semvec/pypi-packages huggingface dataset:
ntropy 0.0.5
Train machine learning models on data across multiple data silos.
Requires Python: >=3.6

# ntropy-client

A python package to train machine learning models on data residing in multiple data silos. Combining data from different sources can boost accuracy and robustness of a machine learning model far beyond what is possible on just a single dataset.

To achieve this, a data generator on the server is trained on data from all participants in the network. Each participant can then sample a dataset in their local feature encoding. The overall data flow is the following:

<img src="https://raw.githubusercontent.com/ntropy-network/ntropy-client/master/images/img1.png" width="50%">

For example, from combining 4 datasets of credit card transactions from 3 different organizations, ROC AUC already improves by more than 5%. See https://medium.com/ntropy-network/dissolving-data-silos-21e5eaab11f6 for more details.

<img src="https://raw.githubusercontent.com/ntropy-network/ntropy-client/master/images/img2.png" width="50%">

## FAQ

#### What data privacy guarantees does this have?

Local datasets are not sent directly to the server. Instead, a generator is first trained on its local dataset on the client. Then, noise is added to the weights of the trained generator to enforce mathematical privacy guarantees. Only then is the weights vector from that generator sent to the server.

When the client requests a new dataset, this data is sampled from the global generator on the server, and hence does not have associated privacy risks.

#### What kind of data is the global model currently trained on?

The framework itself is data agnostic. However, Ntropy is currently focusing on financial fraud and underwriting data.

#### How do you prevent malicious data from polluting the global model? 

The server has a number of validation metrics which are used to weigh each dataset by how much it improves performance. If performance does not improve, the respective dataset is not added to the server model. 

#### How do I start using this?

Please contact partners@ntropy.network to join our partner network.

#### How do I contribute to this project?

Check out https://ntropy.network for more info or write to jobs@ntropy.network. We are actively hiring.
pyindia 1.1.5
India's states and cities

# India package

![deployment](https://github.com/rishabhbatra10/in/workflows/deployment/badge.svg)
#### Version 1.1.5
### Installation
Installation Using pip
```bash
pip install pyindia
```

#### Running Tests

```bash
python -m unittest discover -s tests
```
### Usage
This repository makes it easy to access metadata of states and union territories of India.
```python
>>> import india
>>> india.states.AR
OUT: <State: Arunachal Pradesh>
```

You can also lookup whether your input is state or not.
```python
>>> import india
>>> india.states.lookup('arunachal pradesh')
OUT: <State: Arunachal Pradesh>
```

New release enables you to access districts/cities across india with several attributes
```python
>>> import india
>>> india.cities.KA_BLR
OUT: <City: Bangalore>

```

Lookup for cities
```python
>>> import india
>>> india.cities.lookup('Bangalore')
OUT: <City: Bangalore>
```

Check whether the city is capital
```python
>>> import india
>>> india.cities.lookup('Bangalore').iscapital()
OUT: True
```

### Attributes

Attributes of state object

```python
state.name       # name of the state
state.capital    # returns the capital of that state
state.population # returns the population of the state
state.area       # returns the area of state in km^2
state.density    # returns population density per km^2 of state
state.language   # primary official language of the state
```

Attributes of Cities object

```python
city.name       # name of the city
city.state      # returns the state of city
city.population # returns the population of the city
city.area       # returns the area of the district
city.density    # returns population density per km^2 of the city
city.url        # returns official webpage of the city
```

### Release Notes
<li> Added cities of india with there attributes and states they belong to.
<li> Added attributes to state objects including population, area and language.
<li> Added cities string access.
<li> Added states string access.
<li> Added uts string access.
<li> Added Jammu and Kashmir and Ladhakh as Union territories.
super-distributions 0.1.2
Gaussian and Binomial distributions

Thanks for installing super_distributions!

