评估模型的性能并调参

本次学习首先讲述了使用网格搜索与随机网格搜索，开始用make_pipeline封装了SVC，
pipe_svc = make_pipeline(StandardScalar(), SVC(random_state=1)
然后分别用sklearn中的GridSearchCV和RandomizedSearchCV进行调参优化。

• 当类别为两类时，可采用绘制混淆矩阵和ROC曲线
  • 混淆矩阵
    代码为：confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
    绘制混淆矩阵图：ax.matshow(confmat, cmap=plt.cm.Blues, alpha=0.3)
  • ROC曲线
    代码：fpr, tpr, threshold = roc_curve(y_test, y_pred) # 计算真阳率和假阳率
    绘制ROC曲线：plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc) # 假阳率为横坐标，真阳率为纵坐标

小任务

• 采用了学习小组中提供的参考代码进行了学习；
  其中主要步骤为将数据集划分为训练集和测试集，其中训练集进行训练时，分为了训练集和验证集， 通过验证集选择最优模型，测试集测试其性能；本次学习主要通过PCA分解提取特征。

from time import time
import logging
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import fetch_lfw_people
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.decomposition import PCA
from sklearn.svm import SVC


print(__doc__)

# Display progress logs on stdout
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')


# #############################################################################
# Download the data, if not already on disk and load it as numpy arrays

lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)

# introspect the images arrays to find the shapes (for plotting)
n_samples, h, w = lfw_people.images.shape

# for machine learning we use the 2 data directly (as relative pixel
# positions info is ignored by this model)
X = lfw_people.data
n_features = X.shape[1]

# the label to predict is the id of the person
y = lfw_people.target
target_names = lfw_people.target_names
n_classes = target_names.shape[0]

print("Total dataset size:")
print("n_samples: %d" % n_samples)
print("n_features: %d" % n_features)
print("n_classes: %d" % n_classes)


# #############################################################################
# Split into a training set and a test set using a stratified k fold

# split into a training and testing set
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, random_state=42)


# #############################################################################
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 150

print("Extracting the top %d eigenfaces from %d faces"
      % (n_components, X_train.shape[0]))
t0 = time()
pca = PCA(n_components=n_components, svd_solver='randomized',
          whiten=True).fit(X_train)
print("done in %0.3fs" % (time() - t0))

参考文章为：https://blog.csdn.net/cwlseu/article/details/52356665