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Feat/add num threads param #2

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d10630f
fix: indentation
FlorentRamb Dec 10, 2021
b9ca631
feat: add num_threads init param
FlorentRamb Dec 10, 2021
1d950df
fix: typing
FlorentRamb Dec 10, 2021
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71 changes: 39 additions & 32 deletions src/macest/classification/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,17 +77,18 @@ class ModelWithConfidence:
"""This class creates a model which returns a prediction and a confidence interval."""

def __init__(
self,
point_pred_model: _ClassificationPointPredictionModel,
x_train: np.ndarray,
y_train: Iterable[int],
macest_model_params: MacestConfModelParams = MacestConfModelParams(),
precomputed_neighbour_info: Optional[PrecomputedNeighbourInfo] = None,
graph: Optional[Dict[int, nmslib.dist.FloatIndex]] = None,
search_method_args: HnswGraphArgs = HnswGraphArgs(),
training_preds_by_class: Optional[Dict[int, np.ndarray]] = None,
verbose_training: bool = True,
empirical_conflict_constant: float = 0.5,
self,
point_pred_model: _ClassificationPointPredictionModel,
x_train: np.ndarray,
y_train: Iterable[int],
macest_model_params: MacestConfModelParams = MacestConfModelParams(),
precomputed_neighbour_info: Optional[PrecomputedNeighbourInfo] = None,
graph: Optional[Dict[int, nmslib.dist.FloatIndex]] = None,
search_method_args: HnswGraphArgs = HnswGraphArgs(),
training_preds_by_class: Optional[Dict[int, np.ndarray]] = None,
verbose_training: bool = True,
empirical_conflict_constant: float = 0.5,
num_threads: Optional[int] = num_threads_available,
):
"""
Init.
Expand All @@ -109,6 +110,7 @@ def __init__(
:param verbose_training: If true, information such as training progress will be shown
:param empirical_conflict_constant: Constant to set confidence conflicting predictions
calculated during calibration
:param num_threads: Number of threads to use to query the HNSW graph
"""
self.point_pred_model = point_pred_model
self.x_train = x_train
Expand All @@ -122,6 +124,7 @@ def __init__(
self.search_method_args = search_method_args
self._check_consistent_search_method_args()
self._check_data_consistent_with_search_args()
self.num_threads = num_threads

self.training_preds_by_class = training_preds_by_class
if training_preds_by_class is None:
Expand Down Expand Up @@ -187,7 +190,7 @@ def build_class_graphs(self) -> Dict[int, nmslib.dist.FloatIndex]:
return self.graph

def calc_dist_to_neighbours(
self, x_star: np.ndarray, cls: int
self, x_star: np.ndarray, cls: int
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
Calculate the distance to nearest neighbours, the index of them within the class graph and
Expand All @@ -204,7 +207,9 @@ def calc_dist_to_neighbours(
self.build_class_graphs()
neighbours = np.array(
self.graph[cls].knnQueryBatch( # type: ignore
x_star, k=self._num_neighbours, num_threads=num_threads_available
x_star,
k=self._num_neighbours,
num_threads=self.num_threads,
)
)
class_dist = neighbours[:, 1, :].clip(min=10 ** -15)
Expand Down Expand Up @@ -239,7 +244,7 @@ def calc_dist_to_neighbours(
return neighbour_info

def calc_linear_distance_error_func(
self, local_distance: np.ndarray, local_error: np.ndarray,
self, local_distance: np.ndarray, local_error: np.ndarray,
) -> Tuple[np.ndarray, np.ndarray]:
"""
Calculate the parametric linear distance function using the local error and distance.
Expand All @@ -260,7 +265,7 @@ def calc_linear_distance_error_func(
return dist, error

def predict_proba(
self, x_star: np.ndarray, change_conflicts: bool = False,
self, x_star: np.ndarray, change_conflicts: bool = False,
) -> np.ndarray:
"""
Compute a confidence score for each class for a given points(s) x_star.
Expand All @@ -286,7 +291,7 @@ def predict_proba(
return relative_conf

def predict_confidence_of_point_prediction(
self, x_star: np.ndarray, change_conflicts: bool = False,
self, x_star: np.ndarray, change_conflicts: bool = False,
) -> np.ndarray:
"""
Estimate a single confidence score, this represents the confidence of the point prediction
Expand All @@ -312,7 +317,7 @@ def predict_confidence_of_point_prediction(
return point_prediction_confidence

