semvec/pypi-packages huggingface dataset:
nbtester 0.8.0
Test utilities for Jupyter Notebook file

========
nbtester
========

Test Utility for Jupyter Notebook file.

Loader
======

Jupyter Notebook File (test.ipynb)::

     ...
     "cells": [
      {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
        "collapsed": true
       },
       "outputs": [],
       "source": ["num = 8"]
      }
     ],
     ...

.. code-block:: python

   import unittest
   from nbtester.loader import load_cells


   class Test(TestCase):
       def test_it(self):
          d = {}
          load_cells(d, "test.ipynb")
          self.assertEqual(d['num'], 8)


Supported Magic Commands
========================

* ``% run child.ipynb``
* ``%% ...``
tabular-augmentation 0.0.18
Implementing easy-to-use methods for classical and novel tabular data augmentation and synthesis.
Requires Python: >=3.9

# Description

`tabular_augmentation` contains some classical and novel methods used for data augmentation, making tabular data
augmentation easier, especially for few-shot learning case.

# Usage

SMOTE-based methods

```python
from tabular_augmentation import smote_augmentation
method = 'SVMSMOTE'
x_synthesis, y_synthesis = smote_augmentation(x_few_train, y_few_train, method, seed=seed,
                                              oversample_num=100, positive_ratio=None,
                                              knn_neighbors=3)
tabular_model_test(x_synthesis, y_synthesis, x_test, y_test, model_name='xgb')
```

Mixup-base methods
```python
from tabular_augmentation import mixup_augmentation_with_weight
method = 'vanilla'
x_synthesis, y_synthesis, sample_weight = mixup_augmentation_with_weight(
            x_few_train, y_few_train, oversample_num=200, alpha=1, beta=1, mixup_type=method, seed=seed, rebalanced_ita=1)
tabular_model_test(x_synthesis, y_synthesis, x_test, y_test, model_name='xgb', sample_weight=sample_weight)
```

CTGAN/TVAE-based methods

Methods(CTGAN/TVAE/DeltaTVAE/DiffTVAE) use `sdv_synthesis` function to generate synthetic data, and ConditionalTVAE use `sdv_synthesis_cvae` function
```python
from tabular_augmentation import sdv_synthesis, sdv_synthesis_cvae
method = 'CTGAN'

x_synthesis, y_synthesis = sdv_synthesis(
            x_few_train, y_few_train, method, oversample_num=5000,
            seed=seed, init_synthesizer=True, positive_ratio=0.5,
        )
tabular_model_test(x_synthesis, y_synthesis, x_test, y_test, model_name='xgb')

```

TabDDPM-based methods
```python
from tabular_augmentation import ddpm_synthesis

method = "DDPM"

x_synthesis, y_synthesis = ddpm_synthesis(
            x_few_train, y_few_train, method, oversample_num=5000, seed=seed, init_synthesizer=True, positive_ratio=None, train_steps=10000)
tabular_model_test(x_synthesis, y_synthesis, x_test, y_test, model_name='xgb')

```
# Example
For details, please refer to  [example.ipynb](https://github.com/zhxfei/tabular_augmentation/blob/master/example.ipynb)

# Cite
#### SMOTE
[imbalanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn)

#### MIXUP
[ICLR' 18]mixup: BEYOND EMPIRICAL RISK MINIMIZATION [Mixup](https://github.com/facebookresearch/mixup-cifar10)

[ICLR' 22]Noisy Feature Mixup
[NoisyMixup](https://github.com/erichson/NFM)

[ECCV' 20]Remix: Rebalanced Mixup

#### CTGAN/TVAE
[NIPS' 19]Modeling Tabular data using Conditional GAN
[CTGAN](https://github.com/sdv-dev/CTGAN)

#### TabDDPM
[ICML' 23] TabDDPM: Modelling Tabular Data with Diffusion Models
[TabDDPM](https://github.com/yandex-research/tab-ddpm)
rocketmq-python-cvs 2.1.0.3
RocketMQ Python Client CVS
Keywords: rocketmq

# rocketmq-python-cvs

[![License](https://img.shields.io/badge/license-Apache%202-4EB1BA.svg)](https://www.apache.org/licenses/LICENSE-2.0.html)
[![Build Status](https://travis-ci.org/apache/rocketmq-client-python.svg?branch=master)](https://travis-ci.org/apache/rocketmq-client-python)
[![codecov](https://codecov.io/gh/apache/rocketmq-client-python/branch/ctypes/graph/badge.svg)](https://codecov.io/gh/apache/rocketmq-client-python/branch/ctypes)
[![PyPI](https://img.shields.io/pypi/v/rocketmq-client-python.svg)](https://pypi.org/project/rocketmq-client-python)

