I have a Confusion Matrix with really small sized numbers but I can't find a way to change them.
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, rf_predictions)
ax = plt.subplot()
sns.set(font_scale=3.0) #edited as suggested
sns.heatmap(cm, annot=True, ax=ax, cmap="Blues", fmt="g"); # annot=True to annotate cells
# labels, title and ticks
ax.set_xlabel('Predicted labels');
ax.set_ylabel('Observed labels');
ax.set_title('Confusion Matrix');
ax.xaxis.set_ticklabels(['False', 'True']);
ax.yaxis.set_ticklabels(['Flase', 'True']);
plt.show()
thats the code I am using and the pic I get looks like:
I would not mind changing the numbers of the classification by hand but I dont really want to do it for the labels aswell.
EDIT: Figures are bigger now but the labels stay very small
Cheers
Use sns.set to change the font size of the heatmap values. You can specify the font size of the labels and the title as a dictionary in ax.set_xlabel, ax.set_ylabel and ax.set_title, and the font size of the tick labels with ax.tick_params.
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, rf_predictions)
ax = plt.subplot()
sns.set(font_scale=3.0) # Adjust to fit
sns.heatmap(cm, annot=True, ax=ax, cmap="Blues", fmt="g");
# Labels, title and ticks
label_font = {'size':'18'} # Adjust to fit
ax.set_xlabel('Predicted labels', fontdict=label_font);
ax.set_ylabel('Observed labels', fontdict=label_font);
title_font = {'size':'21'} # Adjust to fit
ax.set_title('Confusion Matrix', fontdict=title_font);
ax.tick_params(axis='both', which='major', labelsize=10) # Adjust to fit
ax.xaxis.set_ticklabels(['False', 'True']);
ax.yaxis.set_ticklabels(['False', 'True']);
plt.show()
Use rcParams to change all text in the plot:
fig, ax = plt.subplots(figsize=(10,10))
plt.rcParams.update({'font.size': 16})
disp = plot_confusion_matrix(clf, Xt, Yt,
display_labels=classes,
cmap=plt.cm.Blues,
normalize=normalize,
ax=ax)
Found it
import itertools
import matplotlib.pyplot as plt
def plot_confusion_matrix(cm,classes,normalize=False,title='Confusion
matrix',cmap=plt.cm.Blues):
plt.figure(figsize=(15,10))
plt.imshow(cm,interpolation='nearest',cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks=np.arange(len(classes))
plt.xticks(tick_marks,classes,rotation=45,fontsize=15)
plt.yticks(tick_marks,classes,fontsize=15,rotation=90)
if normalize:
cm=cm.astype('float')/cm.sum(axis=1)[:,np.newaxis]
cm=np.around(cm,decimals=2)
cm[np.isnan(cm)]=0.0
print('Normalized confusion matrix')
else:
print('Confusion matrix, without normalization')
thresh=cm.max()/2
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",fontsize=15,
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label',fontsize=20)
plt.xlabel('Predicted label',fontsize=20)
The code changed as such
Related
Here is the output of the code :
array = [[64,7,5],
[9,195,1],
[6,17,2]]
df_cm = pd.DataFrame(array, range(3), range(3))
sn.set(font_scale=1.4) # for l)abel size
sn.heatmap(df_cm, annot=True, annot_kws={"size": 16}, cmap='Blues', fmt='g') # font size
class_names = ['dog','cat','bear']
plt.gca().xaxis.tick_top()
plt.gca().xaxis.set_label_position('top')
tick_marks = np.arange(len(class_names))
plt.xticks(tick_marks, class_names, rotation=45, rotation_mode='anchor')
plt.yticks(tick_marks, class_names, rotation='horizontal')# rotation='horizontal', ha='right', rotation_mode='anchor'
plt.tight_layout()
plt.ylabel('True label',size=14)
plt.xlabel('Predicted label',size=14)
plt.show()
I would like to align labels of x and y with center position, So please how can I change the above
With tick_marks = np.arange(len(class_names)) you're setting new tick marks. Just get the existing ones with ax.get_xticks()/ax.get_yticks():
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sn
array = [[64,7,5],
[9,195,1],
[6,17,2]]
df_cm = pd.DataFrame(array, range(3), range(3))
sn.set(font_scale=1.4) # for l)abel size
fig, ax = plt.subplots()
sn.heatmap(df_cm, annot_kws={"size": 16}, cmap='Blues', fmt='g') # font size
class_names = ['dog','cat','bear']
plt.gca().xaxis.tick_top()
plt.gca().xaxis.set_label_position('top')
plt.xticks(ax.get_xticks(), class_names, rotation=45, rotation_mode='anchor')
plt.yticks(ax.get_yticks(), class_names, rotation='horizontal')# rotation='horizontal', ha='right', rotation_mode='anchor'
plt.tight_layout()
plt.ylabel('True label',size=14)
plt.xlabel('Predicted label',size=14)
plt.show()
Output:
Edit: you'll achieve the same result by replacing your plt.xticks(...) and plt.yticks(...) with the following:
plt.gca().set_xticklabels(class_names, rotation=45, rotation_mode='anchor')
plt.gca().set_yticklabels(class_names, rotation='horizontal')
How can I move the vertical legend slightly upwards
(to the centre of rows)?
