test

customers/test.csv

@@ -0,0 +1,16 @@
+,customer_id,name,age
+96,97,Customer_97,40
+4,5,Customer_5,26
+42,43,Customer_43,35
+77,78,Customer_78,32
+10,11,Customer_11,71
+0,1,Customer_1,55
+9,10,Customer_10,71
+69,70,Customer_70,54
+73,74,Customer_74,21
+83,84,Customer_84,22
+44,45,Customer_45,65
+76,77,Customer_77,40
+39,40,Customer_40,29
+33,34,Customer_34,31
+30,31,Customer_31,67

customers/train.csv

@@ -0,0 +1,71 @@
+,customer_id,name,age
+11,12,Customer_12,52
+47,48,Customer_48,40
+85,86,Customer_86,24
+28,29,Customer_29,38
+93,94,Customer_94,66
+5,6,Customer_6,46
+66,67,Customer_67,31
+65,66,Customer_66,24
+35,36,Customer_36,62
+16,17,Customer_17,39
+49,50,Customer_50,69
+34,35,Customer_35,51
+7,8,Customer_8,70
+95,96,Customer_96,29
+27,28,Customer_28,79
+19,20,Customer_20,56
+81,82,Customer_82,69
+25,26,Customer_26,50
+62,63,Customer_63,68
+13,14,Customer_14,21
+24,25,Customer_25,57
+3,4,Customer_4,30
+17,18,Customer_18,23
+38,39,Customer_39,22
+8,9,Customer_9,71
+78,79,Customer_79,35
+6,7,Customer_7,53
+64,65,Customer_65,68
+36,37,Customer_37,68
+89,90,Customer_90,70
+56,57,Customer_57,28
+99,100,Customer_100,58
+54,55,Customer_55,53
+43,44,Customer_44,51
+50,51,Customer_51,45
+67,68,Customer_68,60
+46,47,Customer_47,46
+68,69,Customer_69,46
+61,62,Customer_62,52
+97,98,Customer_98,35
+79,80,Customer_80,30
+41,42,Customer_42,72
+58,59,Customer_59,64
+48,49,Customer_49,37
+98,99,Customer_99,46
+57,58,Customer_58,65
+75,76,Customer_76,27
+32,33,Customer_33,79
+94,95,Customer_95,49
+59,60,Customer_60,56
+63,64,Customer_64,59
+84,85,Customer_85,21
+37,38,Customer_38,69
+29,30,Customer_30,77
+1,2,Customer_2,56
+52,53,Customer_53,30
+21,22,Customer_22,52
+2,3,Customer_3,55
+23,24,Customer_24,71
+87,88,Customer_88,36
+91,92,Customer_92,76
+74,75,Customer_75,44
+86,87,Customer_87,30
+82,83,Customer_83,37
+20,21,Customer_21,51
+60,61,Customer_61,51
+71,72,Customer_72,49
+14,15,Customer_15,23
+92,93,Customer_93,24
+51,52,Customer_52,29

customers/val.csv

@@ -0,0 +1,16 @@
+,customer_id,name,age
+26,27,Customer_27,58
+53,54,Customer_54,43
+70,71,Customer_71,22
+15,16,Customer_16,49
+45,46,Customer_46,57
+88,89,Customer_89,73
+40,41,Customer_41,79
+12,13,Customer_13,69
+72,73,Customer_73,79
+55,56,Customer_56,43
+80,81,Customer_81,27
+18,19,Customer_19,58
+90,91,Customer_91,23
+31,32,Customer_32,33
+22,23,Customer_23,53

products/test.csv

@@ -0,0 +1,13 @@
+,product_id,name,price
+4,5,Product_5,269.37079112122996
+73,74,Product_74,685.4914088169825
+30,31,Product_31,455.75885589146424
+55,56,Product_56,417.2148362971622
+33,34,Product_34,535.8877036986996
+12,13,Product_13,850.2555800942548
+35,36,Product_36,884.8205869267379
+0,1,Product_1,781.976413555275
+64,65,Product_65,343.7487279386133
+28,29,Product_29,744.4778261439482
+22,23,Product_23,245.88954088766758
+67,68,Product_68,675.1697127782456

