def _bql_logpdf(bdb, population_id, generator_id, modelnos, targets,
constraints):
# P(T | C) = \sum_M P(T, M | C)
# = \sum_M P(T | C, M) P(M | C)
# = \sum_M P(T | C, M) P(M) P(C | M) / P(C)
# = \sum_M P(T | C, M) P(M) P(C | M) / \sum_M' P(C, M')
# = \sum_M P(T | C, M) P(M) P(C | M) / \sum_M' P(C | M') P(M')
#
# For a generator M, logpdf(M) computes P(T | C, M), and
# loglikelihood(M) computes P(C | M). For now, we weigh each
# generator uniformly; eventually, we ought to allow the user to
# specify a prior weight (XXX and update some kind of posterior
# weight?).
rowid, constraints = _retrieve_rowid_constraints(bdb, population_id,
constraints)
def logpdf(generator_id, backend):
return backend.logpdf_joint(bdb, generator_id, modelnos, rowid,
targets, constraints)
def loglikelihood(generator_id, backend):
if not constraints:
return 0
return backend.logpdf_joint(bdb, generator_id, modelnos, rowid,
constraints, [])
generator_ids = _retrieve_generator_ids(bdb, population_id, generator_id)
backends = [core.bayesdb_generator_backend(bdb, g) for g in generator_ids]
loglikelihoods = map(loglikelihood, generator_ids, backends)
logpdfs = map(logpdf, generator_ids, backends)
return logavgexp_weighted(loglikelihoods, logpdfs)
def _bql_logpdf(bdb, population_id, generator_id, modelnos, targets,
constraints):
# P(T | C) = \sum_M P(T, M | C)
# = \sum_M P(T | C, M) P(M | C)
# = \sum_M P(T | C, M) P(M) P(C | M) / P(C)
# = \sum_M P(T | C, M) P(M) P(C | M) / \sum_M' P(C, M')
# = \sum_M P(T | C, M) P(M) P(C | M) / \sum_M' P(C | M') P(M')
#
# For a generator M, logpdf(M) computes P(T | C, M), and
# loglikelihood(M) computes P(C | M). For now, we weigh each
# generator uniformly; eventually, we ought to allow the user to
# specify a prior weight (XXX and update some kind of posterior
# weight?).
rowid, constraints = _retrieve_rowid_constraints(
bdb, population_id, constraints)
def logpdf(generator_id, backend):
return backend.logpdf_joint(
bdb, generator_id, modelnos, rowid, targets, constraints)
def loglikelihood(generator_id, backend):
if not constraints:
return 0
return backend.logpdf_joint(
bdb, generator_id, modelnos, rowid, constraints, [])
generator_ids = _retrieve_generator_ids(bdb, population_id, generator_id)
backends = [
core.bayesdb_generator_backend(bdb, g)
for g in generator_ids
]
loglikelihoods = map(loglikelihood, generator_ids, backends)
logpdfs = map(logpdf, generator_ids, backends)
return logavgexp_weighted(loglikelihoods, logpdfs)
def generator_mutinf(generator_id):
backend = core.bayesdb_generator_backend(bdb, generator_id)
return backend.column_mutual_information(bdb,
generator_id,
modelnos,
colnos0,
colnos1,
constraints=constraints,
numsamples=numsamples)
def bayesdb_simulate(
bdb, population_id, generator_id, modelnos, constraints, colnos,
numpredictions=1, accuracy=None):
"""Simulate rows from a generative model, subject to constraints.
Returns a list of `numpredictions` tuples, with a value for each
column specified in the list `colnos`, conditioned on the
constraints in the list `constraints` of tuples ``(colno,
value)``.
The results are simulated from the predictive distribution on
fresh rows.
"""
modelnos = _retrieve_modelnos(modelnos)
rowid, constraints = _retrieve_rowid_constraints(
bdb, population_id, constraints)
def loglikelihood(generator_id, backend):
if not constraints:
return 0
return backend.logpdf_joint(
bdb, generator_id, modelnos, rowid, constraints, [])
def simulate(generator_id, backend, n):
return backend.simulate_joint(
bdb, generator_id, modelnos, rowid, colnos, constraints,
num_samples=n, accuracy=accuracy)
generator_ids = _retrieve_generator_ids(bdb, population_id, generator_id)
backends = [
core.bayesdb_generator_backend(bdb, generator_id)
for generator_id in generator_ids
]
if len(generator_ids) > 1:
loglikelihoods = map(loglikelihood, generator_ids, backends)
likelihoods = map(math.exp, loglikelihoods)
total_likelihood = sum(likelihoods)
if total_likelihood == 0:
# XXX Show the constraints with symbolic names.
raise BQLError(bdb, 'Impossible constraints: %r' % (constraints,))
probabilities = [
likelihood / total_likelihood
for likelihood in likelihoods
]
countses = bdb.np_prng.multinomial(
numpredictions, probabilities, size=1)
counts = countses[0]
elif len(generator_ids) == 1:
counts = [numpredictions]
else:
counts = []
rowses = map(simulate, generator_ids, backends, counts)
all_rows = [row for rows in rowses for row in rows]
assert all(isinstance(row, (tuple, list)) for row in all_rows)
return all_rows
def bql_predict(
bdb, population_id, generator_id, modelnos, rowid, colno, threshold,
numsamples):
# XXX Randomly sample 1 generator from the population, until we figure out
# how to aggregate imputations across different hypotheses.
modelnos = _retrieve_modelnos(modelnos)
if generator_id is None:
generator_ids = core.bayesdb_population_generators(bdb, population_id)
index = bdb.np_prng.randint(0, high=len(generator_ids))
generator_id = generator_ids[index]
backend = core.bayesdb_generator_backend(bdb, generator_id)
return backend.predict(
bdb, generator_id, modelnos, rowid, colno, threshold,
numsamples=numsamples)
def bql_predict_confidence(
bdb, population_id, generator_id, modelnos, rowid, colno, numsamples):
# XXX Do real imputation here!
# XXX Randomly sample 1 generator from the population, until we figure out
# how to aggregate imputations across different hypotheses.
if generator_id is None:
generator_ids = core.bayesdb_population_generators(bdb, population_id)
index = bdb.np_prng.randint(0, high=len(generator_ids))
generator_id = generator_ids[index]
modelnos = _retrieve_modelnos(modelnos)
backend = core.bayesdb_generator_backend(bdb, generator_id)
value, confidence = backend.predict_confidence(
bdb, generator_id, modelnos, rowid, colno, numsamples=numsamples)
# XXX Whattakludge!
return json.dumps({'value': value, 'confidence': confidence})
def bql_predict(bdb, population_id, generator_id, modelnos, rowid, colno,
threshold, numsamples):
# XXX Randomly sample 1 generator from the population, until we figure out
# how to aggregate imputations across different hypotheses.
modelnos = _retrieve_modelnos(modelnos)
if generator_id is None:
generator_ids = core.bayesdb_population_generators(bdb, population_id)
index = bdb.np_prng.randint(0, high=len(generator_ids))
generator_id = generator_ids[index]
backend = core.bayesdb_generator_backend(bdb, generator_id)
return backend.predict(bdb,
generator_id,
modelnos,
rowid,
colno,
threshold,
numsamples=numsamples)
def bql_predict_confidence(bdb, population_id, generator_id, modelnos, rowid,
colno, numsamples):
# XXX Do real imputation here!
# XXX Randomly sample 1 generator from the population, until we figure out
# how to aggregate imputations across different hypotheses.
if generator_id is None:
generator_ids = core.bayesdb_population_generators(bdb, population_id)
index = bdb.np_prng.randint(0, high=len(generator_ids))
generator_id = generator_ids[index]
modelnos = _retrieve_modelnos(modelnos)
backend = core.bayesdb_generator_backend(bdb, generator_id)
value, confidence = backend.predict_confidence(bdb,
generator_id,
modelnos,
rowid,
colno,
numsamples=numsamples)
# XXX Whattakludge!
return json.dumps({'value': value, 'confidence': confidence})
def dump_models(self, line, cell=None):
population_name = line
if not bayesdb_has_population(self._bdb, population_name):
raise ValueError('No such population: %r' % (population_name, ))
population_id = bayesdb_get_population(self._bdb, population_name)
generators = bayesdb_population_generators(self._bdb, population_id)
if len(generators) == 0:
raise ValueError('No generator for population %r' %
(population_name, ))
if len(generators) > 1:
raise ValueError('Generator not unique for population %r' %
(population_name, ))
generator_id = generators[0]
backend = bayesdb_generator_backend(self._bdb, generator_id)
if backend.name() != 'cgpm':
self.write_stderr('%dump_models requires generator from the '
'cgpm backend')
return
j = backend.json_ready_models(self._bdb, population_id, generator_id)
path = population_name + '_models.json'
with open(path, 'w') as outfile:
json.dump(j, outfile, indent=2)
def generator_predprob(generator_id):
backend = core.bayesdb_generator_backend(bdb, generator_id)
return backend.logpdf_joint(
bdb, generator_id, modelnos, fresh_rowid, cgpm_targets,
cgpm_constraints)
def generator_similarity(generator_id):
backend = core.bayesdb_generator_backend(bdb, generator_id)
return backend.predictive_relevance(
bdb, generator_id, modelnos, rowid_target, rowid_query,
hypotheticals, colno)
def generator_similarity(generator_id):
backend = core.bayesdb_generator_backend(bdb, generator_id)
# XXX Change [colno] to colno by updating BayesDB_Backend.
similarity_list = backend.row_similarity(
bdb, generator_id, modelnos, rowid, target_rowid, [colno])
return stats.arithmetic_mean(similarity_list)
def bayesdb_simulate(bdb,
population_id,
generator_id,
modelnos,
constraints,
colnos,
numpredictions=1,
accuracy=None):
"""Simulate rows from a generative model, subject to constraints.
