def create_joined_df(perf_table):
delinquency_12_expr = (ibis.case().when(
perf_table["current_loan_delinquency_status"].notnull(),
perf_table["current_loan_delinquency_status"],
).else_(-1).end())
upb_12_expr = (ibis.case().when(
perf_table["current_actual_upb"].notnull(),
perf_table["current_actual_upb"]).else_(999999999).end())
joined_df = perf_table["loan_id", perf_table["monthly_reporting_period"].
month().name("timestamp_month").cast("int32"),
perf_table["monthly_reporting_period"].year(
).name("timestamp_year").cast("int32"),
delinquency_12_expr.name("delinquency_12"),
upb_12_expr.name("upb_12"), ]
return joined_df
def test_case_where(backend, alltypes, df):
table = alltypes
table = table.mutate(
new_col=(
ibis.case()
.when(table['int_col'] == 1, 20)
.when(table['int_col'] == 0, 10)
.else_(0)
.end()
.cast('int64')
)
)
result = table.execute()
expected = df.copy()
mask_0 = expected['int_col'] == 1
mask_1 = expected['int_col'] == 0
expected['new_col'] = 0
expected.loc[mask_0, 'new_col'] = 20
expected.loc[mask_1, 'new_col'] = 10
expected['new_col'] = expected['new_col']
backend.assert_frame_equal(result, expected)
def test_multiple_case_null_else(table):
expr = ibis.case().when(table.g == "foo", "bar").end()
op = expr.op()
assert isinstance(expr, ir.StringColumn)
assert isinstance(op.default, ir.ValueExpr)
assert isinstance(op.default.op(), ops.NullLiteral)
def test_case_in_projection(alltypes):
t = alltypes
expr = (
t.g.case().when('foo', 'bar').when('baz', 'qux').else_('default').end()
)
expr2 = ibis.case().when(t.g == 'foo', 'bar').when(t.g == 'baz', t.g).end()
proj = t[expr.name('col1'), expr2.name('col2'), t]
result = Compiler.to_sql(proj)
expected = """\
SELECT
CASE `g`
WHEN 'foo' THEN 'bar'
WHEN 'baz' THEN 'qux'
ELSE 'default'
END AS `col1`,
CASE
WHEN `g` = 'foo' THEN 'bar'
WHEN `g` = 'baz' THEN `g`
ELSE CAST(NULL AS string)
END AS `col2`, *
FROM alltypes"""
assert result == expected
def test_select_filter_mutate(backend, alltypes, df):
"""Test that select, filter and mutate are executed in right order.
Before Pr 2635, try_fusion in analysis.py would fuse these operations
together in a way that the order of the operations were wrong. (mutate
was executed before filter).
"""
t = alltypes
# Prepare the float_col so that filter must execute
# before the cast to get the correct result.
t = t.mutate(
float_col=ibis.case()
.when(t['bool_col'], t['float_col'])
.else_(np.nan)
.end()
)
# Actual test
t = t[t.columns]
t = t[~t['float_col'].isnan()]
t = t.mutate(float_col=t['float_col'].cast('int32'))
result = t.execute()
expected = df.copy()
expected.loc[~df['bool_col'], 'float_col'] = None
expected = expected[~expected['float_col'].isna()]
expected = expected.assign(float_col=expected['float_col'].astype('int32'))
backend.assert_frame_equal(result, expected)
def test_multiple_case_null_else(self):
expr = ibis.case().when(self.table.g == "foo", "bar").end()
op = expr.op()
assert isinstance(expr, ir.StringArray)
assert isinstance(op.default, ir.ValueExpr)
assert isinstance(op.default.op(), ir.NullLiteral)
def ifelse(
self,
true_expr: ir.Value,
false_expr: ir.Value,
) -> ir.Value:
"""Construct a ternary conditional expression.
Parameters
----------
true_expr
Expression to return if `self` evaluates to `True`
false_expr
Expression to return if `self` evaluates to `False`
Returns
-------
Value
The value of `true_expr` if `arg` is `True` else `false_expr`
Examples
--------
>>> import ibis
>>> t = ibis.table([("is_person", "boolean")], name="t")
>>> expr = t.is_person.ifelse("yes", "no")
>>> print(ibis.impala.compile(expr))
SELECT CASE WHEN `is_person` THEN 'yes' ELSE 'no' END AS `tmp`
FROM t
"""
import ibis
# Result will be the result of promotion of true/false exprs. These
# might be conflicting types; same type resolution as case expressions
# must be used.
return ibis.case().when(self, true_expr).else_(false_expr).end()
def _extract_quarter(t, expr):
(arg, ) = expr.op().args
expr_new = ops.ExtractMonth(arg).to_expr()
expr_new = (ibis.case().when(expr_new.isin([1, 2, 3]),
1).when(expr_new.isin([4, 5, 6]),
2).when(expr_new.isin([7, 8, 9]),
3).else_(4).end())
return sa.cast(t.translate(expr_new), sa.Integer)
def test_searched_case_column(batting, batting_df):
t = batting
df = batting_df
expr = (ibis.case().when(t.RBI < 5, 'really bad team').when(
t.teamID == 'PH1', 'ph1 team').else_(t.teamID).end())
result = expr.execute()
expected = pd.Series(
np.select([df.RBI < 5, df.teamID == 'PH1'],
['really bad team', 'ph1 team'], df.teamID))
tm.assert_series_equal(result, expected)
def test_search_case(self):
expr = ibis.case().when(self.table.f > 0, self.table.d * 2).when(self.table.c < 0, self.table.a * 2).end()
result = self._translate(expr)
expected = """CASE
WHEN f > 0 THEN d * 2
WHEN c < 0 THEN a * 2
ELSE NULL
END"""
assert result == expected
def test_search_case(self):
expr = (ibis.case().when(self.table.f > 0, self.table.d * 2).when(
self.table.c < 0, self.table.a * 2).end())
result = self._translate(expr)
expected = """CASE
WHEN f > 0 THEN d * 2
WHEN c < 0 THEN a * 2
ELSE NULL
END"""
assert result == expected
def _get_hist_disease_expr(
cls,
disease: str,
geocodes: List[int],
year_week_start: int,
year_week_end: int,
) -> ibis.expr.types.Expr:
"""
Return an ibis expression with the history for a given disease.
