def run_experiment(argin):
num_iters = argin["num_iters"]
num_chains = argin["num_chains"]
num_rows = argin["num_rows"]
num_cols = argin["num_cols"]
num_views = argin["num_views"]
num_clusters = argin["num_clusters"]
prop_missing = argin["prop_missing"]
separation = argin["separation"]
ct_kernel = argin["ct_kernel"]
multinomial_categories = argin["multinomial_categories"]
seed = argin["seed"]
random.seed(seed)
# TODO: use dha.csv
ofilename = "reasonably_calibrated_ofile.csv"
table_name = "reasonably_calibrated"
argin["distargs"] = [{"K": multinomial_categories}] * num_cols
argin["cctypes"] = ["multinomial"] * num_cols
argin["separation"] = [argin["separation"]] * num_views
T_array, structure = eu.gen_data(ofilename, argin, save_csv=True)
filename, indices, col_names = eu.gen_missing_data_csv(ofilename, prop_missing, [])
# create a client
client = Client()
# caluclate empirical frequency of each point
frequencies = []
for col in range(num_cols):
frequencies.append(numpy.zeros(multinomial_categories))
T_int = numpy.array(T_array, dtype=int)
n_indices = len(indices[0])
for i in range(n_indices):
r = indices[0][i]
c = indices[1][i]
x = T_int[r, c]
frequencies[c][x] += 1.0
frequencies = [f / numpy.sum(f) for f in frequencies]
# set up a dict fro the different config data
result = dict()
# do analyses
for config in ["cc", "crp", "nb"]:
config_string = eu.config_map[config]
table = table_name + "-" + config
# drop old btable, create a new one with the new data and init models
client("DROP BTABLE %s;" % table, yes=True)
client("CREATE BTABLE %s FROM %s;" % (table, filename))
client("INITIALIZE %i MODELS FOR %s %s;" % (num_chains, table, config_string))
if ct_kernel == 1:
client("ANALYZE %s FOR %i ITERATIONS WITH MH KERNEL WAIT;" % (table, num_iters))
else:
client("ANALYZE %s FOR %i ITERATIONS WAIT;" % (table, num_iters))
# imput each index in indices and calculate the squared error
results_config = []
for col in range(num_cols):
results_config.append(numpy.zeros(multinomial_categories))
for col in range(num_cols):
col_name = col_names[col]
out = client(
"INFER %s FROM %s WITH CONFIDENCE .95 WITH 1 SAMPLES;" % (col_name, table),
pretty=False,
pandas_output=False,
)
for i in range(n_indices):
r = indices[0][i]
c = indices[1][i]
if c == col:
x = out[0]["data"][r][1]
results_config[c][int(x)] += 1.0
results_config = [f / sum(f) for f in results_config]
result[config] = results_config
retval = dict()
retval["actual_frequencies"] = frequencies
retval["inferred_P_cc"] = result["cc"]
retval["inferred_P_crp"] = result["crp"]
retval["inferred_P_nb"] = result["nb"]
retval["config"] = argin
return retval
def run_experiment(argin):
num_iters = argin["num_iters"]
num_chains = argin["num_chains"]
num_rows = argin["num_rows"]
num_cols = argin["num_cols"]
num_views = argin["num_views"]
num_clusters = argin["num_clusters"]
prop_missing = argin["prop_missing"]
impute_samples = argin["impute_samples"]
separation = argin["separation"]
ct_kernel = argin["ct_kernel"]
seed = argin["seed"]
if seed > 0 :
random.seed(seed)
filename = "exp_fills_in_ofile.csv"
table_name = 'exp_fills_in'
argin['cctypes'] = ['continuous']*num_cols
argin['separation'] = [argin['separation']]*num_views
eu.gen_data(filename, argin, save_csv=True)
# generate a new csv
all_filenames = []
all_indices = []
for p in prop_missing:
data_filename, indices, col_names, extra = eu.gen_missing_data_csv(filename,
p, [], True)
all_indices.append(indices)
all_filenames.append(data_filename)
# get the starting table so we can calculate errors
T_array = extra['array_filled']
num_rows, num_cols = T_array.shape
# create a client
client = Client()
# set up a dict fro the different config data
result = dict()
result['cc'] = numpy.zeros(len(prop_missing))
result['crp'] = numpy.zeros(len(prop_missing))
result['nb'] = numpy.zeros(len(prop_missing))
# do analyses
for p in range(len(prop_missing)):
this_indices = all_indices[p]
this_filename = all_filenames[p]
for config in ['cc', 'crp', 'nb']:
config_string = eu.config_map[config]
table = table_name + '-' + config
# drop old btable, create a new one with the new data and init models
client('DROP BTABLE %s;' % table, yes=True)
