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[AINode] Add window_step options for dataset #15857

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Merged
CRZbulabula merged 1 commit into from
Jul 2, 2025
Merged

[AINode] Add window_step options for dataset #15857

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2 changes: 2 additions & 0 deletions iotdb-core/ainode/ainode/core/ingress/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,7 @@ def __init__(
seq_len: int,
input_token_len: int,
output_token_len: int,
window_step: int,
):
super().__init__(ip, port)
# The number of the time series data points of the model input
Expand All @@ -42,3 +43,4 @@ def __init__(
# The number of the time series data points of the model output
self.output_token_len = output_token_len
self.token_num = self.seq_len // self.input_token_len
self.window_step = window_step
40 changes: 30 additions & 10 deletions iotdb-core/ainode/ainode/core/ingress/iotdb.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,6 +59,7 @@ def __init__(
seq_len: int,
input_token_len: int,
output_token_len: int,
window_step: int,
data_schema_list: list,
ip: str = AINodeDescriptor().get_config().get_ain_cluster_ingress_address(),
port: int = AINodeDescriptor().get_config().get_ain_cluster_ingress_port(),
Expand All @@ -74,7 +75,9 @@ def __init__(
use_rate: float = 1.0,
offset_rate: float = 0.0,
):
super().__init__(ip, port, seq_len, input_token_len, output_token_len)
super().__init__(
ip, port, seq_len, input_token_len, output_token_len, window_step
)

self.SHOW_TIMESERIES = "show timeseries %s%s"
self.COUNT_SERIES_SQL = "select count(%s) from %s%s"
Expand Down Expand Up @@ -139,7 +142,9 @@ def _fetch_schema(self, data_schema_list: list):
window_sum = 0
for seq_name, seq_value in sorted_series:
# calculate and sum the number of training data windows for each time series
window_count = seq_value[1] - self.seq_len - self.output_token_len + 1
window_count = (
seq_value[1] - self.seq_len - self.output_token_len + 1
) // self.window_step
if window_count <= 1:
continue
use_window_count = int(window_count * self.use_rate)
Expand Down Expand Up @@ -176,14 +181,16 @@ def __getitem__(self, index):
# try to get the training data window from cache first
series_data = torch.tensor(series_data)
result = series_data[
window_index : window_index + self.seq_len + self.output_token_len
window_index * self.window_step : window_index * self.window_step
+ self.seq_len
+ self.output_token_len
]
return (
result[0 : self.seq_len],
result[self.input_token_len : self.seq_len + self.output_token_len],
np.ones(self.token_num, dtype=np.int32),
)
result = []
series_data = []
sql = ""
try:
if self.cache_enable:
Expand All @@ -204,12 +211,18 @@ def __getitem__(self, index):
)
with self.session.execute_query_statement(sql) as query_result:
while query_result.has_next():
result.append(get_field_value(query_result.next().get_fields()[0]))
series_data.append(
get_field_value(query_result.next().get_fields()[0])
)
except Exception as e:
logger.error("Executing sql: {} with exception: {}".format(sql, e))
if self.cache_enable:
self.cache.put(cache_key, result)
result = torch.tensor(result)
self.cache.put(cache_key, series_data)
result = series_data[
window_index * self.window_step : window_index * self.window_step
+ self.seq_len
+ self.output_token_len
]
return (
result[0 : self.seq_len],
result[self.input_token_len : self.seq_len + self.output_token_len],
Expand All @@ -230,6 +243,7 @@ def __init__(
seq_len: int,
input_token_len: int,
output_token_len: int,
window_step: int,
data_schema_list: list,
ip: str = AINodeDescriptor().get_config().get_ain_cluster_ingress_address(),
port: int = AINodeDescriptor().get_config().get_ain_cluster_ingress_port(),
Expand All @@ -245,7 +259,9 @@ def __init__(
use_rate: float = 1.0,
offset_rate: float = 0.0,
):
super().__init__(ip, port, seq_len, input_token_len, output_token_len)
super().__init__(
ip, port, seq_len, input_token_len, output_token_len, window_step
)

table_session_config = TableSessionConfig(
node_urls=[f"{ip}:{port}"],
Expand Down Expand Up @@ -302,7 +318,9 @@ def _fetch_schema(self, data_schema_list: list):
window_sum = 0
for seq_name, seq_values in series_map.items():
# calculate and sum the number of training data windows for each time series
window_count = len(seq_values) - self.seq_len - self.output_token_len + 1
window_count = (
len(seq_values) - self.seq_len - self.output_token_len + 1
) // self.window_step
if window_count <= 1:
continue
use_window_count = int(window_count * self.use_rate)
Expand Down Expand Up @@ -331,7 +349,9 @@ def __getitem__(self, index):
window_index -= self.series_with_prefix_sum[series_index - 1][2]
window_index += self.series_with_prefix_sum[series_index][3]
result = self.series_with_prefix_sum[series_index][4][
window_index : window_index + self.seq_len + self.output_token_len
window_index * self.window_step : window_index * self.window_step
+ self.seq_len
+ self.output_token_len
]
result = torch.tensor(result)
return (
Expand Down
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