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1.导入库
1 import os 2 import h5py 3 import shutil 4 import sklearn 5 import tempfile 6 import numpy as np 7 import pandas as pd 8 import sklearn.datasets 9 import sklearn.linear_model 10 import matplotlib.pyplot as plt 11 %matplotlib inline
2.产生数据
sklearn.datasets.make_classification产生测试数据。
10000组数据，特征向量维数为4。
sklearn.cross_validation.train_test_split为交叉验证。就是把data拆分为不同的train set和test set。
这里拆分为7500：2500
1 X, y = sklearn.datasets.make_classification( 2 n_samples=10000, n_features=4, n_redundant=0, n_informative=2, 3 n_clusters_per_class=2, hypercube=False, random_state=0 4 ) 5 6 # Split into train and test 7 X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)
3.数据可视化
1 # Visualize sample of the data 2 # np.random.permutation产生序列或随机交换序列 3 # X.shape=7500 4 # 在此产生0-7499乱序序列并取前1000 5 ind = np.random.permutation(X.shape[0])[:1000] 6 df = pd.DataFrame(X[ind]) 7 # 绘图 ‘kde‘核密度估计，‘hist‘直方图 8 _ = pd.scatter_matrix(df, figsize=(9, 9), diagonal=‘kde‘, marker=‘o‘, s=40, alpha=.4, c=y[ind])
pd.scatter_matrix函数说明
1 def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, 2 diagonal=‘hist‘, marker=‘.‘, density_kwds=None, 3 hist_kwds=None, range_padding=0.05, **kwds): 4 """ 5 Draw a matrix of scatter plots. 6 7 Parameters 8 ---------- 9 frame : DataFrame 10 alpha : float, optional 11 amount of transparency applied 12 figsize : (float,float), optional 13 a tuple (width, height) in inches 14 ax : Matplotlib axis object, optional 15 grid : bool, optional 16 setting this to True will show the grid 17 diagonal : {‘hist‘, ‘kde‘} 18 pick between ‘kde‘ and ‘hist‘ for 19 either Kernel Density Estimation or Histogram 20 plot in the diagonal 21 marker : str, optional 22 Matplotlib marker type, default ‘.‘ 23 hist_kwds : other plotting keyword arguments 24 To be passed to hist function 25 density_kwds : other plotting keyword arguments 26 To be passed to kernel density estimate plot 27 range_padding : float, optional 28 relative extension of axis range in x and y 29 with respect to (x_max - x_min) or (y_max - y_min), 30 default 0.05 31 kwds : other plotting keyword arguments 32 To be passed to scatter function 33 34 Examples 35 -------- 36 >>> df = DataFrame(np.random.randn(1000, 4), columns=[‘A‘,‘B‘,‘C‘,‘D‘]) 37 >>> scatter_matrix(df, alpha=0.2) 38 """
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原文地址：http://www.cnblogs.com/nwpuxuezha/p/4297853.html
