From f8d7c439ded993cea390f22130c494fca9dc9f83 Mon Sep 17 00:00:00 2001 From: XiaoSu97 <54052267+XiaoSu97@users.noreply.github.com> Date: Fri, 25 Sep 2026 16:04:01 +0800 Subject: [PATCH 1/3] Add Lingjiang 2.0 wrapper dependencies --- models/lingjiang2_api/requirements.txt | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 models/lingjiang2_api/requirements.txt diff --git a/models/lingjiang2_api/requirements.txt b/models/lingjiang2_api/requirements.txt new file mode 100644 index 0000000..30c1f56 --- /dev/null +++ b/models/lingjiang2_api/requirements.txt @@ -0,0 +1,4 @@ +alibabacloud_brain_industrial20200920==2.1.2 +alibabacloud_tea_openapi==0.4.6 +datasets==3.6.0 +numpy==2.1.3 From 9dd74ab9fae1722a74815d63f42d6fa79947d06e Mon Sep 17 00:00:00 2001 From: XiaoSu97 <54052267+XiaoSu97@users.noreply.github.com> Date: Fri, 25 Sep 2026 16:08:04 +0800 Subject: [PATCH 2/3] Add Lingjiang 2.0 FEV model wrapper Implement FEV adapter for LingJiang 2.0 forecasting API with input handling, prediction dataset creation, and AICS client integration. --- models/lingjiang2_api/model.py | 330 +++++++++++++++++++++++++++++++++ 1 file changed, 330 insertions(+) create mode 100644 models/lingjiang2_api/model.py diff --git a/models/lingjiang2_api/model.py b/models/lingjiang2_api/model.py new file mode 100644 index 0000000..8284b41 --- /dev/null +++ b/models/lingjiang2_api/model.py @@ -0,0 +1,330 @@ +"""FEV adapter for the hosted LingJiang 2.0 forecasting API.""" +from __future__ import annotations + +import base64 +import hashlib +import io +import json +import lzma +import os +import time +import uuid +from types import SimpleNamespace + +import datasets +import fev +import numpy as np + + +QUANTILES = tuple(str(level / 10) for level in range(1, 10)) +MAX_CONTEXT = 15360 +MAX_RESPONSE_BYTES = 256 * 1024 * 1024 +MAX_AICS_REQUEST_BYTES = 900_000 +MAX_CHUNK_CHARS = 820_000 + + +def _numeric(values): + array = np.asarray(values) + if array.dtype.kind in "biuf": + return array.astype(np.float32) + try: + return np.asarray( + [np.nan if value is None or value == "" else float(value) for value in values], + dtype=np.float32, + ) + except (TypeError, ValueError): + pass + encoded = [] + for value in values: + if value is None or value == "": + encoded.append(np.nan) + else: + digest = hashlib.sha256(str(value).encode("utf-8")).digest() + encoded.append(float(int.from_bytes(digest[:4], "little") % 1000000)) + return np.asarray(encoded, dtype=np.float32) + + +def _make_inputs(task, window): + past, future = window.get_input_data() + if len(past) != len(future): + raise ValueError("Past and future item counts differ") + items = [] + for old, new in zip(past, future, strict=True): + if str(old[task.id_column]) != str(new[task.id_column]): + raise ValueError("Past and future item IDs differ") + context = np.stack([_numeric(old[column]) for column in task.target_columns]) + past_length = context.shape[-1] + context = context[:, -MAX_CONTEXT:] + history = context.shape[-1] + past_covariates = None + if task.past_dynamic_columns: + past_covariates = np.stack( + [_numeric(old[column])[-history:] for column in task.past_dynamic_columns] + ) + known_covariates = None + if task.known_dynamic_columns: + rows = [] + for column in task.known_dynamic_columns: + past_values = _numeric(old[column]) + future_values = _numeric(new[column]) + if len(past_values) != past_length or len(future_values) != task.horizon: + raise ValueError(f"Invalid length for known covariate {column}") + rows.append(np.concatenate([past_values[-history:], future_values])) + known_covariates = np.stack(rows) + items.append(SimpleNamespace( + context=context, + past_covariates=past_covariates, + known_covariates=known_covariates, + )) + return items + + +def _predictions_dataset(task, forecasts): + if not forecasts: + raise ValueError("Empty API prediction") + targets = {} + for target_index, target in enumerate(task.target_columns): + columns = {"predictions": []} + columns.update({quantile: [] for quantile in QUANTILES}) + for forecast in forecasts: + array = np.asarray(forecast, dtype=np.float32) + if array.shape != (9, task.horizon, len(task.target_columns)): + raise ValueError("Invalid API prediction shape") + if not np.isfinite(array).all(): + raise ValueError("API prediction contains non-finite values") + columns["predictions"].append(array[4, :, target_index].astype(float).tolist()) + for quantile_index, quantile in enumerate(QUANTILES): + columns[quantile].append( + array[quantile_index, :, target_index].astype(float).tolist() + ) + targets[target] = datasets.Dataset.from_dict( + columns, features=task.predictions_schema + ) + return datasets.DatasetDict(targets) + + +def _pack_array(value): + if value is None: + return None + stream = io.BytesIO() + np.save(stream, np.asarray(value, dtype=np.float32), allow_pickle=False) + return base64.b64encode(stream.getvalue()).decode("ascii") + + +def _unpack_array(value): + if not isinstance(value, str): + raise ValueError("Expected an encoded prediction array") + array = np.load(io.BytesIO(base64.b64decode(value, validate=True)), allow_pickle=False) + if array.dtype != np.float32 or array.size == 0: + raise ValueError("Invalid prediction array") + return array + + +def _encode_request(batch, horizon): + payload = { + "schema_version": 1, + "prediction_length": horizon, + "contexts": [_pack_array(item.context) for item in batch], + "past_only_covariates": [_pack_array(item.past_covariates) for item in batch], + "past_future_covariates": [_pack_array(item.known_covariates) for item in batch], + } + return base64.b64encode( + lzma.compress(json.dumps(payload, allow_nan=False).encode("utf-8")) + ).decode("ascii") + + +def _request_params(batch, horizon): + params = {"input": _encode_request(batch, horizon)} + wire_bytes = len(json.dumps(params, separators=(",", ":")).encode("utf-8")) + return params, wire_bytes + + +def _pad_batch(batch, compute_size): + if not batch or not len(batch) <= compute_size <= 32: + raise ValueError("Invalid compute batch size") + return list(batch) + [batch[0]] * (compute_size - len(batch)) + + +def _cloud_plans(items, max_items, horizon): + if max_items < 1: + raise ValueError("max_items must be positive") + + def split(batch, compute_size): + _, wire_bytes = _request_params(_pad_batch(batch, compute_size), horizon) + if wire_bytes <= MAX_AICS_REQUEST_BYTES or len(batch) == 1: + yield batch, compute_size + else: + middle = len(batch) // 2 + yield from split(batch[:middle], compute_size) + yield from split(batch[middle:], compute_size) + + for start in range(0, len(items), max_items): + original = items[start:start + max_items] + yield from split(original, len(original)) + + +def _decode_response(value): + for _ in range(8): + if isinstance(value, str): + try: + value = json.loads(value) + except json.JSONDecodeError: + raw = lzma.LZMADecompressor() + data = raw.decompress( + base64.b64decode(value, validate=True), max_length=MAX_RESPONSE_BYTES + 1 + ) + if len(data) > MAX_RESPONSE_BYTES or not raw.eof: + raise ValueError("Incomplete or oversized API response") + value = json.loads(data) + if isinstance(value, dict): + if value.get("schema_version") == 1: + return value + if