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
argsdict = eu.parser_args_to_dict(args)
generate_plots = not argsdict['no_plots']
use_runner = not argsdict['no_runner']
results_filename = 'haystacks_break_results'
dirname_prefix = 'haystacks_break'
er = ExperimentRunner(run_experiment, dirname_prefix=dirname_prefix, bucket_str='experiment_runner', storage_type='fs')
if use_runner:
er.do_experiments([argsdict], do_multiprocessing=False)
if generate_plots:
for id in er.frame.index:
result = er._get_result(id)
this_dirname = eru._generate_dirname(dirname_prefix, 10, result['config'])
filename_img = os.path.join(dirname_prefix, this_dirname, results_filename+'.png')
eu.plot_haystacks_break(result, filename=filename_img)
pass
else:
result = run_experiment(argsdict)
if generate_plots:
this_dirname = eru._generate_dirname(dirname_prefix, 10, result['config'])
filename_img = os.path.join(dirname_prefix, this_dirname, results_filename+'.png')
eu.make_folder(os.path.join(dirname_prefix, this_dirname))
eu.plot_haystacks_break(result, filename=filename_img)
