pyautoweka is a python wrapper for Auto-WEKA, a Java application for algorithm selection and hyperparameter optimizations, that is build on WEKA.
Download, go to the project sources and install:
git clone [email protected]:tdomhan/pyautoweka.git
cd pyautoweka
python setup.py install
AutoWeka for python.
import pyautoweka
#Create an experiment
experiment = pyautoweka.ClassificationExperiment(tuner_timeout=360)tuner_timeout is the time the optimization will run in seconds. So e.g. 360 seconds = 6 minutes. The longer you run the optimization, the better of course. (Note that the experiment object has an interface similar to sklearn classifiers.)
First we need to load some data. Let's for example the famous Iris dataset. Download it using this link.
Let's load it into python:
#load the data:
import numpy as np
import random
X = np.loadtxt("iris.data", delimiter=",", usecols=range(4))
y = np.loadtxt("iris.data", delimiter=",", usecols=[4], dtype="object")
#shuffle the data:
indices = range(len(X))
random.shuffle(indices)
X = X[indices]
y = y[indices]
#split into train and test set:
X_train = X[0:100]
y_train = y[0:100]
X_test = X[100:]
y_test = y[100:]
#now we can fit a model:
experiment.fit(X_train, y_train)
#and predict the labels of the held out data:
y_predict = experiment.predict(X_test)
#Let's check what accuracy we get:
num_correct = sum([1 for predicted, correct in zip(y_predict, y_test) if predicted == correct])
print "Accuracy: %f" % (float(num_correct) / len(y_test))This should give you an accuracy in the high 90%s.
import pyautoweka
#Create an experiment
experiment = pyautoweka.RegressionExperiment(tuner_timeout=360)First we need to load some data. Let's for example the Boston housing dataset. Download it using this link.
#load the data:
import numpy as np
import random
X = np.loadtxt("housing.data.txt", usecols=range(13))
y = np.loadtxt("housing.data.txt", usecols=[13])
#shuffle the data:
indices = range(len(X))
random.shuffle(indices)
X = X[indices]
y = y[indices]
#split into train and test set:
X_train = X[0:100]
y_train = y[0:100]
X_test = X[100:]
y_test = y[100:]
#now we can fit a model:
experiment.fit(X_train, y_train)
#and regress on held out test data:
y_predict = experiment.predict(X_test)
#RMSE of the prediction:
rmse = np.sqrt(((y_predict-y_test)**2).mean())When you don't set a specific classifier all available classifiers will be tried. You have the option to limit the search to certain classifiers as follows:
First of all let's see what classifiers are available:
import pyautoweka
print pyautoweka.AVAILABLE_CLASSIFIERSNow let's say we want to just use the Simple Logistic classifier:
experiment.add_classfier("weka.classifiers.functions.SimpleLogistic")When you create a new experiment theres a bunch of files that will be generated before and during the run of AutoWeka. For each experiment there will be a new folder within in the experiments folder. The folder will have the name of the experiment, if it was specified in the constructor. Each time you fit data a tempraroy arff file will be created that holds all the data in it. This file will be delete after the fit call.
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