Returns a list of `numpredictions` tuples, with a value for each
column specified in the list `colnos`, conditioned on the
constraints in the list `constraints` of tuples ``(colno,
value)``.
The results are simulated from the predictive distribution on
fresh rows.
"""
modelnos = _retrieve_modelnos(modelnos)
rowid, constraints = _retrieve_rowid_constraints(bdb, population_id,
constraints)
def loglikelihood(generator_id, backend):
if not constraints:
return 0
return backend.logpdf_joint(bdb, generator_id, modelnos, rowid,
constraints, [])
def simulate(generator_id, backend, n):
return backend.simulate_joint(bdb,
generator_id,
modelnos,
rowid,
colnos,
constraints,
num_samples=n,
accuracy=accuracy)
generator_ids = _retrieve_generator_ids(bdb, population_id, generator_id)
backends = [
core.bayesdb_generator_backend(bdb, generator_id)
for generator_id in generator_ids
]
if len(generator_ids) > 1:
loglikelihoods = map(loglikelihood, generator_ids, backends)
likelihoods = map(math.exp, loglikelihoods)
total_likelihood = sum(likelihoods)
if total_likelihood == 0:
# XXX Show the constraints with symbolic names.
raise BQLError(bdb, 'Impossible constraints: %r' % (constraints, ))
probabilities = [
likelihood / total_likelihood for likelihood in likelihoods
]
countses = bdb.np_prng.multinomial(numpredictions,
probabilities,
size=1)
counts = countses[0]
elif len(generator_ids) == 1:
counts = [numpredictions]
else:
counts = []
rowses = map(simulate, generator_ids, backends, counts)
all_rows = [row for rows in rowses for row in rows]
assert all(isinstance(row, (tuple, list)) for row in all_rows)
return all_rows
def generator_predprob(generator_id):
backend = core.bayesdb_generator_backend(bdb, generator_id)
return backend.logpdf_joint(bdb, generator_id, modelnos, fresh_rowid,
cgpm_targets, cgpm_constraints)
def generator_similarity(generator_id):
backend = core.bayesdb_generator_backend(bdb, generator_id)
return backend.predictive_relevance(bdb, generator_id, modelnos,
rowid_target, rowid_query,
hypotheticals, colno)
def generator_similarity(generator_id):
backend = core.bayesdb_generator_backend(bdb, generator_id)
# XXX Change [colno] to colno by updating BayesDB_Backend.
similarity_list = backend.row_similarity(bdb, generator_id, modelnos,
rowid, target_rowid, [colno])
return stats.arithmetic_mean(similarity_list)
def execute_phrase(bdb, phrase, bindings=()):
"""Execute the BQL AST phrase `phrase` and return a cursor of results."""
if isinstance(phrase, ast.Parametrized):
n_numpar = phrase.n_numpar
nampar_map = phrase.nampar_map
phrase = phrase.phrase
assert 0 < n_numpar
else:
n_numpar = 0
nampar_map = None
# Ignore extraneous bindings. XXX Bad idea?
if ast.is_query(phrase):
# Compile the query in the transaction in case we need to
# execute subqueries to determine column lists. Compiling is
# a quick tree descent, so this should be fast.
out = compiler.Output(n_numpar, nampar_map, bindings)
with bdb.savepoint():
compiler.compile_query(bdb, phrase, out)
winders, unwinders = out.getwindings()
return execute_wound(bdb, winders, unwinders, out.getvalue(),
out.getbindings())
if isinstance(phrase, ast.Begin):
txn.bayesdb_begin_transaction(bdb)
return empty_cursor(bdb)
if isinstance(phrase, ast.Rollback):
txn.bayesdb_rollback_transaction(bdb)
return empty_cursor(bdb)
if isinstance(phrase, ast.Commit):
txn.bayesdb_commit_transaction(bdb)
return empty_cursor(bdb)
if isinstance(phrase, ast.CreateTabAs):
assert ast.is_query(phrase.query)
with bdb.savepoint():
if core.bayesdb_has_table(bdb, phrase.name):
if phrase.ifnotexists:
return empty_cursor(bdb)
else:
raise BQLError(
bdb, 'Name already defined as table: %s' %
(repr(phrase.name), ))
out = compiler.Output(n_numpar, nampar_map, bindings)
qt = sqlite3_quote_name(phrase.name)
temp = 'TEMP ' if phrase.temp else ''
ifnotexists = 'IF NOT EXISTS ' if phrase.ifnotexists else ''
out.write('CREATE %sTABLE %s%s AS ' % (temp, ifnotexists, qt))
compiler.compile_query(bdb, phrase.query, out)
winders, unwinders = out.getwindings()
with compiler.bayesdb_wind(bdb, winders, unwinders):
bdb.sql_execute(out.getvalue(), out.getbindings())
return empty_cursor(bdb)
if isinstance(phrase, ast.CreateTabCsv):
with bdb.savepoint():
table_exists = core.bayesdb_has_table(bdb, phrase.name)
if table_exists:
if phrase.ifnotexists:
return empty_cursor(bdb)
else:
raise BQLError(
bdb,
'Table already exists: %s' % (repr(phrase.name), ))
bayesdb_read_csv_file(bdb,
phrase.name,
phrase.csv,
header=True,
create=True)
return empty_cursor(bdb)
if isinstance(phrase, ast.DropTab):
with bdb.savepoint():
sql = 'SELECT COUNT(*) FROM bayesdb_population WHERE tabname = ?'
cursor = bdb.sql_execute(sql, (phrase.name, ))
if 0 < cursor_value(cursor):
raise BQLError(
bdb, 'Table still in use by populations: %s' %
(repr(phrase.name), ))
bdb.sql_execute('DELETE FROM bayesdb_column WHERE tabname = ?',
(phrase.name, ))
ifexists = 'IF EXISTS ' if phrase.ifexists else ''
qt = sqlite3_quote_name(phrase.name)
return bdb.sql_execute('DROP TABLE %s%s' % (ifexists, qt))
if isinstance(phrase, ast.AlterTab):
with bdb.savepoint():
table = phrase.table
if not core.bayesdb_has_table(bdb, table):
raise BQLError(bdb, 'No such table: %s' % (repr(table), ))
for cmd in phrase.commands:
if isinstance(cmd, ast.AlterTabRenameTab):
# If the names differ only in case, we have to do
# some extra work because SQLite will reject the
# table rename. Note that we may even have table
# == cmd.name here, but if the stored table name
# differs in case from cmd.name, we want to update
# it anyway.
if casefold(table) == casefold(cmd.name):
# Go via a temporary table.
temp = table + '_temp'
while core.bayesdb_has_table(bdb, temp):
temp += '_temp'
rename_table(bdb, table, temp)
rename_table(bdb, temp, cmd.name)
else:
# Make sure nothing else has this name and
# rename it.
if core.bayesdb_has_table(bdb, cmd.name):
raise BQLError(
bdb, 'Name already defined as table: %s' %
(repr(cmd.name), ))
rename_table(bdb, table, cmd.name)
# If table has implicit population, rename it too.
if core.bayesdb_table_has_implicit_population(
bdb, cmd.name):
populations = \
core.bayesdb_table_populations(bdb, cmd.name)
assert len(populations) == 1
population_name = core.bayesdb_population_name(
bdb, populations[0])
qt = sqlite3_quote_name(cmd.name)
qp = sqlite3_quote_name(population_name)
bdb.execute('ALTER POPULATION %s RENAME TO %s' %
(qp, qt))
# Remember the new name for subsequent commands.
table = cmd.name
elif isinstance(cmd, ast.AlterTabRenameCol):
# XXX Need to deal with this in the compiler.
raise NotImplementedError('Renaming columns'
' not yet implemented.')
# Make sure the old name exist and the new name does not.
old_folded = casefold(cmd.old)
new_folded = casefold(cmd.new)
if old_folded != new_folded:
if not core.bayesdb_table_has_column(
bdb, table, cmd.old):
raise BQLError(
bdb, 'No such column in table %s'
': %s' % (repr(table), repr(cmd.old)))
if core.bayesdb_table_has_column(bdb, table, cmd.new):
raise BQLError(
bdb, 'Column already exists'
' in table %s: %s' %
(repr(table), repr(cmd.new)))
# Update bayesdb_column. Everything else refers
# to columns by (tabname, colno) pairs rather than
# by names.
update_column_sql = '''
UPDATE bayesdb_column SET name = :new
WHERE tabname = :table AND name = :old
'''
total_changes = bdb._sqlite3.totalchanges()
bdb.sql_execute(update_column_sql, {
'table': table,
'old': cmd.old,
'new': cmd.new,
})
assert bdb._sqlite3.totalchanges() - total_changes == 1
# ...except backends may have the (case-folded) name cached.
if old_folded != new_folded:
populations_sql = '''
SELECT id FROM bayesdb_population WHERE tabname = ?
