automl 2.9.9
Automated machine learning for production and analytics
License: MIT
Keywords: machine learning,data science,automated machine learning,regressor,regressors,regression,classification,classifiers,classifier,estimators,predictors,XGBoost,Random Forest,sklearn,scikit-learn,analytics,analysts,coefficients,feature importancesanalytics,artificial intelligence,subpredictors,ensembling,stacking,blending,feature engineering,feature extraction,feature selection,production,pandas,dataframes,machinejs,deep learning,tensorflow,deeplearning,lightgbm,gradient boosting,gbm,keras,production ready,test coverage

auto\_ml
========

    Automated machine learning for production and analytics

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Installation
------------

-  ``pip install auto_ml``

Getting started
---------------

.. code:: python

    from auto_ml import Predictor
    from auto_ml.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!
----------------------------

auto\_ml 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 auto_ml import Predictor
    from auto_ml.utils import get_boston_dataset
    from auto_ml.utils_models import load_ml_model

    # Load data
    df_train, df_test = get_boston_dataset()

    # Tell auto_ml which column is 'output'
    # Also note columns that aren't purely numerical
    # 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)

    # auto_ml is specifically tuned for running in production
    # It can get predictions on an individual row (passed in as a dictionary)
    # A single prediction like this takes ~1 millisecond
    # Here we will demonstrate saving the trained model, and loading it again
    file_name = ml_predictor.save()

    trained_model = load_ml_model(file_name)

    # .predict and .predict_proba take in either:
    # A pandas DataFrame
    # A list of dictionaries
    # 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
--------------------------------------------------------------------------------------

auto\_ml 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`` -
``LGBMClassifer`` 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 auto\_ml's default installation.
You are responsible for installing them yourself. auto\_ml will run fine
without them installed (we check what's isntalled before choosing which
algorithm to use).

Feature Responses
-----------------

Get linear-model-esque interpretations from non-linear models. See the
`docs <http://auto-ml.readthedocs.io/en/latest/feature_responses.html>`__
for more information and caveats.

Classification
--------------

Binary and multiclass classification are both supported. Note that for
now, labels must be integers (0 and 1 for binary classification).
auto\_ml 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 auto\_ml, 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 auto\_ml supports multiclass classification.

Categorical Ensembling
----------------------

Ever wanted to train one market 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
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

http://auto-ml.readthedocs.io/en/latest/

Advice
~~~~~~

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 ``auto_ml``, 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.

Docs
----

The full docs are available at https://auto\_ml.readthedocs.io Again
though, I'd strongly recommend running this on an actual dataset before
referencing the docs any futher.

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 auto\_ml 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
auto\_ml, I happily welcome your contributions here!

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