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• 【莫烦Python】Scikit-learn (sklearn) 优雅地学会机器学习

课程

3 如何选择机器学习方法

流程图

• classification 分类

• regression 回归

• clustering 聚类

• dimensionality reduction 数据降维

4 通用学习模式

导入数据集

查看iris的属性:

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split  # from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier  # KNN K临近算法

iris = datasets.load_iris()
iris_X = iris.data
iris_y = iris.target

iris_X[:2, :]

array([[5.1, 3.5, 1.4, 0.2],
       [4.9, 3. , 1.4, 0.2]])

查看iris的分类结果:

iris_y

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

train_test_split 分离数据集（测试集／训练集）：

# 测试集 : 训练集 = 7 : 3
X_train, X_test, y_train, y_test = train_test_split(iris_X, iris_y, test_size=0.3)
y_train

array([1, 0, 2, 2, 2, 0, 2, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 2,
       0, 0, 1, 0, 0, 1, 1, 2, 0, 1, 2, 2, 2, 2, 2, 2, 1, 2, 0, 0, 2, 2,
       0, 1, 1, 2, 2, 2, 2, 0, 0, 2, 1, 1, 1, 2, 2, 1, 0, 2, 1, 2, 0, 0,
       1, 0, 2, 1, 2, 0, 1, 2, 1, 2, 1, 0, 0, 1, 1, 2, 2, 1, 0, 2, 1, 1,
       0, 0, 1, 1, 1, 2, 0, 0, 2, 1, 2, 2, 1, 2, 1, 2, 2])

使用KNN算法进行分类

knn = KNeighborsClassifier()
# fit 训练
knn.fit(X_train, y_train)
# predict 生成预测结果
knn.predict(X_test)

array([2, 2, 0, 2, 1, 1, 1, 1, 1, 0, 0, 2, 0, 2, 1, 2, 0, 2, 2, 0, 1, 1,
       1, 0, 0, 1, 1, 0, 2, 0, 1, 2, 0, 1, 2, 1, 0, 0, 2, 0, 0, 0, 0, 0,
       1])

y_test

array([1, 2, 0, 2, 1, 1, 1, 1, 1, 0, 0, 2, 0, 2, 1, 1, 0, 2, 2, 0, 1, 1,
       1, 0, 0, 1, 1, 0, 1, 0, 1, 2, 0, 1, 2, 1, 0, 0, 2, 0, 0, 0, 0, 0,
       1])

看出预测结果与y_test基本相似

5 sklearn 的 datasets 数据库

from sklearn import datasets
from sklearn.linear_model import LinearRegression  # 线性回归

loaded_data = datasets.load_boston()  # 波士顿房价数据库
data_X = loaded_data.data
data_y = loaded_data.target

model = LinearRegression()
model.fit(data_X, data_y)

model.predict(data_X[:4, :])

# 这里原作者忘记分割数据集了..

​

array([30.00384338, 25.02556238, 30.56759672, 28.60703649])

data_y[:4]

array([24. , 21.6, 34.7, 33.4])

创造数据库

import matplotlib.pyplot as plt

plt.figure()
X, y = datasets.make_regression(n_samples=100, n_features=1, n_targets=1, noise=10)
plt.scatter(X, y)
plt.show()

6 model 常用属性和功能

from sklearn import datasets
from sklearn.linear_model import LinearRegression  # 线性回归

loaded_data = datasets.load_boston()  # 波士顿房价数据库
data_X = loaded_data.data
data_y = loaded_data.target

model = LinearRegression()
model.fit(data_X, data_y)

​

model.coef_ 斜率

在fit之后输出的coef_和intercept_才会比较准确

model.coef_  # 各个属性对应的斜率

array([-1.08011358e-01,  4.64204584e-02,  2.05586264e-02,  2.68673382e+00,
       -1.77666112e+01,  3.80986521e+00,  6.92224640e-04, -1.47556685e+00,
        3.06049479e-01, -1.23345939e-02, -9.52747232e-01,  9.31168327e-03,
       -5.24758378e-01])

model.intercept_ 截距

model.intercept_  # 截距(与Y轴的交点)

36.45948838509036

model.get_params() 查看定义的参数

model.get_params()

{'copy_X': True,
 'fit_intercept': True,
 'n_jobs': None,
 'normalize': 'deprecated',
 'positive': False}

model.score() 对model学到的东西进行打分

model.score(data_X, data_y)  # 确定性系数R^2, 将预测结果与实际结果进行分析, 看吻合程度

0.7406426641094095

7 normalization 标准化数据

preprocessing.scale()

from sklearn import preprocessing  # 预处理
import numpy as np

a = np.array([[10, 2.7, 3.6],
              [-100, 5, -2],
              [120, 20, 40]], dtype=np.float64)
# 每个列代表一个属性
# 属性1取值范围: [-100, 120]
# 属性2取值范围: [2.7, 20]
# 属性3取值范围: [-2, 40]
# 各个属性取值范围差距较大
preprocessing.scale(a)

array([[ 0.        , -0.85170713, -0.55138018],
       [-1.22474487, -0.55187146, -0.852133  ],
       [ 1.22474487,  1.40357859,  1.40351318]])

