def test_online_batch_consistent():
# Batch
batch = (
preprocessing.StandardScaler() |
multiclass.OneVsRestClassifier(
linear_model.LogisticRegression()
)
)
dataset = datasets.ImageSegments()
batch_metric = metrics.MacroF1()
for i, x in enumerate(pd.read_csv(dataset.path, chunksize=1)):
y = x.pop('category')
y_pred = batch.predict_many(x)
batch.fit_many(x, y)
for yt, yp in zip(y, y_pred):
if yp is not None:
batch_metric.update(yt, yp)
if i == 30:
break
# Online
online = (
preprocessing.StandardScaler() |
multiclass.OneVsRestClassifier(
linear_model.LogisticRegression()
)
)
online_metric = metrics.MacroF1()
X = pd.read_csv(dataset.path)
Y = X.pop('category')
for i, (x, y) in enumerate(stream.iter_pandas(X, Y)):
y_pred = online.predict_one(x)
online.fit_one(x, y)
if y_pred is not None:
online_metric.update(y, y_pred)
if i == 30:
break
assert online_metric.get() == batch_metric.get()
def __init__(self, config: Config = None, seed: int = 42):
self._config = config
self._rng = RandomState(seed)
self._oracle = self.build_oracle()
self._oracle_metric = metrics.MacroF1()
self._times = 1
self._arms_selected = []
self._arms = list(self._config.arms.keys())
self.init_default_reward()
def default_metrics(self):
return [
metrics.Accuracy(),
metrics.CrossEntropy(),
metrics.MacroPrecision(),
metrics.MacroRecall(),
metrics.MacroF1(),
metrics.MicroPrecision(),
metrics.MicroRecall(),
metrics.MicroF1()
]
average='macro'), [0, 1, 2, 2, 2], [0, 0, 2, 2, 1]),
(metrics.MicroRecall(),
functools.partial(sk_metrics.recall_score,
average='micro'), [0, 1, 2, 2, 2], [0, 0, 2, 2, 1]),
(metrics.FBeta(beta=0.5),
functools.partial(sk_metrics.fbeta_score, beta=0.5),
[True, False, True, True, True], [True, True, False, True, True]),
(metrics.MacroFBeta(beta=0.5),
functools.partial(sk_metrics.fbeta_score, beta=0.5,
average='macro'), [0, 1, 0, 2, 2], [0, 0, 1, 1, 2]),
(metrics.MicroFBeta(beta=0.5),
functools.partial(sk_metrics.fbeta_score, beta=0.5,
average='micro'), [0, 1, 0, 2, 2], [0, 0, 1, 1, 2]),
(metrics.F1(), sk_metrics.f1_score, [True, False, True, True, True
], [True, True, False, True, True]),
(metrics.MacroF1(), functools.partial(
sk_metrics.f1_score, average='macro'), [0, 1, 2, 2,
2], [0, 0, 2, 2, 1]),
(metrics.MicroF1(), functools.partial(
sk_metrics.f1_score, average='micro'), [0, 1, 2, 2,
2], [0, 0, 2, 2, 1]),
(metrics.LogLoss(), sk_metrics.log_loss, [True, False, False, True
], [0.9, 0.1, 0.2, 0.65]),
(metrics.CrossEntropy(),
functools.partial(sk_metrics.log_loss, labels=[0, 1, 2]), [0, 1, 2, 2],
[[0.29450637, 0.34216758, 0.36332605],
[0.21290077, 0.32728332, 0.45981591],
[0.42860913, 0.33380113, 0.23758974],
[0.44941979, 0.32962558, 0.22095463]])])
def test_metric(metric, sk_metric, y_true, y_pred):
(metrics.MacroRecall(),
functools.partial(sk_metrics.recall_score, average='macro')),
(metrics.MicroRecall(),
functools.partial(sk_metrics.recall_score, average='micro')),
(metrics.WeightedRecall(),
functools.partial(sk_metrics.recall_score, average='weighted')),
(metrics.FBeta(beta=.5), functools.partial(sk_metrics.fbeta_score,
beta=.5)),
(metrics.MacroFBeta(beta=.5),
functools.partial(sk_metrics.fbeta_score, beta=.5, average='macro')),
(metrics.MicroFBeta(beta=.5),
functools.partial(sk_metrics.fbeta_score, beta=.5, average='micro')),
(metrics.WeightedFBeta(beta=.5),
functools.partial(sk_metrics.fbeta_score, beta=.5, average='weighted')),
(metrics.F1(), sk_metrics.f1_score),
(metrics.MacroF1(), functools.partial(sk_metrics.f1_score,
average='macro')),
(metrics.MicroF1(), functools.partial(sk_metrics.f1_score,
average='micro')),
(metrics.WeightedF1(),
functools.partial(sk_metrics.f1_score, average='weighted')),
(metrics.MCC(), sk_metrics.matthews_corrcoef),
(metrics.MAE(), sk_metrics.mean_absolute_error),
(metrics.MSE(), sk_metrics.mean_squared_error),
]
@pytest.mark.parametrize('metric, sk_metric', TEST_CASES)
@pytest.mark.filterwarnings('ignore::RuntimeWarning')
