An Open Source Project from the Data to AI Lab, at MIT

MLPrimitives

Pipelines and primitives for machine learning and data science.

• Documentation: https://MLBazaar.github.io/MLPrimitives
• Github: https://github.com/MLBazaar/MLPrimitives
• License: MIT
• Development Status: Pre-Alpha

Overview

This repository contains primitive annotations to be used by the MLBlocks library, as well as the necessary Python code to make some of them fully compatible with the MLBlocks API requirements.

There is also a collection of custom primitives contributed directly to this library, which either combine third party tools or implement new functionalities from scratch.

Why did we create this library?

• Too many libraries in a fast growing field
• Huge societal need to build machine learning apps
• Domain expertise resides at several places (knowledge of math)
• No documented information about hyperparameters, behavior...

Installation

Requirements

MLPrimitives has been developed and tested on Python 3.8, 3.9, 3.10, and 3.11

Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid interfering with other software installed in the system where MLPrimitives is run.

Install with pip

The easiest and recommended way to install MLPrimitives is using pip:

pip install mlprimitives

This will pull and install the latest stable release from PyPi.

If you want to install from source or contribute to the project please read the Contributing Guide.

Quickstart

This section is a short series of tutorials to help you getting started with MLPrimitives.

In the following steps you will learn how to load and run a primitive on some data.

Later on you will learn how to evaluate and improve the performance of a primitive by tuning its hyperparameters.

Running a Primitive

In this first tutorial, we will be executing a single primitive for data transformation.

1. Load a Primitive

The first step in order to run a primitive is to load it.

This will be done using the mlprimitives.load_primitive function, which will load the indicated primitive as an MLBlock Object from MLBlocks

In this case, we will load the mlprimitives.custom.feature_extraction.CategoricalEncoder primitive.

from mlprimitives import load_primitive

primitive = load_primitive('mlprimitives.custom.feature_extraction.CategoricalEncoder')

2. Load some data

The CategoricalEncoder is a transformation primitive which applies one-hot encoding to all the categorical columns of a pandas.DataFrame.

So, in order to be able to run our primitive, we will first load some data that contains categorical columns.

This can be done with the mlprimitives.datasets.load_census function:

from mlprimitives.datasets import load_census

dataset = load_census()

This dataset object has an attribute data which contains a table with several categorical columns.

We can have a look at this table by executing dataset.data.head(), which will return a table like this:

                             0                    1                   2
age                         39                   50                  38
workclass            State-gov     Self-emp-not-inc             Private
fnlwgt                   77516                83311              215646
education            Bachelors            Bachelors             HS-grad
education-num               13                   13                   9
marital-status   Never-married   Married-civ-spouse            Divorced
occupation        Adm-clerical      Exec-managerial   Handlers-cleaners
relationship     Not-in-family              Husband       Not-in-family
race                     White                White               White
sex                       Male                 Male                Male
capital-gain              2174                    0                   0
capital-loss                 0                    0                   0
hours-per-week              40                   13                  40
native-country   United-States        United-States       United-States

3. Fit the primitive

In order to run our pipeline, we first need to fit it.

This is the process where it analyzes the data to detect which columns are categorical

This is done by calling its fit method and assing the dataset.data as X.

primitive.fit(X=dataset.data)

4. Produce results

Once the pipeline is fit, we can process the data by calling the produce method of the primitive instance and passing agin the data as X.

transformed = primitive.produce(X=dataset.data)

After this is done, we can see how the transformed data contains the newly generated one-hot vectors:

                                                0      1       2       3       4
age                                            39     50      38      53      28
fnlwgt                                      77516  83311  215646  234721  338409
education-num                                  13     13       9       7      13
capital-gain                                 2174      0       0       0       0
capital-loss                                    0      0       0       0       0
hours-per-week                                 40     13      40      40      40
workclass= Private                              0      0       1       1       1
workclass= Self-emp-not-inc                     0      1       0       0       0
workclass= Local-gov                            0      0       0       0       0
workclass= ?                                    0      0       0       0       0
workclass= State-gov                            1      0       0       0       0
workclass= Self-emp-inc                         0      0       0       0       0
...                                             ...    ...     ...     ...     ...

Tuning a Primitive

In this short tutorial we will teach you how to evaluate the performance of a primitive and improve its performance by modifying its hyperparameters.

To do so, we will load a primitive that can learn from the transformed data that we just generated and later on make predictions based on new data.

1. Load another primitive

Firs of all, we will load the xgboost.XGBClassifier primitive that we will use afterwards.

primitive = load_primitive('xgboost.XGBClassifier')

2. Split the dataset

Before being able to evaluate the primitive perfomance, we need to split the data in two parts: train, which will be used for the primitive to learn, and test, which will be used to make the predictions that later on will be evaluated.

In order to do this, we will get the first 75% of rows from the transformed data that we obtained above and call it X_train, and then set the next 25% of rows as X_test.

train_size = int(len(transformed) * 0.75)
X_train = transformed.iloc[:train_size]
X_test = transformed.iloc[train_size:]

Similarly, we need to obtain the y_train and y_test variables containing the corresponding output values.

y_train = dataset.target[:train_size]
y_test = dataset.target[train_size:]

3. Fit the new primitive

Once we have have splitted the data, we can fit the primitive by passing X_train and y_train to its fit method.

primitive.fit(X=X_train, y=y_train)

4. Make predictions

Once the primitive has been fitted, we can produce predictions using the X_test data as input.

predictions = primitive.produce(X=X_test)

5. Evalute the performance

We can now evaluate how good the predictions from our primitive are by using the score method from the dataset object on both the expected output and the real output from the primitive:

dataset.score(y_test, predictions)

This will output a float value between 0 and 1 indicating how good the predicitons are, being 0 the worst score possible and 1 the best one.

In this case we will obtain a score around 0.866

6. Set new hyperparameter values

In order to improve the performance of our primitive we will try to modify a couple of its hyperparameters.

First we will see which hyperparameter values the primitive has by calling its get_hyperparameters method.

primitive.get_hyperparameters()

which will return a dictionary like this:

{
    "n_jobs": -1,
    "n_estimators": 100,
    "max_depth": 3,
    "learning_rate": 0.1,
    "gamma": 0,
    "min_child_weight": 1
}

Next, we will see which are the valid values for each one of those hyperparameters by calling its get_tunable_hyperparameters method:

primitive.get_tunable_hyperparameters()

For example, we will see that the max_depth hyperparameter has the following specification:

{
    "type": "int",
    "default": 3,
    "range": [
        3,
        10
    ]
}

Next, we will choose a valid value, for example 7, and set it into the pipeline using the set_hyperparameters method:

primitive.set_hyperparameters({'max_depth': 7})

7. Re-evaluate the performance

Once the new hyperparameter value has been set, we repeat the fit/train/score cycle to evaluate the performance of this new hyperparameter value:

primitive.fit(X=X_train, y=y_train)
predictions = primitive.produce(X=X_test)
dataset.score(y_test, predictions)

This time we should see that the performance has improved to a value around 0.724

What's Next?

Do you want to learn more about how the project, about how to contribute to it or browse the API Reference? Please check the corresponding sections of the documentation!