Docker下，五分钟极速体验机器学习

docker run --rm -p 8888:8888 bolingcavalry/anaconda3-jupyter:0.0.1

(base) [root@centos7 ~]# docker run --rm -p 8888:8888 bolingcavalry/anaconda3-jupyter:0.0.1
Starting jupyter
[I 06:30:17.712 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret
[I 06:30:18.460 NotebookApp] JupyterLab extension loaded from /opt/conda/lib/python3.7/site-packages/jupyterlab
[I 06:30:18.460 NotebookApp] JupyterLab application directory is /opt/conda/share/jupyter/lab
[I 06:30:18.461 NotebookApp] Serving notebooks from local directory: /opt/notebooks
[I 06:30:18.461 NotebookApp] The Jupyter Notebook is running at:
[I 06:30:18.461 NotebookApp] http://(a61298ac6493 or 127.0.0.1):8888/?token=708d365fce9d9a76f98b2ade7e9aefcbc8401dbf5027ffa6
[I 06:30:18.462 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 06:30:18.465 NotebookApp] 
    
    To access the notebook, open this file in a browser:
        file:///root/.local/share/jupyter/runtime/nbserver-6-open.html
    Or copy and paste one of these URLs:
        http://(a61298ac6493 or 127.0.0.1):8888/?token=708d365fce9d9a76f98b2ade7e9aefcbc8401dbf5027ffa6
[W 06:31:15.960 NotebookApp] Clearing invalid/expired login cookie username-192-168-121-137-8888
[W 06:31:15.961 NotebookApp] Couldn't authenticate WebSocket connection
[W 06:31:15.961 NotebookApp] Clearing invalid/expired login cookie username-192-168-121-137-8888
[W 06:31:15.962 NotebookApp] Clearing invalid/expired login cookie username-192-168-121-137-8888
[W 06:31:15.981 NotebookApp] 403 GET /api/kernels/5e3f93d5-4f80-4ed3-ad56-b04db61c8487/channels?session_id=33173ba03fee449da0839df0e10cfb6e (192.168.121.1) 22.41ms referer=None

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier

#可以在浏览器上实时显示图像
%matplotlib inline

#取得鸾尾花数据集
iris_dataset = load_iris()

#将整个数据集分割成两部分：train用来训练，test用来测试，这里test_size等于10表示测试数据只有10条，其余的全部用作训练，
#注意，train_test_split会将iris_dataset的数据顺序打乱再分割
#X表示特征数据，每一行表示一朵鸾尾花的完整特征，该特征有四列：花萼长、花萼宽、花瓣长、花瓣宽
#y表示标签，例如y[0]=1，就表示X[0]的四个特征，对应的鸾尾花是第二类，总共有三类鸾尾花
X_train, X_test, y_train, y_test = train_test_split(iris_dataset['data'], iris_dataset['target'], test_size=10)

#在绘图的时候，指定数据列的名称来自数据集的'feature_names'
iris_dataframe=pd.DataFrame(X_train, columns=iris_dataset['feature_names'])

#绘图，散点矩阵图，每个小图和横轴是一个特征，纵轴是另个特征，
#例如花萼长做横轴、花萼宽做纵轴，可以看出不同的花萼长、花萼宽数据对应的鸾尾花类型分布情况
grr=scatter_matrix(iris_dataframe, c=y_train, figsize=(15,15), marker='o', hist_kwds={'bins':20}, s=60, alpha=.8)

#使用K最邻近算法来做训练
knn = KNeighborsClassifier(n_neighbors=1)

#用前面分割好的数据来做训练，X是特征，y是确定的鸾尾花的类型
knn.fit(X_train, y_train)

#训练完毕后，输入前面分割好的10组特征值，用K最邻近算法预测这10组特征值分别是哪10种鸾尾花
y_pred = knn.predict(X_test)

#把预测结果打印出来
print("测试集预测结果：\n{}".format(y_pred))

#y_test是早就准备好的，那10组特征对应的实际的类型
print("测试集实际结果：\n{}".format(y_test))

#将预测结果和实际结果做对比，可以得到预测的准确率
print("knn打分：{:.2f}".format(knn.score(X_test, y_test)))