semvec/pypi-packages huggingface dataset:
CHEESEML 0.0.1
AutoML tool
License: MIT
Requires Python: >=3.6
Keywords: AutoML

# CHEESEML - AutoML tool

![cheese-ml](https://user-images.githubusercontent.com/45726271/141406928-98a05345-0f46-48d7-89d3-00540c6b3dee.png)

cheese-ml is a AutoML Python library that finds most accurate machine learning models automatically and efficiently.
It frees users from selecting models and hyper-parameters for each model.

## Installation

```bash
pip install CHEESEML
```
## Quickstart

```python
from CHEESEML import autoML
aml = autoML()
aml.fit(X_train, X_test, y_train, y_test, "classification", "f1_score")
```
Task can be either classification or regression and metric can be selected accordingly.

hyper-parameter optimization is done using optuna.

After training, use this function to get the best model:

```python
aml.get_best_model()
```

You can use predict() function for custom predicitions.

```python
aml.predict(X_val)
```

Change Log
==========

0.0.1 (12/11/2021)
-------------------
- First Release
CheeseML 0.0.1
AutoML tool
License: MIT
Requires Python: >=3.6
Keywords: AutoML

# CheeseML - AutoML tool

![cheese-ml](https://user-images.githubusercontent.com/45726271/141406928-98a05345-0f46-48d7-89d3-00540c6b3dee.png)

cheese-ml is a AutoML Python library that finds most accurate machine learning models automatically and efficiently.
It frees users from selecting models and hyper-parameters for each model.

## Installation

```bash
pip install CheeseML
```
## Quickstart

```python
from CheeseML import autoML
aml = autoML()
aml.fit(X_train, X_test, y_train, y_test, "classification", "f1_score")
```
Task can be either classification or regression and metric can be selected accordingly.

hyper-parameter optimization is done using optuna.

After training, use this function to get the best model:

```python
aml.get_best_model()
```

You can use predict() function for custom predicitions.

```python
aml.predict(X_val)
```

Change Log
==========

0.0.1 (12/11/2021)
-------------------
- First Release
cheeseml 0.0.1
AutoML tool
License: MIT
Requires Python: >=3.6
Keywords: AutoML

# cheeseML - AutoML tool

![cheese-ml](https://user-images.githubusercontent.com/45726271/141406928-98a05345-0f46-48d7-89d3-00540c6b3dee.png)

cheese-ml is a AutoML Python library that finds most accurate machine learning models automatically and efficiently.
It frees users from selecting models and hyper-parameters for each model.

## Installation

```bash
pip install cheeseml
```
## Quickstart

```python
from cheeseml import autoML
aml = autoML()
aml.fit(X_train, X_test, y_train, y_test, "classification", "f1_score")
```
Task can be either classification or regression and metric can be selected accordingly.

hyper-parameter optimization is done using optuna.

After training, use this function to get the best model:

```python
aml.get_best_model()
```

You can use predict() function for custom predicitions.

```python
aml.predict(X_val)
```

Change Log
==========

0.0.1 (12/11/2021)
-------------------
- First Release
cheeseML 0.0.1
AutoML tool
License: MIT
Requires Python: >=3.6
Keywords: AutoML

# cheeseML - AutoML tool

![cheese-ml](https://user-images.githubusercontent.com/45726271/141406928-98a05345-0f46-48d7-89d3-00540c6b3dee.png)

cheese-ml is a AutoML Python library that finds most accurate machine learning models automatically and efficiently.
It frees users from selecting models and hyper-parameters for each model.

## Installation

```bash
pip install flaml
```
## Quickstart

```python
from cheese-ml import autoML
aml = autoML()
aml.fit(X_train, X_test, y_train, y_test, "classification", "f1_score")
```
Task can be either classification or regression and metric can be selected accordingly.

hyper-parameter optimization is done using optuna.

After training, use this function to get the best model:

```python
aml.get_best_model()
```

You can use predict() function for custom predicitions.

```python
aml.predict(X_val)
```

Change Log
==========

0.0.1 (12/11/2021)
-------------------
- First Release
python-multimapwithttl 0.1.0
An implementation of multimap with per-item expiration backed up by Redis.
License: MIT
Requires Python: >=3.6.2,<4.0

# MultiMapWithTTL


[![pypi](https://img.shields.io/pypi/v/python-multimapwithttl.svg)](https://pypi.org/project/python-multimapwithttl/)
[![python](https://img.shields.io/pypi/pyversions/python-multimapwithttl.svg)](https://pypi.org/project/python-multimapwithttl/)
[![Build Status](https://github.com/loggi/python-multimapwithttl/actions/workflows/dev.yml/badge.svg)](https://github.com/loggi/python-multimapwithttl/actions/workflows/dev.yml)
[![codecov](https://codecov.io/gh/loggi/python-multimapwithttl/branch/main/graphs/badge.svg)](https://codecov.io/github/loggi/python-multimapwithttl)



An implementation of multimap with per-item expiration backed up by Redis.


