def test_prediction_setitem():
"""compare prediction._setitem__ with merge"""
data = nx.play_data()
p1 = nx.production(nx.linear(), data, 'kazutsugi', verbosity=0)
p2 = nx.production(nx.linear(), data, 8, verbosity=0)
p3 = nx.production(nx.linear(), data, 8, verbosity=0)
p4 = nx.backtest(nx.linear(), data, 8, verbosity=0)
p = nx.Prediction()
p[('linear', 1)] = p1
p[('linear', 2)] = p2
p[('linear', 3)] = p3
p[('linear', 4)] = p4
pp = nx.Prediction()
pp = pp.merge(p1)
pp = pp.merge(p2)
pp = pp.merge(p3)
pp = pp.merge(p4)
pd.testing.assert_frame_equal(p.df, pp.df)
assert_raises(ValueError, p.__setitem__, ('linear', 1), p1)
assert_raises(ValueError, p.__setitem__, ('linear', 1), p)
def test_model_rename():
"""Test renaming a model"""
model = nx.linear()
ok_(model.name == 'linear', 'wrong name')
model.rename('LR')
ok_(model.name == 'LR', 'wrong name')
model = model.rename('logreg')
ok_(model.name == 'logreg', 'wrong name')
ok_(model.__repr__().startswith('logreg'), 'wrong name')
model = nx.linear()
ok_(model.rename(None).name == 'linear', 'wrong name')
assert_raises(ValueError, model.rename, 1)
def test_prediction_regression():
"""regression test of prediction performance evaluation"""
d = nx.play_data()
p = nx.production(nx.linear(), d, tournament=None, verbosity=0)
for number, name in nx.tournament_iter():
p2 = nx.production(nx.linear(), d, tournament=name, verbosity=0)
df = p.performance_mean(d['validation'], mean_of='tournament')
logloss1 = float('%.3f' % (df.loc[name]['mse']))
logloss2 = float('%.3f' %
(p2.summary(d['validation']).loc['mean']['mse']))
diff = np.abs(logloss1 - logloss2)
msg = f"failed on {name}"
ok_(diff < 1e-6, msg)
def improve_model(data, tournament='kazutsugi'):
"""
Run multiple models: fit on training data, predict for tournament data.
Then change the data, rerun and compare performance with and without the
change.
"""
# we'll look at 5 models
models = [nx.linear(), nx.extratrees(), nx.randomforest(), nx.mlpc(),
nx.linearPCA()]
print('\nStandard dataset:\n')
# first run the base case
prediction = nx.production(models, data, tournament, verbosity=1)
# let's now make a change, could be anything; as an example let's add
# the square of each feature to the dataset
x = np.hstack((data.x, data.x * data.x))
data2 = data.xnew(x)
print('\nDataset expanded with squared features:\n')
# rerun all models with the new expanded data
prediction2 = nx.production(models, data2, tournament, verbosity=1)
# compare performance
print('\nCompare (1 is regular dataset; 2 expanded dataset):\n')
print(prediction.compare(data['validation'], prediction2, tournament))
def test_multiple_runs():
"""test running multiple models through multiple tournaments"""
d = testing.play_data()
models = [nx.linear(), nx.fifty()]
with testing.HiddenPrints():
p = nx.production(models, d, 'kazutsugi')
ok_(p.shape[1] == 2, 'wrong number of tournaments')
p = nx.backtest(models, d, 8)
ok_(p.shape[1] == 2, 'wrong number of tournaments')
p = nx.run(models, nx.ValidationSplitter(d), 'kazutsugi')
ok_(p.shape[1] == 2, 'wrong number of tournaments')
p = nx.production(models, d)
ok_(p.shape[1] == 2, 'wrong number of tournaments')
p = nx.backtest(models, d)
ok_(p.shape[1] == 2, 'wrong number of tournaments')
p = nx.run(models, nx.ValidationSplitter(d))
ok_(p.shape[1] == 2, 'wrong number of tournaments')
p = nx.production(models, d, [8])
ok_(p.shape[1] == 2, 'wrong number of tournaments')
ok_(p.tournaments() == ['kazutsugi'], 'wrong tournaments')
p = nx.backtest(models, d, ['kazutsugi'])
ok_(p.shape[1] == 2, 'wrong number of tournaments')
ok_(p.tournaments() == ['kazutsugi'], 'wrong tournaments')
p = nx.run(models, nx.ValidationSplitter(d), ['kazutsugi'])
ok_(p.shape[1] == 2, 'wrong number of tournaments')
ok_(p.tournaments() == ['kazutsugi'], 'wrong tournaments')
def test_prediction_check():
"""make sure prediction.check runs"""
d = nx.play_data()
p1 = nx.production(nx.linear(), d, 'kazutsugi', verbosity=0)
p2 = p1.copy()
p2 = p2.rename('example_predictions')
p = p1 + p2
with nx.testing.HiddenPrints():
df = p.check(d)
ok_(isinstance(df, dict), 'expecting a dictionary')
def concordance(data, tournament='kazutsugi'):
"""
Example showing how to calculate concordance.
