c50py 0.2.1

C5.0-like Decision Trees in Python (scikit-learn style)

c5py — C5.0‑style Decision Trees for Python (clean 0.2.0)

c5py provides transparent, easily inspectable decision trees modelled on Quinlan’s C5.0 algorithm. Both classification and regression trees are supported and expose a scikit‑learn‑like API. The implementation is written from scratch in pure Python/Numpy and includes support for numeric and categorical variables, missing values, pre‑ and post‑pruning, boosting, rule tracing/export and Graphviz visualisation.

Features

• Scikit-learn API: fit(X, y), predict(X), score(X, y).
• Categorical support: Pass categorical_features=[0, 2] to handle categories natively.
• Sample weights: Supports sample_weight in fit for weighted splitting and pruning.
• Missing values: Handles missing values using C5.0's fractional propagation strategy.
• Boosting: Set trials=10 to train a boosted ensemble.
• Rule export: call export_rules() to get a list of human-readable rules.
• Graphviz export: call export_graphviz() to visualize the tree.
• Pretty printing: call print_tree to display the learned splits in a readable nested if/else format (single trees only).

Documentation

For a comprehensive guide on how to use c50py, including advanced features and examples, please see the Usage Guide.

Installation (development mode)

Install the package into your environment in editable mode:

pip install -e .

Quickstart (Classification)

import pandas as pd
from time import perf_counter
from c50py import C5Classifier

df = pd.read_csv("titanic.csv")
t0 = perf_counter(); clf.fit(X, y); print(f"fit: {perf_counter()-t0:.3f}s")

# Inspect the tree
clf.print_tree(feature_names=features, class_names=["No", "Yes"])

# Extract rules for each sample
rules = clf.predict_rule(X, feature_names=features)
print(rules[:5])

# Export as Graphviz
path = clf.export_graphviz(
    "titanic_tree",
    feature_names=features,
    class_names=["No", "Yes"],
    format="dot"  # save a .dot file directly
)
print(f"DOT file written to {path}")

Quickstart – Regression (Diabetes)

Fit a regression tree to the diabetes dataset and obtain a visualisation:

import pandas as pd
from time import perf_counter
from c5py import C5Regressor

df = pd.read_csv("diabetes.csv")
y = df["target"].values
X_df = df.drop(columns=["target"])
X = X_df.values.astype(object)
features = list(X_df.columns)

reg = C5Regressor(
    min_samples_split=30,
    min_samples_leaf=10,
    pruning=True, cf=0.25, global_pruning=True,
    feature_names=features,
    random_state=42,
    infer_categorical=False, int_as_categorical=False,
    numeric_threshold_strategy="quantile", max_numeric_thresholds=64
)

start = perf_counter(); reg.fit(X, y); print(f"fit: {perf_counter()-start:.3f}s")

# Export to DOT (Graphviz installed optional)
dot_path = reg.export_graphviz("diabetes_tree", feature_names=features, format="dot")
print(f"Tree saved to {dot_path}")

# Export human‑readable rules (single trees only)
rules = reg.export_rules(feature_names=features)
print(rules[:3])

Performance tuning

Several hyperparameters influence model complexity and performance:

• numeric_threshold_strategy ('quantile' | 'all'): subsample candidate numeric thresholds. With 'quantile' the number of splits considered is limited to max_numeric_thresholds per feature per node. 'all' evaluates every unique midpoint (slower on large datasets).
• max_numeric_thresholds: number of candidate thresholds when using 'quantile' (typically 32–64).
• categorical_features: list of names or indices marking categorical columns.
• max_categories_exhaustive: maximum cardinality for exhaustive subset search on categorical features; beyond this a simpler one‑vs‑rest strategy is used.
• infer_categorical/int_as_categorical: enable automatic detection of categorical/boolean/integer columns when dtype information is not explicit.
• max_depth: optional depth limit for extremely noisy or deep trees.

When boosting (trials > 1) the same hyperparameters apply to each base tree.