def test_get_hilo_activation_examples_many(self):
"""Ensures correct output from _get_hilo_activation_examples.
In this case, many examples are returned.
"""
(these_high_indices,
these_low_indices) = model_activation.get_hilo_activation_examples(
storm_activations=STORM_ACTIVATIONS,
num_low_activation_examples=NUM_LOW_ACTIVATION_EXAMPLES_MANY,
num_high_activation_examples=NUM_HIGH_ACTIVATION_EXAMPLES_MANY)
self.assertTrue(
numpy.array_equal(these_high_indices, HIGH_INDICES_MANY))
self.assertTrue(numpy.array_equal(these_low_indices, LOW_INDICES_MANY))
def test_get_hilo_activations_few(self):
"""Ensures correct output from _get_hilo_activation_examples.
In this case, only few examples are returned.
"""
these_high_indices, these_low_indices = (
model_activation.get_hilo_activation_examples(
storm_activations=STORM_ACTIVATIONS,
num_low_activation_examples=NUM_LOW_ACTIVATIONS_FEW,
num_high_activation_examples=NUM_HIGH_ACTIVATIONS_FEW,
unique_storm_cells=False))
self.assertTrue(numpy.array_equal(these_high_indices,
HIGH_INDICES_FEW))
self.assertTrue(numpy.array_equal(these_low_indices, LOW_INDICES_FEW))
def test_get_hilo_activations_few_unique(self):
"""Ensures correct output from _get_hilo_activation_examples.
In this case, only few examples (from unique storm cells) are returned.
"""
these_high_indices, these_low_indices = (
model_activation.get_hilo_activation_examples(
storm_activations=STORM_ACTIVATIONS,
num_low_activation_examples=NUM_LOW_ACTIVATIONS_FEW,
num_high_activation_examples=NUM_HIGH_ACTIVATIONS_FEW,
unique_storm_cells=True,
full_storm_id_strings=FULL_STORM_ID_STRINGS))
self.assertTrue(
numpy.array_equal(these_high_indices, HIGH_INDICES_FEW_UNIQUE))
self.assertTrue(
numpy.array_equal(these_low_indices, LOW_INDICES_FEW_UNIQUE))
def _run(input_activation_file_name, unique_storm_cells,
num_low_activation_examples, num_high_activation_examples, num_hits,
num_misses, num_false_alarms, num_correct_nulls, top_target_dir_name,
output_dir_name):
"""Finds extreme examples (storm objects), based on model activations.
This is effectively the main method.
:param input_activation_file_name: See documentation at top of file.
:param unique_storm_cells: Same.
:param num_low_activation_examples: Same.
:param num_high_activation_examples: Same.
:param num_hits: Same.
:param num_misses: Same.
:param num_false_alarms: Same.
:param num_correct_nulls: Same.
:param top_target_dir_name: Same.
:param output_dir_name: Same.
:raises: ValueError: if the activation file contains activations for more
than one model component.
"""
# Check input args.
example_counts = numpy.array([
num_low_activation_examples, num_high_activation_examples, num_hits,
num_misses, num_false_alarms, num_correct_nulls
],
dtype=int)
error_checking.assert_is_geq_numpy_array(example_counts, 0)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
# Read activations.
print('Reading activations from: "{0:s}"...'.format(
input_activation_file_name))
activation_matrix, activation_metadata_dict = model_activation.read_file(
input_activation_file_name)
num_model_components = activation_matrix.shape[1]
if num_model_components > 1:
error_string = (
'The file should contain activations for only one model component, '
'not {0:d}.').format(num_model_components)
raise ValueError(error_string)
storm_activations = activation_matrix[:, 0]
full_id_strings = activation_metadata_dict[model_activation.FULL_IDS_KEY]
storm_times_unix_sec = activation_metadata_dict[
model_activation.STORM_TIMES_KEY]
num_storm_objects = len(full_id_strings)
error_checking.assert_is_leq(numpy.sum(example_counts), num_storm_objects)
