def test_aggregate_with_path():
"""Aggregation with column paths as measures which have to be automatically produce merge operation."""
ctx = Prosto("My Prosto")
# Facts
f_tbl = ctx.populate(
table_name="Facts",
attributes=["A", "M"],
func=
"lambda **m: pd.DataFrame({'A': ['a', 'a', 'b', 'b'], 'M': [1.0, 2.0, 3.0, 4.0]})",
tables=[])
# Groups
df = pd.DataFrame({'A': ['a', 'b', 'c'], 'B': [3.0, 2.0, 1.0]})
g_tbl = ctx.populate(
table_name="Groups",
attributes=["A", "B"],
func=
"lambda **m: pd.DataFrame({'A': ['a', 'b', 'c'], 'B': [3.0, 2.0, 1.0]})",
tables=[])
# Link
l_clm = ctx.link(name="Link",
table=f_tbl.id,
type=g_tbl.id,
columns=["A"],
linked_columns=["A"])
# Aggregation
a_clm = ctx.aggregate(name="Aggregate",
table=g_tbl.id,
tables=["Facts"],
link="Link",
func="lambda x, bias, **model: x.sum() + bias",
columns=["Link::B"],
model={"bias": 0.0})
ctx.run()
a_clm_data = g_tbl.get_series('Aggregate')
assert a_clm_data[0] == 6.0
assert a_clm_data[1] == 4.0
assert a_clm_data[2] == 0.0
def test_aggregate():
ctx = Prosto("My Prosto")
# Facts
f_tbl = ctx.populate(
table_name="Facts",
attributes=["A", "M"],
func=
"lambda **m: pd.DataFrame({'A': ['a', 'a', 'b', 'b'], 'M': [1.0, 2.0, 3.0, 4.0], 'N': [4.0, 3.0, 2.0, 1.0]})",
tables=[])
# Groups
df = pd.DataFrame({'A': ['a', 'b', 'c']})
g_tbl = ctx.populate(
table_name="Groups",
attributes=["A"],
func="lambda **m: pd.DataFrame({'A': ['a', 'b', 'c']})",
tables=[])
# Link
l_clm = ctx.link(name="Link",
table=f_tbl.id,
type=g_tbl.id,
columns=["A"],
linked_columns=["A"])
# Aggregation
a_clm = ctx.aggregate(name="Aggregate",
table=g_tbl.id,
tables=["Facts"],
link="Link",
func="lambda x, bias, **model: x.sum() + bias",
columns=["M"],
model={"bias": 0.0})
f_tbl.evaluate()
g_tbl.evaluate()
l_clm.evaluate()
a_clm.evaluate()
g_tbl_data = g_tbl.get_df()
assert len(g_tbl_data) == 3 # Same number of rows
assert len(
g_tbl_data.columns
) == 2 # One aggregate column was added (and one technical "id" column was added which might be removed in future)
a_clm_data = g_tbl.get_series('Aggregate')
assert a_clm_data[0] == 3.0
assert a_clm_data[1] == 7.0
assert a_clm_data[2] == 0.0
#
# Test topology
#
topology = Topology(ctx)
topology.translate() # All data will be reset
layers = topology.elem_layers
assert len(layers) == 3
assert set([x.id for x in layers[0]]) == {"Facts", "Groups"}
assert set([x.id for x in layers[1]]) == {"Link"}
assert set([x.id for x in layers[2]]) == {"Aggregate"}
ctx.run()
a_clm_data = g_tbl.get_series('Aggregate')
assert a_clm_data[0] == 3.0
assert a_clm_data[1] == 7.0
assert a_clm_data[2] == 0.0
#
# Aggregation of multiple columns
#
# Aggregation
a_clm2 = ctx.aggregate(
name="Aggregate 2",
table=g_tbl.id,
tables=["Facts"],
link="Link",
func=
"lambda x, my_param, **model: x['M'].sum() + x['N'].sum() + my_param",
columns=["M", "N"],
model={"my_param": 0.0})
#a_clm2.evaluate()
ctx.translate() # All data will be reset
ctx.run(
) # A new column is NOT added to the existing data frame (not clear where it is)
a_clm2_data = g_tbl.get_series('Aggregate 2')
assert a_clm2_data[0] == 10.0
assert a_clm2_data[1] == 10.0
assert a_clm2_data[2] == 0.0