Parameters
----------
disease : str, {'dengue', 'chik', 'zika'}
geocodes : List[int]
year_week_start : int
The starting Year/Week, e.g.: 202002
year_week_end : int
The ending Year/Week, e.g.: 202005
Returns
-------
ibis.expr.types.Expr
"""
table_suffix = ''
if disease != 'dengue':
table_suffix = '_{}'.format(disease)
schema_city = con.schema('Municipio')
t_hist = schema_city.table('Historico_alerta{}'.format(table_suffix))
case_level = (ibis.case().when(
(t_hist.nivel.cast('string') == '1'), 'verde').when(
(t_hist.nivel.cast('string') == '2'), 'amarelo').when(
(t_hist.nivel.cast('string') == '3'), 'laranja').when(
(t_hist.nivel.cast('string') == '4'),
'vermelho').else_('-').end()).name(f'nivel_{disease}')
hist_keys = [
t_hist.SE.name(f'SE_{disease}'),
t_hist.casos.name(f'casos_{disease}'),
t_hist.p_rt1.name(f'p_rt1_{disease}'),
t_hist.casos_est.name(f'casos_est_{disease}'),
t_hist.p_inc100k.name(f'p_inc100k_{disease}'),
t_hist.nivel.name(f'level_code_{disease}'),
case_level,
t_hist.municipio_geocodigo.name(f'geocode_{disease}'),
]
hist_filter = (t_hist['SE'].between(
year_week_start,
year_week_end)) & (t_hist['municipio_geocodigo'].isin(geocodes))
return t_hist[hist_filter][hist_keys].sort_by(f'SE_{disease}')
def test_searched_case_column(batting, batting_df):
t = batting
df = batting_df
expr = (ibis.case().when(t.RBI < 5, 'really bad team').when(
t.teamID == 'PH1', 'ph1 team').else_(t.teamID).end())
result = expr.compile()
expected = dd.from_array(
np.select(
[df.RBI < 5, df.teamID == 'PH1'],
['really bad team', 'ph1 team'],
df.teamID,
))
tm.assert_series_equal(result.compute(), expected.compute())
def test_substr_with_null_values(backend, alltypes, df):
table = alltypes.mutate(substr_col_null=ibis.case().when(
alltypes['bool_col'], alltypes['string_col']).else_(None).end().substr(
0, 2))
result = table.execute()
expected = df.copy()
mask = ~expected['bool_col']
expected['substr_col_null'] = expected['string_col']
expected.loc[mask, 'substr_col_null'] = None
expected['substr_col_null'] = expected['substr_col_null'].str.slice(0, 2)
backend.assert_frame_equal(result, expected)
def test_search_case(con, alltypes, translate):
t = alltypes
expr = (ibis.case().when(t.float_col > 0,
t.int_col * 2).when(t.float_col < 0,
t.int_col).else_(0).end())
expected = """CASE
WHEN `float_col` > 0 THEN `int_col` * 2
WHEN `float_col` < 0 THEN `int_col`
ELSE 0
END"""
assert translate(expr) == expected
assert len(con.execute(expr))
def test_pickle_multiple_case_node(table):
case1 = table.a == 5
case2 = table.b == 128
case3 = table.c == 1000
result1 = table.f
result2 = table.b * 2
result3 = table.e
default = table.d
expr = (ibis.case().when(case1, result1).when(case2, result2).when(
case3, result3).else_(default).end())
op = expr.op()
assert_pickle_roundtrip(op)
def _bucket(expr):
op = expr.op()
stmt = ibis.case()
if op.closed == 'left':
l_cmp = operator.le
r_cmp = operator.lt
else:
l_cmp = operator.lt
r_cmp = operator.le
user_num_buckets = len(op.buckets) - 1
bucket_id = 0
if op.include_under:
if user_num_buckets > 0:
cmp = operator.lt if op.close_extreme else r_cmp
else:
cmp = operator.le if op.closed == 'right' else operator.lt
stmt = stmt.when(cmp(op.arg, op.buckets[0]), bucket_id)
bucket_id += 1
for j, (lower, upper) in enumerate(zip(op.buckets, op.buckets[1:])):
if op.close_extreme and (
(op.closed == 'right' and j == 0) or
(op.closed == 'left' and j == (user_num_buckets - 1))):
stmt = stmt.when((lower <= op.arg) & (op.arg <= upper), bucket_id)
else:
stmt = stmt.when(
l_cmp(lower, op.arg) & r_cmp(op.arg, upper), bucket_id)
bucket_id += 1
if op.include_over:
if user_num_buckets > 0:
cmp = operator.lt if op.close_extreme else l_cmp
else:
cmp = operator.lt if op.closed == 'right' else operator.le
stmt = stmt.when(cmp(op.buckets[-1], op.arg), bucket_id)
bucket_id += 1
result = stmt.end()
if expr.has_name():
result = result.name(expr.get_name())
return result
def _bucket(translator, expr):
import operator
op = expr.op()
stmt = ibis.case()
if op.closed == 'left':
l_cmp = operator.le
r_cmp = operator.lt
else:
l_cmp = operator.lt
r_cmp = operator.le
user_num_buckets = len(op.buckets) - 1
bucket_id = 0
if op.include_under:
if user_num_buckets > 0:
cmp = operator.lt if op.close_extreme else r_cmp
else:
cmp = operator.le if op.closed == 'right' else operator.lt
stmt = stmt.when(cmp(op.arg, op.buckets[0]), bucket_id)
bucket_id += 1
for j, (lower, upper) in enumerate(zip(op.buckets, op.buckets[1:])):
if (op.close_extreme and
((op.closed == 'right' and j == 0) or
(op.closed == 'left' and j == (user_num_buckets - 1)))):
stmt = stmt.when((lower <= op.arg) & (op.arg <= upper),
bucket_id)
else:
stmt = stmt.when(l_cmp(lower, op.arg) & r_cmp(op.arg, upper),
bucket_id)
bucket_id += 1
if op.include_over:
if user_num_buckets > 0:
cmp = operator.lt if op.close_extreme else l_cmp
else:
cmp = operator.lt if op.closed == 'right' else operator.le
stmt = stmt.when(cmp(op.buckets[-1], op.arg), bucket_id)
bucket_id += 1
case_expr = stmt.end().name(expr._name)
return _searched_case(translator, case_expr)
def test_multiple_case_expr(self):
case1 = self.table.a == 5
case2 = self.table.b == 128
case3 = self.table.c == 1000