def _calc_relative_distance_softmax_normalisation(
self, average_distance_error_func: np.ndarray,
self, average_distance_error_func: np.ndarray,
) -> np.ndarray:
"""
Take a vector of distance functions, we then scale these by the mean distance across
Expand All @@ -332,7 +337,7 @@ def _calc_relative_distance_softmax_normalisation(
return relative_conf

def _renormalise_conf_with_empirical_constant(
self, x_star: np.ndarray, conf_array: np.ndarray
self, x_star: np.ndarray, conf_array: np.ndarray
) -> np.ndarray:
"""
Change conflicting predictions to the empirically learnt constant probability learnt during \
Expand Down Expand Up @@ -374,11 +379,11 @@ def find_conflicting_predictions(self, x_star: np.ndarray) -> np.ndarray:
return conflicting_predictions

def fit(
self,
x_cal: np.ndarray,
y_cal: np.ndarray,
param_range: SearchBounds = SearchBounds(),
optimiser_args: Optional[Dict[Any, Any]] = None,
self,
x_cal: np.ndarray,
y_cal: np.ndarray,
param_range: SearchBounds = SearchBounds(),
optimiser_args: Optional[Dict[Any, Any]] = None,
) -> None:
"""
Fit MACEst model using the calibration data.
Expand Down Expand Up @@ -447,11 +452,11 @@ class _TrainingHelper(object):
"""Class which provides methods used when fitting MACEst model."""

def __init__(
self,
init_conf_model: ModelWithConfidence,
x_cal: np.ndarray,
y_cal: np.ndarray,
param_range: SearchBounds = SearchBounds(),
self,
init_conf_model: ModelWithConfidence,
x_cal: np.ndarray,
y_cal: np.ndarray,
param_range: SearchBounds = SearchBounds(),
):
"""
Init.
Expand Down Expand Up @@ -506,7 +511,9 @@ 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
self.x_cal,
k=max_nbrs,
num_threads=self.model.num_threads,
)
)
max_dist = max_neighbours[x_cal_len_array, 1]
Expand Down Expand Up @@ -573,9 +580,9 @@ def loss(self, params: MacestConfModelParams) -> float:
return expected_calibration_error(self.model.point_preds, self.y_cal, pred_conf)

def fit(
self,
optimiser: Literal["de"] = "de",
optimiser_args: Optional[Dict[Any, Any]] = None,
self,
optimiser: Literal["de"] = "de",
optimiser_args: Optional[Dict[Any, Any]] = None,
) -> ModelWithConfidence:
"""
Fit MACEst model using the calibration data.
Expand Down
11 changes: 9 additions & 2 deletions src/macest/regression/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -78,6 +78,7 @@ def __init__(
prec_point_preds: Optional[np.ndarray] = None,
prec_graph: Optional[nmslib.dist.FloatIndex] = None,
search_method_args: HnswGraphArgs = HnswGraphArgs(),
num_threads: Optional[int] = num_threads_available,
):
"""
Init.
Expand All @@ -95,6 +96,7 @@ def __init__(
:param prec_distance_to_nn: The pre-computed nearest neighbour distances for the calibration and test data
:param prec_ind_of_nn: The pre-computed nearest neighbour indices for the calibration and test data
:param prec_graph: The pre-computed graph to use for online hnsw search
:param num_threads: Number of threads to use to query the HNSW graph
"""
self.model = model
self.x_train = x_train
Expand All @@ -117,6 +119,7 @@ def __init__(
self.search_method_args = search_method_args
self._check_consistent_search_method_args()
self._check_data_consistent_with_search_args()
self.num_threads = num_threads

def predict(self, x_star: np.ndarray) -> np.ndarray:
"""
Expand Down Expand Up @@ -155,7 +158,9 @@ 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
x_star,
k=self._num_neighbours,
num_threads=self.num_threads,
)
)
dist = neighbours[:, 1, :]
Expand Down Expand Up @@ -436,7 +441,9 @@ 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
self.x_cal,
k=int(max_nbrs),
num_threads=self.model.num_threads,
)
)

Expand Down