机器学习基础（二）——训练集和测试集的划分

1. 测试集和训练集的划分

from sklearn.datasets import load_iris, fetch_20newsgroups, load_boston
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report
from sklearn.feature_extraction import DictVectorizer
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.ensemble import RandomForestClassifier
import pandas as pd

li = load_iris()
# li的属性有‘data’，‘target’

print("获取特征值：")
print(li.data[0:5])
print("目标值：")
print(li.target[0:5])

获取特征值：
[[5.1 3.5 1.4 0.2]
 [4.9 3.  1.4 0.2]
 [4.7 3.2 1.3 0.2]
 [4.6 3.1 1.5 0.2]
 [5.  3.6 1.4 0.2]]
目标值：
[0 0 0 0 0]

注意返回值:训练集 train x_train, y_train 测试集 test x_test, y_test

1.1 划分训练集和测试集

# 调用格式为：
x_train, x_test, y_train, y_test = train_test_split(li.data, li.target, test_size=0.25)
# 数据量较大，打印前5个样本即可。
print("训练集特征值和目标值：", x_train[:5], y_train[:5])
print("测试集特征值和目标值：", x_test[:5], y_test[:5])

训练集特征值和目标值： [[6.3 3.3 6.  2.5]
 [6.  3.  4.8 1.8]
 [7.  3.2 4.7 1.4]
 [4.9 2.5 4.5 1.7]
 [6.7 3.1 4.4 1.4]] [2 2 1 2 1]
测试集特征值和目标值： [[7.7 3.  6.1 2.3]
 [4.9 2.4 3.3 1. ]
 [7.7 2.8 6.7 2. ]
 [7.9 3.8 6.4 2. ]
 [5.4 3.9 1.3 0.4]] [2 1 2 2 0]

print(li.DESCR) # 该数据集的一些描述信息

.. _iris_dataset:

Iris plants dataset
--------------------

**Data Set Characteristics:**

    :Number of Instances: 150 (50 in each of three classes)
    :Number of Attributes: 4 numeric, predictive attributes and the class
    :Attribute Information:
        - sepal length in cm
        - sepal width in cm
        - petal length in cm
        - petal width in cm
        - class:
                - Iris-Setosa
                - Iris-Versicolour
                - Iris-Virginica
                
    :Summary Statistics:

    ============== ==== ==== ======= ===== ====================
                    Min  Max   Mean    SD   Class Correlation
    ============== ==== ==== ======= ===== ====================
    sepal length:   4.3  7.9   5.84   0.83    0.7826
    sepal width:    2.0  4.4   3.05   0.43   -0.4194
    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)
    ============== ==== ==== ======= ===== ====================

    :Missing Attribute Values: None
    :Class Distribution: 33.3% for each of 3 classes.
    :Creator: R.A. Fisher
    :Donor: Michael Marshall (MARSHALL%[email protected])
    :Date: July, 1988

The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken
from Fisher's paper. Note that it's the same as in R, but not as in the UCI
Machine Learning Repository, which has two wrong data points.

This is perhaps the best known database to be found in the
pattern recognition literature.  Fisher's paper is a classic in the field and
is referenced frequently to this day.  (See Duda & Hart, for example.)  The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant.  One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.

.. topic:: References

   - Fisher, R.A. "The use of multiple measurements in taxonomic problems"
     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
     Mathematical Statistics" (John Wiley, NY, 1950).
   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
     Structure and Classification Rule for Recognition in Partially Exposed
     Environments".  IEEE Transactions on Pattern Analysis and Machine
     Intelligence, Vol. PAMI-2, No. 1, 67-71.
   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
     on Information Theory, May 1972, 431-433.
   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
     conceptual clustering system finds 3 classes in the data.
   - Many, many more ...

data_url = "http://lib.stat.cmu.edu/datasets/boston"
raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
target = raw_df.values[1::2, 2]
data[:5]

array([[6.3200e-03, 1.8000e+01, 2.3100e+00, 0.0000e+00, 5.3800e-01,
        6.5750e+00, 6.5200e+01, 4.0900e+00, 1.0000e+00, 2.9600e+02,
        1.5300e+01, 3.9690e+02, 4.9800e+00],
       [2.7310e-02, 0.0000e+00, 7.0700e+00, 0.0000e+00, 4.6900e-01,
        6.4210e+00, 7.8900e+01, 4.9671e+00, 2.0000e+00, 2.4200e+02,
        1.7800e+01, 3.9690e+02, 9.1400e+00],
       [2.7290e-02, 0.0000e+00, 7.0700e+00, 0.0000e+00, 4.6900e-01,
        7.1850e+00, 6.1100e+01, 4.9671e+00, 2.0000e+00, 2.4200e+02,
        1.7800e+01, 3.9283e+02, 4.0300e+00],
       [3.2370e-02, 0.0000e+00, 2.1800e+00, 0.0000e+00, 4.5800e-01,
        6.9980e+00, 4.5800e+01, 6.0622e+00, 3.0000e+00, 2.2200e+02,
        1.8700e+01, 3.9463e+02, 2.9400e+00],
       [6.9050e-02, 0.0000e+00, 2.1800e+00, 0.0000e+00, 4.5800e-01,
        7.1470e+00, 5.4200e+01, 6.0622e+00, 3.0000e+00, 2.2200e+02,
        1.8700e+01, 3.9690e+02, 5.3300e+00]])

target[:5]

array([24. , 21.6, 34.7, 33.4, 36.2])

以上展示了两种不同类型的数据集，一种target为离散型（类别），一种为连续型（价格）。

2. fit和transform

fit( ): Method calculates the parameters μ and σ and saves them as internal objects.

可以理解为在对数据集进行转换操作之前，对数据的一些基本属性如：均值，方差，最大值，最小值做个类似pd.info()的概况。

transform( ): Method using these calculated parameters apply the transformation to a particular dataset.
在调用transform之前，需要对数据做一个fit预处理，然后可以进行标准化，降维，归一化等操作。（如PCA，StandardScaler等）。

fit_transform(): joins the fit() and transform() method for transformation of dataset.

想当于是fit和transform的组合，既包括了预处理和数据转换。

from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler, StandardScaler
sample_1 = [[2,4,2,3],[6,4,3,2],[8,4,5,6]]
s = StandardScaler()
s.fit_transform(sample_1)

array([[-1.33630621,  0.        , -1.06904497, -0.39223227],
       [ 0.26726124,  0.        , -0.26726124, -0.98058068],
       [ 1.06904497,  0.        ,  1.33630621,  1.37281295]])

ss = StandardScaler()
ss.fit(sample_1)

StandardScaler()

ss.transform(sample_1)

array([[-1.33630621,  0.        , -1.06904497, -0.39223227],
       [ 0.26726124,  0.        , -0.26726124, -0.98058068],
       [ 1.06904497,  0.        ,  1.33630621,  1.37281295]])