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import json
import attr
import torch
import networkx as nx
from seq2struct.utils import registry
from seq2struct.datasets.spider_lib import evaluation
@attr.s
class SpiderItem:
text = attr.ib()
code = attr.ib()
schema = attr.ib()
orig = attr.ib()
orig_schema = attr.ib()
@attr.s
class Column:
id = attr.ib()
table = attr.ib()
name = attr.ib()
unsplit_name = attr.ib()
orig_name = attr.ib()
type = attr.ib()
foreign_key_for = attr.ib(default=None)
@attr.s
class Table:
id = attr.ib()
name = attr.ib()
unsplit_name = attr.ib()
orig_name = attr.ib()
columns = attr.ib(factory=list)
primary_keys = attr.ib(factory=list)
@attr.s
class Schema:
db_id = attr.ib()
tables = attr.ib()
columns = attr.ib()
foreign_key_graph = attr.ib()
orig = attr.ib()
def load_tables(paths):
schemas = {}
eval_foreign_key_maps = {}
for path in paths:
schema_dicts = json.load(open(path))
for schema_dict in schema_dicts:
tables = tuple(
Table(
id=i,
name=name.split(),
unsplit_name=name,
orig_name=orig_name,
)
for i, (name, orig_name) in enumerate(zip(
schema_dict['table_names'], schema_dict['table_names_original']))
)
columns = tuple(
Column(
id=i,
table=tables[table_id] if table_id >= 0 else None,
name=col_name.split(),
unsplit_name=col_name,
orig_name=orig_col_name,
type=col_type,
)
for i, ((table_id, col_name), (_, orig_col_name), col_type) in enumerate(zip(
schema_dict['column_names'],
schema_dict['column_names_original'],
schema_dict['column_types']))
)
# Link columns to tables
for column in columns:
if column.table:
column.table.columns.append(column)
for column_id in schema_dict['primary_keys']:
# Register primary keys
column = columns[column_id]
column.table.primary_keys.append(column)
foreign_key_graph = nx.DiGraph()
for source_column_id, dest_column_id in schema_dict['foreign_keys']:
# Register foreign keys
source_column = columns[source_column_id]
dest_column = columns[dest_column_id]
source_column.foreign_key_for = dest_column
foreign_key_graph.add_edge(
source_column.table.id,
dest_column.table.id,
columns=(source_column_id, dest_column_id))
foreign_key_graph.add_edge(
dest_column.table.id,
source_column.table.id,
columns=(dest_column_id, source_column_id))
db_id = schema_dict['db_id']
assert db_id not in schemas
schemas[db_id] = Schema(db_id, tables, columns, foreign_key_graph, schema_dict)
eval_foreign_key_maps[db_id] = evaluation.build_foreign_key_map(schema_dict)
return schemas, eval_foreign_key_maps
def load_tables_from_schema_dict(schema_dict):
schemas = {}
eval_foreign_key_maps = {}
schema_dicts = [schema_dict]
for schema_dict in schema_dicts:
tables = tuple(
Table(
id=i,
name=name.split(),
unsplit_name=name,
orig_name=orig_name,
)
for i, (name, orig_name) in enumerate(zip(
schema_dict['table_names'], schema_dict['table_names_original']))
)
columns = tuple(
Column(
id=i,
table=tables[table_id] if table_id >= 0 else None,
name=col_name.split(),
unsplit_name=col_name,
orig_name=orig_col_name,
type=col_type,
)
for i, ((table_id, col_name), (_, orig_col_name), col_type) in enumerate(zip(
schema_dict['column_names'],
schema_dict['column_names_original'],
schema_dict['column_types']))
)
# Link columns to tables
for column in columns:
if column.table:
column.table.columns.append(column)
for column_id in schema_dict['primary_keys']:
# Register primary keys
column = columns[column_id]
column.table.primary_keys.append(column)
foreign_key_graph = nx.DiGraph()
