def run_experiment(argin):
num_iters = argin["num_iters"]
num_chains = argin["num_chains"]
num_rows = argin["num_rows"]
num_cols = argin["num_cols"]
with_id = argin["with_id"]
needles = argin["needles"]
mixed_types = argin["mixed_types"]
multinomial_categories = argin["multinomial_categories"]
separation = argin["separation"]
num_indep_queries = argin["num_indep_queries"]
independent_clusters = argin["independent_clusters"]
ct_kernel = argin["ct_kernel"]
seed = argin["seed"]
if seed > 0:
random.seed(seed)
# generate column indices and header
col_names = [ "col_%i" % i for i in range(num_cols)]
if mixed_types and multinomial_categories > 0:
data_mode = 'mixed'
elif multinomial_categories > 0:
data_mode = 'multinomial'
else:
data_mode = 'continuous'
if needles:
T = [[0]*num_cols]*num_rows
Zv = [0,0,1,1] # our needles
Zv.extend(range(2,num_cols-2))
# random.shuffle(Zv)
num_views = max(Zv)+1
separation = [.95]*2
separation.extend([0.0]*(num_views-2))
min_clusters = 4
max_clusters = 5
cluster_weights = []
# generate weights.
for v in range(num_views):
if v < 2:
num_clusters = random.randrange(min_clusters, max_clusters)
else:
if independent_clusters:
num_clusters = random.randrange(min_clusters, max_clusters)
else:
num_clusters = 1
cluster_weights.append( [1.0/num_clusters]*num_clusters )
cctypes, distargs = eu.get_column_types(data_mode, num_cols, multinomial_categories)
T, _ = sdg.gen_data(cctypes, num_rows, Zv, cluster_weights, separation, distargs=distargs)
else:
T, cctypes = eu.generate_noise(data_mode, num_rows, num_cols)
# # preprend the row_id
# if with_id:
# needle_a_cols = (1,2)
# needle_b_cols = (3,4)
# col_names.insert(0, 'ID')
# # TODO: ID type
# cctypes.insert(0,'continuous')
# # header = "ID,%s" % header
# if needles:
# Zv.insert(0, num_views)
# for row in range(num_rows):
# T[row].insert(0, row)
# else:
needle_a_cols = (col_names[0],col_names[1])
needle_b_cols = (col_names[2],col_names[3])
# save file to .csv
filename = "needles_exp.csv"
table = "needles_exp"
T.insert(0, col_names)
eu.list_to_csv(filename, T)
# generate queries
queries, pairs = generate_dependence_queries(needle_a_cols, needle_b_cols,
col_names, table, num_indep_queries)
num_queries = len(queries)
dependence_probs = numpy.zeros( (num_iters, num_queries) )
client = Client()
client('DROP BTABLE %s;' % table, yes=True)
client('CREATE BTABLE %s FROM %s;' % (table, filename))
init_string = 'INITIALIZE %i MODELS FOR %s;' % (num_chains, table)
print init_string
client(init_string)
client('SHOW DIAGNOSTICS FOR %s;' % table)
# do the analyses
for i in range(num_iters):
if ct_kernel == 1:
client( 'ANALYZE %s FOR 1 ITERATIONS WITH MH KERNEL WAIT;' % table )
else:
client( 'ANALYZE %s FOR 1 ITERATIONS WAIT;' % table )
for q in range(num_queries):
query = queries[q]
out = client(query, pretty=False, pandas_output=False)
dependence_probs[i,q] = out[0]['data'][0][1]
result = dict()
# store the queries in result
result['query_col1'] = []
result['query_col2'] = []
result['dependence_probs'] = dependence_probs
for pair in pairs:
result['query_col1'].append(pair[0])
result['query_col2'].append(pair[1])
# for each query, get wether those columns were actually independent
independent = [True]*num_queries
if needles:
for i in range(num_queries):
col_idx_0 = pairs[i][0]
col_idx_1 = pairs[i][1]
if Zv[col_idx_0] == Zv[col_idx_1]:
independent[i] = False
result['cols_independent'] = independent
result['config'] = argin
result['config']['data_mode'] = data_mode
client('SHOW DIAGNOSTICS FOR %s;' % table)
return result
def run_experiment(argin):
num_iters = argin["num_iters"]
num_chains = argin["num_chains"]
num_rows = argin["num_rows"]
max_cols = argin["max_cols"]
rho = argin["rho"]
num_indep_queries = argin["num_indep_queries"]
independent_clusters = argin["independent_clusters"]
ct_kernel = argin["ct_kernel"]
multimodal = argin["multimodal"]
separation = argin["separation"]
all_cols = max_cols + 4 # max_cols plus number of dependent columns
seed = argin["seed"]
if seed > 0:
random.seed(seed)
numpy.random.seed(seed)
# build full data file
# generate column indices and header
col_names = [ "col_%i" % i for i in range(all_cols)]
Zv = [0,0,1,1] # our needles
Zv.extend(range(2,all_cols-2))
min_clusters = 3
max_clusters = 10
T_array = numpy.zeros( (num_rows, all_cols) )
Sigma = numpy.array( [[1.0,rho],[rho,1.0]])
mu = numpy.array([0,0])
if multimodal:
T = [[0]*num_cols]*num_rows
Zv = [0,0,1,1] # our needles
Zv.extend(range(2,num_cols-2))
random.shuffle(Zv)
num_views = max(Zv)+1
separation = [separation]*2
separation.extend([separation]*(num_views-2))
min_clusters = 4
max_clusters = 5
cluster_weights = []
