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Notes ： <Hands-on ML with Sklearn & TF> Chapter 3

2024-09-27 15:34:01   204人阅读

Chapter 3-Classification

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MNIST¶

 
  • MNIST is a dataset which has 70,000 small images
  • "Hello World" of Machine Learning
In [1]:
# fetch MNIST, from sklearn.datasets import fetch_mldatamnist = fetch_mldata(‘MNIST original‘)#但是总是显示下载失败，下载mnist-original.mat到～/scikit_learn_data/mldata/内。#mldata.org//google
 
  • A DESCR key describing the dataset
  • A data key containing an array with one row per instance and one column per feature
  • A target containing an array with the labels
In [2]:
X, y = mnist["data"],mnist["target"]print(X.shape,y.shape) #784 = 28pixels x 28pixels from 0-255(white-black)
 
(70000, 784) (70000,)
In [3]:
#show%matplotlib inlineimport matplotlibimport matplotlib.pyplot as pltsome_digit = X[12345]some_digit_image = some_digit.reshape(28,28)plt.imshow(some_digit_image, cmap=matplotlib.cm.binary, interpolation="nearest")plt.axis(‘off‘)plt.show()
 
技术分享
In [4]:
# EXTRAimport numpy as npdef plot_digits(instances, images_per_row=10, **options):    size = 28    images_per_row = min(len(instances), images_per_row)    images = [instance.reshape(size,size) for instance in instances] #转换成100个像素阵    n_rows = (len(instances) - 1) // images_per_row + 1    row_images = []    n_empty = n_rows * images_per_row - len(instances)    images.append(np.zeros((size, size * n_empty)))    for row in range(n_rows):        rimages = images[row * images_per_row : (row + 1) * images_per_row]        row_images.append(np.concatenate(rimages, axis=1))      #实现list的reshape    image = np.concatenate(row_images, axis=0)      plt.imshow(image, cmap = matplotlib.cm.binary, **options)    plt.axis("off")plt.figure(figsize=(9,9))example_images = np.r_[X[:12000:600], X[13000:30600:600], X[30600:60000:590]]  #把这100个图连起来plot_digits(example_images, images_per_row=10)plt.show()
 
技术分享
In [5]:
y[12345]
Out[5]:
1.0
 
  • shuffle the train set
    • similar cross-validation folds
    • some algorithms sensitive to instance‘s order , similar instances in a row performs poorly
In [6]:
import numpy as npX_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]shuffle_index = np.random.permutation(60000)X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]
 

Train a Binary Classifer¶

 
  • 判断一张图是不是某个数字就是一个 Binary Classifer问题。 如： 5 or not-5
  • Stochastic Grandient Descant（SGD） Classifer 随机梯度下降分类
    • train instance independently
In [7]:
y_train_5 = (y_train == 5)y_test_5 = (y_test == 5)from sklearn.linear_model import SGDClassifiersgd_clf = SGDClassifier(random_state = 42)sgd_clf.fit(X_train, y_train_5)sgd_clf.predict([X[36000]])
Out[7]:
array([ True], dtype=bool)
 

Preformance Measures¶

 

Measuing Accuracy Using Cross-Validation

  1. need more control
In [8]:
from sklearn.model_selection import StratifiedKFoldfrom sklearn.base import clone#StratifiedKFold performs stratified samplingskfolds = 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_current = sum(y_pred == y_test_fold)    print(n_current/len(y_pred))
 
0.9530.95250.95515
In [9]:
# use cross_val_scorefrom sklearn.model_selection import cross_val_scorecross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=‘accuracy‘)
Out[9]:
array([ 0.953  ,  0.9525 ,  0.95515])
 

这并不代表精确度高，因为即使全为no-5s的交叉验证的正确率也有90%

In [10]:
from sklearn.base import BaseEstimatorclass Never5Classifier(BaseEstimator):    def fit(self, X, y=None):        pass    def predict(self, X):        return np.zeros((len(X), 1), dtype=bool)    never_5_clf = Never5Classifier()cross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring=‘accuracy‘)
Out[10]:
array([ 0.90825,  0.9112 ,  0.9095 ])
 

Confusion Matrix

 

技术分享 技术分享 来源 wikipeadia confusion matrix

In [11]:
from sklearn.metrics import confusion_matrixfrom sklearn.model_selection import cross_val_predicty_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)confusion_matrix(y_train_5, y_train_pred)
Out[11]:
array([[53207,  1372],       [ 1415,  4006]])
In [12]:
y_train_perfect_predictions = y_train_5confusion_matrix(y_train_5, y_train_perfect_predictions)
Out[12]:
array([[54579,     0],       [    0,  5421]])
 

Precision and Recall

In [13]:
from sklearn.metrics import precision_score, recall_scoreprint(precision_score(y_train_5, y_train_pred))print(recall_score(y_train_5, y_train_pred))
 
