def store_k_evaluation_measures(store_path, k_list, k_evaluation_measures, feature_column_names): number_of_folds = k_evaluation_measures[0].shape[1] h5_store = h5_open(store_path + "results.h5") for fold_index in range(number_of_folds): data_frame = pd.DataFrame(k_evaluation_measures[0][:, fold_index], columns=["kendall_tau"], index=k_list) h5store_at(h5_store, "/data/kendall_tau/fold" + str(fold_index), data_frame) data_frame = pd.DataFrame(k_evaluation_measures[1][:, fold_index], columns=["p_value"], index=k_list) h5store_at(h5_store, "/data/p_value/fold" + str(fold_index), data_frame) data_frame = pd.DataFrame(k_evaluation_measures[2][:, fold_index], columns=["mse"], index=k_list) h5store_at(h5_store, "/data/mse/fold" + str(fold_index), data_frame) data_frame = pd.DataFrame(k_evaluation_measures[3][:, fold_index], columns=["jaccard"], index=k_list) h5store_at(h5_store, "/data/top_k_jaccard/fold" + str(fold_index), data_frame) data_frame = pd.DataFrame(k_evaluation_measures[4][:, fold_index, :], columns=feature_column_names, index=k_list) h5store_at(h5_store, "/data/feature_importances/fold" + str(fold_index), data_frame) h5_close(h5_store)
def store_dataset_k(dataset_k_path, dataset_k, X_k_min_dict, X_t_next_dict, index): h5_store = h5_open(dataset_k_path) for osn_name in dataset_k.keys(): h5store_at( h5_store, "/data/" + osn_name + "/X_branching", pd.DataFrame(dataset_k[osn_name]["X_branching"], columns=sorted( list(get_branching_feature_names(osn_name))))) h5store_at( h5_store, "/data/" + osn_name + "/X_usergraph", pd.DataFrame(dataset_k[osn_name]["X_usergraph"], columns=sorted( list(get_usergraph_feature_names(osn_name))))) h5store_at( h5_store, "/data/" + osn_name + "/X_temporal", pd.DataFrame(dataset_k[osn_name]["X_temporal"], columns=sorted( list(get_temporal_feature_names(osn_name))))) utility_arrays = dict() utility_arrays["X_k_min_array"] = X_k_min_dict[osn_name] utility_arrays["X_t_next_array"] = X_t_next_dict[osn_name] h5store_at(h5_store, "/data/" + osn_name + "/utility_arrays", pd.DataFrame(utility_arrays)) h5_close(h5_store)
def store_dataset_full(dataset_full_path, dataset_full, index, branching_feature_names_list_dict, usergraph_feature_names_list_dict, temporal_feature_names_list_dict): h5_store = h5_open(dataset_full_path) for osn_name in dataset_full.keys(): h5store_at( h5_store, "/data/" + osn_name + "/X_branching", pd.DataFrame(dataset_full[osn_name]["X_branching"], columns=branching_feature_names_list_dict[osn_name])) h5store_at( h5_store, "/data/" + osn_name + "/X_usergraph", pd.DataFrame(dataset_full[osn_name]["X_usergraph"], columns=usergraph_feature_names_list_dict[osn_name])) h5store_at( h5_store, "/data/" + osn_name + "/X_temporal", pd.DataFrame(dataset_full[osn_name]["X_temporal"], columns=temporal_feature_names_list_dict[osn_name])) y_raw_dict = dict() for target_name in dataset_full[osn_name]["y_raw"].keys(): y_raw_dict[target_name] = dataset_full[osn_name]["y_raw"][ target_name] h5store_at(h5_store, "/data/" + osn_name + "/y_raw", pd.DataFrame(y_raw_dict)) h5_close(h5_store)
def store_dataset_k(dataset_k_path, dataset_k, X_k_min_dict, X_t_next_dict, index): h5_store = h5_open(dataset_k_path) for osn_name in dataset_k.keys(): h5store_at(h5_store, "/data/" + osn_name + "/X_branching", pd.DataFrame(dataset_k[osn_name]["X_branching"], columns=sorted(list(get_branching_feature_names(osn_name))))) h5store_at(h5_store, "/data/" + osn_name + "/X_usergraph", pd.DataFrame(dataset_k[osn_name]["X_usergraph"], columns=sorted(list(get_usergraph_feature_names(osn_name))))) h5store_at(h5_store, "/data/" + osn_name + "/X_temporal", pd.DataFrame(dataset_k[osn_name]["X_temporal"], columns=sorted(list(get_temporal_feature_names(osn_name))))) utility_arrays = dict() utility_arrays["X_k_min_array"] = X_k_min_dict[osn_name] utility_arrays["X_t_next_array"] = X_t_next_dict[osn_name] h5store_at(h5_store, "/data/" + osn_name + "/utility_arrays", pd.DataFrame(utility_arrays)) h5_close(h5_store)
def store_dataset_full(dataset_full_path, dataset_full, index, branching_feature_names_list_dict, usergraph_feature_names_list_dict, temporal_feature_names_list_dict): h5_store = h5_open(dataset_full_path) for osn_name in dataset_full.keys(): h5store_at(h5_store, "/data/" + osn_name + "/X_branching", pd.DataFrame(dataset_full[osn_name]["X_branching"], columns=branching_feature_names_list_dict[osn_name])) h5store_at(h5_store, "/data/" + osn_name + "/X_usergraph", pd.DataFrame(dataset_full[osn_name]["X_usergraph"], columns=usergraph_feature_names_list_dict[osn_name])) h5store_at(h5_store, "/data/" + osn_name + "/X_temporal", pd.DataFrame(dataset_full[osn_name]["X_temporal"], columns=temporal_feature_names_list_dict[osn_name])) y_raw_dict = dict() for target_name in dataset_full[osn_name]["y_raw"].keys(): y_raw_dict[target_name] = dataset_full[osn_name]["y_raw"][target_name] h5store_at(h5_store, "/data/" + osn_name + "/y_raw", pd.DataFrame(y_raw_dict)) h5_close(h5_store)
def store_k_evaluation_measures(store_path, k_list, k_evaluation_measures, feature_column_names): number_of_folds = k_evaluation_measures[0].shape[1] h5_store = h5_open(store_path + "results.h5") for fold_index in range(number_of_folds): data_frame = pd.DataFrame(k_evaluation_measures[0][:, fold_index], columns=["kendall_tau"], index=k_list) h5store_at(h5_store, "/data/kendall_tau/fold" + str(fold_index), data_frame) data_frame = pd.DataFrame(k_evaluation_measures[1][:, fold_index], columns=["p_value"], index=k_list) h5store_at(h5_store, "/data/p_value/fold" + str(fold_index), data_frame) data_frame = pd.DataFrame(k_evaluation_measures[2][:, fold_index], columns=["mse"], index=k_list) h5store_at(h5_store, "/data/mse/fold" + str(fold_index), data_frame) data_frame = pd.DataFrame(k_evaluation_measures[3][:, fold_index], columns=["jaccard"], index=k_list) h5store_at(h5_store, "/data/top_k_jaccard/fold" + str(fold_index), data_frame) data_frame = pd.DataFrame(k_evaluation_measures[4][:, fold_index, :], columns=feature_column_names, index=k_list) h5store_at(h5_store, "/data/feature_importances/fold" + str(fold_index), data_frame) h5_close(h5_store)