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"""Program Synthesis dataset from dreamcoder. https://github.com/ellisk42/ec""" |
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from random import choice, shuffle |
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import datasets |
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import pandas as pd |
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from dreamcoder.domains.text.makeTextTasks import makeTasks as textMakeTasks |
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from dreamcoder.domains.list.main import main as listMakeTasks |
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_DESCRIPTION = """\ |
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Generated program synthesis datasets used to train dreamcoder. |
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""" |
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_FEATURES = datasets.Features( |
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{ |
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"description": datasets.Value("string"), |
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"input": datasets.Value("string"), |
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"output": datasets.Value("string"), |
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"types": datasets.Value("string") |
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} |
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) |
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_HOMEPAGE = "https://github.com/ellisk42/ec" |
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_LICENSE = "MIT License" |
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_MAX_STEPS = 3782 |
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class infIterator: |
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def __init__(self, make_mthd): |
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self.make_mthd = make_mthd |
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self.i = None |
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def reset(self): |
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tasks = self.make_mthd() |
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rows = [] |
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for task in tasks: |
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base = { |
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'types': str(task.request), |
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"description": task.name, |
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} |
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for (inp, outp) in task.examples: |
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rows.append(dict(input=str(inp), output=str(outp), **base)) |
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shuffle(rows) |
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self.rows = rows |
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self.i = 0 |
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def step(self): |
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if self.i is None: |
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self.reset() |
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row = self.rows[self.i] |
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self.i += 1 |
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if self.i >= len(self.rows): |
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self.reset() |
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return row |
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class ProgramSynthesis(datasets.GeneratorBasedBuilder): |
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"""Program Synthesis dataset from dreamcoder.""" |
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VERSION = datasets.Version("1.1.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="text", version=VERSION, description="Text tasks."), |
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datasets.BuilderConfig(name="list", version=VERSION, description="List tasks."), |
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datasets.BuilderConfig(name="all", version=VERSION, description="All tasks at once."), |
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] |
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DEFAULT_CONFIG_NAME = "all" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=_FEATURES, |
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supervised_keys=("input", "output"), |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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) |
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def _split_generators(self, dl_manager): |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={'split': 'train'} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={'split': 'test'} |
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), |
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] |
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def _generate_examples(self, split): |
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if split == 'test': |
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df_list = pd.read_csv('_t.list.csv') |
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df_text = pd.read_csv('_t.text.csv') |
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if self.config.name == 'all': |
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df = pd.concat(df_list, df_text) |
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elif self.config.name == 'list': |
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df = df_list |
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elif self.config.name == 'text': |
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df = df_text |
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else: |
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raise Exception('Bad Config') |
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for i, row in df.iterrows(): |
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yield i, dict(row) |
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return |
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task_samples = { |
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'text': infIterator(textMakeTasks), |
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'list': infIterator(listMakeTasks), |
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} |
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ks = list(task_samples.keys()) |
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for key in range(_MAX_STEPS): |
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if self.config.name == 'all': |
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dataset_type = choice(ks) |
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else: |
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dataset_type = self.config.name |
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yield key, task_samples[dataset_type].step() |
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