Source code for dea_tools.validation

## validation.py
"""
Tools for validating outputs and producing accuracy assessment metrics.

License: The code in this notebook is licensed under the Apache License,
Version 2.0 (https://www.apache.org/licenses/LICENSE-2.0). Digital Earth
Australia data is licensed under the Creative Commons by Attribution 4.0
license (https://creativecommons.org/licenses/by/4.0/).

Contact: If you need assistance, please post a question on the Open Data
Cube Slack channel (http://slack.opendatacube.org/) or on the GIS Stack
Exchange (https://gis.stackexchange.com/questions/ask?tags=open-data-cube)
using the `open-data-cube` tag (you can view previously asked questions
here: https://gis.stackexchange.com/questions/tagged/open-data-cube).

If you would like to report an issue with this script, you can file one
on GitHub (https://github.com/GeoscienceAustralia/dea-notebooks/issues/new).

Last modified: April 2023
"""

import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from math import sqrt
from scipy import stats




def eval_metrics(x, y, round=3, all_regress=False):
    """
    Calculate a set of common statistical metrics
    based on two input actual and predicted vectors.

    These include:
        - Pearson correlation
        - Root Mean Squared Error
        - Mean Absolute Error
        - R-squared
        - Bias
        - Linear regression parameters (slope,
          p-value, intercept, standard error)

    Parameters
    ----------
    x : numpy.array
        An array providing "actual" variable values
    y : numpy.array
        An array providing "predicted" variable values
    round : int
        Number of decimal places to round each metric
        to. Defaults to 3
    all_regress : bool
        Whether to return linear regression p-value,
        intercept and standard error (in addition to
        only regression slope). Defaults to False

    Returns
    -------
    A pandas.Series containing calculated metrics
    """

    # Create dataframe to drop na
    xy_df = pd.DataFrame({"x": x, "y": y}).dropna()

    # Compute linear regression
    lin_reg = stats.linregress(x=xy_df.x, y=xy_df.y)

    # Calculate statistics
    stats_dict = {
        "Correlation": xy_df.corr().iloc[0, 1],
        "RMSE": sqrt(mean_squared_error(xy_df.x, xy_df.y)),
        "MAE": mean_absolute_error(xy_df.x, xy_df.y),
        "R-squared": lin_reg.rvalue**2,
        "Bias": (xy_df.y - xy_df.x).mean(),
        "Regression slope": lin_reg.slope,
    }

    # Additional regression params
    if all_regress:
        stats_dict.update(
            {
                "Regression p-value": lin_reg.pvalue,
                "Regression intercept": lin_reg.intercept,
                "Regression standard error": lin_reg.stderr,
            }
        )

    # Return as
    return pd.Series(stats_dict).round(round)