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_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_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 predict1(host, id, cookies=None, proxies=None):
dataset = cfdg.ohlcv()
competition = CompetitionSpec(
title='Predict next day volume',
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))
resp = safe_post(construct_path(host, 'api/v1/competition'),
data=ujson.dumps({
'id': id,
'spec': competition.to_dict()
}),
cookies=cookies,
proxies=proxies)
return resp
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 getCFData(type, n_categories=5, n=100, **kwargs):
if type == 'scatter':
return scatter(n_categories,
n,
prefix=kwargs.get('prefix', 'category'),
mode=kwargs.get('mode', None))[['x', 'y', 'categories', 'text']]
elif type == 'scatter3d':
return scatter3d(n_categories,
n,
prefix=kwargs.get('prefix', 'category'),
mode=kwargs.get('mode', None))
elif type == 'bubble':
return bubble(n_categories,
n,
prefix=kwargs.get('prefix', 'category'),
mode=kwargs.get('mode', None))[['x', 'y', 'categories', 'size', 'text']]
elif type == 'bubble3d':
return bubble3d(n_categories,
n,
prefix=kwargs.get('prefix', 'category'),
mode=kwargs.get('mode', None))
elif type == 'pie':
return pie(n_labels=kwargs.get('n_lablels', 5),
mode=kwargs.get('mode', None))
elif type == 'heatmap':
return heatmap(n_x=kwargs.get('n_x', 5),
n_y=kwargs.get('n_y', 10))
elif type == 'bars':
return bars(n,
n_categories,
prefix=kwargs.get('prefix', 'category'),
columns=kwargs.get('columns', None),
mode=kwargs.get('mode', 'abc'))
elif type == 'ohlc':
return ohlc(n)
elif type == 'ohlcv':
return ohlcv(n)
elif type == 'box':
return box(n_traces=kwargs.get('n_traces', 5),
n=n,
mode=kwargs.get('mode', None))
elif type == 'histogram':
return histogram(n_traces=kwargs.get('n_traces', 1),
n=n,
mode=None)
elif type == 'surface':
return surface(n_x=kwargs.get('n_x', 20),
n_y=kwargs.get('n_y', 20))
elif type == 'sinwave':
return sinwave(n=n,
inc=kwargs.get('inc', .25))
if type == 'scattergeo':
return scattergeo()
elif type == 'choropleth':
return choropleth()
elif type == 'stock':
return getName(n=1,
name=kwargs.get('name', 3),
exchange=kwargs.get('exchange', 2),
columns=kwargs.get('columns', None),
mode=kwargs.get('mode', 'abc'))
else:
return lines(n_traces=kwargs.get('n_traces', 5),
n=n,
columns=kwargs.get('columns', None),
dateIndex=kwargs.get('dateIndex', True),
mode=kwargs.get('mode', None))