Core Modules
Morphological Catalog
Supervised Machine Learning
Outlier Detection
Convolutional Neural Networks
Case Studies
Godines & Prescott (2026)
API Reference
API Reference
Index
Index
_
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A
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B
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C
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D
|
E
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F
|
G
|
H
|
I
|
J
|
K
|
L
|
M
|
N
|
O
|
P
|
R
|
S
|
T
|
U
|
V
|
W
|
X
|
Y
|
Z
_
__call__() (pyBIA.optimization.objective_nn method)
(pyBIA.optimization.objective_rf method)
(pyBIA.optimization.objective_xgb method)
_aperture_photometry() (pyBIA.catalog.Catalog method)
_auto_detect_sources() (pyBIA.catalog.Catalog method)
_extract_single_feature() (pyBIA.outlier_detection.Classifier method)
_get_param_names() (pyBIA.feature_selection.BorutaSHAP class method)
_plot_negative() (pyBIA.cnn_model.Classifier method)
_plot_positive() (pyBIA.cnn_model.Classifier method)
_set_style_() (in module pyBIA.ensemble_model)
_subtract_global_background() (pyBIA.catalog.Catalog method)
A
accepted (pyBIA.feature_selection.BorutaSHAP attribute)
activation_conv (pyBIA.cnn_model.Classifier attribute)
activation_dense (pyBIA.cnn_model.Classifier attribute)
AlexNet() (in module pyBIA.cnn_model)
align_error_array() (in module pyBIA.catalog)
all_columns (pyBIA.feature_selection.BorutaSHAP attribute)
amsgrad (pyBIA.cnn_model.Classifier attribute)
annulus_in (pyBIA.catalog.Catalog attribute)
annulus_out (pyBIA.catalog.Catalog attribute)
aperture (pyBIA.catalog.Catalog attribute)
apply_pca (pyBIA.outlier_detection.Classifier attribute)
augment_data (pyBIA.cnn_model.Classifier attribute)
augment_negative() (pyBIA.cnn_model.Classifier method)
augment_positive() (pyBIA.cnn_model.Classifier method)
augmentation() (in module pyBIA.data_augmentation)
B
balance (pyBIA.cnn_model.Classifier attribute)
(pyBIA.ensemble_model.Classifier attribute)
batch_negative (pyBIA.cnn_model.Classifier attribute)
batch_positive (pyBIA.cnn_model.Classifier attribute)
batch_size (pyBIA.cnn_model.Classifier attribute)
best_params (pyBIA.ensemble_model.Classifier attribute)
,
[1]
beta_1 (pyBIA.cnn_model.Classifier attribute)
beta_2 (pyBIA.cnn_model.Classifier attribute)
binomial_H0_test() (pyBIA.feature_selection.BorutaSHAP static method)
bkg (pyBIA.catalog.Catalog attribute)
blend_negative (pyBIA.cnn_model.Classifier attribute)
blend_positive (pyBIA.cnn_model.Classifier attribute)
blending_func (pyBIA.cnn_model.Classifier attribute)
bonferoni_corrections() (pyBIA.feature_selection.BorutaSHAP static method)
boruta_model (pyBIA.ensemble_model.Classifier attribute)
boruta_trials (pyBIA.ensemble_model.Classifier attribute)
BorutaSHAP (class in pyBIA.feature_selection)
borutashap_opt() (in module pyBIA.optimization)
C
calculate_central_moments() (in module pyBIA.image_moments)
calculate_geometrically_centered_moments() (in module pyBIA.image_moments)
calculate_hits() (pyBIA.feature_selection.BorutaSHAP method)
calculate_hu_moments() (in module pyBIA.image_moments)
calculate_legendre_moments() (in module pyBIA.image_moments)
