def _create_histogram(self, M_c, data, columns, mc_col_indices, filename):
dir = S.path.web_resources_data_dir
full_filename = os.path.join(dir, filename)
num_rows = data.shape[0]
num_cols = data.shape[1]
#
pylab.figure()
# col_i goes from 0 to number of predicted columns
# mc_col_idx is the original column's index in M_c
for col_i in range(num_cols):
mc_col_idx = mc_col_indices[col_i]
data_i = data[:, col_i]
ax = pylab.subplot(1, num_cols, col_i, title=columns[col_i])
if M_c['column_metadata'][mc_col_idx][
'modeltype'] == 'normal_inverse_gamma':
pylab.hist(data_i, orientation='horizontal')
else:
str_data = [
du.convert_code_to_value(M_c, mc_col_idx, code)
for code in data_i
]
unique_labels = list(set(str_data))
np_str_data = numpy.array(str_data)
counts = []
for label in unique_labels:
counts.append(sum(np_str_data == label))
num_vals = len(
M_c['column_metadata'][mc_col_idx]['code_to_value'])
rects = pylab.barh(range(num_vals), counts)
heights = numpy.array([rect.get_height() for rect in rects])
ax.set_yticks(numpy.arange(num_vals) + heights / 2)
ax.set_yticklabels(unique_labels)
pylab.tight_layout()
pylab.savefig(full_filename)
def convert_row(row, M_c):
"""
Helper function to convert a row from its 'code' (as it's stored in T) to its 'value'
(the human-understandable value).
"""
ret = []
for cidx, code in enumerate(row):
if not numpy.isnan(code) and not code=='nan':
ret.append(du.convert_code_to_value(M_c, cidx, code))
else:
ret.append(code)
return tuple(ret)
def convert_row(row, M_c):
"""
Helper function to convert a row from its 'code' (as it's stored in T) to its 'value'
(the human-understandable value).
"""
ret = []
for cidx, code in enumerate(row):
if not numpy.isnan(code) and not code == 'nan':
ret.append(du.convert_code_to_value(M_c, cidx, code))
else:
ret.append(code)
return tuple(ret)
def simulate(self, tablename, columnstring, newtablename, whereclause,
numpredictions, order_by):
"""Simple predictive samples. Returns one row per prediction, with all the given and predicted variables."""
X_L_list, X_D_list, M_c = self.persistence_layer.get_latent_states(
tablename)
M_c, M_r, T = self.persistence_layer.get_metadata_and_table(tablename)
numrows = len(M_r['idx_to_name'])
name_to_idx = M_c['name_to_idx']
# parse whereclause
where_col_idxs_to_vals = dict()
if whereclause == "" or '=' not in whereclause:
Y = None
else:
varlist = [[c.strip() for c in b.split('=')]
for b in whereclause.split('AND')]
Y = []
for colname, colval in varlist:
if type(colval) == str or type(colval) == unicode:
colval = ast.literal_eval(colval)
where_col_idxs_to_vals[name_to_idx[colname]] = colval
Y.append((numrows + 1, name_to_idx[colname], colval))
# map values to codes
Y = [(r, c, du.convert_value_to_code(M_c, c, colval))
for r, c, colval in Y]
## Parse queried columns.
colnames = [colname.strip() for colname in columnstring.split(',')]
col_indices = [name_to_idx[colname] for colname in colnames]
query_col_indices = [
idx for idx in col_indices
if idx not in where_col_idxs_to_vals.keys()
]
Q = [(numrows + 1, col_idx) for col_idx in query_col_indices]
args_dict = dict()
args_dict['M_c'] = M_c
args_dict['X_L'] = X_L_list
args_dict['X_D'] = X_D_list
args_dict['Y'] = Y
args_dict['Q'] = Q
args_dict['n'] = numpredictions
out = self.backend.simple_predictive_sample(M_c, X_L_list, X_D_list, Y,
Q, numpredictions)
# convert to data, columns dict output format
# map codes to original values
## TODO: Add histogram call back in, but on Python client locally!
#self._create_histogram(M_c, numpy.array(out), columns, col_indices, tablename+'_histogram')
data = []
for vals in out:
row = []
i = 0
for idx in col_indices:
if idx in where_col_idxs_to_vals:
row.append(where_col_idxs_to_vals[idx])
else:
row.append(du.convert_code_to_value(M_c, idx, vals[i]))
i += 1
data.append(row)
ret = {'message': 'Simulated data:', 'columns': colnames, 'data': data}
return ret
def infer(self,
tablename,
columnstring,
newtablename,
confidence,
whereclause,
limit,
numsamples,
order_by=False):
"""Impute missing values.
Sample INFER: INFER columnstring FROM tablename WHERE whereclause WITH confidence LIMIT limit;
Sample INFER INTO: INFER columnstring FROM tablename WHERE whereclause WITH confidence INTO newtablename LIMIT limit;
Argument newtablename == null/emptystring if we don't want to do INTO
"""
# TODO: actually impute only missing values, instead of all values.
X_L_list, X_D_list, M_c = self.persistence_layer.get_latent_states(
tablename)
M_c, M_r, T = self.persistence_layer.get_metadata_and_table(tablename)
numrows = len(T)
t_array = numpy.array(T, dtype=float)
name_to_idx = M_c['name_to_idx']
if '*' in columnstring:
col_indices = name_to_idx.values()
else:
colnames = [colname.strip() for colname in columnstring.split(',')]
col_indices = [name_to_idx[colname] for colname in colnames]
Q = []
for row_idx in range(numrows):
for col_idx in col_indices:
if numpy.isnan(t_array[row_idx, col_idx]):
Q.append([row_idx, col_idx])
# FIXME: the purpose of the whereclause is to specify 'given'
# p(missing_value | X_L, X_D, whereclause)
## TODO: should all observed values besides the ones being imputed be givens?
if whereclause == "" or '=' not in whereclause:
Y = None
else:
varlist = [[c.strip() for c in b.split('=')]
for b in whereclause.split('AND')]
Y = [(numrows + 1, name_to_idx[colname], colval)
for colname, colval in varlist]
Y = [(r, c, du.convert_value_to_code(M_c, c, colval))
for r, c, colval in Y]
# Call backend
args_dict = dict()
args_dict['M_c'] = M_c
args_dict['X_L'] = X_L_list
args_dict['X_D'] = X_D_list
args_dict['Y'] = Y # givens
args_dict['n'] = numsamples
counter = 0
ret = []
for q in Q:
args_dict['Q'] = q # querys
out = self.backend.impute_and_confidence(M_c, X_L_list, X_D_list,
Y, [q], numsamples)
value, conf = out
if conf >= confidence:
row_idx = q[0]
col_idx = q[1]
ret.append((row_idx, col_idx, value))
counter += 1
if counter >= limit:
break
imputations_list = [(r, c, du.convert_code_to_value(M_c, c, code))
for r, c, code in ret]
## Convert into dict with r,c keys
imputations_dict = defaultdict(dict)
for r, c, val in imputations_list:
imputations_dict[r][c] = val
ret = self.select(tablename,
columnstring,
whereclause,
limit,
order_by=order_by,
imputations_dict=imputations_dict)
return ret
