damccorm commented on code in PR #27516:
URL: https://github.com/apache/beam/pull/27516#discussion_r1269491633
##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,968 @@
+{
+ "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/apache/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": null,
+ "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 a file from [Google Cloud
Storage](https://https://cloud.google.com/storage), and calls the [Google Cloud
Healthcare NLP API](https://cloud.google.com/healthcare-api/docs/how-tos/nlp)
to extract information from unstructured data. This application can be used in
contexts such as reading scanned clinical documents and extracting structure
from it.\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."
Review Comment:
This renders poorly in GitHub, could you just make it one line? Also, this
sentence is just unclear to me; what is the expected user action from
instruction number 2?
##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,968 @@
+{
+ "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/apache/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": null,
+ "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 a file from [Google Cloud
Storage](https://https://cloud.google.com/storage), and calls the [Google Cloud
Healthcare NLP API](https://cloud.google.com/healthcare-api/docs/how-tos/nlp)
to extract information from unstructured data. This application can be used in
contexts such as reading scanned clinical documents and extracting structure
from it.\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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+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Note that below, **us-central1** is hardcoded as the location. This
is because of the limited number of
[locations](https://cloud.google.com/healthcare-api/docs/how-tos/nlp) the API
currently supports."
+ ],
+ "metadata": {
+ "id": "D7lJqW2PRFcN"
+ }
+ },
+ {
+ "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=f'https://healthcare.googleapis.com/v1/projects/{PROJECT}/locations/{LOCATION}/services/nlp:analyzeEntities'\n",
+
"NLP_SERVICE=f'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, we create 3 tables
to organize the data"
+ ],
+ "metadata": {
+ "id": "DI_Qkyn75LO-"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.cloud import bigquery\n",
Review Comment:
This is a pretty big block/hard to read - maybe we could split it into 3
cells (one for each table)?
##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,968 @@
+{
+ "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/apache/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": null,
+ "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 a file from [Google Cloud
Storage](https://https://cloud.google.com/storage), and calls the [Google Cloud
Healthcare NLP API](https://cloud.google.com/healthcare-api/docs/how-tos/nlp)
to extract information from unstructured data. This application can be used in
contexts such as reading scanned clinical documents and extracting structure
from it.\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
/usr/local/lib/python3.10/dist-packages (2.49.0)\n",
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+ "Requirement already satisfied: google-apitools<0.5.32,>=0.5.31 in
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+ "Requirement already satisfied: google-cloud-datastore<3,>=2.0.0
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+ "Requirement already satisfied: google-cloud-bigquery<4,>=2.0.0 in
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+ "Requirement already satisfied: rsa<5,>=3.1.4 in
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in /usr/local/lib/python3.10/dist-packages (from
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+ "Requirement already satisfied: packaging>=14.3 in
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+ "Requirement already satisfied:
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+ "Requirement already satisfied:
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+ "Requirement already satisfied: sqlparse>=0.4.4 in
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+ "Requirement already satisfied:
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+ "Requirement already satisfied: exceptiongroup in
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(1.1.2)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Note that below, **us-central1** is hardcoded as the location. This
is because of the limited number of
[locations](https://cloud.google.com/healthcare-api/docs/how-tos/nlp) the API
currently supports."
+ ],
+ "metadata": {
+ "id": "D7lJqW2PRFcN"
+ }
+ },
+ {
+ "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=f'https://healthcare.googleapis.com/v1/projects/{PROJECT}/locations/{LOCATION}/services/nlp:analyzeEntities'\n",
+
"NLP_SERVICE=f'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, we create 3 tables
to organize the data"
Review Comment:
This would benefit from a little more explanation of the tables/their
purposes
##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,968 @@
+{
+ "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/apache/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": null,
+ "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 a file from [Google Cloud
Storage](https://https://cloud.google.com/storage), and calls the [Google Cloud
Healthcare NLP API](https://cloud.google.com/healthcare-api/docs/how-tos/nlp)
to extract information from unstructured data. This application can be used in
contexts such as reading scanned clinical documents and extracting structure
from it.\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": "markdown",
+ "source": [
+ "Note that below, **us-central1** is hardcoded as the location. This
is because of the limited number of
[locations](https://cloud.google.com/healthcare-api/docs/how-tos/nlp) the API
currently supports."