calculate_moments() (in module pyBIA.image_moments)
calculate_rejected_accepted_tentative() (pyBIA.feature_selection.BorutaSHAP method)
calculate_tp_fp() (in module pyBIA.cnn_model)
calculate_zernike_moments() (in module pyBIA.image_moments)
calculate_Zscore() (pyBIA.feature_selection.BorutaSHAP static method)
cat (pyBIA.catalog.Catalog attribute)
Catalog (class in pyBIA.catalog)
Check_if_chose_train_or_test_and_train_model() (pyBIA.feature_selection.BorutaSHAP method)
check_missing_values() (pyBIA.feature_selection.BorutaSHAP method)
check_model() (pyBIA.feature_selection.BorutaSHAP method)
check_X() (pyBIA.feature_selection.BorutaSHAP method)
classification (pyBIA.feature_selection.BorutaSHAP attribute)
Classifier (class in pyBIA.cnn_model)
(class in pyBIA.ensemble_model)
(class in pyBIA.outlier_detection)
clf (pyBIA.cnn_model.Classifier attribute)
(pyBIA.ensemble_model.Classifier attribute)
,
[1]
(pyBIA.outlier_detection.Classifier attribute)
compute_layered_segmentation() (in module pyBIA.catalog)
concat_channels() (in module pyBIA.data_processing)
connectivity (pyBIA.catalog.Catalog attribute)
conv_init (pyBIA.cnn_model.Classifier attribute)
conv_reg (pyBIA.cnn_model.Classifier attribute)
create() (pyBIA.catalog.Catalog method)
(pyBIA.cnn_model.Classifier method)
(pyBIA.ensemble_model.Classifier method)
(pyBIA.outlier_detection.Classifier method)
create_importance_history() (pyBIA.feature_selection.BorutaSHAP method)
create_list() (pyBIA.feature_selection.BorutaSHAP static method)
create_mapping_between_cols_and_indices() (pyBIA.feature_selection.BorutaSHAP method)
create_mapping_of_features_to_attribute() (pyBIA.feature_selection.BorutaSHAP method)
,
[1]
create_shadow_features() (pyBIA.feature_selection.BorutaSHAP method)
create_training_set() (in module pyBIA.data_processing)
crop_image() (in module pyBIA.data_processing)
csv_file (pyBIA.ensemble_model.Classifier attribute)
custom_model() (in module pyBIA.cnn_model)
D
data (pyBIA.catalog.Catalog attribute)
(pyBIA.outlier_detection.Classifier attribute)
data_x (pyBIA.ensemble_model.Classifier attribute)
,
[1]
(pyBIA.optimization.objective_nn attribute)
(pyBIA.optimization.objective_rf attribute)
(pyBIA.optimization.objective_xgb attribute)
data_y (pyBIA.ensemble_model.Classifier attribute)
,
[1]
(pyBIA.optimization.objective_nn attribute)
(pyBIA.optimization.objective_rf attribute)
(pyBIA.optimization.objective_xgb attribute)
data_y_ (pyBIA.ensemble_model.Classifier attribute)
deblend (pyBIA.catalog.Catalog attribute)
decay (pyBIA.cnn_model.Classifier attribute)
dense_init (pyBIA.cnn_model.Classifier attribute)
dense_reg (pyBIA.cnn_model.Classifier attribute)
dice_loss() (in module pyBIA.cnn_model)
E
epochs (pyBIA.cnn_model.Classifier attribute)
error (pyBIA.catalog.Catalog attribute)
evaluate_model() (in module pyBIA.ensemble_model)
explain() (pyBIA.feature_selection.BorutaSHAP method)
exptime (pyBIA.catalog.Catalog attribute)
F
f1_score() (in module pyBIA.cnn_model)
feats_to_use (pyBIA.ensemble_model.Classifier attribute)
,
[1]
feature_history (pyBIA.ensemble_model.Classifier attribute)
,
[1]
