How to Transform Research Oriented Code into Machine Learning APIs with Python Tetsuya (Jesse) Hirata @JesseTetsuya ———————————————————————————————————————————————————————————————————————————————— Software Engineer at Classi which is an EdTech company. I mostly work in both data science and engineering.

Background and Purpose - Recently, Python Engineers have more opportunities to work with data scientists and researchers than before. - Understanding the processes to develop ML APIs can help make AI/ML projects work more smoothly

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Definition Research oriented code in AI/ML projects is the code written mainly by data scientists or researchers for figuring out the most efficient and suitable machine learning model.

1.Preparation code for accessing data 2.Pre-processing code 3.Machine learning (ML) code Production code (Engineers) Research oriented code (Data Scientists/Researchers) Machine Learning APIs are composed of three elements Research oriented code is developed through an iterative process and integrated into production code.

Data Pre-Processing code Visually trace the code from the top to the bottom Easily and quickly write it

ML code (a part of whole code) Easily handle input data and trace output data with data frame

Refactor both code in Pythonic way This code builds the model in a much faster and simpler way

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What are Python Engineers supposed to do for Research Oriented Code?

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This is a page of research oriented code written with jupyter notebook. This code is procedural and some of them are not classified. The research oriented code seems to be tightly coupled. 2.1. Categorize research oriented code into preparation code, preprocessing code, ML code

Find the code to load input data or access database → preparation code Find the code to make, replace, filter, or delete input data → preprocessing code Find the code to execute calculation or train data → ML code 2.1. Categorize research oriented code into preparation code, preprocessing code, ML code

Module name Functions Preparation code preparation.py - Access big query, execute query, and load input data - Rename columns Preprocessing code preprocessing.py - Replace categorical data with discrete numbers - Filter input data ML code prediction.py - Calculate icc parameters, logistic regression, and item response theory (IRT) The research oriented code became loosely coupled 2.2. Break them out into functions and make them testable

app.py @app.route("/v1/probabilities", methods=['GET']) def probabilities(): return calc_results(), 200 return get_probs(), 200 ← noun ← the same endpoint name ← verb (+ noun) INPUT OUTPUT *item means a question INPUT: results of student answers OUTPUT: probabilities to answer questions correctly 2.3. Clarify input and output of the whole code and define URI

3FTFBSDI 0SJFOUFE $PEF .-"1*T Steps to transform Research Oriented Code into ML APIs Refactor 1. Prepare for refactoring 2. Simplify I/O in preparation code 3. Pandas → Python in preprocessing code

. ᵓᴷᴷ ml_api ᴹ ᵓᴷᴷ api ᴹ ᴹ ᵓᴷᴷ app.py ᴹ ᴹ ᵓᴷᴷ config ᴹ ᴹ ᵓᴷᴷ prediction.py ᴹ ᴹ ᵓᴷᴷ preparation.py ᴹ ᴹ ᵓᴷᴷ preprocessing.py ᴹ ᵓᴷᴷ requirements.txt ᴹ ᵓᴷᴷ run.py ᴹ ᵋᴷᴷ tests ᴹ ᵓᴷᴷ test_app.py ᴹ ᵓᴷᴷ test_prediction.py ᴹ ᵓᴷᴷ test_preparation.py ᴹ ᵋᴷᴷ test_preprocessing.py ᵋᴷᴷ setup.py 3.1 Prepare for refactoring Narrow down requirements of each code by writing test code and take notes about requirements on the comments for refactoring (or you can tell data scientist to write comments in advance) def func(arg1, arg2): """Summary line. Extended description of function. Args: arg1 (int): Description of arg1 arg2 (str): Description of arg2 Returns: bool: Description of return value """ return True ex) Google Style #comments out or doc strings (reStructuredText style /Numpy style/Google Style)

