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