ramp 0.1.4

Rapid machine learning prototyping

Ramp is a python module for rapid prototyping of machine learning solutions. It is essentially a [pandas](http://pandas.pydata.org) wrapper around various python machine learning and statistics libraries ([scikit-learn](http://scikit-learn.org), [rpy2](http://rpy.sourceforge.net/rpy2.html), etc.), providing a simple, declarative syntax for exploring features, algorithms and transformations quickly and efficiently.

Documentation: http://ramp.readthedocs.org

Why Ramp?

• Clean, declarative syntax

  No more hackish one-off spaghetti scripts!

• Complex feature transformations

  Chain and combine features:

`python Normalize(Log('x')) Interactions([Log('x1'), (F('x2') + F('x3')) / 2]) `

Reduce feature dimension:

`python DimensionReduction([F('x%d'%i) for i in range(100)], decomposer=PCA(n_components=3)) `

Incorporate residuals or predictions to blend with other models:

`python 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 in fast HDF5 format (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.

• Easy extensibility

  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, feature selectors, reporters, or estimators.

## Quick start [Getting started with Ramp: Classifying insults](http://www.kenvanharen.com/2012/11/getting-started-with-ramp-detecting.html)

Or, the quintessential Iris example:

```python

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import pandas from ramp import * import urllib2 import sklearn from sklearn import decomposition

# fetch and clean iris data from UCI data = pandas.read_csv(urllib2.urlopen(

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"http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"))

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data = data.drop([149]) # bad line columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class'] data.columns = columns

# all features features = [FillMissing(f, 0) for f in columns[:-1]]

# features, log transformed features, and interaction terms expanded_features = (

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features + [Log(F(f) + 1) for f in features] + [

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F('sepal_width') ** 2, combo.Interactions(features),

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]

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)

# Define several models and feature sets to explore, # run 5 fold cross-validation on each and print the results. # We define 2 models and 4 feature sets, so this will be # 4 * 2 = 8 models tested. shortcuts.cv_factory(

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data=data,

target=[AsFactor('class')], metrics=[[metrics.GeneralizedMCC()]],

# Try out two algorithms model=[

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sklearn.ensemble.RandomForestClassifier(n_estimators=20), sklearn.linear_model.LogisticRegression(), ],

# and 4 feature sets features=[

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expanded_features,

# Feature selection [trained.FeatureSelector(

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expanded_features, # use random forest's importance to trim selectors.RandomForestSelector(classifier=True), target=AsFactor('class'), # target to use n_keep=5, # keep top 5 features )],

# Reduce feature dimension (pointless on this dataset) [combo.DimensionReduction(expanded_features,

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decomposer=decomposition.PCA(n_components=4))],

# Normalized features [Normalize(f) for f in expanded_features],

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]

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)

```

## Status Ramp is very alpha currently, so expect bugs, bug fixes and API changes.

## Requirements

• Numpy
• Scipy
• Pandas
• PyTables
• Sci-kit Learn

## Author Ken Van Haren. Email with feedback/questions: kvh@science.io