damondouglas commented on code in PR #27516:
URL: https://github.com/apache/beam/pull/27516#discussion_r1266128537
##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,973 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "<a
href=\"https://colab.research.google.com/github/svetakvsundhar/beam/blob/healthcarenlp/examples/notebooks/healthcare/beam_nlp.ipynb\"
target=\"_parent\"><img
src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In
Colab\"/></a>"
Review Comment:
Will this URL remain if this PR merged?
##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,973 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "<a
href=\"https://colab.research.google.com/github/svetakvsundhar/beam/blob/healthcarenlp/examples/notebooks/healthcare/beam_nlp.ipynb\"
target=\"_parent\"><img
src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In
Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF),
Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License"
+ ],
+ "metadata": {
+ "id": "lBuUTzxD2mvJ"
+ },
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Natural Language Processing Pipeline**\n",
+ "\n",
+ "**Note**: This example is used from
[here](https://github.com/rasalt/healthcarenlp/blob/main/nlp_public.ipynb).\n",
+ "\n",
+ "\n",
+ "\n",
+ "This example demonstrates how to set up an Apache Beam pipeline that
reads from a [Google Cloud Storage](https://https://cloud.google.com/storage)
file containing text from Shakespeare's work King Lear, tokenizes the text
lines into individual words, and performs a frequency count on each of those
words.\n",
Review Comment:
I see Healthcare NLP and a reference to Shakespeare's King Lear. May we
consider editing this?
##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,973 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "<a
href=\"https://colab.research.google.com/github/svetakvsundhar/beam/blob/healthcarenlp/examples/notebooks/healthcare/beam_nlp.ipynb\"
target=\"_parent\"><img
src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In
Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF),
Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License"
+ ],
+ "metadata": {
+ "id": "lBuUTzxD2mvJ"
+ },
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Natural Language Processing Pipeline**\n",
+ "\n",
+ "**Note**: This example is used from
[here](https://github.com/rasalt/healthcarenlp/blob/main/nlp_public.ipynb).\n",
+ "\n",
+ "\n",
+ "\n",
+ "This example demonstrates how to set up an Apache Beam pipeline that
reads from a [Google Cloud Storage](https://https://cloud.google.com/storage)
file containing text from Shakespeare's work King Lear, tokenizes the text
lines into individual words, and performs a frequency count on each of those
words.\n",
+ "\n",
+ "An Apache Beam pipeline is a pipeline that reads input data,
transforms that data, and writes output data. It consists of PTransforms and
PCollections. A PCollection represents a distributed data set that your Beam
pipeline operates on. A PTransform represents a data processing operation, or a
step, in your pipeline. It takes one or more PCollections as input, performs a
processing function that you provide on the elements of that PCollection, and
produces zero or more output PCollection objects.\n",
+ "\n",
+ "For details about Apache Beam pipelines, including PTransforms and
PCollections, visit the [Beam Programming
Guide](https://beam.apache.org/documentation/programming-guide/).\n",
+ "\n",
+ "You'll be able to use this notebook to explore the data in each
PCollection."
+ ],
+ "metadata": {
+ "id": "nEUAYCTx4Ijj"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Instructions\n",
+ "1. Set the variables in the next cell based upon your project and
preferences\n",
+ "2. The files referred to in this notebook nlpsample*.csv are in the
format with one\n",
+ "blurb of clinical note."
