semvec/pypi-packages huggingface dataset:
DeepEnsemble 0.0.3
An Wrapper for performing ensembling techniques on deep learning models
Requires Python: >=3.7

<h1 align="center">DEEP ENSEMBLE</h1>

## Description
<p>In the context of deep learning, This library is a wrapper, It allows one to perform ensembling techniques such as Stacked Ensembling, Weighted Ensembling, Ensembling based on Votes.
 This library get in an array of predictions made by different models on which the user wants to perform ensembling and it performs either of the three ensembling technique based on user choice.

- What this does?
    * Gets array of predictions(probabilities)
    * Performs ensembling
    * Returns Score of the metrics(user prefers)
    * Returns Y_predicted resulting due to ensembling
    
- Ensembling Techniques included
    * Weighted Ensembling
    * Ensembling by Voting
    * Stacked Ensembling
    
</p>

## Deep Ensemble

<p>Deep Ensemble is a python package for ensembling the prediciton results. </p>

## How to use:

Step 1:
  Install the libaray

````python
pip install DeepEnsemble
````
Step 2:

  Import the library, and specify the path of the csv file. 
````python
from DeepEnsemble.DeepEnsemble import DeepEnsembler

Ensembler = DeepEnsembler(Y_pred, Y_actual, type=None, predThreshold=0.5, metrics="accuracy_score")
score,Y_pred_ensembled = Ensembler.WeightedEnsembling()

````
Note:
  * Y_pred   - Array of predictions made by models
  * Y_actual - Array of actual Class 
  
  There are some optional parameters that you can specify as listed below,

## Usage:

````python
from DeepEnsemble.DeepEnsemble import DeepEnsembler
Y_Pred = np.array([results of model1],
                  [results of model2],
                  [results of model3],
                   ....)
Y_actual = np.array([actual class])

---Example1:
Ensembler = DeepEnsembler(Y_Pred, Y_actual, type="Weighted", predThreshold=0.6, metrics="cohen_kappa_score")
score,Y_pred_ensembled = Ensembler.WeightedClassifier()

---Example2:
Ensembler = DeepEnsembler(Y_Pred, Y_actual, type="Voting", predThreshold=0.6, metrics="cohen_kappa_score")
score,Y_pred_ensembled = Ensembler.VotingClassifier()

---Example3:
Ensembler = DeepEnsembler(Y_Pred, Y_actual, type="Stacking", predThreshold=0.6, metrics="cohen_kappa_score")
score,Y_pred_ensembled = Ensembler.StackingClassifier()
````

## Parameters

------

| Parameter | Default Value | Limit | Example |
| ------ | ------ | ------ | ------ |
| Y_pred | ***none*** | Provide a array with results of different models. | np.array([results of model1],[results of model2],[results of model3],....) |
| Y_actual | ***,*** | Provide a array with actual class | np.array([actual class])
| type | ***Weighted*** | Weighted, Voted, Stacked |  | 
| predThreshold | ***0.5*** | 0 to 1 | 0.5 | 
| metrics | Sklearn Classification metrics | specify any valid classificaiton metrics from sklearn | "accuracy_score" | 



<h2 align="center"> --- THANK YOU, CHEERS --- </h2>
quickcal 0.0.1
A small package for calculation purpose
Requires Python: >=3.6

# QUICK CALCULATION

This library will help to calculate numbers quickly.

# How to install?
'''
pip install quickcal
'''

# initializing package
'''
from quickcal import QuickCal

num_1 = 16
num_2 = 8

cal = QuickCal(num_1, num_2)
'''

# for addition
'''
cal.addition()
'''

# for substraction
'''
cal.substraction()
'''

# for multiplication
'''
cal.multiplication()
'''

# for division
'''
cal.division()
'''
ddmetrics 0.0.1
DataDog Metrics Collector decorator(s)
Requires Python: >=3.6

# datadog-metrics decorator

A python decorator that handles DataDog Metrics API calls.


## Usage

```python
@DDMetrics.send(my_token)
def get_my_metric(x):
    # 1. get metric data and build the metric payload
    metric = [
        {
            "metric": "something",
            "points": [[time(), 55]],
            "tags": ['test1', 'test2']
        }
    ]
    # 2. return it
    return metric
```
fridayAI 0.0.3
fridayAI
Keywords: assistant,AI,Virtual,python

A virtual assistant to automate windows based systems.
To install:
Run in Command Prompt: pip install fridayAI==<current_version>
Warning: Use the latest release only

Get Started:
This package comes with premade SpeechRecognition and Text-to-Speech systems.
Also it has premade function to get started including Automation(Chrome, Youtube, Windows, Instagram, Twitter, Gmail),
tell your loacation, AI-chatbot, Online-Class-Automation(Only for me), Answer any question, weather prediction, How-to-Do something
and many more.

To Use:
create a new pythonn file and write the following code:
from Fryday import Fryday
Fryday.Fryday()