sdk/jobs/automl-standalone-jobs/automl-nlp-text-classification-multilabel-task-paper-categorization/automl-nlp-text-classification-multilabel-task-paper-cat.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML - Train \"the best\" NLP Multi-Label Text Classification model for a paper abstract dataset.\n",
    "\n",
    "**Requirements** - In order to benefit from this tutorial, you will need:\n",
    "- A basic understanding of Machine Learning\n",
    "- An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F)\n",
    "- An Azure ML workspace. [Check this notebook for creating a workspace](../../../resources/workspace/workspace.ipynb) \n",
    "- A Compute Cluster. [Check this notebook to create a compute cluster](../../../resources/compute/compute.ipynb)\n",
    "- A python environment\n",
    "- Installed Azure Machine Learning Python SDK v2 - [install instructions](../../../README.md) - check the getting started section\n",
    "\n",
    "This notebook demonstrates multilabel classification with text data using AutoML NLP.\n",
    "\n",
    "Scenario: Paper submission systems (such as CMT, OpenReview, etc.) require the users to upload paper titles and paper abstracts and then specify the subject areas their papers best belong to. Creating this model, you can classify/suggest what category a corresponding papers could be best associated with.\n",
    "\n",
    "Configuration and remote run of AutoML for a multilabel text classification dataset from [Kaggle](https://www.kaggle.com), [arXiv Paper Abstracts](https://www.kaggle.com/datasets/spsayakpaul/arxiv-paper-abstracts)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Connect to Azure Machine Learning Workspace\n",
    "\n",
    "The [workspace](https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace) is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. In this section we will connect to the workspace in which the job will be run.\n",
    "\n",
    "## 1.1. Import the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gather": {
     "logged": 1634852261599
    }
   },
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "from azure.identity import DefaultAzureCredential\n",
    "from azure.ai.ml import MLClient\n",
    "\n",
    "from azure.ai.ml.constants import AssetTypes\n",
    "from azure.ai.ml import Input\n",
    "\n",
    "from azure.ai.ml import automl\n",
    "\n",
    "# from azure.ai.ml.automl import text_classification_multilabel\n",
    "\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2. Configure workspace details and get a handle to the workspace\n",
    "\n",
    "To connect to a workspace, we need identifier parameters - a subscription, resource group and workspace name. We will use these details in the `MLClient` from `azure.ai.ml` to get a handle to the required Azure Machine Learning workspace. We use the default [default azure authentication](https://docs.microsoft.com/en-us/python/api/azure-identity/azure.identity.defaultazurecredential?view=azure-python) for this tutorial. Check the [configuration notebook](../../configuration.ipynb) for more details on how to configure credentials and connect to a workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gather": {
     "logged": 1634852261884
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "credential = DefaultAzureCredential()\n",
    "ml_client = None\n",
    "try:\n",
    "    ml_client = MLClient.from_config(credential)\n",
    "except Exception as ex:\n",
    "    print(ex)\n",
    "    # Enter details of your AML workspace\n",
    "    subscription_id = \"<SUBSCRIPTION_ID>\"\n",
    "    resource_group = \"<RESOURCE_GROUP>\"\n",
    "    workspace = \"<AML_WORKSPACE_NAME>\"\n",
    "    ml_client = MLClient(credential, subscription_id, resource_group, workspace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data\n",
    "\n",
    "Since the original dataset is very large, we leverage a subsampled dataset to allow for faster training for the purposes of running this example notebook. To run the full dataset (50K+ samples and 1k+ labels) you might need a GPU instance with larger memory and it may take longer to finish training.\n",
    "\n",
    "To run the code below, you can first download 'arxiv_data.csv' from [this link](https://www.kaggle.com/spsayakpaul/arxiv-paper-abstracts) and save it under the same directory as this notebook, and then run preprocessing.py to create a subset of the data for training and evaluation, then split in training, validation and test dataset files. However, for your convenience, we already provide it prepared within the several mltable folders.\n",
    "\n",
    "**NOTE:** In this PRIVATE PREVIEW we're defining the MLTable in a separate folder and .YAML file.\n",
    "In later versions, you'll be able to do it all in Python APIs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MLTable folders\n",
    "training_mltable_path = \"./training-mltable-folder/\"\n",
    "validation_mltable_path = \"./validation-mltable-folder/\"\n",
    "\n",
    "# Training MLTable defined locally, with local data to be uploaded\n",
    "my_training_data_input = Input(type=AssetTypes.MLTABLE, path=training_mltable_path)\n",
    "\n",
    "# Validation MLTable defined locally, with local data to be uploaded\n",
    "my_validation_data_input = Input(type=AssetTypes.MLTABLE, path=validation_mltable_path)\n",
    "\n",
    "# WITH REMOTE PATH: If available already in the cloud/workspace-blob-store\n",
    "# my_training_data_input = Input(type=AssetTypes.MLTABLE, path=\"azureml://datastores/workspaceblobstore/paths/my_training_mltable\")\n",
    "# my_validation_data_input = Input(type=AssetTypes.MLTABLE, path=\"azureml://datastores/workspaceblobstore/paths/my_validation_mltable\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Configure and run the AutoML NLP Text Classification Multilabel training job\n",
    "In this section we will configure and run the AutoML job, for training the model.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# general job parameters\n",
    "compute_name = \"gpu-cluster\"\n",
    "exp_name = \"dpv2-nlp-text-classification-multilabel-experiment\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gather": {
     "logged": 1634852262026
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "name": "text-classification-multilabel-configuration",
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "# Create the AutoML job with the related factory-function.\n",
    "\n",
    "text_classification_multilabel_job = automl.text_classification_multilabel(\n",
    "    compute=compute_name,\n",
    "    # name=\"dpv2-nlp-text-classification-multilabel-job-02\",\n",
    "    experiment_name=exp_name,\n",
    "    training_data=my_training_data_input,\n",
    "    validation_data=my_validation_data_input,\n",
    "    target_column_name=\"terms\",\n",
    "    primary_metric=\"accuracy\",\n",
    "    tags={\"my_custom_tag\": \"My custom value\"},\n",
    ")\n",
    "\n",
    "text_classification_multilabel_job.set_limits(timeout_minutes=120)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Run the Command\n",
    "Using the `MLClient` created earlier, we will now run this Commandas a job in the workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "gather": {
     "logged": 1634852267930
    },
    "jupyter": {
     "outputs_hidden": false,
     "source_hidden": false
    },
    "nteract": {
     "transient": {
      "deleting": false
     }
    }
   },
   "outputs": [],
   "source": [
    "# Submit the AutoML job\n",
    "\n",
    "returned_job = ml_client.jobs.create_or_update(\n",
    "    text_classification_multilabel_job\n",
    ")  # submit the job to the backend\n",
    "\n",
    "print(f\"Created job: {returned_job}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_client.jobs.stream(returned_job.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Steps\n",
    "You can see further examples of other AutoML tasks such as Regression, Image-Object-Detection, NLP-Text-Classification, Time-Series-Forcasting, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3-azureml"
  },
  "kernelspec": {
   "display_name": "Python 3.10 - SDK V2",
   "language": "python",
   "name": "python310-sdkv2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  },
  "microsoft": {
   "host": {
    "AzureML": {
     "notebookHasBeenCompleted": true
    }
   }
  },
  "nteract": {
   "version": "nteract-front-end@1.0.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}