+ ],
+ "metadata": {
+ "id": "D7lJqW2PRFcN"
+ }
+ },
+ {
+ "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=f'https://healthcare.googleapis.com/v1/projects/{PROJECT}/locations/{LOCATION}/services/nlp:analyzeEntities'\n",
+
"NLP_SERVICE=f'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, we create 3 tables
to organize the data"
+ ],
+ "metadata": {
+ "id": "DI_Qkyn75LO-"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.cloud import bigquery\n",
+ "\n",
+ "# Construct a BigQuery client object.\n",
+ "\n",
+ "TABLE_ENTITY=\"entity\"\n",
+ "TABLE_REL=\"relations\"\n",
+ "TABLE_ENTITYMENTIONS=\"entitymentions\"\n",
+ "\n",
+ "schemaEntity = [\n",
+ " bigquery.SchemaField(\"entityId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"preferredTerm\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"vocabularyCodes\", \"STRING\",
mode=\"REPEATED\"),\n",
+ "]\n",
+ "\n",
+ "schemaRelations = [\n",
+ " bigquery.SchemaField(\"subjectId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"objectId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"id\", \"STRING\", mode=\"NULLABLE\"),\n",
+ "]\n",
+ "\n",
+ "schemaEntityMentions = [\n",
+ " bigquery.SchemaField(\"mentionId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"type\", \"STRING\", mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\n",
+ " \"text\",\n",
+ " \"RECORD\",\n",
+ " mode=\"NULLABLE\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"content\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"beginOffset\", \"INTEGER\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\n",
+ " \"linkedEntities\",\n",
+ " \"RECORD\",\n",
+ " mode=\"REPEATED\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"entityId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\n",
+ " \"temporalAssessment\",\n",
+ " \"RECORD\",\n",
+ " mode=\"NULLABLE\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"value\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\n",
+ " \"certaintyAssessment\",\n",
+ " \"RECORD\",\n",
+ " mode=\"NULLABLE\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"value\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\n",
+ " \"subject\",\n",
+ " \"RECORD\",\n",
+ " mode=\"NULLABLE\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"value\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"id\", \"STRING\", mode=\"NULLABLE\")\n",
+ "]\n",
+ "\n",
+ "client = bigquery.Client()\n",
+ "\n",
+ "# Create Table IDs\n",
+ "table_ent = PROJECT+\".\"+DATASET+\".\"+TABLE_ENTITY\n",
+ "table_rel = PROJECT+\".\"+DATASET+\".\"+TABLE_REL\n",
+ "table_mentions = PROJECT+\".\"+DATASET+\".\"+TABLE_ENTITYMENTIONS\n",
+ "\n",
+ "# If table exists, delete the tables.\n",
+ "client.delete_table(table_ent, not_found_ok=True)\n",
+ "client.delete_table(table_rel, not_found_ok=True)\n",
+ "client.delete_table(table_mentions, not_found_ok=True)\n",
+ "\n",
+ "# Create tables\n",
+ "\n",
+ "table = bigquery.Table(table_ent, schema=schemaEntity)\n",
+ "table = client.create_table(table) # Make an API request.\n",
+ "\n",
+ "print(\n",
+ " \"Created table {}.{}.{}\".format(table.project,
table.dataset_id, table.table_id)\n",
+ ")\n",
+ "\n",
+ "table = bigquery.Table(table_rel, schema=schemaRelations)\n",
+ "table = client.create_table(table) # Make an API request.\n",
+ "print(\n",
+ " \"Created table {}.{}.{}\".format(table.project,
table.dataset_id, table.table_id)\n",
+ ")\n",
+ "table = bigquery.Table(table_mentions,
schema=schemaEntityMentions)\n",
+ "table = client.create_table(table) # Make an API request.\n",
+ "print(\n",
+ " \"Created table {}.{}.{}\".format(table.project,
table.dataset_id, table.table_id)\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "bZDqtFVE5Wd_"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Pipeline Setup**\n",
+ "\n",
+ "For the purpose of experimenting, I have setup an interactiveRunner.
