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.
Documentation: http://ramp.readthedocs.org
Why Ramp?
Clean, declarative syntax
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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(simple_model_def) + Predictions(complex_model_def)-
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.
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Composability
All features, estimators, and their fits are composable, pluggable and storable.
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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
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([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 = (
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()],
],
# report feature importance scores from Random Forest
reporters=[
[reporters.RFImportance()],
],
# 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],
]
)Status
Ramp is alpha currently, so expect bugs, bug fixes and API changes.
Requirements
- Numpy
- Scipy
- Pandas
- PyTables
- Sci-kit Learn
- gensim
Author
Ken Van Haren. Email with feedback/questions: kvh@science.io @squaredloss