client('CREATE BTABLE %s FROM %s;' % (table, this_filename))
client('INITIALIZE %i MODELS FOR %s %s;' % (num_chains, table, config_string))
if ct_kernel == 1:
client('ANALYZE %s FOR %i ITERATIONS WITH MH KENEL WAIT;' % (table, num_iters) )
else:
client('ANALYZE %s FOR %i ITERATIONS WAIT;' % (table, num_iters) )
MSE = 0.0
count = 0.0
# imput each index in indices and calculate the squared error
for col in range(0,num_cols):
col_name = col_names[col]
# confidence is set to zero so that a value is always returned
out = client('INFER %s from %s WITH CONFIDENCE %f WITH %i SAMPLES;' % (col_name, table, 0, impute_samples), pretty=False, pandas_output=False )
data = out[0]['data']
# calcaulte MSE
for row, tcol in zip(this_indices[0], this_indices[1]):
if tcol == col:
MSE += ( T_array[row,col] - data[row][1] )**2.0
count += 1.0
result[config][p] = MSE/count
print "error = %f" % result[config][p]
retval = dict()
retval['MSE_naive_bayes_indexer'] = result['nb']
retval['MSE_crp_mixture_indexer'] = result['crp']
retval['MSE_crosscat_indexer'] = result['cc']
retval['prop_missing'] = prop_missing
retval['config'] = argin
return retval
def run_experiment(argin):
num_iters = argin["num_iters"]
num_chains = argin["num_chains"]
num_runs = argin["num_runs"]
prop_missing = argin["prop_missing"]
confidence = argin["confidence"]
seed = argin["seed"]
n_queries = 2
# random.seed(seed)
# using dha, for now
start_filename = "../data/dha.csv"
table = 'exp_shrinks_with_iters'
filename, indices, col_names = eu.gen_missing_data_csv(start_filename, prop_missing, [0])
# get some random column pairs to do DEPENDENCE PROBABILITY queries on
# don't do queries on the first column
columns = range(1,len(col_names))
column_queries = [ random.sample(columns, 2) for _ in range(n_queries)]
dependence_queries = []
for q in column_queries:
col_1 = col_names[q[0]].lower()
col_2 = col_names[q[1]].lower()
this_query = "SELECT DEPENDENCE PROBABILITY OF %s WITH %s FROM %s;" % (col_1, col_2, table)
dependence_queries.append(this_query)
# get some inference queries
column_queries = random.sample(columns, n_queries)
infer_queries = []
for q in column_queries:
col = col_names[q].lower()
this_query = 'INFER %s FROM %s WITH CONFIDENCE %f;' % (col, table, confidence)
infer_queries.append(this_query)
# create a client
client = Client()
dependence_results = []
inference_results = []
for _ in range(num_runs):
# drop old table, create new table, init models
client('DROP BTABLE %s;' % table, yes=True)
client('CREATE BTABLE %s FROM %s;' % (table, filename))
client('INITIALIZE %i MODELS FOR %s;' % (num_chains, table))
dependence_results_run = numpy.zeros((n_queries, num_iters))
inference_results_run = numpy.zeros((n_queries, num_iters))
for i in range(num_iters):
# analyze
client('ANALYZE %s FOR 1 ITERATIONS;' % (table) )
# dependence
for q in range(n_queries):
out_dep = client(dependence_queries[q], pretty=False, pandas_output=False)
dep = out_dep[0]['data'][0][1]
dependence_results_run[q,i] = dep
# infer
for q in range(n_queries):
out_inf = client(infer_queries[q], pretty=False, pandas_output=False)
prop = _get_prop_inferred(out_inf[0]['data'], indices, column_queries[q])
inference_results_run[q,i] = prop
dependence_results.append( dependence_results_run )
inference_results.append( inference_results_run )
# calculate mean and errors (dependence)
dep_means = numpy.zeros( (n_queries, num_iters) )
dep_error = numpy.zeros( (n_queries, num_iters) )
for i in range(num_iters):
X = numpy.zeros( (n_queries,num_runs) )
for r in range(num_runs):
X[:,r] = dependence_results[r][:,i]
dep_means[:,i] = numpy.mean(X, axis=1)
dep_error[:,i] = numpy.std(X, axis=1)/float(num_runs)**.5
# calculate mean and errors (infer)
inf_means = numpy.zeros( (n_queries, num_iters) )
inf_error = numpy.zeros( (n_queries, num_iters) )
for i in range(num_iters):
X = numpy.zeros( (n_queries,num_runs) )
for r in range(num_runs):
X[:,r] = inference_results[r][:,i]
inf_means[:,i] = numpy.mean(X, axis=1)
inf_error[:,i] = numpy.std(X, axis=1)/float(num_runs)**.5
result = dict()
result['config'] = argin
result['num_queries'] = n_queries
result['iteration'] = range(1,num_iters+1)
result['dependence_probability_mean'] = dep_means
result['dependence_probability_error'] = dep_error