'''
cursor = bdb.sql_execute(populations_sql, (table, ))
generators = [
core.bayesdb_population_generators(
bdb, population_id)
for (population_id, ) in cursor
]
for generator_id in set(generators):
backend = core.bayesdb_generator_backend(
bdb, generator_id)
backend.rename_column(bdb, generator_id,
old_folded, new_folded)
else:
assert False, 'Invalid alter table command: %s' % \
(cmd,)
return empty_cursor(bdb)
if isinstance(phrase, ast.GuessSchema):
if not core.bayesdb_has_table(bdb, phrase.table):
raise BQLError(bdb, 'No such table : %s' % phrase.table)
out = compiler.Output(0, {}, {})
with bdb.savepoint():
qt = sqlite3_quote_name(phrase.table)
temptable = bdb.temp_table_name()
qtt = sqlite3_quote_name(temptable)
cursor = bdb.sql_execute('SELECT * FROM %s' % (qt, ))
column_names = [d[0] for d in cursor.description]
rows = cursor.fetchall()
stattypes = bayesdb_guess_stattypes(column_names, rows)
distinct_value_counts = [
len(set([row[i] for row in rows]))
for i in range(len(column_names))
]
out.winder(
'''
CREATE TEMP TABLE %s (
column TEXT,
stattype TEXT,
num_distinct INTEGER,
reason TEXT
)
''' % (qtt, ), ())
for cn, st, ct in zip(column_names, stattypes,
distinct_value_counts):
out.winder(
'''
INSERT INTO %s VALUES (?, ?, ?, ?)
''' % (qtt), (cn, st[0], ct, st[1]))
out.write('SELECT * FROM %s' % (qtt, ))
out.unwinder('DROP TABLE %s' % (qtt, ), ())
winders, unwinders = out.getwindings()
return execute_wound(bdb, winders, unwinders, out.getvalue(),
out.getbindings())
if isinstance(phrase, ast.CreatePop):
with bdb.savepoint():
_create_population(bdb, phrase)
return empty_cursor(bdb)
if isinstance(phrase, ast.DropPop):
with bdb.savepoint():
if not core.bayesdb_has_population(bdb, phrase.name):
if phrase.ifexists:
return empty_cursor(bdb)
raise BQLError(bdb, 'No such population: %r' % (phrase.name, ))
population_id = core.bayesdb_get_population(bdb, phrase.name)
generator_ids = core.bayesdb_population_generators(
bdb, population_id)
if generator_ids:
generators = [
core.bayesdb_generator_name(bdb, gid)
for gid in generator_ids
]
raise BQLError(
bdb, 'Population %r still has generators: %r' %
(phrase.name, generators))
# XXX helpful error checking if generators still exist
# XXX check change counts
bdb.sql_execute(
'''
DELETE FROM bayesdb_variable WHERE population_id = ?
''', (population_id, ))
bdb.sql_execute(
'''
DELETE FROM bayesdb_population WHERE id = ?
''', (population_id, ))
return empty_cursor(bdb)
if isinstance(phrase, ast.AlterPop):
with bdb.savepoint():
population = phrase.population
if not core.bayesdb_has_population(bdb, population):
raise BQLError(bdb,
'No such population: %s' % (repr(population), ))
population_id = core.bayesdb_get_population(bdb, population)
for cmd in phrase.commands:
if isinstance(cmd, ast.AlterPopRenamePop):
table = core.bayesdb_population_table(bdb, population_id)
# Prevent renaming of implicit population directly, unless
# being called by ast.AlterTabRenameTab in which case the
# table name and population name will not be matching.
if core.bayesdb_population_is_implicit(bdb, population_id) \
and casefold(population) == casefold(table):
raise BQLError(
bdb, 'Cannot rename implicit'
'population %s; rename base table instead' %
(population, ))
# Make sure nothing else has this name.
if casefold(population) != casefold(cmd.name):
if core.bayesdb_has_population(bdb, cmd.name):
raise BQLError(
bdb, 'Name already defined as population'
': %s' % (repr(cmd.name), ))
# Update bayesdb_population. Everything else
# refers to it by id.
update_generator_sql = '''
UPDATE bayesdb_population SET name = ? WHERE id = ?
'''
total_changes = bdb._sqlite3.totalchanges()
bdb.sql_execute(update_generator_sql,
(cmd.name, population_id))
assert bdb._sqlite3.totalchanges() - total_changes == 1
# If population has implicit generator, rename it too.
if core.bayesdb_population_has_implicit_generator(
bdb, population_id):
generators = core.bayesdb_population_generators(
bdb, population_id)
assert len(generators) == 1
generator_name = core.bayesdb_generator_name(
bdb, generators[0])
qp = sqlite3_quote_name(cmd.name)
qg = sqlite3_quote_name(generator_name)
bdb.execute('ALTER GENERATOR %s RENAME TO %s' % (
qg,
qp,
))
# Remember the new name for subsequent commands.
population = cmd.name
elif isinstance(cmd, ast.AlterPopAddVar):
# Ensure column exists in base table.
table = core.bayesdb_population_table(bdb, population_id)
if not core.bayesdb_table_has_column(bdb, table, cmd.name):
raise BQLError(
bdb,
'No such variable in base table: %s' % (cmd.name))
# Ensure variable not already in population.
if core.bayesdb_has_variable(bdb, population_id, None,
cmd.name):
raise BQLError(
bdb,
'Variable already in population: %s' % (cmd.name))
# Ensure there is at least observation in the column.
qt = sqlite3_quote_name(table)
qc = sqlite3_quote_name(cmd.name)
cursor = bdb.sql_execute(
'SELECT COUNT(*) FROM %s WHERE %s IS NOT NULL' %
(qt, qc))
if cursor_value(cursor) == 0:
raise BQLError(
bdb, 'Cannot add variable without any values: %s' %
(cmd.name))
# If stattype is None, guess.
if cmd.stattype is None:
cursor = bdb.sql_execute('SELECT %s FROM %s' %
(qc, qt))
rows = cursor.fetchall()
[stattype,
reason] = bayesdb_guess_stattypes([cmd.name], rows)[0]
# Fail if trying to model a key.
if stattype == 'key':
raise BQLError(
bdb, 'Values in column %s appear to be keys.' %
(cmd.name, ))
# Fail if cannot determine a stattype.
elif stattype == 'ignore':
raise BQLError(
bdb, 'Failed to determine a stattype for %s, '
'please specify one manually.' % (cmd.name, ))
# If user specified stattype, ensure it exists.
elif not core.bayesdb_has_stattype(bdb, cmd.stattype):
raise BQLError(bdb,
'Invalid stattype: %s' % (cmd.stattype))
else:
stattype = cmd.stattype
# Check that strings are not being modeled as numerical.
if stattype == 'numerical' \
and _column_contains_string(bdb, table, cmd.name):
raise BQLError(
bdb,
'Numerical column contains string values: %r ' %
(qc, ))
with bdb.savepoint():
# Add the variable to the population.
core.bayesdb_add_variable(bdb, population_id, cmd.name,
stattype)
colno = core.bayesdb_variable_number(
bdb, population_id, None, cmd.name)
# Add the variable to each (initialized) generator in
# the population.
generator_ids = filter(
lambda g: core.bayesdb_generator_modelnos(bdb, g),
core.bayesdb_population_generators(
bdb, population_id),
)
for generator_id in generator_ids:
backend = core.bayesdb_generator_backend(
bdb, generator_id)
backend.add_column(bdb, generator_id, colno)
elif isinstance(cmd, ast.AlterPopStatType):
# Check the no generators are defined for this population.
generators = core.bayesdb_population_generators(
bdb, population_id)
if generators:
raise BQLError(
bdb,
'Cannot update statistical types for population '
'%s, it has generators: %s' % (
repr(population),
repr(generators),
))
# Check all the variables are in the population.
unknown = [
c for c in cmd.names if not core.bayesdb_has_variable(
bdb, population_id, None, c)
]
if unknown:
raise BQLError(
bdb, 'No such variables in population: %s' %
(repr(unknown)))
# Check the statistical type is valid.
if not core.bayesdb_has_stattype(bdb, cmd.stattype):
raise BQLError(
bdb, 'Invalid statistical type: %r' %
(repr(cmd.stattype), ))
# Check that strings are not being modeled as numerical.
if cmd.stattype == 'numerical':
table = core.bayesdb_population_table(
bdb, population_id)
numerical_string_vars = [
col for col in cmd.names
if _column_contains_string(bdb, table, col)
]
if numerical_string_vars:
raise BQLError(
bdb, 'Columns with string values modeled as '
'numerical: %r' % (numerical_string_vars, ))
# Perform the stattype update.
colnos = [
core.bayesdb_variable_number(bdb, population_id, None,
c) for c in cmd.names
]
qcolnos = ','.join('%d' % (colno, ) for colno in colnos)
update_stattype_sql = '''
UPDATE bayesdb_variable SET stattype = ?