# Find high- and low-activation examples.
if num_low_activation_examples + num_high_activation_examples > 0:
high_indices, low_indices = (
model_activation.get_hilo_activation_examples(
storm_activations=storm_activations,
num_low_activation_examples=num_low_activation_examples,
num_high_activation_examples=num_high_activation_examples,
unique_storm_cells=unique_storm_cells,
full_storm_id_strings=full_id_strings))
else:
high_indices = numpy.array([], dtype=int)
low_indices = numpy.array([], dtype=int)
# Write high-activation examples to file.
if len(high_indices) > 0:
high_activation_file_name = '{0:s}/high_activation_examples.p'.format(
output_dir_name)
print((
'Writing IDs and times for high-activation examples to: "{0:s}"...'
).format(high_activation_file_name))
this_activation_matrix = numpy.reshape(storm_activations[high_indices],
(len(high_indices), 1))
model_activation.write_file(
pickle_file_name=high_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[full_id_strings[k] for k in high_indices],
storm_times_unix_sec=storm_times_unix_sec[high_indices],
model_file_name=activation_metadata_dict[
model_activation.MODEL_FILE_NAME_KEY],
component_type_string=activation_metadata_dict[
model_activation.COMPONENT_TYPE_KEY],
target_class=activation_metadata_dict[
model_activation.TARGET_CLASS_KEY],
layer_name=activation_metadata_dict[
model_activation.LAYER_NAME_KEY],
neuron_index_matrix=activation_metadata_dict[
model_activation.NEURON_INDICES_KEY],
channel_indices=activation_metadata_dict[
model_activation.CHANNEL_INDICES_KEY])
# Write low-activation examples to file
if len(low_indices) > 0:
low_activation_file_name = '{0:s}/low_activation_examples.p'.format(
output_dir_name)
print(
('Writing IDs and times for low-activation examples to: "{0:s}"...'
).format(low_activation_file_name))
this_activation_matrix = numpy.reshape(storm_activations[low_indices],
(len(low_indices), 1))
model_activation.write_file(
pickle_file_name=low_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[full_id_strings[k] for k in low_indices],
storm_times_unix_sec=storm_times_unix_sec[low_indices],
model_file_name=activation_metadata_dict[
model_activation.MODEL_FILE_NAME_KEY],
component_type_string=activation_metadata_dict[
model_activation.COMPONENT_TYPE_KEY],
target_class=activation_metadata_dict[
model_activation.TARGET_CLASS_KEY],
layer_name=activation_metadata_dict[
model_activation.LAYER_NAME_KEY],
neuron_index_matrix=activation_metadata_dict[
model_activation.NEURON_INDICES_KEY],
channel_indices=activation_metadata_dict[
model_activation.CHANNEL_INDICES_KEY])
if num_hits + num_misses + num_false_alarms + num_correct_nulls == 0:
return
print(SEPARATOR_STRING)
target_value_dict = _read_target_values(
top_target_dir_name=top_target_dir_name,
storm_activations=storm_activations,
activation_metadata_dict=activation_metadata_dict)
print(SEPARATOR_STRING)
full_id_strings = target_value_dict[FULL_IDS_KEY]
storm_times_unix_sec = target_value_dict[STORM_TIMES_KEY]
storm_activations = target_value_dict[STORM_ACTIVATIONS_KEY]
storm_target_values = target_value_dict[TARGET_VALUES_KEY]
ct_extreme_dict = model_activation.get_contingency_table_extremes(
storm_activations=storm_activations,
storm_target_values=storm_target_values,
num_hits=num_hits,
num_misses=num_misses,
num_false_alarms=num_false_alarms,
num_correct_nulls=num_correct_nulls,
unique_storm_cells=unique_storm_cells,
full_storm_id_strings=full_id_strings)
hit_indices = ct_extreme_dict[model_activation.HIT_INDICES_KEY]
miss_indices = ct_extreme_dict[model_activation.MISS_INDICES_KEY]
false_alarm_indices = (
ct_extreme_dict[model_activation.FALSE_ALARM_INDICES_KEY])
correct_null_indices = (
ct_extreme_dict[model_activation.CORRECT_NULL_INDICES_KEY])
hit_id_strings = [full_id_strings[k] for k in hit_indices]