result['infer_means'] = inf_means
result['infer_stderr'] = inf_error
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"]
num_views = argin["num_views"]
num_clusters = argin["num_clusters"]
prop_missing = argin["prop_missing"]
impute_samples = argin["impute_samples"]
separation = argin["separation"]
ct_kernel = argin["ct_kernel"]
seed = argin["seed"]
if seed > 0:
random.seed(seed)
filename = "exp_fills_in_ofile.csv"
table_name = 'exp_fills_in'
argin['cctypes'] = ['continuous'] * num_cols
argin['separation'] = [argin['separation']] * num_views
eu.gen_data(filename, argin, save_csv=True)
# generate a new csv
all_filenames = []
all_indices = []
for p in prop_missing:
data_filename, indices, col_names, extra = eu.gen_missing_data_csv(
filename, p, [], True)
all_indices.append(indices)
all_filenames.append(data_filename)
# get the starting table so we can calculate errors
T_array = extra['array_filled']
num_rows, num_cols = T_array.shape
# create a client
client = Client()
# set up a dict fro the different config data
result = dict()
result['cc'] = numpy.zeros(len(prop_missing))
result['crp'] = numpy.zeros(len(prop_missing))
result['nb'] = numpy.zeros(len(prop_missing))
# do analyses
for p in range(len(prop_missing)):
this_indices = all_indices[p]
this_filename = all_filenames[p]
for config in ['cc', 'crp', 'nb']:
config_string = eu.config_map[config]
table = table_name + '-' + config
# drop old btable, create a new one with the new data and init models
client('DROP BTABLE %s;' % table, yes=True)
client('CREATE BTABLE %s FROM %s;' % (table, this_filename))
client('INITIALIZE %i MODELS FOR %s %s;' %
(num_chains, table, config_string))
if ct_kernel == 1:
client('ANALYZE %s FOR %i ITERATIONS WITH MH KENEL WAIT;' %
(table, num_iters))
else:
client('ANALYZE %s FOR %i ITERATIONS WAIT;' %
(table, num_iters))
MSE = 0.0
count = 0.0
# imput each index in indices and calculate the squared error
for col in range(0, num_cols):
col_name = col_names[col]
# confidence is set to zero so that a value is always returned
out = client(
'INFER %s from %s WITH CONFIDENCE %f WITH %i SAMPLES;' %
(col_name, table, 0, impute_samples),
pretty=False,
pandas_output=False)
data = out[0]['data']
# calcaulte MSE
for row, tcol in zip(this_indices[0], this_indices[1]):
if tcol == col:
MSE += (T_array[row, col] - data[row][1])**2.0
count += 1.0
result[config][p] = MSE / count
print "error = %f" % result[config][p]
retval = dict()
retval['MSE_naive_bayes_indexer'] = result['nb']
retval['MSE_crp_mixture_indexer'] = result['crp']
retval['MSE_crosscat_indexer'] = result['cc']
retval['prop_missing'] = prop_missing
retval['config'] = argin
return retval
def run_experiment(argin):
num_iters = argin["num_iters"]
num_chains = argin["num_chains"]
num_runs = argin["num_runs"]
prop_missing = argin["prop_missing"]
confidence = argin["confidence"]
seed = argin["seed"]
n_queries = 2
# random.seed(seed)
# using dha, for now
start_filename = "../data/dha.csv"
table = 'exp_shrinks_with_iters'
filename, indices, col_names = eu.gen_missing_data_csv(
start_filename, prop_missing, [0])
# get some random column pairs to do DEPENDENCE PROBABILITY queries on
# don't do queries on the first column
columns = range(1, len(col_names))
column_queries = [random.sample(columns, 2) for _ in range(n_queries)]
dependence_queries = []
for q in column_queries:
col_1 = col_names[q[0]].lower()
col_2 = col_names[q[1]].lower()
this_query = "SELECT DEPENDENCE PROBABILITY OF %s WITH %s FROM %s;" % (
col_1, col_2, table)
dependence_queries.append(this_query)
# get some inference queries
column_queries = random.sample(columns, n_queries)
infer_queries = []
for q in column_queries:
col = col_names[q].lower()
this_query = 'INFER %s FROM %s WITH CONFIDENCE %f;' % (col, table,
confidence)
infer_queries.append(this_query)
# create a client
client = Client()
dependence_results = []
inference_results = []
for _ in range(num_runs):
# drop old table, create new table, init models
client('DROP BTABLE %s;' % table, yes=True)
client('CREATE BTABLE %s FROM %s;' % (table, filename))
client('INITIALIZE %i MODELS FOR %s;' % (num_chains, table))
dependence_results_run = numpy.zeros((n_queries, num_iters))