feature_importance() (pyBIA.feature_selection.BorutaSHAP method)
fft_energy_feature_extraction() (in module pyBIA.outlier_detection)
field_name (pyBIA.catalog.Catalog attribute)
find_duplicate_features() (in module pyBIA.data_processing)
find_index_of_true_in_array() (pyBIA.feature_selection.BorutaSHAP static method)
find_sample() (pyBIA.feature_selection.BorutaSHAP method)
fit() (pyBIA.feature_selection.BorutaSHAP method)
,
[1]
flag (pyBIA.catalog.Catalog attribute)
flatten_list() (pyBIA.feature_selection.BorutaSHAP static method)
focal_loss() (in module pyBIA.cnn_model)
format_labels() (in module pyBIA.cnn_model)
(in module pyBIA.ensemble_model)
G
generate_matrix() (in module pyBIA.ensemble_model)
generate_plot() (in module pyBIA.ensemble_model)
get_5_percent() (pyBIA.feature_selection.BorutaSHAP static method)
get_5_percent_splits() (pyBIA.feature_selection.BorutaSHAP method)
get_display_limits() (in module pyBIA.catalog)
get_extent() (in module pyBIA.catalog)
get_loss_function() (in module pyBIA.cnn_model)
get_optimizer() (in module pyBIA.cnn_model)
get_params() (pyBIA.feature_selection.BorutaSHAP method)
get_segmentation() (in module pyBIA.catalog)
H
history (pyBIA.cnn_model.Classifier attribute)
,
[1]
history_shadow (pyBIA.feature_selection.BorutaSHAP attribute)
history_x (pyBIA.feature_selection.BorutaSHAP attribute)
hits (pyBIA.feature_selection.BorutaSHAP attribute)
hog_feature_extraction() (in module pyBIA.outlier_detection)
horizontal (pyBIA.cnn_model.Classifier attribute)
hyper_opt() (in module pyBIA.optimization)
I
image_blending() (in module pyBIA.data_augmentation)
image_size (pyBIA.cnn_model.Classifier attribute)
img_num_channels (pyBIA.cnn_model.Classifier attribute)
(pyBIA.outlier_detection.Classifier attribute)
imp_method (pyBIA.ensemble_model.Classifier attribute)
(pyBIA.outlier_detection.Classifier attribute)
importance_measure (pyBIA.feature_selection.BorutaSHAP attribute)
impute (pyBIA.ensemble_model.Classifier attribute)
(pyBIA.outlier_detection.Classifier attribute)
impute_missing_values() (in module pyBIA.optimization)
imputer (pyBIA.ensemble_model.Classifier attribute)
,
[1]
imputers (pyBIA.outlier_detection.Classifier attribute)
,
[1]
invert (pyBIA.catalog.Catalog attribute)
isolation_forest() (pyBIA.feature_selection.BorutaSHAP static method)
J
jaccard_loss() (in module pyBIA.cnn_model)
K
kernel_size (pyBIA.catalog.Catalog attribute)
L
lbp_feature_extraction() (in module pyBIA.outlier_detection)
limit_search (pyBIA.ensemble_model.Classifier attribute)
(pyBIA.optimization.objective_xgb attribute)
load() (pyBIA.cnn_model.Classifier method)
(pyBIA.ensemble_model.Classifier method)
(pyBIA.outlier_detection.Classifier method)
lr (pyBIA.cnn_model.Classifier attribute)
M
make_dataframe() (in module pyBIA.catalog)
make_moments_table() (in module pyBIA.image_moments)
make_table() (in module pyBIA.catalog)
mask_size (pyBIA.cnn_model.Classifier attribute)
max_pixel (pyBIA.cnn_model.Classifier attribute)
(pyBIA.outlier_detection.Classifier attribute)
metric (pyBIA.cnn_model.Classifier attribute)
min_pixel (pyBIA.cnn_model.Classifier attribute)
(pyBIA.outlier_detection.Classifier attribute)
