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Code: Visual Studio Code Pandas: Pandas Python: Python Scklearn: Scikit Learn Docker Flask linting: pylint

Final Project

Problem Explanation

Imagine that you're working on a company that want to predict if an employee is leaving or not the company based on several factors like age, salary, etc.

The main focus of this project is to build a model to predict it, register it in a register service and deploy it.

Diagram

Project-Architecture

Reproducibility

If you want to reproduce the results of this project, run the following commands:

Add a whylog_token.json to the root of the project with your token
pip install -r requirements.txt
python model.py
make build
make up

If you only want to see the final apps

make build
make up

Step by Step

Follow the steps below to reproduce the problem and build the model.

Enviroment

Create a conda enviroment for the project with python=3.9

conda create -n project_enviroment python=3.9

Active the enviroment

conda activate project_enviroment

Install the dependencies

pip install -r requirements.txt

Model: Classification model that predict if an employee is leaving the company.

Dataset: IBM HR Analytics Employee Attrition & Performance

Download dataset here

Download dataset with the following command:

wget https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset?resource=download

Working on model.ipynb

Install ipython kernel

conda install -n project_enviroment ipykernel --update-deps --force-reinstall

Tracking Experiment with Mlflow

Run the following command in your terminal to track the experiment in your local machine:

mlflow ui --backend-store-uri sqlite:///mydb.sqlite

That command create a database file called mydb.sqlite in the current directory that'll be used to store the experiment data.

Add this code to your notebook to track the experiment in your local machine using a SQLite database:

import mlflow


mlflow.set_tracking_uri('sqlite:///mydb.sqlite')

And start a run with:

mlflow.start_run()

Model Register

I'm using a sklearn library, mlflow provides a way to register the model with the following command:

  #Model Register
  mlflow.sklearn.log_model(
        sk_model = logreg,
        artifact_path='models/logreg',
        registered_model_name='sk-learn-logreg-model'
  )

Registered model

Orchestration of the project

I'm going to use Prefect==2.7.3 to orchestrate the project.

Install prefect

conda install prefect -c conda-forge

Authenticating with Prefect Cloud (Optional)

prefect cloud login -k YOUR_KEY

Start UI

Go to prefect-cloud-web

Deployment

See the options wit the following command:

 prefect deployment build --help

Build the deployment

prefect deployment build .\model.py:applying_model --name Project-Deployment --tag MLOps

Apply the deployment

prefect deployment apply applying_model-deployment.yaml

Work queues and agents

We can't run the deployment from the UI yet. We nned a work queue and an agent to run the deployment.

Work queues and agents are the mechanisms by which the Prefect API orchestrates deployment flow runs in remote execution environments.

Work queues let you organize flow runs into queues for execution. Agents pick up work from queues and execute the flows

Create a Workflow in the UI

Work Queue

Start an agent

prefect agent start -t tag where tag is the tag you used to build the deployment.

Now, when you run a deployment with the -t tag option, the agent will pick up the work from the queue and execute the flows.

Schedule the deployment

  • Go to the UI
  • Select Add Schedule Schedule
  • I'm going to select Cron with a value of 0 0 * * * that means every day at 12:00 AM.
  • Timezone is important, so, be sure to select the correct timezone.

Monitoring

I'm going to use Evidently and Whylogsto monitor the experiment.

Evidently

Install evidently

You can install it with the following command:

pip install evidently

Dashboard for classification report

Classification Performance report evaluates the quality of a classification model. It works both for binary and multi-class classification. If you have a probabilistic classification, refer to a separate report. This report can be generated for a single model, or as a comparison. You can contrast your current production model performance against the past or an alternative model.

More info here

Results

Using train data and valid data to evaluate the model I've created the following dashboard: Results You can see the resuls in the dashboard folder.

Whylogs

Install whylogs

pip install "whylogs<1.0"

We're installing this version because the platform doesn't yet support v1.

Get your API key

Go to whylogs.com and create an account, then go to your profile and click on the API tab.

First approach: Connect dataset

As a first approach, we can connect the dataset to the experiment.

