def __iter__(self):
buffer = []
for df in self.source_datapipe:
buffer.append(df)
if len(buffer) == self.n_batch:
yield df_wrapper.concat(buffer)
buffer = []
if len(buffer):
yield df_wrapper.concat(buffer)
def __iter__(self):
size = None
all_buffer = []
for df in self.source_datapipe:
if size is None:
size = df_wrapper.get_len(df)
for i in range(df_wrapper.get_len(df)):
all_buffer.append(df_wrapper.get_item(df, i))
random.shuffle(all_buffer)
buffer = []
for df in all_buffer:
buffer.append(df)
if len(buffer) == size:
yield df_wrapper.concat(buffer)
buffer = []
if len(buffer):
yield df_wrapper.concat(buffer)
def __iter__(self):
size = None
all_buffer = []
filter_res = []
for df in self.source_datapipe:
if size is None:
size = len(df.index)
for i in range(len(df.index)):
all_buffer.append(df[i:i + 1])
filter_res.append(self.filter_fn(df.iloc[i]))
buffer = []
for df, res in zip(all_buffer, filter_res):
if res:
buffer.append(df)
if len(buffer) == size:
yield df_wrapper.concat(buffer)
buffer = []
if len(buffer):
yield df_wrapper.concat(buffer)
def _returnIfTrue(self, data):
condition = self.filter_fn(data, *self.args, **self.kwargs)
if df_wrapper.is_column(condition):
# We are operating on DataFrames filter here
result = []
for idx, mask in enumerate(df_wrapper.iterate(condition)):
if mask:
result.append(df_wrapper.get_item(data, idx))
if len(result):
return df_wrapper.concat(result)
else:
return None
if not isinstance(condition, bool):
raise ValueError("Boolean output is required for `filter_fn` of FilterIterDataPipe, got", type(condition))
if condition:
return data
