Automated Machine Learning for production and analytics

## brainless

Current status: Barely even being maintained, if you can call it that

You’ve found brainless!

brainless is an acronym for “Combined Algorithm Selection and Hyperparameter optimization for Machine Learning & Deep Learning”

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## Installation

Coming soon™ - pip install brainless

## Getting started

.. code:: python

from brainless import Predictor from brainless.utils import get_boston_dataset

df_train, df_test = get_boston_dataset()

column_descriptions = { 'MEDV': 'output', 'CHAS': 'categorical' }

ml_predictor = Predictor(type_of_estimator='regressor', column_descriptions=column_descriptions)

ml_predictor.train(df_train)

ml_predictor.score(df_test, df_test.MEDV)

## Show off some more features!

brainless is designed for production. Here’s an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you’d likely follow to deploy the trained model.

.. code:: python

from brainless import Predictor from brainless.utils import get_boston_dataset from brainless.utils_models import load_ml_model

df_train, df_test = get_boston_dataset()

# Examples include ['nlp', 'date', 'categorical', 'ignore']

column_descriptions = { 'MEDV': 'output' , 'CHAS': 'categorical' }

ml_predictor = Predictor(type_of_estimator='regressor', column_descriptions=column_descriptions)

ml_predictor.train(df_train)

# Score the model on test data

test_score = ml_predictor.score(df_test, df_test.MEDV)

# Here we will demonstrate saving the trained model, and loading it again

file_name = ml_predictor.save()

# A single dictionary (optimized for speed in production evironments)

predictions = trained_model.predict(df_test) print(predictions)

## 3rd Party Packages- Deep Learning with TensorFlow & Keras, XGBoost, LightGBM, CatBoost

brainless has all of these awesome libraries integrated! Generally, just pass one of them in for model_names. ml_predictor.train(data, model_names=['DeepLearningClassifier'])

Available options are - DeepLearningClassifier and DeepLearningRegressor - XGBClassifier and XGBRegressor - LGBMClassifier and LGBMRegressor - CatBoostClassifier and CatBoostRegressor

All of these projects are ready for production. These projects all have prediction time in the 1 millisecond range for a single prediction, and are able to be serialized to disk and loaded into a new environment after training.

Depending on your machine, they can occasionally be difficult to install, so they are not included in brainless’s default installation. You are responsible for installing them yourself. brainless will run fine without them installed (we check what’s installed before choosing which algorithm to use).

## Classification

Binary and multiclass classification are both supported. Note that for now, labels must be integers (0 and 1 for binary classification). brainless will automatically detect if it is a binary or multiclass classification problem - you just have to pass in ml_predictor = Predictor(type_of_estimator='classifier', column_descriptions=column_descriptions)

## Feature Learning

Also known as “finally found a way to make this deep learning stuff useful for my business”. Deep Learning is great at learning important features from your data. But the way it turns these learned features into a final prediction is relatively basic. Gradient boosting is great at turning features into accurate predictions, but it doesn’t do any feature learning.

In brainless, you can now automatically use both types of models for what they’re great at. If you pass feature_learning=True, fl_data=some_dataframe to .train(), we will do exactly that: train a deep learning model on your fl_data. We won’t ask it for predictions (standard stacking approach), instead, we’ll use it’s penultimate layer to get it’s 10 most useful features. Then we’ll train a gradient boosted model (or any other model of your choice) on those features plus all the original features.

Across some problems, we’ve witnessed this lead to a 5% gain in accuracy, while still making predictions in 1-4 milliseconds, depending on model complexity.

ml_predictor.train(df_train, feature_learning=True, fl_data=df_fl_data)

This feature only supports regression and binary classification currently. The rest of brainless supports multiclass classification.

## Categorical Ensembling

Ever wanted to train one model for every store/customer, but didn’t want to maintain hundreds of thousands of independent models? With ml_predictor.train_categorical_ensemble(), we will handle that for you. You’ll still have just one consistent API, ml_predictor.predict(data), but behind this single API will be one model for each category you included in your training data.

Just tell us which column holds the category you want to split on, and we’ll handle the rest. As always, saving the model, loading it in a different environment, and getting speedy predictions live in production is baked right in.

ml_predictor.train_categorical_ensemble(df_train, categorical_column='store_name')

More details available in the docs



~~~~~~

Before you go any further, try running the code. Load up some data
(either a DataFrame, or a list of dictionaries, where each dictionary is
a row of data). Make a column_descriptions dictionary that tells us
which attribute name in each row represents the value we’re trying to
predict. Pass all that into brainless, and see what happens!

Everything else in these docs assumes you have done at least the above.
Start there and everything else will build on top. But this part gets
you the output you’re probably interested in, without unnecessary
complexity.

What this project does
----------------------

Automates the whole machine learning process, making it super easy to
use for both analytics, and getting real-time predictions in production.

A quick overview of buzzwords, this project automates:

-  Analytics (pass in data, and brainless will tell you the relationship
of each variable to what it is you’re trying to predict).
-  Feature Engineering (particularly around dates, and NLP).
-  Robust Scaling (turning all values into their scaled versions between
the range of 0 and 1, in a way that is robust to outliers, and works
with sparse data).
-  Feature Selection (picking only the features that actually prove
useful).
-  Data formatting (turning a DataFrame or a list of dictionaries into a
sparse matrix, one-hot encoding categorical variables, taking the
natural log of y for regression problems, etc).
-  Model Selection (which model works best for your problem- we try
roughly a dozen apiece for classification and regression problems,
including favorites like XGBoost if it’s installed on your machine).
-  Hyperparameter Optimization (what hyperparameters work best for that
model).
-  Big Data (feed it lots of data- it’s fairly efficient with
resources).
-  Unicorns (you could conceivably train it to predict what is a unicorn
and what is not).
-  Ice Cream (mmm, tasty…).
-  Hugs (this makes it much easier to do your job, hopefully leaving you
more time to hug those those you care about).

Running the tests
~~~~~~~~~~~~~~~~~

If you’ve cloned the source code and are making any changes (highly
encouraged!), or just want to make sure everything works in your
environment, run nosetests -v tests.

CI is also set up, so if you’re developing on this, you can just open a
PR, and the tests will run automatically on Travis-CI.

The tests are relatively comprehensive, though as with everything with
brainless, I happily welcome your contributions here!

Credit where credit is due
--------------------------

This entire project is based *quite heavily* on the work previously
done by Preston Parry. <https://github.com/ClimbsRocks/auto_ml>__ and
work previously done by Jesse
Toftum <https://github.com/jesse-toftum/brainless>__



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