semvec/pypi-packages huggingface dataset:
Feature-Analysis 0.0.4
It performs feature analysis for data preprocessing or usage of data in Machine Learning.
Requires Python: >=3.6

# Feature Analysis: Simple Feature Analysis in Python

It performs feature analysis for data preprocessing or usage of data in Machine Learning.
# Methods

There are four methods used to identify features to remove:

1. Finding Missing Values (find_missing(missing_threshold)) 

   %% Find the features with a fraction of missing values above `missing_threshold`
2. Single Unique Values (find_unique())

    %% Find the features having a single unique value. NaNs do not count as a unique value.
3. Collinear Features (find_collinear(correlation_threshold,one_hot=False))

   %% Finds collinear features based on the `correlation coefficient` between features.
4. Low Importance Features (find_low_impt(cumulative_impt,label))

   %% Finds the lowest importance features not needed to account for `cumulative_importance` fraction
        of the total feature importance from the gradient boosting machine.
        label corresponding to regession or classification

## Usage

Refer to the [testing.py](https://github.com/coolestbnslz/Feature_Analysis/blob/main/testing.py) for how to use the different methods in module.

## Visualizations

The `FeatureAnalysis` methods also includes a number of visualization in each methods mentioned above to inspect 
characteristics of a dataset. 

### Histogram for missing values
![](images/Figure_1.png)

### Histogram for unique values
![](images/Figure_2.png)

### Correlation Heat map
![](images/Figure_3.png)

### Important Features
![](images/Figure_4.png)

### Cumulative Feature Importance
![](images/Figure_5.png)

# Requirements:
Install dependencies just mentioned in requirements.txt by typing command in shell.

`pip install -r requirements.txt`

## Contact

Any questions can be directed to nbansal1_be18@thapar.edu.

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.
pyopenls9 1.1.0
A library to control the Yamaha LS9

# open-ls9-control

Open LS9 Control is a C++ library for controlling the Yamaha LS9 mixer.
There are also Python bindings included known as PyOpenLS9.

It depends on the [Yamaha Network-MIDI driver](https://uk.yamaha.com/en/support/updates/nwd_mac.html) and the port name is "<Device Name> Port 1" where <Device Name> is the name from System Preferences.

### Parameters

A parameter is defined by an element, index and channel, defined in C++ as an aggregate thus:

```
struct Parameter {
  int element;
  int index;
  int channel;
};
```

And in Python as a dataclass thus:

```
@dataclass
class Parameter:
  "Some parameter of the desk"
  element : int
  index : int
  channel : int
```

### The LS9 class

In C++ the LS9 class is constructed with the port name as a std::string_view and exposes the following methods:

- static auto portNames() -> std::vector<std::string>
- void addGlobalCallback(std::function<void(Parameter, int32_t)> callback)
- void addParamCallback(Parameter param, std::function<void(Parameter, int32_t)> callback)
- auto get(Parameter param, std::chrono::milliseconds timeout) -> int32_t
- void set(Parameter param, int32_t value)
- void fade(Parameter param, int32_t value, std::chrono::milliseconds duration, std::chrono::milliseconds timeout)
- auto nextParamTouched() -> Parameter
- auto getChannelName(int ch, std::chrono::milliseconds timeout) -> std::string

The Python class is much the same: any callable can be used where a std::function is expected and an integer number of milliseconds is used where a std::chrono::milliseconds is expected.
It also has the portNames static method which returns a list of strings.