helperfns 1.0.1

This package provide some python helper functions that are useful in machine learning.

helperfns

🎀 This is a python package that contains some helper functions for machine leaning.

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Table of Contents

• helperfns
• Table of Contents
• Getting started
• Usage
• tables
• text
• utils
• visualization
• Contributing to helperfns.
• License

Getting started

To start using helperfns in your project you run the following command:

pip install helperfns

Or if you wan to install it in notebooks such as jupyter notebooks you can run the code cell with the following code:

!pip install helperfns

Usage

The helperfns package is made up of different sub packages such as:

1. tables
2. text
3. utils
4. visualization

tables

In the tables sub package you can print your data in tabular form for example:

from helperfns.tables import tabulate_data

column_names = ["SUBSET", "EXAMPLE(s)", "Hello"]
row_data = [["training", 5, 4],['validation', 4, 4],['test', 3, '']]
tabulate_data(column_names, row_data)

Output:

Table
+------------+------------+-------+
| SUBSET     | EXAMPLE(s) | Hello |
+------------+------------+-------+
| training   |          5 |     4 |
| validation |          4 |     4 |
| test       |          3 |       |
+------------+------------+-------+

text

The text package offers two main function which are clean_sentence, de_contract, generate_ngrams and generate_bigrams

from helperfns.text import *

# cleans the sentence
print(clean_sentence("text 1 # https://url.com/bla1/blah1/"))
# list of all english words
print(english_words)
# converts strings like `I'm` to 'I am'
print(de_contract("I'm"))

# generate bigrams from a list of word
print(text.generate_bigrams(['This', 'film', 'is', 'terrible']))

# generates n-grams from a list of words
print(text.generate_ngrams(['This', 'film', 'is', 'terrible']))

utils

utils package comes with a simple helper function for converting seconds to hours, minutes and seconds.

Example:

from helperfns.utils import hms_string

start = time.time()
for i in range(100000):
   pass
end = time.time()

print(hms_string(end - start))

Output:

'0:00:00.01'

visualization

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

Examples:

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)

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

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

# 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)

# 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)

Contributing to helperfns.

To contribute to helperfns read the CONTRIBUTION.md file.

License

This project is licensed under the MIT License - see the LICENSE file for details.