[机器学习] Yellowbrick使用笔记4-目标可视化
目标可视化工具专门用于直观地描述用于监督建模的因变量,通常称为y目标。
代码下载
当前实现了以下可视化:
- 平衡箱可视化Balanced Binning:生成带有垂直线的直方图,垂直线显示推荐值点,以将数据装箱到均匀分布的箱中。
- 类平衡Class Balance:可视化来检查目标,以显示每个类对最终估计器的支持。
- 特征相关Feature Correlation:绘制特征和因变量之间的相关性。
文章目录
头文件调用如下:
# Target Visualizers Imports
from yellowbrick.target import BalancedBinningReference
from yellowbrick.target import ClassBalance
from yellowbrick.target import FeatureCorrelatio
本文如果数据集下载不下来,查看下面地址,然后放入yellowbrick安装目录\datasets\fixtures文件夹:
{
"bikeshare": {
"url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/bikeshare.zip",
"signature": "4ed07a929ccbe0171309129e6adda1c4390190385dd6001ba9eecc795a21eef2"
},
"hobbies": {
"url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/hobbies.zip",
"signature": "6114e32f46baddf049a18fb05bad3efa98f4e6a0fe87066c94071541cb1e906f"
},
"concrete": {
"url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/concrete.zip",
"signature": "5807af2f04e14e407f61e66a4f3daf910361a99bb5052809096b47d3cccdfc0a"
},
"credit": {
"url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/credit.zip",
"signature": "2c6f5821c4039d70e901cc079d1404f6f49c3d6815871231c40348a69ae26573"
},
"energy": {
"url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/energy.zip",
"signature": "174eca3cd81e888fc416c006de77dbe5f89d643b20319902a0362e2f1972a34e"
},
"game": {
"url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/game.zip",
"signature": "ce799d1c55fcf1985a02def4d85672ac86c022f8f7afefbe42b20364fba47d7a"
},
"mushroom": {
"url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/mushroom.zip",
"signature": "f79fdbc33b012dabd06a8f3cb3007d244b6aab22d41358b9aeda74417c91f300"
},
"occupancy": {
"url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/occupancy.zip",
"signature": "0b390387584586a05f45c7da610fdaaf8922c5954834f323ae349137394e6253"
},
"spam": {
"url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/spam.zip",
"signature": "000309ac2b61090a3001de3e262a5f5319708bb42791c62d15a08a2f9f7cb30a"
},
"walking": {
"url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/walking.zip",
"signature": "7a36615978bc3bb74a2e9d5de216815621bd37f6a42c65d3fc28b242b4d6e040"
},
"nfl": {
"url": "https://s3.amazonaws.com/ddl-data-lake/yellowbrick/v1.0/nfl.zip",
"signature": "4989c66818ea18217ee0fe3a59932b963bd65869928c14075a5c50366cb81e1f"
}
}
1 平衡箱可视化Balanced Binning
通常,现实世界中的机器学习问题会受到维数诅咒的影响;训练实例比预期的要少,而且预测信号分布在许多不同的特征上。有时,当目标变量连续赋值时,根本没有足够的实例来预测这些值达到回归的精度。在这种情况下,我们有时可以将问题转化为连续的分类问题。
为了帮助用户选择最佳的仓位数量,BalancedBiningReference visualizer将目标变量y作为输入,并生成一个直方图,其中竖线表示建议的值点,以确保数据均匀地分布到每个仓位中。
可视化器 | BalancedBinningReference |
---|---|
快速使用方法 | balanced_binning_reference() |
模型 | 分类 |
工作流程 | 特征分析,目标分析,模型选择 |