value.get("success") is False: + raise RuntimeError("AICS invocation failed") + for key in ("data", "output", "result", "re", "value"): + if key in value: + value = value[key] + break + else: + raise ValueError("Unrecognized API response") + else: + raise ValueError("Unrecognized API response type") + raise ValueError("API response nesting is too deep") + + +class _AICSClient: + def __init__(self, service_id, timeout=3600): + from alibabacloud_brain_industrial20200920.client import Client + from alibabacloud_brain_industrial20200920.models import AicsOpenApiInvokeRequest + from alibabacloud_tea_openapi.models import Config + + config = Config( + access_key_id=os.environ["ALIBABA_CLOUD_ACCESS_KEY_ID"], + access_key_secret=os.environ["ALIBABA_CLOUD_ACCESS_KEY_SECRET"], + region_id="cn-hangzhou", + read_timeout=60000, + connect_timeout=60000, + ) + config.endpoint = "brain-industrial.cn-hangzhou.aliyuncs.com" + self.client = Client(config) + self.request_type = AicsOpenApiInvokeRequest + self.service_id = service_id + self.timeout = timeout + + def _invoke(self, params): + job_id = uuid.uuid4().hex + deadline = time.monotonic() + self.timeout + errors = 0 + while time.monotonic() < deadline: + request = self.request_type( + service_id=self.service_id, param=params, + type="EXPERIMENT", job_id=job_id, + ) + try: + response = self.client.aics_open_api_invoke(request) + except Exception as exc: + errors += 1 + retryable = str(getattr(exc, "status_code", "")) in ( + "429", "500", "502", "503", "504" + ) + if errors >= (12 if retryable else 3): + raise + if retryable: + params = {"input": ""} + time.sleep(min(30, 3 * 2 ** min(errors - 1, 3))) + continue + data = response.body.data + errors = 0 + if isinstance(data, str): + data = json.loads(data) + status = data.get("jobStatus") + if status in ("FAIL", "FAILED", "FAILURE", "CANCELLED", "CANCELED", "ERROR", "STOPPED"): + raise RuntimeError(f"AICS job ended with status {status}") + if status in (None, "SUCCESS"): + for key in ("output", "result"): + if data.get(key) not in (None, "null", ""): + return _decode_response(data[key]) + params = {"input": ""} + time.sleep(3) + raise TimeoutError(f"AICS job exceeded {self.timeout} seconds") + + def predict(self, batch, horizon, target_count, compute_size=None): + padded = _pad_batch(batch, compute_size or len(batch)) + params, wire_bytes = _request_params(padded, horizon) + if wire_bytes <= MAX_AICS_REQUEST_BYTES: + payload = self._invoke(params) + else: + encoded = params["input"] + transfer_id = uuid.uuid4().hex + chunk_chars = MAX_CHUNK_CHARS + while True: + parts = [encoded[start:start + chunk_chars] + for start in range(0, len(encoded), chunk_chars)] + if len(parts) > 128: + raise ValueError("Chunked request exceeds 128 API calls") + requests = [] + for index, part in enumerate(parts): + chunk = {"schema_version": 1, "transport": "chunk-v1", + "transfer_id": transfer_id, "chunk_index": index, + "chunk_count": len(parts), "data": part} + request = {"input": base64.b64encode( + lzma.compress(json.dumps(chunk, allow_nan=False).encode("utf-8")) + ).decode("ascii")} + size = len(json.dumps(request, separators=(",", ":")).encode()) + requests.append((request, size)) + if all(size <= MAX_AICS_REQUEST_BYTES for _, size in requests): + break + chunk_chars = int(chunk_chars * 0.8) + if chunk_chars < 50_000: + raise ValueError("API chunk cannot fit the request limit") + for index, (request, _) in enumerate(requests): + result = self._invoke(request) + if index < len(parts) - 1: + if (result.get("transport") != "chunk-ack-v1" or + result.get("transfer_id") != transfer_id