经过标准化处理后, 各个属性取值范围基本一致

实例

from sklearn import preprocessing  # 预处理
import numpy as np
from sklearn.model_selection import train_test_split  # 分割数据集
from sklearn.datasets import make_classification  # 创建数据
from sklearn.svm import SVC  # Support Vector classifier 支持向量分类器
import matplotlib.pyplot as plt  # 可视化

X, y = make_classification(n_samples=300, n_features=2, n_redundant=0, n_informative=2,
                          random_state=22, n_clusters_per_class=1, scale=100)
plt.figure()
plt.scatter(X[:, 0], X[:, 1], c=y)  # c=y: color=yellow
plt.show()

X, y = make_classification(n_samples=300, n_features=2, n_redundant=0, n_informative=2,
                          random_state=22, n_clusters_per_class=1, scale=1000)
X = preprocessing.scale(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)
clf = SVC()
clf.fit(X_train, y_train)
# 用training data学习, 用test data 去预测
clf.score(X_test, y_test)

0.9222222222222223

8 cross validation 交叉验证1

常规

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

iris = load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=4)
knn = KNeighborsClassifier(n_neighbors=5)  # 考虑数据附近的5给neighbor
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
knn.score(X_test, y_test)

0.9736842105263158

交叉验证

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

iris = load_iris()
X = iris.data
y = iris.target

from sklearn.model_selection import cross_val_score
knn = KNeighborsClassifier(n_neighbors=5)  # 考虑数据附近的5给neighbor
scores = cross_val_score(knn, X, y, cv=5, scoring="accuracy")  # 判断其准确度是否够高
scores

array([0.96666667, 1.        , 0.93333333, 0.96666667, 1.        ])

scores.mean()  # 平均结果

0.9733333333333334

判断如何选择参数取值最佳

看精确度

from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt

k_range = range(1, 31)
k_scores = []
for k in k_range:
    knn = KNeighborsClassifier(n_neighbors=k)
    scores = cross_val_score(knn, X, y, cv=10, scoring="accuracy")
    k_scores.append(scores.mean())
plt.figure()
plt.plot(k_range, k_scores)
plt.xlabel("Value of K for KNN")
plt.ylabel('Cross-Validated Acuracy')
plt.show()

• 如果K过小, 会出现欠拟合问题
• 如果K过大, 会出现overfitting问题(过拟合)

9 cross validation 交叉验证2

过拟合问题

from sklearn.model_selection import learning_curve  # 可视化训练的过程
from sklearn.datasets import load_digits
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np

digits = load_digits()
X = digits.data
y = digits.target
train_sizes, train_loss, test_loss = learning_curve(
    SVC(gamma=0.01), X, y, cv=10, scoring='neg_mean_squared_error',
    train_sizes=[0.1, 0.25, 0.5, 0.75, 1])
train_loss_mean = -np.mean(train_loss, axis=1)
test_loss_mean = -np.mean(test_loss, axis=1)

plt.figure()
plt.plot(train_sizes, train_loss_mean, 'o-', color='r', label='Training')
plt.plot(train_sizes, test_loss_mean, 'o-', color='g', label='Cross-validation')
plt.xlabel('Training examples')
plt.ylabel('Loss')
plt.legend(loc='best')
plt.show()

出现过拟合, 当样本数据量变大时误差率反而增加

10 cross validation 交叉验证3

from sklearn.model_selection import validation_curve
from sklearn.datasets import load_digits
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np

digits = load_digits()
X = digits.data
y = digits.target
param_range = np.logspace(-6, -2.3, 5)
train_loss, test_loss = validation_curve(
    SVC(), X, y, param_name='gamma', param_range=param_range, cv=10, 
    scoring='neg_mean_squared_error')
train_loss_mean = -np.mean(train_loss, axis=1)
test_loss_mean = -np.mean(test_loss, axis=1)

plt.figure()
plt.plot(param_range, train_loss_mean, 'o-', color='r', label='Training')
plt.plot(param_range, test_loss_mean, 'o-', color='g', label='Cross-validation')
plt.xlabel('gamma')
plt.ylabel('Loss')
plt.legend(loc='best')
plt.show()

11 保存model

from sklearn import svm
from sklearn import datasets

clf = svm.SVC()
iris = datasets.load_iris()
X, y = iris.data, iris.target
clf.fit(X, y)

SVC()

方法1: pickle

导出

import pickle

with open('save/clf.pickle', 'wb') as f:
    pickle.dump(clf, f)

导入

with open('save/clf.pickle', 'rb') as f:
    clf2 = pickle.load(f)
clf2.predict(X[0:1])

array([0])

方法2: joblib

导出

import joblib

joblib.dump(clf, 'save/clf.pkl')

['save/clf.pkl']

导入

clf3 = joblib.load('save/clf.pkl')
clf3.predict(X[0:1])

array([0])

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