sklearn-weka-plugin

Makes Weka algorithms available in scikit-learn.

Built on top of the python-weka-wrapper3 library, it uses the jpype library under the hood for communicating with Weka objects in the Java Virtual Machine.

Functionality

The following is currently available:

• Classifiers (classification/regression)
• Clusters
• Filters

Things to be aware of:

• You need to start the JVM in your Python code before you can use Weka (and stop it again).
• Unlikely to work in multi-threaded/process environments (like flask).
• Jupyter Notebooks do not play nice with jpype, as you might have to restart the kernel in order to be able to restart the JVM (e.g., with additional packages).
• The conversion to Weka data structures involves guesswork, i.e., if targets are to be treated as nominal, you need to convert the numeric values to strings (e.g., using to_nominal_labels and/or to_nominal_attributes functions from sklweka.dataset or the MakeNominal transformer from sklweka.preprocessing).
• Check the list of known problems before reporting one.

Requirements

The library has the following requirements:

• Python 3 (does not work with Python 2)

  • python-weka-wrapper (>=0.3.0, required)

• OpenJDK 8 or later (11 is recommended)

Installation

• install the python-weka-wrapper3 library in a virtual environment, see instructions here:

  https://fracpete.github.io/python-weka-wrapper3/install.html

• install the sklearn-weka-plugin library itself in the same virtual environment

  • latest release from PyPI

    ./venv/bin/pip install sklearn-weka-plugin
    

  • from local source

    ./venv/bin/pip install .   
    

  • from Github repository

    ./venv/bin/pip install git+https://github.com/fracpete/sklearn-weka-plugin.git   
    

Examples

Here is a quick example (of which you need to adjust the paths to the datasets, of course):

import sklweka.jvm as jvm
from sklweka.dataset import load_arff, to_nominal_labels
from sklweka.classifiers import WekaEstimator
from sklweka.clusters import WekaCluster
from sklweka.preprocessing import WekaTransformer
from sklearn.model_selection import cross_val_score
from sklweka.datagenerators import DataGenerator, generate_data

# start JVM with Weka package support
jvm.start(packages=True)

# regression
X, y, meta = load_arff("/some/where/bolts.arff", class_index="last")
lr = WekaEstimator(classname="weka.classifiers.functions.LinearRegression")
scores = cross_val_score(lr, X, y, cv=10, scoring='neg_root_mean_squared_error')
print("Cross-validating LR on bolts (negRMSE)\n", scores)

# classification
X, y, meta = load_arff("/some/where/iris.arff", class_index="last")
y = to_nominal_labels(y)
j48 = WekaEstimator(classname="weka.classifiers.trees.J48", options=["-M", "3"])
j48.fit(X, y)
scores = j48.predict(X)
probas = j48.predict_proba(X)
print("\nJ48 on iris\nactual label -> predicted label, probabilities")
for i in range(len(y)):
    print(y[i], "->", scores[i], probas[i])

# clustering
X, y, meta = load_arff("/some/where/iris.arff", class_index="last")
cl = WekaCluster(classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
clusters = cl.fit_predict(X)
print("\nSimpleKMeans on iris\nclass label -> cluster")
for i in range(len(y)):
    print(y[i], "->", clusters[i])

# preprocessing
X, y, meta = load_arff("/some/where/bolts.arff", class_index="last")
tr = WekaTransformer(classname="weka.filters.unsupervised.attribute.Standardize", options=["-unset-class-temporarily"])
X_new, y_new = tr.fit(X, y).transform(X, y)
print("\nStandardize filter")
print("\ntransformed X:\n", X_new)
print("\ntransformed y:\n", y_new)

# generate data
gen = DataGenerator(
    classname="weka.datagenerators.classifiers.classification.BayesNet",
    options=["-S", "2", "-n", "10", "-C", "10"])
X, y, X_names, y_name = generate_data(gen, att_names=True)
print("X:", X_names)
print(X)
print("y:", y_name)
print(y)

# stop JVM
jvm.stop()

See the example repository for more examples:

https://github.com/fracpete/sklearn-weka-plugin-examples

Direct links:

• classifiers
• clusters
• preprocessing
• datagenerators
• pipeline

Documentation

You can find the project documentation here:

https://fracpete.github.io/sklearn-weka-plugin/