result1 = self.table.f
result2 = self.table.b * 2
result3 = self.table.e
default = self.table.d
expr = (ibis.case().when(case1, result1).when(case2, result2).when(
case3, result3).else_(default).end())
op = expr.op()
assert isinstance(expr, ir.DoubleArray)
assert isinstance(op, ops.SearchedCase)
assert op.default is default
def test_multiple_case_expr(table):
case1 = table.a == 5
case2 = table.b == 128
case3 = table.c == 1000
result1 = table.f
result2 = table.b * 2
result3 = table.e
default = table.d
expr = (ibis.case().when(case1, result1).when(case2, result2).when(
case3, result3).else_(default).end())
op = expr.op()
assert isinstance(expr, ir.FloatingColumn)
assert isinstance(op, ops.SearchedCase)
assert op.default is default
def tpc_h08(
con,
NATION="BRAZIL",
REGION="AMERICA",
TYPE="ECONOMY ANODIZED STEEL",
DATE="1995-01-01",
):
part = con.table("part")
supplier = con.table("supplier")
lineitem = con.table("lineitem")
orders = con.table("orders")
customer = con.table("customer")
region = con.table("region")
n1 = con.table("nation")
n2 = n1.view()
q = part
q = q.join(lineitem, part.p_partkey == lineitem.l_partkey)
q = q.join(supplier, supplier.s_suppkey == lineitem.l_suppkey)
q = q.join(orders, lineitem.l_orderkey == orders.o_orderkey)
q = q.join(customer, orders.o_custkey == customer.c_custkey)
q = q.join(n1, customer.c_nationkey == n1.n_nationkey)
q = q.join(region, n1.n_regionkey == region.r_regionkey)
q = q.join(n2, supplier.s_nationkey == n2.n_nationkey)
q = q[orders.o_orderdate.year().cast("string").name("o_year"),
(lineitem.l_extendedprice *
(1 - lineitem.l_discount)).name("volume"),
n2.n_name.name("nation"), region.r_name, orders.o_orderdate,
part.p_type, ]
q = q.filter([
q.r_name == REGION,
q.o_orderdate.between(DATE, add_date(DATE, dy=2, dd=-1)),
q.p_type == TYPE,
])
q = q.mutate(nation_volume=ibis.case().when(q.nation == NATION,
q.volume).else_(0).end())
gq = q.group_by([q.o_year])
q = gq.aggregate(mkt_share=q.nation_volume.sum() / q.volume.sum())
q = q.sort_by([q.o_year])
return q
def test_searched_case_column(batting, batting_df):
t = batting
df = batting_df
expr = (
ibis.case()
.when(t.RBI < 5, 'really bad team')
.when(t.teamID == 'PH1', 'ph1 team')
.else_(t.teamID)
.end()
)
result = expr.execute()
expected = pd.Series(
np.select(
[df.RBI < 5, df.teamID == 'PH1'],
['really bad team', 'ph1 team'],
df.teamID,
)
)
tm.assert_series_equal(result, expected)
def case_expression(self, when_expressions: List[Union[Tuple[Value, Value],
Value]]):
"""
Handles sql_to_ibis case expressions
:param when_expressions:
:return:
"""
case_expression = ibis.case()
for i, when_expression in enumerate(when_expressions):
if isinstance(when_expression, tuple):
conditional_boolean = when_expression[0].get_value()
conditional_value = when_expression[1].get_value()
case_expression = case_expression.when(conditional_boolean,
conditional_value)
else:
case_expression = case_expression.else_(
when_expression.get_value()).end()
return Expression(value=case_expression)
def test_multiple_case_expr(self):
case1 = self.table.a == 5
case2 = self.table.b == 128
case3 = self.table.c == 1000
result1 = self.table.f
result2 = self.table.b * 2
result3 = self.table.e
default = self.table.d
expr = (ibis.case()
.when(case1, result1)
.when(case2, result2)
.when(case3, result3)
.else_(default)
.end())
op = expr.op()
assert isinstance(expr, ir.DoubleArray)
assert isinstance(op, ops.SearchedCase)
assert op.default is default
def create_12_mon_features(joined_df):
delinq_df = None
n_months = 12
for y in range(1, n_months + 1):
year_dec = (ibis.case().when(
joined_df["timestamp_month"] < ibis.literal(y), 1).else_(0).end())
tmp_df = joined_df["loan_id", "delinquency_12", "upb_12",
(joined_df["timestamp_year"] -
year_dec).name("timestamp_year"), ]
delinquency_12 = (tmp_df["delinquency_12"].max() > 3).cast("int32") + (
tmp_df["upb_12"].min() == 0).cast("int32")
tmp_df = tmp_df.groupby(["loan_id", "timestamp_year"]).aggregate(
delinquency_12.name("delinquency_12"))
tmp_df = tmp_df.mutate(timestamp_month=ibis.literal(y, "int32"))
if delinq_df is None:
delinq_df = tmp_df
else:
delinq_df = delinq_df.union(tmp_df)
return delinq_df
def test_searched_case_scalar(client):
expr = ibis.case().when(True, 1).when(False, 2).end()
result = client.execute(expr)
expected = np.int8(1)
assert result == expected
def search_case(con):
t = con.table('alltypes')
return ibis.case().when(t.f > 0, t.d * 2).when(t.c < 0, t.a * 2).end()
def etl_ibis(
filename,
columns_names,
columns_types,
database_name,
table_name,
omnisci_server_worker,
delete_old_database,
create_new_table,
ipc_connection,
validation,
run_import_queries,
etl_keys,
import_mode,
):
tmp_table_name = "tmp_table"
etl_times = {key: 0.0 for key in etl_keys}
omnisci_server_worker.create_database(database_name,
delete_if_exists=delete_old_database)
if run_import_queries:
etl_times_import = {
"t_readcsv_by_ibis": 0.0,
"t_readcsv_by_COPY": 0.0,
"t_readcsv_by_FSI": 0.0,
}
# SQL statemnts preparation for data file import queries
connect_to_db_sql_template = "\c {0} admin HyperInteractive"
create_table_sql_template = """
CREATE TABLE {0} ({1});
"""
