def _fit(self, classif, X, a, r, p):
obs_take = np.in1d(a, self.tree.node_comparisons[classif][0])
X_node = X[obs_take, :]
a_node = a[obs_take]
r_node = r[obs_take]
p_node = p[obs_take]
r_more_onehalf = r_node >= .5
y = (np.in1d(a_node,
self.tree.node_comparisons[classif][2])).astype('uint8')
y_node = y.copy()
y_node[r_more_onehalf] = 1 - y[r_more_onehalf]
w_node = (.5 - r_node) / p_node
w_node[r_more_onehalf] = ((r_node - .5) / p_node)[r_more_onehalf]
w_node = w_node * w_node.shape[0] / np.sum(w_node)
if y_node.shape[0] == 0:
self._oracles[classif] = _RandomPredictor()
elif y_node.sum() == y_node.shape[0]:
self._oracles[classif] = _OnePredictor()
elif y_node.sum() == 0:
self._oracles[classif] = _ZeroPredictor()
else:
self._oracles[classif].fit(X_node, y_node, sample_weight=w_node)
def fit(self, X, a, r, p):
"""
Fits the Offset Tree estimator to partially-labeled data collected from a different policy.
Parameters
----------
X : array (n_samples, n_features)
Matrix of covariates for the available data.
a : array (n_samples), int type
Arms or actions that were chosen for each observations.
r : array (n_samples), {0,1}
Rewards that were observed for the chosen actions. Must be binary rewards 0/1.
p : array (n_samples)
Reward estimates for the actions that were chosen by the policy.
"""
X, a, r = _check_fit_input(X, a, r)
p = _check_1d_inp(p)
assert p.shape[0] == X.shape[0]
if self.c is not None:
p = self.c * p
if self.pmin is not None:
p = np.clip(p, a_min=self.pmin, a_max=None)
self._oracles = [
deepcopy(self.base_algorithm) for c in range(self.nchoices - 1)
]
for classif in range(len(self._oracles)):
obs_take = np.in1d(a, self.tree.node_comparisons[classif][0])
X_node = X[obs_take, :]
a_node = a[obs_take]
r_node = r[obs_take]
p_node = p[obs_take]
r_more_onehalf = r_node >= .5
y = (np.in1d(
a_node,
self.tree.node_comparisons[classif][2])).astype('uint8')
y_node = y.copy()
y_node[r_more_onehalf] = 1 - y[r_more_onehalf]
w_node = (.5 - r_node) / p_node
w_node[r_more_onehalf] = ((r_node - .5) / p_node)[r_more_onehalf]
w_node = w_node * w_node.shape[0] / np.sum(w_node)
if y_node.shape[0] == 0:
self._oracles[classif] = _RandomPredictor()
elif y_node.sum() == y_node.shape[0]:
self._oracles[classif] = _OnePredictor()
elif y_node.sum() == 0:
self._oracles[classif] = _ZeroPredictor()
else:
self._oracles[classif].fit(X_node,
y_node,
sample_weight=w_node)