# 多行输出
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
1.1 基本使用
BalancedBinningReference实际就是应用numpy中的histogram进行数据可视化,histogram()会对区间中数组所对应的权值进行求和,bins决定分箱个数。关于numpy中的histogram函数具体见:
numpy之histogram
from yellowbrick.datasets import load_concrete
from yellowbrick.target import BalancedBinningReference
import numpy as np
# Load the concrete dataset
X, y = load_concrete()
# Instantiate the visualizer
# 可视化器,求各个区间的平均值
visualizer = BalancedBinningReference(bins=5)
# Fit the data to the visualizer
# 拟合数据
a=visualizer.fit(y)
# 显示数据
visualizer.show();
<Figure size 800x550 with 1 Axes>
1.2 快速方法
上面的相同功能可以通过关联的快速方法来实现balanced_binning_reference。此方法将BalancedBinningReference使用关联的参数构建对象,将其拟合,然后(可选)立即显示它。
from yellowbrick.datasets import load_concrete
from yellowbrick.target import balanced_binning_reference
# Load the dataset
X, y = load_concrete()
# Use the quick method and immediately show the figure
balanced_binning_reference(y);
2 类平衡Class Balance
分类模型面临的最大挑战之一是训练数据中类的不平衡。严重的类不平衡可能被相对较好的F1和准确度分数掩盖-分类器只是猜测大多数类,而不对代表性不足的类进行任何评估。
有几种处理类不平衡的技术,例如分层抽样,对多数类进行下采样,加权等。但是,在采取这些措施之前,了解训练数据中的类平衡是什么很重要。ClassBalance visualizer通过为每个类创建支持的条形图来支持这一点,即数据集中类表示的频率。
可视化器 | ClassBalance |
---|---|
快速使用方法 | class_balance() |
模型 | 分类 |
工作流程 | 特征分析,目标分析,模型选择 |
2.1 基本使用
结果图使我们能够诊断余额问题的严重性。 在此图中,我们可以看到“ win”类主导了其他两个类。 一种可能的解决方案是创建一个二进制分类器:“ win”与“ not win”,并将“ loss”和“ draw”类组合为一个类。ClassBalance函数的功能就是计算各个类下样本数。
from yellowbrick.datasets import load_game
from yellowbrick.target import ClassBalance
# Load the classification dataset
# 载入分类数据库
X, y = load_game()
# Instantiate the visualizer
# ClassBalance函数的功能就是计算各个类下样本数。
visualizer = ClassBalance(labels=["draw", "loss", "win"])
visualizer.fit(y) # Fit the data to the visualizer
visualizer.show(); # Finalize and render the figure
如果在评估期间必须保持类别不平衡(例如,被分类的事件实际上如频率所暗示的那样罕见),则应使用分层抽样来创建训练和测试集。
这确保了测试数据与训练数据具有大致相同的类比例。虽然SCRICIT-LEARN默认在Train_Test_Split和其他cv方法中执行此操作,但是比较两个Split中每个类的支持情况可能是有用的。
ClassBalance可视化工具具有“比较”模式,在该模式下,可以将训练和测试数据传递给FIT(),从而创建并排条形图而不是单个条形图,如下所示:
from sklearn.model_selection import TimeSeriesSplit
from yellowbrick.datasets import load_occupancy
from yellowbrick.target import ClassBalance
# Load the classification dataset
X, y = load_occupancy()
# Create the training and test data
# 时间分割序列数据
tscv = TimeSeriesSplit()
for train_index, test_index in tscv.split(X):
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
# Instantiate the visualizer
visualizer = ClassBalance(labels=["unoccupied", "occupied"])
visualizer.fit(y_train, y_test) # Fit the data to the visualizer
visualizer.show();
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/model_selection/_split.py:752: FutureWarning: You should specify a value for 'n_splits' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.
