ztree 0.1.1

ZTree is a Python wrapper around a Java decision tree algorithm.

ZTree

ZTree is a Python wrapper around a Java-based decision tree algorithm using JPype.

Features

• Classification and regression via Java backend.
• Classification and regression tasks will be auto-detected via the inputted target array
• tree = ZTree(feature_names=col_names, z_thresh=0.2) to insantiate a model.
• If no feauture names provided, dummy names will be made. If no z_thresh provided, it will default to 0.5.
• tree.fit(X, y) to fit a decision tree with X as the instance array and y as the target array.
• tree.optimal_fit(X, y) to fit a decision tree with optimized z_thresh.
• tree.search_optimal_z_thresh(X, y) to return the optimal z_thresh.
• tree.predict(X) returns the predicted class labels for the input features X.
• tree.predict_proba(X) returns the predicted class probabilities for classification tasks. For each input sample in X, it returns the probability of belonging to each class (e.g., [0.25, 0.75] for class 0 and class 1).
• print_tree() to print out the trained tree
• more features to be added in the future, i.e. JSON

Installation

Install via pip: bash pip install ztree

Requirements

To use ZTree, ensure the following dependencies are available in your environment.

Python

• Python >= 3.8

Python Dependencies

These will be installed automatically when using pip install ztree:

• numpy – Array and numerical operations
• scikit-learn – ML utilities and estimator API
• jpype1 – Bridge to call Java from Python

Java

• Java Development Kit (JDK) 8 or higher
• Make sure java is available in your system PATH or set JAVA_HOME

Note: JPype requires a working Java installation to start the JVM from Python.

ZTree Parameters

• z_thresh: float
  Default = 0.5. The Z-statistic threshold used for feature evaluation.

• feature_names: list[str]
  Optional. Used to map column names to features in Java.

Basic Usage / Quick start

# basic imports
import pandas as pd
import numpy as np
import zipfile
from sklearn.metrics import roc_auc_score
from ztree import ZTree
from sklearn.model_selection import train_test_split

# import dataset, UCI Adult Income for example
zip_path = "C:/Users/ericr/Downloads/adult.zip"
with zipfile.ZipFile(zip_path, 'r') as z:
    with z.open("adult.data") as f1:
        df1 = pd.read_csv(f1, header=None, sep=",", skipinitialspace=True)
    with z.open("adult.test") as f2:
        df2 = pd.read_csv(f2, header=None, sep=",", skipinitialspace=True, skiprows=1)
datafile = pd.concat([df1, df2], ignore_index=True)

# create column names for printing / preprocessing
col_names = ["age", "workclass", "fnlwgt", "education", "education-num", "marital-status", "occupation", "relationship",
             "race", "sex", "capital-gain", "capital-loss",  "hours-per-week", "native-country", "income"]
datafile.columns = col_names
datafile['income'] = datafile['income'].apply(lambda x: 1 if x in {'>50K', '>50K.'} else 0)
datafile = datafile.drop(columns=["fnlwgt"])
col_names.remove("fnlwgt")
# notice one-hot encoding is not necessary, force categorical features to strings and continuous features leave as floats/ints
X = datafile.drop(columns=["income"])
for col in X.select_dtypes(include=['object', 'category']).columns:
    X[col] = X[col].astype(str) 
col_names = list(X.columns)
y = datafile["income"]
X = X.values
y = y.values
y = y.astype("int64")
# train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.7)
# make tree
ztree1 = ZTree(feature_names=col_names, z_thresh=0.5)
ztree1.fit(X_train, y_train)
y_pred1 = ztree1.predict_proba(X_test)[:, 1]
auc = roc_auc_score(y_test, y_pred1)
print(f"{auc:.6f}")
ztree1.print_tree()
ztree2 = ZTree(feature_names=col_names)
ztree2.fit_optimal(X_train, y_train)
y_pred2 = ztree2.predict_proba(X_test)[:, 1]
auc = roc_auc_score(y_test, y_pred2)
print(f"{auc:.6f}")
ztree2.print_tree()