import_by_COPY_sql_template = """
COPY {0} FROM '{1}' WITH (header='{2}');
"""
import_by_FSI_sql_template = """
CREATE TEMPORARY TABLE {0} ({1}) WITH (storage_type='CSV:{2}');
"""
drop_table_sql_template = """
DROP TABLE IF EXISTS {0};
"""
import_query_cols_list = (
["ID_code TEXT ENCODING NONE, \n", "target SMALLINT, \n"] +
["var_%s DOUBLE, \n" % i for i in range(199)] + ["var_199 DOUBLE"])
import_query_cols_str = "".join(import_query_cols_list)
create_table_sql = create_table_sql_template.format(
tmp_table_name, import_query_cols_str)
import_by_COPY_sql = import_by_COPY_sql_template.format(
tmp_table_name, filename, "true")
import_by_FSI_sql = import_by_FSI_sql_template.format(
tmp_table_name, import_query_cols_str, filename)
# data file import by ibis
columns_types_import_query = ["string", "int64"
] + ["float64" for _ in range(200)]
schema_table_import = ibis.Schema(names=columns_names,
types=columns_types_import_query)
omnisci_server_worker.create_table(
table_name=tmp_table_name,
schema=schema_table_import,
database=database_name,
)
table_import_query = omnisci_server_worker.database(
database_name).table(tmp_table_name)
t0 = timer()
table_import_query.read_csv(filename, delimiter=",")
etl_times_import["t_readcsv_by_ibis"] = round((timer() - t0) * 1000)
# data file import by FSI
omnisci_server_worker.drop_table(tmp_table_name)
t0 = timer()
omnisci_server_worker.execute_sql_query(import_by_FSI_sql)
etl_times_import["t_readcsv_by_FSI"] = round((timer() - t0) * 1000)
omnisci_server_worker.drop_table(tmp_table_name)
# data file import by SQL COPY statement
omnisci_server_worker.execute_sql_query(create_table_sql)
t0 = timer()
omnisci_server_worker.execute_sql_query(import_by_COPY_sql)
etl_times_import["t_readcsv_by_COPY"] = round((timer() - t0) * 1000)
omnisci_server_worker.drop_table(tmp_table_name)
etl_times.update(etl_times_import)
if create_new_table:
# Create table and import data for ETL queries
schema_table = ibis.Schema(names=columns_names, types=columns_types)
if import_mode == "copy-from":
omnisci_server_worker.create_table(
table_name=table_name,
schema=schema_table,
database=database_name,
)
table_import = omnisci_server_worker.database(database_name).table(
table_name)
t0 = timer()
table_import.read_csv(filename,
header=True,
quotechar="",
delimiter=",")
etl_times["t_readcsv"] = round((timer() - t0) * 1000)
elif import_mode == "pandas":
# Datafiles import
columns_types_converted = [
"float64" if (x.startswith("decimal")) else x
for x in columns_types
]
t_import_pandas, t_import_ibis = omnisci_server_worker.import_data_by_ibis(
table_name=table_name,
data_files_names=filename,
files_limit=1,
columns_names=columns_names,
columns_types=columns_types_converted,
header=0,
nrows=None,
compression_type="gzip" if filename.endswith("gz") else None,
validation=validation,
)
etl_times["t_readcsv"] = round(
(t_import_pandas + t_import_ibis) * 1000)
elif import_mode == "fsi":
try:
unzip_name = None
if filename.endswith("gz"):
import gzip
unzip_name = "/tmp/santander-fsi.csv"
with gzip.open(filename, "rb") as gz_input:
with open(unzip_name, "wb") as output:
output.write(gz_input.read())
t0 = timer()
omnisci_server_worker._conn.create_table_from_csv(
table_name, unzip_name or filename, schema_table)
etl_times["t_readcsv"] = round((timer() - t0) * 1000)
finally:
if filename.endswith("gz"):
import os
os.remove(unzip_name)
# Second connection - this is ibis's ipc connection for DML
omnisci_server_worker.connect_to_server(database_name, ipc=ipc_connection)
table = omnisci_server_worker.database(database_name).table(table_name)
# group_by/count, merge (join) and filtration queries
# We are making 400 columns and then insert them into original table thus avoiding
# nested sql requests
t_etl_start = timer()
count_cols = []
orig_cols = ["ID_code", "target"] + ["var_%s" % i for i in range(200)]
cast_cols = []
cast_cols.append(table["target"].cast("int64").name("target0"))
gt1_cols = []
for i in range(200):
col = "var_%d" % i
col_count = "var_%d_count" % i
col_gt1 = "var_%d_gt1" % i
w = ibis.window(group_by=col)
count_cols.append(table[col].count().over(w).name(col_count))
gt1_cols.append(ibis.case().when(
table[col].count().over(w).name(col_count) > 1,
table[col].cast("float32"),
).else_(ibis.null()).end().name("var_%d_gt1" % i))
cast_cols.append(table[col].cast("float32").name(col))
table = table.mutate(count_cols)
table = table.drop(orig_cols)
table = table.mutate(gt1_cols)
table = table.mutate(cast_cols)
table_df = table.execute()
etl_times["t_etl"] = round((timer() - t_etl_start) * 1000)
return table_df, etl_times
def etl_ibis(
filename,
columns_names,
columns_types,
database_name,
table_name,
omnisci_server_worker,
delete_old_database,
create_new_table,
ipc_connection,
validation,
etl_keys,
import_mode,
):
import ibis
etl_times = {key: 0.0 for key in etl_keys}
omnisci_server_worker.create_database(database_name,
delete_if_exists=delete_old_database)
# Create table and import data
if create_new_table:
schema_table = ibis.Schema(names=columns_names, types=columns_types)
if import_mode == "copy-from":
# Create table and import data for ETL queries
omnisci_server_worker.create_table(
