本文是对书《机器学习实战：基于Scikit-Learn和Tensorflow》第三章的知识学习以及代码复现，欢迎大家一起学习一起进步。

获取数据集

提前将MNIST数据集下载好，并放在'\scikit_learn_data'目录之下

from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original', data_home=r'C:\Users\12637\scikit_learn_data')
mnist

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X, y = mnist["data"], mnist["target"]
X.shape

002.png

一共有70000张图片，每张图片有784个特征。因为图片是28x28像素，每个特征代表了一个像素点的强度，从0（白色）到255（黑色）。随手抓取一个实例的特征向量，将其重新形成一个28X28数组，然后用Matplotlib的imshow()函数将其显示出来

%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt

some_digit = X[36000]
some_digit_image = some_digit.reshape(28, 28)

plt.imshow(some_digit_image, cmap=matplotlib.cm.binary, interpolation="nearest")
plt.axis("off")
plt.show()

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# MNIST数据集中的部分数字图像
X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]

# 给训练集数据洗牌
import numpy as np

shuffle_index = np.random.permutation(60000)
X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]

训练一个二元分类器

# 训练一个二元分类器
y_train_5 = (y_train == 5)         # True for all 5s, False for all other digits
y_test_5 = (y_test == 5)

from sklearn.linear_model import SGDClassifier

sgd_clf = SGDClassifier(random_state=42)
sgd_clf.fit(X_train, y_train_5)

sgd_clf.predict([some_digit])

004.png

实施交叉验证

# 实施交叉验证

from sklearn.model_selection import StratifiedKFold
from sklearn.base import clone

skfolds = StratifiedKFold(n_splits=3, random_state=42)
for train_index, test_index in skfolds.split(X_train, y_train_5):
    clone_clf = clone(sgd_clf)
    X_train_folds = X_train[train_index]
    y_train_folds = (y_train_5[train_index])
    X_test_fold = X_train[test_index]
    y_test_fold = (y_train_5[test_index])
    
    clone_clf.fit(X_train_folds, y_train_folds)
    y_pred = clone_clf.predict(X_test_fold)
    n_correct = sum(y_pred == y_test_fold)
    print(n_correct / len(y_pred))

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混淆矩阵

# 混淆矩阵

from sklearn.model_selection import cross_val_predict

y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)

from sklearn.metrics import confusion_matrix

confusion_matrix(y_train_5, y_train_pred)

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精度和召回率以及精度/召回率平衡

008.png

y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3, method="decision_function")

from sklearn.metrics import precision_recall_curve

precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)

def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):
    plt.plot(thresholds, precisions[:-1], "b--", label="Precision")
    plt.plot(thresholds, recalls[:-1], "g-", label="Recall")
    plt.xlabel("Threshold")
    plt.legend(loc="upper left")
    plt.ylim([0, 1])
    
plot_precision_recall_vs_threshold(precisions, recalls, thresholds)
plt.show()

009.png

ROC曲线绘制

# ROC曲线
from sklearn.metrics import roc_curve

fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)
def plot_roc_curve(fpr, tpr, label=None):
    plt.plot(fpr, tpr, linewidth=2, label=label)
    plt.plot([0, 1], [0, 1], 'k--')
    plt.axis([0, 1, 0, 1])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
plot_roc_curve(fpr, tpr)
plt.show()

010.png

from sklearn.ensemble import RandomForestClassifier

forest_clf = RandomForestClassifier(random_state=42)
y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3, method="predict_proba")

y_scores_forest = y_probas_forest[:, 1]
fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5, y_scores_forest)

plt.plot(fpr, tpr, "b:", label="SGD")
plot_roc_curve(fpr_forest, tpr_forest, "Random Forest")
plt.legend(loc="bottom right")
plt.show()

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多类别分类器

012.png 013.png

错误分析

# 错误分析

y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)
conf_mx = confusion_matrix(y_train, y_train_pred)
conf_mx

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多标签分类

# 多标签分类

from sklearn.neighbors import KNeighborsClassifier

y_train_large = (y_train >= 7)
y_train_odd = (y_train % 2 == 1)
y_multilabel = np.c_[y_train_large, y_train_odd]

knn_clf = KNeighborsClassifier()
knn_clf.fit(X_train, y_multilabel)
knn_clf.predict([some_digit])

多输出分类

# 多输出分类

noise = np.random.randint(0, 100, (len(X_train), 784))
X_train_mod = X_train + noise
noise = np.random.randint(0, 100, (len(X_test), 784))
X_test_mod = X_test + noise
y_train_mod = X_train
y_test_mod = X_test

def plot_digit(data):
    image = data.reshape(28, 28)
    plt.imshow(image, cmap = matplotlib.cm.binary,
               interpolation="nearest")
    plt.axis("off")
    
some_index = 5500

plt.subplot(121); plot_digit(X_test_mod[some_index])
plt.subplot(122); plot_digit(y_test_mod[some_index])
plt.show()

017.png

knn_clf.fit(X_train_mod, y_train_mod)
clean_digit = knn_clf.predict([X_test_mod[some_index]])
plot_digit(clean_digit)

018.png