Duplicate from MaxKJC/TSDBench

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+Climateset_500km/input_multimodal/time_series/climate_monthly/values.csv filter=lfs diff=lfs merge=lfs -text
+M5/input_multimodal/node_level_task_graphs/item_graph/edge_info.csv filter=lfs diff=lfs merge=lfs -text
+METR-LA/input_multimodal/time_series/traffic_speed/values.csv filter=lfs diff=lfs merge=lfs -text
+SDWPF/input_multimodal/time_series/sdwpf_scada/values.csv filter=lfs diff=lfs merge=lfs -text
+Store_Sales/tasks/sales_flow_forecasting/labels_long.csv filter=lfs diff=lfs merge=lfs -text
+Terra/input_multimodal/meta.csv filter=lfs diff=lfs merge=lfs -text
+US_Covid_Google/input_multimodal/time_series/weekly_cases_raw/weekly_cases_raw.csv filter=lfs diff=lfs merge=lfs -text

Climateset_500km/dataset.py

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+from __future__ import annotations
+
+import argparse
+import tempfile
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict, Iterable
+from torch.utils.data import Dataset, DataLoader
+
+import pandas as pd
+import yaml
+import ipdb
+
+# Record whether the required fields are provided.
+field_dict = {
+    'dataset': {'modalities': ['meta_features', 'node_level_task_graphs', 'time_series']},
+    'graphs': ['', '', '', ''],
+    'time_series': ['', '', '', ' ', '']
+}
+
+def read_yaml(path: Path) -> dict[str, Any]:
+    with path.open("r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+def read_all_configs(dataset_yaml_path: str) -> Dict[str, Any]:
+    """
+    Read all related configuration files, starting from dataset.yaml.
+    
+    Args:
+        dataset_yaml_path: Path to dataset.yaml.
+        
+    Returns:
+        A dictionary containing all configurations.
+    """
+    # Get the protocol directory path.
+    protocol_dir = Path(dataset_yaml_path).parent
+    
+    # 1. Read dataset.yaml.
+    dataset_config = read_yaml(Path(dataset_yaml_path))
+    print(f"已读取 dataset.yaml: {dataset_config['dataset_name']}")
+    
+    # 2. Read graph configurations.
+    graph_configs = {}
+    if 'node_level_task_graphs' in dataset_config['modalities']:
+        graph_ids = dataset_config['modalities']['node_level_task_graphs'].get('graph_ids', [])
+        
+        for graph_id in graph_ids:
+            graph_yaml_path = protocol_dir / f"input_multimodal/node_level_task_graphs/{graph_id}/graph.yaml"
+            if graph_yaml_path.exists():
+                graph_config = read_yaml(Path(graph_yaml_path))
+                graph_configs[graph_id] = graph_config
+                print(f"已读取 graph.yaml: {graph_id}")
+            else:
+                print(f"警告: 未找到 graph.yaml: {graph_yaml_path}")
+    
+    # 3. Read time series configurations.
+    ts_configs = {}
+    if 'time_series' in dataset_config['modalities']:
+        ts_ids = dataset_config['modalities']['time_series'].get('time_series_ids', [])
+        
+        for ts_id in ts_ids:
+            ts_yaml_path = protocol_dir / f"input_multimodal/time_series/{ts_id}/series.yaml"
+            if ts_yaml_path.exists():
+                ts_config = read_yaml(Path(ts_yaml_path))
+                ts_configs[ts_id] = ts_config
+                print(f"已读取 series.yaml: {ts_id}")
+            else:
+                print(f"警告: 未找到 series.yaml: {ts_yaml_path}")
+    
+    # 4. Combine all configurations.
+    all_configs = {
+        'dataset': dataset_config,
+        'graphs': graph_configs,
+        'time_series': ts_configs
+    }
+    
+    return all_configs
+
+def _first_existing(candidates: Iterable[Path]) -> Path:
+    candidate_list = [Path(p).expanduser() for p in candidates]
+    for p in candidate_list:  # Return an existing path from the candidate paths.
+        if p.is_file():
+            return p.resolve()
+    shown = ", ".join(str(c) for c in candidate_list)
+    raise FileNotFoundError(f"None of the candidate paths exist: {shown}")
+
+
+def _load_frame(path: Path) -> pd.DataFrame:
+    """Load a single-table file as used in protocol YAMLs (csv / parquet / h5)."""
+    path = Path(path).expanduser().resolve()
+    suf = path.suffix.lower()
+    if suf == ".csv":
+        return pd.read_csv(path)
+    if suf in (".tsv", ".txt"):
+        return pd.read_csv(path, sep="\t")
+    if suf in (".parquet", ".pq"):
+        return pd.read_parquet(path)
+    if suf in (".h5", ".hdf5", ".hdf"):
+        return pd.read_hdf(path)
+    raise ValueError(f"Unsupported data file extension for {path}")
+
+
+def _meta_candidates(dataset_dir: Path, file_name: str) -> list[Path]:
+    p = Path(file_name).expanduser()
+    if p.is_absolute():
+        return [p]
+    return [
+        dataset_dir / "input_multimodal" / file_name,
+    ]
+
+
+def _graph_file_candidates(dataset_dir: Path, graph_level: str, graph_id: str, file_name: str) -> list[Path]:
+    p = Path(file_name).expanduser()
+    if p.is_absolute():
+        return [p]
+    base = [
+        dataset_dir
+        / "input_multimodal"
+        / graph_level
+        / graph_id
+        / file_name,
+    ]
+    return base
+
+
+def _series_file_candidates(dataset_dir: Path, series_id: str, file_name: str) -> list[Path]: # Generate all candidate storage paths.
+    p = Path(file_name).expanduser()
+    if p.is_absolute():
+        return [p]
+    return [
+        dataset_dir / "input_multimodal" / "time_series" / series_id / file_name,
+        dataset_dir / file_name,
+        dataset_dir / "input_multimodal" / file_name,
+    ]
+
+def _parse_meta(meta: pd.DataFrame) -> Dict[str, Any]:
+    # Parse each column in meta_df and convert the data to a dictionary.
+    meta_dict = {}
+    for col in meta.columns:
+        meta_dict[col] = list(meta[col])
+    return meta_dict
+
+def _parse_graph(graph: pd.DataFrame, graph_features: list[str] | None) -> Dict[str, Any]:
+    ''' 
+    Required information in the graph dictionary includes:
+    - A sparse adjacency list
+    - The weight of each edge
+    - Edge features (optional)
+    '''
+    graph_dict = {}
+    required_fields = ['src_id', 'dst_id', 'weight']
+    for field in required_fields:
+        if field not in graph.columns:
+            raise ValueError(f"Missing required column: {field}")
+
+    graph_dict['edge'] = list(zip(graph['src_id'], graph['dst_id']))
+    graph_dict['weight'] = graph['weight'].tolist()
+    if all(x == 1.0 for x in list(graph_dict['weight'])):
+        graph_dict['weighted'] = False
+    else:
+        graph_dict['weighted'] = True
+
+    if graph_features is not None: # Add graph features to the dictionary if they are available.
+        for feature in graph_features:
+            graph_dict[feature] = list(graph[feature])
+    
+    return graph_dict
+
+def _parse_time_series(time_series: pd.DataFrame, scfg: dict[str, Any], time2graph_map: Dict[str, Any]|None) -> Dict[str, Any]:
+    
+    time_series_dict = {}   
+    variables_mode = scfg.get("variables_mode")
+    time_series_dict['variables_mode'] = variables_mode # Use mode to distinguish single-variable and multivariable data.
+
+    if variables_mode == "single": # For a single variable, the index is timestamp, column names are sample_id values, and the feature name is series_id.
+        time_type = str(time_series.index.dtype)
+
+        time_series_dict['time_type'] = time_type
+        time_series_dict['timestamp'] = time_series.index.tolist()
+        time_series_dict['value'] = {}
+        if time2graph_map: # 
+            for col in time_series.columns:
+                time_series_dict['value'][time2graph_map.get(int(col), col)] = list(time_series[col])
+        else:
+            for col in time_series.columns:
+                time_series_dict['value'][col] = list(time_series[col])
+    else: 
+        # For multiple variables, the columns are sample_id, timestamp, feature_name1, feature_name2, ...
+        # Extract timestamps and feature values in timestamp order, using the structure feature_name*: {sample_id: [values]}.
+        index_cfg = scfg.get("index") or {}
+        id_col = index_cfg.get("id_colunm") or index_cfg.get("id_column")
+        time_col = index_cfg.get("time_column")
+        if not id_col:
+            raise ValueError("time_series.index must configure id_colunm or id_column")
+        if not time_col:
+            raise ValueError("time_series.index must configure time_column")
+
+        required_columns = [id_col, time_col]
+        missing_columns = [col for col in required_columns if col not in time_series.columns]
+        if missing_columns:
+            raise ValueError(f"Missing required time series column(s): {missing_columns}")
+
+        series_features = index_cfg.get("feature_column")
+        if not series_features:
+            series_features = [col for col in time_series.columns if col not in required_columns]
+        elif isinstance(series_features, str):
+            series_features = [series_features]
+        else:
+            series_features = list(series_features)
+
+        if not series_features:
+            raise ValueError("No feature columns configured or inferred for time series")
+
+        missing_features = [col for col in series_features if col not in time_series.columns]
+        if missing_features:
+            raise ValueError(f"Missing configured feature column(s): {missing_features}")
+
+        sorted_time_series = time_series.sort_values([id_col, time_col])
+        time_series_dict['feature_names'] = series_features
+        time_series_dict['time_type'] = str(time_series[time_col].dtype)
+        time_series_dict['timestamp'] = sorted(time_series[time_col].drop_duplicates().tolist())
+        timestamp_by_sample = {}
+        time_series_dict['value'] = {feature: {} for feature in series_features}
+
+        for sample_id, sample_df in sorted_time_series.groupby(id_col, sort=False):
+            output_sample_id = time2graph_map.get(sample_id, sample_id) if time2graph_map else sample_id
+            timestamp_by_sample[output_sample_id] = sample_df[time_col].tolist()
+            for feature in series_features:
+                time_series_dict['value'][feature][output_sample_id] = sample_df[feature].tolist()
+
+        if any(timestamps != time_series_dict['timestamp'] for timestamps in timestamp_by_sample.values()):
+            time_series_dict['timestamp_by_sample'] = timestamp_by_sample
+        
+    return time_series_dict
+
+@dataclass
+class LoadedDataset:
+    dataset_dir: Path
+    config: dict[str, Any]
+    meta_features: Dict[str, Any]
+    graphs: Dict[str, Any]
+    time_series: Dict[str, Any]
+ 
+    
+# def _check_graphs(graph_config: dict[str, Any]) -> None:
+#     check 
+
+
+# Read all YAML files into one configuration.
+class MultiModalDataset(LoadedDataset):
+    """
+    Use the configuration returned by ``read_all_configs`` to resolve protocol paths under ``dataset_dir`` and load tables.
+
+    ``dataset_dir`` is usually the protocol root containing ``input_multimodal/`` and ``dataset.yaml``,
+    or an equivalent data root. Path resolution tries ``dataset_dir/<file>`` and
+    ``dataset_dir/input_multimodal/...``, among other candidate locations, in order.
+    """
+
+    def __init__(self, config: dict[str, Any], dataset_dir: Path | str) -> None:
+        root = Path(dataset_dir).expanduser().resolve()
+        dataset_cfg = config["dataset"]
+        modalities = dataset_cfg.get("modalities") or {}
+        time2graph_map = {}
+
+        # Read meta_features data.
+        meta_block: Dict[str, Any] = {}
+        if isinstance(modalities.get("meta_features"), dict):
+            mf = modalities["meta_features"]
+            meta_file = mf.get("file_name")
+            if mf.get("required") and not meta_file:
+                raise ValueError("meta_features is required but file_name is not configured")
+            if meta_file:
+                meta_path = _first_existing(_meta_candidates(root, str(meta_file)))
+                meta_tmp = _load_frame(meta_path)
+                meta_features = _parse_meta(meta_tmp) # Parse meta_df.
+                sample_def = dataset_cfg.get("sample_defination") or dataset_cfg.get(
+                    "sample_definition"
+                )
+                id_col_graph = None
+                id_col_time_series = None
+                if isinstance(sample_def, dict):
+                    id_col_graph = sample_def.get("sample_id_graph")
+                    id_col_time_series = sample_def.get("sample_id_time_series")
+
+                meta_block = {
+                    "path": str(meta_path),
+                    "features": meta_features,
+                    "sample_id_graph": id_col_graph,
+                    "sample_id_time_series": id_col_time_series,
+                }
+                if id_col_graph is not None and id_col_time_series is not None: 
+                    # If both id_col_graph and id_col_time_series exist, build a mapping between them to align the two modalities.
+                    # By default, map time series IDs from id_col_time_series to graph IDs in id_col_graph.
+                    time2graph_map = {time_id: graph_id for time_id, graph_id in zip(meta_tmp[id_col_time_series], meta_tmp[id_col_graph])}
+        # Read graph data.
+        graphs: Dict[str, Any] = {}
+        for gid, gcfg in (config.get("graphs") or {}).items():
+            if not isinstance(gcfg, dict):
+                continue
+            fname = gcfg.get("file_name")
+            graph_level = gcfg.get("graph_level") # Graph level, for example: node_level_task_graph, graph_level_task_graph.
+            graph_directed = gcfg.get("directed") # Graph type, for example: directed, undirected.
+            edge_features_cfg = gcfg.get("edge_features") or {}
+            graph_features = edge_features_cfg.get("feature_name") if edge_features_cfg.get("available") else None # Graph features, for example: edge_features.
+            if not fname:
+                continue
+            
+            gpath = _first_existing(_graph_file_candidates(root, str(graph_level), str(gid), str(fname)))
+            tmp_graph = _load_frame(gpath)
+            # Continue parsing individual graph features.
+            tmp_graph_dict = _parse_graph(tmp_graph, graph_features)
+            tmp_graph_dict['directed'] = graph_directed
+            tmp_graph_dict['edge_features'] = graph_features
+            graphs[str(gid)] = tmp_graph_dict # Load graph data into the dictionary.
+
+        time_series: Dict[str, Any] = {}
+        for sid, scfg in (config.get("time_series") or {}).items():
+            if not isinstance(scfg, dict):
+                continue
+            fname = scfg.get("file_name")
+            if not fname:
+                continue
+            spath = _first_existing(_series_file_candidates(root, str(sid), str(fname)))
+            tmp_time_series = _load_frame(spath)
+            
+            frequency = scfg.get("temporal_granularity")
+            tmp_time_series_dict = _parse_time_series(tmp_time_series, scfg, time2graph_map)
+            tmp_time_series_dict['frequency'] = frequency
+            time_series[str(sid)] = tmp_time_series_dict
+
+        super().__init__(
+            dataset_dir=root,
+            config=config,
+            meta_features=meta_block,
+            graphs=graphs,
+            time_series=time_series,
+        )
+
+
+if __name__ == '__main__':
+    protocol_dir = Path(__file__).resolve().parent
+    dataset_yaml_path = protocol_dir / "dataset.yaml"
+
+    configs = read_all_configs(str(dataset_yaml_path))
+    dataset = MultiModalDataset(configs, protocol_dir)
+    ipdb.set_trace()
+    # At the breakpoint, check whether all required information has been loaded.
+
+    # assert isinstance(dataset.config, dict), "dataset.config should be a dict"
+    # assert "dataset" in dataset.config, "dataset.config should include dataset config"
+    # assert isinstance(dataset.meta_features, dict), "dataset.meta_features should be a dict"
+    # assert "features" in dataset.meta_features, "dataset.meta_features should include parsed features"
+    # assert isinstance(dataset.meta_features["features"], dict), (
+    #     "dataset.meta_features['features'] should be a dict"
+    # )
+    # assert isinstance(dataset.graphs, dict), "dataset.graphs should be a dict"
+    # assert "road_distance_graph" in dataset.graphs, (
+    #     "dataset.graphs should include road_distance_graph"
+    # )
+    # assert isinstance(dataset.graphs["road_distance_graph"], dict), (
+    #     "road_distance_graph should be a parsed dict"
+    # )
+    # assert isinstance(dataset.time_series, dict), "dataset.time_series should be a dict"
+    # assert "traffic_flow" in dataset.time_series, (
+    #     "dataset.time_series should include traffic_flow"
+    # )
+    # traffic_flow = dataset.time_series["traffic_flow"]
+    # assert isinstance(traffic_flow, dict), (
+    #     "traffic_flow should be a parsed dict"
+    # )
+    # for field in ("variables_mode", "timestamp", "value", "frequency"):
+    #     assert field in traffic_flow, f"traffic_flow should include {field}"
+
+    print(f"Dataset: {dataset.config['dataset'].get('dataset_name')}")
+    print(f"Protocol dir: {dataset.dataset_dir}")
+    print(f"Meta features: columns={len(dataset.meta_features.get('features', {}))}")
+    for graph_id, graph_dict in dataset.graphs.items():
+        print(
+            f"Graph[{graph_id}]: edges={len(graph_dict.get('edge', []))}, "
+            f"directed={graph_dict.get('directed')}"
+        )
+    for series_id, series_data in dataset.time_series.items():
+        if isinstance(series_data, dict):
+            value = series_data.get("value", {})
+            if series_data.get("variables_mode") == "single":
+                feature_count = 1
+                sample_count = len(value)
+            else:
+                feature_count = len(value)
+                sample_count = len(next(iter(value.values()), {}))
+            print(
+                f"TimeSeries[{series_id}]: mode={series_data.get('variables_mode')}, "
+                f"features={feature_count}, timestamps={len(series_data.get('timestamp', []))}, "
+                f"samples={sample_count}"
+            )
+        else:
+            print(f"TimeSeries[{series_id}]: shape={series_data.shape}")

