Doc Quality Transform: update readme and add sample notebook #790
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "afd55886-5f5b-4794-838e-ef8179fb0394", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "##### **** These pip installs need to be adapted to use the appropriate release level. Alternatively, The venv running the jupyter lab could be pre-configured with a requirement file that includes the right release. Example for transform developers working from git clone:\n", | ||
| "```\n", | ||
| "make venv \n", | ||
| "source venv/bin/activate \n", | ||
| "pip install jupyterlab\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "id": "4c45c3c6-e4d7-4e61-8de6-32d61f2ce695", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%%capture\n", | ||
| "## This is here as a reference only\n", | ||
| "# Users and application developers must use the right tag for the latest from pypi\n", | ||
| "%pip install data-prep-toolkit\n", | ||
| "%pip install data-prep-toolkit-transforms\n", | ||
| "%pip install data-prep-connector\n", | ||
| "%pip install dpk-doc-quality-transform-python" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "407fd4e4-265d-4ec7-bbc9-b43158f5f1f3", | ||
| "metadata": { | ||
| "jp-MarkdownHeadingCollapsed": true | ||
| }, | ||
| "source": [ | ||
| "##### **** Configure the transform parameters. The set of dictionary keys holding DocQualityTransform configuration for values are as follows: \n", | ||
| "* text_lang - specifies language used in the text content. By default, \"en\" is used.\n", | ||
| "* doc_content_column - specifies column name that contains document text. By default, \"contents\" is used.\n", | ||
| "* bad_word_filepath - specifies a path to bad word file: local folder (file or directory) that points to bad word file. You don't have to set this parameter if you don't need to set bad words.\n", | ||
| "#####" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "ebf1f782-0e61-485c-8670-81066beb734c", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "##### ***** Import required classes and modules" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "id": "c2a12abc-9460-4e45-8961-873b48a9ab19", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "import sys\n", | ||
| "\n", | ||
| "from data_processing.runtime.pure_python import PythonTransformLauncher\n", | ||
| "from data_processing.utils import ParamsUtils\n", | ||
| "from doc_quality_transform import (bad_word_filepath_cli_param, doc_content_column_cli_param, text_lang_cli_param,)\n", | ||
| "from doc_quality_transform_python import DocQualityPythonTransformConfiguration" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "7234563c-2924-4150-8a31-4aec98c1bf33", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "##### ***** Setup runtime parameters for this transform" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 9, | ||
| "id": "e90a853e-412f-45d7-af3d-959e755aeebb", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "\n", | ||
| "# create parameters\n", | ||
| "input_folder = os.path.join(\"python\", \"test-data\", \"input\")\n", | ||
| "output_folder = os.path.join( \"python\", \"output\")\n", | ||
| "local_conf = {\n", | ||
| " \"input_folder\": input_folder,\n", | ||
| " \"output_folder\": output_folder,\n", | ||
| "}\n", | ||
| "code_location = {\"github\": \"github\", \"commit_hash\": \"12345\", \"path\": \"path\"}\n", | ||
| "params = {\n", | ||
| " # Data access. Only required parameters are specified\n", | ||
| " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", | ||
| " # execution info\n", | ||
| " \"runtime_pipeline_id\": \"pipeline_id\",\n", | ||
| " \"runtime_job_id\": \"job_id\",\n", | ||
| " \"runtime_code_location\": ParamsUtils.convert_to_ast(code_location),\n", | ||
| " # doc_quality params\n", | ||
| " text_lang_cli_param: \"en\",\n", | ||
| " doc_content_column_cli_param: \"contents\",\n", | ||
| " bad_word_filepath_cli_param: os.path.join(\"python\", \"ldnoobw\", \"en\"),\n", | ||
| "}" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "7949f66a-d207-45ef-9ad7-ad9406f8d42a", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "##### ***** Use python runtime to invoke the transform" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 10, | ||
| "id": "0775e400-7469-49a6-8998-bd4772931459", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stderr", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "10:38:40 INFO - doc_quality parameters are : {'text_lang': 'en', 'doc_content_column': 'contents', 'bad_word_filepath': 'python/ldnoobw/en', 's3_cred': None, 'docq_data_factory': <data_processing.data_access.data_access_factory.DataAccessFactory object at 0x11206e010>}\n", | ||
| "10:38:40 INFO - pipeline id pipeline_id\n", | ||
| "10:38:40 INFO - code location {'github': 'github', 'commit_hash': '12345', 'path': 'path'}\n", | ||
| "10:38:40 INFO - data factory data_ is using local data access: input_folder - python/test-data/input output_folder - python/output\n", | ||
| "10:38:40 INFO - data factory data_ max_files -1, n_sample -1\n", | ||
| "10:38:40 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", | ||
| "10:38:40 INFO - orchestrator docq started at 2024-11-22 10:38:40\n", | ||
| "10:38:40 INFO - Number of files is 1, source profile {'max_file_size': 0.0009870529174804688, 'min_file_size': 0.0009870529174804688, 'total_file_size': 0.0009870529174804688}\n", | ||
| "10:38:40 INFO - Load badwords found locally from python/ldnoobw/en\n", | ||
| "10:38:49 INFO - Completed 1 files (100.0%) in 0.146 min\n", | ||
| "10:38:49 INFO - Done processing 1 files, waiting for flush() completion.\n", | ||
| "10:38:49 INFO - done flushing in 0.0 sec\n", | ||
| "10:38:49 INFO - Completed execution in 0.146 min, execution result 0\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "%%capture\n", | ||
| "sys.argv = ParamsUtils.dict_to_req(d=params)\n", | ||
| "launcher = PythonTransformLauncher(runtime_config=DocQualityPythonTransformConfiguration())\n", | ||
| "launcher.launch()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "c3df5adf-4717-4a03-864d-9151cd3f134b", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "##### **** The specified folder will include the transformed parquet files." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 11, | ||
| "id": "7276fe84-6512-4605-ab65-747351e13a7c", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "['python/output/metadata.json', 'python/output/test1.parquet']" | ||
| ] | ||
| }, | ||
| "execution_count": 11, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "import glob\n", | ||
| "glob.glob(\"python/output/*\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "845a75cf-f4a9-467d-87fa-ccbac1c9beb8", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": ".venv", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.0" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
|
There was a problem hiding this comment. @shahrokhDaijavad @dtsuzuku-ibm This shows how to run the example script once we have cloned the repo. I wonder if we should also add a section to it that would explain how to use it in a notebook or a python script without cloning the repo but only with pip install . i.e.: !pip install data-prep-toolkit from doc_quality_transform python import ... params = { laucher.launch() There was a problem hiding this comment.
