example = json.loads(line)
// Convert all IDs to strings initially,
// for consistency with csv and megam formats.
curr_id = str(example.get("id",
"EXAMPLE_{}".format(example_num)))
class_name = (safe_float(example["y"],
replace_dict=self.class_map)
if "y" in example else None)
example = example["x"]
if self.ids_to_floats:
try:
curr_id = float(curr_id)
except ValueError:
raise ValueError(("You set ids_to_floats to true, but" +
" ID {} could not be converted to " +
"float").format(curr_id))
After Change
// create a data frame; if it"s empty,
// then return `_parse_dataframe()`, which
// will raise an error
df = pd.DataFrame(lines)
if df.empty:
return self._parse_dataframe(df, None, None)
// if it"s PY2 and `id` is in the
// data frame, make sure it"s a string
if PY2 and "id" in df:
df["id"] = df["id"].astype(str)
// convert the features to a
// list of dictionaries