warnings.warn(NSPLIT_WARNING, FutureWarning)
这种可视化使我们可以快速检查,以确保每个类在两个拆分中的比例大致相似。这种可视化应该是第一步,尤其是当评估指标在不同的分割中高度可变时。
2.2 快速使用
from yellowbrick.datasets import load_game
from yellowbrick.target import class_balance
# Load the dataset
X, y = load_game()
# Use the quick method and immediately show the figure
class_balance(y);
3 特征相关Feature Correlation
该可视化工具计算皮尔逊相关系数和特征与因变量之间的互信息。
这种可视化可以用于特征选择,以识别与因变量具有高相关性或大互信息的特征。
默认计算是Pearson相关性,这是使用scipy.stats.Pearsonr执行的。
可视化器 | FeatureCorrelation |
---|---|
快速使用方法 | feature_correlation() |
模型 | 回归/分类/聚类 |
工作流程 | 特征分析/模型选择 |
3.1 Person分析
FeatureCorrelation通过method设置来确定分析方法,默认是person分析,除此之外还有mutual_info-regression,通过sklearn.feature_selection中的mutual_info-regression计算,还有mutual_info-classification,通过sklearn.feature_selection中的mutual_info_classif计算。
from sklearn import datasets
from yellowbrick.target import FeatureCorrelation
# Load the regression dataset
data = datasets.load_diabetes()
X, y = data['data'], data['target']
# Create a list of the feature names
features = np.array(data['feature_names'])
# Instantiate the visualizer
# 计算x中的每个特征与y的相关性
visualizer = FeatureCorrelation(labels=features)
# Fit the data to the visualizer
visualizer.fit(X, y)
visualizer.show();
3.2 Mutual Information Regression分析
互信息详细见:https://www.cntofu.com/book/48/shu-xue-ji-chu/xin-xi-lun/hu-xin-xi.md
互信息与相关性的区别见:https://blog.csdn.net/gdanskamir/article/details/54913233
但是回归中,连续数据需要转换为离散数据。特征和因变量之间的相互信息是使用sklearn.feature_selection.mutual_info_classifwhen method='mutual_info-classification’和mutual_info_regressionwhen 计算的method=‘mutual_info-regression’。在计算互信息时,指定离散特征非常重要,因为连续变量和离散变量的计算是不同的。sklearn中参考文档见http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_classif.html
from sklearn import datasets
from yellowbrick.target import FeatureCorrelation
import numpy as np
# Load the regression dataset
data = datasets.load_diabetes()
X, y = data['data'], data['target']
# Create a list of the feature names
features = np.array(data['feature_names'])
# Create a list of the discrete features
# 创建离散变量列表
discrete = [False for _ in range(len(features))]
# discrete为age需要变为离散变量
discrete[1] = True
# Instantiate the visualizer
visualizer = FeatureCorrelation(method='mutual_info-regression', labels=features)
visualizer.fit(X, y, discrete_features=discrete, random_state=0)
visualizer.show();
3.3 Mutual Information Classification分析
通过与pandas DataFrame配合,可以从列名称中自动获取功能标签。该可视化器还允许根据计算出互信息(或Pearson相关系数)对条形图进行排序,并通过指定特征名称或特征索引来选择要绘制的特征。
import pandas as pd
from sklearn import datasets
from yellowbrick.target import FeatureCorrelation
# Load the regression dataset
# 导入分类数据
data = datasets.load_wine()
X, y = data['data'], data['target']
X_pd = pd.DataFrame(X, columns=data['feature_names'])
# Create a list of the features to plot
features = ['alcohol', 'ash', 'hue', 'proline', 'total_phenols']
# Instaniate the visualizer
# sort设置是否排序
visualizer = FeatureCorrelation(
method='mutual_info-classification', feature_names=features, sort=True
)
visualizer.fit(X_pd, y) # Fit the data to the visualizer
visualizer.show();
3.4 快速使用
上面的相同功能可以通过关联的快速方法来实现feature_correlation。此方法将FeatureCorrelation使用关联的参数构建对象,将其拟合,然后(可选)立即显示它
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from yellowbrick.target.feature_correlation import feature_correlation
#Load the diabetes dataset
data = datasets.load_iris()
X, y = data['data'], data['target']
features = np.array(data['feature_names'])
visualizer = feature_correlation(X, y, labels=features)
plt.tight_layout();
<Figure size 432x288 with 0 Axes>
4 参考
https://www.scikit-yb.org/en/latest/api/target/binning.html
https://www.scikit-yb.org/en/latest/api/target/class_balance.html
本文来自博客园,作者:落痕的寒假,转载请注明原文链接:https://www.cnblogs.com/luohenyueji/p/16970250.html
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