Climateset_500km/dataset.yaml

@@ -0,0 +1,33 @@
+dataset_id: climateset_500km
+dataset_name: ClimateSet 500km sample Benchmark
+domain: earth_science
+
+sample_definition:
+  sample_type: climate_grid_cell
+  sample_id_column: episode_id
+  sample_id_graph: episode_id
+modalities:
+  meta_features:
+    required: true
+    file_name: meta_features.csv
+
+  node_level_task_graphs:
+    available: true
+    graph_ids:
+      - geo_grid_8neighbor_polar
+      
+  time_series:
+    available: true
+    time_series_ids:
+      - climate_monthly
+
+tasks:
+  - climate_response_forecasting
+
+license:
+  data_license: GPL-3.0
+  redistribution_allowed: true
+
+source:
+  name: ClimateSet
+  url: https://github.com/climate-model-analysis/ClimateSet

Climateset_500km/input_multimodal/node_level_task_graphs/geo_grid_8neighbor_polar/graph.yaml

@@ -0,0 +1,18 @@
+graph_id: geo_grid_8neighbor_polar
+graph_type: static
+graph_level: node_level_task_graphs
+directed: false
+weighted: false
+description: Grpah constructed by 8-neighbor queen adjacency on lon-lat grid with polar closure
+node_set: all
+
+file_name: edges.csv
+
+self_loop: false
+
+edge_features:
+  available: false
+  feature_name: ""
+
+allowed_tasks:
+  - climate_response_emulation

Climateset_500km/input_multimodal/time_series/climate_monthly/series.yaml

@@ -0,0 +1,40 @@
+series_id: climate_monthly
+series_type: sample_level
+temporal_granularity: 1M
+
+file_name: values.csv
+variables_mode: multi
+variables:
+  BC:
+    dtype: float
+    description: ''
+    
+  CH4:
+    dtype: float
+    description: ''
+    
+  SO2:
+    dtype: float
+    description: ''
+    
+  pr:
+    dtype: float
+    description: Precipitation
+
+  tas:
+    dtype: float
+    description: Near-Surface Air Temperature
+
+index:
+  id_column: episode_id
+  time_column: timestamp
+  feature_columns:
+    - BC
+    - CH4
+    - SO2
+    - pr
+    - tas
+
+missing_policy:
+  missing_timestamp: drop_rows_without_timestamp
+  missing_value: leave_missing

Climateset_500km/input_multimodal/time_series/climate_monthly/values.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95087fd17049ddcb70c92dc0dc734aaebf5af8204c34f4b5ed0b52dae152a1fd
+size 829626227

Climateset_500km/tasks/climate_response_forecasting/labels.csv

@@ -0,0 +1,294 @@
+timestamp,t_index,context_start_index,context_end_index_exclusive,forecast_start_index,forecast_end_index_exclusive,target_series_ref,target_tensor_shape
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Climateset_500km/tasks/climate_response_forecasting/task.yaml