WHERE population_id = ? AND colno IN (%s)
''' % (qcolnos, )
bdb.sql_execute(update_stattype_sql, (
casefold(cmd.stattype),
population_id,
))
else:
assert False, 'Invalid ALTER POPULATION command: %s' % \
(repr(cmd),)
return empty_cursor(bdb)
if isinstance(phrase, ast.CreateGen):
# Find the population.
if not core.bayesdb_has_population(bdb, phrase.population):
raise BQLError(bdb,
'No such population: %r' % (phrase.population, ))
population_id = core.bayesdb_get_population(bdb, phrase.population)
# Find the backend, or use the default.
backend_name = phrase.backend
if phrase.backend is None:
backend_name = 'cgpm'
if backend_name not in bdb.backends:
raise BQLError(bdb, 'No such backend: %s' % (repr(backend_name), ))
backend = bdb.backends[backend_name]
# Retrieve the (possibility implicit) generator name.
generator_name = phrase.name or phrase.population
implicit = 1 if phrase.name is None else 0
with bdb.savepoint():
if core.bayesdb_has_generator(bdb, population_id, generator_name):
if not phrase.ifnotexists:
raise BQLError(
bdb, 'Name already defined as generator: %s' %
(repr(generator_name), ))
else:
# Insert a record into bayesdb_generator and get the
# assigned id.
bdb.sql_execute(
'''
INSERT INTO bayesdb_generator
(name, population_id, backend, implicit)
VALUES (?, ?, ?, ?)
''', (generator_name, population_id, backend.name(), implicit))
generator_id = core.bayesdb_get_generator(
bdb, population_id, generator_name)
# Do any backend-specific initialization.
backend.create_generator(bdb, generator_id, phrase.schema)
# All done. Nothing to return.
return empty_cursor(bdb)
if isinstance(phrase, ast.DropGen):
with bdb.savepoint():
if not core.bayesdb_has_generator(bdb, None, phrase.name):
if phrase.ifexists:
return empty_cursor(bdb)
raise BQLError(bdb,
'No such generator: %s' % (repr(phrase.name), ))
generator_id = core.bayesdb_get_generator(bdb, None, phrase.name)
backend = core.bayesdb_generator_backend(bdb, generator_id)
# Backend-specific destruction.
backend.drop_generator(bdb, generator_id)
# Drop latent variables, models, and, finally, generator.
drop_columns_sql = '''
DELETE FROM bayesdb_variable WHERE generator_id = ?
'''
bdb.sql_execute(drop_columns_sql, (generator_id, ))
drop_model_sql = '''
DELETE FROM bayesdb_generator_model WHERE generator_id = ?
'''
bdb.sql_execute(drop_model_sql, (generator_id, ))
drop_generator_sql = '''
DELETE FROM bayesdb_generator WHERE id = ?
'''
bdb.sql_execute(drop_generator_sql, (generator_id, ))
return empty_cursor(bdb)
if isinstance(phrase, ast.AlterGen):
with bdb.savepoint():
generator = phrase.generator
if not core.bayesdb_has_generator(bdb, None, generator):
raise BQLError(bdb,
'No such generator: %s' % (repr(generator), ))
generator_id = core.bayesdb_get_generator(bdb, None, generator)
cmds_generic = []
for cmd in phrase.commands:
if isinstance(cmd, ast.AlterGenRenameGen):
population_id = core.bayesdb_generator_population(
bdb, generator_id)
population = core.bayesdb_population_name(
bdb, population_id)
# Prevent renaming of implicit generator directly, unless
# being called by ast.AlterPopRenamePop in which case the
# population name and generator name will not be matching.
if core.bayesdb_population_is_implicit(bdb, generator_id) \
and casefold(generator) == casefold(population):
raise BQLError(
bdb, 'Cannot rename implicit '
'generator; rename base population instead')
# Disable modelnos with AlterGenRenameGen.
if phrase.modelnos is not None:
raise BQLError(bdb, 'Cannot specify models for RENAME')
# Make sure nothing else has this name.
if casefold(generator) != casefold(cmd.name):
if core.bayesdb_has_generator(bdb, None, cmd.name):
raise BQLError(
bdb, 'Name already defined'
' as generator: %s' % (repr(cmd.name), ))
# Update bayesdb_generator. Everything else
# refers to it by id.
update_generator_sql = '''
UPDATE bayesdb_generator SET name = ? WHERE id = ?
'''
total_changes = bdb._sqlite3.totalchanges()
bdb.sql_execute(update_generator_sql,
(cmd.name, generator_id))
assert bdb._sqlite3.totalchanges() - total_changes == 1
# Remember the new name for subsequent commands.
generator = cmd.name
elif isinstance(cmd, ast.AlterGenGeneric):
cmds_generic.append(cmd.command)
else:
assert False, 'Invalid ALTER GENERATOR command: %s' % \
(repr(cmd),)
if cmds_generic:
modelnos = phrase.modelnos
modelnos_invalid = None if modelnos is None else [
modelno for modelno in modelnos
if not core.bayesdb_generator_has_model(
bdb, generator_id, modelno)
]
if modelnos_invalid:
raise BQLError(
bdb, 'No such models in generator %s: %s' %
(repr(phrase.generator), repr(modelnos)))
# Call generic alternations on the backend.
backend = core.bayesdb_generator_backend(bdb, generator_id)
backend.alter(bdb, generator_id, modelnos, cmds_generic)
return empty_cursor(bdb)
if isinstance(phrase, ast.InitModels):
if not core.bayesdb_has_generator(bdb, None, phrase.generator):
raise BQLError(bdb, 'No such generator: %s' % (phrase.generator, ))
generator_id = core.bayesdb_get_generator(bdb, None, phrase.generator)
modelnos = range(phrase.nmodels)
with bdb.savepoint():
# Find the model numbers. Omit existing ones for
# ifnotexists; reject existing ones otherwise.
if phrase.ifnotexists:
modelnos = set(modelno for modelno in modelnos
if not core.bayesdb_generator_has_model(
bdb, generator_id, modelno))
else:
existing = set(modelno for modelno in modelnos
if core.bayesdb_generator_has_model(
bdb, generator_id, modelno))
if 0 < len(existing):
raise BQLError(
bdb, 'Generator %s already has models: %s' %
(repr(phrase.generator), sorted(existing)))
# Stop now if there's nothing to initialize.
if len(modelnos) == 0:
return
# Create the bayesdb_generator_model records.
modelnos = sorted(modelnos)
insert_model_sql = '''
INSERT INTO bayesdb_generator_model
(generator_id, modelno)
VALUES (:generator_id, :modelno)
'''
for modelno in modelnos:
bdb.sql_execute(insert_model_sql, {
'generator_id': generator_id,
'modelno': modelno,
})
# Do backend-specific initialization.
backend = core.bayesdb_generator_backend(bdb, generator_id)
backend.initialize_models(bdb, generator_id, modelnos)
return empty_cursor(bdb)
if isinstance(phrase, ast.AnalyzeModels):
# WARNING: It is the backend's responsibility to work in a
# transaction.
#
# WARNING: It is the backend's responsibility to update the
# iteration count in bayesdb_generator_model records.
#
# We do this so that the backend can save incremental
# progress in case of ^C in the middle.
#
# XXX Put these warning somewhere more appropriate.
if not core.bayesdb_has_generator(bdb, None, phrase.generator):
raise BQLError(bdb, 'No such generator: %s' % (phrase.generator, ))
generator_id = core.bayesdb_get_generator(bdb, None, phrase.generator)
backend = core.bayesdb_generator_backend(bdb, generator_id)
# XXX Should allow parameters for iterations and ckpt/iter.
backend.analyze_models(bdb,
generator_id,
modelnos=phrase.modelnos,
iterations=phrase.iterations,
max_seconds=phrase.seconds,
ckpt_iterations=phrase.ckpt_iterations,
ckpt_seconds=phrase.ckpt_seconds,
program=phrase.program)
return empty_cursor(bdb)
if isinstance(phrase, ast.DropModels):
with bdb.savepoint():
generator_id = core.bayesdb_get_generator(bdb, None,
phrase.generator)
backend = core.bayesdb_generator_backend(bdb, generator_id)
modelnos = None
if phrase.modelnos is not None:
lookup_model_sql = '''
SELECT COUNT(*) FROM bayesdb_generator_model
WHERE generator_id = :generator_id
AND modelno = :modelno
'''
modelnos = sorted(list(phrase.modelnos))
for modelno in modelnos:
cursor = bdb.sql_execute(lookup_model_sql, {
'generator_id': generator_id,
'modelno': modelno,
})
if cursor_value(cursor) == 0:
raise BQLError(
bdb, 'No such model'
' in generator %s: %s' %
(repr(phrase.generator), repr(modelno)))
backend.drop_models(bdb, generator_id, modelnos=modelnos)
if modelnos is None:
drop_models_sql = '''
DELETE FROM bayesdb_generator_model WHERE generator_id = ?
