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Python 3.7.6 (default, Jan 8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)]
Type "copyright", "credits" or "license" for more information.
IPython 7.12.0 -- An enhanced Interactive Python.
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)
# Where to save the figures
PROJECT_ROOT_DIR = "."
CHAPTER_ID = "ensembles"
IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID)
os.makedirs(IMAGES_PATH, exist_ok=True)
def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300):
path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
print("Saving figure", fig_id)
if tight_layout:
plt.tight_layout()
plt.savefig(path, format=fig_extension, dpi=resolution)
#这是因为大数定理导致的： 随着你不断投掷硬币， 正面朝上的比率越来越接近于正面的概率（51%）
heads_proba = 0.51
coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)
cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)
plt.figure(figsize=(8,3.5))
plt.plot(cumulative_heads_ratio)
plt.plot([0, 10000], [0.51, 0.51], "k--", linewidth=2, label="51%")
plt.plot([0, 10000], [0.5, 0.5], "k-", label="50%")
plt.xlabel("Number of coin tosses")
plt.ylabel("Heads ratio")
plt.legend(loc="lower right")
plt.axis([0, 10000, 0.42, 0.58])
save_fig("law_of_large_numbers_plot")
plt.show()
Saving figure law_of_large_numbers_plot
#用Scikit-Learn创建并训练一个投票分类器，由三种不同的分类器组成
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_moons
X, y = make_moons(n_samples=500, noise=0.30, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
log_clf = LogisticRegression(solver="liblinear", random_state=42)
rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)
svm_clf = SVC(gamma="auto", random_state=42)
voting_clf = VotingClassifier(
estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],
voting='hard')
voting_clf.fit(X_train, y_train)
Out[5]:
VotingClassifier(estimators=[('lr',
LogisticRegression(C=1.0, class_weight=None,
dual=False, fit_intercept=True,
intercept_scaling=1,
l1_ratio=None, max_iter=100,
multi_class='auto',
n_jobs=None, penalty='l2',
random_state=42,
solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)),
('rf',
RandomForestClassifier(bootstrap=True,
ccp_alpha=0.0,
class_weight=None,...
oob_score=False,
random_state=42, verbose=0,
warm_start=False)),
('svc',
SVC(C=1.0, break_ties=False, cache_size=200,
class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3,
gamma='auto', kernel='rbf', max_iter=-1,
probability=False, random_state=42,
shrinking=True, tol=0.001, verbose=False))],
flatten_transform=True, n_jobs=None, voting='hard',
weights=None)
from sklearn.metrics import accuracy_score
for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(clf.__class__.__name__, accuracy_score(y_test, y_pred))
LogisticRegression 0.864
RandomForestClassifier 0.872
SVC 0.888
VotingClassifier 0.896
'''
如果所有分类器都能够估算出类别的概率（即有predict_proba（） 方法） ，
那么你可以将概率在所有单个分类器上平均， 然后让Scikit-Learn给出平均概率最高的类别作为预测。
这被称为软投票法。 通常来说， 它比硬投票法的表现更优， 因为它给予那些高度自信的投票更高的权重。
而所有你需要做的就是用voting="soft"代替voting="hard"， 并确保所有分类器都可以估算出概率。
默认情况下， SVC类是不行的， 所以你需要将其超参数probability设置为True
'''
log_clf = LogisticRegression(solver="liblinear", random_state=42)
rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)
svm_clf = SVC(gamma="auto", probability=True, random_state=42)
voting_clf = VotingClassifier(
estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],
voting='soft')
voting_clf.fit(X_train, y_train)
Out[7]:
VotingClassifier(estimators=[('lr',
LogisticRegression(C=1.0, class_weight=None,
dual=False, fit_intercept=True,
intercept_scaling=1,
l1_ratio=None, max_iter=100,
multi_class='auto',
n_jobs=None, penalty='l2',
random_state=42,
solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)),
('rf',
RandomForestClassifier(bootstrap=True,
ccp_alpha=0.0,
class_weight=None,...