But this should be changed to a DatafowRunner once you are comfortable with it"
+ ],
+ "metadata": {
+ "id": "jc_iS_BP5aS4"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Python's regular expression library\n",
+ "import re\n",
+ "from sys import argv\n",
+ "# Beam and interactive Beam imports\n",
+ "import apache_beam as beam\n",
+ "from apache_beam.runners.interactive.interactive_runner import
InteractiveRunner\n",
+ "import apache_beam.runners.interactive.interactive_beam as ib\n",
+ "\n",
+ "#Reference
https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_1\n";,
+ "from apache_beam.options.pipeline_options import PipelineOptions\n",
+ "if debug:\n",
+ " runnertype = \"InteractiveRunner\"\n",
+ "else:\n",
+ " runnertype = \"DataflowRunner\"\n",
+ "\n",
+ "options = PipelineOptions(\n",
+ " flags=argv,\n",
+ " runner=runnertype,\n",
+ " project=PROJECT,\n",
+ " job_name=\"my-healthcare-nlp-job\",\n",
+ " temp_location=TEMP_LOCATION,\n",
+ " region=LOCATION)"
+ ],
+ "metadata": {
+ "id": "07ct6kf55ihP"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The following defines a `PTransform` named `ReadLinesFromText`, that
extracts lines from a file."
+ ],
+ "metadata": {
+ "id": "dO1A9_WK5lb4"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "class ReadLinesFromText(beam.PTransform):\n",
+ "\n",
+ " def __init__(self, file_pattern):\n",
+ " self._file_pattern = file_pattern\n",
+ "\n",
+ " def expand(self, pcoll):\n",
+ " return (pcoll.pipeline\n",
+ " | beam.io.ReadFromText(self._file_pattern))"
+ ],
+ "metadata": {
+ "id": "t5iDRKMK5n_B"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The following sets up an Apache Beam pipeline with the *Interactive
Runner*. The *Interactive Runner* is the runner suitable for running in
notebooks. A runner is an execution engine for Apache Beam pipelines."
+ ],
+ "metadata": {
+ "id": "HI_HVB185sMQ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.auth import default\n",
+ "\n",
+ "credentials = default()"
+ ],
+ "metadata": {
+ "id": "dMc10Dlgtp1c"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "p = beam.Pipeline(options = options)"
+ ],
+ "metadata": {
+ "id": "7osCZ1om5ql0"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The following sets up a PTransform that extracts words from a Google
Cloud Storage file that contains lines with each line containing a In our
example, each line is a medical notes excerpt that will be passed through the
Healthcare NLP API\n",
+ "\n",
+ "| is an overloaded operator that applies a PTransform to a
PCollection to produce a new PCollection. Together with |, >> allows you to
optionally name a PTransform.\n",
+ "\n",
+ "Usage: [PCollection] | [PTransform] or [PCollection] | [name] >>
[PTransform]"
+ ],
+ "metadata": {
+ "id": "EaF8NfC_521y"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "lines = p | 'read' >> ReadLinesFromText(GCS_BUCKET +
\"nlpsample500.csv\")"
+ ],
+ "metadata": {
+ "id": "2APAh6XQ6NYd"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "if debug:\n",
+ " ib.show(lines)"
+ ],
+ "metadata": {
+ "id": "NH_04EXw6P8F"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "if debug:\n",
Review Comment:
There's a lot of consecutive code blocks here with not much explanation as
to what they're doing, I think dropping some text snippets in here would help.
##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,968 @@
+{
+ "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/apache/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": null,
+ "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 a file from [Google Cloud
Storage](https://https://cloud.google.com/storage), and calls the [Google Cloud
Healthcare NLP API](https://cloud.google.com/healthcare-api/docs/how-tos/nlp)
to extract information from unstructured data. This application can be used in
contexts such as reading scanned clinical documents and extracting structure
from it.\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": "markdown",
+ "source": [
+ "Note that below, **us-central1** is hardcoded as the location. This
is because of the limited number of
[locations](https://cloud.google.com/healthcare-api/docs/how-tos/nlp) the API
currently supports."