inference_results_run = numpy.zeros((n_queries, num_iters))
for i in range(num_iters):
# analyze
client('ANALYZE %s FOR 1 ITERATIONS;' % (table))
# dependence
for q in range(n_queries):
out_dep = client(dependence_queries[q],
pretty=False,
pandas_output=False)
dep = out_dep[0]['data'][0][1]
dependence_results_run[q, i] = dep
# infer
for q in range(n_queries):
out_inf = client(infer_queries[q],
pretty=False,
pandas_output=False)
prop = _get_prop_inferred(out_inf[0]['data'], indices,
column_queries[q])
inference_results_run[q, i] = prop
dependence_results.append(dependence_results_run)
inference_results.append(inference_results_run)
# calculate mean and errors (dependence)
dep_means = numpy.zeros((n_queries, num_iters))
dep_error = numpy.zeros((n_queries, num_iters))
for i in range(num_iters):
X = numpy.zeros((n_queries, num_runs))
for r in range(num_runs):
X[:, r] = dependence_results[r][:, i]
dep_means[:, i] = numpy.mean(X, axis=1)
dep_error[:, i] = numpy.std(X, axis=1) / float(num_runs)**.5
# calculate mean and errors (infer)
inf_means = numpy.zeros((n_queries, num_iters))
inf_error = numpy.zeros((n_queries, num_iters))
for i in range(num_iters):
X = numpy.zeros((n_queries, num_runs))
for r in range(num_runs):
X[:, r] = inference_results[r][:, i]
inf_means[:, i] = numpy.mean(X, axis=1)
inf_error[:, i] = numpy.std(X, axis=1) / float(num_runs)**.5
result = dict()
result['config'] = argin
result['num_queries'] = n_queries
result['iteration'] = range(1, num_iters + 1)
result['dependence_probability_mean'] = dep_means
result['dependence_probability_error'] = dep_error
result['infer_means'] = inf_means
result['infer_stderr'] = inf_error
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"]
num_views = argin["num_views"]
num_clusters = argin["num_clusters"]
prop_missing = argin["prop_missing"]
separation = argin["separation"]
ct_kernel = argin["ct_kernel"]
multinomial_categories = argin["multinomial_categories"]
seed = argin["seed"]
random.seed(seed)
# TODO: use dha.csv
ofilename = "reasonably_calibrated_ofile.csv"
table_name = 'reasonably_calibrated'
argin['distargs'] = [{"K": multinomial_categories}] * num_cols
argin['cctypes'] = ['multinomial'] * num_cols
argin['separation'] = [argin['separation']] * num_views
T_array, structure = eu.gen_data(ofilename, argin, save_csv=True)
filename, indices, col_names = eu.gen_missing_data_csv(
ofilename, prop_missing, [])
# create a client
client = Client()
# caluclate empirical frequency of each point
frequencies = []
for col in range(num_cols):
frequencies.append(numpy.zeros(multinomial_categories))
T_int = numpy.array(T_array, dtype=int)
n_indices = len(indices[0])
for i in range(n_indices):
r = indices[0][i]
c = indices[1][i]
x = T_int[r, c]
frequencies[c][x] += 1.0
frequencies = [f / numpy.sum(f) for f in frequencies]
# set up a dict fro the different config data
result = dict()
# do analyses
for config in ['cc', 'crp', 'nb']:
config_string = eu.config_map[config]
table = table_name + '-' + config
# drop old btable, create a new one with the new data and init models
client('DROP BTABLE %s;' % table, yes=True)
client('CREATE BTABLE %s FROM %s;' % (table, filename))
client('INITIALIZE %i MODELS FOR %s %s;' %
(num_chains, table, config_string))
if ct_kernel == 1:
client('ANALYZE %s FOR %i ITERATIONS WITH MH KERNEL WAIT;' %
(table, num_iters))
else:
client('ANALYZE %s FOR %i ITERATIONS WAIT;' % (table, num_iters))
# imput each index in indices and calculate the squared error
results_config = []
for col in range(num_cols):
results_config.append(numpy.zeros(multinomial_categories))
for col in range(num_cols):
col_name = col_names[col]
out = client(
"INFER %s FROM %s WITH CONFIDENCE .95 WITH 1 SAMPLES;" %
(col_name, table),
pretty=False,
pandas_output=False)
for i in range(n_indices):
r = indices[0][i]
c = indices[1][i]
if c == col:
x = out[0]['data'][r][1]
results_config[c][int(x)] += 1.0
results_config = [f / sum(f) for f in results_config]
result[config] = results_config
retval = dict()
retval['actual_frequencies'] = frequencies
retval['inferred_P_cc'] = result['cc']
retval['inferred_P_crp'] = result['crp']
retval['inferred_P_nb'] = result['nb']
retval['config'] = argin
return retval