table_name=table_name,
schema=schema_table,
database=database_name,
)
table_import = omnisci_server_worker.database(database_name).table(
table_name)
t0 = timer()
table_import.read_csv(filename,
header=True,
quotechar="",
delimiter=",")
etl_times["t_readcsv"] = round((timer() - t0) * 1000)
elif import_mode == "pandas":
# Datafiles import
t_import_pandas, t_import_ibis = omnisci_server_worker.import_data_by_ibis(
table_name=table_name,
data_files_names=filename,
files_limit=1,
columns_names=columns_names,
columns_types=columns_types,
header=0,
nrows=None,
compression_type="gzip" if filename.endswith("gz") else None,
validation=validation,
)
etl_times["t_readcsv"] = round(
(t_import_pandas + t_import_ibis) * 1000)
elif import_mode == "fsi":
try:
unzip_name = None
if filename.endswith("gz"):
import gzip
unzip_name = "/tmp/census-fsi.csv"
with gzip.open(filename, "rb") as gz_input:
with open(unzip_name, "wb") as output:
output.write(gz_input.read())
t0 = timer()
omnisci_server_worker._conn.create_table_from_csv(
table_name, unzip_name or filename, schema_table)
etl_times["t_readcsv"] = round((timer() - t0) * 1000)
finally:
if filename.endswith("gz"):
import os
os.remove(unzip_name)
# Second connection - this is ibis's ipc connection for DML
omnisci_server_worker.connect_to_server(database_name, ipc=ipc_connection)
table = omnisci_server_worker.database(database_name).table(table_name)
t_etl_start = timer()
keep_cols = [
"YEAR0",
"DATANUM",
"SERIAL",
"CBSERIAL",
"HHWT",
"CPI99",
"GQ",
"PERNUM",
"SEX",
"AGE",
"INCTOT",
"EDUC",
"EDUCD",
"EDUC_HEAD",
"EDUC_POP",
"EDUC_MOM",
"EDUCD_MOM2",
"EDUCD_POP2",
"INCTOT_MOM",
"INCTOT_POP",
"INCTOT_MOM2",
"INCTOT_POP2",
"INCTOT_HEAD",
"SEX_HEAD",
]
if import_mode == "pandas" and validation:
keep_cols.append("id")
table = table[keep_cols]
# first, we do all filters and eliminate redundant fillna operations for EDUC and EDUCD
table = table[table.INCTOT != 9999999]
table = table[table["EDUC"].notnull()]
table = table[table["EDUCD"].notnull()]
table = table.set_column("INCTOT", table["INCTOT"] * table["CPI99"])
cols = []
# final fillna and casting for necessary columns
for column in keep_cols:
cols.append(ibis.case().when(
table[column].notnull(),
table[column]).else_(-1).end().cast("float64").name(column))
table = table.mutate(cols)
df = table.execute()
if import_mode == "pandas" and validation:
df.index = df["id"].values
# here we use pandas to split table
y = df["EDUC"]
X = df.drop(["EDUC", "CPI99"], axis=1)
etl_times["t_etl"] = round((timer() - t_etl_start) * 1000)
print("DataFrame shape:", X.shape)
return df, X, y, etl_times
def test_searched_case_scalar(client):
expr = ibis.case().when(True, 1).when(False, 2).end()
result = client.execute(expr)
expected = np.int8(1)
assert result == expected
def etl_ibis(
filename,
columns_names,
columns_types,
database_name,
table_name,
omnisci_server_worker,
delete_old_database,
create_new_table,
ipc_connection,
validation,
etl_keys,
import_mode,
fragments_size,
):
etl_times = {key: 0.0 for key in etl_keys}
fragments_size = check_fragments_size(fragments_size,
count_table=1,
import_mode=import_mode)
omnisci_server_worker.create_database(database_name,
delete_if_exists=delete_old_database)
if create_new_table:
# Create table and import data for ETL queries
schema_table = ibis.Schema(names=columns_names, types=columns_types)
if import_mode == "copy-from":
t0 = timer()
omnisci_server_worker.create_table(
table_name=table_name,
schema=schema_table,
database=database_name,
fragment_size=fragments_size[0],
)
table_import = omnisci_server_worker.database(database_name).table(
table_name)
etl_times["t_connect"] += timer() - t0
t0 = timer()
table_import.read_csv(filename,
header=True,
quotechar="",
delimiter=",")
etl_times["t_readcsv"] = timer() - t0
elif import_mode == "pandas":
# decimal(8, 4) is converted to decimal(9, 6) in order to provide better data conversion
# accuracy during import from Pandas into OmniSciDB for proper results validation
columns_types = [
"decimal(9, 6)" if (x == "decimal(8, 4)") else x
for x in columns_types
]
t_import_pandas, t_import_ibis = omnisci_server_worker.import_data_by_ibis(
table_name=table_name,
data_files_names=filename,
files_limit=1,
columns_names=columns_names,
columns_types=columns_types,
header=0,
nrows=None,
compression_type="gzip" if filename.endswith(".gz") else None,
use_columns_types_for_pd=False,
)
etl_times["t_readcsv"] = t_import_pandas + t_import_ibis
etl_times[
"t_connect"] += omnisci_server_worker.get_conn_creation_time()
elif import_mode == "fsi":
try:
unzip_name = None
if filename.endswith(".gz"):
import gzip
unzip_name = get_tmp_filepath("santander-fsi.csv")
with gzip.open(filename, "rb") as gz_input:
with open(unzip_name, "wb") as output:
output.write(gz_input.read())
t0 = timer()
omnisci_server_worker._conn.create_table_from_csv(
table_name,
unzip_name or filename,
schema_table,
fragment_size=fragments_size[0],
)
etl_times["t_readcsv"] = timer() - t0
etl_times[
"t_connect"] += omnisci_server_worker.get_conn_creation_time(
)
finally:
if filename.endswith("gz"):
import os
os.remove(unzip_name)
# Second connection - this is ibis's ipc connection for DML
t0 = timer()