or + result.get("chunk_index") != index): + raise ValueError("Invalid chunk acknowledgment") + else: + payload = result + if payload.get("schema_version") != 1 or payload.get("model") != "lingjiang2-only": + raise ValueError("Unexpected API model or schema") + if payload.get("forecast_keys") != list(QUANTILES): + raise ValueError("Unexpected forecast quantiles") + forecasts = [_unpack_array(item) for item in payload["forecasts"]] + if len(forecasts) != len(padded): + raise ValueError("Unexpected forecast count") + for forecast in forecasts: + if forecast.shape != (9, horizon, target_count) or not np.isfinite(forecast).all(): + raise ValueError("Unexpected forecast shape or value") + return forecasts[:len(batch)] + + +class LingJiang2API(fev.ForecastingModel): + """Zero-shot forecasts through the hosted LingJiang 2.0 API.""" + + model_name = "lingjiang2_api" + trained_on_datasets = [] + + def __init__(self, service_id=None, batch_uni=32, batch_multi=8): + super().__init__() + self.service_id = service_id or os.environ["LINGJIANG2_FEV_CLOUD_SERVICE_ID"] + self.batch_uni = batch_uni + self.batch_multi = batch_multi + self.client = None + + def _fit_predict(self, task: fev.Task): + if self.client is None: + self.client = _AICSClient(self.service_id) + predictions = [] + for window in task.iter_windows(): + inputs = _make_inputs(task, window) + batch_size = self.batch_multi if len(task.target_columns) > 1 else self.batch_uni + forecasts = [] + for batch, compute_size in _cloud_plans(inputs, batch_size, task.horizon): + with self._record_inference_time(): + forecasts.extend( + self.client.predict(batch, task.horizon, len(task.target_columns), + compute_size=compute_size) + ) + predictions.append(_predictions_dataset(task, forecasts)) + return predictions From 50d2b5c421b08e3d1bea21817fd92b6ec374116d Mon Sep 17 00:00:00 2001 From: XiaoSu97 <54052267+XiaoSu97@users.noreply.github.com> Date: Fri, 25 Sep 2026 16:09:18 +0800 Subject: [PATCH 3/3] Add Lingjiang 2.0 fev-bench results --- .../fev_bench/results/lingjiang2_api.csv | 101 ++++++++++++++++++ 1 file changed, 101 insertions(+) create mode 100644 benchmarks/fev_bench/results/lingjiang2_api.csv diff --git a/benchmarks/fev_bench/results/lingjiang2_api.csv b/benchmarks/fev_bench/results/lingjiang2_api.csv new file mode 100644 index 0000000..8b3367a --- /dev/null +++ b/benchmarks/fev_bench/results/lingjiang2_api.csv @@ -0,0 +1,101 @@ +model_name,model_class,dataset_path,dataset_config,horizon,num_windows,initial_cutoff,window_step_size,min_context_length,max_context_length,seasonality,eval_metric,extra_metrics,quantile_levels,id_column,timestamp_column,target,generate_univariate_targets_from,known_dynamic_columns,past_dynamic_columns,static_columns,task_name,test_error,training_time_s,inference_time_s,num_forecasts,dataset_fingerprint,trained_on_this_dataset,fev_version,SQL,MASE,WAPE,WQL,model_kwargs,fev_commit +Lingjiang2.0,lingjiang2_api,autogluon/fev_datasets,proenfo_gfc12,168,10,-1680,168,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,['airtemperature'],[],[],proenfo_gfc12,0.7444744221499711,0.0,47.66649924986996,110,542e70fafa6c1141,False,0.11.0.dev0,0.7444744221499711,0.9276254632103849,0.07693885983146653,0.06155860927736928,{}, +Lingjiang2.0,lingjiang2_api,autogluon/fev_datasets,proenfo_gfc14,168,20,-3360,168,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,['airtemperature'],[],[],proenfo_gfc14,0.4327560107145577,0.0,34.82898658560589,20,b496a2fe259b9b2e,False,0.11.0.dev0,0.4327560107145577,0.5535882891494981,0.027109231298312348,0.021197055832333465,{}, +Lingjiang2.0,lingjiang2_api,autogluon/fev_datasets,proenfo_gfc17,168,20,-3360,168,1,,24,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,['airtemperature'],[],[],proenfo_gfc17,0.5885223962924011,0.0,85.03986683371477,160,52bd28b0bd086271,False,0.11.0.dev0,0.5885223962924011,0.7459165123703264,0.048166401662915345,0.038064550604105477,{}, +Lingjiang2.0,lingjiang2_api,autogluon/fev_datasets,rohlik_sales_1D,14,1,2023-12-15T00:00:00,14,14,,7,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,sales,,"['holiday', 