Matplotlib matshow with many string labels

Question

Today I tried to build a confusion matrix from my classification model.

After searching on some pages, I found that matshow from pyplot might help me.

 import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues, labels=None): fig = plt.figure() ax = fig.add_subplot(111) cax = ax.matshow(cm) plt.title(title) fig.colorbar(cax) if labels: ax.set_xticklabels([''] + labels) ax.set_yticklabels([''] + labels) plt.xlabel('Predicted') plt.ylabel('True') plt.show() 

Works well if I have multiple shortcuts

 y_true = ['a', 'b', 'c', 'd', 'a', 'b', 'c', 'a', 'c', 'd', 'b', 'a', 'b', 'a'] y_pred = ['a', 'b', 'c', 'd', 'a', 'b', 'b', 'a', 'c', 'a', 'a', 'a', 'a', 'a'] labels = list(set(y_true)) cm = confusion_matrix(y_true, y_pred) plot_confusion_matrix(cm, labels=labels) 

But if I have many tags, some tags do not display correctly

 y_true = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n'] y_pred = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n'] labels = list(set(y_true)) cm = confusion_matrix(y_true, y_pred) plot_confusion_matrix(cm, labels=labels) 

My question is, how can I display ALL shortcuts in matshow graphics? I tried something like fontdict but it still doesn't work

Answer 1

You can control the frequency of ticks using the matplotlib.ticker module.

In this case, you want to check every few 1 , so we can use MultipleLocator

Add these two lines before calling plt.show() :

 ax.xaxis.set_major_locator(ticker.MultipleLocator(1)) ax.yaxis.set_major_locator(ticker.MultipleLocator(1)) 

And it will tick and label for each letter in your y_true and y_pred .

I also changed your matshow call to use the color set that you specify when calling the function:

 cax = ax.matshow(cm,cmap=cmap) 

For completeness, your entire function will look like this:

 import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix import matplotlib.ticker as ticker def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues, labels=None): fig = plt.figure() ax = fig.add_subplot(111) # I also added cmap=cmap here, to make use of the # colormap you specify in the function call cax = ax.matshow(cm,cmap=cmap) plt.title(title) fig.colorbar(cax) if labels: ax.set_xticklabels([''] + labels) ax.set_yticklabels([''] + labels) ax.xaxis.set_major_locator(ticker.MultipleLocator(1)) ax.yaxis.set_major_locator(ticker.MultipleLocator(1)) plt.xlabel('Predicted') plt.ylabel('True') plt.savefig('confusionmatrix.png') 

Answer 2

You can use the xticks method to specify labels. Your function will look like this (changing a function from the answer above):

 import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues, labels=None): fig = plt.figure() ax = fig.add_subplot(111) # I also added cmap=cmap here, to make use of the # colormap you specify in the function call cax = ax.matshow(cm,cmap=cmap) plt.title(title) fig.colorbar(cax) if labels: plt.xticks(range(len(labels)), labels) plt.yticks(range(len(labels)), labels) plt.xlabel('Predicted') plt.ylabel('True') plt.savefig('confusionmatrix.png')