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plot_utils.py
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plot_utils.py
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import itertools
import numpy as np
from sklearn.metrics import classification_report,accuracy_score,confusion_matrix
import numpy as np
import matplotlib.pyplot as plt
def model_history(model_history):
#ploting 2 plots on horizontal axis
fig,(ax1,ax2) = plt.subplots(1,2,figsize=(16,8))
# summarize history for accuracy
ax1.plot(model_history.history['accuracy'],c ="darkblue")
ax1.plot(model_history.history['val_accuracy'],c ="crimson")
ax1.set_title('model accuracy')
ax1.set_ylabel('accuracy')
ax1.set_xlabel('epoch')
ax1.legend(['train', 'test'], loc='upper right')
# summarize history for loss
ax2.plot(model_history.history['loss'],c ="darkblue")
ax2.plot(model_history.history['val_loss'],c ="crimson")
ax2.set_title('model loss')
ax2.set_ylabel('loss')
ax2.set_xlabel('epoch')
ax2.legend(['train', 'test'], loc='upper right')
fig.suptitle("Model History")
#Prints a classifcation report with accuracy below it
def c_report(y_true,y_pred,target_names=[]):
print("Classifictaion Report")
print(classification_report(y_true, y_pred, target_names=target_names))
acc_scr = accuracy_score(y_true, y_pred)
print("Accuracy : "+ str(acc_scr))
#plots confusion matrix
def plot_confusion_matrix(cm,
target_names,
title='Confusion matrix',
cmap=None,
normalize=True):
accuracy = np.trace(cm) / float(np.sum(cm))
misclass = 1 - accuracy
#give blueish color mapping
if cmap is None:
cmap = plt.get_cmap('Blues')
plt.figure(figsize=(10, 7))
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
if target_names is not None:
tick_marks = np.arange(len(target_names))
plt.xticks(tick_marks, target_names, rotation=0)
plt.yticks(tick_marks, target_names)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 1.5 if normalize else cm.max() / 2
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
if normalize:
plt.text(j, i, "{:0.4f}".format(cm[i, j]),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
else:
plt.text(j, i, "{:,}".format(cm[i, j]),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.ylabel('True label')
plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
plt.show()