+ ],
+ "metadata": {
+ "id": "D7lJqW2PRFcN"
+ }
+ },
+ {
+ "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=f'https://healthcare.googleapis.com/v1/projects/{PROJECT}/locations/{LOCATION}/services/nlp:analyzeEntities'\n",
+
"NLP_SERVICE=f'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, we create 3 tables
to organize the data"
+ ],
+ "metadata": {
+ "id": "DI_Qkyn75LO-"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.cloud import bigquery\n",
+ "\n",
+ "# Construct a BigQuery client object.\n",
+ "\n",
+ "TABLE_ENTITY=\"entity\"\n",
+ "TABLE_REL=\"relations\"\n",
+ "TABLE_ENTITYMENTIONS=\"entitymentions\"\n",
+ "\n",
+ "schemaEntity = [\n",
+ " bigquery.SchemaField(\"entityId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"preferredTerm\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"vocabularyCodes\", \"STRING\",
mode=\"REPEATED\"),\n",
+ "]\n",
+ "\n",
+ "schemaRelations = [\n",
+ " bigquery.SchemaField(\"subjectId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"objectId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"id\", \"STRING\", mode=\"NULLABLE\"),\n",
+ "]\n",
+ "\n",
+ "schemaEntityMentions = [\n",
+ " bigquery.SchemaField(\"mentionId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"type\", \"STRING\", mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\n",
+ " \"text\",\n",
+ " \"RECORD\",\n",
+ " mode=\"NULLABLE\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"content\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"beginOffset\", \"INTEGER\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\n",
+ " \"linkedEntities\",\n",
+ " \"RECORD\",\n",
+ " mode=\"REPEATED\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"entityId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\n",
+ " \"temporalAssessment\",\n",
+ " \"RECORD\",\n",
+ " mode=\"NULLABLE\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"value\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\n",
+ " \"certaintyAssessment\",\n",
+ " \"RECORD\",\n",
+ " mode=\"NULLABLE\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"value\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\n",
+ " \"subject\",\n",
+ " \"RECORD\",\n",
+ " mode=\"NULLABLE\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"value\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"id\", \"STRING\", mode=\"NULLABLE\")\n",
+ "]\n",
+ "\n",
+ "client = bigquery.Client()\n",
+ "\n",
+ "# Create Table IDs\n",
+ "table_ent = PROJECT+\".\"+DATASET+\".\"+TABLE_ENTITY\n",
+ "table_rel = PROJECT+\".\"+DATASET+\".\"+TABLE_REL\n",
+ "table_mentions = PROJECT+\".\"+DATASET+\".\"+TABLE_ENTITYMENTIONS\n",
+ "\n",
+ "# If table exists, delete the tables.\n",
+ "client.delete_table(table_ent, not_found_ok=True)\n",
+ "client.delete_table(table_rel, not_found_ok=True)\n",
+ "client.delete_table(table_mentions, not_found_ok=True)\n",
+ "\n",
+ "# Create tables\n",
+ "\n",
+ "table = bigquery.Table(table_ent, schema=schemaEntity)\n",
+ "table = client.create_table(table) # Make an API request.\n",
+ "\n",
+ "print(\n",
+ " \"Created table {}.{}.{}\".format(table.project,
table.dataset_id, table.table_id)\n",
+ ")\n",
+ "\n",
+ "table = bigquery.Table(table_rel, schema=schemaRelations)\n",
+ "table = client.create_table(table) # Make an API request.\n",
+ "print(\n",
+ " \"Created table {}.{}.{}\".format(table.project,
table.dataset_id, table.table_id)\n",
+ ")\n",
+ "table = bigquery.Table(table_mentions,
schema=schemaEntityMentions)\n",
+ "table = client.create_table(table) # Make an API request.\n",
+ "print(\n",
+ " \"Created table {}.{}.{}\".format(table.project,
table.dataset_id, table.table_id)\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "bZDqtFVE5Wd_"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Pipeline Setup**\n",
+ "\n",
+ "For the purpose of experimenting, I have setup an interactiveRunner.
But this should be changed to a DatafowRunner once you are comfortable with it"
+ ],
+ "metadata": {
+ "id": "jc_iS_BP5aS4"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Python's regular expression library\n",
+ "import re\n",
+ "from sys import argv\n",
+ "# Beam and interactive Beam imports\n",
+ "import apache_beam as beam\n",
+ "from apache_beam.runners.interactive.interactive_runner import
InteractiveRunner\n",
+ "import apache_beam.runners.interactive.interactive_beam as ib\n",
+ "\n",
+ "#Reference
https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_1\n";,
+ "from apache_beam.options.pipeline_options import PipelineOptions\n",
+ "if debug:\n",
+ " runnertype = \"InteractiveRunner\"\n",
+ "else:\n",
+ " runnertype = \"DataflowRunner\"\n",
+ "\n",
+ "options = PipelineOptions(\n",
+ " flags=argv,\n",
+ " runner=runnertype,\n",
+ " project=PROJECT,\n",
+ " job_name=\"my-healthcare-nlp-job\",\n",
+ " temp_location=TEMP_LOCATION,\n",
+ " region=LOCATION)"
+ ],
+ "metadata": {
+ "id": "07ct6kf55ihP"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The following defines a `PTransform` named `ReadLinesFromText`, that
extracts lines from a file."
