def test_imputation_output(feat_list, exp_imp_cols, feat_table):
with testing.postgresql.Postgresql() as psql:
engine = sqlalchemy.create_engine(psql.url())
engine.execute('create table states (entity_id int, as_of_date date)')
for state in states_table:
engine.execute('insert into states values (%s, %s)', state)
feat_sql = '\n'.join(
[', prefix_entity_id_1y_%s_max int' % f for f in feat_list])
engine.execute('''create table prefix_aggregation (
entity_id int
, as_of_date date
%s
)''' % feat_sql)
ins_sql = 'insert into prefix_aggregation values (%s, %s'+\
(', %s' * len(feat_list))+')'
for rec in feat_table:
engine.execute(ins_sql, rec)
for imp in available_imputations.keys():
# skip error imputation
if imp == 'error':
continue
for coltype in ['aggregate', 'categorical']:
# only consider
if not imputation_values[imp][coltype]['avail']:
continue
impargs = imputation_values[imp][coltype]['kwargs']
aggs = [
Aggregate(feat, ['max'], {
'coltype': coltype,
'all': dict(type=imp, **impargs)
}) for feat in feat_list
]
st = SpacetimeAggregation(
aggregates=aggs,
from_obj='prefix_events',
prefix='prefix',
groups=['entity_id'],
intervals=['1y'],
dates=['2016-01-01', '2016-02-03', '2016-03-14'],
state_table='states',
state_group='entity_id',
date_column='as_of_date',
input_min_date='2000-01-01',
output_date_column='as_of_date')
conn = engine.connect()
trans = conn.begin()
# excute query to find columns with null values and create lists of columns
# that do and do not need imputation when creating the imputation table
res = conn.execute(st.find_nulls())
null_counts = list(zip(res.keys(), res.fetchone()))
impute_cols = [col for col, val in null_counts if val > 0]
nonimpute_cols = [col for col, val in null_counts if val == 0]
# sql to drop and create the imputation table
drop_imp = st.get_drop(imputed=True)
create_imp = st.get_impute_create(
impute_cols=impute_cols, nonimpute_cols=nonimpute_cols)
# create the imputation table
conn.execute(drop_imp)
conn.execute(create_imp)
trans.commit()
# check the results
df = pd.read_sql('SELECT * FROM prefix_aggregation_imputed',
engine)
# we should have a record for every entity/date combo
assert df.shape[0] == len(states_table)
for feat in feat_list:
# all of the input columns should be in the result and be null-free
assert 'prefix_entity_id_1y_%s_max' % feat in df.columns.values
assert df['prefix_entity_id_1y_%s_max' %
feat].isnull().sum() == 0
# for non-categoricals, should add an "imputed" column and be non-null
# (categoricals are expected to be handled through the null category)
# zero_noflag imputation should not generate a flag either
if feat in exp_imp_cols and coltype != 'categorical' and imp != 'zero_noflag':
assert 'prefix_entity_id_1y_%s_max_imp' % feat in df.columns.values
assert df['prefix_entity_id_1y_%s_max_imp' %
feat].isnull().sum() == 0
else:
# should not generate an imputed column when not needed
assert 'prefix_entity_id_1y_%s_max_imp' % feat not in df.columns.values
def test_imputation_output(feat_list, exp_imp_cols, feat_table):
with testing.postgresql.Postgresql() as psql:
engine = sqlalchemy.create_engine(psql.url())
engine.execute("create table states (entity_id int, as_of_date date)")
for state in states_table:
engine.execute("insert into states values (%s, %s)", state)
feat_sql = "\n".join(
[", prefix_entity_id_1y_%s_max int" % f for f in feat_list])
engine.execute("""create table prefix_aggregation (
entity_id int
, as_of_date date
%s
)""" % feat_sql)
ins_sql = ("insert into prefix_aggregation values (%s, %s" +
(", %s" * len(feat_list)) + ")")
for rec in feat_table:
engine.execute(ins_sql, rec)
for imp in available_imputations.keys():
# skip error imputation
if imp == "error":
continue
for coltype in ["aggregate", "categorical"]:
# only consider
if not imputation_values[imp][coltype]["avail"]:
continue
impargs = imputation_values[imp][coltype]["kwargs"]
aggs = [
Aggregate(
feat,
["max"],
{
"coltype": coltype,
"all": dict(type=imp, **impargs)
},
) for feat in feat_list
]
st = SpacetimeAggregation(
aggregates=aggs,
from_obj="prefix_events",
prefix="prefix",
groups=["entity_id"],
intervals=["1y"],
dates=["2016-01-01", "2016-02-03", "2016-03-14"],
state_table="states",
state_group="entity_id",
date_column="as_of_date",
input_min_date="2000-01-01",
output_date_column="as_of_date",
)
conn = engine.connect()
trans = conn.begin()
# excute query to find columns with null values and create lists of columns
# that do and do not need imputation when creating the imputation table
res = conn.execute(st.find_nulls())
null_counts = list(zip(res.keys(), res.fetchone()))
impute_cols = [col for col, val in null_counts if val > 0]
nonimpute_cols = [col for col, val in null_counts if val == 0]
# sql to drop and create the imputation table
drop_imp = st.get_drop(imputed=True)
create_imp = st.get_impute_create(
impute_cols=impute_cols, nonimpute_cols=nonimpute_cols)
# create the imputation table
conn.execute(drop_imp)
conn.execute(create_imp)
trans.commit()
# check the results
df = pd.read_sql("SELECT * FROM prefix_aggregation_imputed",
engine)
# we should have a record for every entity/date combo
assert df.shape[0] == len(states_table)
for feat in feat_list:
# all of the input columns should be in the result and be null-free
assert "prefix_entity_id_1y_%s_max" % feat in df.columns.values
assert df["prefix_entity_id_1y_%s_max" %
feat].isnull().sum() == 0
# for non-categoricals, should add an "imputed" column and be non-null
# (categoricals are expected to be handled through the null category)
# zero_noflag imputation should not generate a flag either
if (feat in exp_imp_cols and coltype != "categorical"
and imp != "zero_noflag"):
assert ("prefix_entity_id_1y_%s_imp" % feat
in df.columns.values)
assert (df["prefix_entity_id_1y_%s_imp" %
feat].isnull().sum() == 0)
else:
# should not generate an imputed column when not needed
assert ("prefix_entity_id_1y_%s_imp" % feat
not in df.columns.values)