semvec/pypi-packages huggingface dataset:
buildbot-mattermost-reporter 0.2.0
Mattermost status reporter for Buildbot
Requires Python: >=3.8

# Mattermost status reporter for Buildbot

Install:

```
pip install buildbot-mattermost-reporter
```

Usage example in Buildbot `master.cfg`:

```python
from buildbot.plugins import reporters

c['services'].append(
    reporters.MattermostStatusPush(
        webhook_url="https://mattermost.example.com/hooks/hookidstring",
    ),
)
```

Parameters to `MattermostStatusPush`:

```
MattermostStatusPush(
    webhook_url, channel=None, icon_url=None, icon_emoji=None,
    timezone=None, username_fn=None, buildstatusgenerator_kwargs=None,
    generators=None,
    **kwargs)
```

* `webhook_url` (*string*): Mattermost Webhook URL given when webhook is
  created
* `channel` (*string*): See [Mattermost incoming webhooks docs][]
* `icon_url` (*string*): See [Mattermost incoming webhooks docs][]
* `icon_emoji` (*string*): See [Mattermost incoming webhooks docs][]
* `timezone` (*datetime.tzinfo*): Convert timestamps in messages to a timezone,
  if set. Otherwise timestamps are UTC. Concrete `tzinfo` objects could be
  created with [dateutil][], which Buildbot has as dependency.
* `username_fn` (*callable*): Callable that takes a string argument, which is
  one of the individuals in the builds *owners* property, and returns a
  Mattermost username. The username is used for failing builds, where the
  message includes a line like `@owner1, @owner2, ...`. By default a function
  is used that assumes that each owner is an e-mail address and returns the
  part before @, i.e. `user@example.com` returns `user`.
  An example where failing builds would notify the whole channel instead would
  be `username_fn=lambda _: "channel"`.
* `buildstatusgenerator_kwargs` (*dict*): Any extra keyword arguments to
  `reporters.BuildStatusGenerator`, e.g.
  `buildstatusgenerator_kwargs={'report_new': True}`.
* `generators` (*list*): To override generator created by
  `MattermostStatusPush` and format Mattermost messages in a custom manner.
* `**kwargs`: Any extra arguments to `reporters.HttpStatusPush`.

[Mattermost incoming webhooks docs]: https://developers.mattermost.com/integrate/webhooks/incoming/
[dateutil]: https://pypi.org/project/python-dateutil/
get-ua 20231212.3
Simple library for generating random user agents

# get_ua

## Overview

`get_ua` is a Python library for generating random user agents. It provides a simple interface to retrieve user agents based on different criteria such as browser, operating system, and device.

## Installation

You can install the library using pip:

```bash
pip install get-ua
```

## Usage
```python
import get_ua

# Create an instance of the user agent generator
ua = get_ua.ua()

# Generate a random user agent
random_ua = ua.random()
print("Random User Agent:", random_ua)

# Get a random user agent for a specific browser, OS, and device type
random_agent = ua.random(browser='Chrome', os='Windows', device='desktop')
print("Random User Agent (filtered):", random_agent)

# Generate a random user agent based on browser
chrome_ua = ua.by_browser('Chrome')
print("Chrome User Agent:", chrome_ua)

# Generate a random user agent based on operating system
windows_ua = ua.by_os('Windows')
print("Windows User Agent:", windows_ua)

# Generate a random user agent based on device
iphone_ua = ua.by_device('iPhone')
print("iPhone User Agent:", iphone_ua)

# Get a list of user agents for a specific browser
firefox_agents = ua.list_by_browser('Firefox')
print("Firefox User Agents:", firefox_agents)

# Get a list of user agents for a specific operating system
linux_agents = ua.list_by_os('Linux')
print("Linux User Agents:", linux_agents)

# Get a list of user agents for a specific device
tablet_agents = ua.list_by_device('Tablet')
print("Tablet User Agents:", tablet_agents)

# Get a list of all available user agents
all_agents = ua.list_all()
print("All User Agents:", all_agents)
```

## .random() Possible Filters:
### Browsers:
   * "Chrome"
   * "Edge"
   * "Firefox"
   * "Internet Explorer"
   * "Safari"

### Device Types:
   * "desktop"
   * "mobile"

### Operating Systems:
   * "Android"
   * "iOS"
   * "macOS"
   * "Windows"
df-autosk 0.0.4
A CascadeForestClassifier extension of AutoSklearn classifier
Requires Python: >=3.8

# Deep Forest for Auto-Sklearn

Extension of AutoSklearnClassifier with DF21 - [CascadeForestClassifier](https://deep-forest.readthedocs.io/en/latest/index.html) - a Deep Forest implementantion. Based on an [example extension](https://automl.github.io/auto-sklearn/master/examples/80_extending/example_extending_classification.html#sphx-glr-download-examples-80-extending-example-extending-classification-py) from Auto-Sklearn documentation.

## Requirements

* Linux operating system (requirement of Auto-Sklearn)
* numpy version <= 1.19
* installation of CascadeForestClassifier
* installation of [Auto-Sklearn](https://automl.github.io/auto-sklearn/master/installation.html)
* input variables have to be converted to numeric without missing values 
* output variables also should be converted to numeric values (you can use sklearn LabelEncoder)

## Example use

```
# import libraries
import sklearn.metrics
import autosklearn.classification
import autosklearn.pipeline.components.classification


# import DFClassifier
from df_autosk.df_autosk import DFClassifier

# add DFClassifier to autosklearn classifier
autosklearn.pipeline.components.classification.add_classifier(DFClassifier)

# initialize and get hyperparameter search space
cs = DFClassifier.get_hyperparameter_search_space()
print(cs)

# load the dataset
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# use the classifier
clf = autosklearn.classification.AutoSklearnClassifier(
        	time_left_for_this_task=5400,
        	include={"classifier": ['DFClassifier']},
        	initial_configurations_via_metalearning=0,
        	memory_limit = 102400,
                # Not recommended for a real implementation
               	smac_scenario_args={"runcount_limit": 2}
                )

clf.fit(X_train, y_train)

#get result
y_pred = clf.predict(X_test)
print("accuracy: ", sklearn.metrics.accuracy_score(y_pred, y_test))
```
Example based on an [example extension](https://automl.github.io/auto-sklearn/master/examples/80_extending/example_extending_classification.html#sphx-glr-download-examples-80-extending-example-extending-classification-py) from Auto-Sklearn documentation.