def number_pred(save, show):
y_true = [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200]
y_pred = [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200]
cm = ConfusionMatrix(y_true, y_pred)
# print(cm.binarize(100).P)
# cm.enlarge(300)
# cm.enlarge([300, 400])
print(cm)
cm.plot()
filename = 'numbers.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
# print("")
# print(cm.classes)
# print("")
# cm.print_stats(None)
cm.print_stats()
def size_pred(save, show):
df = pd.DataFrame({
'Height': [150, 150, 151, 151, 152, 155, 155, 157, 157, 157, 157, 158, 158, 159, 159, 159, 160, 160, 162, 162, 163, 164, 165, 168, 169, 169, 169, 170, 171, 171, 173, 173, 174, 176, 177, 177, 179, 179, 179, 179, 179, 181, 181, 182, 183, 184, 186, 190, 190],
'Weight': [54, 55, 55, 47, 58, 53, 59, 60, 56, 55, 62, 56, 55, 55, 64, 61, 59, 59, 63, 66, 64, 62, 66, 66, 72, 65, 75, 71, 70, 70, 75, 65, 79, 78, 83, 75, 84, 78, 74, 75, 74, 90, 80, 81, 90, 81, 91, 87, 100],
'Size': ['S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'XL', 'XL', 'XL', 'XL', 'XL', 'XL', 'XL', 'XL'],
'SizePred': ['S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'S', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'L', 'M', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'XL', 'L', 'L', 'XL', 'L', 'XL', 'XL', 'XL'],
})
cm = ConfusionMatrix(df["Size"], df["SizePred"])
print(cm)
cm.print_stats()
cm.plot()
filename = 'size.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
def get_confusion_matrix(_results_file):
df = pd.read_csv(results_file, sep='\t', header=None)
true_lbls = df[1]
pred_lbls = df[2]
confusion_matrix = ConfusionMatrix(true_lbls, pred_lbls)
confusion_matrix.plot()
cm_file = _results_file.replace('.txt', '_cm.jpg')
plt.savefig(cm_file)
print()
print(confusion_matrix)
print()
cm = confusion_matrix.to_dataframe()
correct = 0
for i in range(cm.shape[0]):
correct += cm.iloc[i][i]
recall = cm.iloc[i][i] * 100 / cm.sum(axis=0)[i]
prec = cm.iloc[i][i] * 100 / cm.sum(axis=1)[i]
print('Class %s recall = %.4f precision = %.4f' %
(cm.columns[i], recall, prec))
print('Overall accuracy = %.4f' %
float(correct * 100 / sum(cm.sum(axis=0))))
def evaluateModel(pp, preds, fold):
# ************ PROCESSING THE PREDICTIONS
preds[preds >= 0.5] = 1
preds[preds < 0.5] = 0
print("F1 SCORE:")
print(f1_score(pp.test_y, preds, average=None))
print("Hamming Loss:")
print(hamming_loss(pp.test_y, preds))
print("Zero-one loss:")
print(zero_one_loss(pp.test_y, preds))
# I reach here in plus_one_hot_encode, I want to transform it in one hot
y_test_preds = utils.from_plus_to_one_hot(np.array(pp.test_y))
preds_transf = utils.from_plus_to_one_hot(np.array(preds))
cm = ConfusionMatrix(
np.array(y_test_preds).argmax(1),
np.array(preds_transf).argmax(1))
print(cm)
ax = cm.plot()
ax.set_xticklabels(classes, rotation="vertical")
ax.set_yticklabels(classes)
plt.savefig("cmpre{0}.png".format(fold))
from pandas_confusion import ConfusionMatrix
cm=ConfusionMatrix(y_test,pred)
cm.plot(cmap=c.get_cmap('PuBu'))
def main(save, show):
basepath = os.path.dirname(__file__)
# y_true = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
# y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
# cm = ConfusionMatrix(y_true, y_pred)
# cm = ConfusionMatrix(y_true, y_pred, labels=["ant", "bird", "cat"])
# y_true = [2, 0, 2, 2, 0, 1]
# y_pred = [0, 0, 2, 2, 0, 2]
# cm = ConfusionMatrix(y_true, y_pred)
# cm = ConfusionMatrix(y_true, y_pred, labels=["ant", "bird", "cat"])
y_true = ['rabbit', 'cat', 'rabbit', 'rabbit', 'cat', 'dog', 'dog', 'rabbit', 'rabbit', 'cat', 'dog', 'rabbit']
