semvec/pypi-packages huggingface dataset:
egoist 0.0.4
-
Requires Python: >=3.7

![Python package](https://github.com/podhmo/egoist/workflows/Python%20package/badge.svg) [![PyPi version](https://img.shields.io/pypi/v/egoist.svg)](https://pypi.python.org/pypi/egoist) [![](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/download/releases/3.7.0/) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://black.readthedocs.io/en/stable/)

# egoist

:construction: this project is under construction :construction:

- [getting started](https://github.com/podhmo/egoist/blob/master/docs/00getting-started.md)
- [ja](./docs/ja/README.md)

## installing

```console
$ pip install egoist
```

## features

- go code generation
- general code generation

### go code generation

```console
$ egoist init clikit
level:INFO	message:create .                                	name:egoist.cliL25
$ tree .
.
└── definitions.py

0 directories, 1 file
$ python definitions.py generate
[D]	create	cmd/hello
[F]	create	cmd/hello/main.go
$ go run cmd/hello/main.go -name="world"
hello world
```

��

### general code generation

```console
$ egoist init dirkit
level:INFO	message:create .                                	name:egoist.cliL25
$ tree .
.
├── definitions.py
└── input
    └── hello.tmpl

1 directories, 2 file
$ python definitions.py generate
[D]	create	./output
[F]	create	./output/foo.json
[F]	create	./output/bar.json
[F]	create	./output/boo.json
$ cat ./output/*.json
hello bar
hello boo
hello foo
```

✨
KnnRegressionRobGin 1.0.0
A package for KNN Regression
Requires Python: >=3.6

# KNN Regression Algorithm.
Anyone of you can use this library to do KNN Regression in Google Colab
with a all numeric valued dataset.
[Github Open Source](https://github.com/Mrrobi/python/tree/master/packages/KnnRegression)

## Existing methods
* KnnRegression(path,TargetAtLast) - It takes two parameter first one is a string of the csv file path and the second one is a boolean to specify, the position of the target coloum it target is at last then the value will be True.Returns a object of KnnClassification class
* loadToList() - It returns the loaded dataset as a python list.
* list_split(DataList) - It takes a single parameter the dataset list from the which is the return value of previous method,and it return a tuple containing three list Splited into Train(70%), Validation(15%), Test(15%).
* knn(x_list,y_list,k) - It takes three parameter first one is either validation list or test list, second one is train list and the third one is value of k, It returns the root mean square error.


## using process

### 1st need to add the library
```python
pip install KnnRegressionRobGin
```
### 2nd You must need to mount your google drive if you want to load csv from your drive
```python
from google.colab import drive
drive.mount('/content/gdrive')
# You must need to run this code script at first to mount your drive with colab 
```
[Mount Drive with colab](https://colab.research.google.com/notebooks/io.ipynb)

### 3rd you need to copy the csv file path for further use

### 4th import KnnRegressionRobGin and set the file path and target position
```python
import KnnRegressionRobGin as KNN
KNN = KNN.KnnRegression("/content/sample_data/file_name.csv",True) #set path
```
### 5th load the dataset into a list and split it 
```python
DataList = KNN.loadToList() #Loading the list of given dataset
Train,Validation,Test = KNN.list_split(DataList) # Spliting dataset into three
```
### Run the knn to show the accuracy of the dataset 
```python
RMSE = KNN.knn(Validation,Train,5) #Put Validation list to train or put test list to test.(here k=5)
print(RMSE)
```

## N.B: Its not neccessary to split your dataset using given method(fix size) you could split your dataset by your own custom size as well :)
KnnClassificationRobGin 1.0.0
A package for KNN Classification
Requires Python: >=3.6

# KNN Classification Algorithm.
Anyone of you can use this library to do KNN Classification in Google Colab
with a all numeric valued dataset.
[Github Open Source](https://github.com/Mrrobi/python/tree/master/packages/KnnClassification)

## Existing methods
* KnnClassification(path,TargetAtLast) - It takes two parameter first one is a string of the csv file path and the second one is a boolean to specify, the position of the target coloum it target is at last then the value will be True.Returns a object of KnnClassification class
* loadToList() - It returns the loaded dataset as a python list.
* list_split(DataList) - It takes a single parameter the dataset list from the which is the return value of previous method,and it return a tuple containing three list Splited into Train(70%), Validation(15%), Test(15%).
* knn(x_list,y_list,k) - It takes three parameter first one is either validation list or test list, second one is train list and the third one is value of k, It returns the accuracy of the dataset.


## using process

### 1st need to add the library
```python
pip install KnnClassificationRobGin
```
### 2nd You must need to mount your google drive if you want to load csv from your drive
```python
from google.colab import drive
drive.mount('/content/gdrive')
# You must need to run this code script at first to mount your drive with colab 
```
[Mount Drive with colab](https://colab.research.google.com/notebooks/io.ipynb)

### 3rd you need to copy the csv file path for further use

### 4th import KnnClassificationRobGin and set the file path and target position
```python
import KnnClassificationRobGin as KNN
KNN = KNN.KnnClassification("/content/sample_data/file_name.csv",True) #set path
```
### 5th load the dataset into a list and split it 
```python
DataList = KNN.loadToList() #Loading the list of given dataset
Train,Validation,Test = KNN.list_split(DataList) # Spliting dataset into three
```
### Run the knn to show the accuracy of the dataset 
```python
Accuracy = KNN.knn(Validation,Train,5) #Put Validation list to train or put test list to test.(here k=5)
print(Accuracy)
```