0.7448865749350.738978048331
In [14]:
# f1 is the harmonic meanfrom sklearn.metrics import f1_scoref1_score(y_train_5, y_train_pred)
Out[14]:
0.7419205481989074
 

f1 favor classifier that has similar precision and recall</br> 但情况并不总是这样</br> 宁可错杀一百，不可放过一个：low recall, high precision ， 如：视频等级划分;或者情况相反：如抓小偷</br>

 

Precision / Recall Tradeoff

 
  1. lowing the threshold increase recall and reduce precision
  2. sklearn doesn‘t let you set the threshold directly and give you access to the decision secores(use to prediction)
In [15]:
some_digit_index = 36000some_digit = X[some_digit_index]y_scores = sgd_clf.decision_function([some_digit])y_scores
Out[15]:
array([ 45981.28253526])
In [16]:
threshold = 0y_some_digit_pred = (y_scores > threshold)y_some_digit_pred
Out[16]:
array([ True], dtype=bool)
In [17]:
threshold = 200000y_some_digit_pred = (y_scores > threshold)y_some_digit_pred
Out[17]:
array([False], dtype=bool)
 

decide which threshlod to use

In [18]:
#使用交叉验证获取分数y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3, method="decision_function")
In [19]:
from sklearn.metrics import precision_recall_curve#计算所有precision和recallprecisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)
In [20]:
#画出来def plot_precision_recall_vs_threshold(precisions, recalls, threshold):    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()
 
技术分享
In [21]:
y_train_pred_90 = (y_scores > 250000)precision_score(y_train_5, y_train_pred_90)
Out[21]:
0.96514161220043571
In [22]:
recall_score(y_train_5, y_train_pred_90)
Out[22]:
0.32687695997048516
 

just set the a high enough threshold to creat a classifier with virtually any precision

 

ROC 受试者工作特征曲线 : the true positive rate against the false positive rate

In [23]:
from sklearn.metrics import roc_curvefpr, 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()
 
技术分享
In [24]:
# compute the area under the curve(AUC)from sklearn.metrics import roc_auc_scoreroc_auc_score(y_train_5, y_scores)
Out[24]:
0.9568006259068953
 
  1. positive calss is rare or more care the false negatives use the PR curve
  2. otherwise use the ROC(ROC, AUC)
  3. sklearn give decision_function() or predict_proba()(return an array containing an row per instance and a column per class, each containing the probability that the given instance belongs to the given calss)
In [25]:
from sklearn.ensemble import RandomForestClassifierforest_clf = RandomForestClassifier(random_state = 42)y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3, method="predict_proba")
In [26]:
# use the posobility as the scoresy_scores_forest = y_probas_forest[:,1]fprs_forest, tprs_forest, thresholds_forest = roc_curve(y_train_5, y_scores_forest)
In [27]:
plt.plot(fpr, tpr, ‘b:‘, label="SGD")plot_roc_curve(fprs_forest, tprs_forest, "Random forest")plt.legend(loc=‘bottom right‘)plt.show()
 
/usr/local/lib/python3.5/dist-packages/matplotlib/legend.py:326: UserWarning: Unrecognized location "bottom right". Falling back on "best"; valid locations are	lower left	center right	upper right	center	right	upper center	lower right	upper left	center left	lower center	best  six.iterkeys(self.codes))))
 
技术分享
In [28]:
roc_auc_score(y_train_5, y_scores_forest)
Out[28]:
0.99114321301880992
 
  1. how to train binary classifier
  2. choose metric for task
  3. evaluate your classifiers using cross-validation
  4. select the Precision/Recall tradeoff that fits your needs and compare various medel using ROC curve and ROC/AUC scores
 

Multiclass Classification¶

 
  1. 有些算法本身支持多分类
  2. 也可使用多个二分类代替的策略
    1. 多个二分类，要分类时，每个都进行分类，选最高分（OvA）
    2. 为每一对训练一个分类，如：1-2，1-3，...，9-8，...,一共需要N（N-1）/2,称为one versus one(OvO)
    3. 一些数据集规模和算法规模关联性不强的使用OvO,如：SVM;其他的使用OvA
    4. sklearn在使用二分类处理多分类时，自动合适的使用OvA或者OvO
In [29]:
#try SGDClassifiersgd_clf.fit(X_train, y_train)sgd_clf.predict([some_digit])
Out[29]:
array([ 5.])
In [30]:
some_digit_secores = sgd_clf.decision_function([some_digit])some_digit_secores
Out[30]:
array([[-305117.56076994, -572405.6562905 , -386686.20587505,        -198578.92561098, -312977.5748752 ,   45981.28253526,        -752588.92027703, -425193.41816061, -692575.39314386,        -732446.97820597]])
In [31]:
np.argmax(some_digit_secores)
Out[31]:
5
In [32]:
sgd_clf.classes_
Out[32]:
array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])
In [33]:
sgd_clf.classes_[5]  #巧了
Out[33]:
5.0
In [34]:
#force sklearn to use OvO or OvA: use OneVsOneClassifer or OneVsRestClassiferfrom sklearn.multiclass import OneVsOneClassifierovo_clf=OneVsOneClassifier(SGDClassifier(random_state=42))ovo_clf.fit(X_train, y_train)ovo_clf.predict([some_digit])
Out[34]:
array([ 5.])
In [35]:
forest_clf.fit(X_train, y_train)forest_clf.predict([some_digit])
Out[35]:
array([ 5.])
In [36]:
forest_clf.predict_proba([some_digit])
Out[36]:
array([[ 0. ,  0. ,  0. ,  0. ,  0.1,  0.9,  0. ,  0. ,  0. ,  0. ]])
In [37]:
cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=‘accuracy‘)
Out[37]:
array([ 0.87037592,  0.88059403,  0.84912737])
In [38]:
#简单的对输入的缩放：StandardScalerfrom sklearn.preprocessing import StandardScalerscaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=‘accuracy‘)
Out[38]:
array([ 0.91071786,  0.90684534,  0.91233685])
 

