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helper_functions.py
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import itertools
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix
# Our function needs a different name to sklearn's plot_confusion_matrix
def make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15, norm=False, savefig=False):
"""Makes a labelled confusion matrix comparing predictions and ground truth labels.
If classes is passed, confusion matrix will be labelled, if not, integer class values
will be used.
Args:
y_true: Array of truth labels (must be same shape as y_pred).
y_pred: Array of predicted labels (must be same shape as y_true).
classes: Array of class labels (e.g. string form). If `None`, integer labels are used.
figsize: Size of output figure (default=(10, 10)).
text_size: Size of output figure text (default=15).
norm: normalize values or not (default=False).
savefig: save confusion matrix to file (default=False).
Returns:
A labelled confusion matrix plot comparing y_true and y_pred.
Example usage:
make_confusion_matrix(y_true=test_labels, # ground truth test labels
y_pred=y_preds, # predicted labels
classes=class_names, # array of class label names
figsize=(15, 15),
text_size=10)
"""
# Create the confustion matrix
cm = confusion_matrix(y_true, y_pred)
cm_norm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis] # normalize it
n_classes = cm.shape[0] # find the number of classes we're dealing with
# Plot the figure and make it pretty
fig, ax = plt.subplots(figsize=figsize)
cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better
fig.colorbar(cax)
# Are there a list of classes?
if classes:
labels = classes
else:
labels = np.arange(cm.shape[0])
# Label the axes
ax.set(title="Confusion Matrix",
xlabel="Predicted label",
ylabel="True label",
xticks=np.arange(n_classes), # create enough axis slots for each class
yticks=np.arange(n_classes),
xticklabels=labels, # axes will labeled with class names (if they exist) or ints
yticklabels=labels)
# Make x-axis labels appear on bottom
ax.xaxis.set_label_position("bottom")
ax.xaxis.tick_bottom()
# Set the threshold for different colors
threshold = (cm.max() + cm.min()) / 2.
# Plot the text on each cell
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
if norm:
plt.text(j, i, f"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)",
horizontalalignment="center",
color="white" if cm[i, j] > threshold else "black",
size=text_size)
else:
plt.text(j, i, f"{cm[i, j]}",
horizontalalignment="center",
color="white" if cm[i, j] > threshold else "black",
size=text_size)
# Save the figure to the current working directory
if savefig:
fig.savefig("confusion_matrix.png")
def top_1_accuracy(y_true, y_pred):
"""
Computes the top-1 accuracy of a classifier.
Parameters:
y_true (array-like): True labels of the data.
y_pred (array-like): Predicted labels of the data.
Returns:
top_1_acc (float): The top-1 accuracy of the classifier.
"""
# Ensure that the inputs have the same shape
assert y_true.shape == y_pred.shape
# Calculate the number of correct predictions
num_correct = (y_true == y_pred).sum()
# Calculate the top-1 accuracy
top_1_acc = num_correct / y_true.shape[0]
return top_1_acc
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score, roc_curve
def calculate_and_plot_auc_roc(ground_truth, predicted_probs):
"""
Calculate the AUC-ROC and plot the ROC curve for a binary classification problem.
Parameters:
ground_truth (list): The ground truth labels.
predicted_probs (list): The predicted probabilities.
Returns:
float: The AUC-ROC value.
"""
# Calculate AUC-ROC
auc_roc = roc_auc_score(ground_truth, predicted_probs)
# Calculate ROC curve
fpr, tpr, thresholds = roc_curve(ground_truth, predicted_probs)
# Plot ROC curve
plt.figure()
plt.plot(fpr, tpr, label='ROC curve (AUC = %0.2f)' % auc_roc)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC)')
plt.legend(loc="lower right")
plt.show()
return auc_roc