omnisci_server_worker.connect_to_server(database_name, ipc=ipc_connection)
table = omnisci_server_worker.database(database_name).table(table_name)
etl_times["t_connect"] += timer() - t0
# group_by/count, merge (join) and filtration queries
# We are making 400 columns and then insert them into original table thus avoiding
# nested sql requests
t_etl_start = timer()
count_cols = []
orig_cols = ["ID_code", "target"] + ["var_%s" % i for i in range(200)]
cast_cols = []
cast_cols.append(table["target"].cast("int64").name("target0"))
gt1_cols = []
for i in range(200):
col = "var_%d" % i
col_count = "var_%d_count" % i
col_gt1 = "var_%d_gt1" % i
w = ibis.window(group_by=col)
count_cols.append(table[col].count().over(w).name(col_count))
gt1_cols.append(ibis.case().when(
table[col].count().over(w).name(col_count) > 1,
table[col].cast("float32"),
).else_(ibis.null()).end().name(col_gt1))
cast_cols.append(table[col].cast("float32").name(col))
table = table.mutate(count_cols)
table = table.drop(orig_cols)
table = table.mutate(gt1_cols)
table = table.mutate(cast_cols)
table_df = table.execute()
etl_times["t_etl"] = timer() - t_etl_start
return table_df, etl_times
def etl_ibis(
filename,
columns_names,
columns_types,
database_name,
table_name,
omnisci_server_worker,
delete_old_database,
create_new_table,
ipc_connection,
validation,
etl_keys,
):
import ibis
etl_times = {key: 0.0 for key in etl_keys}
omnisci_server_worker.create_database(database_name,
delete_if_exists=delete_old_database)
# Create table and import data
if create_new_table:
# Datafiles import
t_import_pandas, t_import_ibis = omnisci_server_worker.import_data_by_ibis(
table_name=table_name,
data_files_names=filename,
files_limit=1,
columns_names=columns_names,
columns_types=columns_types,
header=0,
nrows=None,
compression_type="gzip",
validation=validation,
)
etl_times["t_readcsv"] = t_import_pandas + t_import_ibis
# Second connection - this is ibis's ipc connection for DML
omnisci_server_worker.connect_to_server(database_name, ipc=ipc_connection)
table = omnisci_server_worker.database(database_name).table(table_name)
t_etl_start = timer()
keep_cols = [
"YEAR0",
"DATANUM",
"SERIAL",
"CBSERIAL",
"HHWT",
"CPI99",
"GQ",
"PERNUM",
"SEX",
"AGE",
"INCTOT",
"EDUC",
"EDUCD",
"EDUC_HEAD",
"EDUC_POP",
"EDUC_MOM",
"EDUCD_MOM2",
"EDUCD_POP2",
"INCTOT_MOM",
"INCTOT_POP",
"INCTOT_MOM2",
"INCTOT_POP2",
"INCTOT_HEAD",
"SEX_HEAD",
]
if validation:
keep_cols.append("id")
table = table[keep_cols]
# first, we do all filters and eliminate redundant fillna operations for EDUC and EDUCD
table = table[table.INCTOT != 9999999]
table = table[table["EDUC"].notnull()]
table = table[table["EDUCD"].notnull()]
table = table.set_column("INCTOT", table["INCTOT"] * table["CPI99"])
cols = []
# final fillna and casting for necessary columns
for column in keep_cols:
cols.append(ibis.case().when(
table[column].notnull(),
table[column]).else_(-1).end().cast("float64").name(column))
table = table.mutate(cols)
df = table.execute()
# here we use pandas to split table
y = df["EDUC"]
X = df.drop(["EDUC", "CPI99"], axis=1)
etl_times["t_etl"] = round((timer() - t_etl_start) * 1000)
print("DataFrame shape:", X.shape)
return df, X, y, etl_times
def _calculate_difference(field_differences, datatype, validation, is_value_comparison):
pct_threshold = ibis.literal(validation.threshold)
if isinstance(datatype, ibis.expr.datatypes.Timestamp):
source_value = field_differences["differences_source_value"].epoch_seconds()
target_value = field_differences["differences_target_value"].epoch_seconds()
elif isinstance(datatype, ibis.expr.datatypes.Float64):
# Float64 type results from AVG() aggregation
source_value = field_differences["differences_source_value"].round(digits=4)
target_value = field_differences["differences_target_value"].round(digits=4)
elif isinstance(datatype, ibis.expr.datatypes.Decimal):
source_value = (
field_differences["differences_source_value"]
.cast("float64")
.round(digits=4)
)
target_value = (
field_differences["differences_target_value"]
.cast("float64")
.round(digits=4)
)
else:
source_value = field_differences["differences_source_value"]
target_value = field_differences["differences_target_value"]
# Does not calculate difference between agg values for row hash due to int64 overflow
if is_value_comparison:
difference = pct_difference = ibis.null()
validation_status = (
ibis.case()
.when(
target_value.isnull() & source_value.isnull(),
consts.VALIDATION_STATUS_SUCCESS,
)
.when(target_value == source_value, consts.VALIDATION_STATUS_SUCCESS)
.else_(consts.VALIDATION_STATUS_FAIL)
.end()
)
# String data types i.e "None" can be returned for NULL timestamp/datetime aggs
elif isinstance(datatype, ibis.expr.datatypes.String):
difference = pct_difference = ibis.null().cast("float64")
validation_status = (
ibis.case()
.when(
target_value.isnull() & source_value.isnull(),
consts.VALIDATION_STATUS_SUCCESS,
)
.else_(consts.VALIDATION_STATUS_FAIL)
.end()
)
else:
difference = (target_value - source_value).cast("float64")
pct_difference_nonzero = (
ibis.literal(100.0)
* difference
/ (
source_value.case()
.when(ibis.literal(0), target_value)
