对于像下面这样的具有低平滑度的散点图矩阵，Python 代码是什么？
我不确定图表的原始来源。我在this post上看到了在交叉验证上。省略号根据原始帖子定义协方差。我不确定这些数字是什么意思。
请您参考如下方法:
我修改了 pandas scatter_matrix 函数并得到了不错的结果:
import pandas as pd
import numpy as np
frame = pd.DataFrame(np.random.randn(100, 4), columns=['A','B','C','D'])
fig = scatter_matrix_lowess(frame, alpha=0.4, figsize=(12,12));
fig.suptitle('Scatterplot matrix with lowess smoother', fontsize=16);
这是 scatter_matrix_lowess 的代码:
def scatter_matrix_lowess(frame, alpha=0.5, figsize=None, grid=False,
diagonal='hist', marker='.', density_kwds=None,
hist_kwds=None, range_padding=0.05, **kwds):
"""
Draw a matrix of scatter plots with lowess smoother.
This is an adapted version of the pandas scatter_matrix function.
Parameters
----------
frame : DataFrame
alpha : float, optional
amount of transparency applied
figsize : (float,float), optional
a tuple (width, height) in inches
ax : Matplotlib axis object, optional
grid : bool, optional
setting this to True will show the grid
diagonal : {'hist', 'kde'}
pick between 'kde' and 'hist' for
either Kernel Density Estimation or Histogram
plot in the diagonal
marker : str, optional
Matplotlib marker type, default '.'
hist_kwds : other plotting keyword arguments
To be passed to hist function
density_kwds : other plotting keyword arguments
To be passed to kernel density estimate plot
range_padding : float, optional
relative extension of axis range in x and y
with respect to (x_max - x_min) or (y_max - y_min),
default 0.05
kwds : other plotting keyword arguments
To be passed to scatter function
Examples
--------
>>> df = DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
>>> scatter_matrix_lowess(df, alpha=0.2)
"""
import matplotlib.pyplot as plt
from matplotlib.artist import setp
import pandas.core.common as com
from pandas.compat import range, lrange, lmap, map, zip
from statsmodels.nonparametric.smoothers_lowess import lowess
df = frame._get_numeric_data()
n = df.columns.size
fig, axes = plt.subplots(nrows=n, ncols=n, figsize=figsize, squeeze=False)
# no gaps between subplots
fig.subplots_adjust(wspace=0, hspace=0)
mask = com.notnull(df)
marker = _get_marker_compat(marker)
hist_kwds = hist_kwds or {}
density_kwds = density_kwds or {}
# workaround because `c='b'` is hardcoded in matplotlibs scatter method
kwds.setdefault('c', plt.rcParams['patch.facecolor'])
boundaries_list = []
for a in df.columns:
values = df[a].values[mask[a].values]
rmin_, rmax_ = np.min(values), np.max(values)
rdelta_ext = (rmax_ - rmin_) * range_padding / 2.
boundaries_list.append((rmin_ - rdelta_ext, rmax_+ rdelta_ext))
for i, a in zip(lrange(n), df.columns):
for j, b in zip(lrange(n), df.columns):
ax = axes[i, j]
if i == j:
values = df[a].values[mask[a].values]
# Deal with the diagonal by drawing a histogram there.
if diagonal == 'hist':
ax.hist(values, **hist_kwds)
elif diagonal in ('kde', 'density'):
from scipy.stats import gaussian_kde
y = values
gkde = gaussian_kde(y)
ind = np.linspace(y.min(), y.max(), 1000)
ax.plot(ind, gkde.evaluate(ind), **density_kwds)
ax.set_xlim(boundaries_list[i])
else:
common = (mask[a] & mask[b]).values
ax.scatter(df[b][common], df[a][common],
marker=marker, alpha=alpha, **kwds)
# The following 2 lines are new and add the lowess smoothing
ys = lowess(df[a][common], df[b][common])
ax.plot(ys[:,0], ys[:,1], 'red', linewidth=1)
ax.set_xlim(boundaries_list[j])
ax.set_ylim(boundaries_list[i])
ax.set_xlabel('')
ax.set_ylabel('')
_label_axis(ax, kind='x', label=b, position='bottom', rotate=True)
_label_axis(ax, kind='y', label=a, position='left')
if j!= 0:
ax.yaxis.set_visible(False)
if i != n-1:
ax.xaxis.set_visible(False)
for ax in axes.flat:
setp(ax.get_xticklabels(), fontsize=8)
setp(ax.get_yticklabels(), fontsize=8)
return fig
def _label_axis(ax, kind='x', label='', position='top',
ticks=True, rotate=False):
from matplotlib.artist import setp
if kind == 'x':
ax.set_xlabel(label, visible=True)
ax.xaxis.set_visible(True)
ax.xaxis.set_ticks_position(position)
ax.xaxis.set_label_position(position)
if rotate:
setp(ax.get_xticklabels(), rotation=90)
elif kind == 'y':
ax.yaxis.set_visible(True)
ax.set_ylabel(label, visible=True)
# ax.set_ylabel(a)
ax.yaxis.set_ticks_position(position)
ax.yaxis.set_label_position(position)
return
def _get_marker_compat(marker):
import matplotlib.lines as mlines
import matplotlib as mpl
if mpl.__version__ < '1.1.0' and marker == '.':
return 'o'
if marker not in mlines.lineMarkers:
return 'o'
return marker
