semvec/pypi-packages huggingface dataset:
simager 0.1.2
Simple tools for auto classification and text preprocessing
License: MIT
Requires Python: >=3.6
Keywords: nlp,text-processing,machine-learning,data-scientist,text-cleaner

# simager
Tools for Auto Machine Learning and Text Preprocessing.
End to end ML research (preprocessing, modelling, hyperparameter tuning) just using a few line of codes

## Features
```
- Auto Classification
- Text Preprocessing
```

## Instalation
```
pip install simager
```
## Getting Started
- Auto Classification
```
from simager.ml import ConfigData, ConfigPreprocess, ConfigModel, AutoClassifier

config_data = ConfigData(
    target="target",
    cat_features = ["column1", "column2"],
    num_features = ["column3","column4", "column5"]
)
config_preprocess = ConfigPreprocess(
    cat_imputer="SimpleImputer",
    num_imputer="SimpleImputer",
    scaler="RobustScaler",
    encoder="OneHotEncoder"
)
config_model=ConfigModel(algoritm=algoritm=[
    "DecisionTreeClassifier",
    "KNeighborsClassifier",
    "LogisticRegression",
    "SVC",
    "RandomForestClassifier",
    "AdaBoostClassifier",
    "XGBClassifier",
    "LGBMClassifier",
    "CatBoostClassifier"
])

model = AutoClassifier(config_data = config_data,
                 config_preprocess=config_preprocess,
                 config_model=config_model)

model.fit(df)

model.hp_tuning()
```

- Text Preprocessing
```
from simager.preprocess import TextPreprocess

methods = [
    "rm_hastag",
    "rm_mention",
    "rm_nonascii",
    "rm_emoticons",
    "rm_html",
    "rm_url",
    "sparate_str_numb",
    "pad_punct",
    "rm_punct",
    "rm_repeat_char",
    "rm_repeat_word",
    "rm_numb",
    "rm_whitespace",
    "normalize",
    "stopwords"
]

cleaner = TextPreprocess(methods=methods)

cleaner("your text here)
```

Full Example of Usage [Here](https://github.com/ulwan/simager/tree/master/simager/example)
ashcount 1.0.1
A set of things that can be done with files. For example, finding the number of lines in a file Find the number of words Find the net code count of a Python file.
License: MIT
Keywords: python,package


**This package helps you to get some information from the files.**

- Find the number of lines in a file
- Find the number of original lines in a file
- Find the word count of a file
- Find the number of codes in a Python file
- Find the net code count of a Python file
- Find the number of comments in a Python file
recipe 0.37.2
A construction kit for SQL
License: MIT

========
Overview
========

.. start-badges

.. |version| image:: https://img.shields.io/pypi/v/recipe.svg
    :alt: PyPI Package latest release
    :target: https://pypi.python.org/pypi/recipe

.. |commits-since| image:: https://img.shields.io/github/commits-since/chrisgemignani/recipe/v0.37.2.svg
    :alt: Commits since latest release
    :target: https://github.com/chrisgemignani/recipe/compare/v0.37.2...main

.. |downloads| image:: https://img.shields.io/pypi/dm/recipe.svg
    :alt: PyPI Package monthly downloads
    :target: https://pypi.python.org/pypi/recipe

.. |wheel| image:: https://img.shields.io/pypi/wheel/recipe.svg
    :alt: PyPI Wheel
    :target: https://pypi.python.org/pypi/recipe

.. |supported-versions| image:: https://img.shields.io/pypi/pyversions/recipe.svg
    :alt: Supported versions
    :target: https://pypi.python.org/pypi/recipe

.. |supported-implementations| image:: https://img.shields.io/pypi/implementation/recipe.svg
    :alt: Supported implementations
    :target: https://pypi.python.org/pypi/recipe


.. end-badges

Legos for SQL

Recipe is an MIT licensed cross-database querying library, written
in Python. It allows you to reuse SQL fragments to answer data questions
consistently. Extension classes allow you to support data anonymization,
automatic generation of where clauses, user permissioning to data, subselects,
and response formatting.

Installation
============

::

    pip install recipe

Documentation
=============

https://recipe.readthedocs.io/

Development
===========

To run the all tests run::

    tox

Note, to combine the coverage data from all the tox environments run:

.. list-table::
    :widths: 10 90
    :stub-columns: 1

    - - Windows
      - ::

            set PYTEST_ADDOPTS=--cov-append
            tox

    - - Other
      - ::

            PYTEST_ADDOPTS=--cov-append tox
cjvalpy 0.4.1
The Python bindings of the official CityJSON validator
License: MIT

# cjvalpy

[![GitHub license](https://img.shields.io/github/license/cityjson/cjvalpy)](https://github.com/cityjson/cjvalpy/blob/main/LICENSE) 
[![PyPI version](https://badge.fury.io/py/cjvalpy.svg)](https://badge.fury.io/py/cjvalpy)

Python bindings of [cjval](https://github.com/cityjson/cjval), the official validator for [CityJSON](https://cityjson.org) files.


## Installation

### pip

To install the latest release: `pip install cjvalpy`


### If you want to compile it yourself

1. install latest [Rust](https://www.rust-lang.org/)
2. install [maturin](https://github.com/PyO3/maturin)
3. `maturin build --release`
4. `cd ./target/wheels/`
5. `pip install [name-wheel].whl` will install it to your local Python


### Development

  1. install [Rust](https://www.rust-lang.org/) (v1.39+)
  2. install [maturin](https://github.com/PyO3/maturin) 
  3. `maturin develop`
  4. move to another folder, and `import cjvalpy` shouldn't return any error



## Usage

Made to be used with [cjio](https://github.com/cityjson/cjio): 

```bash
cjio myfile.city.json validate
```

but can be used directly in python:

```python
import cjvalpy
import json
import urllib.request

f = open("~/data/noise.city.json")
fj = json.loads(f.read())
js = []
js.append(json.dumps(fj))
print("Downloading the Extension JSON schema file(s):")
if "extensions" in fj:
    for ext in fj["extensions"]:
        theurl = fj["extensions"][ext]["url"]
        try:
            with urllib.request.urlopen(fj["extensions"][ext]["url"]) as f:
                sf = f.read().decode('utf-8')
                js.append(sf)
        except:
            s = "'%s' cannot be downloaded\nAbort" % fj["extensions"][ext]["url"]
            raise Exception(s)
val = cjvalpy.CJValidator(js)
val.validate()
re = val.get_report()
print(val.get_report())
```