_location).with_transform(scale_to_z_score_transform))
+```
+
+In this example, `MLTransform` receives `artifact_location`. This location is
used to store artifacts generated by the `MLTransform`. We will talk about the
`artifacts` later. The data processing transform can be passed using the
`with_transform` method of `MLTransform` or transforms can be passed as list to
the MLTransform.
Review Comment:
```suggestion
In this example, `MLTransform` receives a value for `artifact_location`.
`MLTransform` then uses this location value to store artifacts generated by the
transform. To pass the data processing transform, you can use either the
with_transform` method of `MLTransform` or a list.
```
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
+
+Let's look at an example
+
+```
+scale_to_z_score_transform = ScaleToZScore(columns=['x', 'y'])
+with beam.Pipeline() as p:
+ (data |
MLTransform(artifact_location).with_transform(scale_to_z_score_transform))
+```
+
+In this example, `MLTransform` receives `artifact_location`. This location is
used to store artifacts generated by the `MLTransform`. We will talk about the
`artifacts` later. The data processing transform can be passed using the
`with_transform` method of `MLTransform` or transforms can be passed as list to
the MLTransform.
+
+```
+MLTransform(transforms=transforms, artifact_location=artifact_location)
+```
+
+All the transforms passed to `MLTransform` are applied sequentially on the
dataset. `MLTransform` expects a keyed data (Link to the supported inputs).
Review Comment:
```suggestion
The transforms passed to `MLTransform` are applied sequentially on the
dataset. `MLTransform` expects keyed data (Link to the supported inputs).
```
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
+
+Let's look at an example
+
+```
+scale_to_z_score_transform = ScaleToZScore(columns=['x', 'y'])
+with beam.Pipeline() as p:
+ (data |
MLTransform(artifact_location).with_transform(scale_to_z_score_transform))
+```
+
+In this example, `MLTransform` receives `artifact_location`. This location is
used to store artifacts generated by the `MLTransform`. We will talk about the
`artifacts` later. The data processing transform can be passed using the
`with_transform` method of `MLTransform` or transforms can be passed as list to
the MLTransform.
+
+```
+MLTransform(transforms=transforms, artifact_location=artifact_location)
+```
+
+All the transforms passed to `MLTransform` are applied sequentially on the
dataset. `MLTransform` expects a keyed data (Link to the supported inputs).
+
+
+## Example 1
+
+In the example we will create a pipeline that uses MLTransform to scale the
data between 0 and 1.
Review Comment:
```suggestion
This example creates a pipeline that uses `MLTransform` to scale data
between 0 and 1.
The example takes a list of ints and converts them into the range of 0 to 1
using the transform `ScaleTo01`.
```
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
+
+Let's look at an example
+
+```
+scale_to_z_score_transform = ScaleToZScore(columns=['x', 'y'])
+with beam.Pipeline() as p:
+ (data |
MLTransform(artifact_location).with_transform(scale_to_z_score_transform))
+```
+
+In this example, `MLTransform` receives `artifact_location`. This location is
used to store artifacts generated by the `MLTransform`. We will talk about the
`artifacts` later. The data processing transform can be passed using the
`with_transform` method of `MLTransform` or transforms can be passed as list to
the MLTransform.
+
+```
+MLTransform(transforms=transforms, artifact_location=artifact_location)
+```
+
+All the transforms passed to `MLTransform` are applied sequentially on the
dataset. `MLTransform` expects a keyed data (Link to the supported inputs).
+
+
+## Example 1
+
+In the example we will create a pipeline that uses MLTransform to scale the
data between 0 and 1.
+
+{{< highlight language="py"
file="sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
+ class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
mltransform_scale_to_0_1 >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform_test.py"
mltransform_scale_to_0_1 >}}
+{{< /highlight >}}
+
+
+The example takes a list of ints and converts them into the range of 0 to 1
using the transform `ScaleTo01`.
Review Comment:
Remove; I moved this content to the intro.
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
+
+Let's look at an example
+
+```
+scale_to_z_score_transform = ScaleToZScore(columns=['x', 'y'])
+with beam.Pipeline() as p:
+ (data |
MLTransform(artifact_location).with_transform(scale_to_z_score_transform))
+```
+
+In this example, `MLTransform` receives `artifact_location`. This location is
used to store artifacts generated by the `MLTransform`. We will talk about the
`artifacts` later. The data processing transform can be passed using the
`with_transform` method of `MLTransform` or transforms can be passed as list to
the MLTransform.
