time_start=0,
classification_col=c)
x_train, y_train = timestamped_to_vector(train,
vector_col=v,
time_start=0,
classification_col=c)
# Random search with thresholding
rand_params = Configs.get_all()
expt = Experiments.Experiment(rand_params,
search_algorithm="random",
data=(x_train, y_train),
folds=10,
folder_name="random_search_reults",
thresholding=True,
threshold=0.5)
# parameter configurations
A_B_C = Configs.get_A_B_C
# Ensemble model
ensemble_config = Experiments.Ensemble_configurations(
list(A_B_C.values()),
x_test=x_test,
y_test=y_test,
x_train=x_train,
y_train=y_train,
folder_name="test_train_results",
batch_size=64)
ensemble_config.run_experiments()
params_A["step"] = [1, 2, 3, 4, 5] # snapshot intervals (s)
increase_snap_expt = Experiments.Increase_Snaphot_Experiment(
params_A,
search_algorithm="grid",
data=(x, y),
folds=10,
folder_name="increase_intervals",
thresholding=False,
run_on_factors=True)
increase_snap_expt.run_experiments()
# Ensemble model
ensemble_config = Experiments.Ensemble_configurations(
list(A_B_C.values()),
data=(x, y),
folds=10,
folder_name="ensemble_ABC",
batch_size=58)
ensemble_config.run_experiments()
# Time step ensemble model
params_A["step"] = [1]
ensemble_sub_seq = Experiments.Ensemble_sub_sequences(
params_A, data=(x, y), folds=10, folder_name="ensemble_sub_sequence")
ensemble_sub_seq.run_experiments()
# Leave out test data
for config_description in A_B_C:
params = A_B_C[config_description]
params["sequence_length"] = [4, 8, 16]
omit_test = Experiments.Omit_test_data(
