def pair_partition_to_vec(input_data: Tuple[Dict, Tuple[str,str], Iterable[Union[PosNegExample, UnlabeledExample, np.ndarray]]]):
processed_specs, fields, partiton_data = input_data
columns = get_nested_index(fields)
dfs = []
for example in partiton_data:
Encoding.encoding_cnt = 0
# hack to get named tuples to work in parallel
if isinstance(example, np.ndarray):
example = PosNegExample(*example)
# use numbers because we odn't know the names here
neg_feature_vec = count_violations_memoized(processed_specs,
Task(example.data, Query.from_vegalite(example[4]), example.task))
pos_feature_vec = count_violations_memoized(processed_specs,
Task(example.data, Query.from_vegalite(example[5]), example.task))
# Reformat the json data so that we can insert it into a multi index data frame.
# https://stackoverflow.com/questions/24988131/nested-dictionary-to-multiindex-dataframe-where-dictionary-keys-are-column-label
specs = {(fields[0], key): values for key, values in neg_feature_vec.items()}
specs.update({(fields[1], key): values for key, values in pos_feature_vec.items()})
specs[('source', '')] = example.source
specs[('task', '')] = example.task
dfs.append(pd.DataFrame(specs, columns=columns, index=[example.pair_id]))
return pd.concat(dfs)
def __mutate_spec(self, base_spec: Spec, props: List[str], prop_index: int,
seen: Set[Spec], specs: List[Spec]):
# base case
if (prop_index == len(props)):
self.model.post_improve(base_spec, props)
base_spec['data'] = {'url': self.data_url}
# within a group, don't repeat the same specs
if not (base_spec in seen):
seen.add(base_spec)
query = Query.from_vegalite(base_spec)
if (is_valid(Task(self.data, query))):
specs.append(base_spec)
# recursive case
else:
prop_to_mutate = props[prop_index]
for enum in self.model.get_enums(prop_to_mutate):
spec = deepcopy(base_spec)
self.model.mutate_prop(spec, prop_to_mutate, enum)
# recursive call
self.__mutate_spec(spec, props, prop_index + 1, seen, specs)
return
def test_stack_agg(self):
query = Query.from_vegalite({
'mark': 'bar',
'encoding': {
'x': {
'type': 'nominal',
'field': 'n1',
},
'y': {
'type': 'quantitative',
'field': 'q1',
'stack': 'zero',
'aggregate': 'sum'
},
'detail': {
'type': 'nominal',
'field': 'n2'
},
'color': {
'type': 'quantitative',
'field': 'q2',
'aggregate': 'mean'
}
}
})
assert is_valid(Task(data, query), True) == True
def test_is_valid():
data = Data(fields=[Field('foo', 'number')])
invalid = Query.from_vegalite({
'mark': 'text',
'encoding': {
'x': {'field': 'foo', 'type': 'quantitative'}
}
})
assert is_valid(Task(data, invalid)) == False
valid = Query.from_vegalite({
'mark': 'point',
'encoding': {
'x': {'field': 'foo', 'type': 'quantitative'}
}
})
assert is_valid(Task(data, valid)) == True
def test_one_bar(self):
query = Query.from_vegalite({
'mark': 'bar',
'encoding': {
'y': {
'type': 'quantitative',
'field': 'q1'
}
}
})
assert is_valid(Task(data, query), True) == True
def test_row_only(self):
query = Query.from_vegalite({
'mark': 'point',
'encoding': {
'row': {
'type': 'nominal',
'field': 'n1'
}
}
})
assert is_valid(Task(data, query), True) == False
def test_only_one_agg(self):
query = Query.from_vegalite({
'mark': 'point',
'encoding': {
'x': {
'type': 'quantitative',
'field': 'q1'
},
'y': {
'type': 'quantitative',
'field': 'q2',
'aggregate': 'mean'
}
}
})
assert is_valid(Task(data, query), True) == False
def test_heatmap(self):
query = Query.from_vegalite({
'mark': 'rect',
'encoding': {
'x': {
'type': 'nominal',
'field': 'n1',
},
'y': {
'type': 'ordinal',
'field': 'q1',
'bin': True
}
}
})
assert is_valid(Task(data, query), True) == True
def test_hist(self):
query = Query.from_vegalite({
'mark': 'bar',
'encoding': {
'x': {
'type': 'quantitative',
'field': 'q1',
'bin': True
},
'y': {
'type': 'quantitative',
'aggregate': 'count'
}
}
})
assert is_valid(Task(data, query), True) == True
def test_no_auto_bin(self):
q = Query.from_vegalite({
'mark': 'bar',
'encoding': {
'x': {
'type': 'nominal'
},
"y": {
'type': 'quantitative',
'aggregate': 'mean'
},
'color': {
'type': 'nominal'
}
}
})
encs = [e for e in q.encodings if e.stack]
assert len(encs) == 0
def test_count_violations():
data = Data.from_csv('examples/data/cars.csv')
query_json = {
'mark': 'bar',
'data': {
'url': 'data/cars.csv'
},
'encoding': {
'x': {
'field': 'origin',
'type': 'ordinal'
},
'y': {
'field': 'horsepower',
'type': 'quantitative',
'aggregate': 'mean'
}
}
}
violations = count_violations(Task(data, Query.from_vegalite(query_json)))
assert 'encoding' in violations.keys()
assert violations.get('encoding') == 2
def test_scatter(self):
query = Query.from_vegalite({
'mark': 'point',
'encoding': {
'x': {
'type': 'quantitative',
'field': 'q1',
},
'y': {
'type': 'quantitative',
'field': 'q2'
},
'color': {
'type': 'nominal',
'field': 'n2'
},
'size': {
'type': 'quantitative',
'field': 'q3'
}
}
})
assert is_valid(Task(data, query), True) == True
def test_stack_q_q(self):
query = Query.from_vegalite({
'mark': 'area',
'encoding': {
'x': {
'type': 'quantitative',
'field': 'q1',
'scale': {
'zero': False
}
},
'y': {
'type': 'quantitative',
'field': 'q2',
'stack': 'zero'
},
'color': {
'type': 'nominal',
'field': 'n1'
}
}
})
assert is_valid(Task(data, query), True) == True
def run_spec(data, spec):
query = Query.from_vegalite(spec)
input_task = Task(data, query)
return run(input_task)