Here I have made a few changes that make it easier to plot confusion matrices where the true and predicted sets of labels are not the same. This is a case that can occur when doing something like applying "new" categories to a dataset with an older set of categories.

The changes included are the following:

Fix an issue with nan values showing up when unbalanced confusion matrices are normalized. Where rows with zero entries would sum to zero and then divide by zero when normalizing each cell.

Add options to limit the labels displayed on the true and predicted axes, as with unbalanced confusion matrices some of the labels can be only in the set of true labels or only in the set of predicted labels.

You can see the effect of the new options here:

import numpy as np
import matplotlib.pyplot as plt
import scikitplot as sciplt

y_true = np.array(["A", "A", "B", "B", "B", "C", "D"])
y_pred = np.array(["A", "A", "Ba", "Bb", "Ba", "C", "D"])

print(y_true.shape)
print(y_pred.shape)

true_labels = np.unique(y_true)
pred_labels = np.unique(y_pred)

labels = np.sort(np.unique(np.concatenate([true_labels, pred_labels])))

true_label_indexes = np.where(np.isin(labels, true_labels))
pred_label_indexes = np.where(np.isin(labels, pred_labels))

sciplt.plotters.plot_confusion_matrix(y_true, y_pred, hide_zeros=True, normalize=True, true_label_indexes=true_label_indexes, pred_label_indexes=pred_label_indexes, labels=labels)
plt.show()

I've just tried to upgrade the package, but it gave the following error:

Collecting scikit-plot
  Using cached scikit-plot-0.2.6.tar.gz
    Complete output from command python setup.py egg_info:
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "/tmp/pip-build-7wut1485/scikit-plot/setup.py", line 9, in <module>
        import scikitplot
      File "/tmp/pip-build-7wut1485/scikit-plot/scikitplot/__init__.py", line 5, in <module>
        from scikitplot.classifiers import classifier_factory
      File "/tmp/pip-build-7wut1485/scikit-plot/scikitplot/classifiers.py", line 5, in <module>
        import matplotlib.pyplot as plt
      File "/home/paulo/Programs/repos/pyenv/versions/3.6.1/envs/work-3.6.1/lib/python3.6/site-packages/matplotlib/pyplot.py", line 115, in <module>
        _backend_mod, new_figure_manager, draw_if_interactive, _show = pylab_setup()
      File "/home/paulo/Programs/repos/pyenv/versions/3.6.1/envs/work-3.6.1/lib/python3.6/site-packages/matplotlib/backends/__init__.py", line 32, in pylab_setup
        globals(),locals(),[backend_name],0)
      File "/home/paulo/Programs/repos/pyenv/versions/3.6.1/envs/work-3.6.1/lib/python3.6/site-packages/matplotlib/backends/backend_tkagg.py", line 6, in <module>
        from six.moves import tkinter as Tk
      File "/home/paulo/Programs/repos/pyenv/versions/3.6.1/envs/work-3.6.1/lib/python3.6/site-packages/six.py", line 92, in __get__
        result = self._resolve()
      File "/home/paulo/Programs/repos/pyenv/versions/3.6.1/envs/work-3.6.1/lib/python3.6/site-packages/six.py", line 115, in _resolve
        return _import_module(self.mod)
      File "/home/paulo/Programs/repos/pyenv/versions/3.6.1/envs/work-3.6.1/lib/python3.6/site-packages/six.py", line 82, in _import_module
        __import__(name)
      File "/home/paulo/Programs/repos/pyenv/versions/3.6.1/lib/python3.6/tkinter/__init__.py", line 36, in <module>
        import _tkinter # If this fails your Python may not be configured for Tk
    ModuleNotFoundError: No module named '_tkinter'
    
    ----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-build-7wut1485/scikit-plot/

Unfortunately, I don't know how to debug this problem. If you need some info, please don't hesitate to ask!

Hi @reiinakano,

Thank you for this great repo! I am using plot_confusion_matrix() but my counts are quite large so the overlaid counts end up overlapping each other and result in a cluttered plot. I was wondering if I could submit a pull request to update this function to add a hide_counts parameter to give the option to not plot the counts? I've already forked and created a branch with the changes. Thank you!

Hello i have a precision-recall curve where i plot as the following:

skplt.metrics.plot_precision_recall_curve(y_test, y_probas, curves=['each_class'])

I have two classes in the data (one positive and one negative class with labels 1 and -1 respectively). Questions: How can I plot ONLY the positive class?

Thank you

Hello I want to plot a precision-recall curve for SVC (support vector machine classifier), but the scikit-learn svm classifier does not implement a predict_proba method. How can I do that in scikit-plot (as far as I can see in the documentation it accepts prediction probabilities to plot the curve)?

