Fundamentals of machine learning (II) -- division of training set and test set

1. Division of test set and training set

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 The attributes of are ‘data’,‘target’

print(" Get eigenvalues ：")
print(li.data[0:5])
print(" The target ：")
print(li.target[0:5])

 Get eigenvalues ：
[[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]]
 The target ：
[0 0 0 0 0]

Be careful Return value : Training set train x_train, y_train Test set test x_test, y_test

1.1 Divide the training set and the test set

#  The invocation format is ：
x_train, x_test, y_train, y_test = train_test_split(li.data, li.target, test_size=0.25)
#  Large amount of data , Before printing 5 One sample is enough .
print(" Training set eigenvalues and target values ：", x_train[:5], y_train[:5])
print(" Test set eigenvalues and target values ：", x_test[:5], y_test[:5])

 Training set eigenvalues and target values ： [[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]
 Test set eigenvalues and target values ： [[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) #  Some descriptive information of the data set 

.. _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])

The above shows two different types of data sets , A kind of target It is discrete （ Category ）, One is continuity type （ Price ）.

2. fit and transform

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

It can be understood as before the data set is converted , For some basic attributes of data, such as ： mean value , variance , Maximum , The minimum value is similar pd.info() Overview of .

transform( ): Method using these calculated parameters apply the transformation to a particular dataset.
Calling transform Before , You need to do a... On the data fit Preprocessing , Then it can be standardized , Dimension reduction , Normalization and other operations .（ Such as PCA,StandardScaler etc. ）.

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

Want to be so fit and transform The combination of , It includes preprocessing and data conversion .

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]])