@pytest.mark.filterwarnings(
'ignore::sklearn.metrics.classification.UndefinedMetricWarning')
import pickle
import json
#read configurations
config = json.loads(input())
target = config['target']
predict = config['predict']
m = config['metric']
if (m == "Accuracy"):
metric = metrics.Accuracy()
elif (m == "CrossEntropy"):
metric = metrics.CrossEntropy()
elif (m == "MacroF1"):
metric = metrics.MacroF1()
elif (m == "MacroPrecision"):
metric = metrics.MacroPrecision()
elif (m == "MacroRecall"):
metric = metrics.MacroRecall()
elif (m == "MicroF1"):
metric = metrics.MicroF1()
elif (m == "MicroPrecision"):
metric = metrics.MicroPrecision()
elif (m == "MicroRecall"):
metric = metrics.MicroRecall()
while True:
#wait request
data = input()
TEST_CASES = [
(metrics.Accuracy(), sk_metrics.accuracy_score),
(metrics.Precision(), sk_metrics.precision_score),
(metrics.MacroPrecision(), functools.partial(sk_metrics.precision_score, average='macro')),
(metrics.MicroPrecision(), functools.partial(sk_metrics.precision_score, average='micro')),
(metrics.WeightedPrecision(), functools.partial(sk_metrics.precision_score, average='weighted')),
(metrics.Recall(), sk_metrics.recall_score),
(metrics.MacroRecall(), functools.partial(sk_metrics.recall_score, average='macro')),
(metrics.MicroRecall(), functools.partial(sk_metrics.recall_score, average='micro')),
(metrics.WeightedRecall(), functools.partial(sk_metrics.recall_score, average='weighted')),
(metrics.FBeta(beta=.5), functools.partial(sk_metrics.fbeta_score, beta=.5)),
(metrics.MacroFBeta(beta=.5), functools.partial(sk_metrics.fbeta_score, beta=.5, average='macro')),
(metrics.MicroFBeta(beta=.5), functools.partial(sk_metrics.fbeta_score, beta=.5, average='micro')),
(metrics.WeightedFBeta(beta=.5), functools.partial(sk_metrics.fbeta_score, beta=.5, average='weighted')),
(metrics.F1(), sk_metrics.f1_score),
(metrics.MacroF1(), functools.partial(sk_metrics.f1_score, average='macro')),
(metrics.MicroF1(), functools.partial(sk_metrics.f1_score, average='micro')),
(metrics.WeightedF1(), functools.partial(sk_metrics.f1_score, average='weighted')),
(metrics.MCC(), sk_metrics.matthews_corrcoef),
(metrics.MAE(), sk_metrics.mean_absolute_error),
(metrics.MSE(), sk_metrics.mean_squared_error),
]
@pytest.mark.parametrize('metric, sk_metric', TEST_CASES)
@pytest.mark.filterwarnings('ignore::RuntimeWarning')
@pytest.mark.filterwarnings('ignore::sklearn.metrics.classification.UndefinedMetricWarning')
def test_metric(metric, sk_metric):
# Check str works
str(metric)
(metrics.WeightedPrecision(),
partial(sk_metrics.precision_score, average='weighted')),
(metrics.Recall(), sk_metrics.recall_score),
(metrics.MacroRecall(), partial(sk_metrics.recall_score, average='macro')),
(metrics.MicroRecall(), partial(sk_metrics.recall_score, average='micro')),
(metrics.WeightedRecall(),
partial(sk_metrics.recall_score, average='weighted')),
(metrics.FBeta(beta=.5), partial(sk_metrics.fbeta_score, beta=.5)),
(metrics.MacroFBeta(beta=.5),
partial(sk_metrics.fbeta_score, beta=.5, average='macro')),
(metrics.MicroFBeta(beta=.5),
partial(sk_metrics.fbeta_score, beta=.5, average='micro')),
(metrics.WeightedFBeta(beta=.5),
partial(sk_metrics.fbeta_score, beta=.5, average='weighted')),
(metrics.F1(), sk_metrics.f1_score),
(metrics.MacroF1(), partial(sk_metrics.f1_score, average='macro')),
(metrics.MicroF1(), partial(sk_metrics.f1_score, average='micro')),
(metrics.WeightedF1(), partial(sk_metrics.f1_score, average='weighted')),
(metrics.MCC(), sk_metrics.matthews_corrcoef),
(metrics.MAE(), sk_metrics.mean_absolute_error),
(metrics.MSE(), sk_metrics.mean_squared_error),
]
@pytest.mark.parametrize('metric, sk_metric', [
pytest.param(metric, sk_metric, id=f'{metric.__class__.__name__}')
for metric, sk_metric in TEST_CASES
])
@pytest.mark.filterwarnings('ignore::RuntimeWarning')
@pytest.mark.filterwarnings(
'ignore::sklearn.metrics.classification.UndefinedMetricWarning')