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fima5
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linting fixes
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obsidian/experiment/advanced_design.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -33,7 +33,7 @@ def __init__(
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- (low, high, "geometric"): Geometric spacing (doubling)
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- (low, high, "logarithmic"): Logarithmic spacing (powers of 10)
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- [value1, value2, ...]: Custom list of specific levels to sample from
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- {'levels': [value1, value2, ...], 'biases': [weight1, weight2, ...]}:
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- {'levels': [value1, value2, ...], 'biases': [weight1, weight2, ...]}:
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Custom levels with optional biases/weights for non-uniform sampling.
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Biases will be normalized to sum to 1.0 if they don't already.
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:param conditional_subparameters: A dictionary containing the conditional subparameters for the design.
@@ -303,7 +303,7 @@ def sample_continuous_lhs(continuous_params, n_samples, seed):
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# Use biased sampling with inverse transform method
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biases = np.array(biases)
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if len(biases) != len(possible):
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raise ValueError(f"Length of biases ({len(biases)}) must match length of levels ({len(possible)}) for parameter '{key}'")
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raise ValueError(f"Bias length ({len(biases)}) must match levels length ({len(possible)}) for parameter '{key}'")
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if not np.isclose(sum(biases), 1.0):
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biases = biases / np.sum(biases)
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@@ -797,7 +797,7 @@ def calculate_max_mixed_correlation(design, continuous_keys, categorical_keys, s
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:param design: The design DataFrame
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:param continuous_keys: List of continuous parameter names
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:param categorical_keys: List of categorical parameter names
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:param categorical_keys: List of categorical parameter names
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:param subparam_mapping: Dictionary mapping categorical variables to their subparameters
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:return: Maximum absolute mixed correlation value
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"""

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