画的混淆矩阵非常漂亮，自从用了这个来分析图像分类，整个人都精神了。
看一下图
from pandas import DataFrame
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
from matplotlib.collections import QuadMesh
import seaborn as sn
def get_new_fig(fn, figsize=[9,9]):
""" Init graphics """
fig1 = plt.figure(fn, figsize)
ax1 = fig1.gca() #Get Current Axis
ax1.cla() # clear existing plot
return fig1, ax1
def configcell_text_and_colors(array_df, lin, col, oText, facecolors, posi, fz, fmt, show_null_values=0):
text_add = []; text_del = [];
cell_val = array_df[lin][col]
tot_all = array_df[-1][-1]
per = (float(cell_val) / tot_all) * 100
curr_column = array_df[:,col]
ccl = len(curr_column)
#last line and/or last column
if(col == (ccl - 1)) or (lin == (ccl - 1)):
#tots and percents
if(cell_val != 0):
if(col == ccl - 1) and (lin == ccl - 1):
tot_rig = 0
for i in range(array_df.shape[0] - 1):
tot_rig += array_df[i][i]
per_ok = (float(tot_rig) / cell_val) * 100
elif(col == ccl - 1):
tot_rig = array_df[lin][lin]
per_ok = (float(tot_rig) / cell_val) * 100
elif(lin == ccl - 1):
tot_rig = array_df[col][col]
per_ok = (float(tot_rig) / cell_val) * 100
per_err = 100 - per_ok
else:
per_ok = per_err = 0
per_ok_s = ['%.2f%%'%(per_ok), '100%'] [per_ok == 100]
text_del.append(oText)
font_prop = fm.FontProperties(weight='bold', size=fz)
text_kwargs = dict(color='w', ha="center", va="center", gid='sum', fontproperties=font_prop)
lis_txt = ['%d'%(cell_val), per_ok_s, '%.2f%%'%(per_err)]
lis_kwa = [text_kwargs]
dic = text_kwargs.copy(); dic['color'] = 'g'; lis_kwa.append(dic);
dic = text_kwargs.copy(); dic['color'] = 'r'; lis_kwa.append(dic);
lis_pos = [(oText._x, oText._y-0.3), (oText._x, oText._y), (oText._x, oText._y+0.3)]
for i in range(len(lis_txt)):
newText = dict(x=lis_pos[i][0], y=lis_pos[i][1], text=lis_txt[i], kw=lis_kwa[i])
#print 'lin: %s, col: %s, newText: %s' %(lin, col, newText)
text_add.append(newText)
#print '\n'
carr = [0.27, 0.30, 0.27, 1.0]
if(col == ccl - 1) and (lin == ccl - 1):
carr = [0.17, 0.20, 0.17, 1.0]
facecolors[posi] = carr
else:
if(per > 0):
txt = '%s\n%.2f%%' %(cell_val, per)
else:
if(show_null_values == 0):
txt = ''
elif(show_null_values == 1):
txt = '0'
else:
txt = '0\n0.0%'
oText.set_text(txt)
#main diagonal
if(col == lin):
#set color of the textin the diagonal to white
oText.set_color('w')
# set background color in the diagonal to blue
facecolors[posi] = [0.35, 0.8, 0.55, 1.0]
else:
oText.set_color('r')
return text_add, text_del
def insert_totals(df_cm):
""" insert total column and line (the last ones) """
sum_col = []
for c in df_cm.columns:
sum_col.append( df_cm[c].sum() )
sum_lin = []
for item_line in df_cm.iterrows():
sum_lin.append( item_line[1].sum() )
df_cm['sum_lin'] = sum_lin
sum_col.append(np.sum(sum_lin))
df_cm.loc['sum_col'] = sum_col
def pretty_plot_confusion_matrix(df_cm, annot=True, cmap="Oranges", fmt='.2f', fz=11,
lw=0.5, cbar=False, figsize=[8,8], show_null_values=0, pred_val_axis='y'):
"""
print conf matrix with default layout (like matlab)
params:
df_cm dataframe (pandas) without totals
annot print text in each cell
cmap Oranges,Oranges_r,YlGnBu,Blues,RdBu, ... see:
fz fontsize
lw linewidth
pred_val_axis where to show the prediction values (x or y axis)
'col' or 'x': show predicted values in columns (x axis) instead lines
'lin' or 'y': show predicted values in lines (y axis)
"""
if(pred_val_axis in ('col', 'x')):
xlbl = 'Predicted'
ylbl = 'Actual'
else:
xlbl = 'Actual'
ylbl = 'Predicted'
df_cm = df_cm.T
# create "Total" column
insert_totals(df_cm)
#this is for print allways in the same window
fig, ax1 = get_new_fig('Conf matrix default', figsize)
#thanks for seaborn
ax = sn.heatmap(df_cm, annot=annot, annot_kws={"size": fz}, linewidths=lw, ax=ax1,
cbar=cbar, cmap=cmap, linecolor='w', fmt=fmt)
#set ticklabels rotation
ax.set_xticklabels(ax.get_xticklabels(), rotation = 45, fontsize = 10)
ax.set_yticklabels(ax.get_yticklabels(), rotation = 25, fontsize = 10)
# Turn off all the ticks
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
#face colors list
quadmesh = ax.findobj(QuadMesh)[0]
facecolors = quadmesh.get_facecolors()
#iter in text elements
array_df = np.array( df_cm.to_records(index=False).tolist() )
text_add = []; text_del = [];
posi = -1 #from left to right, bottom to top.
