| """Error statistics and least-squares fitting shared across the analysis modules. |
| |
| numpy.linalg.lstsq, not statsmodels: fitting by formula string through statsmodels' patsy parser is |
| about 1000x slower in a loop that fits many (solvent, solute, formula) combinations, so every fitting |
| harness here builds a plain design matrix and calls ols_fit directly. Where a module still needs to |
| report a p-value or similar statsmodels-only diagnostic, keep a separate, explicitly-named |
| statsmodels code path as a test oracle, not the harness itself. |
| """ |
| import numpy as np |
|
|
|
|
| def rmse(predicted, actual): |
| predicted = np.asarray(predicted, dtype=float) |
| actual = np.asarray(actual, dtype=float) |
| return float(np.sqrt(np.mean(np.square(predicted - actual)))) |
|
|
|
|
| def mae(predicted, actual): |
| predicted = np.asarray(predicted, dtype=float) |
| actual = np.asarray(actual, dtype=float) |
| return float(np.mean(np.abs(predicted - actual))) |
|
|
|
|
| def median_ae(predicted, actual): |
| predicted = np.asarray(predicted, dtype=float) |
| actual = np.asarray(actual, dtype=float) |
| return float(np.median(np.abs(predicted - actual))) |
|
|
|
|
| def summarize_errors(predicted, actual): |
| """{"n", "rmse", "mae", "median_ae"} for one (predicted, actual) pair.""" |
| predicted = np.asarray(predicted, dtype=float) |
| actual = np.asarray(actual, dtype=float) |
| return { |
| "n": int(predicted.size), |
| "rmse": rmse(predicted, actual), |
| "mae": mae(predicted, actual), |
| "median_ae": median_ae(predicted, actual), |
| } |
|
|
|
|
| def ols_fit(design, response): |
| """Least-squares coefficient vector for `design @ beta ~ response`. Callers build `design` |
| (including any intercept column) and drop non-finite rows themselves -- this is a thin, |
| single-purpose wrapper, not a formula parser.""" |
| design = np.asarray(design, dtype=float) |
| response = np.asarray(response, dtype=float) |
| beta, _residuals, _rank, _sv = np.linalg.lstsq(design, response, rcond=None) |
| return beta |
|
|
|
|
| def linear_fit_1d(x, y): |
| """(intercept, slope) of the least-squares line through (x, y).""" |
| x = np.asarray(x, dtype=float) |
| y = np.asarray(y, dtype=float) |
| design = np.column_stack([np.ones(len(x)), x]) |
| intercept, slope = ols_fit(design, y) |
| return float(intercept), float(slope) |
|
|
