快速開發機器學習原型：Ramp

Ramp是一個Python庫用于快速搭建機器學習解決方案原型。它是一個輕量級基于pandas的機器學習框架，可插入已有的Python機器學習和統計工具（如scikit-learn, rpy2等）。Ramp提供了一個簡單的聲明性語法探索功能，算法和快速，高效地轉換。

Why Ramp?

• Clean, declarative syntax

• 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.

• Composability

  All features, estimators, and their fits are composable, pluggable and storable.

• 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.

快速入門

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],
    ]
)

項目主頁：http://www.baiduhome.net/lib/view/home/1404267926436