.else_(source_value)
.end()
).cast("float64")
).cast("float64")
# Considers case that source and target agg values can both be 0
pct_difference = (
ibis.case()
.when(difference == ibis.literal(0), ibis.literal(0).cast("float64"))
.else_(pct_difference_nonzero)
.end()
)
th_diff = (pct_difference.abs() - pct_threshold).cast("float64")
validation_status = (
ibis.case()
.when(
source_value.isnull() & target_value.isnull(),
consts.VALIDATION_STATUS_SUCCESS,
)
.when(th_diff.isnan() | (th_diff > 0.0), consts.VALIDATION_STATUS_FAIL)
.else_(consts.VALIDATION_STATUS_SUCCESS)
.end()
)
return (
difference.name("difference"),
pct_difference.name("pct_difference"),
pct_threshold.name("pct_threshold"),
validation_status.name("validation_status"),
)
def etl_ibis(
filename,
columns_names,
columns_types,
database_name,
table_name,
omnisci_server_worker,
delete_old_database,
create_new_table,
ipc_connection,
validation,
run_import_queries,
etl_keys,
):
tmp_table_name = "tmp_table"
etl_times = {key: 0.0 for key in etl_keys}
omnisci_server_worker.create_database(database_name,
delete_if_exists=delete_old_database)
if run_import_queries:
etl_times_import = {
"t_readcsv_by_ibis": 0.0,
"t_readcsv_by_COPY": 0.0,
"t_readcsv_by_FSI": 0.0,
}
# SQL statemnts preparation for data file import queries
connect_to_db_sql_template = "\c {0} admin HyperInteractive"
create_table_sql_template = """
CREATE TABLE {0} ({1});
"""
import_by_COPY_sql_template = """
COPY {0} FROM '{1}' WITH (header='{2}');
"""
import_by_FSI_sql_template = """
CREATE TEMPORARY TABLE {0} ({1}) WITH (storage_type='CSV:{2}');
"""
drop_table_sql_template = """
DROP TABLE IF EXISTS {0};
"""
import_query_cols_list = (
["ID_code TEXT ENCODING NONE, \n", "target SMALLINT, \n"] +
["var_%s DOUBLE, \n" % i for i in range(199)] + ["var_199 DOUBLE"])
import_query_cols_str = "".join(import_query_cols_list)
create_table_sql = create_table_sql_template.format(
tmp_table_name, import_query_cols_str)
import_by_COPY_sql = import_by_COPY_sql_template.format(
tmp_table_name, filename, "true")
import_by_FSI_sql = import_by_FSI_sql_template.format(
tmp_table_name, import_query_cols_str, filename)
# data file import by ibis
columns_types_import_query = ["string", "int64"
] + ["float64" for _ in range(200)]
schema_table_import = ibis.Schema(names=columns_names,
types=columns_types_import_query)
omnisci_server_worker.create_table(
table_name=tmp_table_name,
schema=schema_table_import,
database=database_name,
)
table_import_query = omnisci_server_worker.database(
database_name).table(tmp_table_name)
t0 = timer()
table_import_query.read_csv(filename, delimiter=",")
etl_times_import["t_readcsv_by_ibis"] = round((timer() - t0) * 1000)
# data file import by FSI
omnisci_server_worker.drop_table(tmp_table_name)
t0 = timer()
omnisci_server_worker.execute_sql_query(import_by_FSI_sql)
etl_times_import["t_readcsv_by_FSI"] = round((timer() - t0) * 1000)
omnisci_server_worker.drop_table(tmp_table_name)
# data file import by SQL COPY statement
omnisci_server_worker.execute_sql_query(create_table_sql)
t0 = timer()
omnisci_server_worker.execute_sql_query(import_by_COPY_sql)
etl_times_import["t_readcsv_by_COPY"] = round((timer() - t0) * 1000)
omnisci_server_worker.drop_table(tmp_table_name)
etl_times.update(etl_times_import)
if create_new_table:
# Create table and import data for ETL queries
schema_table = ibis.Schema(names=columns_names, types=columns_types)
omnisci_server_worker.create_table(
table_name=table_name,
schema=schema_table,
database=database_name,
)
table_import = omnisci_server_worker.database(database_name).table(
table_name)
t0 = timer()
table_import.read_csv(filename, delimiter=",")
etl_times["t_readcsv"] = round((timer() - t0) * 1000)
omnisci_server_worker.connect_to_server(database_name, ipc=ipc_connection)
table = omnisci_server_worker.database(database_name).table(table_name)
# group_by/count, merge (join) and filtration queries
# We are making 400 columns and then insert them into original table thus avoiding
# nested sql requests
t_etl_start = timer()
count_cols = []
orig_cols = ["ID_code", "target"] + ['var_%s' % i for i in range(200)]
cast_cols = []
cast_cols.append(table["target"].cast("int64").name("target0"))
gt1_cols = []
for i in range(200):
col = "var_%d" % i
col_count = "var_%d_count" % i
col_gt1 = "var_%d_gt1" % i
w = ibis.window(group_by=col)
count_cols.append(table[col].count().over(w).name(col_count))
gt1_cols.append(ibis.case().when(
table[col].count().over(w).name(col_count) > 1,
table[col].cast("float32"),
).else_(ibis.null()).end().name("var_%d_gt1" % i))
cast_cols.append(table[col].cast("float32").name(col))
table = table.mutate(count_cols)
table = table.drop(orig_cols)
table = table.mutate(gt1_cols)
table = table.mutate(cast_cols)
table_df = table.execute()
etl_times["t_etl"] = round((timer() - t_etl_start) * 1000)
return table_df, etl_times
def etl_ibis(args, run_import_queries, columns_names, columns_types, validation=False):
filename = args.file
database_name = args.name
table_name = args.table
delete_old_database = not args.dnd
create_new_table = not args.dni
run_import_queries = str_arg_to_bool(run_import_queries)
