rulefit-rules-output.py

# -*- coding: utf-8 -*-
"""
This a implementation of the rulefit algorithm for predict bio datasets and output rules.
Derived from: Christoph Molnar
Source: https://github.com/christophM/rulefit
Date: 2018-12-11
"""
import os
import time
import numpy as np
import pandas as pd
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter

__author__ = 'Min'


if __name__ == "__main__":
    start_time = time.time()
    parser = ArgumentParser(description="This a implementation of the rulefit algorithm for predict bio datasets and output rules.",
                            formatter_class=ArgumentDefaultsHelpFormatter)

    parser.add_argument("-t", "--treesize", type=int,
                        help="Number of terminal nodes in generated trees. If exp_rand_tree_size=True, this will be the mean number of terminal nodes.", default=4)
    parser.add_argument("-m", "--maxrules", type=int,
                        help="approximate total number of rules generated for fitting. Note that actual number of rules will usually be lower than this due to duplicates.", default=2000)
    parser.add_argument("--rfmode", type=str,
                        help="'regress' for regression or 'classify' for binary classification.", default='classify')
    parser.add_argument("-k", "--kfolds", type=int,
                        help="Number of folds. Must be at least 2.", default=10)
    parser.add_argument("-r", "--randomseed", type=int,
                        help="pseudo-random number generator state used for shuffling.", default=None)
    parser.add_argument("--datapath", type=str,
                        help="The path of dataset.", required=True)

    args = parser.parse_args()

    # 构建 estimator
    from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
    from sklearn.preprocessing import StandardScaler
    from rulefit import RuleFit
    from rulefit import utils

    # 读取数据
    dataset = pd.read_csv(args.datapath)

    # 设定分类信息和特征矩阵
    X_origin = dataset.iloc[:, 1:].values
    y = dataset.iloc[:, 0].values
    # 读取特征名称
    features = dataset.columns[1:]
    print("\nDataset shape: ", dataset.shape, " Number of features: ", features.size)
    # 不同 Class 统计 (根据第1列)
    class_info = dataset.groupby(dataset.columns[0]).size()
    print('\n', class_info)
    # print("\nClass info:", np.unique(y, return_counts=True))
    print("\nTraining Start...")

    # 标准化处理
    scaler = StandardScaler().fit(X_origin)
    X = scaler.transform(X_origin)

    # 初始化 estimator
    rf = RuleFit(tree_size=args.treesize, rfmode=args.rfmode,
                 max_rules=args.maxrules, random_state=args.randomseed)
    # 模型训练
    rf.fit(X, y, features)
    # 输出 rulefit 相关训练参数
    print("\n=== Model parameters ===")
    print("\nTree generator:{0}, \n\nMax rules:{1}, Tree size:{2}, Random state:{3}".format(
        rf.tree_generator, rf.max_rules, rf.tree_size, rf.random_state))
    # 验证
    y_pred = rf.predict(X)
    # 输出统计结果
    num_categories = class_info.values.size
    if(num_categories > 2):
        utils.model_evaluation(num_categories, y, y_pred)
    else:
        utils.bi_model_evaluation(y, y_pred)
    # 输出 rules
    rules = rf.get_rules()
    rules = rules[rules.coef != 0].sort_values("support", ascending=False)
    # 输出 rules 至 CSV 文件
    rules.to_csv('dataset_rules.csv')
    print("\nThe inspect rules have been saved to 'dataset_rules.csv'")
    end_time = time.time()  # 程序结束时间
    print("\n[Finished in: {0:.6f} mins = {1:.6f} seconds]".format(
        ((end_time - start_time) / 60), (end_time - start_time)))