for source_column_id, dest_column_id in schema_dict['foreign_keys']:
# Register foreign keys
source_column = columns[source_column_id]
dest_column = columns[dest_column_id]
source_column.foreign_key_for = dest_column
foreign_key_graph.add_edge(
source_column.table.id,
dest_column.table.id,
columns=(source_column_id, dest_column_id))
foreign_key_graph.add_edge(
dest_column.table.id,
source_column.table.id,
columns=(dest_column_id, source_column_id))
db_id = schema_dict['db_id']
assert db_id not in schemas
schemas[db_id] = Schema(db_id, tables, columns, foreign_key_graph, schema_dict)
eval_foreign_key_maps[db_id] = evaluation.build_foreign_key_map(schema_dict)
return schemas, eval_foreign_key_maps
@registry.register('dataset_infer', 'spider')
class SpiderDatasetInfer(torch.utils.data.Dataset):
def __init__(self, schemas, eval_foreign_key_maps, db_path):
self.db_path = db_path
self.examples = []
self.schemas, self.eval_foreign_key_maps = schemas, eval_foreign_key_maps
def __len__(self):
return len(self.examples)
def __getitem__(self, idx):
return self.examples[idx]
@registry.register('dataset', 'spider')
class SpiderDataset(torch.utils.data.Dataset):
def __init__(self, paths, tables_paths, db_path, limit=None):
self.paths = paths
self.db_path = db_path
self.examples = []
self.schemas, self.eval_foreign_key_maps = load_tables(tables_paths)
for path in paths:
raw_data = json.load(open(path))
for entry in raw_data:
item = SpiderItem(
text=entry['question_toks'],
code=entry['sql'],
schema=self.schemas[entry['db_id']],
orig=entry,
orig_schema=self.schemas[entry['db_id']].orig)
self.examples.append(item)
def __len__(self):
return len(self.examples)
def __getitem__(self, idx):
return self.examples[idx]
class Metrics:
def __init__(self, dataset):
self.dataset = dataset
self.foreign_key_maps = {
db_id: evaluation.build_foreign_key_map(schema.orig)
for db_id, schema in self.dataset.schemas.items()
}
self.evaluator = evaluation.Evaluator(
self.dataset.db_path,
self.foreign_key_maps,
'match')
self.results = []
def add(self, item, inferred_code, orig_question=None):
ret_dict = self.evaluator.evaluate_one(
item.schema.db_id, item.orig['query'], inferred_code)
if orig_question:
ret_dict["orig_question"] = orig_question
self.results.append(ret_dict)
def add_beams(self, item, inferred_codes, orig_question=None):
beam_dict = {}
if orig_question:
beam_dict["orig_question"] = orig_question
for i, code in enumerate(inferred_codes):
ret_dict = self.evaluator.evaluate_one(
item.schema.db_id, item.orig['query'], code)
beam_dict[i] = ret_dict
if ret_dict["exact"] is True:
break
self.results.append(beam_dict)
def finalize(self):
self.evaluator.finalize()
return {
'per_item': self.results,
'total_scores': self.evaluator.scores
}
@registry.register('dataset', 'spider_idiom_ast')
class SpiderIdiomAstDataset(torch.utils.data.Dataset):
def __init__(self, paths, tables_paths, db_path, limit=None):
self.paths = paths
self.db_path = db_path
self.examples = []
self.schemas, self.eval_foreign_key_maps = load_tables(tables_paths)
for path in paths:
for line in open(path):
entry = json.loads(line)
item = SpiderItem(
text=entry['orig']['question_toks'],
code=entry['rewritten_ast'],
schema=self.schemas[entry['orig']['db_id']],
orig=entry['orig'],
orig_schema=self.schemas[entry['orig']['db_id']].orig)
self.examples.append(item)
def __len__(self):
return len(self.examples)
def __getitem__(self, idx):
return self.examples[idx]
Metrics = SpiderDataset.Metrics |