missing_values_y() (pyBIA.feature_selection.BorutaSHAP method)
model (pyBIA.cnn_model.Classifier attribute)
,
[1]
(pyBIA.ensemble_model.Classifier attribute)
,
[1]
(pyBIA.feature_selection.BorutaSHAP attribute)
model_reg (pyBIA.cnn_model.Classifier attribute)
model_train_metrics (pyBIA.cnn_model.Classifier attribute)
model_val_metrics (pyBIA.cnn_model.Classifier attribute)
models (pyBIA.outlier_detection.Classifier attribute)
,
[1]
module
pyBIA
pyBIA.catalog
pyBIA.cnn_model
pyBIA.data_augmentation
pyBIA.data_processing
pyBIA.ensemble_model
pyBIA.feature_selection
pyBIA.image_moments
pyBIA.optimization
pyBIA.outlier_detection
momentum (pyBIA.cnn_model.Classifier attribute)
morph_parameters() (in module pyBIA.catalog)
morph_params (pyBIA.catalog.Catalog attribute)
N
n_classes (pyBIA.optimization.objective_xgb attribute)
n_iter (pyBIA.ensemble_model.Classifier attribute)
negative_class (pyBIA.cnn_model.Classifier attribute)
nesterov (pyBIA.cnn_model.Classifier attribute)
normalize (pyBIA.cnn_model.Classifier attribute)
(pyBIA.outlier_detection.Classifier attribute)
normalize_pixels() (in module pyBIA.data_processing)
npixels (pyBIA.catalog.Catalog attribute)
nsig (pyBIA.catalog.Catalog attribute)
num_images_to_blend (pyBIA.cnn_model.Classifier attribute)
num_masks (pyBIA.cnn_model.Classifier attribute)
O
obj_name (pyBIA.catalog.Catalog attribute)
objective_nn (class in pyBIA.optimization)
objective_rf (class in pyBIA.optimization)
objective_xgb (class in pyBIA.optimization)
opt_cv (pyBIA.cnn_model.Classifier attribute)
(pyBIA.ensemble_model.Classifier attribute)
(pyBIA.optimization.objective_nn attribute)
(pyBIA.optimization.objective_rf attribute)
(pyBIA.optimization.objective_xgb attribute)
optimization_results (pyBIA.ensemble_model.Classifier attribute)
,
[1]
optimize (pyBIA.ensemble_model.Classifier attribute)
optimizer (pyBIA.cnn_model.Classifier attribute)
P
padding (pyBIA.cnn_model.Classifier attribute)
path (pyBIA.cnn_model.Classifier attribute)
,
[1]
(pyBIA.ensemble_model.Classifier attribute)
patience (pyBIA.cnn_model.Classifier attribute)
pca_components (pyBIA.outlier_detection.Classifier attribute)
pcas (pyBIA.outlier_detection.Classifier attribute)
,
[1]
percentile (pyBIA.feature_selection.BorutaSHAP attribute)
plot() (in module pyBIA.data_augmentation)
plot_conf_matrix() (pyBIA.ensemble_model.Classifier method)
plot_feature_opt() (pyBIA.ensemble_model.Classifier method)
plot_hyper_opt() (pyBIA.ensemble_model.Classifier method)
plot_hyper_param_importance() (pyBIA.ensemble_model.Classifier method)
plot_images_grid_2x2() (in module pyBIA.catalog)
plot_objects_segmentation() (in module pyBIA.catalog)
plot_performance() (pyBIA.cnn_model.Classifier method)
plot_roc_curve() (pyBIA.ensemble_model.Classifier method)
plot_tsne() (pyBIA.cnn_model.Classifier method)
(pyBIA.ensemble_model.Classifier method)
positive_class (pyBIA.cnn_model.Classifier attribute)
predict() (pyBIA.cnn_model.Classifier method)
(pyBIA.ensemble_model.Classifier method)
(pyBIA.outlier_detection.Classifier method)
print_params() (in module pyBIA.cnn_model)
process_class() (in module pyBIA.data_processing)
pvalue (pyBIA.feature_selection.BorutaSHAP attribute)
