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Posted to github@beam.apache.org by "svetakvsundhar (via GitHub)" <gi...@apache.org> on 2023/07/20 16:02:30 UTC
[GitHub] [beam] svetakvsundhar commented on a diff in pull request #27516: Healthcare NLP Colab Example
svetakvsundhar commented on code in PR #27516:
URL: https://github.com/apache/beam/pull/27516#discussion_r1269676516
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examples/notebooks/healthcare/beam_nlp.ipynb:
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+{
+ "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"
Review Comment:
I agree, but I think if we want to standardize this we should write some documentation on notebooks guidelines. I can take this as a follow up AI.
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