This package provide some python helper functions that are useful in machine learning.
Project description
helperfns
🎀 This is a python package that contains some helper functions for machine leaning.
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:
- tables
- text
- utils
- 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.
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