結合sklearn的可視化工具Yellowbrick：超參與行為的可視化帶來更優秀的實現

https://blog.csdn.net/qq_34739497/article/details/80508262

Yellowbrick 是一套名為「Visualizers」的視覺診斷工具，它擴展了 Scikit-Learn API 以允許我們監督模型的選擇過程。簡而言之，Yellowbrick 將 Scikit-Learn 與 Matplotlib 結合在一起，並以傳統 Scikit-Learn 的方式對模型進行可視化。

• 可視化器 
  可視化器（Visualizers）是一種從數據中學習的估計器，其主要目標是創建可理解模型選擇過程的可視化。在 Scikit-Learn 的術語中，它們類似於轉換器（transformer），其在可視化數據空間或包裝模型估計器上類似「ModelCV」（例如 RidgeCV 和 LassoCV）方法的過程。Yellowbrick 的主要目標是創建一個類似於 Scikit-Learn 的 API，其中一些流行的可視化器包括：

  特征可視化

  • Rank Features：單個或成對特征排序以檢測關系
  • Radial Visualization：圍繞圓形圖分離實例
  • PCA Projection：基於主成分分析映射實例
  • Manifold Visualization：通過流形學習實現高維可視化
  • Feature Importances：基於模型性能對特征進行排序
  • Recursive Feature Elimination：按重要性搜索最佳特征子集
  • Scatter and Joint Plots：通過特征選擇直接進行數據可視化

  分類可視化

  • Class Balance：了解類別分布如何影響模型
  • Class Prediction Error：展示分類的誤差與主要來源
  • Classification Report：可視化精度、召回率和 F1 分數的表征
  • ROC/AUC Curves：受試者工作曲線和曲線下面積
  • Confusion Matrices：類別決策制定的視覺描述
  • Discrimination Threshold：搜索最佳分離二元類別的閾值

  回歸可視化

  • Prediction Error Plots：沿着目標域尋找模型崩潰的原因
  • Residuals Plot：以殘差的方式展示訓練和測試數據中的差異
  • Alpha Selection：展示 alpha 的選擇如何影響正則化

  聚類可視化

  • K-Elbow Plot：使用肘法（elbow method）和多個指標來選擇 k
  • Silhouette Plot：通過可視化輪廓系數值來選擇 k

  模型選擇可視化

  • Validation Curve：對模型的單個超參數進行調整
  • Learning Curve：展示模型是否能從更多的數據或更低的復雜性中受益

  文本可視化

  • Term Frequency：可視化語料庫中詞項的頻率分布
  • t-SNE Corpus Visualization：使用隨機近鄰嵌入來投影文檔

  實例

#特征之間協方差可視化 from yellowbrick.features import Rank2D from sklearn.datasets import load_iris data=load_iris() visualizer = Rank2D(features=data['feature_names'], algorithm='covariance') visualizer.fit(data['data'], data['target']) # Fit the data to the visualizer visualizer.transform(data['data']) # Transform the data visualizer.poof() # Draw/show/poof the data

#梯度提升樹中特征重要性可視化 import matplotlib.pyplot as plt from sklearn.ensemble import GradientBoostingClassifier from yellowbrick.features import FeatureImportances from sklearn.datasets import load_iris data=load_iris() fig = plt.figure() ax = fig.add_subplot() viz = FeatureImportances(GradientBoostingClassifier(), relative=False) viz.fit(data['data'],data['target']) # Fit the data to the visualizer viz.poof() # Draw/show/poof the data

#線性支持向量機ROC曲線可視化 from sklearn.svm import LinearSVC from yellowbrick.classifier import ROCAUC model = LinearSVC() model.fit(data['data'],data['target']) visualizer = ROCAUC(model) visualizer.score(data['data'],data['target']) visualizer.poof()

#主成分分析二維降維可視化 from yellowbrick.features.pca import PCADecomposition visualizer = PCADecomposition(scale=True, center=False, color="g", proj_dim=2) visualizer.fit_transform(data['data'],data['target']) visualizer.poof()

#線性支持向量機准確率、召回率、f1-score可視化 from sklearn.svm import LinearSVC from yellowbrick.classifier import ClassificationReport from sklearn.model_selection import train_test_split model = LinearSVC() X_train, X_test, y_train, y_test = train_test_split(data['data'],data['target'], test_size=0.2) visualizer = ClassificationReport(model, classes=data['target_names']) visualizer.fit(X_train, y_train) # Fit the visualizer and the model visualizer.score(X_test, y_test) # Evaluate the model on the test data g = visualizer.poof() # Draw/show/poof the data

#alpha 的選擇如何影響正則化可視化 import numpy as np from sklearn.linear_model import LassoCV from yellowbrick.regressor import AlphaSelection # Create a list of alphas to cross-validate against alphas = np.logspace(-10, 1, 400)#以10為底對數，-10到1分成400份 # Instantiate the linear model and visualizer model = LassoCV(alphas=alphas) visualizer = AlphaSelection(model) visualizer.fit(data['data'],data['target']) g = visualizer.poof()

#肘部法則選擇最佳聚類的k from sklearn.cluster import MiniBatchKMeans from yellowbrick.cluster import KElbowVisualizer # Instantiate the clustering model and visualizer visualizer = KElbowVisualizer(MiniBatchKMeans(), k=(4,12)) visualizer.fit(data["data"]) # Fit the training data to the visualizer visualizer.poof() # Draw/show/poof the data

#訓練集數量對模型表現可視化 import numpy as np from sklearn.naive_bayes import MultinomialNB from sklearn.model_selection import StratifiedKFold from yellowbrick.model_selection import LearningCurve # Create the learning curve visualizer cv = StratifiedKFold(12)#k折交叉切分 sizes = np.linspace(0.3, 1.0, 10) viz = LearningCurve( MultinomialNB(), cv=cv, train_sizes=sizes, scoring='f1_weighted', n_jobs=4 ) # Fit and poof the visualizer viz.fit(data['data'],data['target']) viz.poof()

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