README.md

Ramp - Rapid Machine Learning Prototyping

Ramp is a python module for rapid prototyping of machine learning solutions. It is essentially a pandas wrapper around various python machine learning and statistics libraries (scikit-learn, rpy2, etc.), providing a simple, declarative syntax for exploring features, algorithms and transformations quickly and efficiently.

Complex feature transformations

Chain basic feature transformations: Normalize(Log('x')) Interactions([Log('x1'), (F('x2') + F('x3')) / 2]) Reduce feature dimension SVDDimensionReduction([F('x%d'%i) for i in range(100)], n_keep=20) Incorporate residuals or predictions to blend with other models Residuals(config_model1) + Predictions(config_model2) Any feature that uses the target ("y") variable will automatically respect the current training and test sets.

Caching

Ramp caches and stores on disk (or elsewhere if you want) all features and models it computes, so nothing is recomputed unnecessarily. Results are stored and can be retrieved, compared, blended, and reused between runs.

Easily extensible

Ramp has a simple API, allowing you to plug in estimators from scikit-learn, rpy2 and elsewhere, or easily build your own feature transformations, metrics and feature selectors.

Quick example

import urllib2
import tempfile
import pandas
import sklearn

# fetch iris data from UCI
data = pandas.read_csv(urllib2.urlopen(
    "http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"))
data = data.drop([149])
columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class']
data.columns = columns

# create ramp analysis context
ctx = DataContext(store=HDFPickleStore(tempfile.mkdtemp()), data=data)

# all features
features = [FillMissing(f, 0) for f in columns[:-1]]
# features, log transformed features, and interaction terms
expanded_features = features + [Log(F(f) + 1) for f in features] + [Interactions(features)]

# base configuration
base_conf = Configuration(
    target=AsFactor('class'),
    metric=GeneralizedMCC()
    )

# define several models and feature sets to explore
factory = ConfigFactory(base_conf,
    model=[
        sklearn.ensemble.RandomForestClassifier(n_estimators=20),
        sklearn.linear_model.LogisticRegression(),
        ],
    features=[
        expanded_features,
        # Feature selection
        [FeatureSelector(
            expanded_features,
            RandomForestSelector(classifier=True), # use random forest's importance to trim
            AsFactor('class'), # target to use
            5, # keep top 5 features
            )],
        # Reduce feature dimension (pointless on this dataset)
        [SVDDimensionReduction(expanded_features, n_keep=5)],
        # Normalized features
        [Normalize(f) for f in expanded_features],
        ]
    )

for conf in factory:
    print conf
    # perform cross validation and report MCC scores
    models.print_scores(models.cv(ctx, conf))

TODO

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