In this project we used requirements.txt file. To install the packages please write:

cd brain-cogito-hackathon-2021/
pip install -r requirements.txt

The requirements.txt file includes the following Python libraries installed::

• NumPy
• Pandas
• matplotlib
• scikit-learn
• pandas-profiling
• plotly
• torch
• tqdm
• statsmodels

You will also need to have software installed to run and execute a Jupyter Notebook.

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included.

To run this project you'll need to have access to the dataset files hackathon_kpis_anonymised.csv and relative_distance.csv. All code to recreate our results are in this repository.

'hackathon_kpis_anonymised.csv'
'relative_distance.csv'

These files can be found at Telenor's google drive.

All actions for this step can be found in the preprocessing.py file located at root folder.

from preprocessing import DataPreProcessor
from hierarchical_clustering import HierarchicalClustering
import pandas as pd

path_distance = 'data/relative_distance.csv'
path_harc = 'data/cell_clusters.csv'

# preprocessing the data for hierarchical clustering
hiarc_df = HierarchicalClustering.from_path(path = path_distance).cluster_data().extract_cell_name_from_clusters()

hiarc_df.to_csv(path_harc)

path = 'data/hackathon_kpis_anonymised-1.csv'

# preprocessing by extracting each feature from the cell name. 
# fixing the failure rates, for example 0 calls divided by 0 becomes NaN, this is now changed to 0.
# Process path is using hierarchical clustering data from csv. 

df = DataPreProcessor().read_file(read_kpis_path=path).extract_cell_name_data().fix_failure_rates().process_from_path(path_harc).fetch_data()

print(df.head())

The example above shows how to use every function in our arsenal to preprocess the data given for this assignment.

To run DeepAnT is pretty easy. Configure whether you want to train on cuda or cpu at the top of DeepAnT.py, then run the file.

python DeepAnt.py

To run isolationForest is pretty easy. Go in the jupyter notebook : isolationForest.ipynb file and run each cell from top to down.

jupyter notebook isolationForest.ipynb

To run STD is pretty easy. Go in the jupyter notebook : std-baseline.ipynb file and run each cell from top to down.

jupyter notebook std-baseline.ipynb