+ ],
+ "metadata": {
+ "id": "ZLBB0PTG5CHw"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "pip install apache-beam[gcp]"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "O7hq2sse8K4u",
+ "outputId": "c6f559a6-1419-43f2-b88d-49fedca1c686"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: apache-beam[gcp] in
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+ "Requirement already satisfied: anyio<5.0,>=3.0 in
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+ "Requirement already satisfied: exceptiongroup in
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(1.1.2)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#Change this variable to True if you want to debug the Interactive
Runner Pipeline else it uses Dataflow\n",
+ "debug = False\n",
+ "DATASET=\"<YOUR_DATASET_NAME>\"\n",
+ "TEMP_LOCATION=\"<YOUR_TEMP_LOCATION>\"\n",
+ "PROJECT='<YOUR_PROJECT_ID>'\n",
+ "LOCATION='us-central1'\n",
+ "URL='https://healthcare.googleapis.com/v1/projects/'+PROJECT+
'/locations/'+LOCATION+'/services/nlp:analyzeEntities'\n",
+
"NLP_SERVICE='projects/'+PROJECT+'/locations/'+LOCATION+'/services/nlp'\n",
Review Comment:
Could you use python's f-string syntax instead of concatenating using '+'?
##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,973 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "<a
href=\"https://colab.research.google.com/github/svetakvsundhar/beam/blob/healthcarenlp/examples/notebooks/healthcare/beam_nlp.ipynb\"
target=\"_parent\"><img
src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In
Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF),
Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License"
+ ],
+ "metadata": {
+ "id": "lBuUTzxD2mvJ"
+ },
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Natural Language Processing Pipeline**\n",
+ "\n",
+ "**Note**: This example is used from
[here](https://github.com/rasalt/healthcarenlp/blob/main/nlp_public.ipynb).\n",
+ "\n",
+ "\n",
+ "\n",
+ "This example demonstrates how to set up an Apache Beam pipeline that
reads from a [Google Cloud Storage](https://https://cloud.google.com/storage)
file containing text from Shakespeare's work King Lear, tokenizes the text
lines into individual words, and performs a frequency count on each of those
words.\n",
+ "\n",
+ "An Apache Beam pipeline is a pipeline that reads input data,
transforms that data, and writes output data. It consists of PTransforms and
PCollections. A PCollection represents a distributed data set that your Beam
pipeline operates on. A PTransform represents a data processing operation, or a
step, in your pipeline. It takes one or more PCollections as input, performs a
processing function that you provide on the elements of that PCollection, and
produces zero or more output PCollection objects.\n",
+ "\n",
+ "For details about Apache Beam pipelines, including PTransforms and
PCollections, visit the [Beam Programming
Guide](https://beam.apache.org/documentation/programming-guide/).\n",
+ "\n",
+ "You'll be able to use this notebook to explore the data in each
PCollection."
+ ],
+ "metadata": {
+ "id": "nEUAYCTx4Ijj"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Instructions\n",
+ "1. Set the variables in the next cell based upon your project and
preferences\n",
+ "2. The files referred to in this notebook nlpsample*.csv are in the
format with one\n",
+ "blurb of clinical note."
+ ],
+ "metadata": {
+ "id": "ZLBB0PTG5CHw"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "pip install apache-beam[gcp]"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "O7hq2sse8K4u",
+ "outputId": "c6f559a6-1419-43f2-b88d-49fedca1c686"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
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+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "#Change this variable to True if you want to debug the Interactive
Runner Pipeline else it uses Dataflow\n",
+ "debug = False\n",
+ "DATASET=\"<YOUR_DATASET_NAME>\"\n",
+ "TEMP_LOCATION=\"<YOUR_TEMP_LOCATION>\"\n",
+ "PROJECT='<YOUR_PROJECT_ID>'\n",
+ "LOCATION='us-central1'\n",
+ "URL='https://healthcare.googleapis.com/v1/projects/'+PROJECT+
'/locations/'+LOCATION+'/services/nlp:analyzeEntities'\n",
+
"NLP_SERVICE='projects/'+PROJECT+'/locations/'+LOCATION+'/services/nlp'\n",
+ "GCS_BUCKET=PROJECT"
+ ],
+ "metadata": {
+ "id": "s9lhe5CZ5F3o"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**BigQuery Setup**\n",
+ "\n",
+ "We will be using BigQuery to warehouse the structured data revealed
in the output of the Healthcare NLP API. For this purpose I create 3 tables to
organize the data"
Review Comment:
In tutorial speech, might this say "For this purpose we create ..."
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