y_pred = ['cat', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'cat', 'rabbit', 'rabbit']
cm = ConfusionMatrix(y_true, y_pred)
# y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
# y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
# >>> cm(y_true, y_pred, labels=["ant", "bird", "cat"])
# array([[2, 0, 0],
# [0, 0, 1],
# [1, 0, 2]])
# cm = ConfusionMatrix(y_true, y_pred)
print("Confusion matrix:\n%s" % cm)
df = cm.to_dataframe()
print(df)
print(df.dtypes)
cm.plot()
filename = 'cm.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
cm.plot(normalized=True)
filename = 'cm_norm.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
cm.print_stats()
print(cm.classification_report)
print("sklearn confusion_matrix:\n%s" % confusion_matrix(y_true, y_pred))
print(classification_report(y_true, y_pred))
# stat = 'precision'
# print(cm._avg_stat(stat))
# print(cm.ACC)
# import seaborn as sns
# cm.plot(normalized=True, backend=Backend.Seaborn)
# sns.plt.show()
print("Binarize a confusion matrix")
y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
cm = ConfusionMatrix(y_true, y_pred)
print(cm)
binary_cm = cm.binarize(['ant', 'cat'])
# A bird is not a "land_animal"
print(binary_cm)
def main(save, show):
basepath = os.path.dirname(__file__)
# y_true = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
# y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
# cm = ConfusionMatrix(y_true, y_pred)
# cm = ConfusionMatrix(y_true, y_pred, labels=["ant", "bird", "cat"])
# y_true = [2, 0, 2, 2, 0, 1]
# y_pred = [0, 0, 2, 2, 0, 2]
# cm = ConfusionMatrix(y_true, y_pred)
# cm = ConfusionMatrix(y_true, y_pred, labels=["ant", "bird", "cat"])
y_true = [
'rabbit', 'cat', 'rabbit', 'rabbit', 'cat', 'dog', 'dog', 'rabbit',
'rabbit', 'cat', 'dog', 'rabbit'
]
y_pred = [
'cat', 'cat', 'rabbit', 'dog', 'cat', 'rabbit', 'dog', 'cat', 'rabbit',
'cat', 'rabbit', 'rabbit'
]
cm = ConfusionMatrix(y_true, y_pred)
# y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
# y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
# >>> cm(y_true, y_pred, labels=["ant", "bird", "cat"])
# array([[2, 0, 0],
# [0, 0, 1],
# [1, 0, 2]])
# cm = ConfusionMatrix(y_true, y_pred)
print("Confusion matrix:\n%s" % cm)
df = cm.to_dataframe()
print(df)
print(df.dtypes)
cm.plot()
filename = 'cm.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
cm.plot(normalized=True)
filename = 'cm_norm.png'
if save:
plt.savefig(os.path.join(basepath, '..', 'screenshots', filename))
if show:
plt.show()
cm.print_stats()
print(cm.classification_report)
print("sklearn confusion_matrix:\n%s" % confusion_matrix(y_true, y_pred))
print(classification_report(y_true, y_pred))
# stat = 'precision'
# print(cm._avg_stat(stat))
# print(cm.ACC)
# import seaborn as sns
# cm.plot(normalized=True, backend=Backend.Seaborn)
# sns.plt.show()
print("Binarize a confusion matrix")
y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
cm = ConfusionMatrix(y_true, y_pred)
print(cm)
binary_cm = cm.binarize(['ant', 'cat'])
# A bird is not a "land_animal"
print(binary_cm)
plt.plot(c, cv_scores, '-o')
plt.xscale('log')
# In[ ]:
predicted = clf.predict(X_test)
expected = y_test
print(accuracy_score(expected, predicted))
# In[ ]:
predicted_probs = clf.predict_proba(X_test)
print(log_loss(y_test, predicted_probs))
# In[ ]:
cm = ConfusionMatrix(expected, predicted)
cm_stats = cm.to_dataframe().apply(lambda x: x/sum(x), axis=1)
cm_stats.to_csv('data/confusion_matrix_stats.csv')
# In[ ]:
mpl.rcParams['figure.figsize'] = (10.0, 5.0)
cm.plot(normalized=True)
