semvec/pypi-packages huggingface dataset:
pathtree 0.1.3
Python command line tree printer

# pathtree

[![PyPI](https://img.shields.io/pypi/v/pathtree.svg)](https://pypi.org/project/pathtree/)

## Installation

`pip install pathtree`

## Example

* Use in Shell

Run command `find pathtree | python pathtree` will print this:

```txt
pathtree
├── pathtree.py
├── __main__.py
├── __pycache__
│   ├── pathtree.cpython-36.pyc
│   └── __main__.cpython-36.pyc
└── __init__.py
```

* Use Programmatically

```python
from pathtree import Tree

# Init
tree = Tree()
# Add path
tree.add_path(<path>)
# Print tree
print(tree.dump())
```

## License
The MIT License.
django-searchable 0.1.2
Easy FTS with Django and PostgreSQL
Keywords: search,searchable,fts

Django Searchable
=================

Easy full text search with Django and PostgreSQL. Sane defaults + auto creation of vector fields, indexes, and database triggers.


Example
--------------

Extend your model with the :code:`SearchableModel` class, and use the :code:`SearchableTextField` class to automatically setup full text search:

.. code-block:: python

    from django.db.models import TextField
    from django_searchable.models import SearchableModel, SearchableTextField

    class Blog(SearchableModel):
        author_name = TextField() # will NOT have FTS setup automatically
        title = SearchableTextField() # will have FTS setup automatically
        text = SearchableTextField() # will have FTS setup automatically

Then search away via the Blog manager:

.. code-block:: python

    # takes a string of space separated terms
    results = Blog.objects.search('spiderman suits')

    # or a list of terms
    results = Blog.objects.search(['water', 'baskets', 'leaking'])

    # or a SearchQuery object
    from django.contrib.postgres.search import SearchQuery
    query = ~SearchQuery('superman') & SearchQuery('batman')
    results = Blog.objects.search(query)

:code:`.search` adds a :code:`rank` annotation and automatically filters and sorts the resulting queryset.

By default, :code:`.search` will search through all :code:`SearchableTextField` fields on the model, but you can specify any subset:

.. code-block:: python

    results = Blog.objects.search('who is venom', fields=['title'])

Contributing
--------------

.. code-block:: bash

    pipenv install --dev
    createuser -s -P test_user  # use 'password'
    pipenv run ./manage.py test
snips-nlu-rust 0.57.0
Python wrapper of the Snips NLU engine

Snips NLU Rust Wrapper
======================

Installation
------------

It is recommended to install the package with a virtualenv:

.. code-block:: bash

    virtualenv venv
    . venv/bin/activate

The package is available on pypi, and can be installed with `pip`:

.. code-block:: bash

    pip install snips-nlu-rust
mllearn 1.2.3
a package for multi-label classify
Requires Python: ~=3.5
Keywords: multi label classification

# multi-label-learn

mlleran is a python library for multi-label classification bulti on scikit-learn and numpy.

## Implementation
The implementation is based on the paper [A Review on Multi-Label Learning Algorithms](https://ieeexplore.ieee.org/document/6471714/), and the implementated algorithms include:

**Problem Transformation**

- [x] Binary Relevance
- [x] Classifier Chains
- [x] Calibrated Label Ranking
- [x] Random k-Labelsets

**Algorithm Adaptation**

- [x] Multi-Label k-Nearest Neighbor
- [x] Multi-Label Decision Tree
- [ ] Ranking Support Vector Machine
- [ ] Collective Multi-Label Classifier

## Installation
```bash
pip install mllearn
```
**Note: Support Python3 only.** 

## Data Format
All data type should be `ndarray`, especially y should be the binary format. For example, if your dataset totally have 5 labels and one of your samples has only first and last labels, then the corresponding output should be `[1, 0, 0, 0, 1]`.
```python
samples, features = X_train.shape
samples, labels = y_train.shape
samples_test, features = X_test.shape
samples_test, labels = y_test.shape
```
You can also find multi-label dataset provided by Mulan [here](http://mulan.sourceforge.net/datasets-mlc.html).

## Example Usage
This library includes 2 parts, algorithms and metrics.
```python
from mllearn.problem_transform import BinaryRelevance

classif = BinaryRelevance()
classif.fit(X_train, y_train)
predictions = classif.predict(X_test)
```

```python
from mllearn.metrics import subset_acc
acc = subset_acc(y_test, predictions)
```