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+task:
+  id: climate_response_forecasting
+  type: time_series_forecasting
+  description: Forecast monthly climate response variables pr and tas from historical
+    climate response and forcing covariates.
+inherit_general: true
+sample:
+  primary_key: timestamp
+  label_mode: index_reference
+inputs:
+  time_series:
+    - fields:
+      - CH4
+      - SO2
+      - BC
+      - pr
+      source: climate_monthly
+      window:
+        length: 48
+        unit: 1month
+      sampling:
+        native_rate: 1month
+        task_rate: 1month
+        downsample_method: mean
+        upsample_method: ffill
+  tabular_static:
+    source: climate_grid_metadata
+    fields:
+      - lat_idx
+      - lon_idx
+      - lat_center
+      - lon_center
+      - is_north_pole_row
+      - is_south_pole_row
+      - hemisphere
+  graph:
+    source: geo_grid_8neighbor_polar
+prediction:
+  field: pr
+  source: climate_monthly
+  horizon:
+    length: 6
+    unit: 1month
+    starts_after: timestamp
+  stride: 6
+  label_storage:
+    ref_column: target_series_ref
+    shape_column: target_tensor_shape
+leakage:
+  observed_past_only_columns:
+  - pr
+  - CH4
+  - SO2
+  - BC
+  known_future_columns:
+  - CH4
+  - SO2
+  - BC
+  forbidden_input_columns:
+  - target_series_ref
+  - target_tensor_shape
+  future_values_removed_from_input: true
+# data_refs:
+#   time_series:
+#     climate_monthly:
+#       file: climateset_500km/input_multimodal/time_series/climate_monthly/values.csv
+#       value_field:
+#       - BC
+#       - CH4
+#       - SO2
+#       - pr
+#       - tas
+
+#   tabular_static:
+#     climate_grid_metadata:
+#       file: climateset_500km/input_multimodal/meta_features.csv
+#       id_column: episode_id
+#   graph:
+#     geo_grid_8neighbor_polar:
+#       file: climateset_500km/input_multimodal/node_level_task_graphs/geo_grid_8neighbor_polar/edges.csv

Climateset_500km/tasks/general_task.yaml

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+# ============================================================
+# LargeST_protocol/tasks/general_task.yaml
+# ============================================================
+# Purpose:
+# - This file contains only processing rules shared by all tasks and naming conventions.
+
+# Recommended reading order for each task.yaml:
+# 1) Start with sample in each task.yaml: what constitutes one sample.
+# 2) Read inputs: what the model can access at inference time.
+# 3) Read prediction: what the model should predict.
+# 4) Finally, read leakage: which columns must be excluded from model inputs.
+# ============================================================
+
+general_defaults:
+  # ------------------------------------------------------------
+  # Default input processing rules.
+  # Note: these are processing defaults, not input field defaults.
+  # These rules specify how to process time series, tabular, or graph inputs when a task uses them,
+  # but do not automatically add any input fields to a task.
+  # ------------------------------------------------------------
+  input_processing:
+    tabular_static:
+      # Unique identifier column for sensors / road segments in LargeST.
+      # This column is used only to join labels, splits, and metadata.
+      id_column: episode_id
+
+    time_series:
+      # LargeST historical traffic flow data typically uses timestamp as the time axis and sensor IDs as columns.
+      timestamp_column: timestamp
+      id_column: episode_id
+
+      sampling:
+        # LargeST raw historical traffic flow data typically has a 15-minute resolution.
+        # If your h5 file has been preprocessed to a different resolution, override this in the specific task.
+        native_rate: 1month
+
+        # By default, the task resolution matches the native resolution.
+        # For example, override this to 1h in task.yaml for a ranking task with hourly decisions.
+        task_rate: 1month
+
+        # Use this aggregation method when task_rate is coarser than native_rate, for example 15min -> 1h.
+        # mean is suitable for average traffic flow; sum is suitable for cumulative flow counts, subject to the data semantics.
+        downsample_method: mean
+
+        # Use this interpolation method when task_rate is finer than native_rate, for example 15min -> 5min. This task configuration is generally not recommended.
+        # ffill forward-fills using the most recent observation.
+        upsample_method: ffill
+
+    graph:
+      # Graph inputs are static by default, for example LargeST's adj.npy.
+      # Future dynamic graphs, such as event-induced or construction-impact graphs, can be configured in individual tasks.
+      directed: false
+      weighted: true
+
+
+  # ------------------------------------------------------------
+  # Standard split names.
+  # Use these standard values in splits.csv to avoid mixing train/val/valid/dev.
+  # ------------------------------------------------------------
+  split_names:
+    train: train
+    validation: val
+    test: test
+
+  # ------------------------------------------------------------
+  # Inheritance rules.
+  # A task can set inherit_general: true to inherit this file's processing defaults.
+  # Note: inheritance only affects default rules such as input_processing / columns / split_names,
+  # and does not automatically inherit any specific input fields.
+  # ------------------------------------------------------------
+  inheritance:
+    default_inherit_general: true
+    merge_strategy: recursive_override