'''
bdb.sql_execute(drop_models_sql, (generator_id, ))
else:
drop_model_sql = '''
DELETE FROM bayesdb_generator_model
WHERE generator_id = :generator_id
AND modelno = :modelno
'''
for modelno in modelnos:
bdb.sql_execute(drop_model_sql, {
'generator_id': generator_id,
'modelno': modelno,
})
return empty_cursor(bdb)
if isinstance(phrase, ast.Regress):
# Retrieve the population.
if not core.bayesdb_has_population(bdb, phrase.population):
raise BQLError(bdb,
'No such population: %r' % (phrase.population, ))
population_id = core.bayesdb_get_population(bdb, phrase.population)
# Retrieve the generator
generator_id = None
if phrase.generator:
if not core.bayesdb_has_generator(bdb, population_id,
phrase.generator):
raise BQLError(bdb,
'No such generator: %r' % (phrase.generator, ))
generator_id = core.bayesdb_get_generator(bdb, population_id,
phrase.generator)
# Retrieve the target variable.
if not core.bayesdb_has_variable(bdb, population_id, None,
phrase.target):
raise BQLError(bdb, 'No such variable: %r' % (phrase.target, ))
colno_target = core.bayesdb_variable_number(bdb, population_id, None,
phrase.target)
stattype = core.bayesdb_variable_stattype(bdb, population_id,
generator_id, colno_target)
if stattype != 'numerical':
raise BQLError(
bdb,
'Target variable is not numerical: %r' % (phrase.target, ))
# Build the given variables.
if any(isinstance(col, ast.SelColAll) for col in phrase.givens):
# Using * is not allowed to be mixed with other variables.
if len(phrase.givens) > 1:
raise BQLError(bdb, 'Cannot use (*) with other givens.')
colno_givens = core.bayesdb_variable_numbers(
bdb, population_id, None)
else:
if any(isinstance(col, ast.SelColSub) for col in phrase.givens):
# Subexpression needs special compiling.
out = compiler.Output(n_numpar, nampar_map, bindings)
bql_compiler = compiler.BQLCompiler_None()
givens = compiler.expand_select_columns(
bdb, phrase.givens, True, bql_compiler, out)
else:
givens = phrase.givens
colno_givens = [
core.bayesdb_variable_number(bdb, population_id, None,
given.expression.column)
for given in givens
]
# Build the arguments to bqlfn.bayesdb_simulate.
colno_givens_unique = set(colno for colno in colno_givens
if colno != colno_target)
if len(colno_givens_unique) == 0:
raise BQLError(bdb, 'No matching given columns.')
constraints = []
colnos = [colno_target] + list(colno_givens_unique)
nsamp = 100 if phrase.nsamp is None else phrase.nsamp.value.value
modelnos = None if phrase.modelnos is None else str(phrase.modelnos)
rows = bqlfn.bayesdb_simulate(bdb,
population_id,
generator_id,
modelnos,
constraints,
colnos,
numpredictions=nsamp)
# Retrieve the stattypes.
stattypes = [
core.bayesdb_variable_stattype(bdb, population_id, generator_id,
colno_given)
for colno_given in colno_givens_unique
]
# Separate the target values from the given values.
target_values = [row[0] for row in rows]
given_values = [row[1:] for row in rows]
given_names = [
core.bayesdb_variable_name(bdb, population_id, generator_id, given)
for given in colno_givens_unique
]
# Compute the coefficients. The import to regress_ols is here since the
# feature depends on pandas + sklearn, so avoid module-wide import.
from bayeslite.regress import regress_ols
coefficients = regress_ols(target_values, given_values, given_names,
stattypes)
# Store the results in a winder.
temptable = bdb.temp_table_name()
qtt = sqlite3_quote_name(temptable)
out = compiler.Output(0, {}, {})
out.winder(
'''
CREATE TEMP TABLE %s (variable TEXT, coefficient REAL);
''' % (qtt, ), ())
for variable, coef in coefficients:
out.winder(
'''
INSERT INTO %s VALUES (?, ?)
''' % (qtt), (
variable,
coef,
))
out.write('SELECT * FROM %s ORDER BY variable' % (qtt, ))
out.unwinder('DROP TABLE %s' % (qtt, ), ())
winders, unwinders = out.getwindings()
return execute_wound(bdb, winders, unwinders, out.getvalue(),
out.getbindings())
assert False # XXX
def generator_depprob(generator_id):
backend = core.bayesdb_generator_backend(bdb, generator_id)
depprob_list = backend.column_dependence_probability(
bdb, generator_id, modelnos, colno0, colno1)
return stats.arithmetic_mean(depprob_list)
def execute_phrase(bdb, phrase, bindings=()):
"""Execute the BQL AST phrase `phrase` and return a cursor of results."""
if isinstance(phrase, ast.Parametrized):
n_numpar = phrase.n_numpar
nampar_map = phrase.nampar_map
phrase = phrase.phrase
assert 0 < n_numpar
else:
n_numpar = 0
nampar_map = None
# Ignore extraneous bindings. XXX Bad idea?
if ast.is_query(phrase):
# Compile the query in the transaction in case we need to
# execute subqueries to determine column lists. Compiling is
# a quick tree descent, so this should be fast.
out = compiler.Output(n_numpar, nampar_map, bindings)
with bdb.savepoint():
compiler.compile_query(bdb, phrase, out)
winders, unwinders = out.getwindings()
return execute_wound(bdb, winders, unwinders, out.getvalue(),
out.getbindings())
if isinstance(phrase, ast.Begin):
txn.bayesdb_begin_transaction(bdb)
return empty_cursor(bdb)
if isinstance(phrase, ast.Rollback):
txn.bayesdb_rollback_transaction(bdb)
return empty_cursor(bdb)
if isinstance(phrase, ast.Commit):
txn.bayesdb_commit_transaction(bdb)
return empty_cursor(bdb)
if isinstance(phrase, ast.CreateTabAs):
assert ast.is_query(phrase.query)
with bdb.savepoint():
if core.bayesdb_has_table(bdb, phrase.name):
if phrase.ifnotexists:
return empty_cursor(bdb)
else:
raise BQLError(bdb,
'Name already defined as table: %s' %
(repr(phrase.name),))
out = compiler.Output(n_numpar, nampar_map, bindings)
qt = sqlite3_quote_name(phrase.name)
temp = 'TEMP ' if phrase.temp else ''
ifnotexists = 'IF NOT EXISTS ' if phrase.ifnotexists else ''
out.write('CREATE %sTABLE %s%s AS ' % (temp, ifnotexists, qt))
compiler.compile_query(bdb, phrase.query, out)
winders, unwinders = out.getwindings()
with compiler.bayesdb_wind(bdb, winders, unwinders):
bdb.sql_execute(out.getvalue(), out.getbindings())
return empty_cursor(bdb)
if isinstance(phrase, ast.CreateTabCsv):
with bdb.savepoint():
table_exists = core.bayesdb_has_table(bdb, phrase.name)
if table_exists:
if phrase.ifnotexists:
return empty_cursor(bdb)
else:
raise BQLError(bdb, 'Table already exists: %s' %
(repr(phrase.name),))
bayesdb_read_csv_file(
bdb, phrase.name, phrase.csv, header=True, create=True)
return empty_cursor(bdb)
if isinstance(phrase, ast.DropTab):
with bdb.savepoint():
sql = 'SELECT COUNT(*) FROM bayesdb_population WHERE tabname = ?'
cursor = bdb.sql_execute(sql, (phrase.name,))
if 0 < cursor_value(cursor):
raise BQLError(bdb, 'Table still in use by populations: %s' %
(repr(phrase.name),))
bdb.sql_execute('DELETE FROM bayesdb_column WHERE tabname = ?',
(phrase.name,))
ifexists = 'IF EXISTS ' if phrase.ifexists else ''
qt = sqlite3_quote_name(phrase.name)
return bdb.sql_execute('DROP TABLE %s%s' % (ifexists, qt))
if isinstance(phrase, ast.AlterTab):
with bdb.savepoint():
table = phrase.table
if not core.bayesdb_has_table(bdb, table):
raise BQLError(bdb, 'No such table: %s' % (repr(table),))
for cmd in phrase.commands:
if isinstance(cmd, ast.AlterTabRenameTab):
# If the names differ only in case, we have to do
# some extra work because SQLite will reject the
# table rename. Note that we may even have table
# == cmd.name here, but if the stored table name
# differs in case from cmd.name, we want to update
# it anyway.
if casefold(table) == casefold(cmd.name):
# Go via a temporary table.
temp = table + '_temp'
while core.bayesdb_has_table(bdb, temp):
temp += '_temp'
rename_table(bdb, table, temp)
rename_table(bdb, temp, cmd.name)
else:
# Make sure nothing else has this name and
# rename it.
if core.bayesdb_has_table(bdb, cmd.name):
raise BQLError(bdb,
'Name already defined as table: %s'
% (repr(cmd.name),))
rename_table(bdb, table, cmd.name)
# If table has implicit population, rename it too.
if core.bayesdb_table_has_implicit_population(
bdb, cmd.name):
populations = \
core.bayesdb_table_populations(bdb, cmd.name)
assert len(populations) == 1
population_name = core.bayesdb_population_name(
bdb, populations[0])
qt = sqlite3_quote_name(cmd.name)
qp = sqlite3_quote_name(population_name)
bdb.execute('ALTER POPULATION %s RENAME TO %s'
% (qp, qt))
# Remember the new name for subsequent commands.
table = cmd.name
elif isinstance(cmd, ast.AlterTabRenameCol):
# XXX Need to deal with this in the compiler.
raise NotImplementedError('Renaming columns'
' not yet implemented.')
