def __init__(self,
test_scenes,
study_info_,
constraint_csv_removed_users=None):
Model.__init__(
self,
ProximityModel.name,
test_scenes,
study_info_,
constraint_csv_removed_users=constraint_csv_removed_users)
prototype_array = []
weight_array = []
for feature in self.all_feature_keys:
if feature != "shortest_distance":
prototype_array.append(0)
weight_array.append(0)
else:
x = self.feature_processer.convert_normal_value_to_standardised(
feature, 0)
prototype_array.append(x)
weight_array.append(1)
self.prototype_array = np.array(prototype_array)
self.weight_array = np.array(weight_array)
def __init__(self,
test_scenes,
study_info_,
constraint_csv_removed_users=None):
Model.__init__(
self,
SimpleModel.name,
test_scenes,
study_info_,
constraint_csv_removed_users=constraint_csv_removed_users)
self.prototype_dictionary = dict()
self.weight_dictionary = dict()
in_prototype, in_weights = self.generate_arrays(
{"bbox_overlap_proportion": 1})
self.prototype_dictionary["in"] = self.prototype_dictionary[
"inside"] = in_prototype
self.weight_dictionary["in"] = self.weight_dictionary[
"inside"] = in_weights
on_prototype, on_weights = self.generate_arrays({
"above_proportion": 1,
"contact_proportion": 1
})
self.prototype_dictionary["on"] = self.prototype_dictionary[
"on top of"] = on_prototype
self.weight_dictionary["on"] = self.weight_dictionary[
"on top of"] = on_weights
above_prototype, above_weights = self.generate_arrays({
"above_proportion":
1,
"horizontal_distance":
0
})
self.prototype_dictionary["above"] = self.prototype_dictionary[
"over"] = above_prototype
self.weight_dictionary["above"] = self.weight_dictionary[
"over"] = above_weights
below_prototype, below_weights = self.generate_arrays({
"below_proportion":
1,
"horizontal_distance":
0
})
self.prototype_dictionary["below"] = self.prototype_dictionary[
"under"] = below_prototype
self.weight_dictionary["below"] = self.weight_dictionary[
"under"] = below_weights
against_prototype, against_weights = self.generate_arrays({
"contact_proportion":
1,
"horizontal_distance":
0
})
self.prototype_dictionary["against"] = against_prototype
self.weight_dictionary["against"] = against_weights
def __init__(self,
preposition_model_dict,
test_scenes,
study_info_,
constraint_csv_removed_users=None):
self.preposition_model_dict = preposition_model_dict
Model.__init__(
self,
CSModel.name,
test_scenes,
study_info_,
constraint_csv_removed_users=constraint_csv_removed_users)
def __init__(
self,
preposition_model_dict: Dict[str,
GeneratePrepositionModelParameters],
test_scenes,
study_info_,
train_test_proportion=float(1),
number_of_epochs=200,
make_plots=False,
test_prepositions=None):
Model.__init__(self,
self.name,
test_scenes,
study_info_,
test_prepositions=test_prepositions)
self.models = dict()
self.preposition_model_dict = preposition_model_dict
self.training_data_dict = {}
self.callbacks = None
self.number_of_epochs = number_of_epochs
self.train_test_proportion = train_test_proportion
self.make_plots = make_plots
for p in self.test_prepositions:
self.training_data_dict[
p] = self.convert_train_dataframe_to_tfdataset(
preposition_model_dict[p].train_dataset, p)
if self.train_test_proportion == 1:
train = self.training_data_dict[p]
model = self.train_model(p, train)
self.callbacks = [high_acc_callback]
else:
train_amount = int(
len(self.training_data_dict[p]) *
self.train_test_proportion)
train = self.training_data_dict[p].take(train_amount)
test = self.training_data_dict[p].skip(train_amount)
model = self.train_model(p, train, test)
self.models[p] = model
def __init__(self,
name,
test_scenes,
study_info_,
test_prepositions=PREPOSITION_LIST):
"""Summary
Args:
name (TYPE): Description
test_scenes (TYPE): Description
study_info_ (TYPE): Description
:param test_prepositions:
"""
Model.__init__(self,
name,
test_scenes,
study_info_,
test_prepositions=test_prepositions)