RocketMQ Python client, based on [rocketmq-client-cpp](https://github.com/apache/rocketmq-client-cpp), supports Linux and macOS

use librocketmq.so custom 

## Installation

```bash
pip install rocketmq-python-cvs
```

## Usage

### Producer

```python
from rocketmq.client import Producer, Message

producer = Producer('PID-XXX')
producer.set_log_root_path('/opt/app/karl_demo')
producer.set_name_server_address('127.0.0.1:9876')
producer.start()

msg = Message('YOUR-TOPIC')
msg.set_keys('XXX')
msg.set_tags('XXX')
msg.set_body('XXXX')
ret = producer.send_sync(msg)
print(ret.status, ret.msg_id, ret.offset)
producer.shutdown()
```

### PushConsumer

```python
import time

from rocketmq.client import PushConsumer


def callback(msg):
    print(msg.id, msg.body)


consumer = PushConsumer('CID_XXX')
consumer.set_log_root_path('/opt/app/karl_demo')
consumer.set_name_server_address('127.0.0.1:9876')
consumer.subscribe('YOUR-TOPIC', callback)
consumer.start()

while True:
    time.sleep(3600)

consumer.shutdown()

```

## License
[Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0.html) Copyright (C) Apache Software Foundation
galaxyxmlfubar 0.4.16
Galaxy XML generation library fork

Galaxy XML Generation Libraries |Build Status| |PyPI|
=====================================================
Forked from https://github.com/hexylena/galaxyxml to work around a PR
blockage.

These libraries will support building of Tool XML and Tool Dependencies
XML. We'd be happy to support any other XML that Galaxy supports, just
make an issue or PR if you're feeling motivated.

Known Bugs
----------

-  no validation of unique names
-  repeats aren't named properly
-  conditional/whens aren't named properly
-  conditionals not handled in CLI

License
-------

-  Apache License, v2

Changelog
---------
-  0.4.15
   - Convert command to XMLParameter so can accept attributes and add profile with default 22.05
-  0.4.14

   - Fix for TestRepeats (@fubar2)

-  0.4.13

   - Add TestOutputCollection, TestRepeat and tests (thanks @fubar2)

-  0.4.12

   - Correct the ordering of elements for planemo (thanks @fubar2)
   - Properly read stdio on loading tools (thanks @fubar2)

-  0.4.11

   - Update to command line override (thanks @fubar2)

-  0.4.10

   - Allow overriding of command line in support of positional args (thanks @fubar2)

-  0.4.9

   - Fix quoting of text params (thanks @fubar2!)

-  0.4.8

   - Fix travis deploy process
   - Fix testing
   - py36 only

-  0.4.6

   -  Deprecate py2
   -  Wrap version command in CDATA

-  0.4.5

   -  Bug fixes:

      -  Write catched error to logger instead of STDOUT
      -  Fix Travis: install ``xmllint``
      -  Fix Travis: Deal with new ``flake8`` restrictions for
         exceptions

-  0.4.3

   -  Bug fixes:

      -  Allow ``<discover_dataset>`` within ``<data>`` in ``<outputs>``
      -  Allow import of existing XML with no description

-  0.4.2

   -  Add methods to check presence of EDAM and citations (thanks @khillion)

-  0.4.0

   -  Add feature to import existing Galaxy xml

-  0.3.3

   -  @khillion implemented the
      following:

      -  ``<options>`` with ``<filter>`` and ``<column>``
      -  ``<container>`` for ``<requirements>``
      -  Started to add ``<tests>`` section
      -  ``<section>`` in ``<inputs>``
      -  ``<collection>`` with ``<discover_datasets>`` in ``<outputs>``

   -  Please note that ``tool.parameters.OutputParam()`` has been
      changed to ``tool.parameters.OutputData()``

-  0.3.2

   -  configfiles
      (`#8 <https://github.com/hexylena/galaxyxml/pull/8>`__)

-  0.3.0

   -  Travis auto-deploys on new tags
   -  Testing
   -  p3k

-  0.2.3

   -  First widely used/stable version

.. |Build Status| image:: https://travis-ci.org/hexylena/galaxyxml.svg?branch=master
   :target: https://travis-ci.org/hexylena/galaxyxml
.. |PyPI| image:: https://img.shields.io/pypi/v/galaxyxml.svg
   :target: https://pypi.python.org/pypi/galaxyxml/