def test_multiple_runs():
"test running multiple models through multiple tournaments"
d = testing.play_data()
models = [nx.logistic(), nx.fifty()]
with testing.HiddenPrints():
p = nx.production(models, d, 'bernie')
ok_(p.shape[1] == 2, 'wrong number of tournaments')
p = nx.backtest(models, d, 2)
ok_(p.shape[1] == 2, 'wrong number of tournaments')
p = nx.run(models, nx.ValidationSplitter(d), 'ken')
ok_(p.shape[1] == 2, 'wrong number of tournaments')
p = nx.production(models, d)
ok_(p.shape[1] == 10, 'wrong number of tournaments')
p = nx.backtest(models, d)
ok_(p.shape[1] == 10, 'wrong number of tournaments')
p = nx.run(models, nx.ValidationSplitter(d))
ok_(p.shape[1] == 10, 'wrong number of tournaments')
p = nx.production(models, d, [1, 5])
ok_(p.shape[1] == 4, 'wrong number of tournaments')
ok_(p.tournaments() == ['bernie', 'charles'], 'wrong tournaments')
p = nx.backtest(models, d, ['charles', 'bernie'])
ok_(p.shape[1] == 4, 'wrong number of tournaments')
ok_(p.tournaments() == ['bernie', 'charles'], 'wrong tournaments')
p = nx.run(models, nx.ValidationSplitter(d), ['ken'])
ok_(p.shape[1] == 2, 'wrong number of tournaments')
ok_(p.tournaments() == ['ken'], 'wrong tournaments')
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_run():
"Make sure run runs"
d = testing.play_data()
models = [nx.logistic(), 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:
nx.run(model, splitter, verbosity=0)
def test_splitter_overlap():
"prediction data should not overlap"
d = nx.play_data()
splitters = [
nx.TournamentSplitter(d),
nx.ValidationSplitter(d),
nx.CheatSplitter(d),
nx.CVSplitter(d),
nx.IgnoreEraCVSplitter(d),
nx.SplitSplitter(d, fit_fraction=0.5)
]
for splitter in splitters:
predict_ids = []
for dfit, dpredict in splitter:
predict_ids.extend(dpredict.ids.tolist())
ok_(len(predict_ids) == len(set(predict_ids)), "ids overlap")
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 test_splitter_reset():
"splitter reset should not change results"
d = nx.play_data()
splitters = [
nx.TournamentSplitter(d),
nx.ValidationSplitter(d),
nx.CheatSplitter(d),
nx.CVSplitter(d),
nx.IgnoreEraCVSplitter(d),
nx.SplitSplitter(d, fit_fraction=0.5)
]
for splitter in splitters:
ftups = [[], []]
ptups = [[], []]
for i in range(2):
for dfit, dpredict in splitter:
ftups[i].append(dfit)
ptups[i].append(dpredict)
splitter.reset()
ok_(ftups[0] == ftups[1], "splitter reset changed fit split")
ok_(ptups[0] == ptups[1], "splitter reset changed predict split")
