def fill_X_handcrafted_full_and_y_raw(
dataset_full, h5_store_files, h5_keys, offset, osn_name, target_list,
branching_feature_names_list_dict, usergraph_feature_names_list_dict,
temporal_feature_names_list_dict, number_of_branching_features_dict,
number_of_usergraph_features_dict, number_of_temporal_features_dict):
for d, h5_key in enumerate(h5_keys):
handcrafted_features_data_frame = h5load_from(h5_store_files[1],
h5_key)
kth_row = get_kth_row(handcrafted_features_data_frame, -1,
branching_feature_names_list_dict[osn_name])
dataset_full[osn_name]["X_branching"][
offset + d, :number_of_branching_features_dict[osn_name]] = kth_row
kth_row = get_kth_row(handcrafted_features_data_frame, -1,
usergraph_feature_names_list_dict[osn_name])
dataset_full[osn_name]["X_usergraph"][
offset + d, :number_of_usergraph_features_dict[osn_name]] = kth_row
kth_row = get_kth_row(handcrafted_features_data_frame, -1,
temporal_feature_names_list_dict[osn_name])
dataset_full[osn_name]["X_temporal"][
offset + d, :number_of_temporal_features_dict[osn_name]] = kth_row
for target_name in target_list:
dataset_full[osn_name]["y_raw"][target_name][
offset + d] = get_target_value(handcrafted_features_data_frame,
target_name)
def fill_X_handcrafted_full_and_y_raw(dataset_full,
h5_store_files,
h5_keys,
offset,
osn_name,
target_list,
branching_feature_names_list_dict,
usergraph_feature_names_list_dict,
temporal_feature_names_list_dict,
number_of_branching_features_dict,
number_of_usergraph_features_dict,
number_of_temporal_features_dict):
for d, h5_key in enumerate(h5_keys):
handcrafted_features_data_frame = h5load_from(h5_store_files[1], h5_key)
kth_row = get_kth_row(handcrafted_features_data_frame,
-1,
branching_feature_names_list_dict[osn_name])
dataset_full[osn_name]["X_branching"][offset + d, :number_of_branching_features_dict[osn_name]] = kth_row
kth_row = get_kth_row(handcrafted_features_data_frame,
-1,
usergraph_feature_names_list_dict[osn_name])
dataset_full[osn_name]["X_usergraph"][offset + d, :number_of_usergraph_features_dict[osn_name]] = kth_row
kth_row = get_kth_row(handcrafted_features_data_frame,
-1,
temporal_feature_names_list_dict[osn_name])
dataset_full[osn_name]["X_temporal"][offset + d, :number_of_temporal_features_dict[osn_name]] = kth_row
for target_name in target_list:
dataset_full[osn_name]["y_raw"][target_name][offset + d] = get_target_value(handcrafted_features_data_frame,
target_name)
def get_dataframe_row(data_frame, k, k_based_on_lifetime_old, feature_list):
lifetime = k
k_based_on_lifetime = get_k_based_on_lifetime(data_frame, lifetime, min_k=k_based_on_lifetime_old, max_k=-1)
kth_row = get_kth_row(data_frame, k_based_on_lifetime, feature_list)
return kth_row, k_based_on_lifetime
def get_dataframe_row(data_frame, k, k_based_on_lifetime_old, feature_list):
lifetime = k
k_based_on_lifetime = get_k_based_on_lifetime(
data_frame, lifetime, min_k=k_based_on_lifetime_old, max_k=-1)
kth_row = get_kth_row(data_frame, k_based_on_lifetime, feature_list)
return kth_row, k_based_on_lifetime
def fill_X_handcrafted_k_actual(dataset_k, h5_store_files, h5_keys, offset, k,
X_k_min_dict, X_t_next_dict,
branching_feature_names_list,
usergraph_feature_names_list,
temporal_feature_names_list, osn_name):
for d, h5_key in enumerate(h5_keys):
if X_k_min_dict[osn_name][offset + d] == -1:
dataset_k[osn_name]["X_branching"][offset + d, :] = np.nan
dataset_k[osn_name]["X_usergraph"][offset + d, :] = np.nan
dataset_k[osn_name]["X_temporal"][offset + d, :] = np.nan
continue
handcrafted_features_data_frame = h5load_from(h5_store_files[1],
h5_key)
# min_index = 0
# max_index = len(branching_feature_names_list)
kth_row = get_kth_row(handcrafted_features_data_frame,
X_k_min_dict[osn_name][offset + d],
branching_feature_names_list)
dataset_k[osn_name]["X_branching"][offset + d, :] = kth_row
# min_index = len(branching_feature_names_list)
# max_index = len(branching_feature_names_list) + len(usergraph_feature_names_list)
kth_row = get_kth_row(handcrafted_features_data_frame,
X_k_min_dict[osn_name][offset + d],
usergraph_feature_names_list)
dataset_k[osn_name]["X_usergraph"][offset + d, :] = kth_row
# min_index = len(branching_feature_names_list) + len(usergraph_feature_names_list)
# max_index = len(branching_feature_names_list) + len(usergraph_feature_names_list) + len(temporal_feature_names_list)
kth_row = get_kth_row(handcrafted_features_data_frame,
X_k_min_dict[osn_name][offset + d],
temporal_feature_names_list)
dataset_k[osn_name]["X_temporal"][offset + d, :] = kth_row