+ ],
+ "metadata": {
+ "id": "dO1A9_WK5lb4"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "class ReadLinesFromText(beam.PTransform):\n",
+ "\n",
+ " def __init__(self, file_pattern):\n",
+ " self._file_pattern = file_pattern\n",
+ "\n",
+ " def expand(self, pcoll):\n",
+ " return (pcoll.pipeline\n",
+ " | beam.io.ReadFromText(self._file_pattern))"
+ ],
+ "metadata": {
+ "id": "t5iDRKMK5n_B"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The following sets up an Apache Beam pipeline with the *Interactive
Runner*. The *Interactive Runner* is the runner suitable for running in
notebooks. A runner is an execution engine for Apache Beam pipelines."
+ ],
+ "metadata": {
+ "id": "HI_HVB185sMQ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.auth import default\n",
+ "\n",
+ "credentials = default()"
+ ],
+ "metadata": {
+ "id": "dMc10Dlgtp1c"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "p = beam.Pipeline(options = options)"
+ ],
+ "metadata": {
+ "id": "7osCZ1om5ql0"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The following sets up a PTransform that extracts words from a Google
Cloud Storage file that contains lines with each line containing a In our
example, each line is a medical notes excerpt that will be passed through the
Healthcare NLP API\n",
+ "\n",
+ "| is an overloaded operator that applies a PTransform to a
PCollection to produce a new PCollection. Together with |, >> allows you to
optionally name a PTransform.\n",
+ "\n",
+ "Usage: [PCollection] | [PTransform] or [PCollection] | [name] >>
[PTransform]"
Review Comment:
Could we separate these examples onto 2 different lines (and maybe bold
"or")? This was confusing on my first readthrough because I thought you were
saying
`[PCollection] | (either [PTransform] or [PCollection]) | [name] >>
[PTransform]"`
and that didn't make sense to me
##########
examples/notebooks/healthcare/beam_nlp.ipynb:
##########
@@ -0,0 +1,968 @@
+{
+ "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/apache/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": null,
+ "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 a file from [Google Cloud
Storage](https://https://cloud.google.com/storage), and calls the [Google Cloud
Healthcare NLP API](https://cloud.google.com/healthcare-api/docs/how-tos/nlp)
to extract information from unstructured data. This application can be used in
contexts such as reading scanned clinical documents and extracting structure
from it.\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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requests<3.0.0,>=2.24.0->apache-beam[gcp]) (2023.5.7)\n",
+ "Requirement already satisfied: charset-normalizer~=2.0.0 in
/usr/local/lib/python3.10/dist-packages (from
requests<3.0.0,>=2.24.0->apache-beam[gcp]) (2.0.12)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in
/usr/local/lib/python3.10/dist-packages (from
requests<3.0.0,>=2.24.0->apache-beam[gcp]) (3.4)\n",
+ "Requirement already satisfied: httpcore>=0.17.3 in
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dnspython<3.0.0,>=1.16.0->pymongo<5.0.0,>=3.8.0->apache-beam[gcp]) (0.17.3)\n",
+ "Requirement already satisfied: sniffio<2.0,>=1.1 in
/usr/local/lib/python3.10/dist-packages (from
dnspython<3.0.0,>=1.16.0->pymongo<5.0.0,>=3.8.0->apache-beam[gcp]) (1.3.0)\n",
+ "Requirement already satisfied:
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+ "Requirement already satisfied: google-crc32c<2.0dev,>=1.0 in
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+ "Requirement already satisfied: pyasn1>=0.1.7 in
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+ "Requirement already satisfied: h11<0.15,>=0.13 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": "markdown",
+ "source": [
+ "Note that below, **us-central1** is hardcoded as the location. This
is because of the limited number of
[locations](https://cloud.google.com/healthcare-api/docs/how-tos/nlp) the API
currently supports."
