def load_dataset_k(dataset_k_path,
feature_osn_name_list,
branching_feature_names_list_dict,
usergraph_feature_names_list_dict,
temporal_feature_names_list_dict):
dataset_k = dict()
X_k_min_dict = dict()
X_t_next_dict = dict()
index = dict()
h5_store = h5_open(dataset_k_path)
for osn_name in feature_osn_name_list:
dataset_k[osn_name] = dict()
df = h5load_from(h5_store, "/data/" + osn_name + "/X_branching")[branching_feature_names_list_dict[osn_name]]
index[osn_name] = list(df.index)
dataset_k[osn_name]["X_branching"] = df.values
dataset_k[osn_name]["X_usergraph"] = h5load_from(h5_store, "/data/" + osn_name + "/X_usergraph")[usergraph_feature_names_list_dict[osn_name]].values
dataset_k[osn_name]["X_temporal"] = h5load_from(h5_store, "/data/" + osn_name + "/X_temporal")[temporal_feature_names_list_dict[osn_name]].values
data_frame = h5load_from(h5_store, "/data/" + osn_name + "/utility_arrays")
X_k_min_dict[osn_name] = data_frame["X_k_min_array"].values
X_t_next_dict[osn_name] = data_frame["X_t_next_array"].values
h5_close(h5_store)
return dataset_k, X_k_min_dict, X_t_next_dict, index
def load_dataset_full(dataset_full_path, target_osn_name,
feature_osn_name_list, target_name_list,
branching_feature_names_list_dict,
usergraph_feature_names_list_dict,
temporal_feature_names_list_dict):
dataset_full = dict()
dataset_full[target_osn_name] = dict()
index = dict()
h5_store = h5_open(dataset_full_path)
for osn_name in feature_osn_name_list:
df = h5load_from(
h5_store, "/data/" + osn_name +
"/X_branching")[branching_feature_names_list_dict[osn_name]]
index[osn_name] = list(df.index)
dataset_full[osn_name]["X_branching"] = df.values
dataset_full[osn_name]["X_usergraph"] = h5load_from(
h5_store, "/data/" + osn_name +
"/X_usergraph")[usergraph_feature_names_list_dict[osn_name]].values
dataset_full[osn_name]["X_temporal"] = h5load_from(
h5_store, "/data/" + osn_name +
"/X_temporal")[temporal_feature_names_list_dict[osn_name]].values
data_frame = h5load_from(h5_store, "/data/" + target_osn_name + "/y_raw")
dataset_full[target_osn_name]["y_raw"] = dict()
for target_name in target_name_list:
dataset_full[target_osn_name]["y_raw"][target_name] = data_frame[
target_name].values
h5_close(h5_store)
return dataset_full, index
def load_dataset_full(dataset_full_path,
target_osn_name,
feature_osn_name_list,
target_name_list,
branching_feature_names_list_dict,
usergraph_feature_names_list_dict,
temporal_feature_names_list_dict):
dataset_full = dict()
dataset_full[target_osn_name] = dict()
index = dict()
h5_store = h5_open(dataset_full_path)
for osn_name in feature_osn_name_list:
df = h5load_from(h5_store, "/data/" + osn_name + "/X_branching")[branching_feature_names_list_dict[osn_name]]
index[osn_name] = list(df.index)
dataset_full[osn_name]["X_branching"] = df.values
dataset_full[osn_name]["X_usergraph"] = h5load_from(h5_store, "/data/" + osn_name + "/X_usergraph")[usergraph_feature_names_list_dict[osn_name]].values
dataset_full[osn_name]["X_temporal"] = h5load_from(h5_store, "/data/" + osn_name + "/X_temporal")[temporal_feature_names_list_dict[osn_name]].values
data_frame = h5load_from(h5_store, "/data/" + target_osn_name + "/y_raw")
dataset_full[target_osn_name]["y_raw"] = dict()
for target_name in target_name_list:
