def convert_dataset(dataset):
data_x_numeric = dataset.loc[:, dataset.columns != "status"]
data_x_numeric = data_x_numeric.loc[:,
data_x_numeric.columns != "time"]
# convert string columns to categorical type
for col in data_x_numeric.columns:
if str(data_x_numeric[col].dtype) == "object":
data_x_numeric[col] = data_x_numeric[col].astype('category')
data_x_numeric = OneHotEncoder().fit_transform(data_x_numeric)
data_y = dataset[["status", "time"]]
data_y = data_y.reindex(columns=["status", "time"])
data_y["status"] = data_y["status"].astype('bool')
pd_y_values = data_y.copy()
pd_y_values = pd_y_values.rename(index=int,
columns={"status": "event"})
pd_y_values = pd_y_values.reindex(columns=["time", "event"])
# test on sorted input data
test_data = data_x_numeric.copy()
test_timed_data = test_data
test_timed_data['time'] = pd_y_values["time"]
return data_x_numeric, pd_y_values, test_timed_data
def convert_dataset(dataset):
# convert string columns to categorical type
for col in dataset.columns:
if str(dataset[col].dtype) == "object":
dataset.loc[:, col] = dataset[col].astype('category')
data_x_numeric = OneHotEncoder().fit_transform(dataset[[
"horTh", "age", "menostat", "tsize", "tgrade", "pnodes", "progrec",
"estrec"
]])
data_y = dataset[["time", "cens"]]
data_y = data_y.reindex(columns=["cens", "time"])
data_y["cens"] = data_y["cens"].astype('bool')
pd_y_values = data_y.copy()
pd_y_values = pd_y_values.rename(index=int, columns={"cens": "event"})
pd_y_values = pd_y_values.reindex(columns=["time", "event"])
# test on sorted input data
test_data = data_x_numeric.copy()
test_timed_data = test_data
test_timed_data['time'] = pd_y_values["time"]
return data_x_numeric, pd_y_values, test_timed_data