LargeST/dataset.py

@@ -0,0 +1,397 @@
+from __future__ import annotations
+
+import argparse
+import tempfile
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict, Iterable
+from torch.utils.data import Dataset, DataLoader
+
+import pandas as pd
+import yaml
+import ipdb
+
+# Record whether the required fields are provided.
+field_dict = {
+    'dataset': {'modalities': ['meta_features', 'node_level_task_graphs', 'time_series']},
+    'graphs': ['', '', '', ''],
+    'time_series': ['', '', '', ' ', '']
+}
+
+def read_yaml(path: Path) -> dict[str, Any]:
+    with path.open("r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+def read_all_configs(dataset_yaml_path: str) -> Dict[str, Any]:
+    """
+    Read all related configuration files, starting from dataset.yaml.
+    
+    Args:
+        dataset_yaml_path: Path to dataset.yaml.
+        
+    Returns:
+        A dictionary containing all configurations.
+    """
+    # Get the protocol directory path.
+    protocol_dir = Path(dataset_yaml_path).parent
+    
+    # 1. Read dataset.yaml.
+    dataset_config = read_yaml(Path(dataset_yaml_path))
+    print(f"已读取 dataset.yaml: {dataset_config['dataset_name']}")
+    
+    # 2. Read graph configurations.
+    graph_configs = {}
+    if 'node_level_task_graphs' in dataset_config['modalities']:
+        graph_ids = dataset_config['modalities']['node_level_task_graphs'].get('graph_ids', [])
+        
+        for graph_id in graph_ids:
+            graph_yaml_path = protocol_dir / f"input_multimodal/node_level_task_graphs/{graph_id}/graph.yaml"
+            if graph_yaml_path.exists():
+                graph_config = read_yaml(Path(graph_yaml_path))
+                graph_configs[graph_id] = graph_config
+                print(f"已读取 graph.yaml: {graph_id}")
+            else:
+                print(f"警告: 未找到 graph.yaml: {graph_yaml_path}")
+    
+    # 3. Read time series configurations.
+    ts_configs = {}
+    if 'time_series' in dataset_config['modalities']:
+        ts_ids = dataset_config['modalities']['time_series'].get('time_series_ids', [])
+        
+        for ts_id in ts_ids:
+            ts_yaml_path = protocol_dir / f"input_multimodal/time_series/{ts_id}/series.yaml"
+            if ts_yaml_path.exists():
+                ts_config = read_yaml(Path(ts_yaml_path))
+                ts_configs[ts_id] = ts_config
+                print(f"已读取 series.yaml: {ts_id}")
+            else:
+                print(f"警告: 未找到 series.yaml: {ts_yaml_path}")
+    
+    # 4. Combine all configurations.
+    all_configs = {
+        'dataset': dataset_config,
+        'graphs': graph_configs,
+        'time_series': ts_configs
+    }
+    
+    return all_configs
+
+def _first_existing(candidates: Iterable[Path]) -> Path:
+    candidate_list = [Path(p).expanduser() for p in candidates]
+    for p in candidate_list:  # Return an existing path from the candidate paths.
+        if p.is_file():
+            return p.resolve()
+    shown = ", ".join(str(c) for c in candidate_list)
+    raise FileNotFoundError(f"None of the candidate paths exist: {shown}")
+
+
+def _load_frame(path: Path) -> pd.DataFrame:
+    """Load a single-table file as used in protocol YAMLs (csv / parquet / h5)."""
+    path = Path(path).expanduser().resolve()
+    suf = path.suffix.lower()
+    if suf == ".csv":
+        return pd.read_csv(path)
+    if suf in (".tsv", ".txt"):
+        return pd.read_csv(path, sep="\t")
+    if suf in (".parquet", ".pq"):
+        return pd.read_parquet(path)
+    if suf in (".h5", ".hdf5", ".hdf"):
+        return pd.read_hdf(path)
+    raise ValueError(f"Unsupported data file extension for {path}")
+
+
+def _meta_candidates(dataset_dir: Path, file_name: str) -> list[Path]:
+    p = Path(file_name).expanduser()
+    if p.is_absolute():
+        return [p]
+    return [
+        dataset_dir / "input_multimodal" / file_name,
+    ]
+
+
+def _graph_file_candidates(dataset_dir: Path, graph_level: str, graph_id: str, file_name: str) -> list[Path]:
+    p = Path(file_name).expanduser()
+    if p.is_absolute():
+        return [p]
+    base = [
+        dataset_dir
+        / "input_multimodal"
+        / graph_level
+        / graph_id
+        / file_name,
+    ]
+    return base
+
+
+def _series_file_candidates(dataset_dir: Path, series_id: str, file_name: str) -> list[Path]: # Generate all candidate storage paths.
+    p = Path(file_name).expanduser()
+    if p.is_absolute():
+        return [p]
+    return [
+        dataset_dir / "input_multimodal" / "time_series" / series_id / file_name,
+        dataset_dir / file_name,
+        dataset_dir / "input_multimodal" / file_name,
+    ]
+
+def _parse_meta(meta: pd.DataFrame) -> Dict[str, Any]:
+    # Parse each column in meta_df and convert the data to a dictionary.
+    meta_dict = {}
+    for col in meta.columns:
+        meta_dict[col] = list(meta[col])
+    return meta_dict
+
+def _parse_graph(graph: pd.DataFrame, graph_features: list[str] | None) -> Dict[str, Any]:
+    ''' 
+    Required information in the graph dictionary includes:
+    - A sparse adjacency list
+    - The weight of each edge
+    - Edge features (optional)
+    '''
+    graph_dict = {}
+    required_fields = ['src_id', 'dst_id', 'weight']
+    for field in required_fields:
+        if field not in graph.columns:
+            raise ValueError(f"Missing required column: {field}")
+
+    graph_dict['edge'] = list(zip(graph['src_id'], graph['dst_id']))
+    graph_dict['weight'] = graph['weight'].tolist()
+    if all(x == 1.0 for x in list(graph_dict['weight'])):
+        graph_dict['weighted'] = False
+    else:
+        graph_dict['weighted'] = True
+
+    if graph_features is not None: # Add graph features to the dictionary if they are available.
+        for feature in graph_features:
+            graph_dict[feature] = list(graph[feature])
+    
+    return graph_dict
+
+def _parse_time_series(time_series: pd.DataFrame, scfg: dict[str, Any], time2graph_map: Dict[str, Any]|None) -> Dict[str, Any]:
+    
+    time_series_dict = {}   
+    variables_mode = scfg.get("variables_mode")
+    time_series_dict['variables_mode'] = variables_mode # Use mode to distinguish single-variable and multivariable data.
+
+    if variables_mode == "single": # For a single variable, the index is timestamp, column names are sample_id values, and the feature name is series_id.
+        time_type = str(time_series.index.dtype)
+
+        time_series_dict['time_type'] = time_type
+        time_series_dict['timestamp'] = time_series.index.tolist()
+        time_series_dict['value'] = {}
+        if time2graph_map: # 
+            for col in time_series.columns:
+                time_series_dict['value'][time2graph_map.get(int(col), col)] = list(time_series[col])
+        else:
+            for col in time_series.columns:
+                time_series_dict['value'][col] = list(time_series[col])
+    else: 
+        # For multiple variables, the columns are sample_id, timestamp, feature_name1, feature_name2, ...
+        # Extract timestamps and feature values in timestamp order, using the structure feature_name*: {sample_id: [values]}.
+        index_cfg = scfg.get("index") or {}
+        id_col = index_cfg.get("id_colunm") or index_cfg.get("id_column")
+        time_col = index_cfg.get("time_column")
+        if not id_col:
+            raise ValueError("time_series.index must configure id_colunm or id_column")
+        if not time_col:
+            raise ValueError("time_series.index must configure time_column")
+
+        required_columns = [id_col, time_col]
+        missing_columns = [col for col in required_columns if col not in time_series.columns]
+        if missing_columns:
+            raise ValueError(f"Missing required time series column(s): {missing_columns}")
+
+        series_features = index_cfg.get("feature_column")
+        if not series_features:
+            series_features = [col for col in time_series.columns if col not in required_columns]
+        elif isinstance(series_features, str):
+            series_features = [series_features]
+        else:
+            series_features = list(series_features)
+
+        if not series_features:
+            raise ValueError("No feature columns configured or inferred for time series")
+
+        missing_features = [col for col in series_features if col not in time_series.columns]
+        if missing_features:
+            raise ValueError(f"Missing configured feature column(s): {missing_features}")
+
+        sorted_time_series = time_series.sort_values([id_col, time_col])
+        time_series_dict['feature_names'] = series_features
+        time_series_dict['time_type'] = str(time_series[time_col].dtype)
+        time_series_dict['timestamp'] = sorted(time_series[time_col].drop_duplicates().tolist())
+        timestamp_by_sample = {}
+        time_series_dict['value'] = {feature: {} for feature in series_features}
+
+        for sample_id, sample_df in sorted_time_series.groupby(id_col, sort=False):
+            output_sample_id = time2graph_map.get(sample_id, sample_id) if time2graph_map else sample_id
+            timestamp_by_sample[output_sample_id] = sample_df[time_col].tolist()
+            for feature in series_features:
+                time_series_dict['value'][feature][output_sample_id] = sample_df[feature].tolist()
+
+        if any(timestamps != time_series_dict['timestamp'] for timestamps in timestamp_by_sample.values()):
+            time_series_dict['timestamp_by_sample'] = timestamp_by_sample
+        
+    return time_series_dict
+
+@dataclass
+class LoadedDataset:
+    dataset_dir: Path
+    config: dict[str, Any]
+    meta_features: Dict[str, Any]
+    graphs: Dict[str, Any]
+    time_series: Dict[str, Any]
+ 
+    
+# def _check_graphs(graph_config: dict[str, Any]) -> None:
+#     check 
+
+
+# Read all YAML files into one configuration.
+class MultiModalDataset(LoadedDataset):
+    """
+    Use the configuration returned by ``read_all_configs`` to resolve protocol paths under ``dataset_dir`` and load tables.
+
+    ``dataset_dir`` is usually the protocol root containing ``input_multimodal/`` and ``dataset.yaml``,
+    or an equivalent data root. Path resolution tries ``dataset_dir/<file>`` and
+    ``dataset_dir/input_multimodal/...``, among other candidate locations, in order.
+    """
+
+    def __init__(self, config: dict[str, Any], dataset_dir: Path | str) -> None:
+        root = Path(dataset_dir).expanduser().resolve()
+        dataset_cfg = config["dataset"]
+        modalities = dataset_cfg.get("modalities") or {}
+        time2graph_map = {}
+
+        # Read meta_features data.
+        meta_block: Dict[str, Any] = {}
+        if isinstance(modalities.get("meta_features"), dict):
+            mf = modalities["meta_features"]
+            meta_file = mf.get("file_name")
+            if mf.get("required") and not meta_file:
+                raise ValueError("meta_features is required but file_name is not configured")
+            if meta_file:
+                meta_path = _first_existing(_meta_candidates(root, str(meta_file)))
+                meta_tmp = _load_frame(meta_path)
+                meta_features = _parse_meta(meta_tmp) # Parse meta_df.
+                sample_def = dataset_cfg.get("sample_defination") or dataset_cfg.get(
+                    "sample_definition"
+                )
+                id_col_graph = None
+                id_col_time_series = None
+                if isinstance(sample_def, dict):
+                    id_col_graph = sample_def.get("sample_id_graph")
+                    id_col_time_series = sample_def.get("sample_id_time_series")
+
+                meta_block = {
+                    "path": str(meta_path),
+                    "features": meta_features,
+                    "sample_id_graph": id_col_graph,
+                    "sample_id_time_series": id_col_time_series,
+                }
+                if id_col_graph is not None and id_col_time_series is not None: 
+                    # If both id_col_graph and id_col_time_series exist, build a mapping between them to align the two modalities.
+                    # By default, map time series IDs from id_col_time_series to graph IDs in id_col_graph.
+                    time2graph_map = {time_id: graph_id for time_id, graph_id in zip(meta_tmp[id_col_time_series], meta_tmp[id_col_graph])}
+        # Read graph data.
+        graphs: Dict[str, Any] = {}
+        for gid, gcfg in (config.get("graphs") or {}).items():
+            if not isinstance(gcfg, dict):
+                continue
+            fname = gcfg.get("file_name")
+            graph_level = gcfg.get("graph_level") # Graph level, for example: node_level_task_graph, graph_level_task_graph.
+            graph_directed = gcfg.get("directed") # Graph type, for example: directed, undirected.
+            edge_features_cfg = gcfg.get("edge_features") or {}
+            graph_features = edge_features_cfg.get("feature_name") if edge_features_cfg.get("available") else None # Graph features, for example: edge_features.
+            if not fname:
+                continue
+            
+            gpath = _first_existing(_graph_file_candidates(root, str(graph_level), str(gid), str(fname)))
+            tmp_graph = _load_frame(gpath)
+            # Continue parsing individual graph features.
+            tmp_graph_dict = _parse_graph(tmp_graph, graph_features)
+            tmp_graph_dict['directed'] = graph_directed
+            tmp_graph_dict['edge_features'] = graph_features
+            graphs[str(gid)] = tmp_graph_dict # Load graph data into the dictionary.
+
+        time_series: Dict[str, Any] = {}
+        for sid, scfg in (config.get("time_series") or {}).items():
+            if not isinstance(scfg, dict):
+                continue
+            fname = scfg.get("file_name")
+            if not fname:
+                continue
+            spath = _first_existing(_series_file_candidates(root, str(sid), str(fname)))
+            tmp_time_series = _load_frame(spath)
+            
+            frequency = scfg.get("temporal_granularity")
+            tmp_time_series_dict = _parse_time_series(tmp_time_series, scfg, time2graph_map)
+            tmp_time_series_dict['frequency'] = frequency
+            time_series[str(sid)] = tmp_time_series_dict
+
+        super().__init__(
+            dataset_dir=root,
+            config=config,
+            meta_features=meta_block,
+            graphs=graphs,
+            time_series=time_series,
+        )
+
+
+if __name__ == '__main__':
+    protocol_dir = Path(__file__).resolve().parent
+    dataset_yaml_path = protocol_dir / "dataset.yaml"
+
+    configs = read_all_configs(str(dataset_yaml_path))
+    dataset = MultiModalDataset(configs, protocol_dir)
+    ipdb.set_trace()
+    # At the breakpoint, check whether all required information has been loaded.
+
+    # assert isinstance(dataset.config, dict), "dataset.config should be a dict"
+    # assert "dataset" in dataset.config, "dataset.config should include dataset config"
+    # assert isinstance(dataset.meta_features, dict), "dataset.meta_features should be a dict"
+    # assert "features" in dataset.meta_features, "dataset.meta_features should include parsed features"
+    # assert isinstance(dataset.meta_features["features"], dict), (
+    #     "dataset.meta_features['features'] should be a dict"
+    # )
+    # assert isinstance(dataset.graphs, dict), "dataset.graphs should be a dict"
+    # assert "road_distance_graph" in dataset.graphs, (
+    #     "dataset.graphs should include road_distance_graph"
+    # )
+    # assert isinstance(dataset.graphs["road_distance_graph"], dict), (
+    #     "road_distance_graph should be a parsed dict"
+    # )
+    # assert isinstance(dataset.time_series, dict), "dataset.time_series should be a dict"
+    # assert "traffic_flow" in dataset.time_series, (
+    #     "dataset.time_series should include traffic_flow"
+    # )
+    # traffic_flow = dataset.time_series["traffic_flow"]
+    # assert isinstance(traffic_flow, dict), (
+    #     "traffic_flow should be a parsed dict"
+    # )
+    # for field in ("variables_mode", "timestamp", "value", "frequency"):
+    #     assert field in traffic_flow, f"traffic_flow should include {field}"
+
+    print(f"Dataset: {dataset.config['dataset'].get('dataset_name')}")
+    print(f"Protocol dir: {dataset.dataset_dir}")
+    print(f"Meta features: columns={len(dataset.meta_features.get('features', {}))}")
+    for graph_id, graph_dict in dataset.graphs.items():
+        print(
+            f"Graph[{graph_id}]: edges={len(graph_dict.get('edge', []))}, "
+            f"directed={graph_dict.get('directed')}"
+        )
+    for series_id, series_data in dataset.time_series.items():
+        if isinstance(series_data, dict):
+            value = series_data.get("value", {})
+            if series_data.get("variables_mode") == "single":
+                feature_count = 1
+                sample_count = len(value)
+            else:
+                feature_count = len(value)
+                sample_count = len(next(iter(value.values()), {}))
+            print(
+                f"TimeSeries[{series_id}]: mode={series_data.get('variables_mode')}, "
+                f"features={feature_count}, timestamps={len(series_data.get('timestamp', []))}, "
+                f"samples={sample_count}"
+            )
+        else:
+            print(f"TimeSeries[{series_id}]: shape={series_data.shape}")