# Make sure the old name exist and the new name does not.
old_folded = casefold(cmd.old)
new_folded = casefold(cmd.new)
if old_folded != new_folded:
if not core.bayesdb_table_has_column(bdb, table,
cmd.old):
raise BQLError(bdb, 'No such column in table %s'
': %s' %
(repr(table), repr(cmd.old)))
if core.bayesdb_table_has_column(bdb, table, cmd.new):
raise BQLError(bdb, 'Column already exists'
' in table %s: %s' %
(repr(table), repr(cmd.new)))
# Update bayesdb_column. Everything else refers
# to columns by (tabname, colno) pairs rather than
# by names.
update_column_sql = '''
UPDATE bayesdb_column SET name = :new
WHERE tabname = :table AND name = :old
'''
total_changes = bdb._sqlite3.totalchanges()
bdb.sql_execute(update_column_sql, {
'table': table,
'old': cmd.old,
'new': cmd.new,
})
assert bdb._sqlite3.totalchanges() - total_changes == 1
# ...except backends may have the (case-folded) name cached.
if old_folded != new_folded:
populations_sql = '''
SELECT id FROM bayesdb_population WHERE tabname = ?
'''
cursor = bdb.sql_execute(populations_sql, (table,))
generators = [
core.bayesdb_population_generators(
bdb, population_id)
for (population_id,) in cursor
]
for generator_id in set(generators):
backend = core.bayesdb_generator_backend(bdb,
generator_id)
backend.rename_column(bdb, generator_id,
old_folded, new_folded)
else:
assert False, 'Invalid alter table command: %s' % \
(cmd,)
return empty_cursor(bdb)
if isinstance(phrase, ast.GuessSchema):
if not core.bayesdb_has_table(bdb, phrase.table):
raise BQLError(bdb, 'No such table : %s' % phrase.table)
out = compiler.Output(0, {}, {})
with bdb.savepoint():
qt = sqlite3_quote_name(phrase.table)
temptable = bdb.temp_table_name()
qtt = sqlite3_quote_name(temptable)
cursor = bdb.sql_execute('SELECT * FROM %s' % (qt,))
column_names = [d[0] for d in cursor.description]
rows = cursor.fetchall()
stattypes = bayesdb_guess_stattypes(column_names, rows)
distinct_value_counts = [
len(set([row[i] for row in rows]))
for i in range(len(column_names))
]
out.winder('''
CREATE TEMP TABLE %s (
column TEXT,
stattype TEXT,
num_distinct INTEGER,
reason TEXT
)
''' % (qtt,), ())
for cn, st, ct in zip(column_names, stattypes, distinct_value_counts):
out.winder('''
INSERT INTO %s VALUES (?, ?, ?, ?)
''' % (qtt), (cn, st[0], ct, st[1]))
out.write('SELECT * FROM %s' % (qtt,))
out.unwinder('DROP TABLE %s' % (qtt,), ())
winders, unwinders = out.getwindings()
return execute_wound(
bdb, winders, unwinders, out.getvalue(), out.getbindings())
if isinstance(phrase, ast.CreatePop):
with bdb.savepoint():
_create_population(bdb, phrase)
return empty_cursor(bdb)
if isinstance(phrase, ast.DropPop):
with bdb.savepoint():
if not core.bayesdb_has_population(bdb, phrase.name):
if phrase.ifexists:
return empty_cursor(bdb)
raise BQLError(bdb, 'No such population: %r' % (phrase.name,))
population_id = core.bayesdb_get_population(bdb, phrase.name)
generator_ids = core.bayesdb_population_generators(
bdb, population_id)
if generator_ids:
generators = [core.bayesdb_generator_name(bdb, gid)
for gid in generator_ids]
raise BQLError(bdb, 'Population %r still has generators: %r' %
(phrase.name, generators))
# XXX helpful error checking if generators still exist
# XXX check change counts
bdb.sql_execute('''
DELETE FROM bayesdb_variable WHERE population_id = ?
''', (population_id,))
bdb.sql_execute('''
DELETE FROM bayesdb_population WHERE id = ?
''', (population_id,))
return empty_cursor(bdb)
if isinstance(phrase, ast.AlterPop):
with bdb.savepoint():
population = phrase.population
if not core.bayesdb_has_population(bdb, population):
raise BQLError(bdb, 'No such population: %s' %
(repr(population),))
population_id = core.bayesdb_get_population(bdb, population)
for cmd in phrase.commands:
if isinstance(cmd, ast.AlterPopRenamePop):
table = core.bayesdb_population_table(bdb, population_id)
# Prevent renaming of implicit population directly, unless
# being called by ast.AlterTabRenameTab in which case the
# table name and population name will not be matching.
if core.bayesdb_population_is_implicit(bdb, population_id) \
and casefold(population) == casefold(table):
raise BQLError(bdb, 'Cannot rename implicit'
'population %s; rename base table instead'
% (population,))
# Make sure nothing else has this name.
if casefold(population) != casefold(cmd.name):
if core.bayesdb_has_population(bdb, cmd.name):
raise BQLError(bdb,
'Name already defined as population' ': %s'
% (repr(cmd.name),))
# Update bayesdb_population. Everything else
# refers to it by id.
update_generator_sql = '''
UPDATE bayesdb_population SET name = ? WHERE id = ?
'''
total_changes = bdb._sqlite3.totalchanges()
bdb.sql_execute(update_generator_sql,
(cmd.name, population_id))
assert bdb._sqlite3.totalchanges() - total_changes == 1
# If population has implicit generator, rename it too.
if core.bayesdb_population_has_implicit_generator(
bdb, population_id):
generators = core.bayesdb_population_generators(
bdb, population_id)
assert len(generators) == 1
generator_name = core.bayesdb_generator_name(
bdb, generators[0])
qp = sqlite3_quote_name(cmd.name)
qg = sqlite3_quote_name(generator_name)
bdb.execute('ALTER GENERATOR %s RENAME TO %s'
% (qg, qp,))
# Remember the new name for subsequent commands.
population = cmd.name
elif isinstance(cmd, ast.AlterPopAddVar):
# Ensure column exists in base table.
table = core.bayesdb_population_table(bdb, population_id)
if not core.bayesdb_table_has_column(
bdb, table, cmd.name):
raise BQLError(bdb,
'No such variable in base table: %s'
% (cmd.name))
# Ensure variable not already in population.
if core.bayesdb_has_variable(
bdb, population_id, None, cmd.name):
raise BQLError(bdb,
'Variable already in population: %s'
% (cmd.name))
# Ensure there is at least observation in the column.
qt = sqlite3_quote_name(table)
qc = sqlite3_quote_name(cmd.name)
cursor = bdb.sql_execute(
'SELECT COUNT(*) FROM %s WHERE %s IS NOT NULL' %
(qt, qc))
if cursor_value(cursor) == 0:
raise BQLError(bdb,
'Cannot add variable without any values: %s'
% (cmd.name))
# If stattype is None, guess.
if cmd.stattype is None:
cursor = bdb.sql_execute(
'SELECT %s FROM %s' % (qc, qt))
rows = cursor.fetchall()
[stattype, reason] = bayesdb_guess_stattypes(
[cmd.name], rows)[0]
# Fail if trying to model a key.
if stattype == 'key':
raise BQLError(bdb,
'Values in column %s appear to be keys.'
% (cmd.name,))
# Fail if cannot determine a stattype.
elif stattype == 'ignore':
raise BQLError(bdb,
'Failed to determine a stattype for %s, '
'please specify one manually.' % (cmd.name,))
# If user specified stattype, ensure it exists.
elif not core.bayesdb_has_stattype(bdb, cmd.stattype):
raise BQLError(bdb,
'Invalid stattype: %s' % (cmd.stattype))
else:
stattype = cmd.stattype
# Check that strings are not being modeled as numerical.
if stattype == 'numerical' \
and _column_contains_string(bdb, table, cmd.name):
raise BQLError(bdb,
'Numerical column contains string values: %r '
% (qc,))
with bdb.savepoint():
# Add the variable to the population.
core.bayesdb_add_variable(
bdb, population_id, cmd.name, stattype)
colno = core.bayesdb_variable_number(
bdb, population_id, None, cmd.name)
# Add the variable to each (initialized) generator in
# the population.
generator_ids = filter(
lambda g: core.bayesdb_generator_modelnos(bdb, g),
core.bayesdb_population_generators(
bdb, population_id),
)
for generator_id in generator_ids:
backend = core.bayesdb_generator_backend(
bdb, generator_id)
backend.add_column(bdb, generator_id, colno)
elif isinstance(cmd, ast.AlterPopStatType):
# Check the no generators are defined for this population.
generators = core.bayesdb_population_generators(
bdb, population_id)
if generators:
raise BQLError(bdb,
'Cannot update statistical types for population '
'%s, it has generators: %s'
% (repr(population), repr(generators),))
# Check all the variables are in the population.
unknown = [
c for c in cmd.names if not
core.bayesdb_has_variable(bdb, population_id, None, c)
]
if unknown:
raise BQLError(bdb,
'No such variables in population: %s'
% (repr(unknown)))
# Check the statistical type is valid.
if not core.bayesdb_has_stattype(bdb, cmd.stattype):
raise BQLError(bdb,
'Invalid statistical type: %r'
% (repr(cmd.stattype),))
# Check that strings are not being modeled as numerical.
if cmd.stattype == 'numerical':
table = core.bayesdb_population_table(
bdb, population_id)
numerical_string_vars = [
col for col in cmd.names
if _column_contains_string(bdb, table, col)
]
if numerical_string_vars:
raise BQLError(bdb,
'Columns with string values modeled as '
'numerical: %r' % (numerical_string_vars,))
# Perform the stattype update.
colnos = [
core.bayesdb_variable_number(
bdb, population_id, None, c) for c in cmd.names
]
qcolnos = ','.join('%d' % (colno,) for colno in colnos)
update_stattype_sql = '''
UPDATE bayesdb_variable SET stattype = ?