I need these very large letters
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
labels_raw=np.array([["98.75\n±0.50" ,"1.25\n±0.82"],[ "1.32\n±0.74", "98.68\n±0.74"]])
cm_raw=np.array([[98.75 ,1.25],[ 1.32, 98.68]])
sns.set(font_scale=2.5)
labels=labels_raw
cm=cm_raw
fig, ax = plt.subplots(figsize=(5,5))
target_names = ['test1','test2']
f=sns.heatmap(cm, annot=labels, fmt=':^', xticklabels=target_names,
yticklabels=target_names,annot_kws={"size": 25},cbar=False)
plt.show(block=False)
#plt.show()
fig=f.get_figure()
You can rotate your labels and set the vertical alignment to be center:
fig, ax = plt.subplots(figsize=(5,5))
target_names = ['test1','test2']
sns.heatmap(cm, annot=labels, fmt=':^', xticklabels=target_names,
yticklabels=target_names,annot_kws={"size": 25},cbar=False,ax=ax)
ax.set_yticklabels(target_names, rotation=0, fontsize="25", va="center")
Or this will keep the y-axis labels vertical:
sns.heatmap(cm, annot=labels, fmt=':^', xticklabels=target_names,
yticklabels=target_names,annot_kws={"size": 25},cbar=False,ax=ax)
ax.set_yticklabels(target_names, fontsize="25", va="center")
I have a Matplotlib function to create a confusion matrix and save it to a file:
def pretty_print_conf_matrix(y_true, y_pred,
classes,
normalize=False,
title='{} Confusion matrix'.format(describe_test_setup()),
cmap=plt.cm.Blues,
out_dir=None):
"""
Code adapted from: http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py
"""
fig, ax = plt.subplots(figsize=(15, 15))
cm = confusion_matrix(y_true, y_pred, labels=classes)
# Configure Confusion Matrix Plot Aesthetics (no text yet)
cax = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.set_title(title, fontsize=16)
ax.set_xticks(np.arange(len(classes)))
ax.set_yticks(np.arange(len(classes)))
ax.set_xticklabels(classes)
ax.set_yticklabels(classes)
ax.tick_params(axis='x', labelsize=14)
ax.tick_params(axis='y', labelsize=14)
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode='anchor')
plt.colorbar(cax)
ax.set_ylabel('True label', fontsize=16)
ax.set_xlabel('Predicted label', fontsize=16, rotation='horizontal')
# Calculate normalized values (so all cells sum to 1) if desired
if normalize:
cm = np.round(cm.astype('float') / cm.sum(), 2) # (axis=1)[:, np.newaxis]
thresh = cm.max() / 1.5 if normalize else cm.max() / 2
# Place Numbers as Text on Confusion Matrix Plot
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
ax.text(j, i, cm[i, j],
ha="center",
va="center",
color="white" if cm[i, j] > thresh else "black",
fontsize=12)
#fig.tight_layout()
plt.show(block=False)
if out_dir is not None:
out_file = os.path.join(out_dir, 'Confusion Matrix{}.png'.format(describe_test_setup()))
fig.savefig(out_file, dpi=300)
This works well on two of my machines, but on the third it produces ugly squashed images. They are all running the same source code.