validation = str_arg_to_bool(validation)
tmp_table_name = "tmp_table"
etl_times = {"t_groupby_merge_where": 0.0, "t_train_test_split": 0.0, "t_etl": 0.0}
if run_import_queries:
etl_times_import = {
"t_readcsv_by_ibis": 0.0,
"t_readcsv_by_COPY": 0.0,
"t_readcsv_by_FSI": 0.0,
}
etl_times.update(etl_times_import)
omnisci_server = OmnisciServer(
omnisci_executable=args.omnisci_executable,
omnisci_port=args.omnisci_port,
database_name=args.name,
user=args.user,
password=args.password,
debug_timer=True,
columnar_output=args.server_columnar_output,
lazy_fetch=args.server_lazy_fetch,
)
omnisci_server.launch()
import ibis
from server_worker import OmnisciServerWorker
omnisci_server_worker = OmnisciServerWorker(omnisci_server)
omnisci_server_worker.create_database(
database_name, delete_if_exists=delete_old_database
)
time.sleep(2)
omnisci_server_worker.connect_to_server()
if run_import_queries:
# SQL statemnts preparation for data file import queries
connect_to_db_sql_template = "\c {0} admin HyperInteractive"
create_table_sql_template = """
CREATE TABLE {0} ({1});
"""
import_by_COPY_sql_template = """
COPY {0} FROM '{1}' WITH (header='{2}');
"""
import_by_FSI_sql_template = """
CREATE TEMPORARY TABLE {0} ({1}) WITH (storage_type='CSV:{2}');
"""
drop_table_sql_template = """
DROP TABLE IF EXISTS {0};
"""
import_query_cols_list = (
["ID_code TEXT ENCODING NONE, \n", "target SMALLINT, \n"]
+ ["var_%s DOUBLE, \n" % i for i in range(199)]
+ ["var_199 DOUBLE"]
)
import_query_cols_str = "".join(import_query_cols_list)
connect_to_db_sql = connect_to_db_sql_template.format(database_name)
create_table_sql = create_table_sql_template.format(
tmp_table_name, import_query_cols_str
)
import_by_COPY_sql = import_by_COPY_sql_template.format(
tmp_table_name, filename, "true"
)
import_by_FSI_sql = import_by_FSI_sql_template.format(
tmp_table_name, import_query_cols_str, filename
)
# data file import by ibis
columns_types_import_query = ["string", "int64"] + [
"float64" for _ in range(200)
]
schema_table_import = ibis.Schema(
names=columns_names, types=columns_types_import_query
)
omnisci_server_worker.get_conn().create_table(
table_name=tmp_table_name,
schema=schema_table_import,
database=database_name,
fragment_size=args.fragment_size,
)
table_import_query = omnisci_server_worker.database(database_name).table(tmp_table_name)
t0 = timer()
table_import_query.read_csv(filename, delimiter=",")
etl_times["t_readcsv_by_ibis"] = timer() - t0
# data file import by FSI
omnisci_server_worker.drop_table(tmp_table_name)
t0 = timer()
omnisci_server_worker.execute_sql_query(import_by_FSI_sql)
etl_times["t_readcsv_by_FSI"] = timer() - t0
omnisci_server_worker.drop_table(tmp_table_name)
# data file import by SQL COPY statement
omnisci_server_worker.execute_sql_query(create_table_sql)
t0 = timer()
omnisci_server_worker.execute_sql_query(import_by_COPY_sql)
etl_times["t_readcsv_by_COPY"] = timer() - t0
omnisci_server_worker.drop_table(tmp_table_name)
if create_new_table:
# Create table and import data for ETL queries
schema_table = ibis.Schema(names=columns_names, types=columns_types)
omnisci_server_worker.get_conn().create_table(
table_name=table_name,
schema=schema_table,
database=database_name,
fragment_size=args.fragment_size,
)
table_import = omnisci_server_worker.database(database_name).table(table_name)
table_import.read_csv(filename, delimiter=",")
if args.server_conn_type == "regular":
omnisci_server_worker.connect_to_server()
elif args.server_conn_type == "ipc":
omnisci_server_worker.ipc_connect_to_server()
else:
print("Wrong connection type is specified!")
sys.exit(0)
db = omnisci_server_worker.database(database_name)
table = db.table(table_name)
# group_by/count, merge (join) and filtration queries
# We are making 400 columns and then insert them into original table thus avoiding
# nested sql requests
t0 = timer()
count_cols = []
orig_cols = ["ID_code", "target"] + ['var_%s'%i for i in range(200)]
cast_cols = []
cast_cols.append(table["target"].cast("int64").name("target0"))
gt1_cols = []
for i in range(200):
col = "var_%d" % i
col_count = "var_%d_count" % i
col_gt1 = "var_%d_gt1" % i
w = ibis.window(group_by=col)
count_cols.append(table[col].count().over(w).name(col_count))
gt1_cols.append(
ibis.case()
.when(
table[col].count().over(w).name(col_count) > 1,
table[col].cast("float32"),
)
.else_(ibis.null())
.end()
.name("var_%d_gt1" % i)
)
cast_cols.append(table[col].cast("float32").name(col))
table = table.mutate(count_cols)
table = table.drop(orig_cols)
table = table.mutate(gt1_cols)
table = table.mutate(cast_cols)
table_df = table.execute()
etl_times["t_groupby_merge_where"] = timer() - t0
# rows split query
t0 = timer()
training_part, validation_part = table_df[:-10000], table_df[-10000:]
etl_times["t_train_test_split"] = timer() - t0
etl_times["t_etl"] = etl_times["t_groupby_merge_where"] + etl_times["t_train_test_split"]
x_train = training_part.drop(['target0'],axis=1)
y_train = training_part['target0']
x_valid = validation_part.drop(['target0'],axis=1)
y_valid = validation_part['target0']
omnisci_server.terminate()
omnisci_server = None
return x_train, y_train, x_valid, y_valid, etl_times