import pandas as pd
class ValidationError(Exception):
"""Raised when an error related to the validation is encountered.
"""
[docs]def validate_ref_point_with_ideal_and_nadir(
dimensions_data: pd.DataFrame, reference_point: pd.DataFrame
):
validate_ref_point_dimensions(dimensions_data, reference_point)
validate_ref_point_data_type(reference_point)
validate_ref_point_with_ideal(dimensions_data, reference_point)
validate_with_ref_point_nadir(dimensions_data, reference_point)
[docs]def validate_ref_point_with_ideal(
dimensions_data: pd.DataFrame, reference_point: pd.DataFrame
):
validate_ref_point_dimensions(dimensions_data, reference_point)
ideal_fitness = dimensions_data.loc["ideal"] * dimensions_data.loc["minimize"]
ref_point_fitness = reference_point * dimensions_data.loc["minimize"]
if not (ideal_fitness <= ref_point_fitness).all(axis=None):
problematic_columns = ideal_fitness.index[
(ideal_fitness > ref_point_fitness).values.tolist()[0]
].values
msg = (
f"Reference point should be worse than or equal to the ideal point\n"
f"The following columns have problematic values: {problematic_columns}"
)
raise ValidationError(msg)
[docs]def validate_with_ref_point_nadir(
dimensions_data: pd.DataFrame, reference_point: pd.DataFrame
):
validate_ref_point_dimensions(dimensions_data, reference_point)
nadir_fitness = dimensions_data.loc["nadir"] * dimensions_data.loc["minimize"]
ref_point_fitness = reference_point * dimensions_data.loc["minimize"]
if not (ref_point_fitness <= nadir_fitness).all(axis=None):
problematic_columns = nadir_fitness.index[
(nadir_fitness < ref_point_fitness).values.tolist()[0]
].values
msg = (
f"Reference point should be better than or equal to the nadir point\n"
f"The following columns have problematic values: {problematic_columns}"
)
raise ValidationError(msg)
[docs]def validate_ref_point_dimensions(
dimensions_data: pd.DataFrame, reference_point: pd.DataFrame
):
if not dimensions_data.shape[1] == reference_point.shape[1]:
msg = (
f"There is a mismatch in the number of columns of the dataframes.\n"
f"Columns in dimensions data: {dimensions_data.columns}\n"
f"Columns in the reference point provided: {reference_point.columns}"
)
raise ValidationError(msg)
if not all(dimensions_data.columns == reference_point.columns):
msg = (
f"There is a mismatch in the column names of the dataframes.\n"
f"Columns in dimensions data: {dimensions_data.columns}\n"
f"Columns in the reference point provided: {reference_point.columns}"
)
raise ValidationError(msg)
[docs]def validate_ref_point_data_type(reference_point: pd.DataFrame):
for dtype in reference_point.dtypes:
if not ((dtype == int) or (dtype == float)):
msg = (
f"Type of data in reference point dataframe should be int or float.\n"
f"Provided datatype: {dtype}"
)
raise ValidationError(msg)
