def test_using_model():
assert parse_bql_string('simulate x from t using model 42'
' limit 10') == \
[ast.Simulate(['x'], 't', ast.ExpLit(ast.LitInt(42)), [],
ast.ExpLit(ast.LitInt(10)))]
with pytest.raises(parse.BQLParseError):
assert parse_bql_string('simulate x from t'
' using model (87)') == \
[ast.Simulate(['x'], 't', ast.ExpLit(ast.LitInt(87)), [],
ast.ExpLit(ast.LitInt(10)))]
assert parse_bql_string('estimate x from t using model (1+2)') == \
[ast.Estimate(ast.SELQUANT_ALL,
[ast.SelColExp(ast.ExpCol(None, 'x'), None)],
't',
ast.ExpOp(ast.OP_ADD, (
ast.ExpLit(ast.LitInt(1)),
ast.ExpLit(ast.LitInt(2)),
)),
None, None, None, None)]
assert parse_bql_string('estimate * from columns of t'
' using model modelno') == \
[ast.EstCols([ast.SelColAll(None)], 't', ast.ExpCol(None, 'modelno'),
None, None, None)]
assert parse_bql_string('estimate 42 from columns of t'
' using model modelno') == \
[ast.EstCols([(ast.ExpLit(ast.LitInt(42)), None)], 't',
ast.ExpCol(None, 'modelno'),
None, None, None)]
assert parse_bql_string('estimate 42 from pairwise columns of t'
' using model modelno') == \
[ast.EstPairCols([(ast.ExpLit(ast.LitInt(42)), None)], 't', None,
ast.ExpCol(None, 'modelno'),
None, None, None)]
assert parse_bql_string('estimate similarity from pairwise t'
' using model modelno') == \
[ast.EstPairRow([ast.SelColExp(ast.ExpBQLSim(None, [ast.ColListAll()]),
None)],
't', ast.ExpCol(None, 'modelno'),
None, None, None)]
assert parse_bql_string('infer x from t using model modelno') == \
[ast.InferAuto([ast.InfColOne('x', None)], ast.ExpLit(ast.LitInt(0)),
't', ast.ExpCol(None, 'modelno'),
None, None, None, None)]
assert parse_bql_string('infer explicit x from t using model modelno') == \
[ast.InferExplicit([ast.SelColExp(ast.ExpCol(None, 'x'), None)],
't', ast.ExpCol(None, 'modelno'),
None, None, None, None)]
def test_infer_explicit_samples():
assert parse_bql_string('infer explicit x, predict y with confidence 0.9,'
' predict p with confidence 0.8 as q, predict u confidence v,'
' predict a as b confidence c,'
' predict h confidence k using 42 samples'
' from p') == \
[ast.InferExplicit([
ast.SelColExp(ast.ExpCol(None, 'x'), None),
ast.SelColExp(
ast.ExpBQLPredict('y', ast.ExpLit(ast.LitFloat(.9)), None),
None,
),
ast.SelColExp(
ast.ExpBQLPredict('p', ast.ExpLit(ast.LitFloat(.8)), None),
'q',
),
ast.PredCol('u', None, 'v', None),
ast.PredCol('a', 'b', 'c', None),
ast.PredCol('h', None, 'k', ast.ExpLit(ast.LitInt(42))),
],
'p', None, None, None, None, None)]
def test_trivial_commands():
assert parse_bql_string('''
create population satellites for satellites_ucs (
MODEL country_of_operator, orbit_type AS categorical;
MODEL launch_mass AS numerical;
MODEL perigee AS numerical;
MODEL apogee, period AS numerical
)
''') == \
[ast.CreatePop(False, 'satellites', 'satellites_ucs', [
ast.PopModelVars(
['country_of_operator', 'orbit_type'], 'categorical'),
ast.PopModelVars(['launch_mass'], 'numerical'),
ast.PopModelVars(['perigee'], 'numerical'),
ast.PopModelVars(['apogee', 'period'], 'numerical'),
])]
assert parse_bql_string('drop population satellites') == \
[ast.DropPop(False, 'satellites')]
assert parse_bql_string('create generator t_cc for t using crosscat'
'(xyz numerical, pqr categorical, lmn cyclic)') == \
[ast.CreateGen('t_cc', False, 't', None, 'crosscat', [
['xyz', 'numerical'],
['pqr', 'categorical'],
['lmn', 'cyclic'],
])]
assert parse_bql_string('create generator t_cc for t with baseline crosscat'
'(xyz numerical, pqr categorical, lmn cyclic)') == \
[ast.CreateGen(
't_cc', False, 't',
ast.Baseline('crosscat', []),
None, # Defaults to cgpm.
[
['xyz', 'numerical'],
['pqr', 'categorical'],
['lmn', 'cyclic'],
])]
assert parse_bql_string('create generator t_cc if not exists'
' for t using crosscat'
'(xyz numerical, pqr categorical, lmn cyclic)') == \
[ast.CreateGen('t_cc', True, 't', None, 'crosscat', [
['xyz', 'numerical'],
['pqr', 'categorical'],
['lmn', 'cyclic'],
])]
assert parse_bql_string('create generator if not exists t_cc'
' for t using crosscat'
'(xyz numerical, pqr categorical, lmn cyclic)') == \
[ast.CreateGen('t_cc', True, 't', None, 'crosscat', [
['xyz', 'numerical'],
['pqr', 'categorical'],
['lmn', 'cyclic'],
])]
