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Fix #848: Pass estimation_sample_size parameter to individual trees in UpliftRandomForestClassifier #850

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Merged
jeongyoonlee merged 1 commit into from
Sep 26, 2025
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Fix #848: Pass estimation_sample_size parameter to individual trees in UpliftRandomForestClassifier #850

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36 changes: 19 additions & 17 deletions causalml/inference/tree/uplift.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -243,7 +243,7 @@ def group_uniqueCounts_to_arr(np.ndarray[TR_TYPE_t, ndim=1] treatment_idx,
tv = treatment_idx[i]
# assume treatment index is in range
out_arr[2*tv] += 1
# assume y should be either 0 or 1, so this is summing
# assume y should be either 0 or 1, so this is summing
out_arr[2*tv + 1] += y[i]
# adjust the entry at index 2*i to be N(Y = 0, T = i) = N(T = i) - N(Y = 1, T = i)
for i in range(n_class):
Expand Down Expand Up @@ -322,7 +322,7 @@ def group_counts_by_divide(
tv = treatment_idx[i]
# assume treatment index is in range
out_arr[2*tv] += 1
# assume y should be either 0 or 1, so this is summing
# assume y should be either 0 or 1, so this is summing
out_arr[2*tv + 1] += y[i]
# adjust the entry at index 2*i to be N(Y = 0, T = i) = N(T = i) - N(Y = 1, T = i)
for i in range(n_class):
Expand Down Expand Up @@ -360,9 +360,9 @@ class UpliftTreeClassifier:
n_reg: int, optional (default=100)
The regularization parameter defined in Rzepakowski et al. 2012, the weight (in terms of sample size) of the
parent node influence on the child node, only effective for 'KL', 'ED', 'Chi', 'CTS' methods.

early_stopping_eval_diff_scale: float, optional (default=1)
If train and valid uplift score diff bigger than
If train and valid uplift score diff bigger than
min(train_uplift_score,valid_uplift_score)/early_stopping_eval_diff_scale, stop.

control_name: string
Expand Down Expand Up @@ -404,7 +404,7 @@ class UpliftTreeClassifier:
self.arr_eval_func = self.arr_evaluate_ED
elif evaluationFunction == 'Chi':
self.evaluationFunction = self.evaluate_Chi
self.arr_eval_func = self.arr_evaluate_Chi
self.arr_eval_func = self.arr_evaluate_Chi
elif evaluationFunction == 'DDP':
self.evaluationFunction = self.evaluate_DDP
self.arr_eval_func = self.arr_evaluate_DDP
Expand Down Expand Up @@ -465,7 +465,7 @@ class UpliftTreeClassifier:
y_val = (y_val > 0).astype(Y_TYPE) # make sure it is 0 or 1, and is int8
treatment_val = np.asarray(treatment_val)
assert len(y_val) == len(treatment_val), 'Data length must be equal for X_val, treatment_val, and y_val.'

# Get treatment group keys. self.classes_[0] is reserved for the control group.
treatment_groups = sorted([x for x in list(set(treatment)) if x != self.control_name])
self.classes_ = [self.control_name]
Expand Down Expand Up @@ -1336,7 +1336,7 @@ class UpliftTreeClassifier:
np.ndarray[N_TYPE_t, ndim=1] right_node_summary_n):
'''
Calculate likelihood ratio test statistic as split evaluation criterion for a given node

NOTE: n_class should be 2.

Args
Expand Down Expand Up @@ -1365,7 +1365,7 @@ class UpliftTreeClassifier:
Has type numpy.int32.
The counts of each of the control
and treament groups of the right node, i.e. [N(T=i)...]

Returns
-------
lrt : Likelihood ratio test statistic
Expand Down Expand Up @@ -1422,7 +1422,7 @@ class UpliftTreeClassifier:
def evaluate_IDDP(nodeSummary):
'''
Calculate Delta P as split evaluation criterion for a given node.

