def create_btable_from_csv(self, tablename, csv_path, cctypes, postgres_coltypes, colnames):
with self.open_db_connection(commit=True) as cur:
## TODO: warning: m_r and m_c have 0-indexed indices
## but the db has 1-indexed keys
t, m_r, m_c, header = du.read_data_objects(csv_path, cctypes=cctypes)
curtime = datetime.datetime.now().ctime()
cur.execute("INSERT INTO preddb.table_index (tablename, numsamples, uploadtime, analyzetime, t, m_r, m_c, cctypes, path) VALUES (%s, %s, %s, NULL, %s, %s, %s, %s, %s);", (tablename, 0, curtime, json.dumps(t), json.dumps(m_r), json.dumps(m_c), json.dumps(cctypes), csv_path))
def create_btable_from_csv(self, tablename, csv_path, cctypes,
postgres_coltypes, colnames):
with self.open_db_connection(commit=True) as cur:
## TODO: warning: m_r and m_c have 0-indexed indices
## but the db has 1-indexed keys
t, m_r, m_c, header = du.read_data_objects(csv_path,
cctypes=cctypes)
curtime = datetime.datetime.now().ctime()
cur.execute(
"INSERT INTO preddb.table_index (tablename, numsamples, uploadtime, analyzetime, t, m_r, m_c, cctypes, path) VALUES (%s, %s, %s, NULL, %s, %s, %s, %s, %s);",
(tablename, 0, curtime, json.dumps(t), json.dumps(m_r),
json.dumps(m_c), json.dumps(cctypes), csv_path))
def update_datatypes(self, tablename, mappings):
"""
mappings is a dict of column name to 'continuous', 'multinomial',
or an int, which signifies multinomial of a specific type.
TODO: FIX HACKS. Current works by reloading all the data from csv,
and it ignores multinomials' specific number of outcomes.
Also, disastrous things may happen if you update a schema after creating models.
"""
max_chainid = self.persistence_layer.get_max_chain_id(tablename)
if max_chainid is not None:
return 'Error: cannot update datatypes after models have already been created. Please create a new table.'
# First, get existing cctypes, and T, M_c, and M_r.
cctypes = self.persistence_layer.get_cctypes(tablename)
m_c, m_r, t = self.persistence_layer.get_metadata_and_table(tablename)
# Now, update cctypes, T, M_c, and M_r
for col, mapping in mappings.items():
## TODO: fix this hack! See method's docstring.
if type(mapping) == int:
mapping = 'multinomial'
cctypes[m_c['name_to_idx'][col]] = mapping
t, m_r, m_c, header = du.read_data_objects(csv_abs_path,
cctypes=cctypes)
# Now, put cctypes, T, M_c, and M_r back into the DB
self.persistence_layer.update_cctypes(tablename, cctypes)
self.persistence_layer.update_metadata_and_table(
tablename, M_r, M_c, T)
colnames = [
m_c['idx_to_name'][str(idx)]
for idx in range(len(m_c['idx_to_name']))
]
return dict(columns=colnames,
data=[cctypes],
message='Updated schema:\n')
from crosscat.LocalEngine import LocalEngine
import crosscat.utils.data_utils as data_utils
data_filename = 'T.csv'
inference_seed = 0
num_full_transitions = 10
# read the data table into internal json representation
data_table, row_metadata, column_metadata, header = \
data_utils.read_data_objects(data_filename)
# create an engine to run analysis, inference
engine = LocalEngine(seed=inference_seed)
# initialize markov chain samples
initial_latent_state, initial_latent_state_clustering = \
engine.initialize(column_metadata, row_metadata, data_table)
# run markov chain transition kernels on samples
latent_state, latent_state_clustering = engine.analyze(column_metadata,
data_table, initial_latent_state, initial_latent_state_clustering,
n_steps=num_full_transitions)
from crosscat.LocalEngine import LocalEngine
import crosscat.utils.data_utils as data_utils
data_filename = "T.csv"
inference_seed = 0
num_full_transitions = 10
# read the data table into internal json representation
data_table, row_metadata, column_metadata, header = data_utils.read_data_objects(data_filename)
# create an engine to run analysis, inference
engine = LocalEngine(seed=inference_seed)
# initialize markov chain samples
initial_latent_state, initial_latent_state_clustering = engine.initialize(column_metadata, row_metadata, data_table)
# run markov chain transition kernels on samples
latent_state, latent_state_clustering = engine.analyze(
column_metadata, data_table, initial_latent_state, initial_latent_state_clustering, n_steps=num_full_transitions
)
#!/usr/bin/env python from crosscat.LocalEngine import LocalEngine import crosscat.utils.data_utils as data_utils data_filename = '/vagrant/DREAM5_network_inference_challenge/Network1/input data/net1_chip_features.tsv' inference_seed = 0 num_full_transitions = 10 data_table, row_metadata, column_metadata, header = data_utils.read_data_objects(data_filename) engine = LocalEngine(seed=inference_seed) initial_latent_state, initial_latent_state_clustering = engine.initialize(column_metadata, row_metadata, data_table) latent_state, latent_state_clustering = engine.analyze( column_metadata, data_table, initial_latent_state, initial_latent_state_clustering, n_steps=num_full_transitions)
