Automatic confusion matrix calculation

SavePredictResult.py

'''
Automatic Keras callback class for:
- Confusion matrix calculation
- Weighted accuracy(AKA. WA)
- Unweighted accuracy(AKA. UA)
- Label distribution
- Predict correctness

Dependences: pandas, numpy

Usage: 
Import at first:

  from libTJNU.SavePredictResult import SavePredictResult

Append to your Keras callback list with test data 
and label, like:

  cb_list = [SavePredictResult(test_data=(X_test, y_test))]
  model.fit(X_train, y_train, batch_size=batch_size, 
            epochs=epoch, validation_data=(X_test, y_test),
            shuffle=True, callbacks=callbacks_list)

Example:
---------------------------------------------------------
              Result for current model
=========================================================
- WA               :  0.6487804878048781
- UA               :  0.5528546658259773
- Y Data length    :  [104  15  61  25]
- Correctness      :  [79  0 30 24]
- Confusion Matrix :
Classified as ->      0     1      2      3 Recall
0                    79     0      1     24  76.0%
1                     4     0      0     11   0.0%
2                    31     0     30      0  49.2%
3                     1     0      0     24  96.0%
Precision         68.7%  nan%  96.8%  40.7%   0.0%
---------------------------------------------------------
'''

import pandas as pd
import numpy as np
from keras.callbacks import Callback

class SavePredictResult(Callback):
    def __init__(self, test_data):
        self.filepath = filepath

    def on_epoch_end(self, epoch, logs={}):
        x, y = self.test_data
        res = self.model.predict(x, batch_size=100)
        
        # Calculate weighted accuracy
        y = np.argmax(y, axis=-1)
        p = np.argmax(res, axis=-1)
        
        eql = np.equal(y, p)
        crt = p[np.where(eql)]

        dst = np.bincount(y)
        prd = np.bincount(crt)
        
        d_len = dst.shape[0]

        # Bug fix
        # April 9, 2018
        # Some times model give no predict result on the last category
        # This will cause prd array has less length than dst
        prd = np.pad(prd, (0, d_len - prd.shape[0]), 'constant', constant_values=(0,0))
        
        print('---------------------------------------------------------')
        print('              Result for current model')
        print('=========================================================')

        print('- WA               : ', np.sum(prd) / np.sum(dst))
        print('- UA               : ', np.average(np.divide(prd, dst)))
        print('- Y Data length    : ', dst)
        print('- Correctness      : ', prd)
        print('- Confusion Matrix :' )

        mtx = np.array([[ y[(y == i) & (p == j)].shape[0] for j in range(d_len) ] for i in range(d_len) ])

        recall = np.array([mtx[i][i] / np.sum(mtx[i]) for i in range(mtx.shape[0]) ])
        precision = np.array([mtx[i][i] / np.sum(mtx.transpose()[i]) for i in range(mtx.shape[0]) ])

        recall = np.array(['%.1f%%' % (i * 100) for i in recall])
        recall = np.expand_dims(recall, axis=-1)
        precision = np.concatenate([precision, [0]])
        precision = np.array(['%.1f%%' % (i * 100) for i in precision])

        mtx = mtx.astype('str')

        mtx = np.hstack([mtx, recall])
        mtx = np.vstack([mtx, [precision]])

        col = [i for i in range(d_len)]
        idx = [i for i in range(d_len)]
        col.append('Recall')
        idx.append('Precision')

        df = pd.DataFrame(mtx, columns=col, index=idx)
        df.columns.names = ['Classified as ->']

        print(df)
        
        print('---------------------------------------------------------')