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Click here to download the full example code

Extended ScoreCard

# mkdocs_gallery_thumbnail_path = 'images/example_thumnail.png'

import numpy as np

from krisi.evaluate import Metric, MetricCategories, SampleTypes, ScoreCard

target, predictions = np.random.rand(100), np.random.rand(100)

sc = ScoreCard(
    y=target,
    predictions=predictions,
    model_name="<your_model_name>",
    dataset_name="<your_dataset_name>",
    sample_type=SampleTypes.insample,
)

""" Predefined metrics """
sc.evaluate(defaults=True)

""" Adding a new metric """
calculated_metric_example = (target - predictions).mean()

sc["own_metric"] = calculated_metric_example
# or
sc.another_metric = calculated_metric_example * 2.0
# or
sc["yet_another_metric"] = Metric(
    name="A new, own Metric",
    category=MetricCategories.residual,
    result=calculated_metric_example * 3.0,
    parameters={"hyper_1": 5.0},
)

# Updating a metric
sc.yet_another_metric = dict(info="Giving description to a metric")

""" Print scorecard summary """
sc.print("extended", with_info=True)

Total running time of the script: ( 0 minutes 0.000 seconds)

Download Python source code: scorecard_extended.py

Download Jupyter notebook: scorecard_extended.ipynb

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