Multi-class Classification examples #28
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| # Binary classification: Is this bottle of wine red or white? | ||
| This is the root directory for an example project for the | ||
| [MLflow Classification Recipe](https://mlflow.org/docs/latest/recipes.html#classification-recipe). | ||
| Follow the instructions [here](../README.md) to set up your environment first, | ||
| then use this directory to create a classifier and evaluate its performance, | ||
| all out of box! | ||
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| In this [notebook](notebooks/jupyter.ipynb) ([the Databricks version](notebooks/databricks.py)), | ||
| we show how to build and evaluate a very simple classifier step by step, | ||
| following the best practices of machine learning engineering. | ||
| By the end of this example, | ||
| you will learn how to use MLflow Recipes to | ||
| - Ingest the raw source data. | ||
| - Splits the dataset into training/validation/test. | ||
| - Create an identity transformer and transform the dataset. | ||
| - Train a linear model (classifier) to tell if a bottle of wine is red. | ||
| - Evaluate the trained model, and improve it by iterating through the `transform` and `train` steps. | ||
| - Register the model for production inference. | ||
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| All of these can be done with Jupyter notebook or on the Databricks environment. | ||
| Finally, challenge yourself to build a better model. Try the following: | ||
| - Find a better data source with more training data and more raw feature columns. | ||
| - Clean the dataset to make it less noisy. | ||
| - Find better feature transformations. | ||
| - Fine tune the hyperparameters of the model. | ||
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| sepal length,sepal width,petal length,petal width,class | ||
| 5.1,3.5,1.4,0.2,setosa | ||
| 4.9,3,1.4,0.2,setosa | ||
| 4.7,3.2,1.3,0.2,setosa | ||
| 4.6,3.1,1.5,0.2,setosa | ||
| 5,3.6,1.4,0.2,setosa | ||
| 5.4,3.9,1.7,0.4,setosa | ||
| 4.6,3.4,1.4,0.3,setosa | ||
| 5,3.4,1.5,0.2,setosa | ||
| 4.4,2.9,1.4,0.2,setosa | ||
| 4.9,3.1,1.5,0.1,setosa | ||
| 5.4,3.7,1.5,0.2,setosa | ||
| 4.8,3.4,1.6,0.2,setosa | ||
| 4.8,3,1.4,0.1,setosa | ||
| 4.3,3,1.1,0.1,setosa | ||
| 5.8,4,1.2,0.2,setosa | ||
| 5.7,4.4,1.5,0.4,setosa | ||
| 5.4,3.9,1.3,0.4,setosa | ||
| 5.1,3.5,1.4,0.3,setosa | ||
| 5.7,3.8,1.7,0.3,setosa | ||
| 5.1,3.8,1.5,0.3,setosa | ||
| 5.4,3.4,1.7,0.2,setosa | ||
| 5.1,3.7,1.5,0.4,setosa | ||
| 4.6,3.6,1,0.2,setosa | ||
| 5.1,3.3,1.7,0.5,setosa | ||
| 4.8,3.4,1.9,0.2,setosa | ||
| 5,3,1.6,0.2,setosa | ||
| 5,3.4,1.6,0.4,setosa | ||
| 5.2,3.5,1.5,0.2,setosa | ||
| 5.2,3.4,1.4,0.2,setosa | ||
| 4.7,3.2,1.6,0.2,setosa | ||
| 4.8,3.1,1.6,0.2,setosa | ||
| 5.4,3.4,1.5,0.4,setosa | ||
| 5.2,4.1,1.5,0.1,setosa | ||
| 5.5,4.2,1.4,0.2,setosa | ||
| 4.9,3.1,1.5,0.2,setosa | ||
| 5,3.2,1.2,0.2,setosa | ||
| 5.5,3.5,1.3,0.2,setosa | ||
| 4.9,3.6,1.4,0.1,setosa | ||
| 4.4,3,1.3,0.2,setosa | ||
| 5.1,3.4,1.5,0.2,setosa | ||
| 5,3.5,1.3,0.3,setosa | ||
| 4.5,2.3,1.3,0.3,setosa | ||
| 4.4,3.2,1.3,0.2,setosa | ||
| 5,3.5,1.6,0.6,setosa | ||
| 5.1,3.8,1.9,0.4,setosa | ||
| 4.8,3,1.4,0.3,setosa | ||
| 5.1,3.8,1.6,0.2,setosa | ||
| 4.6,3.2,1.4,0.2,setosa | ||
| 5.3,3.7,1.5,0.2,setosa | ||
| 5,3.3,1.4,0.2,setosa | ||
| 7,3.2,4.7,1.4,versicolor | ||
| 6.4,3.2,4.5,1.5,versicolor | ||
| 6.9,3.1,4.9,1.5,versicolor | ||
| 5.5,2.3,4,1.3,versicolor | ||
| 6.5,2.8,4.6,1.5,versicolor | ||
| 5.7,2.8,4.5,1.3,versicolor | ||
| 6.3,3.3,4.7,1.6,versicolor | ||
| 4.9,2.4,3.3,1,versicolor | ||
| 6.6,2.9,4.6,1.3,versicolor | ||
| 5.2,2.7,3.9,1.4,versicolor | ||
| 5,2,3.5,1,versicolor | ||
| 5.9,3,4.2,1.5,versicolor | ||
| 6,2.2,4,1,versicolor | ||
| 6.1,2.9,4.7,1.4,versicolor | ||
| 5.6,2.9,3.6,1.3,versicolor | ||
| 6.7,3.1,4.4,1.4,versicolor | ||
| 5.6,3,4.5,1.5,versicolor | ||
| 5.8,2.7,4.1,1,versicolor | ||
| 6.2,2.2,4.5,1.5,versicolor | ||