Note that the scikit-learn documentation page has an example of precision-recall curve for SVC

Thank you, Nikos

It would be nice to have Jupyter notebooks in the "examples" folder showing the different plots as used in a Jupyter notebook. It could contain the same exact code as the examples in the .py files, but adjusted for size (Jupyter notebook plots tend to come out much smaller).

Using the confusion matrix in a jupyter notebook returns a plot that is quite small. If Seaborn is also used, some values in the plot are hard to read (white text on white lines).

I have added a figsize-argument to the plot_confusion_matrix and changed the way that values are displayed (now with neutral background box). For larger plots all text-elements scale with the figsize-argument.

You tagged this issue #14 as help wanted so I thought I'd pitch in. Feel free to edit if it doesn't match your styIe.

I added a little bit of code to allow the user to pass a list to the roc curve plotting functions to allow them to suppress/show each of the three types of curves: class-specific curves, micro averages, and macro averages.

Hi there, I am getting an error installing it

pip install scikit-plot                                                              ~ 1
Collecting scikit-plot
  Downloading scikit-plot-0.2.1.tar.gz
    Complete output from command python setup.py egg_info:
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "c:\users\arthur\.babun\cygwin\tmp\pip-build-yrgynz\scikit-plot\setup.py", line 9, in <module>
        import scikitplot
      File "c:\users\arthur\.babun\cygwin\tmp\pip-build-yrgynz\scikit-plot\scikitplot\__init__.py", line 5, in <module>
        from scikitplot.classifiers import classifier_factory
      File "c:\users\arthur\.babun\cygwin\tmp\pip-build-yrgynz\scikit-plot\scikitplot\classifiers.py", line 7, in <module>
        from scikitplot import plotters
      File "c:\users\arthur\.babun\cygwin\tmp\pip-build-yrgynz\scikit-plot\scikitplot\plotters.py", line 9, in <module>
        from sklearn.metrics import confusion_matrix
    ImportError: No module named sklearn.metrics

    ----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in c:\users\arthur\.babun\cygwin\tmp\pip-build-yrgynz\scikit-plot\

For binary classification, when I input numpy arrays having test label and test probabilities, it throws the following error :

y_true = np.array(ytest)
y_probas = np.array(p_test)
skplt.metrics.plot_roc_curve(y_true,y_probas)
plt.show()

IndexError                                Traceback (most recent call last)
<ipython-input-49-1b02f082006a> in <module>()
----> 1 skplt.metrics.plot_roc_curve(y_true,y_probas)
      2 plt.show()


/Users/tarun/anaconda/envs/gl-env/lib/python2.7/site-packages/scikitplot/metrics.pyc in plot_roc_curve(y_true, y_probas, title, curves, ax, figsize, cmap, title_fontsize, text_fontsize)
    247     roc_auc = dict()
    248     for i in range(len(classes)):
--> 249         fpr[i], tpr[i], _ = roc_curve(y_true, probas[:, i],
    250                                       pos_label=classes[i])
    251         roc_auc[i] = auc(fpr[i], tpr[i])

IndexError: too many indices for array

Hello, I have an issue when trying to plot a confusion matrix fewer classes in my test set than in training. The class with 12 000+ occcurences in my sample should be labelled 'O' is it possible to get around this, or to include the label set manually as an input?

it's not a big issue but would be nice if we could fix it. Thanks for your help

In you code I noticed that if we pass classes in the form of their actual meaning instead of (0,1,2 .. ) and we pass it as (c,b,a) then np.unique(y_true) makes the classes in the form of its alphabetical format and this changes the position of the classes that the model was trained on classes = np.unique(y_true)

fpr_dict[i], tpr_dict[i], _ = roc_curve(y_true, probas[:, i], pos_label=classes[i])

Hence if you could add a parameter of class_labels in the function

def plot_roc_multi(y_true, y_probas,class_labels, title='ROC Curves', plot_micro=True, plot_macro=True, classes_to_plot=None, ax=None, figsize=None, cmap='nipy_spectral', title_fontsize="large", text_fontsize="medium"):

where class_labels is in the form of an array [a,b,c] it would be much easier I think

This is just a heads up for the programmers that this is a dead library and it functionalities may break any time. There has been not a single update form 2018 and most probably there will be no bug-fix or feature implementations.

Either implement your own plotting functions or look at other active modules such as yellowbrick.

scikit-plot is dead and it might break any time, if you use this in production.

I'd like to be able to set the names of the classes used in the legend in the the plot_lift_curve() and plot_cumulative_gain() functions.

I've made a pull request (https://github.com/reiinakano/scikit-plot/pull/109) that adds an optional arg class_names to each of these functions to accomplish this.

This differs from issue https://github.com/reiinakano/scikit-plot/issues/78 in that I am trying to change the labels in the plot's legend, not omit one of the classes from the plot.