| Name | helperfns JSON |
| Version |
1.2.1
JSON |
| download |
| home_page | None |
| Summary | This package provides some Python helper functions that are useful in machine learning. |
| upload_time | 2024-10-12 07:04:19 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | 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. |
| keywords |
helperfns
python
python3
helper-functions
text cleaning
visualization
machine-learning
|
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### `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?raw=true" alt="logo" width="60%"/>
</p>
---
<p align="center">
<a href="https://pypi.python.org/pypi/helperfns"><img src="https://badge.fury.io/py/helperfns.svg"></a>
<a href="https://github.com/crispengari/helperfns/actions/workflows/CI.yml"><img src="https://github.com/crispengari/helperfns/actions/workflows/CI.yml/badge.svg"></a>
<a href="/LICENSE"><img src="https://img.shields.io/badge/license-MIT-green"></a>
<a href="https://pypi.python.org/pypi/helperfns"><img src="https://img.shields.io/pypi/pyversions/helperfns.svg"></a>
</p>
### Table of Contents
- [`helperfns`](#helperfns)
- [Table of Contents](#table-of-contents)
- [Getting started](#getting-started)
- [Usage](#usage)
- [tables](#tables)
- [text](#text)
- [utils](#utils)
- [visualization](#visualization)
- [Contributing to `helperfns`.](#contributing-to-helperfns)
- [Documentation](#documentation)
- [License](#license)
### 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
### 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 | |
+------------+------------+-------+
```
The following is the table of arguments for the `tabulate_data` helper function
| Argument | Description | Type |
| -------------- | -------------------- | ------ |
| `column_names` | List of column names | `list` |
| `data` | Data to be tabulated | `list` |
| `title` | Title of the table | `str` |
### 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/"))
```
Here is the table of arguments for the `clean_sentence` helper function.
| Argument | Description | Type |
| -------- | --------------------------------------------- | ------ |
| `sent` | Input sentence | `str` |
| `lower` | Flag to convert to lower case (default: True) | `bool` |
You can get the list of english words as follows:
```py
# list of all english words
print(english_words)
```
You can use the `de_contract` to de-contact strings as follows
```py
# converts strings like `I'm` to 'I am'
print(de_contract("I'm"))
```
Here is the table of arguments for the `de_contract` function.
| Argument | Description | Type |
| -------- | ------------------- | ----- |
| `word` | Word to de-contract | `str` |
The `generate_bigrams` is responsible for generating bi grams from list of words. Here is how you can use the function
```py
# generate bigrams from a list of word
print(text.generate_bigrams(['This', 'film', 'is', 'terrible']))
```
Here is the table of arguments for the `generate_bigrams` function:
| Argument | Description | Type |
| -------- | ---------------------- | ------ |
| `x` | List of input elements | `list` |
The `generate_ngrams` generate the n-grams from a list of words, here is an example on how you can use this function
```py
# generates n-grams from a list of words
print(text.generate_ngrams(['This', 'film', 'is', 'terrible']))
```
Here is the table of arguments for the `generate_ngrams` function:
| Argument | Description | Type |
| -------- | --------------------------------------------------- | ------ |
| `x` | List of input elements | `list` |
| `grams` | Number of grams for generating n-grams (default: 3) | `int` |
### utils
utils package comes with a simple helper function for converting seconds to hours, minutes and seconds.
Example:
```python
from helperfns.utils import hms_string
start = time.time()
for i in range(100000):
pass
end = time.time()
print(hms_string(end - start))
```
Output:
```shell
'0:00:00.01'
```
The `hms_string` takes in the following as arguments.
| Argument | Description | Type |
| ------------- | ------------------------------- | ----- |
| `sec_elapsed` | Time in seconds to be converted | `Any` |
### 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.
```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)
```
The `plot_classification_report` takes the following arguments:
| Argument | Description | Type |
| --------------- | ---------------------------------------------------- | --------------- |
| `y_true` | True labels | `list` |
| `y_pred` | Predicted labels | `list` |
| `title` | Title of the plot (default: "Classification Report") | `str` |
| `figsize` | Size of the figure (default: (10, 5)) | `tuple` |
| `dpi` | Resolution of the figure (default: 70) | `int` |
| `save_fig_path` | Path to save the figure (default: None) | `Any` or `None` |
| \*\*kwargs | Additional keyword arguments | `Any` |
The `plot_images_predictions` plots the image predictions. This functions is very useful when you are doing image classification.
```py
# 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.
```py
# 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.
```py
# 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.
```py
# 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` |
The `plot_wordcloud` function generates and plots a word cloud based on the provided corpus.
