def test_answerPrototype62(self):
# AnswerType.SIX
exp = datetime.now() + timedelta(minutes=2)
competition = CompetitionSpec(
title="",
type=CompetitionType.PREDICT,
when=exp,
prize=1.0,
dataset="https://feeds.citibikenyc.com/stations/stations.json",
dataset_type=DatasetFormat.JSON,
dataset_key="id",
dataset_kwargs={"record_column": "stationBeanList"},
metric=CompetitionMetric.ABSDIFF,
targets=["availableBikes", "availableDocks"],
expiration=exp,
)
dataset = fetchDataset(competition)
df = answerPrototype(competition)[["when", "availableBikes", "availableDocks"]]
ans = pd.DataFrame(
[
{"when": exp, "availableBikes": np.nan, "availableDocks": np.nan}
for x in dataset.id
],
index=[id for id in dataset.id],
)[["when", "availableBikes", "availableDocks"]]
print(df)
print(ans)
assert df.equals(ans)
def test_checkAnswer2(self):
dataset = cfdg.ohlcv()
competition = CompetitionSpec(
title='',
type=CompetitionType.PREDICT,
expiration=datetime.now() + timedelta(minutes=1),
prize=1.0,
dataset=dataset.iloc[:-1],
metric=CompetitionMetric.ABSDIFF,
targets=dataset.columns[-1],
answer=dataset.iloc[-1:],
when=datetime.utcfromtimestamp(
dataset[-1:].index.values[0].astype(datetime) / 1000000000))
c2 = Competition.from_spec(1, competition)
ans = foo3(competition)
print(ans)
d2 = SubmissionSpec.from_dict({
'competition_id': 2,
'answer': ans.to_json(),
'answer_type': DatasetFormat.JSON
})
s = Submission.from_spec(1, 2, c2, d2)
checkAnswer(s)
def test_answerPrototype6(self):
# AnswerType.SIX
dataset = cfdg.ohlcv().reset_index()
exp = datetime.utcfromtimestamp(
dataset[-1:]['index'].values[0].astype(datetime) / 1000000000)
competition = CompetitionSpec(title='',
type=CompetitionType.PREDICT,
expiration=datetime.now() +
timedelta(minutes=1),
prize=1.0,
dataset=dataset.iloc[:-1],
metric=CompetitionMetric.ABSDIFF,
targets=dataset.columns[-1],
answer=dataset.iloc[-1:],
dataset_key='index',
when=exp)
df = answerPrototype(competition)[['when', dataset.columns[-1]]]
ans = pd.DataFrame([{
'when': exp,
dataset.columns[-1]: np.nan
} for x in dataset['index'].iloc[:-1]],
index=[x for x in dataset['index'].iloc[:-1]
])[['when', dataset.columns[-1]]]
print(df)
print(ans)
assert df.equals(ans)
def test_checkAnswer3(self):
exp = datetime.now() + timedelta(minutes=2)
competition = CompetitionSpec(
title='',
type=CompetitionType.PREDICT,
when=exp,
prize=1.0,
dataset='https://feeds.citibikenyc.com/stations/stations.json',
dataset_type=DatasetFormat.JSON,
dataset_key='id',
dataset_kwargs={'record_column': 'stationBeanList'},
metric=CompetitionMetric.ABSDIFF,
targets=['availableBikes', 'availableDocks'],
expiration=exp)
c2 = Competition.from_spec(1, competition)
ans = foo5(competition)
s2 = SubmissionSpec.from_dict({
'competition_id': 2,
'answer': ans.to_json(orient='records'),
'answer_type': DatasetFormat.JSON
})
s = Submission.from_spec(1, 2, c2, s2)
checkAnswer(s)
def test_answerPrototype62(self):
# AnswerType.SIX
exp = datetime.now() + timedelta(minutes=2)
competition = CompetitionSpec(
title='',
type=CompetitionType.PREDICT,
when=exp,
prize=1.0,
dataset='https://feeds.citibikenyc.com/stations/stations.json',
dataset_type=DatasetFormat.JSON,
dataset_key='id',
dataset_kwargs={'record_column': 'stationBeanList'},
metric=CompetitionMetric.ABSDIFF,
targets=['availableBikes', 'availableDocks'],
expiration=exp)
dataset = fetchDataset(competition)
df = answerPrototype(competition)[[
'when', 'availableBikes', 'availableDocks'
]]
ans = pd.DataFrame([{
'when': exp,
'availableBikes': np.nan,
'availableDocks': np.nan
} for x in dataset.id],
index=[id for id in dataset.id])[[
'when', 'availableBikes', 'availableDocks'
]]
print(df)
print(ans)
assert df.equals(ans)
def test_checkAnswer3(self):
exp = datetime.now() + timedelta(minutes=2)
competition = CompetitionSpec(
title="",
type=CompetitionType.PREDICT,
when=exp,
prize=1.0,
dataset="https://feeds.citibikenyc.com/stations/stations.json",
dataset_type=DatasetFormat.JSON,
dataset_key="id",
