本文目的是认识sklearn库的一些基本概念，了解sklearn提供的常用功能。

官方网站

Getting Start：https://scikit-learn.org/stable/getting_started.html
sklearn主页：https://scikit-learn.org/stable/

训练和预测

sklearn定义好了一系列的机器学习算法和模型，称为estimators，调用其fit方法训练模型，下面是一个简单的例子

from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(random_state=0)
X = [[ 1,  2,  3],  # 2 samples, 3 features
y = [0, 1]  # classes of each sample
print(clf.fit(X, y))
# RandomForestClassifier(random_state=0)

clf.predict(X)  # predict classes of the training data
# array([0, 1])

clf.predict([[4, 5, 6], [14, 15, 16]])  # predict classes of new data
# array([0, 1])

fit函数的参数

X

数据的特征，(n_samples, n_features)
y

数据对应的标签

预处理

预处理类继承了estimator 类，先调用fit函数然后调用transform函数获取预处理结果

from sklearn.preprocessing import StandardScaler
X = [[0, 15], [1, -10]]
# scale data according to computed scaling values
print(StandardScaler().fit(X).transform(X))
# array([[-1.,  1.],
#        [ 1., -1.]])

pipeline

将预处理和训练模型整合成为一个对象称为pipeline，下面是一个具体的例子

from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# create a pipeline object
pipe = make_pipeline(
    StandardScaler(),
    LogisticRegression()
)

# load the iris dataset and split it into train and test sets
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# fit the whole pipeline
print(pipe.fit(X_train, y_train))

# we can now use it like any other estimator
acc = accuracy_score(pipe.predict(X_test), y_test)
print(acc)
# 0.9736842105263158

交叉验证

from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_validate

X, y = make_regression(n_samples=1000, random_state=0)
lr = LinearRegression()
result = cross_validate(lr, X, y)  # defaults to 5-fold CV
print(result['test_score'])  # r_squared score is high because dataset is easy
# [1. 1. 1. 1. 1.]

超参数自动搜索

sklearn提供了自动搜索最优组合超参数的类

from sklearn.datasets import fetch_california_housing
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from scipy.stats import randint

X, y = fetch_california_housing(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# define the parameter space that will be searched over
param_distributions = {'n_estimators': randint(1, 5), 'max_depth': randint(5, 10)}

# now create a searchCV object and fit it to the data
search = RandomizedSearchCV(estimator=RandomForestRegressor(random_state=0), n_iter=5,
                            param_distributions=param_distributions, random_state=0)
print(search.fit(X_train, y_train))
print(search.best_params_)
# {'max_depth': 9, 'n_estimators': 4}

# the search object now acts like a normal random forest estimator
# with max_depth=9 and n_estimators=4
print(search.score(X_test, y_test))
# 0.735363411343253