dataset_full[target_osn_name]["y_raw"][target_name] = data_frame[target_name].values
h5_close(h5_store)
return dataset_full, index
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_all_comment_lifetimes(h5_stores_and_keys, osn_focus):
all_comment_timestamps_list = list()
extend_comment_timestamp = all_comment_timestamps_list.extend
for h5_store_files, h5_keys in h5_stores_and_keys:
for h5_key in h5_keys[osn_focus]:
timestamps_data_frame = h5load_from(h5_store_files[0], h5_key)
timestamps_col = timestamps_data_frame["timestamp"]
extend_comment_timestamp(timestamps_col.iloc[1:] - timestamps_col.iloc[0])
return all_comment_timestamps_list
def get_all_comment_lifetimes(h5_stores_and_keys, osn_focus):
all_comment_timestamps_list = list()
extend_comment_timestamp = all_comment_timestamps_list.extend
for h5_store_files, h5_keys in h5_stores_and_keys:
for h5_key in h5_keys[osn_focus]:
timestamps_data_frame = h5load_from(h5_store_files[0], h5_key)
timestamps_col = timestamps_data_frame["timestamp"]
extend_comment_timestamp(timestamps_col.iloc[1:] -
timestamps_col.iloc[0])
return all_comment_timestamps_list
def load_dataset_k(dataset_k_path, feature_osn_name_list,
branching_feature_names_list_dict,
usergraph_feature_names_list_dict,
temporal_feature_names_list_dict):
dataset_k = dict()
X_k_min_dict = dict()
X_t_next_dict = dict()
index = dict()
h5_store = h5_open(dataset_k_path)
for osn_name in feature_osn_name_list:
dataset_k[osn_name] = dict()
df = h5load_from(
h5_store, "/data/" + osn_name +
"/X_branching")[branching_feature_names_list_dict[osn_name]]
index[osn_name] = list(df.index)
dataset_k[osn_name]["X_branching"] = df.values
dataset_k[osn_name]["X_usergraph"] = h5load_from(
h5_store, "/data/" + osn_name +
"/X_usergraph")[usergraph_feature_names_list_dict[osn_name]].values
dataset_k[osn_name]["X_temporal"] = h5load_from(
h5_store, "/data/" + osn_name +
"/X_temporal")[temporal_feature_names_list_dict[osn_name]].values
data_frame = h5load_from(h5_store,
"/data/" + osn_name + "/utility_arrays")
X_k_min_dict[osn_name] = data_frame["X_k_min_array"].values
X_t_next_dict[osn_name] = data_frame["X_t_next_array"].values
h5_close(h5_store)
return dataset_k, X_k_min_dict, X_t_next_dict, index
def get_all_post_lifetimes(h5_stores_and_keys, osn_focus):
all_post_lifetimes_list = list()
append_post_lifetime = all_post_lifetimes_list.append
for h5_store_files, h5_keys in h5_stores_and_keys:
for h5_key in h5_keys[osn_focus]:
timestamps_data_frame = h5load_from(h5_store_files[0], h5_key)
timestamps_col = timestamps_data_frame["timestamp"]
if timestamps_col.size == 1:
index = 0
else:
index = int(np.ceil(0.99 * (timestamps_col.size - 1)))
append_post_lifetime(timestamps_col.iloc[index] - timestamps_col.iloc[0])
return all_post_lifetimes_list
def get_all_post_lifetimes(h5_stores_and_keys, osn_focus):
all_post_lifetimes_list = list()
append_post_lifetime = all_post_lifetimes_list.append
for h5_store_files, h5_keys in h5_stores_and_keys:
for h5_key in h5_keys[osn_focus]:
timestamps_data_frame = h5load_from(h5_store_files[0], h5_key)
timestamps_col = timestamps_data_frame["timestamp"]
if timestamps_col.size == 1:
index = 0
else:
index = int(np.ceil(0.99 * (timestamps_col.size - 1)))
append_post_lifetime(timestamps_col.iloc[index] -
timestamps_col.iloc[0])