| 5.6,2.5,3.9,1.1,versicolor | ||
| 5.9,3.2,4.8,1.8,versicolor | ||
| 6.1,2.8,4,1.3,versicolor | ||
| 6.3,2.5,4.9,1.5,versicolor | ||
| 6.1,2.8,4.7,1.2,versicolor | ||
| 6.4,2.9,4.3,1.3,versicolor | ||
| 6.6,3,4.4,1.4,versicolor | ||
| 6.8,2.8,4.8,1.4,versicolor | ||
| 6.7,3,5,1.7,versicolor | ||
| 6,2.9,4.5,1.5,versicolor | ||
| 5.7,2.6,3.5,1,versicolor | ||
| 5.5,2.4,3.8,1.1,versicolor | ||
| 5.5,2.4,3.7,1,versicolor | ||
| 5.8,2.7,3.9,1.2,versicolor | ||
| 6,2.7,5.1,1.6,versicolor | ||
| 5.4,3,4.5,1.5,versicolor | ||
| 6,3.4,4.5,1.6,versicolor | ||
| 6.7,3.1,4.7,1.5,versicolor | ||
| 6.3,2.3,4.4,1.3,versicolor | ||
| 5.6,3,4.1,1.3,versicolor | ||
| 5.5,2.5,4,1.3,versicolor | ||
| 5.5,2.6,4.4,1.2,versicolor | ||
| 6.1,3,4.6,1.4,versicolor | ||
| 5.8,2.6,4,1.2,versicolor | ||
| 5,2.3,3.3,1,versicolor | ||
| 5.6,2.7,4.2,1.3,versicolor | ||
| 5.7,3,4.2,1.2,versicolor | ||
| 5.7,2.9,4.2,1.3,versicolor | ||
| 6.2,2.9,4.3,1.3,versicolor | ||
| 5.1,2.5,3,1.1,versicolor | ||
| 5.7,2.8,4.1,1.3,versicolor | ||
| 6.3,3.3,6,2.5,virginica | ||
| 5.8,2.7,5.1,1.9,virginica | ||
| 7.1,3,5.9,2.1,virginica | ||
| 6.3,2.9,5.6,1.8,virginica | ||
| 6.5,3,5.8,2.2,virginica | ||
| 7.6,3,6.6,2.1,virginica | ||
| 4.9,2.5,4.5,1.7,virginica | ||
| 7.3,2.9,6.3,1.8,virginica | ||
| 6.7,2.5,5.8,1.8,virginica | ||
| 7.2,3.6,6.1,2.5,virginica | ||
| 6.5,3.2,5.1,2,virginica | ||
| 6.4,2.7,5.3,1.9,virginica | ||
| 6.8,3,5.5,2.1,virginica | ||
| 5.7,2.5,5,2,virginica | ||
| 5.8,2.8,5.1,2.4,virginica | ||
| 6.4,3.2,5.3,2.3,virginica | ||
| 6.5,3,5.5,1.8,virginica | ||
| 7.7,3.8,6.7,2.2,virginica | ||
| 7.7,2.6,6.9,2.3,virginica | ||
| 6,2.2,5,1.5,virginica | ||
| 6.9,3.2,5.7,2.3,virginica | ||
| 5.6,2.8,4.9,2,virginica | ||
| 7.7,2.8,6.7,2,virginica | ||
| 6.3,2.7,4.9,1.8,virginica | ||
| 6.7,3.3,5.7,2.1,virginica | ||
| 7.2,3.2,6,1.8,virginica | ||
| 6.2,2.8,4.8,1.8,virginica | ||
| 6.1,3,4.9,1.8,virginica | ||
| 6.4,2.8,5.6,2.1,virginica | ||
| 7.2,3,5.8,1.6,virginica | ||
| 7.4,2.8,6.1,1.9,virginica | ||
| 7.9,3.8,6.4,2,virginica | ||
| 6.4,2.8,5.6,2.2,virginica | ||
| 6.3,2.8,5.1,1.5,virginica | ||
| 6.1,2.6,5.6,1.4,virginica | ||
| 7.7,3,6.1,2.3,virginica | ||
| 6.3,3.4,5.6,2.4,virginica | ||
| 6.4,3.1,5.5,1.8,virginica | ||
| 6,3,4.8,1.8,virginica | ||
| 6.9,3.1,5.4,2.1,virginica | ||
| 6.7,3.1,5.6,2.4,virginica | ||
| 6.9,3.1,5.1,2.3,virginica | ||
| 5.8,2.7,5.1,1.9,virginica | ||
| 6.8,3.2,5.9,2.3,virginica | ||
| 6.7,3.3,5.7,2.5,virginica | ||
| 6.7,3,5.2,2.3,virginica | ||
| 6.3,2.5,5,1.9,virginica | ||
| 6.5,3,5.2,2,virginica | ||
| 6.2,3.4,5.4,2.3,virginica | ||
| 5.9,3,5.1,1.8,virginica |
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| # Databricks notebook source | ||
| # MAGIC %md | ||
| # MAGIC # MLflow Classification Recipe Databricks Notebook | ||
| # MAGIC This notebook runs the MLflow Classification Recipe on Databricks and inspects its results. | ||
| # MAGIC | ||
| # MAGIC For more information about the MLflow Classification Recipe, including usage examples, | ||
| # MAGIC see the [Classification Recipe overview documentation](https://mlflow.org/docs/latest/recipes.html#classification-recipe) | ||
| # MAGIC and the [Classification Recipe API documentation](https://mlflow.org/docs/latest/python_api/mlflow.recipes.html#module-mlflow.recipes.classification.v1.recipe). | ||
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| # COMMAND ---------- | ||
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| # MAGIC %pip install -r ../../requirements.txt | ||
| # MAGIC %pip install git+https://github.com/mshtelma/mlflow.git@multiclassclassification | ||
|
Contributor
There was a problem hiding this comment. Remove this once MLFlow is released.
Contributor
There was a problem hiding this comment. We just released MLflow 2.1.0 so we should be good to remove this now :) |
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| # COMMAND ---------- | ||
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| # MAGIC %md ### Start with a recipe: | ||