* Documentation: <https://loggi.github.io/python-multimapwithttl>
* GitHub: <https://github.com/loggi/python-multimapwithttl>
* PyPI: <https://pypi.org/project/python-multimapwithttl/>
* Free software: MIT


## Description

This lib is based on: https://quickleft.com/blog/how-to-create-and-expire-list-items-in-redis/
without the need for an extra job to delete old items.

Values are internally stored on Redis using Sorted Sets :

    key1: { (score1, value1), (score2, value2), ... }
    key2: { (score3, value3), (score4, value4), ... }
    ...

Where the `score` is the timestamp when the value was added.
We use the timestamp to filter expired values and when an insertion happens,
we opportunistically garbage collect expired values.

The key itself is set to expire through redis ttl mechanism together with the newest value.
These operations result in a simulated multimap with item expiration.

You can use to keep track of values associated to keys,
when the value has a notion of expiration.

```
>>> s = MultiMapWithTTL(redis_client, 'multimap')
>>> s.add('a', 1, 2, 3)
>>> sorted(s.get('a'))
[1, 2, 3]
>>> s.add_many([('b', (4, 5, 6)), ('c', (7, 8, 9)), ])
>>> sorted(sorted(values) for values in s.get_many('a', 'b', 'c')))
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
```
cheese-ml 0.0.1
AutoML tool
License: MIT
Requires Python: >=3.6
Keywords: AutoML

# cheese-ml - AutoML tool

![cheese-ml](https://user-images.githubusercontent.com/45726271/141406928-98a05345-0f46-48d7-89d3-00540c6b3dee.png)

cheese-ml is a AutoML Python library that finds most accurate machine learning models automatically and efficiently.
It frees users from selecting models and hyper-parameters for each model.

## Installation

```bash
pip install flaml
```
## Quickstart

```python
from cheese-ml import autoML
aml = autoML()
aml.fit(X_train, X_test, y_train, y_test, "classification", "f1_score")
```
Task can be either classification or regression and metric can be selected accordingly.

hyper-parameter optimization is done using optuna.

After training, use this function to get the best model:

```python
aml.get_best_model()
```

You can use predict() function for custom predicitions.

```python
aml.predict(X_val)
```

Change Log
==========

0.0.1 (12/11/2021)
-------------------
- First Release
xorsms 0.0.1
In just 11 lines of code you can find information about the receivers phone number, and send SMS messages using gmail!
License: MIT
Keywords: sms

Thanks for installing "xorsms!"

With xorsms you can send SMS messages almost instantly using gmail, as long as you know the receivers phone provider! If you're not sure of this information don't worry, xorsms will find that information for you!

!!! WARNING !!!
In order for this to work properly make sure you go to https://myaccount.google.com/ and turn on less secure access. It is not recommended you do this! So we recommend creating another gmail account and turing off less secure access after you are done using our services!
pydantic-decoder 1.0.2
Convert json text back into proper Pydantic models.
License: MIT
Keywords: pydantic json decode encode

================
pydantic_decoder
================

Convert json text back into proper Pydantic models.


Simple Example
==============

.. code-block:: python

    from pydantic_decoder import DecoderModel, encode, decode


    class MyModel(DecoderModel):
        first_name: str
        last_name: str


    model = MyModel(first_name='John', last_name='Doe')
    msg = model.json()
    m = model.decode(msg)
    assert m == model
    assert isinstance(m, MyModel)


Registration Examples
=====================

Use your current models by only changing the inheritance from `pydantic.BaseModel` to `pydantic_decoder.DecoderModel`.

.. code-block:: python

    from pydantic_decoder import DecoderModel, register_model, encode, decode

    class MyModel(DecoderModel):
        first_name: str
        last_name: str

    class MyModel2(DecoderModel):
        first_name: str
        last_name: str


    # Subclasses of the DecoderModel are registered automatically
    # register_model(MyModel)
    # register_model('Model2', MyModel2)

    model = MyModel(first_name='John', last_name='Doe')
    msg = encode(model)
    m = decode(msg)
    assert m == model
    assert isinstance(m, MyModel)

    model2 = MyModel2(first_name='John', last_name='Doe')
    msg = encode(model2)
    m2 = decode(msg)
    assert m2 == model2
    assert isinstance(m2, MyModel2)