MLPet

Preprocessing tools for Petrophysics ML projects at Eureka

Installation

• Install the package by running the following (requires python 3.8 or later)

    pip install mlpet
  

Quick start

• Short example for pre-processing data prior to making a regression model:

    from mlpet.Datasets.shear import Sheardata
    # Instantiate an empty dataset object using the example settings and mappings provided
    ds = Sheardata(
            settings="support/settings_shear.yaml", 
            mappings="support/mappings.yaml", 
            folder_path="support/")
    # Populate the dataset with data from a file 
    # (support for multiple file formats and direct cdf data collection exists)
    ds.load_from_pickle("support/data/shear.pkl")
    # The original data will be kept in ds.df_original and will remain unchanged 
    print(ds.df_original.head())
    # Split the data into train-validation sets
    df_train_original, df_valid_original, valid_wells = ds.train_test_split(
            df=ds.df_original, 
            test_size=0.3)
    # Preprocess the data for training
    df_train, train_key_wells, feats = ds.preprocess(df_train_original)
    # Preprocecss accepts some keyword arguments related to various steps 
    # (e.g. the key wells used for normalizing curves such as GR)
    df_valid, valid_key_wells, _ = ds.preprocess(
            df_valid_original, 
            _normalize_curves={'key_wells':train_key_wells})
  

The procedure will be exactly the same for the lithology class. The only difference will be in the "settings". For a full list of possible settings keys see the documentation for the main Dataset class. Make sure that the curve names are consistent with those in the dataset. The mappings will NOT be applied during the load data step.

API Documentation

Full API documentaion of the package can be found under docs/

For developers

• to update the API documentation, from the root directory of the project run

    pip install pdoc
    pdoc --docformat google -o docs mlpet