semvec/pypi-packages huggingface dataset:
MLAC 0.2
Code for comparing different machine learning algorithms for binary classification.
License: MIT
Keywords: Machine Learning,Binary Classification,SciKit-learn,neural networks


# Overview
MLAC is available on PyPi and can be installed via
```none
pip install MLAC
```
This package provides the functionality to quickly compare seven types of feature extraction algorithms and seven types of classifiers. In total there are 49 unique algorithms which can be defined from these FE and classifier algorithms, using the Sci-Kit learn pipeline and grid search functions. Included are two neural networks: an auto-encoder and vanilla fully connected network. The parameters in these are found using the HyperBand algorithm, provided in the KerasTuner package. When a neural network is included in the pipeline, an initial search is performed over the hyper-parameter space using a low number of epochs, patience and factor; this is increased at the end of the search to provide a finer search. This process facilitates quickly determining a good set of hyper-parameters. Included is the ability to see the different hyper-parameter values selected, and whether these are at the bounds of the defined search.    


[pypi]:  https://pypi.org/project/MVPR/

# Example
Import data:
```
data_input = np.random.randn(3000, 1)
ind = np.where(data_input < 0)
data_output = np.zeros(np.shape(data_input))
data_input = data_input + np.random.randn(3000, 1)*0.2 # add noise
data_output[ind] = 1

test_input = np.random.randn(300, 1)
ind = np.where(test_input < 0)
test_output = np.zeros(np.shape(test_input))
test_input = test_input + np.random.randn(300, 1)* 0.2 # add noise
test_output[ind] = 1
```
This looks like:
![data](https://user-images.githubusercontent.com/60707891/131995760-2e2734ca-161b-4482-b758-f4c4d03c8858.png)

We want to find the best ML algorithm that can seperate out this data and classify it:

```
CD_class = CDA.Parameter_Search(
            data_input,
            data_output,
        )
```
To see the available algorithms:
```
keys_FE = CD_class.keys_FE
keys_CA = CD_class.keys_CA
```
Finally:
```
score_arr = []
for i in keys_FE:
   for j in keys_CA:
       CD_class.FE = i
       CD_class.CA = j
       CD_class.trained_model()
       score, predictions = CD_class.predict(test_input, test_output)
       score_arr.append(score)
```
The results are save automatically for the training data:

![image](https://user-images.githubusercontent.com/60707891/131996611-4167bdc2-4533-401e-aa17-cce9bcb5ac66.png)

We can see the results of a specific hyper-parameter search:

![image](https://user-images.githubusercontent.com/60707891/131996887-3e7db117-4453-443d-9ae6-8427adb3f1de.png)

If you want to save the data, you can pull the default filepath:

```
filepath = CD_class.filepath
df = pd.DataFrame([keys_FE,keys_CA,score_arr]).transpose()
df.columns = ["FE", "CA", "Accuracy"]
df.to_excel(filepath + "\\algorithm_performance_test.xlsx", index=False)
```

see:

![image](https://user-images.githubusercontent.com/60707891/131998255-f9143ba2-0af8-4385-8715-6d66a8aca74c.png)
geo1 0.0.0
locates ip by city
License: MIT

# ip-locator
locates city of the passed ip using tkinter as the UI
"""Disclaimer this for educational purposes only I am not responsible for your actions
by using this program you accept sole responsibility

developed by clive hunt
"""
The GeoLiteCity.dat file is from MaxMind and was downloaded before the the new privacy rules 

How to use enter ip in IPv4 format click submit
thats it done
ip-locator-gui01 0.0.0
locates ip by city
License: MIT

# ip-locator
locates city of the passed ip using tkinter as the UI
"""Disclaimer this for educational purposes only I am not responsible for your actions
by using this program you accept sole responsibility

developed by clive hunt
"""
The GeoLiteCity.dat file is from MaxMind and was downloaded before the the new privacy rules 

How to use enter ip in IPv4 format click submit
thats it done
django-ingredient-field 1.4.2
A Django app providing ingredient handling.
License: MIT
Requires Python: >=3.6

=======================
django-ingredient-field
=======================

Quick start
-----------

1. Add "dj_ingredient_field" to your INSTALLED_APPS setting like this::

    INSTALLED_APPS = [
        ...
        'dj_ingredient_field',
    ]

2. To use lazy loaded widgets include this module in your urlpatterns::

    urlpatterns = [
        path('cooking/', include('dj_ingredient_field.urls')),
        ...
    ]

3. That's it!

Usage
-----

Simply add the fields you need to your model::

   from dj_ingredient_field import IngredientField, MeasurementUnitField

   class MyModel(models.Model):
      ingredient = IngredientField()
      unit = MeasurementUnitField()

The fields map to Ingredient and MeasurementUnit classes::

   from dj_ingredient_field import IngredientName, Ingredient, MeasurementUnit, INGREDIENT_UNITS

   model.ingredient = Ingredient(IngredientName.I_ARUGULA)
   model.unit = MeasurementUnit(**INGREDIENT_UNITS["Killogram"])

All the available ingredients can be found in the ``IngredientName`` enum

Ingredients and units can be customized, see 'Settings'

Documentation 
-------------
https://django-ingredient-field.readthedocs.io/en/latest