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Scikit

Scikit-plot画图

在机器学习过程中画图是一个重要的步骤，例如在分类任务中需要画P-R曲线，AUC曲线，混淆曲线等，使用matpotlib, Seaborn等类库作图需要多写几行代码，例如设置title，xlim，ylim，lengend等，如果有一个工具库可以封装这些操作的话可以帮助我们节省时间，提升开发效率，从而专注在算法/业务的改进上。

安装

conda install -c conda-forge scikit-plot
# 或者
pip install scikit-plot

导包

from sklearn.datasets import load_digits, load_breast_cancer, load_iris, make_classification
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.cluster import KMeans
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import scikitplot as skplt
import warningswarnings.filterwarnings('ignore')

混淆矩阵

X, y = load_digits(return_X_y=True)
rf = RandomForestClassifier()
rf.fit(X, y)
preds = rf.predict(X)
skplt.metrics.plot_confusion_matrix(y_true=y, y_pred=preds)
plt.show()

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ROC曲线

X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
nb = GaussianNB()
nb.fit(X_train, y_train)
predicted_probas = nb.predict_proba(X_test)skplt.metrics.plot_roc(y_test, predicted_probas)
plt.show()

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KS统计

X, y = load_breast_cancer(return_X_y=True)
lr = LogisticRegression()
lr.fit(X, y)
probas = lr.predict_proba(X)
skplt.metrics.plot_ks_statistic(y_true=y, y_probas=probas)
plt.show()

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Precision-Recall

X, y = load_digits(return_X_y=True)
nb = GaussianNB()
nb.fit(X, y)
probas = nb.predict_proba(X)
skplt.metrics.plot_precision_recall(y_true=y, y_probas=probas)
plt.show()

聚类

X, y = load_iris(return_X_y=True)
kmeans = KMeans(n_clusters=4, random_state=1)
cluster_labels = kmeans.fit_predict(X)
skplt.metrics.plot_silhouette(X, cluster_labels)
plt.show()

Calibration Curve(校准曲线)

X, y = make_classification(n_samples=100000, n_features=20,n_informative=2, n_redundant=2,random_state=20)X_train, y_train, X_test, y_test = X[:1000], y[:1000], X[1000:], y[1000:]rf_probas = RandomForestClassifier().fit(X_train, y_train).predict_proba(X_test)
lr_probas = LogisticRegression().fit(X_train, y_train).predict_proba(X_test)
nb_probas = GaussianNB().fit(X_train, y_train).predict_proba(X_test)
sv_scores = LinearSVC().fit(X_train, y_train).decision_function(X_test)probas_list = [rf_probas, lr_probas, nb_probas, sv_scores]
clf_names=['Random Forest','Logistic Regression','Gaussian Naive Bayes','Support Vector Machine']skplt.metrics.plot_calibration_curve(y_test,probas_list=probas_list,clf_names=clf_names,n_bins=10)
plt.show()

Cumulative Gain(累计增益)

根据标签和分数/概率生成累积增益图

X, y = load_breast_cancer(return_X_y=True)
lr = LogisticRegression()
lr.fit(X, y)
probas = lr.predict_proba(X)
skplt.metrics.plot_cumulative_gain(y_true=y, y_probas=probas)
plt.show()

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Lift Curve(提升曲线)

从标签和分数/概率生成提升曲线

X, y = load_breast_cancer(return_X_y=True)
lr = LogisticRegression()
lr.fit(X, y)
probas = lr.predict_proba(X)
skplt.metrics.plot_lift_curve(y_true=y, y_probas=probas)
plt.show()

Learning Curve(学习曲线)

X, y = load_breast_cancer(return_X_y=True)
rf = RandomForestClassifier()
skplt.estimators.plot_learning_curve(rf, X, y)
plt.show()

Feature Importances(特征重要性)

X, y = load_iris(return_X_y=True)
rf = RandomForestClassifier()
rf.fit(X, y)
skplt.estimators.plot_feature_importances(rf,feature_names=['petal length','petal width','sepal length','sepal width'])
plt.show()

Elbow Curve

为 KMeans 聚类绘制不同 K 值的肘部曲线。

X, y = load_iris(return_X_y=True)
kmeans = KMeans(random_state=1)
skplt.cluster.plot_elbow_curve(kmeans, X, cluster_ranges=range(1, 11))
plt.show()

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PCA Component Variance

绘制 PCA 组件的解释方差比。

X, y = load_digits(return_X_y=True)
pca = PCA(random_state=1)
pca.fit(X)
skplt.decomposition.plot_pca_component_variance(pca)
plt.show()

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PCA 2d Projection

在给定数据集上绘制 PCA 的二维投影。

X, y = load_digits(return_X_y=True)
pca = PCA(random_state=1)
pca.fit(X)
skplt.decomposition.plot_pca_2d_projection(pca, X, y)
plt.show()

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本文标签： Scikit