## N.B: Its not neccessary to split your dataset using given method(fix size) you could split your dataset by your own custom size as well :)
html-css-gen 2.2.1
snippet to merge html/css files

This is a simple example package to combine html/css files.
![Image of Yaktocat](https://image-mgt.sgp1.digitaloceanspaces.com/gy/shortlisted.jpg)

**Quick start**

```
from html_css_gen import template
payload = {'1': 'file_A',
           '2': 'file_B',
           '3': 'file_C'
           ...
           ...
           ...
         }
emailobj=template.init_template(**payload)
output=emailobj.combine_element()
print(output)
```
---
**combine_element()**
---

**Description**

Combine all the element in the email object

**Return**

A combined html string output

**Attention**

This is compulsory to be called upon init (for now)

---
**add_parent_html(change_data,]html_tags)**
---
**Description**

	Add a parent tag to the combined html file

-   change_data - the **html file variable in str** to manipulate
-   html_tags - [[start_tag, end_tag]

**Return**

A combined html file with a new wrapper tag

---
**add_customised_css(change_data, css_input)**
---
**Description**

Add on universal css after the html file is created. Ideal for adding code relating to the combined output. The additional css string will be appended at the bottom of the css output

 - **change_data** - the html file variable in str to manipulate
- **css_input**- the customised css snippet you wish to append to your current css

**Return**

A combined html string output with the customised css

---
populate(change_data, **payload,)
---
**Description**

Substitute the values you wish to replace in the html file

- change_data - the html file variable in str to manipulate
- **payload - a dictionary with the key being the label in the html file in which you wish the value to be replaced and the value being the data to replace with

**Additional Info**

1.  Insert {variable name} in the parts of html files which you wishes to have the value replaced

2.  Call the method with a dictionary payload that has the variable name inserted in the html as the key and the value as the value you wish to replace with

---
**export(filename, export_data)**
---
**Description**

Export the combined file into the desired location
1.  filename - refers to the path in which the file is to be exported
2.  export_data - being the html string file you wish to export, output of the method above

**Return**
None

---
**Full working example**
---
```
from html_css_gen import template

payload = {'1': 'fileA',
           '2': 'fileB',
           '3': 'fileC'
           }
emailobj=template.init_template(**payload)
template = email_obj.combine_element()
customised_css = '''@media screen and (min-width: 600px) {
                    body {
                        width: 600px; !important
                        }
                    }'''
template_css = email_obj.add_customised_css(
    change_data=template, css_input=customised_css)
template_html = email_obj.add_parent_html(change_data=template_css,
                                          html_tags=[['<div class="sudo">', '</div>']])
final_data = email_obj.populate(template_html,**{'fieldA': 'valueA', 'fieldB': 'valueB'})
email_obj.export(filename='output/unread_email', export_data=final_data)
```
mlflow-extend 0.1.6
Extend MLflow's functionality
Requires Python: >=3.6

# MLflow Extend

[![Documentation Status](https://readthedocs.org/projects/mlflow-extend/badge/?version=latest)](https://mlflow-extend.readthedocs.io/en/latest/?badge=latest)
[![CI](https://github.com/harupy/mlflow-extend/workflows/CI/badge.svg?event=push)](https://github.com/harupy/mlflow-extend/actions?query=workflow%3ACI)
[![codecov](https://codecov.io/gh/harupy/mlflow-extend/branch/master/graph/badge.svg)](https://codecov.io/gh/harupy/mlflow-extend)
[![Total alerts](https://img.shields.io/lgtm/alerts/g/harupy/mlflow-extend.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/harupy/mlflow-extend/alerts/)
[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/harupy/mlflow-extend.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/harupy/mlflow-extend/context:python)
[![version](https://img.shields.io/pypi/v/mlflow-extend?color=brightgreen)](https://pypi.org/project/mlflow-extend/)
![pyversions](https://img.shields.io/pypi/pyversions/mlflow-extend?color=brightgreen)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
![GitHub](https://img.shields.io/github/license/harupy/mlflow-extend?color=brightgreen)

Extend MLflow's functionality.

## Installation

From PyPI

```bash
pip install mlflow-extend
```

From GitHub (development version)

```
pip install git+https://github.com/harupy/mlflow-extend.git
```

## Example

```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly import graph_objects as go

from mlflow_extend import mlflow

with mlflow.start_run():
    # mlflow native APIs
    mlflow.log_param('param', 0)
    mlflow.log_metric('metric', 1.0)

    ##### new APIs mlflow_extend provides #####

    # flatten dict
    mlflow.log_params_flatten({"a": {"b": 0}})
    mlflow.log_metrics_flatten({"a": {"b": 0.0}})

    # dict
    mlflow.log_dict({'a': 0}, 'dict.json')

    # numpy array
    mlflow.log_numpy(np.array([0]), 'array.npy')

    # pandas dataframe
    mlflow.log_df(pd.DataFrame({'a': [0]}), 'df.csv')

    # matplotlib figure
    fig, ax = plt.subplots()
    ax.plot([0, 1], [0, 1])
    mlflow.log_figure(fig, 'figure.png')

    # plotly figure
    fig = go.Figure(data=[go.Bar(x=[1, 2, 3], y=[1, 3, 2])])
    mlflow.log_figure(fig, 'figure.html')

    # confusion matrix
    mlflow.log_confusion_matrix([[1, 2], [3, 4]])

    # run "mlflow ui" and see the result.
```