Hand on Machine Learning第三章课后作业(2)：其余小练习

-#!/usr/bin/env python
-# # # -- coding: utf-8 --
-# # # @Time : 2019.5.22 14:09
-# # # @Author : AndrewCHH
-# # # @File : after_class.py

1. 获取数据

• 使用MNIST数据集练习分类任务

from __future__ import print_function
import pandas as pd

# 导入后加入以下列，再显示时显示完全。
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
from sklearn.datasets import fetch_mldata
from scipy.io import loadmat

mnist = fetch_mldata('MNIST original', transpose_data=True, data_home='files')
print("mnist",mnist)
# *DESCR为description,即数据集的描述
# *CLO_NAMES为列名
# *target键，带有标记的数组
# *data键，每个实例为一行，每个特征为1列
# 共七万张图片，每张图片784个特征点
X, y = mnist["data"], mnist["target"]
print(X.shape, y.shape)
print(type(X))
# 显示图片
import matplotlib
import matplotlib.pyplot as plt

some_digit = X[36001]
some_digit_image = some_digit.reshape(28, 28)  # 将一维数组转化为28*28的数组
# cmap->颜色图谱（colormap)
# interpolation: 图像插值参数，图像插值就是利用已知邻近像素点的灰度值（或rgb图像中的三色值）来产生未知像素点的灰度值，以便由原始图像再生出具有更高分辨率的图像。
# * If interpolation is None, it defaults to the image.interpolation rc parameter.
# If the interpolation is 'none', then no interpolation is performed for the Agg, ps and pdf backends. Other backends will default to 'nearest'.
# For the Agg, ps and pdf backends, interpolation = 'none' works well when a big image is scaled down,
# while interpolation = 'nearest' works well when a small image is scaled up.
plt.imshow(some_digit_image, cmap=matplotlib.cm.binary,
           interpolation="nearest")
plt.axis("off")
plt.show()
print(y[36001])

D:Anaconda3libsite-packagessklearnutilsdeprecation.py:77: DeprecationWarning: Function fetch_mldata is deprecated; fetch_mldata was deprecated in version 0.20 and will be removed in version 0.22
  warnings.warn(msg, category=DeprecationWarning)
D:Anaconda3libsite-packagessklearnutilsdeprecation.py:77: DeprecationWarning: Function mldata_filename is deprecated; mldata_filename was deprecated in version 0.20 and will be removed in version 0.22
  warnings.warn(msg, category=DeprecationWarning)


mnist {'DESCR': 'mldata.org dataset: mnist-original', 'COL_NAMES': ['label', 'data'], 'target': array([0., 0., 0., ..., 9., 9., 9.]), 'data': 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]], dtype=uint8)}
(70000, 784) (70000,)
<class 'numpy.ndarray'>



<Figure size 640x480 with 1 Axes>


5.0

2. 创建测试集训练集

x_train, x_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]

2.1 数据洗牌（注意数据的顺序敏感性）

import numpy as np

# Randomly permute a sequence, or return a permuted range.
shuffle_index = np.random.permutation(60000)
x_train, y_train = x_train[shuffle_index], y_train[shuffle_index]

# # Q1：
# ## 1.1 创建KNN分类器
y_train_5 =(y_train==5)
from sklearn.neighbors import KNeighborsClassifier
# knn_clf = KNeighborsClassifier()
# knn_clf = KNeighborsClassifier(n_jobs=-1, weights='distance', n_neighbors=4)
# knn_clf.fit(x_train, y_train)
# array = knn_clf.predict(x_test)
# print("knnResult",array)

# # ## 1.2 对knn执行网格搜索
# from sklearn.model_selection import GridSearchCV
# para_grid = [
#     {'n_neighbors':[3,4,5,6],'weights':["uniform","distance",]}
# ]
# knn_clf = KNeighborsClassifier()
# grid_search = GridSearchCV(knn_clf,para_grid,cv=5,verbose=3,n_jobs=-1,scoring="neg_mean_squared_error")
# grid_search.fit(x_train,y_train)
# print(grid_search.best_params_)
# print(grid_search.best_estimator_)

# from sklearn.model_selection import GridSearchCV
#
# param_grid = [{'weights': ["uniform", "distance"], 'n_neighbors': [3, 4, 5]}]
#
# knn_clf = KNeighborsClassifier()
# grid_search = GridSearchCV(knn_clf, param_grid, cv=5, verbose=3, n_jobs=-1)
# grid_search.fit(x_train, y_train)
# print(grid_search.best_params_)
# print(grid_search.best_estimator_)