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| # COMMAND ---------- | ||
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| from mlflow.recipes import Recipe | ||
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| r = Recipe(profile="databricks") | ||
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| # COMMAND ---------- | ||
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| r.clean() | ||
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| # COMMAND ---------- | ||
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| # MAGIC %md ### Inspect recipe DAG: | ||
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| # COMMAND ---------- | ||
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| r.inspect() | ||
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| # COMMAND ---------- | ||
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| # MAGIC %md ### Ingest the dataset: | ||
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| # COMMAND ---------- | ||
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| r.run("ingest") | ||
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| # COMMAND ---------- | ||
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| # MAGIC %md ### Split the dataset into train, validation and test: | ||
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| # COMMAND ---------- | ||
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| r.run("split") | ||
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Contributor
There was a problem hiding this comment. Can we do a bit of EDA here before proceeding to split? |
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| # COMMAND ---------- | ||
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| r.run("transform") | ||
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| # COMMAND ---------- | ||
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| # MAGIC %md ### Train the model: | ||
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| # COMMAND ---------- | ||
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| r.run("train") | ||
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| # COMMAND ---------- | ||
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| # MAGIC %md ### Evaluate the model: | ||
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| # COMMAND ---------- | ||
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| r.run("evaluate") | ||
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| # COMMAND ---------- | ||
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| # MAGIC %md ### Register the model: | ||
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| # COMMAND ---------- | ||
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| r.run("register") | ||
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| # COMMAND ---------- | ||
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| r.inspect("train") | ||
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| # COMMAND ---------- | ||
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| training_data = r.get_artifact("training_data") | ||
| training_data.describe() | ||
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| # COMMAND ---------- | ||
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| training_data[:1].to_json() | ||
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| # COMMAND ---------- | ||
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| trained_model = r.get_artifact("model") | ||
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Contributor
There was a problem hiding this comment. Can we use the model to make a prediction on one hand-generated input example here? |
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| print(trained_model) | ||
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| # COMMAND ---------- | ||
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[MLflow Classification Recipe] --> [MLflow Multi Class Classification Recipe]
What do you think?