I've used the following command to connect the dataset to the experiment:

import whylogs as why
from whylogs.app import Session
from whylogs.app.writers import WhyLabsWriter

writer = WhyLabsWriter("", formats=[])
    session = Session(project="model-1", pipeline="mlops-project-pipeline", writers=[writer])

with session.logger(tags={"datasetId": "model-1"}) as ylog:
        ylog.log_dataframe(df)

Results

images

Classifier Report

First: Make sure that you have selected Classification model in your Whylabs Project. Whylabs-Classification

Generate performance report

    scores = [max(p) for p in logreg.predict_proba(X_val)]
    with session.logger(tags={"datasetId": "model-1"}, dataset_timestamp = datetime.now()) as ylog:
        ylog.log_metrics(
            targets = list(y_val),
            predictions = list(y_pred),
            scores = scores,
            model_type = ModelType.CLASSIFICATION,
            target_field="Attrition",
            prediction_field="prediction",
            score_field = "Normalized Prediction Probability",
        )

Results

Classification Results

Activate Presets

We can activate some Preset monitors to monitor different part of the experiment.

You can receive alerts from these Preset monitors, in my case I've enabled: Presets

Tests

I'll use Pytest to test the model.

Install pytest with the following command:

pip install pytest

Configure Tests

  1. Go to tests extension in VS Code and select a folder that contains the tests, in this case tests/.
  2. Select Pytest as the test runner.

Linting and Formatting

I'm going to use Pylint to lint and format the code.

Intall Pylint

Use this command to install pylint:

pip install pylint

Lint the code

You can lint your python file as follows:

pylint my_file.py

In my case, pylint model.py.

Runs Results

  1. In the first time I'd obtained a score 5.23/10 (very bad).
  2. Score of 5.88/10 (still bad).
  3. Score of 6.47/10 (quite good).
  4. After creating pyproject.toml my score raises to 8.35/10 (very good).
  5. Now my score is 9.76/10 (excellent).

View Results in Visual Studio Code

  1. Press Ctrl + Shift + P and then type linting and select Pylint.
  2. Run linting with Ctrl + Shift + P and Run linting.

Add .pylintrc file

You can add a .pylintrc file in the root of the project to configure pylint.

I'm going to use pyproject.toml instead.

Formatting with black and isort

Install black and isort with the following command:

pip install black isort

Before you run black, you can check the changes that will do with the following command:

black --diff my_file.py

After that, you can run black with the following command:

black my_file.py

Add Black to pyproject.toml

You can add some configurations to pyproject.toml, in my case:

[tool.black]
line-length = 120
target-version = ['py39']
skip-string-normalization = true

where:

  • line-length is the maximum length of a line.
  • target-version is the version of python that you want to use.
  • skip-string-normalization is a boolean that indicates if you want to skip string normalization.

Apply Isort

You can apply isort with the following command:

isort my_file.py

Add Isort to pyproject.toml

Add the following configurations to pyproject.toml:

multi_line_output = 3
length_sort = true
order_by_type = true

where:

  • multi_line_output is the number of lines that will be used to output a multiline string.
  • length_sort is a boolean that indicates if you want to sort by length.
  • order_by_type is a boolean that indicates if you want to order by type.

Git pre-commits hooks

Example I'm going to install pre-commit library. More info here.

Install pre-commit

pip install pre-commit

See pre-commit hooks examples in the root of the project (Optional)

  1. Go to .git/hooks folder and select pre-commit.
  2. Open pre-commit.sample file and see the examples.
  3. Example content:
#!/bin/sh
#
# An example hook script to verify what is about to be committed.
# Called by "git commit" with no arguments.  The hook should
# exit with non-zero status after issuing an appropriate message if
# it wants to stop the commit.
#
# To enable this hook, rename this file to "pre-commit".

if git rev-parse --verify HEAD >/dev/null 2>&1
then
	against=HEAD
else
	# Initial commit: diff against an empty tree object
	against=$(git hash-object -t tree /dev/null)
fi

# If you want to allow non-ASCII filenames set this variable to true.
allownonascii=$(git config --type=bool hooks.allownonascii)

# Redirect output to stderr.
exec 1>&2

# Cross platform projects tend to avoid non-ASCII filenames; prevent
# them from being added to the repository. We exploit the fact that the
# printable range starts at the space character and ends with tilde.
if [ "$allownonascii" != "true" ] &&
	# Note that the use of brackets around a tr range is ok here, (it's
	# even required, for portability to Solaris 10's /usr/bin/tr), since
	# the square bracket bytes happen to fall in the designated range.
	test $(git diff --cached --name-only --diff-filter=A -z $against |
	  LC_ALL=C tr -d '[ -~]\0' | wc -c) != 0
then
	cat <<\EOF
Error: Attempt to add a non-ASCII file name.