# generate weights.
for v in range(num_views):
if v < 2:
num_clusters = random.randrange(min_clusters, max_clusters)
else:
num_clusters = 1
cluster_weights.append( [1.0/num_clusters]*num_clusters )
cctypes, distargs = eu.get_column_types(data_mode, num_cols, multinomial_categories)
T, _ = sdg.gen_data(cctypes, num_rows, Zv, cluster_weights, separation, distargs=distargs)
T_array = numpy.array(T)
else:
T_array[:, 0:1+1] = numpy.random.multivariate_normal(mu, Sigma, num_rows)
T_array[:, 2:3+1] = numpy.random.multivariate_normal(mu, Sigma, num_rows)
separation = .5
for col in range(4, all_cols):
num_clusters = random.randrange(min_clusters, max_clusters)+1
for row in range(num_rows):
k = random.randrange(num_clusters)
T_array[row, col] = numpy.random.randn()+k*6*separation
T = T_array.tolist()
# save file to .csv
exp_path = 'expdata/hb/'
eu.make_folder(exp_path)
filename = exp_path + "haystack_break_exp.csv"
table = "haystack_break_exp"
T.insert(0, col_names)
eu.list_to_csv(filename, T)
# done building data file
# get colum step size (powers of two)
num_steps = int( math.log(max_cols, 2) )-1
step_size = [2**t for t in range(2, num_steps+1)]
assert step_size[-1] <= max_cols
if step_size[-1] < max_cols:
step_size.append(max_cols)
assert step_size[0] == 4 and step_size[-1] == max_cols
# the needle column names
needle_a_cols = (col_names[0],col_names[1])
needle_b_cols = (col_names[2],col_names[3])
result = dict()
result['steps'] = []
for num_distractor_columns in step_size:
# create subdata
T_sub = take_T_column_subset(T, range(4+num_distractor_columns) )
subpath = exp_path+'d_'+str(num_distractor_columns)+'/'
eu.make_folder(subpath)
subfilename = subpath + "haystack_break_exp_" + str(num_distractor_columns) + ".csv"
eu.list_to_csv(subfilename, T_sub)
col_names_sub = T_sub[0]
# generate queries
queries, pairs = generate_dependence_queries(needle_a_cols, needle_b_cols,
col_names_sub, table, num_indep_queries)
num_queries = len(queries)
dependence_probs = numpy.zeros( (num_iters+1, num_queries) )
client = Client()
client('DROP BTABLE %s;' % table, yes=True)
client('CREATE BTABLE %s FROM %s;' % (table, subfilename))
init_string = 'INITIALIZE %i MODELS FOR %s;' % (num_chains, table)
print init_string
client(init_string)
client('SHOW DIAGNOSTICS FOR %s;' % table)
# do the analyses
for i in range(0,num_iters+1):
if i > 0:
if ct_kernel == 1:
client( 'ANALYZE %s FOR 1 ITERATIONS WITH MH KERNEL WAIT;' % table )
else:
client( 'ANALYZE %s FOR 1 ITERATIONS WAIT;' % table )
for q in range(num_queries):
query = queries[q]
out = client(query, pretty=False, pandas_output=False)
dependence_probs[i,q] = out[0]['data'][0][1]
subresult = dict()
# store the queries in subresult
subresult['query_col1'] = []
subresult['query_col2'] = []
subresult['dependence_probs'] = dependence_probs
for pair in pairs:
subresult['query_col1'].append(pair[0])
subresult['query_col2'].append(pair[1])
# for each query, get wether those columns were actually independent
independent = [True]*num_queries
for i in range(num_queries):
col_idx_0 = pairs[i][0]
col_idx_1 = pairs[i][1]
if Zv[col_idx_0] == Zv[col_idx_1]:
independent[i] = False
subresult['cols_independent'] = independent
subresult['distractor_cols'] = num_distractor_columns
result['steps'].append(subresult)
result['config'] = argin
result['data'] = T_array
return result
def run_experiment(argin):
num_iters = argin["num_iters"]
num_chains = argin["num_chains"]