Args
----
nodeSummary : dictionary
Expand All @@ -1444,7 +1444,7 @@ class UpliftTreeClassifier:
np.ndarray[N_TYPE_t, ndim=1] node_summary_n):
'''
Calculate Delta P as split evaluation criterion for a given node.

Args
----
node_summary_p : array of shape [n_class]
Expand Down Expand Up @@ -1589,7 +1589,7 @@ class UpliftTreeClassifier:
Normalization factor.
'''
cdef N_TYPE_t[::1] cur_summary_n = cur_node_summary_n
cdef N_TYPE_t[::1] left_summary_n = left_node_summary_n
cdef N_TYPE_t[::1] left_summary_n = left_node_summary_n
cdef int n_class = cur_summary_n.shape[0]
cdef int i = 0

Expand Down Expand Up @@ -1929,7 +1929,7 @@ class UpliftTreeClassifier:
cdef np.ndarray[N_TYPE_t, ndim=1] val_left_summary_n = np.zeros(self.n_class, dtype = N_TYPE)
cdef np.ndarray[P_TYPE_t, ndim=1] val_right_summary_p = np.zeros(self.n_class, dtype = P_TYPE)
cdef np.ndarray[N_TYPE_t, ndim=1] val_right_summary_n = np.zeros(self.n_class, dtype = N_TYPE)

# dummy
cdef int has_parent_summary = 0
if parentNodeSummary_p is None:
Expand Down Expand Up @@ -2107,7 +2107,7 @@ class UpliftTreeClassifier:
for k in range(n_class):
if (abs(val_left_summary_p[k] - left_summary_p[k]) >
min(val_left_summary_p[k], left_summary_p[k])/early_stopping_eval_diff_scale or
abs(val_right_summary_p[k] - right_summary_p[k]) >
abs(val_right_summary_p[k] - right_summary_p[k]) >
min(val_right_summary_p[k], right_summary_p[k])/early_stopping_eval_diff_scale):
early_stopping_flag = True
break
Expand Down Expand Up @@ -2160,13 +2160,13 @@ class UpliftTreeClassifier:
norm_factor = self.arr_normI(cur_summary_n, left_summary_n, alpha=0.9)
else:
norm_factor = 1
gain = gain / norm_factor
gain = gain / norm_factor
if (gain > bestGain and len_X_l > min_samples_leaf and len_X_r > min_samples_leaf):
bestGain = gain
bestGainImp = gain_for_imp
best_col = col
best_value = value

# after finding the best split col and value
if best_col is not None:
bestAttribute = (best_col, best_value)
Expand Down Expand Up @@ -2364,7 +2364,7 @@ class UpliftRandomForestClassifier:
child node, only effective for 'KL', 'ED', 'Chi', 'CTS' methods.

early_stopping_eval_diff_scale: float, optional (default=1)
If train and valid uplift score diff bigger than
If train and valid uplift score diff bigger than
min(train_uplift_score,valid_uplift_score)/early_stopping_eval_diff_scale, stop.

control_name: string
Expand Down Expand Up @@ -2427,6 +2427,7 @@ class UpliftRandomForestClassifier:
self.control_name = control_name
self.normalization = normalization
self.honesty = honesty
self.estimation_sample_size = estimation_sample_size
self.n_jobs = n_jobs
self.joblib_prefer = joblib_prefer

Expand Down Expand Up @@ -2477,6 +2478,7 @@ class UpliftRandomForestClassifier:
control_name=self.control_name,
normalization=self.normalization,
honesty=self.honesty,
estimation_sample_size=self.estimation_sample_size,
random_state=random_state.randint(MAX_INT))
for _ in range(self.n_estimators)
]
Expand Down Expand Up @@ -2512,7 +2514,7 @@ class UpliftRandomForestClassifier:
x_val_bt = X_val[bt_val_index]
y_val_bt = y_val[bt_val_index]
treatment_val_bt = treatment_val[bt_val_index]

tree.fit(X=x_train_bt, treatment=treatment_train_bt, y=y_train_bt, X_val=x_val_bt, treatment_val=treatment_val_bt, y_val=y_val_bt)
return tree

Expand Down