```python
# Generate a word cloud from a sample text
corpus = "This is a sample text for generating word clouds"
plot_wordcloud(corpus, max_words=500, mask="wine")
```
This function takes in the following as arguments.
| Argument | Description | Type |
| ------------------ | --------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------ |
| `corpus` | The text or dictionary of word frequencies to generate the word cloud from. | `str` or `dict` |
| `max_words` | Maximum number of words to include in the word cloud, default is 1,000. | `int` |
| `title` | Title of the plot, default is "Word Cloud". | `str` |
| `mask` | The shape mask for the word cloud. Options are "head", "chicken", "wine", "apple", "tree" or None, default is "tree". | `Union[Literal["head", "chicken", "wine", "apple", "tree"], None]` |
| `background_color` | The background color of the word cloud, default is "#E4E0E1". | `str` |
| `contour_width` | Width of the contour around the word cloud, default is 1. | `int` |
| `contour_color` | Color of the contour around the word cloud, default is "#D6C0B3". | `str` |
| `figsize` | The figure size of the word cloud plot, default is (10, 10). | `tuple` |
| `fontsize` | Font size for the plot title, default is 15. | `int` |
| `save_path` | The path to save the plotted figure (default: None). | `str` or `None` |
### Contributing to `helperfns`.
To contribute to `helperfns` read the [CONTRIBUTION.md](https://github.com/CrispenGari/helperfns/blob/main/CONTRIBUTION.md) file.
### Documentation
You can read the full [documentation](https://helperfns.readthedocs.io/en/latest/) here.
### License
This project is licensed under the MIT License - see the [LICENSE](/LISENSE) file for details.
Raw data
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"keywords": "helperfns, python, python3, helper-functions, text cleaning, visualization, machine-learning",
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"author_email": "Crispen Gari <crispengari@gmail.com>",
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"description": "### `helperfns`\n\n\ud83c\udf80 This is a python package that contains some helper functions for machine leaning.\n\n<p align=\"center\">\n <img src=\"https://github.com/CrispenGari/helperfns/blob/main/images/logo.png?raw=true\" alt=\"logo\" width=\"60%\"/>\n</p>\n\n---\n\n<p align=\"center\">\n <a href=\"https://pypi.python.org/pypi/helperfns\"><img src=\"https://badge.fury.io/py/helperfns.svg\"></a>\n <a href=\"https://github.com/crispengari/helperfns/actions/workflows/CI.yml\"><img src=\"https://github.com/crispengari/helperfns/actions/workflows/CI.yml/badge.svg\"></a>\n <a href=\"/LICENSE\"><img src=\"https://img.shields.io/badge/license-MIT-green\"></a>\n <a href=\"https://pypi.python.org/pypi/helperfns\"><img src=\"https://img.shields.io/pypi/pyversions/helperfns.svg\"></a>\n</p>\n\n### Table of Contents\n\n- [`helperfns`](#helperfns)\n- [Table of Contents](#table-of-contents)\n- [Getting started](#getting-started)\n- [Usage](#usage)\n- [tables](#tables)\n- [text](#text)\n- [utils](#utils)\n- [visualization](#visualization)\n- [Contributing to `helperfns`.](#contributing-to-helperfns)\n- [Documentation](#documentation)\n- [License](#license)\n\n### Getting started\n\nTo start using `helperfns` in your project you run the following command:\n\n```shell\npip install helperfns\n```\n\nOr if you wan to install it in notebooks such as jupyter notebooks you can run the code cell with the following code:\n\n```shell\n!pip install helperfns\n```\n\n### Usage\n\nThe `helperfns` package is made up of different sub packages such as:\n\n1. tables\n2. text\n3. utils\n4. visualization\n\n### tables\n\nIn the tables sub package you can print your data in tabular form for example:\n\n```python\nfrom helperfns.tables import tabulate_data\n\ncolumn_names = [\"SUBSET\", \"EXAMPLE(s)\", \"Hello\"]\nrow_data = [[\"training\", 5, 4],['validation', 4, 4],['test', 3, '']]\ntabulate_data(column_names, row_data)\n\n```\n\nOutput:\n\n```shell\nTable\n+------------+------------+-------+\n| SUBSET | EXAMPLE(s) | Hello |\n+------------+------------+-------+\n| training | 5 | 4 |\n| validation | 4 | 4 |\n| test | 3 | |\n+------------+------------+-------+\n```\n\nThe following is the table of arguments for the `tabulate_data` helper function\n\n| Argument | Description | Type |\n| -------------- | -------------------- | ------ |\n| `column_names` | List of column names | `list` |\n| `data` | Data to be tabulated | `list` |\n| `title` | Title of the table | `str` |\n\n### text\n\nThe text package offers two main function which are `clean_sentence`, `de_contract`, `generate_ngrams` and `generate_bigrams`\n\n```python\nfrom helperfns.text import *\n\n# cleans the sentence\nprint(clean_sentence(\"text 1 # https://url.com/bla1/blah1/\"))\n\n```\n\nHere is the table of arguments for the `clean_sentence` helper function.