def __init__(self,
train_scenes,
test_scenes,
study_info_,
features_to_remove,
train_test_proportion=float(1),
number_of_epochs=200,
make_plots=False,
test_prepositions=None):
Model.__init__(self,
self.name,
test_scenes,
study_info_,
test_prepositions=test_prepositions)
self.models = dict()
self.train_scenes = train_scenes
self.features_to_remove = features_to_remove
self.number_of_epochs = number_of_epochs
self.train_test_proportion = train_test_proportion
self.make_plots = make_plots
self.models = dict()
self.train_datasets = dict()
self.callbacks = None
for p in self.test_prepositions:
self.train_datasets[p] = self.prepare_train_dataset(p)
if self.train_test_proportion == 1:
train = self.train_datasets[p]
model = self.train_model(p, train)
self.callbacks = [perfect_acc_callback]
else:
train_amount = int(
len(self.train_datasets[p]) * self.train_test_proportion)
train = self.train_datasets[p].take(train_amount)
test = self.train_datasets[p].skip(train_amount)
model = self.train_model(p, train, test)
self.models[p] = model
def __init__(
self,
preposition_model_dict: Dict[str,
GeneratePrepositionModelParameters],
test_scenes,
study_info_: StudyInfo,
test_prepositions=PREPOSITION_LIST,
constraint_csv_removed_users=None):
self.preposition_model_dict = preposition_model_dict
Model.__init__(
self,
PrototypeModel.name,
test_scenes,
study_info_,
test_prepositions=test_prepositions,
constraint_csv_removed_users=constraint_csv_removed_users)
if len(test_scenes) < len(study_info_.scene_name_list):
for p in self.test_prepositions:
train_scenes = set(self.preposition_model_dict[p].train_scenes)
if (any(x in train_scenes for x in test_scenes)):
raise ValueError("Train and test scene overlap.")
def __init__(self,
test_scenes,
study_info_,
constraint_csv_removed_users=None):
Model.__init__(
self,
BestGuessModel.name,
test_scenes,
study_info_,
constraint_csv_removed_users=constraint_csv_removed_users)
self.prototype_dictionary = dict()
self.weight_dictionary = dict()
inside_prototype, inside_weights = self.generate_arrays(
{"bbox_overlap_proportion": 1})
self.prototype_dictionary["inside"] = inside_prototype
self.weight_dictionary["inside"] = inside_weights
in_prototype, in_weights = self.generate_arrays({
"bbox_overlap_proportion":
1,
"location_control":
1
})
self.prototype_dictionary["in"] = in_prototype
self.weight_dictionary["in"] = in_weights
on_prototype, on_weights = self.generate_arrays({
"above_proportion": 1,
"contact_proportion": 1,
"support": 1
})
self.prototype_dictionary["on"] = on_prototype
self.weight_dictionary["on"] = on_weights
ontopof_prototype, ontopof_weights = self.generate_arrays({
"above_proportion":
1,
"contact_proportion":
1
})
self.prototype_dictionary["on top of"] = ontopof_prototype
self.weight_dictionary["on top of"] = ontopof_weights
above_prototype, above_weights = self.generate_arrays({
"above_proportion":
1,
"horizontal_distance":
0
})
self.prototype_dictionary["above"] = above_prototype
self.weight_dictionary["above"] = above_weights
over_prototype, over_weights = self.generate_arrays({
"above_proportion":
1,
"f_covers_g":
1
})
self.prototype_dictionary["over"] = over_prototype
self.weight_dictionary["over"] = over_weights
below_prototype, below_weights = self.generate_arrays({
"below_proportion":
1,
"horizontal_distance":
0
})
self.prototype_dictionary["below"] = below_prototype
self.weight_dictionary["below"] = below_weights
under_prototype, under_weights = self.generate_arrays({
"below_proportion":
1,
"g_covers_f":
1
})
self.prototype_dictionary["under"] = under_prototype
self.weight_dictionary["under"] = under_weights
against_prototype, against_weights = self.generate_arrays({
"contact_proportion":
1,
"horizontal_distance":
0,
"location_control":
0.5
})
self.prototype_dictionary["against"] = against_prototype
self.weight_dictionary["against"] = against_weights