1. Move into project folder and install all required dependencies.
### Installation
0. Install your favorite virtual environment and activate it, e.g. conda (recommended) or virtualenv. Run all commands inside your virtual
enviroment.
1. Move into your project folder and install all dependencies.
```bash
pip install-r requirements.txt
```
2. Install the program.
2. Install conML.
```bash
python setup.py install
```
### Documentation
You'll find the documentation at [here](https://git.informatik.uni-leipzig.de/ml-group/conml/python/-/wikis/home).
### Quick Start
#### 1. Define main ingredients
1. Begin by importing the conML module:
```python
importconML
```
2. Now, request the constructor. The constructor needs a list of tuples consisting of an instantiated unsupervised machine learning models from scikit learn library and the corresponding abbreviations. In addition, the type of construction must also be specified. Currently only `conceptual` construction is supported.
3. The second component is the feature selector. The feature selector consists of filter methods and embedded methods. It is important to know that embedded methods are applied as soon as a predefined number of features or samples is exceeded, otherwise filter methods are used.
6. The knowledge search operates on blocks of the data type pandas.DataFrame. It is recommended to pass the blocks with the help of a generator. First lets load the example dataset. You should name the features as «0.0.n», where n is the feature number. T column should contain the timestamps, Sigma and Z should be empty.