oob_score=False,
random_state=42, verbose=0,
warm_start=False)),
('svc',
SVC(C=1.0, break_ties=False, cache_size=200,
class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3,
gamma='auto', kernel='rbf', max_iter=-1,
probability=True, random_state=42,
shrinking=True, tol=0.001, verbose=False))],
flatten_transform=True, n_jobs=None, voting='soft',
weights=None)
from sklearn.metrics import accuracy_score
for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(clf.__class__.__name__, accuracy_score(y_test, y_pred))
LogisticRegression 0.864
RandomForestClassifier 0.872
SVC 0.888
VotingClassifier 0.912
############################ bagging和pasting #######################
####这是一个bagging的示例， 如果你想使用pasting， 只需要设置bootstrap=False即可
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
bag_clf = BaggingClassifier(
DecisionTreeClassifier(random_state=42), n_estimators=500,
max_samples=100, bootstrap=True, n_jobs=-1, random_state=42)
bag_clf.fit(X_train, y_train)
y_pred = bag_clf.predict(X_test)
from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, y_pred))
0.904
###使用决策树分类
tree_clf = DecisionTreeClassifier(random_state=42)
tree_clf.fit(X_train, y_train)
y_pred_tree = tree_clf.predict(X_test)
print(accuracy_score(y_test, y_pred_tree))
0.856
#集成预测的泛化效果很可能会比单独的决策树要好一些： 二者偏差相近， 但是集成的方差更小
from matplotlib.colors import ListedColormap
def plot_decision_boundary(clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.5, contour=True):
x1s = np.linspace(axes[0], axes[1], 100)
x2s = np.linspace(axes[2], axes[3], 100)
x1, x2 = np.meshgrid(x1s, x2s)
X_new = np.c_[x1.ravel(), x2.ravel()]
y_pred = clf.predict(X_new).reshape(x1.shape)
custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])
plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)
if contour:
custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])
plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)
plt.plot(X[:, 0][y==0], X[:, 1][y==0], "yo", alpha=alpha)
plt.plot(X[:, 0][y==1], X[:, 1][y==1], "bs", alpha=alpha)
plt.axis(axes)
plt.xlabel(r"$x_1$", fontsize=18)
plt.ylabel(r"$x_2$", fontsize=18, rotation=0)
plt.figure(figsize=(11,4))
plt.subplot(121)
plot_decision_boundary(tree_clf, X, y)
plt.title("Decision Tree", fontsize=14)
plt.subplot(122)
plot_decision_boundary(bag_clf, X, y)
plt.title("Decision Trees with Bagging", fontsize=14)
save_fig("decision_tree_without_and_with_bagging_plot")
plt.show()
Saving figure decision_tree_without_and_with_bagging_plot
####未被采样的训练实例称为包外
bag_clf = BaggingClassifier(
DecisionTreeClassifier(splitter="random", max_leaf_nodes=16, random_state=42),
n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)
bag_clf.fit(X_train, y_train)
y_pred = bag_clf.predict(X_test)
y_pred
Out[16]:
array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1,
1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0,
0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0,
0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0,
1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,
1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0], dtype=int64)
accuracy_score(y_test, y_pred)
Out[18]: 0.92
############################## 随机森林 ###########################
from sklearn.ensemble import RandomForestClassifier
rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)
rnd_clf.fit(X_train, y_train)
y_pred_rf = rnd_clf.predict(X_test)
np.sum(y_pred == y_pred_rf) / len(y_pred)
Out[19]: 0.976
####特征重要性
from sklearn.datasets import load_iris
iris = load_iris()
rnd_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=42)
rnd_clf.fit(iris["data"], iris["target"])
for name, score in zip(iris["feature_names"], rnd_clf.feature_importances_):
print(name, score)
sepal length (cm) 0.11249225099876375
sepal width (cm) 0.02311928828251033
petal length (cm) 0.4410304643639577
petal width (cm) 0.4233579963547682
rnd_clf.feature_importances_
Out[21]: array([0.11249225, 0.02311929, 0.44103046, 0.423358 ])
plt.figure(figsize=(6, 4))
for i in range(15):
tree_clf = DecisionTreeClassifier(max_leaf_nodes=16, random_state=42 + i)
indices_with_replacement = np.random.randint(0, len(X_train), len(X_train))
tree_clf.fit(X[indices_with_replacement], y[indices_with_replacement])
plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.02, contour=False)
plt.show()
####在MNIST数据集上训练一个随机森林分类器，然后绘制其每个像素的重要性
bag_clf = BaggingClassifier(
DecisionTreeClassifier(random_state=42), n_estimators=500,
bootstrap=True, n_jobs=-1, oob_score=True, random_state=40)
bag_clf.fit(X_train, y_train)
bag_clf.oob_score_
Out[23]: 0.9013333333333333
from sklearn.metrics import accuracy_score
y_pred = bag_clf.predict(X_test)
accuracy_score(y_test, y_pred)
Out[24]: 0.912
try:
from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784', version=1, as_frame=False)
mnist.target = mnist.target.astype(np.int64)
except ImportError:
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original')
rnd_clf = RandomForestClassifier(n_estimators=10, random_state=42)
rnd_clf.fit(mnist["data"], mnist["target"])
Out[26]:
RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=10,
n_jobs=None, oob_score=False, random_state=42, verbose=0,
warm_start=False)
def plot_digit(data):
image = data.reshape(28, 28)
plt.imshow(image, cmap = mpl.cm.hot,
interpolation="nearest")
plt.axis("off")
plot_digit(rnd_clf.feature_importances_)
cbar = plt.colorbar(ticks=[rnd_clf.feature_importances_.min(), rnd_clf.feature_importances_.max()])
cbar.ax.set_yticklabels(['Not important', 'Very important'])
save_fig("mnist_feature_importance_plot")
plt.show()
Saving figure mnist_feature_importance_plot
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