把它们放在一起

校验者: @片刻翻译者: @X

模型管道化

我们已经知道一些模型可以做数据转换,一些模型可以用来预测变量。我们可以建立一个组合模型同时完成以上工作:

http://sklearn.apachecn.org/cn/0.19.0/_images/sphx_glr_plot_digits_pipe_001.png

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  3. import pandas as pd
  4. from sklearn import datasets
  5. from sklearn.decomposition import PCA
  6. from sklearn.linear_model import SGDClassifier
  7. from sklearn.pipeline import Pipeline
  8. from sklearn.model_selection import GridSearchCV
  9. # Define a pipeline to search for the best combination of PCA truncation
  10. # and classifier regularization.
  11. logistic = SGDClassifier(loss='log', penalty='l2', early_stopping=True,
  12. max_iter=10000, tol=1e-5, random_state=0)
  13. pca = PCA()
  14. pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])
  15. digits = datasets.load_digits()
  16. X_digits = digits.data
  17. y_digits = digits.target
  18. # Parameters of pipelines can be set using ‘__’ separated parameter names:
  19. param_grid = {
  20. 'pca__n_components': [5, 20, 30, 40, 50, 64],
  21. 'logistic__alpha': np.logspace(-4, 4, 5),
  22. }
  23. search = GridSearchCV(pipe, param_grid, iid=False, cv=5)
  24. search.fit(X_digits, y_digits)
  25. print("Best parameter (CV score=%0.3f):" % search.best_score_)
  26. print(search.best_params_)
  27. # Plot the PCA spectrum
  28. pca.fit(X_digits)
  29. fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(6, 6))
  30. ax0.plot(pca.explained_variance_ratio_, linewidth=2)
  31. ax0.set_ylabel('PCA explained variance')
  32. ax0.axvline(search.best_estimator_.named_steps['pca'].n_components,
  33. linestyle=':', label='n_components chosen')

用特征面进行人脸识别

该实例用到的数据集来自 LFW_(Labeled Faces in the Wild)。数据已经进行了初步预处理

http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)

  1. """

Faces recognition example using eigenfaces and SVMs

The dataset used in this example is a preprocessed excerpt of the"Labeled Faces in the Wild", aka LFW_:

http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)

.. _LFW: http://vis-www.cs.umass.edu/lfw/

Expected results for the top 5 most represented people in the dataset:

================== ============ ======= ========== ======= precision recall f1-score support================== ============ ======= ========== ======= Ariel Sharon 0.67 0.92 0.77 13 Colin Powell 0.75 0.78 0.76 60 Donald Rumsfeld 0.78 0.67 0.72 27 George W Bush 0.86 0.86 0.86 146Gerhard Schroeder 0.76 0.76 0.76 25 Hugo Chavez 0.67 0.67 0.67 15 Tony Blair 0.81 0.69 0.75 36

  1. avg / total 0.80 0.80 0.80 322

================== ============ ======= ========== =======

"""from time import timeimport loggingimport matplotlib.pyplot as plt

from sklearn.model_selection import train_test_splitfrom sklearn.model_selection import GridSearchCVfrom sklearn.datasets import fetch_lfw_peoplefrom sklearn.metrics import classification_reportfrom sklearn.metrics import confusion_matrixfrom sklearn.decomposition import PCAfrom 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.datan_features = X.shape[1]

the label to predict is the id of the person

y = lfw_people.targettarget_names = lfw_people.target_namesn_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))

eigenfaces = pca.components_.reshape((n_components, h, w))

print("Projecting the input data on the eigenfaces orthonormal basis")t0 = time()X_train_pca = pca.transform(X_train)X_test_pca = pca.transform(X_test)print("done in %0.3fs" % (time() - t0))

#

Train a SVM classification model

print("Fitting the classifier to the training set")t0 = time()paramgrid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5], 'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid, cv=5, iid=False)clf = clf.fit(X_train_pca, y_train)print("done in %0.3fs" % (time() - t0))print("Best estimator found by grid search:")print(clf.best_estimator)

#

Quantitative evaluation of the model quality on the test set

print("Predicting people's names on the test set")t0 = time()y_pred = clf.predict(X_test_pca)print("done in %0.3fs" % (time() - t0))

print(classification_report(y_test, y_pred, target_names=target_names))print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))

#

Qualitative evaluation of the predictions using matplotlib

def plot_gallery(images, titles, h, w, n_row=3, n_col=4): """Helper function to plot a gallery of portraits""" plt.figure(figsize=(1.8 n_col, 2.4 n_row)) plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35) for i in range(n_row * n_col): plt.subplot(n_row, n_col, i + 1) plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray) plt.title(titles[i], size=12) plt.xticks(()) plt.yticks(())

plot the result of the prediction on a portion of the test set

def title(y_pred, y_test, target_names, i): pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1] true_name = target_names[y_test[i]].rsplit(' ', 1)[-1] return 'predicted: %s\ntrue: %s' % (pred_name, true_name)

prediction_titles = [title(y_pred, y_test, target_names, i) for i in range(y_pred.shape[0])]

plot_gallery(X_test, prediction_titles, h, w)

plot the gallery of the most significative eigenfaces

eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]plot_gallery(eigenfaces, eigenface_titles, h, w)

plt.show()

predictioneigenfaces
PredictionEigenfaces

数据集中前5名最有代表性样本的预期结果:

  1. precision recall f1-score support
  2. Gerhard_Schroeder 0.91 0.75 0.82 28
  3. Donald_Rumsfeld 0.84 0.82 0.83 33
  4. Tony_Blair 0.65 0.82 0.73 34
  5. Colin_Powell 0.78 0.88 0.83 58
  6. George_W_Bush 0.93 0.86 0.90 129
  7. avg / total 0.86 0.84 0.85 282

开放性问题: 股票市场结构

我们可以预测 Google 在特定时间段内的股价变动吗?

Learning a graph structure


我们一直在努力

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