return all_post_lifetimes_list
def calculate_k_based_on_lifetime(dataset_k, h5_store_files, h5_keys, offset,
k, X_k_min_dict, X_t_next_dict, osn_name):
number_of_keys = len(h5_keys["post"])
for d in range(number_of_keys):
timestamps_data_frame = h5load_from(h5_store_files[0],
h5_keys["post"][d])
if np.isnan(X_t_next_dict[osn_name][offset + d]):
continue
observed_comments,\
next_lifetime = get_k_based_on_lifetime(timestamps_data_frame,
k,
min_k=X_k_min_dict[osn_name][offset + d],
max_k=-1)
X_k_min_dict[osn_name][offset + d] = observed_comments
X_t_next_dict[osn_name][offset + d] = next_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
def calculate_k_based_on_lifetime(dataset_k,
h5_store_files,
h5_keys,
offset,
k,
X_k_min_dict,
X_t_next_dict,
osn_name):
number_of_keys = len(h5_keys["post"])
for d in range(number_of_keys):
timestamps_data_frame = h5load_from(h5_store_files[0], h5_keys["post"][d])
if np.isnan(X_t_next_dict[osn_name][offset + d]):
continue
observed_comments,\
next_lifetime = get_k_based_on_lifetime(timestamps_data_frame,
k,
min_k=X_k_min_dict[osn_name][offset + d],
max_k=-1)
X_k_min_dict[osn_name][offset + d] = observed_comments
X_t_next_dict[osn_name][offset + d] = next_lifetime
def load_k_evaluation_measures(store_path, number_of_folds=10):
h5_store = h5_open(store_path + "results.h5")
kendall_tau_keys = [
"/data/" + "kendall_tau/fold" + str(fold_index)
for fold_index in range(number_of_folds)
]
p_value_keys = [
"/data/" + "p_value/fold" + str(fold_index)
for fold_index in range(number_of_folds)
]
mse_keys = [
"/data/" + "mse/fold" + str(fold_index)
for fold_index in range(number_of_folds)
]
jaccard_keys = [
"/data/" + "top_k_jaccard/fold" + str(fold_index)
for fold_index in range(number_of_folds)
]
feature_importances_keys = [
"/data/" + "feature_importances/fold" + str(fold_index)
for fold_index in range(number_of_folds)
]
if (len(kendall_tau_keys) != len(p_value_keys)) or\
(len(kendall_tau_keys) != len(feature_importances_keys)):
print("Fold number different for evaluation measures load.")
raise RuntimeError
number_of_folds = len(feature_importances_keys)
data_frame = h5load_from(h5_store, feature_importances_keys[0])
k_list = data_frame.index
number_of_samples = k_list.size
feature_names_list = data_frame.columns
number_of_features = len(feature_names_list)
kendall_tau_array = np.empty((number_of_samples, number_of_folds),
dtype=np.float64)
p_value_array = np.empty((number_of_samples, number_of_folds),
dtype=np.float64)
mean_square_error = np.empty((number_of_samples, number_of_folds),
dtype=np.float64)
top_k_jaccard = np.empty((number_of_samples, number_of_folds),
dtype=np.float64)
feature_importances_array = np.empty(
(number_of_samples, number_of_folds, number_of_features),
dtype=np.float64)
for f in range(number_of_folds):
kendall_tau_key = kendall_tau_keys[f]
p_value_key = p_value_keys[f]
mse_key = mse_keys[f]
jaccard_key = jaccard_keys[f]
feature_importances_key = feature_importances_keys[f]
kendall_tau_data_frame = h5load_from(h5_store, kendall_tau_key)
p_value_data_frame = h5load_from(h5_store, p_value_key)
mse_data_frame = h5load_from(h5_store, mse_key)
jaccard_data_frame = h5load_from(h5_store, jaccard_key)
feature_importances_data_frame = h5load_from(h5_store,
feature_importances_key)
