Visualization

This sub package provides different helper functions for visualizing data using plots.

Examples

The following code cell will plot a classification report of true labels versus predicted labels.

from helperfns.visualization import plot_complicated_confusion_matrix, plot_images, plot_images_predictions, plot_simple_confusion_matrix,
plot_classification_report

# plotting classification report
fig, ax = plot_classification_report(labels, preds,
                    title='Classification Report',
                    figsize=(10, 5), dpi=70,
                    target_names = classes)

The plot_classification_report takes the following arguments:

Argument	Description	Type
images	List of images to plot	list
labels_true	True labels	list
labels_pred	Predicted labels	list
classes	List of class labels (default: [])	list
cols	Number of columns in the plot (default: 5)	int
rows	Number of rows in the plot (default: 3)	int
fontsize	Font size for labels (default: 16)	int

The plot_images_predictions plots the image predictions. This functions is very useful when you are doing image classification.

# plot predicted image labels with the images
plot_images_predictions(images, true_labels, preds, classes=["dog", "cat"] ,cols=8)

Here is the table of arguments for the plot_images_predictions

Argument	Description	Type
images	List of images to plot	list
labels_true	True labels	list
labels_pred	Predicted labels	list
classes	List of class labels (default: [])	list
cols	Number of columns in the plot (default: 5)	int
rows	Number of rows in the plot (default: 3)	int
fontsize	Font size for labels (default: 16)	int

The plot_images functions is used to visualize images.

# plot the images with their labels
plot_images(images[:24], true_labels[:24], cols=8)

The plot_images takes the following as arguments:

Argument	Description	Type
images	List of images to plot	list
labels	List of labels corresponding to images	list
cols	Number of columns in the plot (default: 5)	int
rows	Number of rows in the plot (default: 3)	int
fontsize	Font size for labels (default: 16)	int

The plot_simple_confusion_matrix is used to plot a less more verbose confusion matrix of real labels against predicted labels.

# plot a simple confusion matrix
y_true = [random.randint(0, 1) for _ in range (100)]
y_pred = [random.randint(0, 1) for _ in range (100)]
classes =["dog", "cat"]
plot_simple_confusion_matrix(y_true, y_pred, classes)

This function takes in the following in the following as arguments.

Argument	Description	Type
y_true	True labels	list
y_pred	Predicted labels	list
classes	List of class labels (default: [])	list
figsize	Size of the figure (default: (10, 10))	tuple
fontsize	Font size for labels (default: 15)	int

The plot_complicated_confusion_matrix is used to plot a more verbose confusion matrix of real labels against predicted labels.

# plot a confusion matrix with percentage value of confusion
y_true = [random.randint(0, 1) for _ in range (100)]
y_pred = [random.randint(0, 1) for _ in range (100)]
classes =["dog", "cat"]
plot_complicated_confusion_matrix(y_true, y_pred, classes)

This function takes in the following as arguments.

Argument	Description	Type
y_true	True labels	list
y_pred	Predicted labels	list
classes	List of class labels (default: [])	list
figsize	Size of the figure (default: (5, 5))	tuple
fontsize	Font size for labels (default: 20)	int
title	Title of the plot (default: “Confusion Matrix”)	str
xlabel	Label for x-axis (default: “Predicted label”)	str
ylabel	Label for y-axis (default: “True label”)	str