def fit(self, data, args):
self.model = KBinsDiscretizer()
with Timer() as t:
self.model.fit(data.X_train, data.y_train)
return t.interval
def __init__(self, n_bins=5, encode='onehot', strategy='quantile'):
self._hyperparams = {
'n_bins': n_bins,
'encode': encode,
'strategy': strategy
}
self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
class KBinsDiscretizerImpl():
def __init__(self, n_bins=5, encode='onehot', strategy='quantile'):
self._hyperparams = {
'n_bins': n_bins,
'encode': encode,
'strategy': strategy
}
self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def transform(self, X):
return self._wrapped_model.transform(X)
class CreateKBinsDiscretizer(CreateModel):
def fit(self, data, args):
self.model = KBinsDiscretizer()
with Timer() as t:
self.model.fit(data.X_train, data.y_train)
return t.interval
def test(self, data):
assert self.model is not None
return self.model.transform(data.X_test)
def predict(self, data):
with Timer() as t:
self.predictions = self.test(data)
data.learning_task = LearningTask.REGRESSION
return t.interval
from sklearn.preprocessing._discretization import KBinsDiscretizer import numpy as np bins = 10 d = KBinsDiscretizer(bins, encode='ordinal', strategy='uniform') X = np.array(['hello', 'test', 'hello', 'test', 'h', 'a']).reshape(1, -1) d.fit(X) print(d.transform(X))