kendall_tau_array[:, f] = np.squeeze(kendall_tau_data_frame.values)
p_value_array[:, f] = np.squeeze(p_value_data_frame.values)
mean_square_error[:, f] = np.squeeze(mse_data_frame.values)
top_k_jaccard[:, f] = np.squeeze(jaccard_data_frame.values)
try:
feature_importances_array[:, f, :] = np.squeeze(
feature_importances_data_frame.values)
except ValueError:
feature_importances_array[:,
f, :] = feature_importances_data_frame.values
k_evaluation_measures = (kendall_tau_array, p_value_array,
mean_square_error, top_k_jaccard,
feature_importances_array)
return k_list, k_evaluation_measures, feature_names_list
def load_k_evaluation_measures(store_path,
number_of_folds=10):
h5_store = h5_open(store_path + "results.h5")
kendall_tau_keys = ["/data/" + "kendall_tau/fold" + str(fold_index) for fold_index in range(number_of_folds)]
p_value_keys = ["/data/" + "p_value/fold" + str(fold_index) for fold_index in range(number_of_folds)]
mse_keys = ["/data/" + "mse/fold" + str(fold_index) for fold_index in range(number_of_folds)]
jaccard_keys = ["/data/" + "top_k_jaccard/fold" + str(fold_index) for fold_index in range(number_of_folds)]
feature_importances_keys = ["/data/" + "feature_importances/fold" + str(fold_index) for fold_index in range(number_of_folds)]
if (len(kendall_tau_keys) != len(p_value_keys)) or\
(len(kendall_tau_keys) != len(feature_importances_keys)):
print("Fold number different for evaluation measures load.")
raise RuntimeError
number_of_folds = len(feature_importances_keys)
data_frame = h5load_from(h5_store, feature_importances_keys[0])
k_list = data_frame.index
number_of_samples = k_list.size
feature_names_list = data_frame.columns
number_of_features = len(feature_names_list)
kendall_tau_array = np.empty((number_of_samples,
number_of_folds),
dtype=np.float64)
p_value_array = np.empty((number_of_samples,
number_of_folds),
dtype=np.float64)
mean_square_error = np.empty((number_of_samples,
number_of_folds), dtype=np.float64)
top_k_jaccard = np.empty((number_of_samples,
number_of_folds), dtype=np.float64)
feature_importances_array = np.empty((number_of_samples,
number_of_folds,
number_of_features),
dtype=np.float64)
for f in range(number_of_folds):
kendall_tau_key = kendall_tau_keys[f]
p_value_key = p_value_keys[f]
mse_key = mse_keys[f]
jaccard_key = jaccard_keys[f]
feature_importances_key = feature_importances_keys[f]
kendall_tau_data_frame = h5load_from(h5_store, kendall_tau_key)
p_value_data_frame = h5load_from(h5_store, p_value_key)
mse_data_frame = h5load_from(h5_store, mse_key)
jaccard_data_frame = h5load_from(h5_store, jaccard_key)
feature_importances_data_frame = h5load_from(h5_store, feature_importances_key)
kendall_tau_array[:, f] = np.squeeze(kendall_tau_data_frame.values)
p_value_array[:, f] = np.squeeze(p_value_data_frame.values)
mean_square_error[:, f] = np.squeeze(mse_data_frame.values)
top_k_jaccard[:, f] = np.squeeze(jaccard_data_frame.values)
try:
feature_importances_array[:, f, :] = np.squeeze(feature_importances_data_frame.values)
except ValueError:
feature_importances_array[:, f, :] = feature_importances_data_frame.values
k_evaluation_measures = (kendall_tau_array,
p_value_array,
mean_square_error,
top_k_jaccard,
feature_importances_array)
return k_list, k_evaluation_measures, feature_names_list
