Making SVM run faster in python

If you want to stick with SVC as much as possible and train on the full dataset, you can use ensembles of SVCs that are trained on subsets of the data to reduce the number of records per classifier (which apparently has quadratic influence on complexity). Scikit supports that with the BaggingClassifier wrapper. That should give you similar (if not better) accuracy compared to a single classifier, with much less training time. The training of the individual classifiers can also be set to run in parallel using the n_jobs parameter.

Alternatively, I would also consider using a Random Forest classifier - it supports multi-class classification natively, it is fast and gives pretty good probability estimates when min_samples_leaf is set appropriately.

I did a quick tests on the iris dataset blown up 100 times with an ensemble of 10 SVCs, each one trained on 10% of the data. It is more than 10 times faster than a single classifier. These are the numbers I got on my laptop:

Single SVC: 45s

Ensemble SVC: 3s

Random Forest Classifier: 0.5s

See below the code that I used to produce the numbers:

import timeimport numpy as npfrom sklearn.ensemble import BaggingClassifier, RandomForestClassifierfrom sklearn import datasetsfrom sklearn.multiclass import OneVsRestClassifierfrom sklearn.svm import SVCiris = datasets.load_iris()X, y = iris.data, iris.targetX = np.repeat(X, 100, axis=0)y = np.repeat(y, 100, axis=0)start = time.time()clf = OneVsRestClassifier(SVC(kernel='linear', probability=True, class_weight='auto'))clf.fit(X, y)end = time.time()print "Single SVC", end - start, clf.score(X,y)proba = clf.predict_proba(X)n_estimators = 10start = time.time()clf = OneVsRestClassifier(BaggingClassifier(SVC(kernel='linear', probability=True, class_weight='auto'), max_samples=1.0 / n_estimators, n_estimators=n_estimators))clf.fit(X, y)end = time.time()print "Bagging SVC", end - start, clf.score(X,y)proba = clf.predict_proba(X)start = time.time()clf = RandomForestClassifier(min_samples_leaf=20)clf.fit(X, y)end = time.time()print "Random Forest", end - start, clf.score(X,y)proba = clf.predict_proba(X)

If you want to make sure that each record is used only once for training in the BaggingClassifier, you can set the bootstrap parameter to False.

SVM classifiers don't scale so easily. From the docs, about the complexity of sklearn.svm.SVC.

The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to dataset with more than a couple of 10000 samples.

In scikit-learn you have svm.linearSVC which can scale better. Apparently it could be able to handle your data.

Alternatively you could just go with another classifier. If you want probability estimates I'd suggest logistic regression.Logistic regression also has the advantage of not needing probability calibration to output 'proper' probabilities.

Edit:

I did not know about linearSVC complexity, finally I found information in the user guide:

Also note that for the linear case, the algorithm used in LinearSVC by the liblinear implementation is much more efficient than its libsvm-based SVC counterpart and can scale almost linearly to millions of samples and/or features.

To get probability out of a linearSVC check out this link. It is just a couple links away from the probability calibration guide I linked above and contains a way to estimate probabilities. Namely:

    prob_pos = clf.decision_function(X_test)    prob_pos = (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())

Note the estimates will probably be poor without calibration, as illustrated in the link.

It was briefly mentioned in the top answer; here is the code: The quickest way to do this is via the n_jobs parameter: replace the line

clf = OneVsRestClassifier(SVC(kernel='linear', probability=True, class_weight='auto'))

with

clf = OneVsRestClassifier(SVC(kernel='linear', probability=True, class_weight='auto'), n_jobs=-1)

This will use all available CPUs on your Computer, while still doing the same computation as before.