#!/usr/bin/env python
# coding: utf-8

# # Class Prediction Error Visualizer
# The `ClassPredictionError` visualizer is a ScoreVisualizer that takes a fitted scikit-learn classifier and a set of test X and y values and returns a stacked bar graph showing a color-coded break down of predicted classes compared to their actual classes. This visualizer provides a way to quickly understand how good your classifier is at predicting the right classes.
# 
# Below is an example with the visualizer

# In[1]:


import matplotlib
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')

from yellowbrick.classifier import ClassPredictionError

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split as tts
from sklearn.ensemble import RandomForestClassifier


# In[2]:


# We create our own classification dataset
# The data set contains 5 classess and 1000 samples
# We use RandomForest for modeling the data
# I came up with arbitrary names for the classes

X, y = make_classification(n_samples=1000, n_classes=5,
                           n_informative=3, n_clusters_per_class=1)

# Perform 80/20 training/test split
X_train, X_test, y_train, y_test = tts(X, y, test_size=0.20,
                                       random_state=42)

# Pass in model and classes to ClassPredictionError
visualizer = ClassPredictionError(RandomForestClassifier(),
                                  classes=['apple', 'kiwi', 'pear',
                                           'banana', 'orange'])
# Fit the model
visualizer.fit(X_train, y_train)

# Use test data to create visualization
visualizer.score(X_test, y_test)

# Display visualization
visualizer.show()
plt.show()


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