Concordance must be less than 0.12 to pass numerai's check.
For an accurate concordance calculation `data` must be the full dataset.
"""
models = [nx.linear(), nx.extratrees(), nx.mlpc()]
p = nx.production(models, data, tournament)
print("\nA concordance less than 0.12 is passing")
print(p.concordance(data))
def get_models():
models = [nx.linear(),
nx.ridge_mean(),
nx.extratrees(),
nx.randomforest(),
nx.mlpc(),
nx.linearPCA(),
nx.example_predictions(),
nx.fifty()]
return models
def numerox_example():
"""
Example of how to prepare a submission for the Numerai tournament.
It uses Numerox which you can install with: pip install numerox
For more information see: https://github.com/kwgoodman/numerox
"""
# download dataset from numerai, save it and then load it
data = nx.download('numerai_dataset.zip')
# we will use logistic regression; you will want to write your own model
model = nx.linear()
# fit model with train data and make predictions for tournament data
prediction = nx.production(model, data, tournament='kazutsugi')
# save predictions to csv file
prediction.to_csv('linear.csv', verbose=True)
def test_run():
"Make sure run runs"
d = testing.play_data()
models = [nx.linear(), nx.fifty()]
splitters = [nx.TournamentSplitter(d),
nx.ValidationSplitter(d),
nx.CheatSplitter(d),
nx.CVSplitter(d, kfold=2),
nx.SplitSplitter(d, fit_fraction=0.5)]
for model in models:
for splitter in splitters:
p = nx.run(model, splitter, tournament=None, verbosity=0)
ok_(p.shape[1] == 1, 'wrong number of tournaments')
ok_(p.tournaments() == nx.tournament_all(), 'wrong tournaments')
assert_raises(ValueError, nx.run, None, nx.TournamentSplitter(d))
assert_raises(ValueError, nx.run, nx.fifty(), nx.TournamentSplitter(d), {})
def run_all_examples(data=None):
"Run most of the numerox examples"
if data is None:
data = nx.numerai.download_data_object(verbose=True)
backtest = nx.examples.backtest
print_source(backtest)
backtest(data)
concordance = nx.examples.concordance
print_source(concordance)
concordance(data)
improve_model = nx.examples.improve_model
print_source(improve_model)
improve_model(data)
cv_warning = nx.examples.cv_warning
print_source(cv_warning)
cv_warning(nx.linear(), data['train'], nsamples=2)
def backtest(data, tournament='kazutsugi'):
"Simple cross validation on training data using linear regression"
model = nx.linear()
prediction = nx.backtest(model, data, tournament) # noqa
def test_prediction_concordance():
"""make sure prediction.concordance runs"""
d = nx.testing.play_data()
p = nx.production(nx.linear(), d, 8, verbosity=0)
df = p.concordance(d)
ok_(isinstance(df, pd.DataFrame), 'expecting a dataframe')