WHERE population_id = ? AND colno IN (%s)
''' % (qcolnos,)
bdb.sql_execute(
update_stattype_sql,
(casefold(cmd.stattype), population_id,))
else:
assert False, 'Invalid ALTER POPULATION command: %s' % \
(repr(cmd),)
return empty_cursor(bdb)
if isinstance(phrase, ast.CreateGen):
# Find the population.
if not core.bayesdb_has_population(bdb, phrase.population):
raise BQLError(bdb, 'No such population: %r' %
(phrase.population,))
population_id = core.bayesdb_get_population(bdb, phrase.population)
# Find the backend, or use the default.
backend_name = phrase.backend
if phrase.backend is None:
backend_name = 'cgpm'
if backend_name not in bdb.backends:
raise BQLError(bdb, 'No such backend: %s' %
(repr(backend_name),))
backend = bdb.backends[backend_name]
# Retrieve the (possibility implicit) generator name.
generator_name = phrase.name or phrase.population
implicit = 1 if phrase.name is None else 0
with bdb.savepoint():
if core.bayesdb_has_generator(bdb, population_id, generator_name):
if not phrase.ifnotexists:
raise BQLError(
bdb, 'Name already defined as generator: %s' %
(repr(generator_name),))
else:
# Insert a record into bayesdb_generator and get the
# assigned id.
bdb.sql_execute('''
INSERT INTO bayesdb_generator
(name, population_id, backend, implicit)
VALUES (?, ?, ?, ?)
''', (generator_name, population_id, backend.name(), implicit))
generator_id = core.bayesdb_get_generator(
bdb, population_id, generator_name)
# Do any backend-specific initialization.
backend.create_generator(bdb, generator_id, phrase.schema)
# All done. Nothing to return.
return empty_cursor(bdb)
if isinstance(phrase, ast.DropGen):
with bdb.savepoint():
if not core.bayesdb_has_generator(bdb, None, phrase.name):
if phrase.ifexists:
return empty_cursor(bdb)
raise BQLError(bdb, 'No such generator: %s' %
(repr(phrase.name),))
generator_id = core.bayesdb_get_generator(bdb, None, phrase.name)
backend = core.bayesdb_generator_backend(bdb, generator_id)
# Backend-specific destruction.
backend.drop_generator(bdb, generator_id)
# Drop latent variables, models, and, finally, generator.
drop_columns_sql = '''
DELETE FROM bayesdb_variable WHERE generator_id = ?
'''
bdb.sql_execute(drop_columns_sql, (generator_id,))
drop_model_sql = '''
DELETE FROM bayesdb_generator_model WHERE generator_id = ?
'''
bdb.sql_execute(drop_model_sql, (generator_id,))
drop_generator_sql = '''
DELETE FROM bayesdb_generator WHERE id = ?
'''
bdb.sql_execute(drop_generator_sql, (generator_id,))
return empty_cursor(bdb)
if isinstance(phrase, ast.AlterGen):
with bdb.savepoint():
generator = phrase.generator
if not core.bayesdb_has_generator(bdb, None, generator):
raise BQLError(bdb, 'No such generator: %s' %
(repr(generator),))
generator_id = core.bayesdb_get_generator(bdb, None, generator)
cmds_generic = []
for cmd in phrase.commands:
if isinstance(cmd, ast.AlterGenRenameGen):
population_id = core.bayesdb_generator_population(
bdb, generator_id)
population = core.bayesdb_population_name(
bdb, population_id)
# Prevent renaming of implicit generator directly, unless
# being called by ast.AlterPopRenamePop in which case the
# population name and generator name will not be matching.
if core.bayesdb_population_is_implicit(bdb, generator_id) \
and casefold(generator) == casefold(population):
raise BQLError(bdb, 'Cannot rename implicit '
'generator; rename base population instead')
# Disable modelnos with AlterGenRenameGen.
if phrase.modelnos is not None:
raise BQLError(bdb, 'Cannot specify models for RENAME')
# Make sure nothing else has this name.
if casefold(generator) != casefold(cmd.name):
if core.bayesdb_has_generator(bdb, None, cmd.name):
raise BQLError(bdb, 'Name already defined'
' as generator: %s' %
(repr(cmd.name),))
# Update bayesdb_generator. Everything else
# refers to it by id.
update_generator_sql = '''
UPDATE bayesdb_generator SET name = ? WHERE id = ?
'''
total_changes = bdb._sqlite3.totalchanges()
bdb.sql_execute(update_generator_sql,
(cmd.name, generator_id))
assert bdb._sqlite3.totalchanges() - total_changes == 1
# Remember the new name for subsequent commands.
generator = cmd.name
elif isinstance(cmd, ast.AlterGenGeneric):
cmds_generic.append(cmd.command)
else:
assert False, 'Invalid ALTER GENERATOR command: %s' % \
(repr(cmd),)
if cmds_generic:
modelnos = phrase.modelnos
modelnos_invalid = None if modelnos is None else [
modelno for modelno in modelnos if not
core.bayesdb_generator_has_model(bdb, generator_id, modelno)
]
if modelnos_invalid:
raise BQLError(bdb,
'No such models in generator %s: %s' %
(repr(phrase.generator), repr(modelnos)))
# Call generic alternations on the backend.
backend = core.bayesdb_generator_backend(bdb, generator_id)
backend.alter(bdb, generator_id, modelnos, cmds_generic)
return empty_cursor(bdb)
if isinstance(phrase, ast.InitModels):
if not core.bayesdb_has_generator(bdb, None, phrase.generator):
raise BQLError(bdb, 'No such generator: %s' %
(phrase.generator,))
generator_id = core.bayesdb_get_generator(bdb, None, phrase.generator)
modelnos = range(phrase.nmodels)
with bdb.savepoint():
# Find the model numbers. Omit existing ones for
# ifnotexists; reject existing ones otherwise.
if phrase.ifnotexists:
modelnos = set(modelno for modelno in modelnos
if not core.bayesdb_generator_has_model(bdb, generator_id,
modelno))
else:
existing = set(modelno for modelno in modelnos
if core.bayesdb_generator_has_model(bdb, generator_id,
modelno))
if 0 < len(existing):
raise BQLError(bdb, 'Generator %s already has models: %s' %
(repr(phrase.generator), sorted(existing)))
# Stop now if there's nothing to initialize.
if len(modelnos) == 0:
return
# Create the bayesdb_generator_model records.
modelnos = sorted(modelnos)
insert_model_sql = '''
INSERT INTO bayesdb_generator_model
(generator_id, modelno)
VALUES (:generator_id, :modelno)
'''
for modelno in modelnos:
bdb.sql_execute(insert_model_sql, {
'generator_id': generator_id,
'modelno': modelno,
})
# Do backend-specific initialization.
backend = core.bayesdb_generator_backend(bdb, generator_id)
backend.initialize_models(bdb, generator_id, modelnos)
return empty_cursor(bdb)
if isinstance(phrase, ast.AnalyzeModels):
# WARNING: It is the backend's responsibility to work in a
# transaction.
#
# WARNING: It is the backend's responsibility to update the
# iteration count in bayesdb_generator_model records.
#
# We do this so that the backend can save incremental
# progress in case of ^C in the middle.
#
# XXX Put these warning somewhere more appropriate.
if not core.bayesdb_has_generator(bdb, None, phrase.generator):
raise BQLError(bdb, 'No such generator: %s' %
(phrase.generator,))
generator_id = core.bayesdb_get_generator(bdb, None, phrase.generator)
backend = core.bayesdb_generator_backend(bdb, generator_id)
# XXX Should allow parameters for iterations and ckpt/iter.
backend.analyze_models(bdb, generator_id,
modelnos=phrase.modelnos,
iterations=phrase.iterations,
max_seconds=phrase.seconds,
ckpt_iterations=phrase.ckpt_iterations,
ckpt_seconds=phrase.ckpt_seconds,
program=phrase.program)
return empty_cursor(bdb)
if isinstance(phrase, ast.DropModels):
with bdb.savepoint():
generator_id = core.bayesdb_get_generator(
bdb, None, phrase.generator)
backend = core.bayesdb_generator_backend(bdb, generator_id)
modelnos = None
if phrase.modelnos is not None:
lookup_model_sql = '''
SELECT COUNT(*) FROM bayesdb_generator_model
WHERE generator_id = :generator_id
AND modelno = :modelno
'''
modelnos = sorted(list(phrase.modelnos))
for modelno in modelnos:
cursor = bdb.sql_execute(lookup_model_sql, {
'generator_id': generator_id,
'modelno': modelno,
})
if cursor_value(cursor) == 0:
raise BQLError(bdb, 'No such model'
' in generator %s: %s' %
(repr(phrase.generator), repr(modelno)))
backend.drop_models(bdb, generator_id, modelnos=modelnos)
if modelnos is None:
drop_models_sql = '''
DELETE FROM bayesdb_generator_model WHERE generator_id = ?