# XXX Schema of [[]] instead of [] is kinda wacky. Fix? (But
# make sure the empty-parens and no-parens cases are equivalent.)
assert parse_bql_string('create generator t_cc'
' for t using crosscat()') == \
[ast.CreateGen('t_cc', False, 't', None, 'crosscat', [[]])]
assert parse_bql_string('create generator t_cc'
' for t using crosscat') == \
[ast.CreateGen('t_cc', False, 't', None, 'crosscat', [[]])]
assert parse_bql_string('initialize 1 model for t;') == \
[ast.InitModels(False, 't', 1)]
assert parse_bql_string('initialize 1 model if not exists for t;') == \
[ast.InitModels(True, 't', 1)]
assert parse_bql_string('initialize 2 models for t;') == \
[ast.InitModels(False, 't', 2)]
assert parse_bql_string('initialize 2 models if not exists for t;') == \
[ast.InitModels(True, 't', 2)]
assert parse_bql_string('create temporary table tx as'
' infer explicit x, predict x as xi confidence xc from t_cc') == \
[ast.CreateTabAs(True, False, 'tx',
ast.InferExplicit(
[
ast.SelColExp(ast.ExpCol(None, 'x'), None),
ast.PredCol('x', 'xi', 'xc', None),
],
't_cc', None, None, None, None, None,
))]
def p_infer_explicit(self, cols, population, generator, cond, grouping,
ord, lim):
return ast.InferExplicit(cols, population, generator, cond, grouping,
ord, lim)
def test_trivial_commands():
assert parse_bql_string('create generator t_cc for t using crosscat'
'(xyz numerical, pqr categorical, lmn cyclic)') == \
[ast.CreateGen(False, 't_cc', False, 't', 'crosscat', [
['xyz', 'numerical'],
['pqr', 'categorical'],
['lmn', 'cyclic'],
])]
assert parse_bql_string('create default generator t_cc for t using crosscat'
'(xyz numerical, pqr categorical, lmn cyclic)') == \
[ast.CreateGen(True, 't_cc', False, 't', 'crosscat', [
['xyz', 'numerical'],
['pqr', 'categorical'],
['lmn', 'cyclic'],
])]
assert parse_bql_string('create generator t_cc if not exists'
' for t using crosscat'
'(xyz numerical, pqr categorical, lmn cyclic)') == \
[ast.CreateGen(False, 't_cc', True, 't', 'crosscat', [
['xyz', 'numerical'],
['pqr', 'categorical'],
['lmn', 'cyclic'],
])]
assert parse_bql_string('initialize 1 model for t;') == \
[ast.InitModels(False, 't', 1, None)]
assert parse_bql_string('initialize 1 model if not exists for t;') == \
[ast.InitModels(True, 't', 1, None)]
assert parse_bql_string('initialize 2 models for t;') == \
[ast.InitModels(False, 't', 2, None)]
assert parse_bql_string('initialize 2 models if not exists for t;') == \
[ast.InitModels(True, 't', 2, None)]
assert parse_bql_string('analyze t for 1 iteration;') == \
[ast.AnalyzeModels('t', None, 1, None, None, None, False)]
assert parse_bql_string('analyze t for 1 iteration wait;') == \
[ast.AnalyzeModels('t', None, 1, None, None, None, True)]
assert parse_bql_string('analyze t for 1 minute;') == \
[ast.AnalyzeModels('t', None, None, 60, None, None, False)]
assert parse_bql_string('analyze t for 1 minute wait;') == \
[ast.AnalyzeModels('t', None, None, 60, None, None, True)]
assert parse_bql_string('analyze t for 2 minutes;') == \
[ast.AnalyzeModels('t', None, None, 120, None, None, False)]
assert parse_bql_string('analyze t for 2 minutes wait;') == \
[ast.AnalyzeModels('t', None, None, 120, None, None, True)]
assert parse_bql_string('analyze t for 1 second;') == \
[ast.AnalyzeModels('t', None, None, 1, None, None, False)]
assert parse_bql_string('analyze t for 1 second wait;') == \
[ast.AnalyzeModels('t', None, None, 1, None, None, True)]
assert parse_bql_string('analyze t for 2 seconds;') == \
[ast.AnalyzeModels('t', None, None, 2, None, None, False)]
assert parse_bql_string('analyze t for 2 seconds wait;') == \
[ast.AnalyzeModels('t', None, None, 2, None, None, True)]
assert parse_bql_string('analyze t model 1 for 1 iteration;') == \
[ast.AnalyzeModels('t', [1], 1, None, None, None, False)]
assert parse_bql_string('analyze t models 1,2,3 for 1 iteration;') == \
[ast.AnalyzeModels('t', [1,2,3], 1, None, None, None, False)]
assert parse_bql_string('analyze t models 1-3,5 for 1 iteration;') == \
[ast.AnalyzeModels('t', [1,2,3,5], 1, None, None, None, False)]
assert parse_bql_string('analyze t for 10 iterations'
' checkpoint 3 iterations') == \
[ast.AnalyzeModels('t', None, 10, None, 3, None, False)]
assert parse_bql_string('analyze t for 10 seconds'
' checkpoint 3 seconds') == \
[ast.AnalyzeModels('t', None, None, 10, None, 3, False)]
assert parse_bql_string('create temporary table tx as'
' infer explicit x, predict x as xi confidence xc from t_cc') == \
[ast.CreateTabAs(True, False, 'tx',
ast.InferExplicit(
[
ast.SelColExp(ast.ExpCol(None, 'x'), None),
ast.PredCol('x', 'xi', 'xc'),
],
't_cc', ast.ExpLit(ast.LitNull(None)), None, None, None, None,
))]
def p_infer_explicit(self, cols, generator, modelno, cond, grouping, ord,
lim):
return ast.InferExplicit(cols, generator, modelno, cond, grouping, ord,
lim)
