Post Client b2b pricing (WTP model)

Model Training:

• Iterates through countries and configurations:
  • For each country and configuration combination, it:
    • Sets the current configuration
    • Prints progress (e.g., "Country 1/1 - Config 1/1")
    • Prepares the dataset:
      • Takes data for the current country from data_dict
      • If cfg.sample is set, samples a subset of data
      • Cleans the dataset using _clean_dataset (removes outliers, handles missing values)
        • Data Filtering:
          • Removes rows where discount is outside the valid range (clip_min to clip_max)
          • Removes rows with missing acceptance values (accepted.notna())
          • If a country is specified, filters to only that country's data
          • Removes duplicate rows
        • Column Validation:
          • Checks three types of columns:
            • bin_cols: Binary columns
            • cat_cols: Categorical columns
            • num_cols: Numerical columns
          • Issues warnings for any missing columns
          • Creates new lists containing only the columns that exist in the data
        • Returns:
          • Cleaned dataset
          • List of validated columns (bcn_cols) for each type (binary, categorical, numerical)
    • Splits data into training and test sets using split_trn_tst
    • Augments the training data:
      • Uses augment_datato create additional synthetic training examples
      • This helps improve model robustness
    • Trains the model:
      • Splits data into features (X) and target (y)
      • Determines number of features after preprocessing
      • Calls _train_model which:
        • Creates an Optuna study for hyperparameter optimization
        • Trains an XGBoost classifier with the best parameters
        • Returns the trained model and study results
    • Saves results (if save_immediately_to is provided):
      • Creates directory structure for results
      • Saves:
        • Classifier evaluation metrics
        • ROM evaluation metrics
        • Model configuration
        • Evaluation details
  • The results are stored in the trainer's internal dictionaries:
    • elf.models: Trained models
    • self.studies: Optimization study results
    • self.data: Training/test data splits
    • Various evaluation dataframes The key feature is that it handles the entire pipeline from data preparation through training to evaluation and result storage, with support for multiple countries and configurations.

pricing

Overview

This is your new Kedro project, which was generated using kedro 0.19.11.

Take a look at the Kedro documentation to get started.

Rules and guidelines

In order to get the best out of the template:

• Don't remove any lines from the .gitignore file we provide
• Make sure your results can be reproduced by following a data engineering convention
• Don't commit data to your repository
• Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in conf/local/

How to install dependencies

Declare any dependencies in requirements.txt for pip installation.

To install them, run:

pip install -r requirements.txt

How to run your Kedro pipeline

You can run your Kedro project with:

kedro run

How to test your Kedro project

Have a look at the file src/tests/test_run.py for instructions on how to write your tests. You can run your tests as follows:

pytest

You can configure the coverage threshold in your project's pyproject.toml file under the [tool.coverage.report] section.

Project dependencies

To see and update the dependency requirements for your project use requirements.txt. You can install the project requirements with pip install -r requirements.txt.

Further information about project dependencies

How to work with Kedro and notebooks

Note: Using kedro jupyter or kedro ipython to run your notebook provides these variables in scope: context, 'session', catalog, and pipelines.

Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run pip install -r requirements.txt you will not need to take any extra steps before you use them.

Jupyter

To use Jupyter notebooks in your Kedro project, you need to install Jupyter:

pip install jupyter

After installing Jupyter, you can start a local notebook server:

kedro jupyter notebook

JupyterLab

To use JupyterLab, you need to install it:

pip install jupyterlab

You can also start JupyterLab:

kedro jupyter lab

IPython

And if you want to run an IPython session:

kedro ipython

How to ignore notebook output cells in git

To automatically strip out all output cell contents before committing to git, you can use tools like nbstripout. For example, you can add a hook in .git/config with nbstripout --install. This will run nbstripout before anything is committed to git.

Note: Your output cells will be retained locally.

Package your Kedro project

Further information about building project documentation and packaging your project