LargeST/dataset.yaml

@@ -0,0 +1,40 @@
+# Dataset information: the entry point for dataset configuration, including file locations for each modality.
+dataset_id: LeageST
+dataset_name: LargeST
+domain: traffic_management
+
+sample_defination:
+  sample_type: traffic flow monitoring sensor
+  sample_id_graph: ID2 # Corresponding column name in the data; use None if the index is used directly.
+  sample_id_time_series: ID # Corresponding column name in the data; use None if the index is used directly.
+  description: each sample corresponds to a traffic flow monitoring sensor
+
+modalities: 
+# Available feature modalities.
+  meta_features: # Static metadata, analogous to tabular data; read ca_meta.csv from the main directory.
+    required: true # meta_features is required.
+    file_name: ca_meta.csv # All provided files should be directly readable by pandas as a pd.DataFrame.
+
+  node_level_task_graphs:
+    available: true # Whether a graph for node-level tasks is available; if so, load its data from the corresponding directory.
+    graph_ids:
+      - road_distance_graph # Graph name, for example road_distance_graph (shortest driving distance graph).
+
+  time_series: 
+    available: true # Whether time series data is available; if so, load it from the corresponding directory.
+    time_series_ids:
+      - traffic_flow # Time series name, for example traffic_flow (traffic volume).
+
+tasks: # Task construction.
+# Supported task types, for example meta_feature prediction, time series forecasting, and ranking.
+  - lane_count_regression # Lane count regression.
+  - traffic_flow_forecasting # Traffic flow forecasting.
+
+# Data source information.
+license:
+  data_license: source-dependent
+  redistribution_allowed: true
+
+source:
+  name: Citi Bike Trip History
+  url: source url or citation

LargeST/input_multimodal/node_level_task_graphs/road_distance_graph/graph.yaml

@@ -0,0 +1,21 @@
+graph_id: road_distance_graph
+graph_type: static
+graph_level: node_level_task_graphs
+directed: true
+weighted: true
+description: Graph constructed by driving distance between stations
+node_set: all_samples
+
+file_name: edge_infos.csv
+
+self_loop: true
+
+edge_features:
+  available: false
+  feature_name:
+    - road_distance
+
+allowed_tasks:
+  - highway_category_classification
+  - lane_count_regression
+  - traffic_flow_forecasting

LargeST/input_multimodal/time_series/day_of_week/series.yaml

@@ -0,0 +1,30 @@
+series_id: day_of_week
+series_type: sample_level
+temporal_granularity: 900
+
+file_name: calendar_2019.csv
+variables_mode: multiple
+variables:
+  day_of_week:
+    dtype: int
+    description: day of week for each timestamp
+  hour_of_day:
+    dtype: int
+    description: hour of day for each timestamp
+  minute_of_hour:
+    dtype: int
+    description: minute of hour for each timestamp
+  is_weekend:
+    dtype: int
+    description: weekend indicator for each timestamp
+
+index:
+  time_column: timestamp
+  feature_column:
+    - day_of_week
+    - hour_of_day
+    - minute_of_hour
+    - is_weekend
+
+missing_policy:
+  missing_timetamp: fill_zero

LargeST/input_multimodal/time_series/traffic_flow/ca_his_2019.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ea8518d540db1b63859fc7ac72b86a31523d29be233462f7c0b1428cc705ffe
+size 2411192312

LargeST/input_multimodal/time_series/traffic_flow/series.yaml

@@ -0,0 +1,19 @@
+series_id: traffic_flow
+series_type: sample_level
+temporal_granularity: 900
+
+file_name: ca_his_2019.h5
+variables_mode: single
+variables:
+  traffic_flow:
+    dtype: float
+    description: traffic flow at the sensor
+
+index:
+  id_column: ID
+  time_column: timestamp
+  feature_column:
+    - traffic_flow
+
+missing_policy:
+  missing_timetamp: fill_zero

LargeST/tasks/general_task.yaml

@@ -0,0 +1,74 @@
+# ============================================================
+# LargeST_protocol/tasks/general_task.yaml
+# ============================================================
+# Purpose:
+# - This file contains only processing rules shared by all tasks and naming conventions.
+
+# Recommended reading order for each task.yaml:
+# 1) Start with sample in each task.yaml: what constitutes one sample.
+# 2) Read inputs: what the model can access at inference time.
+# 3) Read prediction: what the model should predict.
+# 4) Finally, read leakage: which columns must be excluded from model inputs.
+# ============================================================
+
+general_defaults:
+  # ------------------------------------------------------------
+  # Default input processing rules.
+  # Note: these are processing defaults, not input field defaults.
+  # These rules specify how to process time series, tabular, or graph inputs when a task uses them,
+  # but do not automatically add any input fields to a task.
+  # ------------------------------------------------------------
+  input_processing:
+    tabular_static:
+      # Unique identifier column for sensors / road segments in LargeST.
+      # This column is used only to join labels, splits, and metadata.
+      id_column: ID
+
+    time_series:
+      # LargeST historical traffic flow data typically uses timestamp as the time axis and sensor IDs as columns.
+      timestamp_column: timestamp
+      id_column: ID
+      timezone: UTC
+
+      sampling:
+        # LargeST raw historical traffic flow data typically has a 15-minute resolution.
+        # If your h5 file has been preprocessed to a different resolution, override this in the specific task.
+        native_rate: 15min
+
+        # By default, the task resolution matches the native resolution.
+        # For example, override this to 1h in task.yaml for a ranking task with hourly decisions.
+        task_rate: 15min
+
+        # Use this aggregation method when task_rate is coarser than native_rate, for example 15min -> 1h.
+        # mean is suitable for average traffic flow; sum is suitable for cumulative flow counts, subject to the data semantics.
+        downsample_method: mean
+
+        # Use this interpolation method when task_rate is finer than native_rate, for example 15min -> 5min. This task configuration is generally not recommended.
+        # ffill forward-fills using the most recent observation.
+        upsample_method: ffill
+
+    graph:
+      # Graph inputs are static by default, for example LargeST's adj.npy.
+      # Future dynamic graphs, such as event-induced or construction-impact graphs, can be configured in individual tasks.
+      directed: false
+      weighted: true
+
+
+  # ------------------------------------------------------------
+  # Standard split names.
+  # Use these standard values in splits.csv to avoid mixing train/val/valid/dev.
+  # ------------------------------------------------------------
+  split_names:
+    train: train
+    validation: val
+    test: test
+
+  # ------------------------------------------------------------
+  # Inheritance rules.
+  # A task can set inherit_general: true to inherit this file's processing defaults.
+  # Note: inheritance only affects default rules such as input_processing / columns / split_names,
+  # and does not automatically inherit any specific input fields.
+  # ------------------------------------------------------------
+  inheritance:
+    default_inherit_general: true
+    merge_strategy: recursive_override

LargeST/tasks/lane_count_regression/task.yaml

@@ -0,0 +1,88 @@
+
+
+# ============================================================
+# Task: lane_count_regression
+# ============================================================
+# Task objective:
+# - Predict the lane count, Lanes, from static attributes of LargeST road segments / sensors.
+# - This is an episode-level regression task: each ID corresponds to one static sample.
+#
+# Core semantics:
+# - sample is a road_segment / sensor ID.
+# - inputs are the static attributes available to the model.
+# - prediction.target.column = Lanes is the regression target the model should predict.
+# - Lanes must not be an input field, as this would directly leak the label.
+# ============================================================
+
+task:
+  id: lane_count_regression # Unique task ID; keep it consistent with the directory name.
+  type: episode_regression # Task type: output one continuous value or numeric label per sample.
+  description: >
+    Predict the number of lanes for each LargeST road segment / sensor episode
+    using static metadata features.
+
+inherit_general: true
+
+sample:
+  primary_key: ID # ID joins labels.csv, splits.csv, and the static attribute table.
+
+inputs:
+  # ------------------------------------------------------------
+  # Static tabular inputs.
+  # ------------------------------------------------------------
+  tabular_static:
+    source: largeST_static_metadata # Refers to the static attribute table in data_refs.tabular_static.
+
+    # Explicitly list the static fields available to the model.
+    # The following fields are common in LargeST / PeMS road metadata and may help predict lane count.
+    # If the column names in your metadata file differ, update them here accordingly.
+    fields:
+      - Lat
+      - Lng
+      - District
+      - County
+      - Fwy
+      - Lanes
+      - Type
+      - Direction
+
+
+  time_series:
+    - fields: traffic_flow  # Historical time series fields available to the model.
+      source: traffic_flow_ca_2019 # Refers to the underlying LargeST h5 file defined in data_refs.time_series.
+
+      window:
+        length: 1344
+        unit: 15min
+
+  graph:
+      source: road_distance_graph
+
+
+prediction:
+  field: Lanes # Supervision column in labels.csv.
+
+
+leakage:
+  forbidden_input_columns:
+    - Lanes # Target column; it must never be used as an input.
+    - ID # The primary key is used only for joins / indexing.
+
+  # If Fwy reveals too much about lane count, move it here from inputs.tabular_static.fields.
+  #   - Fwy
+
+data_refs:
+  tabular_static:
+    largeST_static_metadata:
+      file: All_Datasets/LargeST_ca_full/input_multimodal/ca_meta.csv # Adjust this to the actual file location; relative paths are preferred.
+      id_column: ID
+
+  time_series:
+    traffic_flow_ca_2019:
+      file: All_Datasets/LargeST_ca_full/input_multimodal/time_series/traffic_flow/ca_his_2019.h5
+      value_field: traffic_flow
+      native_grid_minutes: 15
+
+  graph:
+    road_distance_graph:
+      file: All_Datasets/LargeST_ca_full/input_multimodal/node_level_task_graphs/road_distance_graph/edge_infos.csv

LargeST/tasks/traffic_flow_forecasting/splits.csv

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LargeST/tasks/traffic_flow_forecasting/task.yaml