Error Analysis¶

 
  1. look at the confusion matrix
  2. plot on the errors
    1. divide each value in confusion matrix by number of images in the corresopnding class
    2. fill the diagonals with zeros to keep only the errors
In [39]:
#1-1y_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
Out[39]:
array([[5729,    2,   23,    8,   11,   50,   49,    9,   40,    2],       [   1, 6505,   42,   21,    6,   40,    6,   10,  100,   11],       [  53,   41, 5336,  102,   81,   26,   84,   67,  154,   14],       [  45,   45,  140, 5359,    6,  220,   36,   49,  134,   97],       [  16,   30,   38,   10, 5361,   11,   50,   33,   77,  216],       [  73,   41,   34,  184,   73, 4588,  104,   32,  195,   97],       [  31,   28,   51,    1,   51,   86, 5613,    8,   49,    0],       [  22,   21,   70,   30,   55,   12,    5, 5815,   18,  217],       [  49,  173,   74,  151,   14,  153,   55,   21, 5021,  140],       [  43,   37,   25,   85,  166,   32,    3,  204,   83, 5271]])
In [40]:
plt.matshow(conf_mx, cmap=plt.cm.gray)plt.show()
 
技术分享
 

most images are on the main diagonal which means that they were classified correctly and 5s is darker means fewer 5s images in the dataset or classifier doesn‘t perform well

In [41]:
#2-1row_sums = conf_mx.sum(axis=1, keepdims=True)norm_conf_mx = conf_mx/row_sums
In [42]:
#2-2np.fill_diagonal(norm_conf_mx, 0)plt.matshow(norm_conf_mx, cmap=plt.cm.gray)plt.show()
 
技术分享
 
  1. row represent the actual classes
  2. improve 8s, 9s, 3/5
    1. count the number of close loops
In [43]:
cl_a, cl_b = 3, 5X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]plt.figure(figsize=(8,8))plt.subplot(221)plot_digits(X_aa[:25], images_per_row=5)plt.subplot(222)plot_digits(X_ab[:25], images_per_row=5)plt.subplot(223)plot_digits(X_ba[:25], images_per_row=5)plt.subplot(224)plot_digits(X_bb[:25], images_per_row=5)plt.show()
 
技术分享
 

看到顶部的直线的底部的弧线中间的连接方式：偏向左边一条直线就是5，偏向右边就是3

 

Multilabel Classification¶

In [44]:
from sklearn.neighbors import KNeighborsClassifier  #support multilabel classificationy_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)
Out[44]:
KNeighborsClassifier(algorithm=‘auto‘, leaf_size=30, metric=‘minkowski‘,           metric_params=None, n_jobs=1, n_neighbors=5, p=2,           weights=‘uniform‘)
In [45]:
knn_clf.predict([some_digit])
Out[45]:
array([[False,  True]], dtype=bool)
In [46]:
#evaluate by f1 scorefrom sklearn.metrics import f1_scorey_train_knn_pred = cross_val_predict(knn_clf, X_train, y_train, cv=3)f1_score(y_train, y_train_knn_pred, average=‘macro‘)
 
---------------------------------------------------------------------------KeyboardInterrupt                         Traceback (most recent call last)KeyboardInterrupt:
 

Multioutput Classification¶

In [53]:
import numpy.random as rndnoise = rnd.randint(0, 100, (len(X_train), 784))X_train_mod = X_train + noisey_train_mod = X_trainnoise = rnd.randint(0, 100, (len(X_test), 784))X_test_mod = X_test + noisey_test_mod = X_test
In [54]:
def plot_digit(data):    image = data.reshape(28, 28)    plt.imshow(image, cmap = matplotlib.cm.binary,               interpolation="nearest")    plt.axis("off")    some_index = 5500plt.subplot(121); plot_digit(X_test_mod[some_index])plt.subplot(122); plot_digit(y_test_mod[some_index])plt.show()
 
技术分享
In [57]:
knn_clf.fit(X_train_mod, y_train_mod)clean_digit=knn_clf.predict([X_test_mod[some_index]])plot_digit(clean_digit)
 
技术分享
In [ ]:
 

Notes ： <Hands-on ML with Sklearn & TF> Chapter 3


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