We can do all of these in jupyter/collab notebook which will be added in the future. There was a problem hiding this comment. @dtsuzuku-ibm . that is great. Once you have the notebook, you can reference it. For now, I don't think this is complete until we have one or the other or both. |
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,13 +1,25 @@ | ||
| # Document Quality Transform | ||
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| Please see the set of | ||
| [transform project conventions](../../../README.md#transform-project-conventions) | ||
| for details on general project conventions, transform configuration, | ||
| testing and IDE set up. | ||
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| ## Contributors | ||
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| - Daiki Tsuzuku ([email protected]) | ||
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| ## Description | ||
| This transform will calculate and annotate several metrics which are useful to assess the quality of the document. | ||
| The document quality transform operates on text documents only | ||
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| ### Input | ||
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| | input column name | data type | description | | ||
| |-|-|-| | ||
| | the one specified in _doc_content_column_ configuration | string | text whose quality will be calculated by this transform | | ||
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| ### Output columns annotated by this transform | ||
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| | output column name | data type | description | supported language | | ||
| |-|-|-|-| | ||
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@@ -27,7 +39,7 @@ In this transform, following metrics will be included: | |
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| You can see more detailed backgrounds of some columns in [Deepmind's Gopher paper](https://arxiv.org/pdf/2112.11446.pdf) | ||
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| ## Configuration | ||
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| The set of dictionary keys holding [DocQualityTransform](src/doc_quality_transform.py) | ||
| configuration for values are as follows: | ||
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@@ -36,13 +48,19 @@ configuration for values are as follows: | |
| * _doc_content_column_ - specifies column name that contains document text. By default, "contents" is used. | ||
| * _bad_word_filepath_ - specifies a path to bad word file: local folder (file or directory) that points to bad word file. You don't have to set this parameter if you don't need to set bad words. | ||
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| Example | ||
| ``` | ||
| { | ||
| text_lang_key: "en", | ||
| doc_content_column_key: "contents", | ||
| bad_word_filepath_key: os.path.join(basedir, "ldnoobw", "en"), | ||
| } | ||
| ``` | ||
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| ## Usage | ||
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| ### Launched Command Line Options | ||
| The following command line arguments are available | ||
| ``` | ||
| --docq_text_lang DOCQ_TEXT_LANG language used in the text content. By default, "en" is used. | ||
| --docq_doc_content_column DOCQ_DOC_CONTENT_COLUMN column name that contain document text. By default, "contents" is used. | ||
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@@ -70,14 +88,37 @@ ls output | |
| ``` | ||
| To see results of the transform. | ||
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| ### Code example | ||
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| [notebook](../doc_quality.ipynb) | ||
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| ### Transforming data using the transform image | ||
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| To use the transform image to transform your data, please refer to the | ||
| [running images quickstart](../../../../doc/quick-start/run-transform-image.md), | ||
| substituting the name of this transform image and runtime as appropriate. | ||
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| ## Testing | ||
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| Following [the testing strategy of data-processing-lib](../../../../data-processing-lib/doc/transform-testing.md) | ||
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| Currently we have: | ||
| - [Unit test](test/test_doc_quality_python.py) | ||
| - [Integration test](test/test_doc_quality.py) | ||
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| ## Further Resource | ||
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| - For those who want to learn C4 heuristic rules | ||
| - https://arxiv.org/pdf/1910.10683.pdf | ||
| - For those who want to learn Gopher statistics | ||
| - https://arxiv.org/pdf/2112.11446.pdf | ||
| - For those who want to see the source of badwords used by default | ||
| - https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words | ||
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| ## Consideration | ||
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| ### Troubleshooting guide | ||
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| For M1 Mac user, if you see following error during make command, `error: command '/usr/bin/clang' failed with exit code 1`, you may better follow [this step](https://freeman.vc/notes/installing-fasttext-on-an-m1-mac) | ||
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Please remove references to
pip install dpk-doc-quality-transform-python(as we no longer publish the individual transforms) and topip install data-prep-connector(as this notebook does not seem to have a dependency on the web crawler).There was a problem hiding this comment.
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@touma-I
Without
dpk-doc-quality-transform-python, we cannot import doc_quality_transform module. Could you tell me what kind of dependency we need to install instead?Got it 👍
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@dtsuzuku-ibm I think if you do: pip install ‘data-prep-toolkit-transforms[all]>=0.2.2.dev3', it has doc_quality_transform included. @touma-I can confirm this. Please see the comment at the top of the notebook that says: "These pip installs need to be adapted to use the appropriate release level."
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Thank you, @dtsuzuku-ibm.