'''
bdb.sql_execute(drop_models_sql, (generator_id,))
else:
drop_model_sql = '''
DELETE FROM bayesdb_generator_model
WHERE generator_id = :generator_id
AND modelno = :modelno
'''
for modelno in modelnos:
bdb.sql_execute(drop_model_sql, {
'generator_id': generator_id,
'modelno': modelno,
})
return empty_cursor(bdb)
if isinstance(phrase, ast.Regress):
# Retrieve the population.
if not core.bayesdb_has_population(bdb, phrase.population):
raise BQLError(bdb, 'No such population: %r' % (phrase.population,))
population_id = core.bayesdb_get_population(bdb, phrase.population)
# Retrieve the generator
generator_id = None
if phrase.generator:
if not core.bayesdb_has_generator(bdb, population_id,
phrase.generator):
raise BQLError(bdb,
'No such generator: %r' % (phrase.generator,))
generator_id = core.bayesdb_get_generator(
bdb, population_id, phrase.generator)
# Retrieve the target variable.
if not core.bayesdb_has_variable(
bdb, population_id, None, phrase.target):
raise BQLError(bdb, 'No such variable: %r' % (phrase.target,))
colno_target = core.bayesdb_variable_number(
bdb, population_id, None, phrase.target)
stattype = core.bayesdb_variable_stattype(bdb, population_id,
generator_id, colno_target)
if stattype != 'numerical':
raise BQLError(bdb,
'Target variable is not numerical: %r' % (phrase.target,))
# Build the given variables.
if any(isinstance(col, ast.SelColAll) for col in phrase.givens):
# Using * is not allowed to be mixed with other variables.
if len(phrase.givens) > 1:
raise BQLError(bdb, 'Cannot use (*) with other givens.')
colno_givens = core.bayesdb_variable_numbers(
bdb, population_id, None)
else:
if any(isinstance(col, ast.SelColSub) for col in phrase.givens):
# Subexpression needs special compiling.
out = compiler.Output(n_numpar, nampar_map, bindings)
bql_compiler = compiler.BQLCompiler_None()
givens = compiler.expand_select_columns(
bdb, phrase.givens, True, bql_compiler, out)
else:
givens = phrase.givens
colno_givens = [
core.bayesdb_variable_number(
bdb, population_id, None, given.expression.column)
for given in givens
]
# Build the arguments to bqlfn.bayesdb_simulate.
colno_givens_unique = set(
colno for colno in colno_givens if colno!= colno_target
)
if len(colno_givens_unique) == 0:
raise BQLError(bdb, 'No matching given columns.')
constraints = []
colnos = [colno_target] + list(colno_givens_unique)
nsamp = 100 if phrase.nsamp is None else phrase.nsamp.value.value
modelnos = None if phrase.modelnos is None else str(phrase.modelnos)
rows = bqlfn.bayesdb_simulate(
bdb, population_id, generator_id, modelnos, constraints,
colnos, numpredictions=nsamp)
# Retrieve the stattypes.
stattypes = [
core.bayesdb_variable_stattype(
bdb, population_id, generator_id, colno_given)
for colno_given in colno_givens_unique
]
# Separate the target values from the given values.
target_values = [row[0] for row in rows]
given_values = [row[1:] for row in rows]
given_names = [
core.bayesdb_variable_name(bdb, population_id, generator_id, given)
for given in colno_givens_unique
]
# Compute the coefficients. The import to regress_ols is here since the
# feature depends on pandas + sklearn, so avoid module-wide import.
from bayeslite.regress import regress_ols
coefficients = regress_ols(
target_values, given_values, given_names, stattypes)
# Store the results in a winder.
temptable = bdb.temp_table_name()
qtt = sqlite3_quote_name(temptable)
out = compiler.Output(0, {}, {})
out.winder('''
CREATE TEMP TABLE %s (variable TEXT, coefficient REAL);
''' % (qtt,), ())
for variable, coef in coefficients:
out.winder('''
INSERT INTO %s VALUES (?, ?)
''' % (qtt), (variable, coef,))
out.write('SELECT * FROM %s ORDER BY variable' % (qtt,))
out.unwinder('DROP TABLE %s' % (qtt,), ())
winders, unwinders = out.getwindings()
return execute_wound(
bdb, winders, unwinders, out.getvalue(), out.getbindings())
assert False # XXX
def generator_mutinf(generator_id):
backend = core.bayesdb_generator_backend(bdb, generator_id)
return backend.column_mutual_information(
bdb, generator_id, modelnos, colnos0, colnos1,
constraints=constraints, numsamples=numsamples)
def _cmd_render_crosscat(self, query, sql=None, **kwargs):
'''Returns a rendering of the specified crosscat state
Usage: .render_crosscat [options] <generator> <modelno>.
Options:
--subsample=<n>
--width=<w>
--height=<c>
--rowlabels=<colname>
--progress=[True|False]
--yticklabeslize=<fontsize>
--xticklabeslize=<fontsize>
The allowable fontsize strings are:
xx-small, x-small, # small, medium, large, x-large, xx-large
'''
tokens = query.split()
if len(tokens) != 2:
self.write_stderr('Usage: .render_crosscat <generator> <modelno>')
return
generator = tokens[0]
modelno = int(tokens[1])
if not bayesdb_has_generator(self._bdb, None, generator):
self.write_stderr('No such generator: %s.' % (generator, ))
return
generator_id = bayesdb_get_generator(self._bdb, None, generator)
population_id = bayesdb_generator_population(self._bdb, generator_id)
backend = bayesdb_generator_backend(self._bdb, generator_id)
if backend.name() != 'cgpm':
self.write_stderr('.render_crosscat requires generator from the '
'cgpm backend')
return
engine = backend._engine(self._bdb, generator_id)
cursor = self._bdb.sql_execute(
'''
SELECT cgpm_modelno FROM bayesdb_cgpm_modelno
WHERE generator_id = ? AND modelno = ?
''', (
generator_id,
modelno,
))
cgpm_modelno = cursor_value(cursor, nullok=True)
if cgpm_modelno is None:
self.write_stderr('No such model number: %d.' % (modelno, ))
return
state = engine.get_state(cgpm_modelno)
row_names = None
row_index_column = kwargs.get('rowlabels', None)
if row_index_column is not None:
table_name = bayesdb_generator_table(self._bdb, generator_id)
qt = bql_quote_name(table_name)
qc = bql_quote_name(row_index_column)
cursor = self._bdb.sql_execute(
'''
SELECT %s FROM %s WHERE oid IN (
SELECT table_rowid FROM bayesdb_cgpm_individual
WHERE generator_id = ?
)
''' % (qc, qt), (generator_id, ))
row_names = [c[0] for c in cursor]
if 'progress' in kwargs:
sys.stdout.write('Creating figure...\n')
import cgpm.utils.render
if 'variable' not in kwargs:
# Plot the entire state.
col_names = [
bayesdb_variable_name(self._bdb, population_id, None, colno)
for colno in state.outputs
]
fig, _ax = cgpm.utils.render.viz_state(state,
col_names=col_names,
row_names=row_names,
**kwargs)
else:
# Plot the view of the requested variable.
varno = bayesdb_variable_number(self._bdb, population_id,
generator_id, kwargs['variable'])
view = state.view_for(varno)
col_names = [
bayesdb_variable_name(self._bdb, population_id, None, colno)
for colno in view.outputs[1:]
]
fig, _ax = cgpm.utils.render.viz_view(view,
col_names=col_names,
row_names=row_names,
**kwargs)
(width, height) = fig.get_size_inches()
if 'width' in kwargs:
width = float(kwargs['width'])
fig.set_size_inches(width, height)
if 'height' in kwargs:
height = float(kwargs['height'])
fig.set_size_inches(width, height)
if 'progress' in kwargs:
sys.stdout.write('Rendering figure...\n')
def generator_depprob(generator_id):
backend = core.bayesdb_generator_backend(bdb, generator_id)
depprob_list = backend.column_dependence_probability(
bdb, generator_id, modelnos, colno0, colno1)
return stats.arithmetic_mean(depprob_list)