- Free software: MIT license
- Documentation: https://cacp.readthedocs.io.
- Article: https://doi.org/10.1016/j.softx.2022.101134
CACP is made for comparing newly developed classification algorithms (both traditional and incremental) in Python with other commonly used classifiers to evaluate classification performance, reproducibility, and statistical reliability. CACP simplifies the entire classifier evaluation process.
To install cacp, run this command in your terminal:
pip install cacpJupyter Notebook on Kaggle: https://www.kaggle.com/sc4444/cacp-example-usage
An example usage of this library is included in the package: https://github.com/sylwekczmil/cacp/tree/main/cacp_examples_simple.
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from cacp import run_experiment, ClassificationDataset
# select datasets
experimental_datasets = [
ClassificationDataset('iris'),
ClassificationDataset('wisconsin'),
ClassificationDataset('pima'),
ClassificationDataset('wdbc'),
]
# select classifiers
experimental_classifiers = [
('SVC', lambda n_inputs, n_classes: SVC()),
('DT', lambda n_inputs, n_classes: DecisionTreeClassifier(max_depth=5)),
('RF', lambda n_inputs, n_classes: RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1)),
('KNN', lambda n_inputs, n_classes: KNeighborsClassifier(3)),
]
# trigger experiment run
run_experiment(
experimental_datasets,
experimental_classifiers,
results_directory='./example_result'
)An advanced example usage of this library is included in the package: https://github.com/sylwekczmil/cacp/tree/main/cacp_examples.
from sklearn.neighbors import KNeighborsClassifier
from skmultiflow.lazy import KNNClassifier
from skmultiflow.meta import LearnPPNSEClassifier
from cacp import all_datasets, run_experiment, ClassificationDataset
from cacp_examples.classifiers import CLASSIFIERS
from cacp_examples.example_custom_classifiers.xgboost import XGBoost
# you can specify datasets by name, all of them will be automatically downloaded
experimental_datasets_example = [
ClassificationDataset('iris'),
ClassificationDataset('wisconsin'),
ClassificationDataset('pima'),
ClassificationDataset('sonar'),
ClassificationDataset('wdbc'),
]
# or use all datasets
experimental_datasets = all_datasets()
# same for classifiers, you can specify list of classifiers
experimental_classifiers_example = [
('KNN_3', lambda n_inputs, n_classes: KNeighborsClassifier(3)),
# you can define classifiers multiple times with different parameters
('KNN_5', lambda n_inputs, n_classes: KNeighborsClassifier(5)),
# you can use classifiers from any lib that
# supports fit/predict methods eg. scikit-learn/scikit-multiflow
('KNNI', lambda n_inputs, n_classes: KNNClassifier(n_neighbors=3)),
# you can also use wrapped algorithms from other libs or custom implementations
('XGB', lambda n_inputs, n_classes: XGBoost()),
('LPPNSEC', lambda n_inputs, n_classes: LearnPPNSEClassifier())
]
# or you can use predefined ones
experimental_classifiers = CLASSIFIERS
# this is how you trigger experiment run
run_experiment(
experimental_datasets,
experimental_classifiers,
results_directory='./example_result'
)Defining custom classifier wrapper: https://github.com/sylwekczmil/cacp/tree/main/cacp_examples/example_custom_classifiers/xgboost.py.
Defining custom dataset: https://github.com/sylwekczmil/cacp/tree/main/cacp_examples/example_custom_datasets/random_dataset.py
Defining local dataset: https://github.com/sylwekczmil/cacp/tree/main/cacp_examples/example_custom_datasets/local_dataset.py
An example usage of this library for incremental classifiers is included in the package: https://github.com/sylwekczmil/cacp/tree/main/cacp_examples_incremental.
import river
from river.ensemble import AdaptiveRandomForestClassifier
from river.naive_bayes import GaussianNB
from river.neighbors import KNNClassifier
from river.tree import HoeffdingTreeClassifier
from cacp import run_incremental_experiment, ClassificationDataset
if __name__ == '__main__':
# select datasets
experimental_datasets = [
ClassificationDataset('iris'),
ClassificationDataset('wisconsin'),
# you can use datasets from river
river.datasets.Phishing(),
river.datasets.Bananas(),
]
# select incremental classifiers
experimental_classifiers = [
('ARF', lambda n_inputs, n_classes: AdaptiveRandomForestClassifier()),
('HAT', lambda n_inputs, n_classes: HoeffdingTreeClassifier()),
('KNN', lambda n_inputs, n_classes: KNNClassifier()),
('GNB', lambda n_inputs, n_classes: GaussianNB()),
]
# trigger experiment run
run_incremental_experiment(
experimental_datasets,
experimental_classifiers,
results_directory='./example_result'
)