miss_id_strings = [full_id_strings[k] for k in miss_indices]
false_alarm_id_strings = [full_id_strings[k] for k in false_alarm_indices]
correct_null_id_strings = [
full_id_strings[k] for k in correct_null_indices
]
hit_primary_id_strings = (
temporal_tracking.full_to_partial_ids(hit_id_strings)[0])
miss_primary_id_strings = (
temporal_tracking.full_to_partial_ids(miss_id_strings)[0])
fa_primary_id_strings = (
temporal_tracking.full_to_partial_ids(false_alarm_id_strings)[0])
cn_primary_id_strings = (
temporal_tracking.full_to_partial_ids(correct_null_id_strings)[0])
these_flags = numpy.array(
[i in miss_primary_id_strings for i in hit_primary_id_strings],
dtype=bool)
print(
('Number of primary IDs in best hits AND worst misses = {0:d}').format(
numpy.sum(these_flags)))
these_flags = numpy.array(
[i in fa_primary_id_strings for i in hit_primary_id_strings],
dtype=bool)
print(('Number of primary IDs in best hits AND worst false alarms = {0:d}'
).format(numpy.sum(these_flags)))
these_flags = numpy.array(
[i in cn_primary_id_strings for i in hit_primary_id_strings],
dtype=bool)
print(('Number of primary IDs in best hits AND best correct nulls = {0:d}'
).format(numpy.sum(these_flags)))
these_flags = numpy.array(
[i in fa_primary_id_strings for i in miss_primary_id_strings],
dtype=bool)
print(
('Number of primary IDs in worst misses AND worst false alarms = {0:d}'
).format(numpy.sum(these_flags)))
these_flags = numpy.array(
[i in cn_primary_id_strings for i in miss_primary_id_strings],
dtype=bool)
print(
('Number of primary IDs in worst misses AND best correct nulls = {0:d}'
).format(numpy.sum(these_flags)))
these_flags = numpy.array(
[i in cn_primary_id_strings for i in fa_primary_id_strings],
dtype=bool)
print((
'Number of primary IDs in worst false alarms AND best correct nulls = '
'{0:d}').format(numpy.sum(these_flags)))
# Write best hits to file.
if len(hit_indices) > 0:
best_hit_file_name = '{0:s}/best_hits.p'.format(output_dir_name)
print('Writing IDs and times for best hits to: "{0:s}"...'.format(
best_hit_file_name))
this_activation_matrix = numpy.reshape(storm_activations[hit_indices],
(len(hit_indices), 1))
model_activation.write_file(
pickle_file_name=best_hit_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[full_id_strings[k] for k in hit_indices],
storm_times_unix_sec=storm_times_unix_sec[hit_indices],
model_file_name=activation_metadata_dict[
model_activation.MODEL_FILE_NAME_KEY],
component_type_string=activation_metadata_dict[
model_activation.COMPONENT_TYPE_KEY],
target_class=activation_metadata_dict[
model_activation.TARGET_CLASS_KEY],
layer_name=activation_metadata_dict[
model_activation.LAYER_NAME_KEY],
neuron_index_matrix=activation_metadata_dict[
model_activation.NEURON_INDICES_KEY],
channel_indices=activation_metadata_dict[
model_activation.CHANNEL_INDICES_KEY])
# Write worst misses to file.
if len(miss_indices) > 0:
worst_miss_file_name = '{0:s}/worst_misses.p'.format(output_dir_name)
print('Writing IDs and times for worst misses to: "{0:s}"...'.format(
worst_miss_file_name))
this_activation_matrix = numpy.reshape(storm_activations[miss_indices],
(len(miss_indices), 1))
model_activation.write_file(
pickle_file_name=worst_miss_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[full_id_strings[k] for k in miss_indices],
storm_times_unix_sec=storm_times_unix_sec[miss_indices],
model_file_name=activation_metadata_dict[
model_activation.MODEL_FILE_NAME_KEY],
component_type_string=activation_metadata_dict[
model_activation.COMPONENT_TYPE_KEY],
target_class=activation_metadata_dict[
model_activation.TARGET_CLASS_KEY],
layer_name=activation_metadata_dict[
model_activation.LAYER_NAME_KEY],
neuron_index_matrix=activation_metadata_dict[
model_activation.NEURON_INDICES_KEY],
channel_indices=activation_metadata_dict[
model_activation.CHANNEL_INDICES_KEY])