CASE STUDY: Refactoring the code to access BigQuery and GCS by using google cloud client libraries with Python 3.2 Simplify I/O in preparation code

from google.cloud import bigquery client = bigquery.Client() query = "SELECT column_1, column_2, column_3 FROM `data set name` where column_1 is not NULL query_job = client.query(query) results = [list(row.values()) for row in query_job.result()] OUTPUT: Two Dimensional Arrays + Filter Values + Drop Null OUTPUT: Two Dimensional Arrays from google.cloud import bigquery client = bigquery.Client() query = "SELECT * FROM `data set name` query_job = client.query(query) results = [list(row.values()) for row in query_job.result()] → Preprocess the data with query as much as possible → It is faster and lower-cost than preprocess data with python Code B Code A 3.2. Simplify I/O in preparation code ex) Big Query with Python

import io, csv, gzip from google.cloud import storage storage_client = storage.Client() bucket = storage_client.get_bucket(‘bucket name’) with io.StringIO() as csv_obj: writer = csv.writer(csv_obj, quotechar='"', quoting=csv.QUOTE_ALL, lineterminator="\n") writer.writerows(two_dimentional_arrays) result = csv_obj.getvalue() with io.BytesIO() as gzip_obj: with gzip.GzipFile(fileobj=gzip_obj, mode="wb") as gzip_file: bytes_f = result.encode() gzip_file.write(bytes_f) blob = bucket.blob(‘storage_path’) blob.upload_from_file(gzip_obj, rewind=True, content_type='application/gzip') Make bytes object and upload it from memory to GCS with Python 3.2 Simplify I/O in preparation code ex) Google Cloud Storage with Python

import gcp_accessor bq = gcp_accessor.BigQueryAccessor() query = "SELECT * FROM `data set name` bq.execute_query(query) gcs = gcp_accessor.GoogleCloudStorageAccessor() gcs.upload_csv_gzip( ‘bucket name', ‘full path on gcs', ‘input data’) 3.2. Simplify I/O more by using wrapper import io, csv, gzip from google.cloud import storage storage_client = storage.Client() bucket = storage_client.get_bucket(‘bucket name’) with io.StringIO() as csv_obj: writer = csv.writer(csv_obj, quotechar='"', quoting=csv.QUOTE_ALL, lineterminator="\n") writer.writerows(two_dimentional_arrays) result = csv_obj.getvalue() with io.BytesIO() as gzip_obj: with gzip.GzipFile(fileobj=gzip_obj, mode="wb") as gzip_file: bytes_f = result.encode() gzip_file.write(bytes_f) blob = bucket.blob(storage_path) blob.upload_from_file(gzip_obj, rewind=True, content_type='application/gzip') from google.cloud import bigquery client = bigquery.Client() query = "SELECT * FROM `data set name` query_job = client.query(query) results = [list(row.values()) for row in query_job.result()] google-cloud-bigquery google-cloud-storage gcp-accessor (wrapper library) (https://pypi.org/project/gcp-accessor/)

All data in the api is processed using the same data type. This improves readability and maintainability as opposed to prioritizing processing speed 3.3. Pandas → Python in preprocessing code

One day, I wondered why I struggled so much with refactoring of preprocessing code in research oriented code that I wrote a previous week. 3.3. Pandas → Python in preprocessing code

3.3. Pandas → Python in preprocessing code Code Styles/ Preprocessing Functions Pandas Python Filter dataframe.where(.query) dataframe.groupby() dataframe[[“”, “”, ‘“]] dataframe.loc[] dataframe.iloc[] if - else + for +.append() [[v1, v2, v3] for value in values] Replace dataframe.fillna() dic = {“key1”: value1, “key2”: value, …} dataframe['column1'].replace(dic, inplace=True) dic = {“key1”: value1, “key2”: value, …} [[dic.get(v, v) for v in value] for value in values] De-duplicate /Be unique duplicated() / drop_duplicates() dataframe['column1'].unique()
 (outuput: array([v1, v2, v3])) set(list) list({v1, v2, v2, …}) list({value[0] for value in values}) Delete/Drop dataframe.dropna() dataframe.drop() dataframe.drop(index=index list) if - else + for +.append() [[v1, v2, v3] for value in values]

