semvec/pypi-packages huggingface dataset:
pdp-lgtm 0.1.5
party of dope pajamas looks good to me.
License: MIT

# pdp-lgtm
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party of dope pajamas looks good to me.

# summary
this package provides a cli tool `pdp`, it composes commands from predefined words and arguments

# install
    pip install pdp-lgtm
needs tk to run, make sure os specific python-tk connector is installed.

# example
![demo](https://media.giphy.com/media/MFNwY4VbCfHWnxZYjp/giphy.gif)

- run `pdp`
- flip to git tab, press `git` then `fetch --prune`
- press enter

# configuration

edit `~/.pdpconfig.yaml` to add tabbed sections and change width / height. config file is auto generated after first run. history is saved as `~/.pdphistory.yaml`.

# actions

- click a word to append it to the entries.
- `enter`: print and run command.
- `esc`: print command but don't run.
- `backspace`: on an empty entry to remove it.
- `tab`: on an entry to move to the next one.
- `up`: on an entry to increment if it's an integer.
- `down`: on an entry to decrement if it's an integer.
- `shift` or other special keys: flip tab.

# develop
to release make a tag like v0.1 that matches package version

    python3 setup.py sdist
    python3 -m twine upload dist/*
sklearn-surrogatesearchcv 0.1.3
Surrogate adaptive randomized search for hyper parametersin sklearn.
License: MIT

# Surrogate Search CV
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This package implements a randomized hyper parameter search for sklearn (similar to `RandomizedSearchCV`) but utilizes surrogate adaptive sampling from pySOT. Use this similarly to GridSearchCV with a few extra paramters.

## Usage

```
pip install sklearn-surrogatesearchcv
```

The interface is unimaginative, stylistically similar to `RandomizedSearchCV`.

```
class SurrogateSearchCV(object):
    """Surrogate search with cross validation for hyper parameter tuning.
    """

    def __init__(self, estimator, n_iter=10, param_def=None, refit=False,
                 **kwargs):
        """
        :param estimator: estimator
        :param n_iter: number of iterations to run (default 10)
        :param param_def: list of dictionaries, e.g.
            [
                {
                    'name': 'alpha',
                    'integer': False,
                    'lb': 0.1,
                    'ub': 0.9,
                },
                {
                    'name': 'max_depth',
                    'integer': True,
                    'lb': 3,
                    'ub': 12,
                }
            ]
        :param **: every other parameter is the same as GridSearchCV
        """
```

The result can be found in the following properties of the class instance after running.

```
params_history_
score_history_
best_params_
best_score_
```

For a complete example, please refer to `src/test/test_basic.py`.

## Resources

A slide about role of surrogate optimization in ml. [link](https://www.slideshare.net/TimTan2/machine-learning-vs-traditional-optimization)