def test_find_most_comm():
deps = classes.Dependencies({}, ['d'])
rels = [(['a'], 'b'), (['b'], 'c'), (['b'], 'a'), (['d'], 'a')]
assert normalize.find_most_comm(rels, deps) == ['b']
rels = [(['a', 'c'], 'b'), (['b'], 'c'), (['b'], 'a'), (['d'], 'a'),
(['a', 'c'], 'b')]
assert normalize.find_most_comm(rels, deps) == ['b']
def test_normalize():
# how to test that relations remain the same???
# check that there are no new relations?
# there can be less however?
dep_dic = {
'A': [],
'B': [],
'C': [],
'D': [['F']],
'E': [['A', 'B', 'C', 'D']],
'F': [['A', 'B']]
}
dep = classes.Dependencies(dep_dic, ['A', 'B', 'C'])
df = pd.DataFrame(columns=['A', 'B', 'C', 'D', 'E', 'F'], dtype='int64')
new = normalize.normalize(dep, df)
dep_dic = dep.serialize()
for x in new:
trans_deps = x.find_trans_deps()
normalize.filter(trans_deps, df)
assert trans_deps == []
part_deps = x.find_partial_deps()
normalize.filter(part_deps, df)
assert part_deps == []
dic = x.serialize()
for rhs in dic:
for lhs in dic[rhs]:
assert lhs in dep_dic[rhs]
def test_normalize_dataframe():
dic = {
'team': [
'Red', 'Red', 'Red', 'Orange', 'Orange', 'Yellow', 'Yellow',
'Green', 'Green', 'Blue'
],
'jersey_num': [1, 2, 3, 1, 2, 1, 5, 8, 2, 2],
'player_name': ['A', 'B', 'C', 'D', 'A', 'E', 'B', 'A', 'G', 'H'],
'city': [
'boston', 'boston', 'boston', 'chicago', 'chicago', 'honolulu',
'honolulu', 'boston', 'boston', 'austin'
],
'state': ['MA', 'MA', 'MA', 'IL', 'IL', 'HI', 'HI', 'MA', 'MA', 'TX']
}
df = pd.DataFrame(dic)
deps = classes.Dependencies(
{
'team': [['player_name', 'jersey_num']],
'jersey_num': [['player_name', 'team']],
'player_name': [['team', 'jersey_num']],
'city': [['team'], ['state'], ['player_name', 'jersey_num']],
'state': [['team'], ['player_name', 'jersey_num'], ['city']]
}, ['team', 'jersey_num'])
depdf = normalize.DepDF(deps, df, deps.get_prim_key())
normalize.normalize_dataframe(depdf)
new_dfs = depdf.return_dfs()
assert len(new_dfs) == 3
dic_one = {
'team': [
'Red', 'Red', 'Red', 'Orange', 'Orange', 'Yellow', 'Yellow',
'Green', 'Green', 'Blue'
],
'jersey_num': [1, 2, 3, 1, 2, 1, 5, 8, 2, 2],
'player_name': ['A', 'B', 'C', 'D', 'A', 'E', 'B', 'A', 'G', 'H']
}
dic_two = {
'team': ['Red', 'Orange', 'Yellow', 'Green', 'Blue', 'Blue'],
'city':
['boston', 'chicago', 'honolulu', 'boston', 'austin', 'austin']
}
dic_three = {
'city': ['boston', 'chicago', 'honolulu', 'austin', 'austin'],
'state': ['MA', 'IL', 'HI', 'TX', 'TX']
}
assert new_dfs[0].equals(
normalize.drop_primary_dups(pd.DataFrame(dic_one),
['team', 'jersey_num']))
assert new_dfs[1].equals(
normalize.drop_primary_dups(pd.DataFrame(dic_two), ['team']))
assert new_dfs[2].equals(
normalize.drop_primary_dups(pd.DataFrame(dic_three), ['city']))
def test_make_indexes():
dic = {
"id": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
"month":
['dec', 'dec', 'jul', 'jul', 'dec', 'jul', 'jul', 'jul', 'dec', 'jul'],
"hemisphere": ['N', 'N', 'N', 'N', 'S', 'S', 'S', 'S', 'S', 'N'],
"is_winter":
[True, True, False, False, False, True, True, True, False, False]
}
df = pd.DataFrame(dic)
deps = classes.Dependencies(
{
'id': [],
'month': [['id'], ['hemisphere', 'is_winter']],
'hemisphere': [['month', 'is_winter'], ['id']],
'is_winter': [['month', 'hemisphere'], ['id']]
}, ['id'])
depdf = normalize.DepDF(deps, df, deps.get_prim_key())
normalize.normalize_dataframe(depdf)
normalize.make_indexes(depdf)
new_dfs = depdf.return_dfs()
mask = (new_dfs[1]['month'] == 'dec') & (new_dfs[1]['hemisphere'] == 'N')
val = new_dfs[1][mask][new_dfs[1].columns[0]].iloc[0]
assert new_dfs[0][new_dfs[1].columns[0]][0] == val
assert new_dfs[0][new_dfs[1].columns[0]][1] == val
mask = (new_dfs[1]['month'] == 'jul') & (new_dfs[1]['hemisphere'] == 'N')
val = new_dfs[1][mask][new_dfs[1].columns[0]].iloc[0]
assert new_dfs[0][new_dfs[1].columns[0]][2] == val
assert new_dfs[0][new_dfs[1].columns[0]][3] == val
assert new_dfs[0][new_dfs[1].columns[0]][9] == val
mask = (new_dfs[1]['month'] == 'dec') & (new_dfs[1]['hemisphere'] == 'S')
val = new_dfs[1][mask][new_dfs[1].columns[0]].iloc[0]
assert new_dfs[0][new_dfs[1].columns[0]][4] == val
assert new_dfs[0][new_dfs[1].columns[0]][8] == val
mask = (new_dfs[1]['month'] == 'jul') & (new_dfs[1]['hemisphere'] == 'S')
val = new_dfs[1][mask][new_dfs[1].columns[0]].iloc[0]
assert new_dfs[0][new_dfs[1].columns[0]][5] == val
assert new_dfs[0][new_dfs[1].columns[0]][6] == val
assert new_dfs[0][new_dfs[1].columns[0]][7] == val
# Make sure new column names are sorted
assert 'hemisphere_month' in new_dfs[0].columns
assert 'hemisphere_month' in new_dfs[1].columns
def test_split_on_dep():
dep_dic = {'A': [], 'B': [], 'C': [['A'], ['B']], 'D': [['B']]}
new = normalize.split_on_dep(['B'], classes.Dependencies(dep_dic))
assert new[0].serialize() == {'A': [], 'B': []}
assert new[1].serialize() == {'B': [], 'C': [['B']], 'D': [['B']]}
