add ace (MockupSampler)

ace.py

@@ -0,0 +1,164 @@
+# Copyright 2020 The HuggingFace Datasets Authors.
+# Copyright 2023 Cyril Zhang.
+#
+# 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.
+
+
+import csv
+import json
+import os
+
+import datasets
+import numpy as np
+
+
+_CITATION = """\
+"""
+
+_DESCRIPTION = """\
+Online dataset mockup.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = ""
+
+_URLS = {}
+
+class MockupDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("0.0.0")
+    BUILDER_CONFIGS = []
+    
+    def __init__(self, name=None, data_config={}, **kwargs):
+        super().__init__(**kwargs)
+        
+        """
+        Set default configs
+        """
+        if name is None:
+            name = 'parity'
+        if 'length' not in data_config:
+            data_config['length'] = 20
+        if 'size' not in data_config:
+            data_config['size'] = 100
+
+        self.data_config = data_config
+        # self.sampler = AutomatonSampler(name, data_config)
+        self.sampler = dataset_map[name](data_config)
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "x": datasets.Value("null"),
+                "y": datasets.Value("null")
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "split": "train",
+                },
+            )
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, split):
+        for i in range(self.data_config['size']):
+            x, y = self.sampler.sample()
+            yield i, {
+                "x": x,
+                "y": y
+            }
+
+
+class AutomatonSampler:
+    def __init__(self, data_config):
+        # self.name = name
+        self.data_config = data_config
+
+        if 'seed' in self.data_config:
+            self.np_rng = np.random.default_rng(self.data_config['seed'])
+        else:
+            self.np_rng = np.random.default_rng()
+
+        self.n_states = data_config['n_states']
+        self.T = self.data_config['length']
+
+    def f(self, x):
+        """
+        Get output sequence given an input seq
+        """
+        raise NotImplementedError()
+
+    def sample(self):
+        raise NotImplementedError()
+
+
+class ParitySampler(AutomatonSampler):
+    def __init__(self, data_config):
+        super(ParitySampler, self).__init__(data_config)
+        self.name = 'parity'
+        self.data_config = data_config
+
+    def f(self, x):
+        return np.cumsum(x) % 2
+
+    def sample(self):
+        x = self.np_rng.binomial(1,0.5,size=self.T)
+        return x, self.f(x)
+
+
+class FlipflopSampler(AutomatonSampler):
+    def __init__(self, data_config):
+        super(FlipflopSampler, self).__init__(data_config)
+        self.name = 'parity'
+        self.data_config = data_config
+
+        self.n_actions = self.n_states + 1
+        self.transition = np.array([list(range(self.n_actions))] + [[i+1]*self.n_actions for i in range(self.n_states)]).T
+
+    def f(self, x):
+        state, states = 0, []
+        for action in x:
+          state = self.transition[state, action]
+          states += state,
+        return np.array(states)
+
+    def sample(self):
+        rand = np.random.uniform(size=self.T)
+        nonzero_pos = (rand < 0.5).astype(np.int64)
+        writes = np.random.choice(range(1, self.n_states+1), size=self.T)
+        x = writes * nonzero_pos
+        return x, self.f(x)
+
+
+dataset_map = {
+  'parity': ParitySampler,
+  'flipflop': FlipflopSampler,
+  # TODO: more datasets
+  }
+
+