def test_prediction_getitem():
"prediction.__getitem__"
p = testing.micro_prediction()
names = ['model2', 'model0']
p2 = p[names]
ok_(isinstance(p2, nx.Prediction), 'expecting a prediction')
ok_(p2.names == names, 'names corrcupted')
def test_prediction_copies():
"prediction properties should be copies"
p = testing.micro_prediction()
ok_(testing.shares_memory(p, p), "looks like shares_memory failed")
ok_(testing.shares_memory(p, p.ids), "p.ids should be a view")
ok_(testing.shares_memory(p, p.y), "p.y should be a view")
ok_(not testing.shares_memory(p, p.copy()), "should be a copy")
def test_metrics_per_era():
"make sure calc_metrics runs"
d = micro_data()
p = micro_prediction()
metrics_per_era(d, p)
metrics_per_era(d, p, 'yhat')
metrics_per_era(d, p, 'inner')
assert_raises(ValueError, metrics_per_era, d, p, 'outer')
def test_metrics_per_name():
"make sure metrics_per_name runs"
d = testing.micro_data()
p = testing.micro_prediction()
metrics_per_name(d, p, 1)
metrics_per_name(d, p, 2, join='yhat')
metrics_per_name(d, p, 3, columns=['sharpe'])
assert_raises(ValueError, metrics_per_name, d, p, 4, 'data', ['wtf'])
def test_prediction_iter():
"test prediction.iter"
p = testing.micro_prediction()
names = []
for pi in p.iter():
n = pi.names
ok_(len(n) == 1, 'should only yield a single name')
names.append(n[0])
ok_(p.names == names, 'prediction.iter failed')
def test_prediction_to_csv():
"make sure prediction.to_csv runs"
p = testing.micro_prediction()
with tempfile.NamedTemporaryFile() as temp:
p['model1'].to_csv(temp.name)
with testing.HiddenPrints():
p['model1'].to_csv(temp.name, verbose=True)
p2 = nx.load_prediction_csv(temp.name, 'model1')
ade(p2, p['model1'], "prediction corrupted during roundtrip")
assert_raises(ValueError, p.to_csv, 'unused')
def test_prediction_save():
"test prediction.save with mode='a'"
p = testing.micro_prediction()
p1 = p['model0']
p2 = p[['model1', 'model2']]
with tempfile.NamedTemporaryFile() as temp:
p1.save(temp.name)
p2.save(temp.name, mode='a')
p12 = nx.load_prediction(temp.name)
ade(p, p12, "prediction corrupted during roundtrip")
def test_prediction_ynew():
"test prediction.ynew"
p = testing.micro_prediction()
y = p.y.copy()
y2 = np.random.rand(*y.shape)
p2 = p.ynew(y2)
np.testing.assert_array_equal(p2.y, y2, 'prediction.ynew failed')
assert_raises(ValueError, p.ynew, y2[:3])
assert_raises(ValueError, p.ynew, y2[:, :2])
assert_raises(ValueError, p.ynew, y2.reshape(-1))
def test_metrics_per_era():
"make sure metrics_per_era runs"
d = testing.micro_data()
p = testing.micro_prediction()
metrics_per_era(d, p, 1)
metrics_per_era(d, p, 2, join='yhat')
metrics_per_era(d, p, 3, join='inner')
assert_raises(ValueError, metrics_per_era, d, p, 4, 'outer')
with testing.HiddenPrints():
metrics_per_era(d, p, tournament=5, era_as_str=True)
def test_prediction_performance():
"make sure prediction.performance runs"
d = testing.micro_data()
p = testing.micro_prediction()
df = p.performance(d)
ok_(isinstance(df, pd.DataFrame), 'expecting a dataframe')
p.performance(d, sort_by='auc')
p.performance(d, sort_by='acc')
p.performance(d, sort_by='ystd')
p.performance(d, sort_by='sharpe')
p.performance(d, sort_by='consis')
def test_prediction_roundtrip():
"save/load roundtrip shouldn't change prediction"
p = testing.micro_prediction()
with tempfile.NamedTemporaryFile() as temp:
p.save(temp.name)
p2 = nx.load_prediction(temp.name)
ade(p, p2, "prediction corrupted during roundtrip")
p.save(temp.name, compress=False)
p2 = nx.load_prediction(temp.name)
ade(p, p2, "prediction corrupted during roundtrip")
def test_prediction_rename():
"prediction.rename"
p = testing.micro_prediction()
rename_dict = {}
names = []
original_names = p.names
for i in range(p.shape[1]):
key = original_names[i]
value = 'm_%d' % i
names.append(value)
rename_dict[key] = value
p2 = p.rename(rename_dict)
ok_(p2.names == names, 'prediction.rename failed')
p = testing.micro_prediction()
assert_raises(ValueError, p.rename, 'modelX')
p = p['model1']
p2 = p.rename('modelX')
ok_(p2.names[0] == 'modelX', 'prediction.rename failed')
def test_prediction_correlation():
"make sure prediction.correlation runs"
p = testing.micro_prediction()
with testing.HiddenPrints():
p.correlation()
def test_merge_predictions():
"test merge_predictions"
p = testing.micro_prediction()
assert_raises(ValueError, nx.merge_predictions, [p, p])
p2 = nx.merge_predictions([p, nx.Prediction()])
ade(p2, p, 'corruption of merge predictions')
p1 = testing.micro_prediction([0, 1, 2, 3, 4])
p2 = testing.micro_prediction([5, 6, 7, 8, 9])
p12 = nx.merge_predictions([p1, p2])
ade(p12, p, 'corruption of merge predictions')
p1 = testing.micro_prediction([0, 1, 2, 3])
p2 = testing.micro_prediction([4, 5, 6])
p3 = testing.micro_prediction([7, 8, 9])
p123 = nx.merge_predictions([p1, p2, p3])
ade(p123, p, 'corruption of merge predictions')
p1 = testing.micro_prediction([9, 4, 3, 2])
p2 = testing.micro_prediction([1, 8, 7])
p3 = testing.micro_prediction([6, 5, 0])
p123 = nx.merge_predictions([p1, p2, p3])
ade(p123, p, 'corruption of merge predictions')
p1 = testing.micro_prediction([0, 1, 2, 3, 4])
p11 = p1[['model0', 'model1']]
p12 = p1['model2']
p2 = testing.micro_prediction([5, 6, 7, 8, 9])
p21 = p2['model0']
p22 = p2[['model1', 'model2']]
p12 = nx.merge_predictions([p11, p21, p22, p12])
ade(p12, p, 'corruption of merge predictions')
def test_prediction_originality():
"make sure prediction.originality runs"
p = testing.micro_prediction()
df = p.originality(['model1'])
ok_(isinstance(df, pd.DataFrame), 'expecting a dataframe')
def test_prediction_summary():
"make sure prediction.summary runs"
d = testing.micro_data()
p = testing.micro_prediction()
df = p['model1'].summary(d)
ok_(isinstance(df, pd.DataFrame), 'expecting a dataframe')
def test_prediction_drop():
"prediction.drop"
p = testing.micro_prediction()
p = p.drop(['model1'])
ok_(p.names == ['model0', 'model2'], 'prediction.drop failed')
def test_prediction_repr():
"make sure prediction.__repr__() runs"
p = micro_prediction()
p.__repr__()
def test_prediction_compare():
"make sure prediction.compare runs"
d = testing.micro_data()
p = testing.micro_prediction()
df = p.compare(d, p)
ok_(isinstance(df, pd.DataFrame), 'expecting a dataframe')