\n\n| Argument | Description | Type |\n| -------- | --------------------------------------------- | ------ |\n| `sent` | Input sentence | `str` |\n| `lower` | Flag to convert to lower case (default: True) | `bool` |\n\nYou can get the list of english words as follows:\n\n```py\n# list of all english words\nprint(english_words)\n```\n\nYou can use the `de_contract` to de-contact strings as follows\n\n```py\n# converts strings like `I'm` to 'I am'\nprint(de_contract(\"I'm\"))\n\n```\n\nHere is the table of arguments for the `de_contract` function.\n\n| Argument | Description | Type |\n| -------- | ------------------- | ----- |\n| `word` | Word to de-contract | `str` |\n\nThe `generate_bigrams` is responsible for generating bi grams from list of words. Here is how you can use the function\n\n```py\n# generate bigrams from a list of word\nprint(text.generate_bigrams(['This', 'film', 'is', 'terrible']))\n```\n\nHere is the table of arguments for the `generate_bigrams` function:\n\n| Argument | Description | Type |\n| -------- | ---------------------- | ------ |\n| `x` | List of input elements | `list` |\n\nThe `generate_ngrams` generate the n-grams from a list of words, here is an example on how you can use this function\n\n```py\n# generates n-grams from a list of words\nprint(text.generate_ngrams(['This', 'film', 'is', 'terrible']))\n\n```\n\nHere is the table of arguments for the `generate_ngrams` function:\n\n| Argument | Description | Type |\n| -------- | --------------------------------------------------- | ------ |\n| `x` | List of input elements | `list` |\n| `grams` | Number of grams for generating n-grams (default: 3) | `int` |\n\n### utils\n\nutils package comes with a simple helper function for converting seconds to hours, minutes and seconds.\n\nExample:\n\n```python\nfrom helperfns.utils import hms_string\n\nstart = time.time()\nfor i in range(100000):\n pass\nend = time.time()\n\nprint(hms_string(end - start))\n```\n\nOutput:\n\n```shell\n'0:00:00.01'\n```\n\nThe `hms_string` takes in the following as arguments.\n\n| Argument | Description | Type |\n| ------------- | ------------------------------- | ----- |\n| `sec_elapsed` | Time in seconds to be converted | `Any` |\n\n### visualization\n\nThis sub package provides different helper functions for visualizing data using plots.\n\nExamples:\n\nThe following code cell will plot a classification report of true labels versus predicted labels.\n\n```python\nfrom helperfns.visualization import plot_complicated_confusion_matrix, plot_images, plot_images_predictions, plot_simple_confusion_matrix,\nplot_classification_report\n\n# plotting classification report\nfig, ax = plot_classification_report(labels, preds,\n title='Classification Report',\n figsize=(10, 5), dpi=70,\n target_names = classes)\n```\n\nThe `plot_classification_report` takes the following arguments:\n\n| Argument | Description | Type |\n| --------------- | ---------------------------------------------------- | --------------- |\n| `y_true` | True labels | `list` |\n| `y_pred` | Predicted labels | `list` |\n| `title` | Title of the plot (default: \"Classification Report\") | `str` |\n| `figsize` | Size of the figure (default: (10, 5)) | `tuple` |\n| `dpi` | Resolution of the figure (default: 70) | `int` |\n| `save_fig_path` | Path to save the figure (default: None) | `Any` or `None` |\n| \\*\\*kwargs | Additional keyword arguments | `Any` |\n\nThe `plot_images_predictions` plots the image predictions. This functions is very useful when you are doing image classification.\n\n```py\n# plot predicted image labels with the images\nplot_images_predictions(images, true_labels, preds, classes=[\"dog\", \"cat\"] ,cols=8)\n```\n\nHere is the table of arguments for the `plot_images_predictions`.\n\n| Argument | Description | Type |\n| ------------- | ------------------------------------------ | ------ |\n| `images` | List of images to plot | `list` |\n| `labels_true` | True labels | `list` |\n| `labels_pred` | Predicted labels | `list` |\n| `classes` | List of class labels (default: []) | `list` |\n| `cols` | Number of columns in the plot (default: 5) | `int` |\n| `rows` | Number of rows in the plot (default: 3) | `int` |\n| `fontsize` | Font size for labels (default: 16) | `int` |\n\nThe `plot_images` functions is used to visualize images.