This can cause problems if you want to work with people on other platforms.

To be portable it is advisable to rename the file.

If you know what you are doing you can disable this check using:

  git config hooks.allownonascii true
EOF
	exit 1
fi

# If there are whitespace errors, print the offending file names and fail.
exec git diff-index --check --cached $against --

Create a sample config file with pre-commit

Type the following command to create a sample config file:

pre-commit sample-config

Create a yaml file with pre-commit settings

Type the following command to create a yaml file with pre-commit settings:

pre-commit sample-config >.pre-commit-config.yaml

Add pre-commit to the .git folder

Type the following command to add pre-commit to the .git folder:

pre-commit install

For one of my commits, I've got the following result:

Example

You can see all the hooks that pre-commit can run at the following link:

https://pre-commit.com/hooks.html

Isort pre-commit hook

Add the following configuration to .pre-commit-config.yaml:

  - repo: https://github.com/pycqa/isort
    rev: 5.10.1
    hooks:
      - id: isort
        name: isort (python)

Black pre-commit hook

Add the following configuration to .pre-commit-config.yaml:

  repos:
-   repo: https://github.com/psf/black
    rev: stable
    hooks:
    - id: black
      language_version: python3.9

Pylint pre-commit hook

Add the following configuration to .pre-commit-config.yaml:

- repo: local
  hooks:
    - id: pylint
      name: pylint
      entry: pylint
      language: system
      types: [python]
      args:
        [
          "-rn", # Only display messages
          "-sn", # Don't display the score
        ]

Pytest pre-commit hook

    hooks:
      - id: pytest-check
        name: pytest-check
        entry: pytest
        language: system
        pass_filenames: false
        always_run: true

Makefiles and Make

Make is a tool which controls the generation of executables and other non-source files of a program from the program's source files. Make gets its knowledge of how to build your program from a file called the makefile, which lists each of the non-source files and how to compute it from other files. When you write a program, you should write a makefile for it, so that it is possible to use Make to build and install the program.

Install make with the following command:

sudo apt install make

or if you are using Windows, you can install make with the following command (as a administrator):

choco install make

where choco is the command to install chocolatey packages.

Example:

test:
	echo test

other_thing:
	echo other thing

run: test other_thing
	echo run

Run make with the following command:

make run

Flask app: Evidently

I'm going to put the html generated by Evidently in a Flask app.

Install Flask

Install Flask with the following command:

pip install flask

Create a Flask app

from flask import Flask, render_template

app = Flask (__name__, template_folder='dashboards')

@app.route('/')
def evidently():
    return render_template('df_model_performance.html')

if __name__ == '__main__':
    app.run(debug=True, host = '0.0.0.0', port = 9696)

where:

  1. dashboards is the folder where the html files are located.
  2. render_template is a function that takes the name of the html file and returns the html code.
  3. I've selected port = 9696.

Streamlit app

We can make a Streamlit app using our model.

Install Streamlit

Install Streamlit with the following command:

pip install streamlit

Model that we'll use

We'll use the last model we trained. This model will be at models folder.

Load the model

Load the model with the following command:

def load_model():
    model = 'models/pipeline.bin'

    with open(model, 'rb') as f_in:
        pipeline = pickle.load(f_in)

    return pipeline
pipeline = load_model()

Run the app

Run the app with the following command:

streamlit run streamlit/streamlit_app.py

where streamlit_app.py is the name of the file.

CI/CD

Github actions

Github actions is a service that allows us to run our code in a virtual machine. We can use this service to run our code when we push our code to Github. We can also use this service to run our code when we create a pull request.

Testing workflow

I've created a workflow to test the code. Basically, this workflow will run the following commands:

  1. Install the requirements.
  2. Run the tests.

Continuous training workflow

This workflow will run the following commands:

  1. Install the requirements.
  2. Run the model
  3. Push the new model to the repository.
  4. This workflow will run every day (6 hours after the last run).