dataset_kwargs={"record_column": "stationBeanList"},
metric=CompetitionMetric.ABSDIFF,
targets=["availableBikes", "availableDocks"],
expiration=exp,
)
c2 = Competition.from_spec(1, competition)
ans = foo5(competition)
s2 = SubmissionSpec.from_dict({
"competition_id": 2,
"answer": ans.to_json(orient="records"),
"answer_type": DatasetFormat.JSON,
})
s = Submission.from_spec(1, 2, c2, s2)
checkAnswer(s)
def test_validateSpec(self):
CompetitionSpec.validate('', '', CompetitionType.CLASSIFY, None, 1.0,
CompetitionMetric.LOGLOSS, 'http://test.com',
'test', DatasetFormat.JSON, None, None, 2,
None, '', None, '')
try:
CompetitionSpec.validate('', '', None, None, 1.0,
CompetitionMetric.LOGLOSS,
'http://test.com', 'test',
DatasetFormat.JSON, None, None, 2, None,
'', None, '')
assert False
except MalformedCompetition:
pass
try:
CompetitionSpec.validate('', '', CompetitionType.CLASSIFY, None,
1.0, None, 'http://test.com', 'test',
DatasetFormat.JSON, None, None, 2, None,
'', None, '')
assert False
except MalformedMetric:
pass
try:
CompetitionSpec.validate('', '', CompetitionType.CLASSIFY, None,
1.0, CompetitionMetric.LOGLOSS,
'http://test.com', 'test', None, None,
None, 2, None, '', None, '')
assert False
except MalformedDataset:
pass
try:
CompetitionSpec.validate('', '', CompetitionType.PREDICT, None,
1.0, CompetitionMetric.LOGLOSS,
'http://test.com', None,
DatasetFormat.JSON, None, None, 2, None,
'', None, '')
assert False
except MalformedTargets:
pass
def test_init(self):
dataset = make_classification()
CompetitionSpec(title='',
type=CompetitionType.CLASSIFY,
expiration=datetime.now() + timedelta(minutes=1),
prize=1.0,
num_classes=2,
dataset=pandas.DataFrame(dataset[0]),
metric=CompetitionMetric.LOGLOSS,
answer=pandas.DataFrame(dataset[1]))
def test_to_dict_from_dict(self):
time = datetime(2018, 1, 1)
s = CompetitionSpec(
title="",
type=CompetitionType.CLASSIFY,
expiration=time,
prize=1.0,
num_classes=2,
dataset="http://test.com",
metric=CompetitionMetric.LOGLOSS,
answer="http://test.com",
)
print(str(s))
d = s.to_dict()
d["expiration"] = time.strftime("%Y-%m-%d %H:%M:%S")
j = s.to_json()
s2 = CompetitionSpec.from_dict(d)
s3 = CompetitionSpec.from_json(j)
for item in ["type", "expiration", "prize", "num_classes", "metric"]:
assert getattr(s, item) == getattr(s2, item) == getattr(s3, item)
def test_answerPrototype5(self):
# AnswerType.FIVE
exp = datetime.now() + timedelta(minutes=2)
competition = CompetitionSpec(title='',
type=CompetitionType.PREDICT,
expiration=exp,
prize=1.0,
dataset='http://bonds.paine.nyc',
dataset_type=DatasetFormat.JSON,
metric=CompetitionMetric.ABSDIFF,
targets={0: ['Price']})
df = answerPrototype(competition)
ans = pd.DataFrame([{'Price': np.nan}])
print(df)
print(ans)
assert df.equals(ans)
def test_answerPrototype1(self):
# AnswerType.ONE
dataset = make_classification()
competition = CompetitionSpec(title='',
type=CompetitionType.CLASSIFY,
expiration=datetime.now() +
timedelta(minutes=1),
prize=1.0,
num_classes=2,
dataset=pd.DataFrame(dataset[0]),
metric=CompetitionMetric.LOGLOSS,
answer=pd.DataFrame(dataset[1]))
dataset = fetchDataset(competition)
df = answerPrototype(competition)
ans = pd.DataFrame([{'class': np.nan} for x in dataset.index])
print(df)
print(ans)
assert df.equals(ans)
def test_answerPrototype9(self):
# AnswerType.NINE
dataset = cfdg.ohlcv()
competition = CompetitionSpec(
title="",
type=CompetitionType.PREDICT,
expiration=datetime.now() + timedelta(minutes=1),
prize=1.0,
dataset=dataset.iloc[:-1],
metric=CompetitionMetric.ABSDIFF,
targets=dataset.columns[-1],
answer=dataset.iloc[-1:],
)
df = answerPrototype(competition)
ans = pd.DataFrame([{dataset.columns[-1]: np.nan} for x in dataset.index])
ans = pd.DataFrame([{dataset.columns[-1]: np.nan}])
print(df)
print(ans)
assert df.equals(ans)
def test_answerPrototype8(self):
# AnswerType.EIGHT
dataset = cfdg.ohlcv()
exp = datetime.utcfromtimestamp(
dataset[-1:].index.values[0].astype(datetime) / 1000000000)
competition = CompetitionSpec(title='',