@@ -0,0 +1,112 @@
+
+# ============================================================
+# Task: traffic_flow_forecasting
+# ============================================================
+# Task objective:
+# - Use a historical window of LargeST traffic_flow to predict a future window.
+# - This is a typical multisensor time series forecasting task.
+#
+# Core semantics:
+# - sample defines one sample: a decision timestamp.
+# - inputs defines what the model can access: historical traffic_flow before timestamp,
+#   plus future covariates known at prediction time, such as day_of_week.
+# - prediction defines what to predict: the traffic_flow sequence over the future horizon.
+# - leakage defines which future values must be excluded from inputs: future traffic_flow must never be an input.
+# ============================================================
+
+task:
+  id: traffic_flow_forecasting # Unique task ID; keep it consistent with the directory name for automatic discovery by scripts.
+  type: time_series_forecasting # Task type: time series forecasting.
+  description: >
+    Predict future LargeST traffic_flow from historical traffic_flow.
+    Each sample corresponds to one decision timestamp, and labels are stored
+    by index reference to avoid expanding all sensor-by-time targets into labels.csv.
+
+# Whether to inherit the general processing rules in ../general_task.yaml.
+inherit_general: true
+
+sample:
+  primary_key: timestamp
+
+  # Label storage mode.
+  # index_reference: labels.csv does not expand every (ID, timestamp, horizon) label;
+  #                  instead, reference columns point to the underlying time series file.
+  # long_table:      labels.csv stores each target value in a separate row; this is larger but easier to inspect.
+  label_mode: index_reference
+
+inputs:
+  # ------------------------------------------------------------
+  # Model input 1: historical traffic flow.
+  # ------------------------------------------------------------
+  time_series:
+    - fields: traffic_flow  # Historical time series fields available to the model.
+      source: traffic_flow_ca_2019 # Refers to the underlying LargeST h5 file defined in data_refs.time_series.
+
+      window:
+        length: 64 # The history window contains 672 task time steps.
+        unit: 15min # Each step is 15 minutes, so the model sees the past 7 days.
+
+      sampling:  # Optional configuration; set this here if the raw data is not sampled every 15min.
+        native_rate: 15min # Native sampling resolution of the LargeST h5 file.
+        task_rate: 15min # Input resolution for this task, matching the native resolution.
+        downsample_method: mean # If changed to 1h, use mean to aggregate four 15min observations.
+        upsample_method: ffill # If changed to a finer resolution, use forward filling.
+
+
+
+  tabular_static:
+    source: largeST_static_metadata # Refers to the static attribute table in data_refs.tabular_static.
+
+    fields:
+      - Lat
+      - Lng
+      - District
+      - County
+      - Fwy
+      - Lanes
+      - Type
+      - Direction
+
+
+  graph:
+      source: road_distance_graph
+
+prediction:
+  field: traffic_flow # Target variable to predict.
+  source: traffic_flow_ca_2019 # Target values come from the same LargeST historical traffic flow h5 file.
+
+  horizon:
+    length: 16 # Predict the next 16 time steps.
+    unit: 15min # Each step is 15 minutes, so the forecast covers the next 4 hours.
+    starts_after: timestamp # The future window starts after sample.timestamp.
+
+  stride: 16 # Timestamps of adjacent samples are 16 task time steps apart.
+
+  label_storage:
+    ref_column: target_series_ref # Reference column in labels.csv pointing to the underlying target sequence.
+    shape_column: target_tensor_shape # Column in labels.csv recording the target tensor shape, for example [num_nodes, horizon, num_fields].
+
+leakage:
+
+  observed_past_only_columns:
+    - traffic_flow # traffic_flow can only be a historical input; future traffic_flow is the prediction target and must not be an input.
+
+
+
+data_refs:
+  time_series:
+    - traffic_flow_ca_2019:
+      file: All_Datasets/LargeST_ca_full/input_multimodal/time_series/traffic_flow/ca_his_2019.h5 # LargeST California 2019 historical traffic flow file.
+      value_field: traffic_flow # Semantics of the corresponding variable in the h5 file.
+      native_grid_minutes: 15 # The native time grid is 15 minutes.
+
+
+
+  tabular_static:
+    largeST_static_metadata:
+      file: All_Datasets/LargeST_ca_full/input_multimodal/ca_meta.csv # Static sensor attribute table.
+      id_column: ID
+
+  graph:
+    road_distance_graph:
+      file: All_Datasets/LargeST_ca_full/input_multimodal/node_level_task_graphs/road_distance_graph/edge_infos.csv