+ ],
+ "metadata": {
+ "id": "D7lJqW2PRFcN"
+ }
+ },
+ {
+ "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=f'https://healthcare.googleapis.com/v1/projects/{PROJECT}/locations/{LOCATION}/services/nlp:analyzeEntities'\n",
+
"NLP_SERVICE=f'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, we create 3 tables
to organize the data"
+ ],
+ "metadata": {
+ "id": "DI_Qkyn75LO-"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.cloud import bigquery\n",
+ "\n",
+ "# Construct a BigQuery client object.\n",
+ "\n",
+ "TABLE_ENTITY=\"entity\"\n",
+ "TABLE_REL=\"relations\"\n",
+ "TABLE_ENTITYMENTIONS=\"entitymentions\"\n",
+ "\n",
+ "schemaEntity = [\n",
+ " bigquery.SchemaField(\"entityId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"preferredTerm\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"vocabularyCodes\", \"STRING\",
mode=\"REPEATED\"),\n",
+ "]\n",
+ "\n",
+ "schemaRelations = [\n",
+ " bigquery.SchemaField(\"subjectId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"objectId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"id\", \"STRING\", mode=\"NULLABLE\"),\n",
+ "]\n",
+ "\n",
+ "schemaEntityMentions = [\n",
+ " bigquery.SchemaField(\"mentionId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"type\", \"STRING\", mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\n",
+ " \"text\",\n",
+ " \"RECORD\",\n",
+ " mode=\"NULLABLE\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"content\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"beginOffset\", \"INTEGER\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\n",
+ " \"linkedEntities\",\n",
+ " \"RECORD\",\n",
+ " mode=\"REPEATED\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"entityId\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\n",
+ " \"temporalAssessment\",\n",
+ " \"RECORD\",\n",
+ " mode=\"NULLABLE\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"value\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\n",
+ " \"certaintyAssessment\",\n",
+ " \"RECORD\",\n",
+ " mode=\"NULLABLE\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"value\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\n",
+ " \"subject\",\n",
+ " \"RECORD\",\n",
+ " mode=\"NULLABLE\",\n",
+ " fields=[\n",
+ " bigquery.SchemaField(\"value\", \"STRING\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " ],\n",
+ " ),\n",
+ " bigquery.SchemaField(\"confidence\", \"FLOAT64\",
mode=\"NULLABLE\"),\n",
+ " bigquery.SchemaField(\"id\", \"STRING\", mode=\"NULLABLE\")\n",
+ "]\n",
+ "\n",
+ "client = bigquery.Client()\n",
+ "\n",
+ "# Create Table IDs\n",
+ "table_ent = PROJECT+\".\"+DATASET+\".\"+TABLE_ENTITY\n",
+ "table_rel = PROJECT+\".\"+DATASET+\".\"+TABLE_REL\n",
+ "table_mentions = PROJECT+\".\"+DATASET+\".\"+TABLE_ENTITYMENTIONS\n",
+ "\n",
+ "# If table exists, delete the tables.\n",
+ "client.delete_table(table_ent, not_found_ok=True)\n",
+ "client.delete_table(table_rel, not_found_ok=True)\n",
+ "client.delete_table(table_mentions, not_found_ok=True)\n",
+ "\n",
+ "# Create tables\n",
+ "\n",
+ "table = bigquery.Table(table_ent, schema=schemaEntity)\n",
+ "table = client.create_table(table) # Make an API request.\n",
+ "\n",
+ "print(\n",
+ " \"Created table {}.{}.{}\".format(table.project,
table.dataset_id, table.table_id)\n",
+ ")\n",
+ "\n",
+ "table = bigquery.Table(table_rel, schema=schemaRelations)\n",
+ "table = client.create_table(table) # Make an API request.\n",
+ "print(\n",
+ " \"Created table {}.{}.{}\".format(table.project,
table.dataset_id, table.table_id)\n",
+ ")\n",
+ "table = bigquery.Table(table_mentions,
schema=schemaEntityMentions)\n",
+ "table = client.create_table(table) # Make an API request.\n",
+ "print(\n",
+ " \"Created table {}.{}.{}\".format(table.project,
table.dataset_id, table.table_id)\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "bZDqtFVE5Wd_"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Pipeline Setup**\n",
+ "\n",
+ "For the purpose of experimenting, I have setup an interactiveRunner.
But this should be changed to a DatafowRunner once you are comfortable with it"
Review Comment:
Why recommend Dataflow here? For notebooks the interactive runner seems like
the best choice.
also "DatafowRunner" -> "DataflowRunner"
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