+
+```
+MLTransform(transforms=transforms, artifact_location=artifact_location)
+```
+
+All the transforms passed to `MLTransform` are applied sequentially on the
dataset. `MLTransform` expects a keyed data (Link to the supported inputs).
+
+
+## Example 1
+
+In the example we will create a pipeline that uses MLTransform to scale the
data between 0 and 1.
+
+{{< highlight language="py"
file="sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
+ class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
mltransform_scale_to_0_1 >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform_test.py"
mltransform_scale_to_0_1 >}}
+{{< /highlight >}}
+
+
+The example takes a list of ints and converts them into the range of 0 to 1
using the transform `ScaleTo01`.
+
+## Example 2
+
+In the example, we will create a pipeline that use MLTransform to compute
vocabulary on the entire dataset and assign indices to each unique vocab.
Review Comment:
```suggestion
This example creates a pipeline that use `MLTransform` to compute vocabulary
on the entire dataset and assign indices to each unique vocabulary item.
It takes a list of strings, computes vocabulary over the entire dataset, and
then applies a unique index to each vocabulary item.
```
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
+
+Let's look at an example
+
+```
+scale_to_z_score_transform = ScaleToZScore(columns=['x', 'y'])
+with beam.Pipeline() as p:
+ (data |
MLTransform(artifact_location).with_transform(scale_to_z_score_transform))
+```
+
+In this example, `MLTransform` receives `artifact_location`. This location is
used to store artifacts generated by the `MLTransform`. We will talk about the
`artifacts` later. The data processing transform can be passed using the
`with_transform` method of `MLTransform` or transforms can be passed as list to
the MLTransform.
+
+```
+MLTransform(transforms=transforms, artifact_location=artifact_location)
+```
+
+All the transforms passed to `MLTransform` are applied sequentially on the
dataset. `MLTransform` expects a keyed data (Link to the supported inputs).
+
+
+## Example 1
+
+In the example we will create a pipeline that uses MLTransform to scale the
data between 0 and 1.
+
+{{< highlight language="py"
file="sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
+ class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
mltransform_scale_to_0_1 >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform_test.py"
mltransform_scale_to_0_1 >}}
+{{< /highlight >}}
+
+
+The example takes a list of ints and converts them into the range of 0 to 1
using the transform `ScaleTo01`.
+
+## Example 2
+
+In the example, we will create a pipeline that use MLTransform to compute
vocabulary on the entire dataset and assign indices to each unique vocab.
+
+{{< highlight language="py"
file="sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
+ class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
mltransform_compute_and_apply_vocabulary >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform_test.py"
mltransform_compute_and_apply_vocabulary >}}
+{{< /highlight >}}
+
+
+This example takes a list of strings, computes vocab over the entire dataset
and applies a unique index to each vocab.
+
+
+The above two examples requires a full pass over the dataset to transform the
dataset. For `ComputeAndApplyVocabulary`, all the unqiue words in the dataset
needs to be known before transforming the data. For `ScaleTo01`, the minimum
and maximum of the dataset needs to be known before transforming the dataset.
This is acheived by `MLTransform`.
+
+
+In this example, we will create a pipeline that use MLTransform to compute
vocabulary on the entire dataset and assign indices to each unique vocab. This
pipeline takes a single element as input instead of list of elements.
Review Comment:
```suggestion
This example creates a pipeline that uses `MLTransform` to compute
vocabulary on the entire dataset and assign indices to each unique vocabulary
item. This pipeline takes a single element as input instead of a list of
elements.
```
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
+
+Let's look at an example
+
+```
+scale_to_z_score_transform = ScaleToZScore(columns=['x', 'y'])
+with beam.Pipeline() as p:
+ (data |
MLTransform(artifact_location).with_transform(scale_to_z_score_transform))
+```
+
+In this example, `MLTransform` receives `artifact_location`. This location is
used to store artifacts generated by the `MLTransform`. We will talk about the
`artifacts` later. The data processing transform can be passed using the
`with_transform` method of `MLTransform` or transforms can be passed as list to
the MLTransform.
Review Comment:
If we include this example on this page, it should be in the example section
and formatted to match the other examples on the page.
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
+
+Let's look at an example
+
+```
+scale_to_z_score_transform = ScaleToZScore(columns=['x', 'y'])
+with beam.Pipeline() as p:
+ (data |
MLTransform(artifact_location).with_transform(scale_to_z_score_transform))
+```
+
+In this example, `MLTransform` receives `artifact_location`. This location is
used to store artifacts generated by the `MLTransform`. We will talk about the
`artifacts` later. The data processing transform can be passed using the
`with_transform` method of `MLTransform` or transforms can be passed as list to
the MLTransform.