'school_holidays', 'sell_price_main', 'shops_closed', 'total_orders', 'type_0_discount', 'type_1_discount', 'type_2_discount', 'type_3_discount', 'type_4_discount', 'type_5_discount', 'type_6_discount', 'winter_school_holidays']",['availability'],"['L1_category_name_en', 'L2_category_name_en', 'L3_category_name_en', 'L4_category_name_en', 'name', 'product_unique_id', 'warehouse']",rohlik_sales_1D,0.953671460917894,0.0,344.261027460685,4116,8fbcde7781725e42,False,0.11.0.dev0,0.953671460917894,1.174382715156477,0.28930748040560994,0.2346924357780413,{}, +Lingjiang2.0,lingjiang2_api,autogluon/fev_datasets,rohlik_orders_1D,61,5,2023-05-01T00:00:00,61,1,,7,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,orders,,"['holiday', 'school_holidays', 'shops_closed', 'winter_school_holidays']","['blackout', 'frankfurt_shutdown', 'mini_shutdown', 'mov_change', 'precipitation', 'shutdown', 'snow', 'user_activity_1', 'user_activity_2']",[],rohlik_orders_1D,0.9916049472655153,0.0,6.1681689587421715,35,e870b9bb33c73005,False,0.11.0.dev0,0.9916049472655153,1.2302111480975104,0.05716598236452719,0.045946923484148645,{}, +Lingjiang2.0,lingjiang2_api,autogluon/fev_datasets,rohlik_sales_1W,8,1,2023-12-15T00:00:00,8,8,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,sales,,"['holiday', 'school_holidays', 'sell_price_main', 'shops_closed', 'total_orders', 'type_0_discount', 'type_1_discount', 'type_2_discount', 'type_3_discount', 'type_4_discount', 'type_5_discount', 'type_6_discount', 'winter_school_holidays']",['availability'],"['L1_category_name_en', 'L2_category_name_en', 'L3_category_name_en', 'L4_category_name_en', 'name', 'product_unique_id', 'warehouse']",rohlik_sales_1W,1.2152954124667474,0.0,132.72175291366875,3942,09ccc385e4401736,False,0.11.0.dev0,1.2152954124667474,1.471851456373061,0.22112633123988895,0.17944566086469863,{}, +Lingjiang2.0,lingjiang2_api,autogluon/fev_datasets,rohlik_orders_1W,8,5,2023-05-01T00:00:00,8,1,,1,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,orders,,"['holiday', 'school_holidays', 'shops_closed', 'winter_school_holidays']","['blackout', 'frankfurt_shutdown', 'mini_shutdown', 'mov_change', 'precipitation', 'shutdown', 'snow', 'user_activity_1', 'user_activity_2']",[],rohlik_orders_1W,1.3855380777694979,0.0,4.476959165884182,35,91789fb8a7648db1,False,0.11.0.dev0,1.3855380777694979,1.7277805483635327,0.05672292923580724,0.04544201421636623,{}, +Lingjiang2.0,lingjiang2_api,autogluon/fev_datasets,entsoe_15T,96,20,-1920,96,1,,96,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 'epsilon': 1.0}]","[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]",id,timestamp,target,,"['radiation_diffuse_horizontal', 'radiation_direct_horizontal', 'temperature']",[],[],entsoe_15T,0.4696437547332352,0.0,99.72905258345418,120,8ec35ddbd7a93b8f,False,0.11.0.dev0,0.4696437547332352,0.5937952217354688,0.04504761343308089,0.03598562323713517,{}, +Lingjiang2.0,lingjiang2_api,autogluon/fev_datasets,entsoe_30T,96,20,-1920,96,1,,48,SQL,"['MASE', {'name': 'WAPE', 'epsilon': 1.0}, {'name': 'WQL', 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