x_test, y_test = timestamped_to_vector(test,
vector_col=v,
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",
classification_col=2)
# test
test_data = np.loadtxt("data/test.txt", delimiter=",")
x_test, y_test = timestamped_to_vector(test_data,
timestamp_col=0,
time_start=1,
classification_col=2)
# all data
x = np.concatenate((x_train, x_test))
y = np.concatenate((y_train, y_test))
# random search of hyperparameters
expt = Experiments.Experiment(Configs.get_all(),
folds=10,
search_algorithm="random",
data=(x_train, y_train),
folder_name="random_search",
thresholding=True,
threshold=0.5)
expt.run_experiments(num_experiments=400)
# Config A with separate test set
params_A = Configs.get_A()
params_A["sequence_length"] = list(range(1, 31)) # total real time length
expt = Experiments.Experiment(params_A,
search_algorithm="grid",
x_test=x_test,
y_test=y_test,
x_train=x_train,
y_train=y_train,