3FTFBSDI 0SJFOUFE $PEF .-"1*T Steps to transform Research Oriented Code into ML APIs $IFDL 1. Write decorators to check parameters 2. Set up production-like environments

4.1. Write decorators to check parameters Error handling Request parameter check Access token check Image of Decorators in APIs 3FRVFTU $MJFOU URIs preparation preprocessing calculation

4.1. Write decorators to check parameters Error handling Request parameter check Access token check Image of Decorators in APIs 3FRVFTU $MJFOU URIs preparation preprocessing calculation

{ "$schema": "http://json-schema.org/draft-04/schema#", "student_name": { "type": "string", "required": "True" }, "student_grade": { "type": "string", "required": "True", "maximum": 120, "minimum": 1 } } curl http://localhost:5000/ -X POST -H "Content-Type: application/json" -d '{"student_name": "test_name", "student_grade": “forth-grade"}' make_name_grade.json request curl command 4.1. Write decorators to check parameters ex) Request parameter check with JSON Schema

def validate_json(f): @wraps(f) def wrapper(*args, **kw): try: request.json except BadRequest as e: msg = “ This is an invalid json" return jsonify({"error": msg}), 400 return f(*args, **kw) return wrapper def validate_schema(schema_name): def decorator(f): @wraps(f) def wrapper(*args, **kw): try: validate(request.json, current_app.config[schema_name]) except ValidationError as e: return jsonify({"error": e.message}), 400 return f(*args, **kw) return wrapper return decorator @app.route('/', methods=['POST']) @validate_json @validate_schema('make_name_grade') def index(): if request.is_post: data = json.loads(request.data) print(data["student_name"]) print(data["student_grade"]) return "Hi! " + data["student_name"] else: return "Hi!" app.py json_validate.py This code of json_validate.py is cited from the URL: https://stackoverflow.com/questions/24238743/flask-decorator-to-verify-json-and-json-schema 4.1. Write decorators to check parameters ex) Request parameter check with JSON Schema

Automate Continuous Integration Visualize data (Load Test) Deploy on GCP 'MBTL"QQ #VJMEFS %BTI 4.2. Set up production-like environments with Flask

Resources LOCUST: https://www.youtube.com/watch?v=XQ4hrbgVysk (Pycon Korea 2015) Refactoring: https://www.youtube.com/watch?v=D_6ybDcU5gc (Pycon US 2016)
 Pytest: https://www.youtube.com/watch?v=G-MAMrJ-CSA (Pycon US 2019) Flask workshop: https://www.youtube.com/watch?v=DIcpEg77gdE (Pycon US 2015) Dash: https://www.youtube.com/watch?v=WLbQYFZc-YY (Pycon Jp 2019) google-cloud-bigquery: https://pypi.org/project/google-cloud-bigquery/ google-cloud-storage: https://pypi.org/project/google-cloud-storage/ gcp-accessor: https://pypi.org/project/gcp-accessor/0.0.1/ Flask-AppBuilder: https://flask-appbuilder.readthedocs.io/en/latest/ Python Tools that I mentioned in this talk Python Packages that I mentioned in this talk

Summary 3FTFBSDI 0SJFOUFE $PEF .-"1*T 3FGBDUPS $IFDL 6OEFSTUBOE .PEVMBSJ[F - What is Research Oriented Code ? - What are ML APIs - How should engineers handle research oriented code ? - Categorize research oriented code into preparation code, preprocessing code, ML code - Break them out into functions and make them testable - Clarify input and output of the code, and define URI - Prepare for refactoring - Simplify I/O in preparation code - Pandas → Python in preprocessing code - Write decorators to check parameters - Set up production-like environments

Tetsuya (Jesse) Hirata @JesseTetsuya ———————————————————————————————————————————————————————————————————————————————— Software Engineer at Classi which is an EdTech company. I mostly work in both data science and engineering. If you have an interest in how I am refactoring, in the EdTech domain, or in what our team is doing, feel free to talk to me later !!