+
+```
+MLTransform(transforms=transforms, artifact_location=artifact_location)
+```
+
+All the transforms passed to `MLTransform` are applied sequentially on the
dataset. `MLTransform` expects a keyed data (Link to the supported inputs).
+
Review Comment:
```suggestion
## Examples
The following examples demonstrate how to to create pipelines that use
`MLTransform` to preprocess data.
MLTransform can do a full pass on the dataset, which is useful when you need
to transform a single element only after analyzing the entire dataset.
The first two examples require a full pass over the dataset to complete the
data transformation.
* For the `ComputeAndApplyVocabulary` transform, the transform needs access
to all of the unique words in the dataset.
* For the `ScaleTo01` transform, the transform needs to know the minimum and
maximum values in the dataset.
```
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
+
+Let's look at an example
+
+```
+scale_to_z_score_transform = ScaleToZScore(columns=['x', 'y'])
+with beam.Pipeline() as p:
+ (data |
MLTransform(artifact_location).with_transform(scale_to_z_score_transform))
+```
+
+In this example, `MLTransform` receives `artifact_location`. This location is
used to store artifacts generated by the `MLTransform`. We will talk about the
`artifacts` later. The data processing transform can be passed using the
`with_transform` method of `MLTransform` or transforms can be passed as list to
the MLTransform.
+
+```
+MLTransform(transforms=transforms, artifact_location=artifact_location)
+```
+
+All the transforms passed to `MLTransform` are applied sequentially on the
dataset. `MLTransform` expects a keyed data (Link to the supported inputs).
+
+
+## Example 1
Review Comment:
```suggestion
### Example 1
```
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
+
+Let's look at an example
+
+```
+scale_to_z_score_transform = ScaleToZScore(columns=['x', 'y'])
+with beam.Pipeline() as p:
+ (data |
MLTransform(artifact_location).with_transform(scale_to_z_score_transform))
+```
+
+In this example, `MLTransform` receives `artifact_location`. This location is
used to store artifacts generated by the `MLTransform`. We will talk about the
`artifacts` later. The data processing transform can be passed using the
`with_transform` method of `MLTransform` or transforms can be passed as list to
the MLTransform.
+
+```
+MLTransform(transforms=transforms, artifact_location=artifact_location)
+```
+
+All the transforms passed to `MLTransform` are applied sequentially on the
dataset. `MLTransform` expects a keyed data (Link to the supported inputs).
+
+
+## Example 1
+
+In the example we will create a pipeline that uses MLTransform to scale the
data between 0 and 1.
+
+{{< highlight language="py"
file="sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
+ class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
mltransform_scale_to_0_1 >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform_test.py"
mltransform_scale_to_0_1 >}}
+{{< /highlight >}}
+
+
+The example takes a list of ints and converts them into the range of 0 to 1
using the transform `ScaleTo01`.
+
+## Example 2
+
+In the example, we will create a pipeline that use MLTransform to compute
vocabulary on the entire dataset and assign indices to each unique vocab.
+
+{{< highlight language="py"
file="sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
+ class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
mltransform_compute_and_apply_vocabulary >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform_test.py"
mltransform_compute_and_apply_vocabulary >}}
+{{< /highlight >}}
+
+
+This example takes a list of strings, computes vocab over the entire dataset
and applies a unique index to each vocab.
Review Comment:
Remove; I moved this content to the intro.
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
+
+Let's look at an example
+
+```
+scale_to_z_score_transform = ScaleToZScore(columns=['x', 'y'])
+with beam.Pipeline() as p:
+ (data |
MLTransform(artifact_location).with_transform(scale_to_z_score_transform))
+```
+
+In this example, `MLTransform` receives `artifact_location`. This location is
used to store artifacts generated by the `MLTransform`. We will talk about the
`artifacts` later. The data processing transform can be passed using the
`with_transform` method of `MLTransform` or transforms can be passed as list to
the MLTransform.
+
+```
+MLTransform(transforms=transforms, artifact_location=artifact_location)
+```
+
+All the transforms passed to `MLTransform` are applied sequentially on the
dataset. `MLTransform` expects a keyed data (Link to the supported inputs).
+
+
+## Example 1
+
+In the example we will create a pipeline that uses MLTransform to scale the
data between 0 and 1.
+
+{{< highlight language="py"
file="sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
+ class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
mltransform_scale_to_0_1 >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform_test.py"
mltransform_scale_to_0_1 >}}
+{{< /highlight >}}
+
+
+The example takes a list of ints and converts them into the range of 0 to 1
using the transform `ScaleTo01`.