# Write worst false alarms to file.
if len(false_alarm_indices) > 0:
worst_fa_file_name = '{0:s}/worst_false_alarms.p'.format(
output_dir_name)
print(('Writing IDs and times for worst false alarms to: "{0:s}"...'
).format(worst_fa_file_name))
this_activation_matrix = numpy.reshape(
storm_activations[false_alarm_indices],
(len(false_alarm_indices), 1))
model_activation.write_file(
pickle_file_name=worst_fa_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[full_id_strings[k] for k in false_alarm_indices],
storm_times_unix_sec=storm_times_unix_sec[false_alarm_indices],
model_file_name=activation_metadata_dict[
model_activation.MODEL_FILE_NAME_KEY],
component_type_string=activation_metadata_dict[
model_activation.COMPONENT_TYPE_KEY],
target_class=activation_metadata_dict[
model_activation.TARGET_CLASS_KEY],
layer_name=activation_metadata_dict[
model_activation.LAYER_NAME_KEY],
neuron_index_matrix=activation_metadata_dict[
model_activation.NEURON_INDICES_KEY],
channel_indices=activation_metadata_dict[
model_activation.CHANNEL_INDICES_KEY])
# Write best correct nulls to file.
if len(correct_null_indices) > 0:
best_cn_file_name = '{0:s}/best_correct_nulls.p'.format(
output_dir_name)
print(('Writing IDs and times for best correct nulls to: "{0:s}"...'
).format(best_cn_file_name))
this_activation_matrix = numpy.reshape(
storm_activations[correct_null_indices],
(len(correct_null_indices), 1))
model_activation.write_file(
pickle_file_name=best_cn_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[full_id_strings[k] for k in correct_null_indices],
storm_times_unix_sec=storm_times_unix_sec[correct_null_indices],
model_file_name=activation_metadata_dict[
model_activation.MODEL_FILE_NAME_KEY],
component_type_string=activation_metadata_dict[
model_activation.COMPONENT_TYPE_KEY],
target_class=activation_metadata_dict[
model_activation.TARGET_CLASS_KEY],
layer_name=activation_metadata_dict[
model_activation.LAYER_NAME_KEY],
neuron_index_matrix=activation_metadata_dict[
model_activation.NEURON_INDICES_KEY],
channel_indices=activation_metadata_dict[
model_activation.CHANNEL_INDICES_KEY])
def _run(prediction_file_names, top_match_dir_name, unique_storm_cells,
num_hits, num_misses, num_false_alarms, num_correct_nulls,
num_disagreements, output_dir_names):
"""Finds extreme examples vis-a-vis two models.
This is effectively the main method.
:param prediction_file_names: See documentation at top of file.
:param top_match_dir_name: Same.
:param unique_storm_cells: Same.
:param num_hits: Same.
:param num_misses: Same.
:param num_false_alarms: Same.
:param num_correct_nulls: Same.
:param num_disagreements: Same.
:param output_dir_names: Same.
"""