num_rows = argin["num_rows"]
num_cols = argin["num_cols"]
with_id = argin["with_id"]
needles = argin["needles"]
mixed_types = argin["mixed_types"]
multinomial_categories = argin["multinomial_categories"]
separation = argin["separation"]
num_indep_queries = argin["num_indep_queries"]
independent_clusters = argin["independent_clusters"]
ct_kernel = argin["ct_kernel"]
seed = argin["seed"]
if seed > 0:
random.seed(seed)
# generate column indices and header
col_names = ["col_%i" % i for i in range(num_cols)]
if mixed_types and multinomial_categories > 0:
data_mode = 'mixed'
elif multinomial_categories > 0:
data_mode = 'multinomial'
else:
data_mode = 'continuous'
if needles:
T = [[0] * num_cols] * num_rows
Zv = [0, 0, 1, 1] # our needles
Zv.extend(range(2, num_cols - 2))
# random.shuffle(Zv)
num_views = max(Zv) + 1
separation = [.95] * 2
separation.extend([0.0] * (num_views - 2))
min_clusters = 4
max_clusters = 5
cluster_weights = []
# generate weights.
for v in range(num_views):
if v < 2:
num_clusters = random.randrange(min_clusters, max_clusters)
else:
if independent_clusters:
num_clusters = random.randrange(min_clusters, max_clusters)
else:
num_clusters = 1
cluster_weights.append([1.0 / num_clusters] * num_clusters)
cctypes, distargs = eu.get_column_types(data_mode, num_cols,
multinomial_categories)
T, _ = sdg.gen_data(cctypes,
num_rows,
Zv,
cluster_weights,
separation,
distargs=distargs)
else:
T, cctypes = eu.generate_noise(data_mode, num_rows, num_cols)
# # preprend the row_id
# if with_id:
# needle_a_cols = (1,2)
# needle_b_cols = (3,4)
# col_names.insert(0, 'ID')
# # TODO: ID type
# cctypes.insert(0,'continuous')
# # header = "ID,%s" % header
# if needles:
# Zv.insert(0, num_views)
# for row in range(num_rows):
# T[row].insert(0, row)
# else:
needle_a_cols = (col_names[0], col_names[1])
needle_b_cols = (col_names[2], col_names[3])
# save file to .csv
filename = "needles_exp.csv"
table = "needles_exp"
T.insert(0, col_names)
eu.list_to_csv(filename, T)
# generate queries
queries, pairs = generate_dependence_queries(needle_a_cols, needle_b_cols,
col_names, table,
num_indep_queries)
num_queries = len(queries)
dependence_probs = numpy.zeros((num_iters, num_queries))
client = Client()
client('DROP BTABLE %s;' % table, yes=True)
client('CREATE BTABLE %s FROM %s;' % (table, filename))
init_string = 'INITIALIZE %i MODELS FOR %s;' % (num_chains, table)
print init_string
client(init_string)
client('SHOW DIAGNOSTICS FOR %s;' % table)
# do the analyses
for i in range(num_iters):
if ct_kernel == 1:
client('ANALYZE %s FOR 1 ITERATIONS WITH MH KERNEL WAIT;' % table)
else:
client('ANALYZE %s FOR 1 ITERATIONS WAIT;' % table)
for q in range(num_queries):
query = queries[q]
out = client(query, pretty=False, pandas_output=False)
dependence_probs[i, q] = out[0]['data'][0][1]
result = dict()
# store the queries in result
result['query_col1'] = []
result['query_col2'] = []
result['dependence_probs'] = dependence_probs
for pair in pairs:
result['query_col1'].append(pair[0])
result['query_col2'].append(pair[1])
# for each query, get wether those columns were actually independent
independent = [True] * num_queries
if needles:
for i in range(num_queries):
col_idx_0 = pairs[i][0]
col_idx_1 = pairs[i][1]
if Zv[col_idx_0] == Zv[col_idx_1]:
independent[i] = False
result['cols_independent'] = independent
result['config'] = argin
result['config']['data_mode'] = data_mode
client('SHOW DIAGNOSTICS FOR %s;' % table)
return result