type=CompetitionType.PREDICT,
expiration=datetime.now() +
timedelta(minutes=1),
prize=1.0,
dataset=dataset.iloc[:-1],
metric=CompetitionMetric.ABSDIFF,
targets=dataset.columns[-1],
answer=dataset.iloc[-1:],
when=exp)
df = answerPrototype(competition)
ans = pd.DataFrame([{dataset.columns[-1]: np.nan}], index=[exp])
print(df)
print(ans)
assert df.equals(ans)
def test_answerPrototype7(self):
# AnswerType.SEVEN
dataset = cfdg.ohlcv().reset_index()
competition = CompetitionSpec(title='',
type=CompetitionType.PREDICT,
expiration=datetime.now() +
timedelta(minutes=1),
prize=1.0,
dataset=dataset.iloc[:-1],
metric=CompetitionMetric.ABSDIFF,
targets=dataset.columns[-1],
dataset_key='index',
answer=dataset.iloc[-1:])
df = answerPrototype(competition)
ans = pd.DataFrame([{
dataset.columns[-1]: np.nan
} for x in dataset['index'].iloc[:-1]],
index=dataset['index'].iloc[:-1])
print(df)
print(ans)
assert df.equals(ans)
def test_answerPrototype2(self):
# AnswerType.TWO
exp = datetime.now() + timedelta(minutes=2)
competition = CompetitionSpec(title='',
type=CompetitionType.PREDICT,
expiration=exp,
when=exp,
prize=1.0,
dataset='http://bonds.paine.nyc',
dataset_type=DatasetFormat.JSON,
metric=CompetitionMetric.ABSDIFF,
dataset_key='Name',
targets={'ABC Corp': ['Price']})
df = answerPrototype(competition)[['Price', 'when']]
ans = pd.DataFrame([{
'when': exp,
'Price': np.nan
}],
index=['ABC Corp'])[['Price', 'when']]
print(df)
print(ans)
assert df.equals(ans)
def test_checkAnswer(self):
dataset = make_classification()
competition = CompetitionSpec(title='',
type=CompetitionType.CLASSIFY,
expiration=datetime.now() +
timedelta(minutes=1),
prize=1.0,
num_classes=2,
dataset=pd.DataFrame(dataset[0]),
metric=CompetitionMetric.LOGLOSS,
answer=pd.DataFrame(dataset[1]))
c2 = Competition.from_spec(1, competition)
d2 = SubmissionSpec.from_dict({
'competition_id':
2,
'answer':
pd.DataFrame(dataset[1]).to_json(),
'answer_type':
DatasetFormat.JSON
})
s = Submission.from_spec(1, 2, c2, d2)
checkAnswer(s)
def test_answerPrototype3(self):
# AnswerType.THREE
competition = CompetitionSpec(title='',
type=CompetitionType.PREDICT,
expiration=datetime.now() +
timedelta(minutes=1),
prize=1.0,
dataset='http://bonds.paine.nyc',
dataset_type=DatasetFormat.JSON,
metric=CompetitionMetric.ABSDIFF,
dataset_key='Name',
targets={'ABC Corp': ['Price']})
dataset = fetchDataset(competition)
df = answerPrototype(competition, dataset)
index = dataset[dataset['Name'] == 'ABC Corp'].index
ans = pd.DataFrame([{
'Name': 'ABC Corp',
'Price': np.nan
} for _ in index],
index=index)
print(df)
print(ans)
assert df.equals(ans)
def test_to_dict_from_dict(self):
time = datetime(2018, 1, 1)
s = CompetitionSpec(title='',
type=CompetitionType.CLASSIFY,
expiration=time,
prize=1.0,
num_classes=2,
dataset='http://test.com',
metric=CompetitionMetric.LOGLOSS,
answer='http://test.com')
print(str(s))
d = s.to_dict()
d['expiration'] = time.strftime("%Y-%m-%d %H:%M:%S")
j = s.to_json()
s2 = CompetitionSpec.from_dict(d)
s3 = CompetitionSpec.from_json(j)
for item in ['type', 'expiration', 'prize', 'num_classes', 'metric']:
assert getattr(s, item) == getattr(s2, item) == getattr(s3, item)
import pandas
import ujson
from datetime import datetime, timedelta
from sklearn.datasets import make_classification
from crowdsource.client import Client
from mock import patch, MagicMock
from crowdsource.types.competition import CompetitionSpec
from crowdsource.enums import CompetitionType, CompetitionMetric
dataset = make_classification()
competition = CompetitionSpec(
title="",
type=CompetitionType.CLASSIFY,
expiration=datetime.now() + timedelta(minutes=1),
prize=1.0,
num_classes=2,
dataset=pandas.DataFrame(dataset[0]),
metric=CompetitionMetric.LOGLOSS,
answer=pandas.DataFrame(dataset[1]),
)
class TestClient:
def test_init(self):
with patch("requests.post") as m:
m.return_value = MagicMock()
m.return_value.text = '{"id":1}'
c = Client("test")
assert c
def test_register(self):