# TODO(thunderhoser): Throw error if multiclass predictions are read.
# Check input args.
example_counts = numpy.array([
num_hits, num_misses, num_false_alarms, num_correct_nulls,
num_disagreements
],
dtype=int)
error_checking.assert_is_geq_numpy_array(example_counts, 0)
first_output_dir_name = output_dir_names[0]
file_system_utils.mkdir_recursive_if_necessary(
directory_name=first_output_dir_name)
second_output_dir_name = output_dir_names[1]
file_system_utils.mkdir_recursive_if_necessary(
directory_name=second_output_dir_name)
# Match storm objects between the two prediction files.
print('Reading data from: "{0:s}"...'.format(prediction_file_names[0]))
first_prediction_dict = prediction_io.read_ungridded_predictions(
prediction_file_names[0])
print('Reading data from: "{0:s}"...'.format(prediction_file_names[1]))
second_prediction_dict = prediction_io.read_ungridded_predictions(
prediction_file_names[1])
print(SEPARATOR_STRING)
first_prediction_dict, second_prediction_dict = _match_storm_objects(
first_prediction_dict=first_prediction_dict,
second_prediction_dict=second_prediction_dict,
top_match_dir_name=top_match_dir_name)
print(SEPARATOR_STRING)
observed_labels = first_prediction_dict[prediction_io.OBSERVED_LABELS_KEY]
first_model_file_name = first_prediction_dict[prediction_io.MODEL_FILE_KEY]
first_full_id_strings = first_prediction_dict[prediction_io.STORM_IDS_KEY]
first_storm_times_unix_sec = first_prediction_dict[
prediction_io.STORM_TIMES_KEY]
first_probabilities = first_prediction_dict[
prediction_io.PROBABILITY_MATRIX_KEY][:, 1]
second_model_file_name = second_prediction_dict[
prediction_io.MODEL_FILE_KEY]
second_full_id_strings = second_prediction_dict[
prediction_io.STORM_IDS_KEY]
second_storm_times_unix_sec = second_prediction_dict[
prediction_io.STORM_TIMES_KEY]
second_probabilities = second_prediction_dict[
prediction_io.PROBABILITY_MATRIX_KEY][:, 1]
if num_disagreements > 0:
second_high_indices, first_high_indices = (
model_activation.get_hilo_activation_examples(
storm_activations=second_probabilities - first_probabilities,
num_low_activation_examples=num_disagreements,
num_high_activation_examples=num_disagreements,
unique_storm_cells=unique_storm_cells,
full_storm_id_strings=first_full_id_strings))
# Print summary to command window.
this_mean_diff = numpy.mean(second_probabilities[second_high_indices] -
first_probabilities[second_high_indices])
print((
'Average prob difference for {0:d} worst disagreements with second '
'model higher: {1:.3f}').format(num_disagreements, this_mean_diff))
this_mean_diff = numpy.mean(second_probabilities[first_high_indices] -
first_probabilities[first_high_indices])
print((
'Average prob difference for {0:d} worst disagreements with first '
'model higher: {1:.3f}').format(num_disagreements, this_mean_diff))
# Write file.
this_activation_file_name = '{0:s}/low_disagreement_examples.p'.format(
first_output_dir_name)
print(('Writing disagreements (second model higher) to: "{0:s}"...'
).format(this_activation_file_name))
this_activation_matrix = numpy.reshape(
first_probabilities[second_high_indices],
(len(second_high_indices), 1))
model_activation.write_file(
pickle_file_name=this_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[
first_full_id_strings[j] for j in second_high_indices
],
storm_times_unix_sec=first_storm_times_unix_sec[
second_high_indices],
model_file_name=first_model_file_name,
component_type_string=CLASS_COMPONENT_STRING,
target_class=1)
# Write file.
this_activation_file_name = '{0:s}/high_disagreement_examples.p'.format(
second_output_dir_name)
print(('Writing disagreements (second model higher) to: "{0:s}"...'
).format(this_activation_file_name))
this_activation_matrix = numpy.reshape(
second_probabilities[second_high_indices],
(len(second_high_indices), 1))
model_activation.write_file(
pickle_file_name=this_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[
second_full_id_strings[j] for j in second_high_indices
],
storm_times_unix_sec=second_storm_times_unix_sec[
second_high_indices],
model_file_name=second_model_file_name,
component_type_string=CLASS_COMPONENT_STRING,
target_class=1)
# Write file.
this_activation_file_name = '{0:s}/high_disagreement_examples.p'.format(
first_output_dir_name)
print(('Writing disagreements (first model higher) to: "{0:s}"...'