Example of it working properly (resolution 4500x4500):
Example of it working poorly (resolution 1028x715):
I thought this could be caused by me running different matplotlib versions, but using pip freeze I can see matplotlib==3.1.2 on both machines.
Any ideas what the cause might be?
I'm trying to display a confusion matrix and can't for the life of my figure out why it refuses to display in an appropriate manner. Here's my code:
import numpy as np
import itertools
from sklearn.metrics import confusion_matrix
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.winter):
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title, fontsize=30)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, fontsize=20)
plt.yticks(tick_marks, classes, fontsize=20)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center",
color="white" if cm[i, j] < thresh else "black", fontsize=40)
plt.tight_layout()
plt.ylabel('True label', fontsize=30)
plt.xlabel('Predicted label', fontsize=30)
return plt
cm = confusion_matrix(y_test, y_predicted_counts)
fig = plt.figure(figsize=(10, 10))
plot = plot_confusion_matrix(cm, classes=['Unsure','No','Yes'], normalize=False, title='Confusion matrix')
plt.show()
print(cm)
And this is what is displayed:
Any help would be appreciated. Thanks in advance.
For the call to imshow you need to specify origin='lower' (the default is 'upper'; they probably changed this at some time and the scikit-learn docs didn't update their example). So the following should do the trick:
plt.imshow(cm, interpolation='nearest', cmap=cmap, origin='lower')
# ^
# |
# added origin='lower' ------------------------------
Using Matplotlib
If you want to keep your matplotlib implementation, just add plt.ylim(-0.5,2.5) at the end of your plot_confusion_matrix function:
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.winter):
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title, fontsize=30)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, fontsize=20)
plt.yticks(tick_marks, classes, fontsize=20)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center",
color="white" if cm[i, j] < thresh else "black", fontsize=40)
plt.tight_layout()
plt.ylabel('True label', fontsize=30)
plt.xlabel('Predicted label', fontsize=30)
plt.ylim(-0.5, 2.5) # <-- SOLUTION
return plt
Using Seaborn
you can try the seaborn package for plotting heatmaps:
from sklearn.metrics import confusion_matrix
import pandas as pd
import seaborn as sn
import matplotlib.pyplot as plt
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.winter):
cm_df = pd.DataFrame(cm, columns=classes, index = classes)
cm_df.index.name = 'Actual'
cm_df.columns.name = 'Predicted'
plt.figure(figsize = (10,7))
sn.set(font_scale=1.4)#for label size
ax =sn.heatmap(cm_df, cmap=cmap, annot=True,annot_kws={"size": 16},fmt="d")# font size
plt.title(title)
bottom, top = ax.get_ylim()
ax.set_ylim(bottom + 0.5, top - 0.5)
plt.show()
plot_confusion_matrix(cm, classes=['Unsure','No','Yes'], normalize=False, title='Confusion matrix')
Confusion Matrix Result
Hope this works for you!
It's likely that you're using matplotlib 3.1.1 which broke the tick default behaviour. Upgrade to 3.1.2 or downgrade to 3.1.0 to fix the issue.
Does anyone know how I can plot confusion matrix for 100 class labels? I did these line of codes but I ended up having a confusion matrix attached. The code is working fine for less class numbers like 5 but as the number of classes is 100, there in no clear confusion matrix.
y_pred = model.predict(X_test)
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
print(confmat)
fig, ax = plt.subplots(figsize=(5, 5))
ax.matshow(confmat, cmap=plt.cm.Blues, alpha=0.3)
for i in range(confmat.shape[0]):
for j in range(confmat.shape[1]):
ax.text(x=j, y=i, s=confmat[i, j], va='center', ha='center')
plt.xlabel('predicted label')
plt.ylabel('true label')
plt.show()