hugeng007_adaboost_demo

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# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier

"""
生成分类面数据点
"""
def make_meshgrid(x, y, h=.02):
    """Create a mesh of points to plot in

    Parameters
    ----------
    x: data to base x-axis meshgrid on
    y: data to base y-axis meshgrid on
    h: stepsize for meshgrid, optional

    Returns
    -------
    xx, yy : ndarray
    """
    x_min, x_max = x.min() - 1, x.max() + 1
    y_min, y_max = y.min() - 1, y.max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    return xx, yy

"""
利用分类器对数据点进行分类
"""
def plot_contours(ax, clf, xx, yy, **params):
    """Plot the decision boundaries for a classifier.

    Parameters
    ----------
    ax: matplotlib axes object
    clf: a classifier
    xx: meshgrid ndarray
    yy: meshgrid ndarray
    params: dictionary of params to pass to contourf, optional
    """
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    out = ax.contourf(xx, yy, Z, **params)
    return out

"""
实验目的：AdaBoost实验

数据集：本程序使用Iris数据集是常用的分类实验数据集，由Fisher, 1936收集整理。
Iris也称鸢尾花卉数据集，是一类多重变量分析的数据集。
数据集包含150个数据集，分为3类，每类50个数据，每个数据包含4个属性。
可通过花萼长度，花萼宽度，花瓣长度，花瓣宽度4个属性预测鸢尾花卉属于
（Setosa，Versicolour，Virginica）三个种类中的哪一类。

注意：为了方面可视化，实验中取Iris数据集中前两维特征进行模型训练
"""

# import some data to play with
iris = datasets.load_iris()
# Take the first two features. We could avoid this by using a two-dim dataset
X = iris.data[:, :2]
y = iris.target

"""
函数说明：
    class sklearn.ensemble.AdaBoostClassifier(base_estimator=None, n_estimators=50, learning_rate=1.0, 
                        algorithm=’SAMME.R’, random_state=None)
 
参数说明：
http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
"""
# Create and fit an AdaBoosted decision tree
clf = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1),
                         algorithm="SAMME",
                         n_estimators=200)
clf.fit(X,y)

# title for the plots
title = ('AdaBoostClassifier')

# Set-up window for plotting.
fig, ax = plt.subplots(1, 1)
plt.subplots_adjust(wspace=0.4, hspace=0.4)

X0, X1 = X[:, 0], X[:, 1]
xx, yy = make_meshgrid(X0, X1)

"""
对平面内的点集分类并进行可视化处理
"""
plot_contours(ax, clf, xx, yy,
              cmap=plt.cm.coolwarm, alpha=0.8)
ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xlabel('Sepal length')
ax.set_ylabel('Sepal width')
ax.set_xticks(())
ax.set_yticks(())
ax.set_title(title)

plt.show()

hugeng007_adaboost_demo

标签：说明   sha   技术分享   ble   min()   title   实验目的   处理   max   

原文地址：https://www.cnblogs.com/hugeng007/p/9471592.html