).format(this_activation_file_name))
this_activation_matrix = numpy.reshape(
first_probabilities[first_high_indices],
(len(first_high_indices), 1))
model_activation.write_file(
pickle_file_name=this_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[
first_full_id_strings[j] for j in first_high_indices
],
storm_times_unix_sec=first_storm_times_unix_sec[
first_high_indices],
model_file_name=first_model_file_name,
component_type_string=CLASS_COMPONENT_STRING,
target_class=1)
# Write file.
this_activation_file_name = '{0:s}/low_disagreement_examples.p'.format(
second_output_dir_name)
print(('Writing disagreements (first model higher) to: "{0:s}"...'
).format(this_activation_file_name))
this_activation_matrix = numpy.reshape(
second_probabilities[first_high_indices],
(len(first_high_indices), 1))
model_activation.write_file(
pickle_file_name=this_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[
second_full_id_strings[j] for j in first_high_indices
],
storm_times_unix_sec=second_storm_times_unix_sec[
first_high_indices],
model_file_name=second_model_file_name,
component_type_string=CLASS_COMPONENT_STRING,
target_class=1)
if num_hits + num_misses + num_false_alarms + num_correct_nulls == 0:
return
mean_probabilities = 0.5 * (first_probabilities + second_probabilities)
ct_extreme_dict = model_activation.get_contingency_table_extremes(
storm_activations=mean_probabilities,
storm_target_values=observed_labels,
num_hits=num_hits,
num_misses=num_misses,
num_false_alarms=num_false_alarms,
num_correct_nulls=num_correct_nulls,
unique_storm_cells=unique_storm_cells,
full_storm_id_strings=first_full_id_strings)
hit_indices = ct_extreme_dict[model_activation.HIT_INDICES_KEY]
miss_indices = ct_extreme_dict[model_activation.MISS_INDICES_KEY]
false_alarm_indices = ct_extreme_dict[
model_activation.FALSE_ALARM_INDICES_KEY]
correct_null_indices = ct_extreme_dict[
model_activation.CORRECT_NULL_INDICES_KEY]
if num_hits > 0:
print((
'Mean probability from first and second model for {0:d} best hits: '
'{1:.3f}, {2:.3f}').format(
num_hits, numpy.mean(first_probabilities[hit_indices]),
numpy.mean(second_probabilities[hit_indices])))
this_activation_file_name = '{0:s}/best_hits.p'.format(
first_output_dir_name)
print('Writing best hits to: "{0:s}"...'.format(
this_activation_file_name))
this_activation_matrix = numpy.reshape(
first_probabilities[hit_indices], (len(hit_indices), 1))
model_activation.write_file(
pickle_file_name=this_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[first_full_id_strings[j] for j in hit_indices],
storm_times_unix_sec=first_storm_times_unix_sec[hit_indices],
model_file_name=first_model_file_name,
component_type_string=CLASS_COMPONENT_STRING,
target_class=1)
this_activation_file_name = '{0:s}/best_hits.p'.format(
second_output_dir_name)
print('Writing best hits to: "{0:s}"...'.format(
this_activation_file_name))
this_activation_matrix = numpy.reshape(
second_probabilities[hit_indices], (len(hit_indices), 1))
model_activation.write_file(
pickle_file_name=this_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[second_full_id_strings[j] for j in hit_indices],
storm_times_unix_sec=second_storm_times_unix_sec[hit_indices],
model_file_name=second_model_file_name,
component_type_string=CLASS_COMPONENT_STRING,
target_class=1)
if num_misses > 0:
print(('Mean probability from first and second model for {0:d} worst '
'misses: {1:.3f}, {2:.3f}').format(
num_misses, numpy.mean(first_probabilities[miss_indices]),
numpy.mean(second_probabilities[miss_indices])))
this_activation_file_name = '{0:s}/worst_misses.p'.format(
first_output_dir_name)
print('Writing worst misses to: "{0:s}"...'.format(
this_activation_file_name))
this_activation_matrix = numpy.reshape(
first_probabilities[miss_indices], (len(miss_indices), 1))
model_activation.write_file(
