def cancer_pipeline():
X_train, X_test, y_train, y_test = get_cancer_data()
pipe_lr = Pipeline([('scl', StandardScaler()),
('pca', PCA(n_components=2)),
('clf', LogisticRegression(random_state=1))])
return pipe_lr, X_train, X_test, y_train, y_test
import numpy as np
import matplotlib.pyplot as plt
from BCW import get_cancer_data
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
from sklearn.metrics import confusion_matrix
pipe_svc = Pipeline([('scl', StandardScaler()), ('clf', SVC(random_state=1))])
X_train, X_test, y_train, y_test = get_cancer_data()
pipe_svc.fit(X_train, y_train)
y_pred = pipe_svc.predict(X_test)
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
fig, ax = plt.subplots(figsize=(2.5, 2.5))
ax.matshow(confmat, cmap=plt.get_cmap('Blues'), alpha=0.3)
for i in range(confmat.shape[0]):
for j in range(confmat.shape[1]):
ax.text(x=j, y=i, s=confmat[i, j], va='center', ha='center')
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.show()