Delete time_series_library.py

time_series_library.py

@@ -1,90 +0,0 @@
-# time_series_library.py
-print("[TSL] custom script loaded")
-import os, pandas as pd
-from datasets import (
-    GeneratorBasedBuilder, Split, SplitGenerator, Features, Value
-)
-
-class TimeSeriesLibrary(GeneratorBasedBuilder):
-    print("[TSL] custom script loaded")
-    BUILDER_CONFIGS = []
-
-    def _info(self):
-        import pandas as pd
-        from datasets import Features, Value
-        df = pd.read_csv(
-            list(self.config.data_files.values())[0]
-            if isinstance(self.config.data_files, dict)
-            else self.config.data_files[0],
-            nrows=10
-        )
-        cols = {}
-        for c in df.columns:
-            cols[c] = Value("string") if df[c].dtype == object else Value("float64")
-        return self.dataset_info_from_configs(features=Features(cols))
-
-
-    from datasets import Split, SplitGenerator
-    
-    def _split_generators(self, dl_manager):
-        print("use spilt")
-        dfiles = self.config.data_files
-        files = list(dfiles) if isinstance(dfiles, (list, tuple)) else [dfiles]
-    
-        gens = []
-    
-        def split_name_for(path, used):
-            """按文件名自动判别 split；若重复则加后缀避免合并"""
-            name = os.path.basename(str(path)).lower()
-            if "train" in name:
-                base = "train"
-            elif any(k in name for k in ["val", "valid", "validation"]):
-                base = "validation"
-            elif "test_label" in name or "label" in name:
-                base = "test_label"
-            elif "test" in name:
-                base = "test"
-            else:
-                # 没有关键字就用文件名（去掉拓展名）
-                base = os.path.splitext(os.path.basename(str(path)))[0]
-    
-            # 避免同名冲突：train/train_1/train_2...
-            s = base
-            idx = 1
-            while s in used:
-                s = f"{base}_{idx}"
-                idx += 1
-            used.add(s)
-            return s
-    
-        used_names = set()
-        for f in files:
-            sname = split_name_for(f, used_names)
-            # 标准 split 用内置常量；自定义用 Split(sname)
-            if sname == "train":
-                gens.append(SplitGenerator(name=Split.TRAIN,       gen_kwargs={"filepath": f}))
-            elif sname == "validation":
-                gens.append(SplitGenerator(name=Split.VALIDATION,  gen_kwargs={"filepath": f}))
-            elif sname == "test":
-                gens.append(SplitGenerator(name=Split.TEST,        gen_kwargs={"filepath": f}))
-            else:
-                gens.append(SplitGenerator(name=Split(sname),      gen_kwargs={"filepath": f}))
-    
-        # 如果列表为空（极端情况），兜底把所有文件各自做一个 split
-        if not gens:
-            for i, f in enumerate(files):
-                gens.append(SplitGenerator(name=Split(f"file_{i}"), gen_kwargs={"filepath": f}))
-    
-        return gens
-
-    
-
-    def _generate_examples(self, filepath, row_slice=None):
-        if isinstance(filepath, (list, tuple)):
-            filepath = filepath[0]
-        df = pd.read_csv(filepath)
-        if row_slice:
-            a, b = row_slice
-            df = df.iloc[a:b]
-        for i, row in df.iterrows():
-            yield i, row.to_dict()