\n\n```py\n# plot the images with their labels\nplot_images(images[:24], true_labels[:24], cols=8)\n\n```\n\nThe `plot_images` takes the following as arguments:\n\n| Argument | Description | Type |\n| ---------- | ------------------------------------------ | ------ |\n| `images` | List of images to plot | `list` |\n| `labels` | List of labels corresponding to images | `list` |\n| `cols` | Number of columns in the plot (default: 5) | `int` |\n| `rows` | Number of rows in the plot (default: 3) | `int` |\n| `fontsize` | Font size for labels (default: 16) | `int` |\n\nThe `plot_simple_confusion_matrix` is used to plot a less more verbose confusion matrix of real labels against predicted labels.\n\n```py\n# plot a simple confusion matrix\ny_true = [random.randint(0, 1) for _ in range (100)]\ny_pred = [random.randint(0, 1) for _ in range (100)]\nclasses =[\"dog\", \"cat\"]\nplot_simple_confusion_matrix(y_true, y_pred, classes)\n\n\n```\n\nThis function takes in the following in the following as arguments.\n\n| Argument | Description | Type |\n| ---------- | -------------------------------------- | ------- |\n| `y_true` | True labels | `list` |\n| `y_pred` | Predicted labels | `list` |\n| `classes` | List of class labels (default: []) | `list` |\n| `figsize` | Size of the figure (default: (10, 10)) | `tuple` |\n| `fontsize` | Font size for labels (default: 15) | `int` |\n\nThe `plot_complicated_confusion_matrix` is used to plot a more verbose confusion matrix of real labels against predicted labels.\n\n```py\n# plot a confusion matrix with percentage value of confusion\ny_true = [random.randint(0, 1) for _ in range (100)]\ny_pred = [random.randint(0, 1) for _ in range (100)]\nclasses =[\"dog\", \"cat\"]\nplot_complicated_confusion_matrix(y_true, y_pred, classes)\n```\n\nThis function takes in the following as arguments.\n\n| Argument | Description | Type |\n| ---------- | ----------------------------------------------- | ------- |\n| `y_true` | True labels | `list` |\n| `y_pred` | Predicted labels | `list` |\n| `classes` | List of class labels (default: []) | `list` |\n| `figsize` | Size of the figure (default: (5, 5)) | `tuple` |\n| `fontsize` | Font size for labels (default: 20) | `int` |\n| `title` | Title of the plot (default: \"Confusion Matrix\") | `str` |\n| `xlabel` | Label for x-axis (default: \"Predicted label\") | `str` |\n| `ylabel` | Label for y-axis (default: \"True label\") | `str` |\n\nThe `plot_wordcloud` function generates and plots a word cloud based on the provided corpus.\n\n```python\n# Generate a word cloud from a sample text\ncorpus = \"This is a sample text for generating word clouds\"\nplot_wordcloud(corpus, max_words=500, mask=\"wine\")\n\n```\n\nThis function takes in the following as arguments.\n\n| Argument | Description | Type |\n| ------------------ | --------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------ |\n| `corpus` | The text or dictionary of word frequencies to generate the word cloud from. | `str` or `dict` |\n| `max_words` | Maximum number of words to include in the word cloud, default is 1,000. | `int` |\n| `title` | Title of the plot, default is \"Word Cloud\". | `str` |\n| `mask` | The shape mask for the word cloud. Options are \"head\", \"chicken\", \"wine\", \"apple\", \"tree\" or None, default is \"tree\". | `Union[Literal[\"head\", \"chicken\", \"wine\", \"apple\", \"tree\"], None]` |\n| `background_color` | The background color of the word cloud, default is \"#E4E0E1\". | `str` |\n| `contour_width` | Width of the contour around the word cloud, default is 1. | `int` |\n| `contour_color` | Color of the contour around the word cloud, default is \"#D6C0B3\". | `str` |\n| `figsize` | The figure size of the word cloud plot, default is (10, 10). | `tuple` |\n| `fontsize` | Font size for the plot title, default is 15. | `int` |\n| `save_path` | The path to save the plotted figure (default: None). | `str` or `None` |\n\n### Contributing to `helperfns`.\n\nTo contribute to `helperfns` read the [CONTRIBUTION.md](https://github.com/CrispenGari/helperfns/blob/main/CONTRIBUTION.md) file.\n\n### Documentation\n\nYou can read the full [documentation](https://helperfns.readthedocs.io/en/latest/) here.\n\n### License\n\nThis project is licensed under the MIT License - see the [LICENSE](/LISENSE) file for details.\n",
"bugtrack_url": null,
"license": "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. ",
"summary": "This package provides some Python helper functions that are useful in machine learning.",
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