Ramp - Rapid Machine Learning Prototyping
Ramp is a python library for rapid prototyping of machine learning solutions. It's a light-weight pandas-based machine learning framework pluggable with existing python machine learning and statistics tools (scikit-learn, rpy2, etc.). Ramp provides a simple, declarative syntax for exploring features, algorithms and transformations quickly and efficiently.
Clean, declarative syntax
No more hackish one-off spaghetti scripts!
Complex feature transformations
Chain and combine features:
Normalize(Log('x')) Interactions([Log('x1'), (F('x2') + F('x3')) / 2])
Reduce feature dimension:
DimensionReduction([F('x%d'%i) for i in range(100)], decomposer=PCA(n_components=3))
Incorporate residuals or predictions to blend with other models:
Residuals(config_model1) + Predictions(config_model2)
Data context awareness
Any feature that uses the target ("y") variable will automatically respect the current training and test sets. Similarly, preparation data (a feature's mean and stdev, for example) is stored and tracked between data contexts.
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.
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.
Or, the quintessential Iris example:
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( "http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data")) data = data.drop() # 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 = ( features + [Log(F(f) + 1) for f in features] + [ F('sepal_width') ** 2, combo.Interactions(features), ] ) # 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( data=data, target=[AsFactor('class')], metrics=[[metrics.GeneralizedMCC()]], # Try out two algorithms model=[ sklearn.ensemble.RandomForestClassifier(n_estimators=20), sklearn.linear_model.LogisticRegression(), ], # and 4 feature sets features=[ expanded_features, # Feature selection [trained.FeatureSelector( 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, decomposer=decomposition.PCA(n_components=4))], # Normalized features [Normalize(f) for f in expanded_features], ] )
Ramp is very alpha currently, so expect bugs, bug fixes and API changes.
- Sci-kit Learn