modelEvaluation.py

from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from pandas import DataFrame as df
import matplotlib.pyplot as plt
import numpy as np


def plot_results(predicted_labels, true_labels, snrs):

  sorted_snrs = np.sort(np.unique(snrs))

  x_axis = []
  y_axis = []
  for snr in sorted_snrs:
    idx = np.where(snrs == snr)
    #print('snr =', snr, '-->', accuracy_score(np.argmax(true_labels[idx], axis = 1), np.argmax(predicted_labels[idx], axis = 1)))
    x_axis.append(snr)
    y_axis.append(accuracy_score(np.argmax(true_labels[idx], axis = 1), np.argmax(predicted_labels[idx], axis = 1)))

  plt.xlabel('SNR')
  plt.ylabel('Accuracy')
  plt.title('Classification Accuracy over different SNRs')
  plt.plot(x_axis, np.array(y_axis) * 100, 'ro--')
  plt.grid(True)
  plt.savefig("./result/metaSGD_result.png")
  plt.show()

def print_results(predicted_labels, true_labels, snrs):

  sorted_snrs = np.sort(np.unique(snrs))

  x_axis = []
  y_axis = []
  for snr in sorted_snrs:
    idx = np.where(snrs == snr)
    #print('snr =', snr, '-->', accuracy_score(np.argmax(true_labels[idx], axis = 1), np.argmax(predicted_labels[idx], axis = 1)))
    x_axis.append(snr)
    y_axis.append(accuracy_score(np.argmax(true_labels[idx], axis = 1), np.argmax(predicted_labels[idx], axis = 1)))

  return df(data = np.array(y_axis).reshape(1, -1) * 100,  columns = sorted_snrs, index = ['accuracy']).round(2)

def plot_confusion_matrix(y_true, y_pred, classes, cmap=plt.cm.Blues):
  y_true, y_pred = np.argmax(y_true, axis = 1), np.argmax(y_pred, axis = 1)
  cm = confusion_matrix(y_true, y_pred)
  cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
  fig, ax = plt.subplots()
  im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
  ax.figure.colorbar(im, ax=ax)
  # We want to show all ticks...
  ax.set(xticks=np.arange(cm.shape[1]),
         yticks=np.arange(cm.shape[0]),
         # ... and label them with the respective list entries
         xticklabels=classes, yticklabels=classes,
         title = 'Confusion Matrix',
         ylabel = 'True label',
         xlabel = 'Predicted label')

  # Rotate the tick labels and set their alignment.
  plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
           rotation_mode="anchor")
  plt.savefig('result/metaSGD_confusion.png')
  plt.show()