How to run a simple example

Author: Jeanselme

README.md

@@ -7,6 +7,54 @@ All the other functions contains functions used for the clustering.
 
 ## Dependencies
 This code has been executed with `python3` with the library indicated in `requirements.txt`. Additionaly, it is necessary to have R installed with the library `conclust`.
+For setting up the environment:
+```
+conda create --name clustering
+conda install -f -y -q --name clustering -c conda-forge --file requirements.txt
+conda activate clustering
+pip install pmlb metric-learn==0.4.0 hyperopt
+```
 
 ## How to compare your method ?
 Import your library in `Experiments.ipynb` and select the algorithms that you want to run. The execution of the notebook saves all results in the result folder (indicated in `config.py` with the timestamp indicated when you run the experiments notebook). Copy this timestamp in the `dates` list in `Visualizations.ipynb` to compare the results (you can indicate several dates to compare methods computed at different time). 
+
+## How to run our model on your dataset ?
+
+### Kernel Clustering
+#### Open your data and constraints
+```
+import pandas as pd
+import numpy as np
+
+dname = 'data' # Data name - Used for saving kernels
+ncluster = 3 # Number cluster
+data = pd.read_csv(dname)
+constraint = np.full((len(data), len(data)), 1) # Constraint matrix : +1 if linked, -1 otherwise - Prefer coomatrix
+```
+
+#### Create kernels
+```
+from models.kernel_opt import kernel_clustering
+from kernels.features import produce_kernels, normalize_and_check_kernels
+
+kernels_name = ['rbf', 'sigmoid', 'polynomial', 
+                'laplacian', 'linear']
+kernel_args = {"normalize": "expectation", 
+               "check_method": "trivial", 
+               "clip": True}
+
+names, kernels = produce_kernels(dname, kernels_name, data, n_jobs = n_jobs)
+names, kernels = normalize_and_check_kernels(names, kernels, ncluster, n_jobs = n_jobs, **kernel_args)
+```
+
+#### Run Kmeans and opitmization
+```
+assignment = kernel_clustering(kernels, ncluster, constraint)
+```
+
+### Use Mahalanobius 
+```
+from models.mahalanobis import mahalanobis_bayes_clustering
+
+assignment = mahalanobis_bayes_clustering(data, ncluster, constraint)
+```

requirements.txt

@@ -1,8 +1,9 @@
 numpy
-scikit-learn
+numba
+scikit-learn=0.20.4
 seaborn
 matplotlib
 rpy2
-pmlb
 GPyOpt
-metric-learn==0.4.0
+jupyter
+tqdm