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fix: choose lighter dtype for distance and error stored values #3

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fix: choose lighter dtype for distance and error stored values #3

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12 changes: 8 additions & 4 deletions src/macest/classification/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -205,15 +205,17 @@ def calc_dist_to_neighbours(
neighbours = np.array(
self.graph[cls].knnQueryBatch( # type: ignore
x_star, k=self._num_neighbours, num_threads=num_threads_available
)
),
dtype='float32',
)
class_dist = neighbours[:, 1, :].clip(min=10 ** -15)
class_ind = neighbours[:, 0, :].astype(int)
if self.training_preds_by_class is None:
raise ValueError("training_preds_by_class has already been cached")
class_preds = self.training_preds_by_class[cls]
class_error = np.array(
[class_preds[class_ind[j]] != cls for j in range(x_star.shape[0])]
[class_preds[class_ind[j]] != cls for j in range(x_star.shape[0])],
dtype='bool',
)
else:
if self.distance_to_neighbours is None:
Expand Down Expand Up @@ -507,7 +509,8 @@ def _precompute_neighbours(self) -> PrecomputedNeighbourInfo:
max_neighbours = np.array(
self.model.graph[class_num].knnQueryBatch( # type: ignore
self.x_cal, k=max_nbrs, num_threads=num_threads_available
)
),
dtype='float32',
)
max_dist = max_neighbours[x_cal_len_array, 1]
max_ind = max_neighbours[x_cal_len_array, 0]
Expand All @@ -519,7 +522,8 @@ def _precompute_neighbours(self) -> PrecomputedNeighbourInfo:
[
cls_preds[ind[j].astype(int)] != class_num
for j in range(self.x_cal.shape[0])
]
],
dtype='bool',
) # type: ignore

dist_dict[k] = dist
Expand Down
6 changes: 4 additions & 2 deletions src/macest/regression/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -156,7 +156,8 @@ def calc_nn_dist(self, x_star: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
neighbours = np.array(
self.prec_graph.knnQueryBatch(
x_star, k=self._num_neighbours, num_threads=num_threads_available
)
),
dtype='float32',
)
dist = neighbours[:, 1, :]
ind = neighbours[:, 0, :].astype(int)
Expand Down Expand Up @@ -437,7 +438,8 @@ def _prec_neighbours(self) -> Tuple[Dict[int, np.ndarray], Dict[int, np.ndarray]
max_neighbours = np.array(
self.prec_graph.knnQueryBatch(
self.x_cal, k=int(max_nbrs), num_threads=num_threads_available
)
),
dtype='float32',
)

max_dist = max_neighbours[x_cal_len_array, 1]
Expand Down