a sythetic generate dataset for testing numerical reasoning

README.md

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+# Numerical Reasoning

numerical_reasoning.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+    Generated dataset for testing numerical reasoning
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("0.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="arithmetic_multiplication", version=VERSION, description="x1 x x2 = y"),
+        datasets.BuilderConfig(name="arithmetic_addition", version=VERSION, description="x1 + x2 = y"),
+        datasets.BuilderConfig(name="op_infer_mult", version=VERSION, description="x1 # x2 = y, must infer that # is multiplication"),
+        datasets.BuilderConfig(name="op_infer_add", version=VERSION, description="x1 # x2 = y, must infer that # is addition"),
+        datasets.BuilderConfig(name="time_unit_min_sec", version=VERSION, description="x minutes equals y seconds"),
+        datasets.BuilderConfig(name="time_unit_hour_min", version=VERSION, description="x hours equals y minutes"),
+        datasets.BuilderConfig(name="time_unit_day_hour", version=VERSION, description="x days equals y hours"),
+        datasets.BuilderConfig(name="time_unit_week_day", version=VERSION, description="x minutes equals y seconds"),
+        datasets.BuilderConfig(name="time_unit_month_week", version=VERSION, description="x months equals y weeks"),
+        datasets.BuilderConfig(name="time_unit_year_month", version=VERSION, description="x years equals y months"),
+        datasets.BuilderConfig(name="time_unit_decade_year", version=VERSION, description="x decades equals y years"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        if ("arithmetic" in self.config.name) or ("op_infer" in self.config.name):  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "x1": datasets.Value("int32"),
+                    "x2": datasets.Value("int32"),
+                    "y": datasets.Value("int32")
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "x1": datasets.Value("int32"),
+                    "time_unit": datasets.Value("string"),
+                    "y": datasets.Value("int32")
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,  # Here we define them above because they are different between the two configurations
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "split": "test"
+                },
+            ),
+        ]
+
+    def _generate_examples(self, split):
+
+        if ("arithmetic" in self.config.name) or ("op_infer" in self.config.name):
+            key = 0
+            for x1 in range(0,100):
+                for x2 in range(1,51):
+                    key += 1
+                    # Yields examples as (key, example) tuples
+                    yield key, {
+                        "x1": x1,
+                        "x2": x2,
+                        "y": x1*x2,
+                    }
+        else:
+            if "min_sec" in self.config.name:
+                time_unit = "minutes"
+                multiplier = 60
+
+            elif "hour_min" in self.config.name:
+                time_unit = "hours"
+                multiplier = 60
+
+            elif "day_hour" in self.config.name:
+                time_unit = "days"
+                multiplier = 24
+
+            elif "week_day" in self.config.name:
+                time_unit = "weeks"
+                multiplier = 7
+
+            elif "month_week" in self.config.name:
+                time_unit = "months"
+                multiplier = 30
+
+            elif "year_month" in self.config.name:
+                time_unit = "years"
+                multiplier = 12
+
+            elif "decade_year" in self.config.name:
+                time_unit = "decades"
+                multiplier = 10
+
+            for key, x1 in enumerate(range(0, 100)):
+                yield key, {
+                    "x1": x1,
+                    "time_unit": time_unit,
+                    "y": multiplier*x1,
+                }