基于sklearn的決策樹實作

本文中講解的是使用

sklearn

實作決策樹及其模組化過程，包含

• 資料的清洗和資料分離

  train_test_split

• 采用不同的名額，基尼系數或者資訊熵進行模組化，使用的是X_train和y_train
  • 執行個體化

  • fit

    拟合
• 預測功能：采用上面的兩種執行個體化進行預測

  y_pred = clf_gini.predict(X_test)

• 結果評估
  • 混淆矩陣
  • 準确率
  • 分類報告

封裝成函數實作

import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix  # 混淆矩陣
from sklearn.model_selection import train_test_split  # 資料分離子產品
from sklearn.tree import DecisionTreeClassifier   #  分類決策樹
from sklearn.metrics import accuracy_score  # 評價名額
from sklearn.metrics import classification_report   # 生成分類結果報告子產品

# 讀取資料 importing data
def load_data():
    balance_data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-'+'databases/balance-scale/balance-scale.data',sep=',',header=None)   # 導入資料集，同時設定頭部
    print("Dataset Length", len(balance_data))

    print(balance_data.head())
    return balance_data

# 訓練集和測試集的分離 splitting the dataset into train and test
def split_dataset(balance_data):

    X = balance_data.values[:, 1:5]  # 提取特征資料
    y = balance_data.values[:, 0]  # 提取資料标簽

    X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.3,
                                                      random_state=100)  # 進行資料分離

    return X, y, X_train, X_test, y_train, y_test

# 使用基尼系數進行訓練 training with giniIndex
def train_using_gini(X_train, y_train):

    # 先建立執行個體，再進行fit拟合
    clf_gini = DecisionTreeClassifier(criterion="gini"   # 執行個體化
                                     ,random_state=100
                                     ,max_depth=3
                                     ,min_samples_leaf=5)
    clf_gini.fit(X_train, y_train)  # fit拟合
    return clf_gini

# 使用資訊熵進行訓練 training with entropy
def train_using_entropy(X_train, y_train):

    # 執行個體化+fit拟合
    clf_entropy = DecisionTreeClassifier(criterion="entropy"
                                     ,random_state=100
                                     ,max_depth=3
                                     ,min_samples_leaf=5)
    clf_entropy.fit(X_train, y_train)
    return clf_entropy

# 預測功能 make predictions
def prediction(X_test, clf_object):

    y_pred = clf_object.predict(X_test)
    print("Predicted vlaues:")
    print(y_pred)
    return y_pred

# 計算準确率 calculate accuracy
def cal_accuracy(y_test, y_pred):

    print("Confusion Matrix:", confusion_matrix(y_test, y_pred))

    print("Accuracy:", accuracy_score(y_test, y_pred)*100)

    print("Report:", classification_report(y_test, y_pred))

def main():
    data = load_data()
    X, y, X_train, X_test, y_train, y_test = split_dataset(data)
    clf_gini = train_using_gini(X_train, y_train)
    clf_entropy = train_using_entropy(X_train, y_train)

    print("result using gini Index:")
    y_pred_gini = prediction(X_test, clf_gini)
    cal_accuracy(y_test, y_pred_gini)

    print("result using Entropy:")
    y_pred_entropy = prediction(X_test, clf_entropy)
    cal_accuracy(y_test, y_pred_entropy)

if __name__ == "__main__":
    main()           

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