It is a simple package for using some of the most popular distributions such as Gaussian & Binomial Distributions (more incoming)

#Setting it up

After you've installed the package, simply import the package with desired distrubution name like:

------->  from  super_distributions import Gaussian

You can then simply create an object for class Gaussian like:

------>   gaussian_one = Gaussian(mean_value, standard_deviation_value)   // Gaussian(20,3)
          gaussian_one.mean   // 20
          gaussian_one.stdev   // 3

Inbuilt functions for all distributions:   

object.read_data_file()

<!-- Function to read in data from a txt file. The txt file should have
one number (float) per line. The numbers are stored in the data attribute.
				
		Args:
			file_name (string): name of a file to read from
		
		Returns:
			None
		
		""" -->
        
        
object.calculate_mean() 
object.calculate_stdev()
object.pdf()
object.plot_histogram()
object.plot_histogram_pdf()



For any doubts, just write to me at narayansharma275@gmail.com or reach out to me on my LinkedIn - https://www.linkedin.com/in/narayansharma277/
jailconf 0.2.2
Parse and edit your FreeBSD jail.conf file
Keywords: jail.conf

Parse and edit your FreeBSD jail.conf file with python.

Installation
~~~~~~~~~~~~

To install jailconf, simply:

.. code-block:: console

    pip3 install jailconf
    
jailconf requires Python 3.

Examples
~~~~~~~~

.. code:: python

    import jailconf
    
Load the configuration from a path

.. code:: python
   
    conf = jailconf.load('/etc/jail.conf')
    
Load the configuration from a string 
    
.. code:: python   
    
    conf = jailconf.loads(open('/etc/jail.conf').read())
    
Create an empty configuration

.. code:: python
    
    conf = jailconf.JailConf()
    
The configuration is represented as a dictionnary (actually a subclass of OrderedDict).
    
Let's modify some settings.

The quoted strings in the configuration should be passed with the quotes.
For example, to obtain the setting:

.. code:: sh
    
    path = "/var/jail/$name";

you write:

.. code:: python
    
    conf['path'] = '"/var/jail/$name"'
    
The string should be exactly what you want to appear on the right side of the
parameter name in the configuration file.
If you want the value of a parameter to be a quoted string, you pass
a string containing a quoted string.
This allows you to specify what kind of quotes you want to see in the output configuration 
file (single quotes, double quotes, or no quote at all).

.. code:: python
    
    conf['exec.start'] = '"/bin/sh /etc/rc"'
    conf['exec.stop'] = '"/bin/sh /etc/rc.shutdown"'
    
Boolean parameters. To obtain:

.. code:: sh

    exec.clean;
    mount.devfs;

you write:

.. code:: python
    
    conf['exec.clean'] = True
    conf['mount.devfs'] = True
    
Add a jail:

.. code:: python
    
    conf['myjail'] = jailconf.JailBlock([
        ('host.hostname', '"example.com"'),
        ('ip4.addr', ['10.1.1.1', '10.1.1.2', '10.1.1.3'])   
    ])
    
Modify a jail

.. code:: python
    
    conf['myjail']['ip4.addr'] = '192.168.1.2' # this will be rendered as the line: ip4.addr = 192.168.1.2
    
    # To set multiple ips, use a list:
    
    conf['myjail']['ip4.addr'] = ['192.168.1.2', '192.168.1.3']
    
Delete a jail

.. code:: python
    
    del conf['uselessjail']
    
Iterate over jails

.. code:: python
    
    for name, jail_block in conf.jails():
        jail_block['host.hostname'] = '"%s"' % name
        
Output the configuration as a string

.. code:: python
    
    >>> print(conf.dumps())

.. code::

    path = "/var/jail/$name";
    exec.start = "/bin/sh /etc/rc";
    exec.stop = "/bin/sh /etc/rc.shutdown";
    exec.clean;
    mount.devfs;
    myjail {
	    host.hostname = "myjail";
	    ip4.addr = 192.168.1.2, 192.168.1.3;
    }
    
Write the configuration to a file

.. code:: python
    
    conf.write('/etc/jail.conf')


GitHub repo: https://github.com/leforestier/jailconf