semvec/pypi-packages huggingface dataset:
dloud-ads 0.0.2
Abstract Data Structures commonly used in CS scenarios. Implemented by Data Loud Labs!

dloud_ads
=========

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Abstract Data Structures commonly used in CS scenarios. Implemented by Data Loud Labs!

Usage
-----

Installation
------------

Requirements
^^^^^^^^^^^^

Compatibility
-------------

Licence
-------

Authors
-------

`dloud_ads` was written by `Pedro Sousa <pjgs.sousa@gmail.com>`_.
skippy 0.1.0
Simplified analysis of sklearn datasets

# The `skippy` python package

Skip the boilerplate of scikit-learn machine learning examples.

## Installation
```bash
pip install skippy
```

## Usage

In a shell environment, you can run `skippy`
with no arguments to perform a Logistic Regression
on the `digits` dataset.

This will produce a 10 x 10 confusion matrix
with the Accuracy Score at the top.

You can also pass arguments to skippy at the command line.

For example, 
```bash
skippy -data diabetes -type linear_model -name Lasso
# Or
skippy -d diabetes -t linear_model -n Lasso
```
will run a linear regression with lasso regularization (L1)
on the `diabetes` dataset.

The `data` argument can be any of
the following built-in scikit-learn datasets:
- Regression
    - `boston`
    - `diabetes`
- Classification
    - `digits`
    - `iris`
    - `wine`
    - `breast_cancer`

The `type` and `name` arguments are
referring to the model type and name from scikit-learn.
The `type` is the submodule, e.g. 
- `linear_model`
- `naive_bayes`
- `ensemble`
- `svm`

while the `name` is the what is actually imported, e.g.
- `LinearRegression`
- `GaussianNB`
- `RandomForestRegressor`
- `SVC`

Simplify code to a single function call per step:
```python
from sklearn.metrics import confusion_matrix, accuracy_score
import skippy as skp

data = skp.get_data('digits')
x_train, x_test, y_train, y_test = skp.split_data(data)

model = skp.get_model(model_type='ensemble',
                      model_name='RandomForestClassifier')

fit = model.fit(x_train, y_train)
skp.pickle_model(filename='digits_rf.pickle', model=fit)
predictions = fit.predict(x_test)

confmat = confusion_matrix(y_true=y_test, y_pred=predictions)
accuracy = accuracy_score(y_true=y_test, y_pred=predictions)

skp.confusion_matrix_plot(cm=confmat,
                          acc=accuracy,
                          filename='digits_rf.png')
```

Or run a whole pipeline with one function:

```python
import skippy as skp

skp.classification(dataset='digits',
                   model_type='ensemble',
                   model_name='RandomForestClassifier',
                   pickle_name='digits_rf.pickle',
                   plot_name='digits_rf.png')
```

For inspiration, look at the example pipelines in the
[pipelines folder](https://github.com/marskar/skippy/tree/master/pipelines)
of the
[skippy repo](https://github.com/marskar/skippy).