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Step by step installation
Jakub edited this page Mar 19, 2019
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7 revisions
- Python3.5 on Ubuntu 16.04 machine
Install requirements in your Python3.5 environment, (preferably create new environment: virtualenv home-credit -p python3.5
)
pip3 install -r requirements.txt
Clone repository
git clone https://github.com/minerva-ml/open-solution-home-credit.git
Update data directories in the neptune.yaml configuration file, specifically:
project: ORGANIZATION/home-credit # ORGANIZATION is your user-name.
...
# Data
train_filepath: YOUR/PATH/TO/application_train.csv
test_filepath: YOUR/PATH/TO/application_test.csv
bureau_balance_filepath: YOUR/PATH/TO/bureau_balance.csv
bureau_filepath: YOUR/PATH/TO/bureau.csv
credit_card_balance_filepath: YOUR/PATH/TO/credit_card_balance.csv
installments_payments_filepath: YOUR/PATH/TO/installments_payments.csv
POS_CASH_balance_filepath: YOUR/PATH/TO/POS_CASH_balance.csv
previous_application_filepath: YOUR/PATH/TO/previous_application.csv
sample_submission_filepath: YOUR/PATH/TO/sample_submission.csv
experiment_directory: YOUR/PATH/WORKDIR
- Register to the neptune.ml (if you wish to use it)
- Go to the neptune.ml, log in and create project
Home-Credit-Default-Risk
(button at the top left side of the screen).
Run experiment based on LightGBM:
π±
neptune login
neptune run --config configs/neptune.yaml main.py train_evaluate_predict_cv --pipeline_name lightGBM
π
python main.py -- train_evaluate_predict_cv --pipeline_name lightGBM
Collect submit from experiment_directory
specified in the neptune.yaml
Happy training π
check our GitHub organization https://github.com/neptune-ml for more cool stuff π
Kamil & Kuba, core contributors
- chestnut π°: LightGBM and basic features
- seedling π±: Sklearn and XGBoost algorithms and groupby features
- blossom πΌ: LightGBM on selected features
- tulip π·: LightGBM with smarter features
- sunflower π»: LightGBM clean dynamic features
- four leaf clover π: Stacking by feature diversity and model diversity