pyBIA
module
pyBIA.catalog
module
pyBIA.cnn_model
module
pyBIA.data_augmentation
module
pyBIA.data_processing
module
pyBIA.ensemble_model
module
pyBIA.feature_selection
module
pyBIA.image_moments
module
pyBIA.optimization
module
pyBIA.outlier_detection
module
R
random_cutout() (in module pyBIA.data_augmentation)
random_skew() (in module pyBIA.data_augmentation)
random_zoom() (in module pyBIA.data_augmentation)
rejected (pyBIA.feature_selection.BorutaSHAP attribute)
remove_features_if_rejected() (pyBIA.feature_selection.BorutaSHAP method)
resize() (in module pyBIA.data_augmentation)
Resnet18() (in module pyBIA.cnn_model)
resnet_block() (in module pyBIA.cnn_model)
results_to_csv() (pyBIA.feature_selection.BorutaSHAP method)
,
[1]
rho (pyBIA.cnn_model.Classifier attribute)
rotation (pyBIA.cnn_model.Classifier attribute)
S
save() (pyBIA.cnn_model.Classifier method)
(pyBIA.ensemble_model.Classifier method)
(pyBIA.outlier_detection.Classifier method)
save_hyper_importance() (pyBIA.ensemble_model.Classifier method)
scale_features (pyBIA.outlier_detection.Classifier attribute)
scaler_type (pyBIA.outlier_detection.Classifier attribute)
scalers (pyBIA.outlier_detection.Classifier attribute)
,
[1]
scoring_metric (pyBIA.ensemble_model.Classifier attribute)
SEED_NO (pyBIA.ensemble_model.Classifier attribute)
,
[1]
(pyBIA.optimization.objective_nn attribute)
(pyBIA.optimization.objective_rf attribute)
(pyBIA.optimization.objective_xgb attribute)
(pyBIA.outlier_detection.Classifier attribute)
segm_find() (in module pyBIA.catalog)
set_params() (pyBIA.feature_selection.BorutaSHAP method)
shift (pyBIA.cnn_model.Classifier attribute)
signed_log_transform() (in module pyBIA.data_processing)
size (pyBIA.catalog.Catalog attribute)
skew_angle (pyBIA.cnn_model.Classifier attribute)
standardize_data() (in module pyBIA.optimization)
statistical_feature_extraction() (in module pyBIA.outlier_detection)
store_feature_importance() (pyBIA.feature_selection.BorutaSHAP method)
Strawman_imputation() (in module pyBIA.optimization)
subtract_background() (in module pyBIA.catalog)
T
tentative (pyBIA.feature_selection.BorutaSHAP attribute)
test_features() (pyBIA.feature_selection.BorutaSHAP method)
threshold (pyBIA.catalog.Catalog attribute)
to_dictionary() (pyBIA.feature_selection.BorutaSHAP static method)
Train_model() (pyBIA.feature_selection.BorutaSHAP method)
U
update_importance_history() (pyBIA.feature_selection.BorutaSHAP method)
use_gpu (pyBIA.cnn_model.Classifier attribute)
V
val_negative (pyBIA.cnn_model.Classifier attribute)
val_positive (pyBIA.cnn_model.Classifier attribute)
verbose (pyBIA.cnn_model.Classifier attribute)
vertical (pyBIA.cnn_model.Classifier attribute)
VGG16() (in module pyBIA.cnn_model)
W
wavelet_energy_feature_extraction() (in module pyBIA.outlier_detection)
weighted_binary_crossentropy() (in module pyBIA.cnn_model)
X
x (pyBIA.catalog.Catalog attribute)
X_boruta (pyBIA.feature_selection.BorutaSHAP attribute)
Y
y (pyBIA.catalog.Catalog attribute)
Z
zernike_radial_polynomial() (in module pyBIA.image_moments)
zoom_range (pyBIA.cnn_model.Classifier attribute)
zp (pyBIA.catalog.Catalog attribute)