pickle_file_name=this_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[first_full_id_strings[j] for j in miss_indices],
storm_times_unix_sec=first_storm_times_unix_sec[miss_indices],
model_file_name=first_model_file_name,
component_type_string=CLASS_COMPONENT_STRING,
target_class=1)
this_activation_file_name = '{0:s}/worst_misses.p'.format(
second_output_dir_name)
print('Writing worst misses to: "{0:s}"...'.format(
this_activation_file_name))
this_activation_matrix = numpy.reshape(
second_probabilities[miss_indices], (len(miss_indices), 1))
model_activation.write_file(
pickle_file_name=this_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[second_full_id_strings[j] for j in miss_indices],
storm_times_unix_sec=second_storm_times_unix_sec[miss_indices],
model_file_name=second_model_file_name,
component_type_string=CLASS_COMPONENT_STRING,
target_class=1)
if num_false_alarms > 0:
print(('Mean probability from first and second model for {0:d} worst '
'false alarms: {1:.3f}, {2:.3f}').format(
num_false_alarms,
numpy.mean(first_probabilities[false_alarm_indices]),
numpy.mean(second_probabilities[false_alarm_indices])))
this_activation_file_name = '{0:s}/worst_false_alarms.p'.format(
first_output_dir_name)
print('Writing worst false alarms to: "{0:s}"...'.format(
this_activation_file_name))
this_activation_matrix = numpy.reshape(
first_probabilities[false_alarm_indices],
(len(false_alarm_indices), 1))
model_activation.write_file(
pickle_file_name=this_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[
first_full_id_strings[j] for j in false_alarm_indices
],
storm_times_unix_sec=first_storm_times_unix_sec[
false_alarm_indices],
model_file_name=first_model_file_name,
component_type_string=CLASS_COMPONENT_STRING,
target_class=1)
this_activation_file_name = '{0:s}/worst_false_alarms.p'.format(
second_output_dir_name)
print('Writing worst false alarms to: "{0:s}"...'.format(
this_activation_file_name))
this_activation_matrix = numpy.reshape(
second_probabilities[false_alarm_indices],
(len(false_alarm_indices), 1))
model_activation.write_file(
pickle_file_name=this_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[
second_full_id_strings[j] for j in false_alarm_indices
],
storm_times_unix_sec=second_storm_times_unix_sec[
false_alarm_indices],
model_file_name=second_model_file_name,
component_type_string=CLASS_COMPONENT_STRING,
target_class=1)
if num_correct_nulls > 0:
print(('Mean probability from first and second model for {0:d} best '
'correct nulls: {1:.3f}, {2:.3f}').format(
num_correct_nulls,
numpy.mean(first_probabilities[correct_null_indices]),
numpy.mean(second_probabilities[correct_null_indices])))
this_activation_file_name = '{0:s}/best_correct_nulls.p'.format(
first_output_dir_name)
print('Writing best correct nulls to: "{0:s}"...'.format(
this_activation_file_name))
this_activation_matrix = numpy.reshape(
first_probabilities[correct_null_indices],
(len(correct_null_indices), 1))
model_activation.write_file(
pickle_file_name=this_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[
first_full_id_strings[j] for j in correct_null_indices
],
storm_times_unix_sec=first_storm_times_unix_sec[
correct_null_indices],
model_file_name=first_model_file_name,
component_type_string=CLASS_COMPONENT_STRING,
target_class=1)
this_activation_file_name = '{0:s}/best_correct_nulls.p'.format(
second_output_dir_name)
print('Writing best correct nulls to: "{0:s}"...'.format(
this_activation_file_name))
this_activation_matrix = numpy.reshape(
second_probabilities[correct_null_indices],
(len(correct_null_indices), 1))
model_activation.write_file(
pickle_file_name=this_activation_file_name,
activation_matrix=this_activation_matrix,
full_id_strings=[
second_full_id_strings[j] for j in correct_null_indices
],
storm_times_unix_sec=second_storm_times_unix_sec[
correct_null_indices],
model_file_name=second_model_file_name,
component_type_string=CLASS_COMPONENT_STRING,
target_class=1)