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SciKit learn wrapper for XCS algorithm implementation.

Project description


skxcs is a SciKit learn wrapper for implementation of XCS algorithm xcs.


Use the package manager pip to install skxcs.

pip install skxcs


Numeric Values

from skxcs.classifiers import XcsClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Numeric values
iris = load_iris()
X, y =,
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

classifier = XcsClassifier()

# If data input is non binary, classifier automatically uses MLDP discretizer for numeric values
# and one hot encoding for categorical values to transform data in both fit and predict methods., y_train)

# Get prediction array
y_pred = classifier.predict(X_test)

# Get pretty rules
for rule in classifier.get_pretty_rules():

# To use get_pretty_rules or pretty_print_prediction methods,
# classifier has to transform train and test data first.

Categorical values

import pandas as pd
from skxcs.classifiers import XcsClassifier
from sklearn.model_selection import train_test_split

# Categorical values
categorical_frame = pd.read_csv(
    header=None, na_values="?").dropna()
y = categorical_frame[0]
X = categorical_frame.select_dtypes(include=[object])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
classifier = XcsClassifier()

# You can transform data yourself. You should either transform both training
# and testing data, or none of them. It is necessary to ensure correct values are passed to classifier.
X_train_bin = classifier.transform_df(X_train, y=y_train), y_train)

# Note that we don't pass 'y' to transform method when we transform test data
X_test_bin = classifier.transform_df(X_test)

# pretty print prediction
result = classifier.pretty_print_prediction(X_test_bin)





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