M5/dataset.py

@@ -0,0 +1,426 @@
+from __future__ import annotations
+
+import argparse
+import tempfile
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict, Iterable
+from torch.utils.data import Dataset, DataLoader
+
+import pandas as pd
+import yaml
+
+# Record whether the required fields are provided.
+field_dict = {
+    'dataset': {'modalities': ['meta_features', 'node_level_task_graphs', 'time_series']},
+    'graphs': ['', '', '', ''],
+    'time_series': ['', '', '', ' ', '']
+}
+
+def read_yaml(path: Path) -> dict[str, Any]:
+    with path.open("r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+def read_all_configs(dataset_yaml_path: str) -> Dict[str, Any]:
+    """
+    Read all related configuration files, starting from dataset.yaml.
+    
+    Args:
+        dataset_yaml_path: Path to dataset.yaml.
+        
+    Returns:
+        A dictionary containing all configurations.
+    """
+    # Get the protocol directory path.
+    protocol_dir = Path(dataset_yaml_path).parent
+    
+    # 1. Read dataset.yaml.
+    dataset_config = read_yaml(Path(dataset_yaml_path))
+    print(f"已读取 dataset.yaml: {dataset_config['dataset_name']}")
+    
+    # 2. Read graph configurations.
+    graph_configs = {}
+    if 'node_level_task_graphs' in dataset_config['modalities']:
+        graph_ids = dataset_config['modalities']['node_level_task_graphs'].get('graph_ids', [])
+        
+        for graph_id in graph_ids:
+            graph_yaml_path = protocol_dir / f"input_multimodal/node_level_task_graphs/{graph_id}/graph.yaml"
+            if graph_yaml_path.exists():
+                graph_config = read_yaml(Path(graph_yaml_path))
+                graph_configs[graph_id] = graph_config
+                print(f"已读取 graph.yaml: {graph_id}")
+            else:
+                print(f"警告: 未找到 graph.yaml: {graph_yaml_path}")
+    
+    # 3. Read time series configurations.
+    ts_configs = {}
+    if 'time_series' in dataset_config['modalities']:
+        ts_ids = dataset_config['modalities']['time_series'].get('time_series_ids', [])
+        
+        for ts_id in ts_ids:
+            ts_yaml_path = protocol_dir / f"input_multimodal/time_series/{ts_id}/series.yaml"
+            if ts_yaml_path.exists():
+                ts_config = read_yaml(Path(ts_yaml_path))
+                ts_configs[ts_id] = ts_config
+                print(f"已读取 series.yaml: {ts_id}")
+            else:
+                print(f"警告: 未找到 series.yaml: {ts_yaml_path}")
+    
+    # 4. Combine all configurations.
+    all_configs = {
+        'dataset': dataset_config,
+        'graphs': graph_configs,
+        'time_series': ts_configs
+    }
+    
+    return all_configs
+
+def _first_existing(candidates: Iterable[Path]) -> Path:
+    candidate_list = [Path(p).expanduser() for p in candidates]
+    for p in candidate_list:  # Return an existing path from the candidate paths.
+        if p.is_file():
+            return p.resolve()
+    shown = ", ".join(str(c) for c in candidate_list)
+    raise FileNotFoundError(f"None of the candidate paths exist: {shown}")
+
+
+def _load_frame(path: Path) -> pd.DataFrame:
+    """Load a single-table file as used in protocol YAMLs (csv / parquet / h5)."""
+    path = Path(path).expanduser().resolve()
+    suf = path.suffix.lower()
+    if suf == ".csv":
+        return pd.read_csv(path)
+    if suf in (".tsv", ".txt"):
+        return pd.read_csv(path, sep="\t")
+    if suf in (".parquet", ".pq"):
+        return pd.read_parquet(path)
+    if suf in (".h5", ".hdf5", ".hdf"):
+        return pd.read_hdf(path)
+    raise ValueError(f"Unsupported data file extension for {path}")
+
+
+def _meta_candidates(dataset_dir: Path, file_name: str) -> list[Path]:
+    p = Path(file_name).expanduser()
+    if p.is_absolute():
+        return [p]
+    return [
+        dataset_dir / "input_multimodal" / file_name,
+    ]
+
+
+def _graph_file_candidates(dataset_dir: Path, graph_level: str, graph_id: str, file_name: str) -> list[Path]:
+    p = Path(file_name).expanduser()
+    if p.is_absolute():
+        return [p]
+    base = [
+        dataset_dir
+        / "input_multimodal"
+        / graph_level
+        / graph_id
+        / file_name,
+    ]
+    return base
+
+
+def _series_file_candidates(dataset_dir: Path, series_id: str, file_name: str) -> list[Path]: # Generate all candidate storage paths.
+    p = Path(file_name).expanduser()
+    if p.is_absolute():
+        return [p]
+    return [
+        dataset_dir / "input_multimodal" / "time_series" / series_id / file_name,
+        dataset_dir / file_name,
+        dataset_dir / "input_multimodal" / file_name,
+    ]
+
+def _parse_meta(meta: pd.DataFrame) -> Dict[str, Any]:
+    # Parse each column in meta_df and convert the data to a dictionary.
+    meta_dict = {}
+    for col in meta.columns:
+        meta_dict[col] = list(meta[col])
+    return meta_dict
+
+def _parse_graph(graph: pd.DataFrame, graph_features: list[str] | None) -> Dict[str, Any]:
+    ''' 
+    Required information in the graph dictionary includes:
+    - A sparse adjacency list
+    - The weight of each edge
+    - Edge features (optional)
+    '''
+    graph_dict = {}
+    required_fields = ['src_id', 'dst_id', 'weight']
+    for field in required_fields:
+        if field not in graph.columns:
+            raise ValueError(f"Missing required column: {field}")
+
+    graph_dict['edge'] = list(zip(graph['src_id'], graph['dst_id']))
+    graph_dict['weight'] = graph['weight'].tolist()
+    if all(x == 1.0 for x in list(graph_dict['weight'])):
+        graph_dict['weighted'] = False
+    else:
+        graph_dict['weighted'] = True
+
+    if graph_features is not None: # Add graph features to the dictionary if they are available.
+        for feature in graph_features:
+            graph_dict[feature] = list(graph[feature])
+    
+    return graph_dict
+
+def _parse_time_series(time_series: pd.DataFrame, scfg: dict[str, Any], time2graph_map: Dict[str, Any]|None) -> Dict[str, Any]:
+    
+    time_series_dict = {}   
+    variables_mode = scfg.get("variables_mode")
+    time_series_dict['variables_mode'] = variables_mode # Use mode to distinguish single-variable and multivariable data.
+
+    if variables_mode == "single": # For a single variable, the index is timestamp, column names are sample_id values, and the feature name is series_id.
+        time_type = str(time_series.index.dtype)
+
+        time_series_dict['time_type'] = time_type
+        time_series_dict['timestamp'] = time_series.index.tolist()
+        time_series_dict['value'] = {}
+        if time2graph_map: # 
+            for col in time_series.columns:
+                time_series_dict['value'][time2graph_map.get(int(col), col)] = list(time_series[col])
+        else:
+            for col in time_series.columns:
+                time_series_dict['value'][col] = list(time_series[col])
+    else: 
+        # For multiple variables, the columns are sample_id, timestamp, feature_name1, feature_name2, ...
+        # Extract timestamps and feature values in timestamp order, using the structure feature_name*: {sample_id: [values]}.
+        index_cfg = scfg.get("index") or {}
+        id_col = index_cfg.get("id_colunm") or index_cfg.get("id_column")
+        time_col = index_cfg.get("time_column")
+        if not id_col:
+            raise ValueError("time_series.index must configure id_colunm or id_column")
+        if not time_col:
+            raise ValueError("time_series.index must configure time_column")
+
+        required_columns = [id_col, time_col]
+        missing_columns = [col for col in required_columns if col not in time_series.columns]
+        if missing_columns:
+            raise ValueError(f"Missing required time series column(s): {missing_columns}")
+
+        series_features = index_cfg.get("feature_column")
+        if not series_features:
+            series_features = [col for col in time_series.columns if col not in required_columns]
+        elif isinstance(series_features, str):
+            series_features = [series_features]
+        else:
+            series_features = list(series_features)
+
+        if not series_features:
+            raise ValueError("No feature columns configured or inferred for time series")
+
+        missing_features = [col for col in series_features if col not in time_series.columns]
+        if missing_features:
+            raise ValueError(f"Missing configured feature column(s): {missing_features}")
+
+        sorted_time_series = time_series.sort_values([id_col, time_col])
+        time_series_dict['feature_names'] = series_features
+        time_series_dict['time_type'] = str(time_series[time_col].dtype)
+        time_series_dict['timestamp'] = sorted(time_series[time_col].drop_duplicates().tolist())
+        timestamp_by_sample = {}
+        time_series_dict['value'] = {feature: {} for feature in series_features}
+
+        for sample_id, sample_df in sorted_time_series.groupby(id_col, sort=False):
+            output_sample_id = time2graph_map.get(sample_id, sample_id) if time2graph_map else sample_id
+            timestamp_by_sample[output_sample_id] = sample_df[time_col].tolist()
+            for feature in series_features:
+                time_series_dict['value'][feature][output_sample_id] = sample_df[feature].tolist()
+
+        if any(timestamps != time_series_dict['timestamp'] for timestamps in timestamp_by_sample.values()):
+            time_series_dict['timestamp_by_sample'] = timestamp_by_sample
+        
+    return time_series_dict
+
+@dataclass
+class LoadedDataset:
+    dataset_dir: Path
+    config: dict[str, Any]
+    meta_features: Dict[str, Any]
+    graphs: Dict[str, Any]
+    time_series: Dict[str, Any]
+ 
+    
+# def _check_graphs(graph_config: dict[str, Any]) -> None:
+#     check 
+
+
+# Read all YAML files into one configuration.
+class MultiModalDataset(LoadedDataset):
+    """
+    Use the configuration returned by ``read_all_configs`` to resolve protocol paths under ``dataset_dir`` and load tables.
+
+    ``dataset_dir`` is usually the protocol root containing ``input_multimodal/`` and ``dataset.yaml``,
+    or an equivalent data root. Path resolution tries ``dataset_dir/<file>`` and
+    ``dataset_dir/input_multimodal/...``, among other candidate locations, in order.
+    """
+
+    def __init__(self, config: dict[str, Any], dataset_dir: Path | str) -> None:
+        root = Path(dataset_dir).expanduser().resolve()
+        dataset_cfg = config["dataset"]
+        modalities = dataset_cfg.get("modalities") or {}
+        time2graph_map = {}
+
+        # Read meta_features data.
+        meta_block: Dict[str, Any] = {}
+        if isinstance(modalities.get("meta_features"), dict):
+            mf = modalities["meta_features"]
+            meta_file = mf.get("file_name")
+            if mf.get("required") and not meta_file:
+                raise ValueError("meta_features is required but file_name is not configured")
+            if meta_file:
+                meta_path = _first_existing(_meta_candidates(root, str(meta_file)))
+                meta_tmp = _load_frame(meta_path)
+                meta_features = _parse_meta(meta_tmp) # Parse meta_df.
+                sample_def = dataset_cfg.get("sample_defination") or dataset_cfg.get(
+                    "sample_definition"
+                )
+                id_col_graph = None
+                id_col_time_series = None
+                if isinstance(sample_def, dict):
+                    id_col_graph = sample_def.get("sample_id_graph")
+                    id_col_time_series = sample_def.get("sample_id_time_series")
+
+                meta_block = {
+                    "path": str(meta_path),
+                    "features": meta_features,
+                    "sample_id_graph": id_col_graph,
+                    "sample_id_time_series": id_col_time_series,
+                }
+                if id_col_graph is not None and id_col_time_series is not None: 
+                    # If both id_col_graph and id_col_time_series exist, build a mapping between them to align the two modalities.
+                    # By default, map time series IDs from id_col_time_series to graph IDs in id_col_graph.
+                    time2graph_map = {time_id: graph_id for time_id, graph_id in zip(meta_tmp[id_col_time_series], meta_tmp[id_col_graph])}
+        # Read graph data.
+        graphs: Dict[str, Any] = {}
+        for gid, gcfg in (config.get("graphs") or {}).items():
+            if not isinstance(gcfg, dict):
+                continue
+            fname = gcfg.get("file_name")
+            graph_level = gcfg.get("graph_level") # Graph level, for example: node_level_task_graph, graph_level_task_graph.
+            graph_directed = gcfg.get("directed") # Graph type, for example: directed, undirected.
+            edge_features_cfg = gcfg.get("edge_features") or {}
+            graph_features = edge_features_cfg.get("feature_name") if edge_features_cfg.get("available") else None # Graph features, for example: edge_features.
+            if not fname:
+                continue
+            
+            gpath = _first_existing(_graph_file_candidates(root, str(graph_level), str(gid), str(fname)))
+            tmp_graph = _load_frame(gpath)
+            # Continue parsing individual graph features.
+            tmp_graph_dict = _parse_graph(tmp_graph, graph_features)
+            tmp_graph_dict['directed'] = graph_directed
+            tmp_graph_dict['edge_features'] = graph_features
+            graphs[str(gid)] = tmp_graph_dict # Load graph data into the dictionary.
+
+        time_series: Dict[str, Any] = {}
+        for sid, scfg in (config.get("time_series") or {}).items():
+            if not isinstance(scfg, dict):
+                continue
+            fname = scfg.get("file_name")
+            if not fname:
+                continue
+            spath = _first_existing(_series_file_candidates(root, str(sid), str(fname)))
+            tmp_time_series = _load_frame(spath)
+            
+            frequency = scfg.get("temporal_granularity")
+            tmp_time_series_dict = _parse_time_series(tmp_time_series, scfg, time2graph_map)
+            tmp_time_series_dict['frequency'] = frequency
+            time_series[str(sid)] = tmp_time_series_dict
+
+        super().__init__(
+            dataset_dir=root,
+            config=config,
+            meta_features=meta_block,
+            graphs=graphs,
+            time_series=time_series,
+        )
+
+
+if __name__ == '__main__':
+    protocol_dir = Path(__file__).resolve().parent
+    dataset_yaml_path = protocol_dir / "dataset.yaml"
+
+    configs = read_all_configs(str(dataset_yaml_path))
+    dataset = MultiModalDataset(configs, protocol_dir)
+
+    print("--- 结构校验 (True/False) ---")
+    mf = dataset.meta_features if isinstance(dataset.meta_features, dict) else {}
+    feats = mf.get("features")
+    if isinstance(feats, dict):
+        print("meta_features['features'] 为 dict: True")
+    else:
+        print("meta_features['features'] 为 dict: False")
+
+    if isinstance(dataset.graphs, dict):
+        print("dataset.graphs 为 dict: True")
+    else:
+        print("dataset.graphs 为 dict: False")
+
+    if isinstance(dataset.graphs, dict) and "item_graph" in dataset.graphs:
+        print("graphs 含 item_graph: True")
+    else:
+        print("graphs 含 item_graph: False")
+
+    if isinstance(dataset.graphs, dict) and isinstance(dataset.graphs.get("item_graph"), dict):
+        print("graphs['item_graph'] 为 dict: True")
+    else:
+        print("graphs['item_graph'] 为 dict: False")
+
+    if isinstance(dataset.time_series, dict):
+        print("dataset.time_series 为 dict: True")
+    else:
+        print("dataset.time_series 为 dict: False")
+
+    _ts_fields = ("variables_mode", "timestamp", "value", "frequency")
+    for sid in (
+        "calendar_month",
+        "calendar_wday",
+        "calendar_year",
+        "sell_price",
+        "snap_CA",
+        "snap_TX",
+        "snap_WI",
+        "unit_sales",
+    ):
+        if isinstance(dataset.time_series, dict) and sid in dataset.time_series:
+            print(f"time_series 含 {sid}: True")
+        else:
+            print(f"time_series 含 {sid}: False")
+            continue
+        s = dataset.time_series[sid]
+        if isinstance(s, dict):
+            print(f"{sid} 为 dict: True")
+        else:
+            print(f"{sid} 为 dict: False")
+            continue
+        for field in _ts_fields:
+            if field in s:
+                print(f"{sid} 含字段 {field}: True")
+            else:
+                print(f"{sid} 含字段 {field}: False")
+
+    print("--- 摘要 ---")
+    print(f"Dataset: {dataset.config['dataset'].get('dataset_name')}")
+    print(f"Protocol dir: {dataset.dataset_dir}")
+    print(f"Meta features: columns={len(dataset.meta_features.get('features', {}))}")
+    for graph_id, graph_dict in dataset.graphs.items():
+        print(
+            f"Graph[{graph_id}]: edges={len(graph_dict.get('edge', []))}, "
+            f"directed={graph_dict.get('directed')}"
+        )
+    for series_id, series_data in dataset.time_series.items():
+        if isinstance(series_data, dict):
+            value = series_data.get("value", {})
+            if series_data.get("variables_mode") == "single":
+                feature_count = 1
+                sample_count = len(value)
+            else:
+                feature_count = len(value)
+                sample_count = len(next(iter(value.values()), {}))
+            print(
+                f"TimeSeries[{series_id}]: mode={series_data.get('variables_mode')}, "
+                f"features={feature_count}, timestamps={len(series_data.get('timestamp', []))}, "
+                f"samples={sample_count}"
+            )
+        else:
+            print(f"TimeSeries[{series_id}]: shape={series_data.shape}")

