def test_multioutput(es):
multioutput = AggregationFeature(
IdentityFeature(es["log"].ww["zipcode"]), "sessions", NMostCommon
)
feat = FeatureOutputSlice(multioutput, 0)
graph = graph_feature(feat).source
feat_name = feat.get_name()
prim_node = "0_{}_n_most_common".format(multioutput.get_name())
groupby_node = "{}_groupby_log--session_id".format(multioutput.get_name())
sessions_table = "\u2605 sessions (target)"
log_table = "log"
groupby_edge = 'log:session_id -> "{}"'.format(groupby_node)
groupby_input = 'log:zipcode -> "{}"'.format(groupby_node)
prim_input = '"{}" -> "{}"'.format(groupby_node, prim_node)
feat_edge = '"{}" -> sessions:"{}"'.format(prim_node, feat_name)
graph_components = [
feat_name,
prim_node,
groupby_node,
sessions_table,
log_table,
groupby_edge,
groupby_input,
prim_input,
feat_edge,
]
for component in graph_components:
assert component in graph
dataframes = {
"log": [log_table, "zipcode", "session_id"],
"sessions": [sessions_table, feat_name],
}
for dataframe in dataframes:
regex = r"{} \[label=<\n<TABLE.*?</TABLE>>".format(dataframe)
matches = re.findall(regex, graph, re.DOTALL)
assert len(matches) == 1
rows = re.findall(r"<TR.*?</TR>", matches[0], re.DOTALL)
assert len(rows) == len(dataframes[dataframe])
for row in rows:
matched = False
for i in dataframes[dataframe]:
if i in row:
matched = True
dataframes[dataframe].remove(i)
break
assert matched
def test_stacked(es, trans_feat):
stacked = AggregationFeature(trans_feat, es['cohorts'], Mode)
graph = graph_feature(stacked).source
feat_name = stacked.get_name()
intermediate_name = trans_feat.get_name()
agg_primitive = '0_{}_mode'.format(feat_name)
trans_primitive = '1_{}_year'.format(intermediate_name)
groupby_node = '{}_groupby_customers--cohort'.format(feat_name)
trans_prim_edge = 'customers:cancel_date -> "{}"'.format(trans_primitive)
intermediate_edge = '"{}" -> customers:"{}"'.format(trans_primitive, intermediate_name)
groupby_edge = 'customers:cohort -> "{}"'.format(groupby_node)
groupby_input = 'customers:"{}" -> "{}"'.format(intermediate_name, groupby_node)
agg_input = '"{}" -> "{}"'.format(groupby_node, agg_primitive)
feat_edge = '"{}" -> cohorts:"{}"'.format(agg_primitive, feat_name)
graph_components = [feat_name, intermediate_name, agg_primitive, trans_primitive,
groupby_node, trans_prim_edge, intermediate_edge, groupby_edge,
groupby_input, agg_input, feat_edge]
for component in graph_components:
assert component in graph
agg_primitive = agg_primitive.replace('(', '\\(').replace(')', '\\)')
agg_node = re.findall('"{}" \\[label.*'.format(agg_primitive), graph)
assert len(agg_node) == 1
assert 'Step 2' in agg_node[0]
trans_primitive = trans_primitive.replace('(', '\\(').replace(')', '\\)')
trans_node = re.findall('"{}" \\[label.*'.format(trans_primitive), graph)
assert len(trans_node) == 1
assert 'Step 1' in trans_node[0]
def test_multioutput(es):
multioutput = AggregationFeature(es['log']['zipcode'], es['sessions'],
NMostCommon)
feat = FeatureOutputSlice(multioutput, 0)
graph = graph_feature(feat).source
feat_name = feat.get_name()
prim_node = '0_{}_n_most_common'.format(multioutput.get_name())
groupby_node = '{}_groupby_log--session_id'.format(multioutput.get_name())
sessions_table = '\u2605 sessions (target)'
log_table = 'log'
groupby_edge = 'log:session_id -> "{}"'.format(groupby_node)
groupby_input = 'log:zipcode -> "{}"'.format(groupby_node)
prim_input = '"{}" -> "{}"'.format(groupby_node, prim_node)
feat_edge = '"{}" -> sessions:"{}"'.format(prim_node, feat_name)
graph_components = [
feat_name, prim_node, groupby_node, sessions_table, log_table,
groupby_edge, groupby_input, prim_input, feat_edge
]
for component in graph_components:
assert component in graph
entities = {
'log': [log_table, 'zipcode', 'session_id'],
'sessions': [sessions_table, feat_name]
}
for entity in entities:
regex = r"{} \[label=<\n<TABLE.*?</TABLE>>".format(entity)
matches = re.findall(regex, graph, re.DOTALL)
assert len(matches) == 1
rows = re.findall(r"<TR.*?</TR>", matches[0], re.DOTALL)
assert len(rows) == len(entities[entity])
for row in rows:
matched = False
for i in entities[entity]:
if i in row:
matched = True
entities[entity].remove(i)
break
assert matched
def test_aggregation(es):
feat = AggregationFeature(IdentityFeature(es['log'].ww['id']), 'sessions',
Count)
graph = graph_feature(feat).source
feat_name = feat.get_name()
prim_node = '0_{}_count'.format(feat_name)
groupby_node = '{}_groupby_log--session_id'.format(feat_name)
sessions_table = '\u2605 sessions (target)'
log_table = 'log'
groupby_edge = 'log:session_id -> "{}"'.format(groupby_node)
groupby_input = 'log:id -> "{}"'.format(groupby_node)
prim_input = '"{}" -> "{}"'.format(groupby_node, prim_node)
feat_edge = '"{}" -> sessions:"{}"'.format(prim_node, feat_name)
graph_components = [
feat_name, prim_node, groupby_node, sessions_table, log_table,
groupby_edge, groupby_input, prim_input, feat_edge
]
for component in graph_components:
assert component in graph
dataframes = {
'log': [log_table, 'id', 'session_id'],
'sessions': [sessions_table, feat_name]
}
for dataframe in dataframes:
regex = r"{} \[label=<\n<TABLE.*?</TABLE>>".format(dataframe)
matches = re.findall(regex, graph, re.DOTALL)
assert len(matches) == 1
rows = re.findall(r"<TR.*?</TR>", matches[0], re.DOTALL)
assert len(rows) == len(dataframes[dataframe])
for row in rows:
matched = False
for i in dataframes[dataframe]:
if i in row:
matched = True
dataframes[dataframe].remove(i)
break
assert matched