def production(model, data, tournament=None, verbosity=2):
"""
Fit a model with train data; make prediction on tournament data.
Parameters
----------
model : nx.Model, list, tuple
Prediction model. Can be a list or tuple of prediction models. Model
names must be unique.
data : nx.Data
The data to run the model through.
tournament : {None, int, str, list, tuple}, optional
The tournament(s) to run the model through. By default (None) the
model is run through all five tournaments. If a list or tuple of
tournaments is given then it must must not contain duplicate
tournaments.
verbosity : int, optional
An integer that determines verbosity. Zero is silent.
Returns
-------
p : nx.Prediction
A prediction object containing the predictions of the specified
model/tournament pairs.
"""
splitter = nx.TournamentSplitter(data)
prediction = run(model, splitter, tournament, verbosity=verbosity)
return prediction
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_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_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")