M5/dataset.yaml

@@ -0,0 +1,46 @@
+# Dataset information: the entry point for dataset configuration, including file locations for each modality.
+dataset_id: M5
+dataset_name: M5 Forecasting Accuracy
+domain: retail_demand_forecasting
+
+sample_defination:
+  sample_type: item-store sales episode
+  sample_id_graph: episode_id
+  description: each sample corresponds to one item sold at one Walmart store
+
+modalities:
+# Available feature modalities.
+  meta_features:
+    required: true
+    file_name: meta.csv
+
+  node_level_task_graphs:
+    available: true
+    graph_ids:
+      - item_graph
+
+  time_series:
+    available: true
+    time_series_ids:
+      - calendar_month
+      - calendar_wday
+      - calendar_year
+      - sell_price
+      - snap_CA
+      - snap_TX
+      - snap_WI
+      - unit_sales
+    
+tasks:
+# Supported task types, for example meta_feature prediction, time series forecasting, and ranking.
+  - item_category_classification
+  - episode_unit_sales_forecasting
+
+# Data source information.
+license:
+  data_license: Kaggle competition data terms
+  redistribution_allowed: source-dependent
+
+source:
+  name: M5 Forecasting - Accuracy
+  url: https://www.kaggle.com/competitions/m5-forecasting-accuracy

M5/input_multimodal/node_level_task_graphs/item_graph/edge_info.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f5c8edaff700028ce6aca98a03a1110433582a8895622917fa38c80f2411561
+size 292087633

M5/input_multimodal/node_level_task_graphs/item_graph/graph.yaml

@@ -0,0 +1,22 @@
+graph_id: item_graph
+graph_type: static
+graph_level: node_level_task_graphs
+directed: false
+weighted: true
+description: >
+  Undirected graph on item-store episodes; edge weight is Pearson correlation
+  of daily sales d_1..d_1885. An edge exists iff abs(correlation) exceeds the
+  build threshold (0.28). src_id/dst_id are integer episode_id (episode_index) from process/episode_id_index.csv.
+node_set: all_samples
+
+file_name: edge_info.csv
+
+self_loop: false
+
+edge_features:
+  available: false
+  feature_name: []
+
+allowed_tasks:
+  - item_category_classification
+  - episode_unit_sales_forecasting

M5/input_multimodal/time_series/calendar_month/calendar_month.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1bea05191e7ddcfd5f26b3b7215ce1255f86af04656e664cdf4d9f5de6670421
+size 238635460

M5/input_multimodal/time_series/calendar_month/series.yaml

@@ -0,0 +1,19 @@
+series_id: calendar_month
+series_type: sample_level # Time series type, for example sample_level.
+temporal_granularity: 86400 # Time series resolution in seconds, for example 5min = 300s, 15min = 900s, 30min = 1800s, and 86400s = 1day.
+
+file_name: calendar_month.h5 # All provided files should be directly readable by pandas as a pd.DataFrame.
+variables_mode: single # For a single variable, IDs are usually column names and timestamp is the row index.name.
+variables: # Time series variables, usually a single variable.
+  calendar_month:
+    dtype: int
+    description: calendar month
+
+index: # For multiple variables, specify id_colunm and feature_column.
+  id_column: episode_id
+  time_column: timestamp
+  feature_column: # The first two fields are fixed: sample_id and timestamp; subsequent fields depend on the dataset.
+    - calendar_month
+  
+missing_policy:
+  missing_timetamp: fill_zero

M5/input_multimodal/time_series/calendar_wday/calendar_wday.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:740818835b1d5c34902b4826003842f8d16bd45f0408d7ef9f5c692b6040f155
+size 238635460

M5/input_multimodal/time_series/calendar_wday/series.yaml

@@ -0,0 +1,19 @@
+series_id: calendar_wday
+series_type: sample_level # Time series type, for example sample_level.
+temporal_granularity: 86400 # Time series resolution in seconds, for example 5min = 300s, 15min = 900s, 30min = 1800s, and 86400s = 1day.
+
+file_name: calendar_wday.h5 # All provided files should be directly readable by pandas as a pd.DataFrame.
+variables_mode: single # For a single variable, IDs are usually column names and timestamp is the row index.name.
+variables: # Time series variables, usually a single variable.
+  calendar_wday:
+    dtype: int
+    description: calendar weekday
+
+index: # For multiple variables, specify id_colunm and feature_column.
+  id_column: episode_id
+  time_column: timestamp
+  feature_column: # The first two fields are fixed: sample_id and timestamp; subsequent fields depend on the dataset.
+    - calendar_wday
+  
+missing_policy:
+  missing_timetamp: fill_zero

M5/input_multimodal/time_series/calendar_year/calendar_year.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb726a24bc3c6582541eb4ef0199e2ef4cc18e2f8d8d028df2a3240b0edc2820
+size 238635460

M5/input_multimodal/time_series/calendar_year/series.yaml

@@ -0,0 +1,19 @@
+series_id: calendar_year
+series_type: sample_level # Time series type, for example sample_level.
+temporal_granularity: 86400 # Time series resolution in seconds, for example 5min = 300s, 15min = 900s, 30min = 1800s, and 86400s = 1day.
+
+file_name: calendar_year.h5 # All provided files should be directly readable by pandas as a pd.DataFrame.
+variables_mode: single # For a single variable, IDs are usually column names and timestamp is the row index.name.
+variables: # Time series variables, usually a single variable.
+  calendar_year:
+    dtype: int
+    description: calendar year
+
+index: # For multiple variables, specify id_colunm and feature_column.
+  id_column: episode_id
+  time_column: timestamp
+  feature_column: # The first two fields are fixed: sample_id and timestamp; subsequent fields depend on the dataset.
+    - calendar_year
+  
+missing_policy:
+  missing_timetamp: fill_zero

M5/input_multimodal/time_series/sell_price/sell_price.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:34c0c72e506969d0fb2becc5cba33f8aa29b4b18fc99f50347e6e672eb22d6c2
+size 238635460

M5/input_multimodal/time_series/sell_price/series.yaml

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+series_id: sell_price
+series_type: sample_level # Time series type, for example sample_level.
+temporal_granularity: 86400 # Time series resolution in seconds, for example 5min = 300s, 15min = 900s, 30min = 1800s, and 86400s = 1day.
+
+file_name: sell_price.h5 # All provided files should be directly readable by pandas as a pd.DataFrame.
+variables_mode: single # For a single variable, IDs are usually column names and timestamp is the row index.name.
+variables: # Time series variables, usually a single variable.
+  sell_price:
+    dtype: float
+    description: sell price
+
+index: # For multiple variables, specify id_colunm and feature_column.
+  id_column: episode_id
+  time_column: timestamp
+  feature_column: # The first two fields are fixed: sample_id and timestamp; subsequent fields depend on the dataset.
+    - sell_price
+  
+missing_policy:
+  missing_timetamp: fill_zero

M5/input_multimodal/time_series/snap_CA/series.yaml

@@ -0,0 +1,19 @@
+series_id: snap_CA
+series_type: sample_level # Time series type, for example sample_level.
+temporal_granularity: 86400 # Time series resolution in seconds, for example 5min = 300s, 15min = 900s, 30min = 1800s, and 86400s = 1day.
+
+file_name: snap_CA.h5 # All provided files should be directly readable by pandas as a pd.DataFrame.
+variables_mode: single # For a single variable, IDs are usually column names and timestamp is the row index.name.
+variables: # Time series variables, usually a single variable.
+  snap_CA:
+    dtype: int
+    description: snap CA index
+
+index: # For multiple variables, specify id_colunm and feature_column.
+  id_column: episode_id
+  time_column: timestamp
+  feature_column: # The first two fields are fixed: sample_id and timestamp; subsequent fields depend on the dataset.
+    - snap_CA
+  
+missing_policy:
+  missing_timetamp: fill_zero

M5/input_multimodal/time_series/snap_CA/snap_CA.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:50f54d25e24c4f72d60b338ab585e193bab8ad8f57f00740ac613b0bfb15e075
+size 238635460

M5/input_multimodal/time_series/snap_TX/series.yaml

@@ -0,0 +1,19 @@
+series_id: snap_TX
+series_type: sample_level # Time series type, for example sample_level.
+temporal_granularity: 86400 # Time series resolution in seconds, for example 5min = 300s, 15min = 900s, 30min = 1800s, and 86400s = 1day.
+
+file_name: snap_TX.h5 # All provided files should be directly readable by pandas as a pd.DataFrame.
+variables_mode: single # For a single variable, IDs are usually column names and timestamp is the row index.name.
+variables: # Time series variables, usually a single variable.
+  snap_TX:
+    dtype: int
+    description: snap TX index
+
+index: # For multiple variables, specify id_colunm and feature_column.
+  id_column: episode_id
+  time_column: timestamp
+  feature_column: # The first two fields are fixed: sample_id and timestamp; subsequent fields depend on the dataset.
+    - snap_TX
+  
+missing_policy:
+  missing_timetamp: fill_zero

M5/input_multimodal/time_series/snap_TX/snap_TX.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:403d1d3cb55ab09654ad02368afee762b5bda1c9ff022c1506a223e38f11d994
+size 238635460

M5/input_multimodal/time_series/snap_WI/series.yaml

@@ -0,0 +1,19 @@
+series_id: snap_WI
+series_type: sample_level # Time series type, for example sample_level.
+temporal_granularity: 86400 # Time series resolution in seconds, for example 5min = 300s, 15min = 900s, 30min = 1800s, and 86400s = 1day.
+
+file_name: snap_WI.h5 # All provided files should be directly readable by pandas as a pd.DataFrame.
+variables_mode: single # For a single variable, IDs are usually column names and timestamp is the row index.name.
+variables: # Time series variables, usually a single variable.
+  snap_WI:
+    dtype: int
+    description: snap WI index
+
+index: # For multiple variables, specify id_colunm and feature_column.
+  id_column: episode_id
+  time_column: timestamp
+  feature_column: # The first two fields are fixed: sample_id and timestamp; subsequent fields depend on the dataset.
+    - snap_WI
+  
+missing_policy:
+  missing_timetamp: fill_zero