semvec/pypi-packages huggingface dataset:
ClassMaker 0.1
A python module to help with meta classes

A helper class to programaticly build a Meta Class

### Install

```
git clone https://gitlab.com/craigukaea/class-maker.git
cd class-builder
./Install
```

### Use

To build a class equivelent to the following:
```python
class Foo:
    def bar():
        print("Hello world")
```

Use:
```
maker = ClassMaker()
maker.setClassName('Foo')
maker.addFunction('bar', lambda : print("Hello World!"))

#make the class
foo = maker.makeClass()

##test the functions
foo.__init__()
foo.bar()

```


### Example
```python
from ClassMaker import ClassMaker

if __name__ == "__main__":

    maker = ClassMaker()
    maker.setClassName('Foo')
    maker.addFunction('bar', lambda : print("Hello World!"))
    maker.addVariable('baz', 5)

    maker.addFunctionToInitialiser(lambda : print("Initialising"))
    maker.addFunctionToInitialiser(lambda : print("!"))

    foo = maker.makeClass()
    foo.__init__()
    foo.bar()
    print(foo.baz)
```
gpam_training 0.0.16
Module to facilitate the integration of a sklearn training pipeline into a deploy and retraining system


gpam_training
=============

Module to facilitate the integration of a sklearn training pipeline into a deploy and retraining system

Install
-------

.. code-block::

   pip install gpam_training

Usage
-----

Multilabel training
^^^^^^^^^^^^^^^^^^^

First of all, it is needed to have in memory a dataframe from pandas.
The csv must be in the following format:

.. code-block::

   process_id,page_text_extract,tema
   1,Lorem ipsum dolor sit amet,1
   2,Lorem ipsum dolor sit amet,2
   2,Lorem ipsum dolor sit amet,3
   42,Lorem ipsum dolor sit amet,2

To train the model, do as shown bellow:

.. code-block::

   from gpam_training import MultilabelTraining
   import pandas as pd

   df = pd.read_csv('example.csv')
   model = MultilabelTraining(df)
   model.train()

To dump a pickle file with the trained model, do the following:

.. code-block::

   model_pickle = model.get_pickle()