products/train.csv

@@ -0,0 +1,57 @@
+,product_id,name,price
+34,35,Product_35,627.0339105804485
+62,63,Product_63,795.2203625971675
+42,43,Product_43,431.42174870634176
+54,55,Product_55,171.29278952097133
+16,17,Product_17,727.2072210972166
+39,40,Product_40,488.4934137670739
+56,57,Product_57,232.33327948040628
+79,80,Product_80,818.5325554700922
+7,8,Product_8,172.55514641687452
+50,51,Product_51,484.58955660187667
+53,54,Product_54,104.3223454128037
+19,20,Product_20,627.8929825553761
+66,67,Product_67,160.2453054361185
+25,26,Product_26,733.2758690991045
+44,45,Product_45,221.6231309201294
+13,14,Product_14,646.7432023479928
+76,77,Product_77,343.43198976410105
+3,4,Product_4,472.5873287639955
+17,18,Product_18,289.6892006326001
+38,39,Product_39,935.7701837570473
+8,9,Product_9,711.887311071343
+65,66,Product_66,580.6405432081264
+6,7,Product_7,267.3146277123249
+36,37,Product_37,30.57509848504054
+72,73,Product_73,893.1409905386581
+58,59,Product_59,691.6589417200158
+46,47,Product_47,136.55068856676792
+78,79,Product_79,853.188338930974
+15,16,Product_16,450.0831695158656
+27,28,Product_28,881.2456312569794
+41,42,Product_42,566.9732082525446
+26,27,Product_27,742.0870715920396
+48,49,Product_49,894.6951757165921
+24,25,Product_25,794.4546339940993
+43,44,Product_44,587.238548414977
+77,78,Product_78,524.5048062872403
+57,58,Product_58,590.7441471934189
+11,12,Product_12,290.99334081899434
+32,33,Product_33,10.535300745444836
+75,76,Product_76,965.8899199420919
+59,60,Product_60,821.4789406713716
+63,64,Product_64,703.1451760515428
+69,70,Product_70,878.63055771838
+37,38,Product_38,475.778730622188
+29,30,Product_30,911.5928884027744
+1,2,Product_2,607.9732982452558
+52,53,Product_53,669.7002011972322
+21,22,Product_22,170.68073655716717
+2,3,Product_3,521.0349099965142
+23,24,Product_24,704.275434148361
+74,75,Product_75,338.606477325675
+20,21,Product_21,89.88173740252655
+60,61,Product_61,666.7241424290982
+71,72,Product_72,798.0284175208953
+14,15,Product_15,646.53290939936
+51,52,Product_52,319.54741973678875

products/val.csv

@@ -0,0 +1,13 @@
+,product_id,name,price
+45,46,Product_46,238.7534646641605
+31,32,Product_32,272.12339271529373
+18,19,Product_19,702.6422504593837
+5,6,Product_6,286.3477557446952
+61,62,Product_62,876.9379619780921
+9,10,Product_10,507.5089689086898
+47,48,Product_48,457.126492113172
+70,71,Product_71,704.57755157279
+49,50,Product_50,622.5561531442168
+68,69,Product_69,809.4552429423823
+40,41,Product_41,553.2586258624842
+10,11,Product_11,347.22224358729665

test.py

@@ -0,0 +1,90 @@
+import os
+import datasets
+
+_CITATION = """\
+Put your dataset citation here.
+"""
+
+_DESCRIPTION = """\
+Description of your dataset goes here.
+"""
+
+_HOMEPAGE = "https://your-dataset-homepage.com"
+
+_LICENSE = "License information goes here."
+
+
+class Test(datasets.GeneratorBasedBuilder):
+    """Your dataset description"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="customers", version=datasets.Version("1.0.0"), description="This is subset A"),
+        datasets.BuilderConfig(name="products", version=datasets.Version("1.0.0"), description="This is subset B"),
+    ]
+
+    def _info(self):
+        if self.config.name == "customers":
+            features = datasets.Features(
+                {
+                    "customer_id": datasets.Value("string"),
+                    "name": datasets.Value("string"),
+                    "age": datasets.Value("int32"),
+                }
+            )
+        elif self.config.name == "products":
+            features = datasets.Features(
+                {
+                    "product_id": datasets.Value("float32"),
+                    "name": datasets.Value("string"),
+                    "price": datasets.Value("float32"),
+                }
+            )
+        else:
+            raise ValueError(f"Unknown subset: {self.config.name}")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = os.path.join(self.config.data_dir, self.config.name)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": os.path.join(data_dir, "train.csv")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": os.path.join(data_dir, "val.csv")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(data_dir, "test.csv")},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            for id_, line in enumerate(f):
+                # Example: Parsing CSV file based on the subset name
+                if self.config.name == "customers":
+                    # Subset A expects three columns: feature_a1, feature_a2, label
+                    feature_a1, feature_a2, label = line.strip().split(",")
+                    yield id_, {
+                        "customer_id": feature_a1,
+                        "name": feature_a2,
+                        "age": int(label),
+                    }
+                elif self.config.name == "products":
+                    # Subset B expects four columns: feature_b1, feature_b2, additional_info, label
+                    feature_b1, feature_b2, additional_info = line.strip().split(",")
+                    yield id_, {
+                        "product_id": feature_b1,
+                        "name": feature_b2,
+                        "price": float(additional_info),
+                    }