+
+## Example 2
+
+In the example, we will create a pipeline that use MLTransform to compute
vocabulary on the entire dataset and assign indices to each unique vocab.
+
+{{< highlight language="py"
file="sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
+ class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
mltransform_compute_and_apply_vocabulary >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform_test.py"
mltransform_compute_and_apply_vocabulary >}}
+{{< /highlight >}}
+
+
+This example takes a list of strings, computes vocab over the entire dataset
and applies a unique index to each vocab.
+
+
+The above two examples requires a full pass over the dataset to transform the
dataset. For `ComputeAndApplyVocabulary`, all the unqiue words in the dataset
needs to be known before transforming the data. For `ScaleTo01`, the minimum
and maximum of the dataset needs to be known before transforming the dataset.
This is acheived by `MLTransform`.
+
Review Comment:
```suggestion
### Example 3
```
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
Review Comment:
```suggestion
To define a data processing transformation by using `MLTransform`, create
instances of data processing transforms with `columns` as input parameters. The
data in the specified `columns` is transformed and outputted to the `beam.Row`
object.
```
##########
website/www/site/content/en/documentation/transforms/python/elementwise/mltransform.md:
##########
@@ -0,0 +1,111 @@
+---
+title: "MLTransform"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# MLTransform for data processing
+
+{{< localstorage language language-py >}}
+
+
+<table>
+ <tr>
+ <td>
+ <a>
+ {{< button-pydoc path="apache_beam.ml.transforms" class="MLTransform" >}}
+ </a>
+ </td>
+ </tr>
+</table>
+
+
+`MLTransform` is used to apply common machine learning processing tasks on
keyed data. Apache Beam provides ML data processing transformations which can
be used with `MLTransform`. You can find the full list of available data
+processing transformations in the
((GitHub)[https://github.com/apache/beam/blob/ab93fb1988051baac6c3b9dd1031f4d68bd9a149/sdks/python/apache_beam/ml/transforms/tft.py#L52])
repository.
+
+
+To define a data processing transformation using `MLTransform`, you need to
create instances of data processing transforms with `columns` as input
paramter. The data in the specified `columns` will be transformed and outputted
in the `beam.Row` object.
+
+Let's look at an example
+
+```
+scale_to_z_score_transform = ScaleToZScore(columns=['x', 'y'])
+with beam.Pipeline() as p:
+ (data |
MLTransform(artifact_location).with_transform(scale_to_z_score_transform))
+```
+
+In this example, `MLTransform` receives `artifact_location`. This location is
used to store artifacts generated by the `MLTransform`. We will talk about the
`artifacts` later. The data processing transform can be passed using the
`with_transform` method of `MLTransform` or transforms can be passed as list to
the MLTransform.
+
+```
+MLTransform(transforms=transforms, artifact_location=artifact_location)
+```
+
+All the transforms passed to `MLTransform` are applied sequentially on the
dataset. `MLTransform` expects a keyed data (Link to the supported inputs).
+
+
+## Example 1
+
+In the example we will create a pipeline that uses MLTransform to scale the
data between 0 and 1.
+
+{{< highlight language="py"
file="sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
+ class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
mltransform_scale_to_0_1 >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform_test.py"
mltransform_scale_to_0_1 >}}
+{{< /highlight >}}
+
+
+The example takes a list of ints and converts them into the range of 0 to 1
using the transform `ScaleTo01`.
+
+## Example 2
+
+In the example, we will create a pipeline that use MLTransform to compute
vocabulary on the entire dataset and assign indices to each unique vocab.
+
+{{< highlight language="py"
file="sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
+ class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform.py"
mltransform_compute_and_apply_vocabulary >}}
+{{</ highlight >}}
+
+{{< paragraph class="notebook-skip" >}}
+Output:
+{{< /paragraph >}}
+{{< highlight class="notebook-skip" >}}
+{{< code_sample
"sdks/python/apache_beam/examples/snippets/transforms/elementwise/mltransform_test.py"
mltransform_compute_and_apply_vocabulary >}}
+{{< /highlight >}}
+
+
+This example takes a list of strings, computes vocab over the entire dataset
and applies a unique index to each vocab.
+
+
+The above two examples requires a full pass over the dataset to transform the
dataset. For `ComputeAndApplyVocabulary`, all the unqiue words in the dataset
needs to be known before transforming the data. For `ScaleTo01`, the minimum
and maximum of the dataset needs to be known before transforming the dataset.
This is acheived by `MLTransform`.
Review Comment:
Remove; I moved this content to the intro of the Example section.
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