for t in ax.collections[0].axes.texts: #ax.texts:
pos = np.array( t.get_position()) - [0.5,0.5]
lin = int(pos[1]); col = int(pos[0]);
posi += 1
#print ('>>> pos: %s, posi: %s, val: %s, txt: %s' %(pos, posi, array_df[lin][col], t.get_text()))
#set text
txt_res = configcell_text_and_colors(array_df, lin, col, t, facecolors, posi, fz, fmt, show_null_values)
text_add.extend(txt_res[0])
text_del.extend(txt_res[1])
#remove the old ones
for item in text_del:
item.remove()
#append the new ones
for item in text_add:
ax.text(item['x'], item['y'], item['text'], **item['kw'])
#titles and legends
ax.set_title('Confusion matrix')
ax.set_xlabel(xlbl)
ax.set_ylabel(ylbl)
plt.tight_layout() #set layout slim
fig.savefig('./混淆矩阵.jpg')
plt.show()
def plot_confusion_matrix_from_data(y_test, predictions, columns=None, annot=True, cmap="Oranges",
fmt='.2f', fz=11, lw=0.5, cbar=False, figsize=[8,8], show_null_values=0, pred_val_axis='lin'):
from sklearn.metrics import confusion_matrix
from pandas import DataFrame
#data
if(not columns):
#labels axis integer:
##columns = range(1, len(np.unique(y_test))+1)
#labels axis string:
from string import ascii_uppercase
columns = ['class %s' %(i) for i in list(ascii_uppercase)[0:len(np.unique(y_test))]]
confm = confusion_matrix(y_test, predictions)
cmap = 'Oranges';
fz = 11;
figsize=[9,9];
show_null_values = 2
df_cm = DataFrame(confm, index=columns, columns=columns)
pretty_plot_confusion_matrix(df_cm, fz=fz, cmap=cmap, figsize=figsize, show_null_values=show_null_values, pred_val_axis=pred_val_axis)
def confusion_matrix_data_class(y_test,predic):
columns = []
annot = True;
cmap = 'Oranges';
fmt = '.2f'
lw = 0.5
cbar = False
show_null_values = 2
pred_val_axis = 'y'
#size::
fz = 12;
figsize = [9,9];
if(len(y_test) > 10):
fz=9; figsize=[14,14];
plot_confusion_matrix_from_data(y_test, predic, columns,
annot, cmap, fmt, fz, lw, cbar, figsize, show_null_values, pred_val_axis)
#
if(__name__ == '__main__'):
y_test = np.array([1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5])
predic = np.array([1,2,4,3,5, 1,2,4,3,5, 1,2,3,4,4, 1,4,3,4,5, 1,2,4,4,5, 1,2,4,4,5, 1,2,4,4,5, 1,2,4,4,5, 1,2,3,3,5, 1,2,3,3,5, 1,2,3,4,4, 1,2,3,4,1, 1,2,3,4,1, 1,2,3,4,1, 1,2,4,4,5, 1,2,4,4,5, 1,2,4,4,5, 1,2,4,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5])
confusion_matrix_data_class(y,predic)
plt.show()