# ## 1.2 评估准确性
# y_knn_pred = knn_clf.predict(x_test)
# from sklearn.metrics import accuracy_score
# a = accuracy_score(y_test,y_knn_pred)
# print(a)

# # Q2:
# 使用shaift方法移动图片中的像素，注意，self传进来的X[1]是一维数组，要使用reshape变成28*28的数组。
# cval参数指的是移动图片后填补的像素值
from scipy.ndimage.interpolation import shift
def movePiexOfImage(self,dx,dy,new=0):
    return shift(self.reshape(28,28),[dx,dy],cval=new)


# 图片显示维度错误检测
def valid_imshow_data(data):
    data = np.asarray(data)
    if data.ndim == 2:
        return True
    elif data.ndim == 3:
        if 3 <= data.shape[2] <= 4:
            return True
        else:
            print('The "data" has 3 dimensions but the last dimension '
                  'must have a length of 3 (RGB) or 4 (RGBA), not "{}".'
                  ''.format(data.shape[2]))
            return False
    else:
        print('To visualize an image the data must be 2 dimensional or '
              '3 dimensional, not "{}".'
              ''.format(data.ndim))
        return False

print("image:",X[1])
imageShift=movePiexOfImage(X[1],5,1,new=100)
imageShift = imageShift.reshape(28,28)
plt.imshow(imageShift,cmap=matplotlib.cm.binary)
plt.show()

print(len(x_train))
for i in range(len(x_train)):
    moveLeft  = movePiexOfImage(x_train[i],1,0,new=100)
    # moveDown  = movePiexOfImage(x_train[i],0,1,new=100)
    # moveRight = movePiexOfImage(x_train[i],-1,0,new=100)
    # moveUp    = movePiexOfImage(x_train[i],0,-1,new=100)
    # moveDown = moveDown.reshape(1,784)
    moveLeft = moveLeft.reshape(1,784)
    # moveRight = moveRight.reshape(1,784)
    # moveUp = moveUp.reshape(1,784)
    x_train = np.concatenate((x_train,moveLeft),axis=0)
print(len(x_train))

image: [  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   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   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  64 253 255  63   0   0   0   0   0   0   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0   0   0  96 205 251 253 205 111
   4   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
   0   0   0  96 189 251 251 253 251 251  31   0   0   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0  16  64 223 244 251 251 211 213
 251 251  31   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
   0  80 181 251 253 251 251 251  94  96 251 251  31   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0  92 253 253 253 255 253 253 253
  95  96 253 253  31   0   0   0   0   0   0   0   0   0   0   0   0   0
   0  92 236 251 243 220 233 251 251 243  82  96 251 251  31   0   0   0
   0   0   0   0   0   0   0   0   0   0  80 253 251 251 188   0  96 251
 251 109   0  96 251 251  31   0   0   0   0   0   0   0   0   0   0   0
   0  96 240 253 243 188  42   0  96 204 109   4   0  12 197 251  31   0
   0   0   0   0   0   0   0   0   0   0   0 221 251 253 121   0   0   0
  36  23   0   0   0   0 190 251  31   0   0   0   0   0   0   0   0   0
   0   0  48 234 253   0   0   0   0   0   0   0   0   0   0   0 191 253
  31   0   0   0   0   0   0   0   0   0   0  44 221 251 251   0   0   0
   0   0   0   0   0   0   0  12 197 251  31   0   0   0   0   0   0   0
   0   0   0 190 251 251 251   0   0   0   0   0   0   0   0   0   0  96
 251 251  31   0   0   0   0   0   0   0   0   0   0 190 251 251 113   0
   0   0   0   0   0   0   0   0  40 234 251 219  23   0   0   0   0   0
   0   0   0   0   0 190 251 251  94   0   0   0   0   0   0   0   0  40
 217 253 231  47   0   0   0   0   0   0   0   0   0   0   0 191 253 253
 253   0   0   0   0   0   0  12 174 253 253 219  39   0   0   0   0   0
   0   0   0   0   0   0   0  67 236 251 251 191 190 111  72 190 191 197
 251 243 121  39   0   0   0   0   0   0   0   0   0   0   0   0   0   0
  63 236 251 253 251 251 251 251 253 251 188  94   0   0   0   0   0   0
   0   0   0   0   0   0   0   0   0   0   0  27 129 253 251 251 251 251
 229 168  15   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
   0   0   0   0   0  95 212 251 211  94  59   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   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   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]

60000