diff options
Diffstat (limited to 'gensvm/gridsearch.py')
| -rw-r--r-- | gensvm/gridsearch.py | 55 |
1 files changed, 32 insertions, 23 deletions
diff --git a/gensvm/gridsearch.py b/gensvm/gridsearch.py index fbb9168..d62ab1d 100644 --- a/gensvm/gridsearch.py +++ b/gensvm/gridsearch.py @@ -99,6 +99,23 @@ class _MockEstimator(ClassifierMixin): return self.predictions +def _wrap_score(y_pred, y_true, scorers, is_multimetric): + start_time = time.time() + results = {} + # we use -1 to signify missing predictions because numpy has no integer NaN + if np.any(y_pred < 0): + if is_multimetric: + for name in scorers: + results[name] = np.nan + else: + results["score"] = np.nan + else: + estimator = _MockEstimator(y_pred) + results = _score(estimator, None, y_true, scorers, is_multimetric) + score_time = time.time() - start_time + return results, score_time + + def _format_results( results, cv_idx, @@ -136,46 +153,38 @@ def _format_results( # Out must be a list of dicts of size n_params x n_splits that iterates # over the params in the list and for each param iterates over the splits. - for param, duration, predictions in zip( - results["params"], results["duration"], results["predictions"] + for param, durations, predictions in zip( + results["params"], results["durations"], results["predictions"] ): - for test_idx in np.unique(cv_idx): + fit_times = durations + is_missing = np.any(np.isnan(durations)) + for test_idx in sorted(np.unique(cv_idx)): ret = [] score_time = 0 if return_train_score: train_pred = predictions[cv_idx != test_idx,] y_train = true_y[cv_idx != test_idx,] - train_mock = _MockEstimator(train_pred) - start_time = time.time() - train_scores = _score( - train_mock, None, y_train, scorers, is_multimetric + train_score, score_t = _wrap_score( + train_pred, y_train, scorers, is_multimetric ) - score_time += time.time() - start_time - ret.append(train_scores) + score_time += score_t + ret.append(train_score) test_pred = predictions[cv_idx == test_idx,] y_test = true_y[cv_idx == test_idx,] - test_mock = _MockEstimator(test_pred) - start_time = time.time() - test_scores = _score( - test_mock, None, y_test, scorers, is_multimetric + test_score, score_t = _wrap_score( + test_pred, y_test, scorers, is_multimetric ) - score_time += time.time() - start_time - ret.append(test_scores) + score_time += score_t + ret.append(test_score) if return_n_test_samples: ret.append(len(y_test)) if return_times: - # Note, the C library returns the duration for a task (i.e. all - # splits). The _skkl_format_cv_results() computes the mean of - # the values, which should represent the average time per - # split. To compute this correctly, we here divide by the - # number of splits. Since we calculate the mean later, the mean - # is still correct, but this is not the exact fit_time for this - # fold. - fit_time = duration / n_splits + fit_time = fit_times[test_idx] + score_time = np.nan if is_missing else score_time ret.extend([fit_time, score_time]) if return_parameters: ret.append(param) |