Metadata-Version: 2.1
Name: helperfns
Version: 0.0.6
Summary: This package provide some python helper functions that are useful in machine learning.
Author: Crispen Gari
Author-email: <crispengari@gmail.com>
Keywords: helperfns,python,python3,helper-functions,text cleaning,visualization,machine-learning
Classifier: Development Status :: 1 - Planning
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Environment :: MacOS X
Classifier: Environment :: MacOS X :: Carbon
Classifier: Environment :: MacOS X :: Carbon
Classifier: Environment :: MacOS X :: Cocoa
Classifier: Environment :: Web Environment
Classifier: Environment :: Win32 (MS Windows)
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Other Audience
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Topic :: Education
Classifier: Topic :: Education :: Testing
Classifier: Topic :: Internet
Classifier: Topic :: Utilities
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
License-File: LICENSE


### helperfns



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



<p align="center">

   <img src="https://github.com/CrispenGari/helperfns/blob/main/images/logo.png" alt="logo" width="60%"/>

</p>



### Getting started



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



```shell

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:



```shell

!pip install helperfns

```



### Usage



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



1. tables

2. text

3. utils

4. visualization

5. torch



### tables



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



```python

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:



```shell

+---------------------------------+

|              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`



```python

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:



```python

start = time.time()

for i in range(100000):

   pass

end = time.time()



```



Output:



```shell

'0:00:00.01'

```



### visualization



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



Examples:



```python

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)

```



### torch



This is a subpackage that contains other subpackages mainly used when working with `pytorch`. These sub packages are:



1. text

2. models

3. accuracy



### `torch.text`



This package contains the `label_pipeline` and `text_pipeline`. This helper function are normally used when doing text processing in python pytorch.



Example:



```python

from helperfns.torch import text



# converting a sentence into sequence of integer representation.

vocab = {'<unk>': 0, 'this': 1, 'is': 2, 'a': 3, 'dog': 4}

tokenizer = lambda x: x.split(' ')

print(text.text_pipeline("This is a dog that is backing", tokenizer=tokenizer, vocab=vocab, unk_token='<unk>', lower=True))



# converting labels into their integer representation.

labels_dict = {l:i for (i, l) in enumerate(['af', 'en', 'st', 'ts', 'xh', 'zu'])}

print(text.label_pipeline("en", labels_dict=labels_dict))



```



### `torch.models`



This package contains a helper function called `model_params`. This function is used to count the model parameters of a pytorch model. Example:



```python

model_params(my_model)

```



> Note that `my_model` is a python class model instance that is inheriting from the `nn.Module` class.



### `torch.accuracy`



This package contains two helper functions called that are used to calculate the accuracy between predicted labels and real label. These functions are:



1. `binary_accuracy` - used to calculate the binary accuracy between predicted labels and real labels.



```python

y = y.to(device)

predictions = model(X).squeeze(1)

loss = criterion(predictions, y)

acc = binary_accuracy(predictions, y)

print(acc)

```



2. `categorical_accuracy` - used to calculate the categorical accuracy between predicted labels and real labels.



```python

y = y.to(device)

predictions = model(X).squeeze(1)

loss = criterion(predictions, y)

acc = categorical_accuracy(predictions, y)

print(acc)

```



### Contributing to `helperfns`.



To contribute to `helperfns` read the [CONTRIBUTION.md](https://github.com/CrispenGari/helperfns/blob/main/CONTRIBUTION.md) file.



### License



In this package the `MIT` license was used which reads as follows:



```

MIT License



Copyright (c) 2022 crispengari



Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction, including without limitation the rights

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

copies of the Software, and to permit persons to whom the Software is

furnished to do so, subject to the following conditions:



The above copyright notice and this permission notice shall be included in all

copies or substantial portions of the Software.



THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

SOFTWARE.



```

