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Avoiding uses of np.float because that has been removed from the latest numpy. #521

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Mar 20, 2024
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Avoiding uses of np.float because that has been removed from the latest numpy. #521

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55e9509
Avoiding uses of np.float because that has been removed from the late…
Mar 17, 2024
96b1e1f
Better yet to use float instead of np.float64.
Mar 17, 2024
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8 changes: 4 additions & 4 deletions aif360/algorithms/inprocessing/kamfadm-2012ecmlpkdd/fadm/lr/pr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -213,15 +213,15 @@ def init_coef(self, itype, X, y, s):
if itype == 0:
# clear by zeros
self.coef_ = np.zeros(self.n_sfv_ * self.n_features_,
dtype=np.float)
dtype=float)
elif itype == 1:
# at random
self.coef_ = np.random.randn(self.n_sfv_ * self.n_features_)

elif itype == 2:
# learned by standard LR
self.coef_ = np.empty(self.n_sfv_ * self.n_features_,
dtype=np.float)
dtype=float)
coef = self.coef_.reshape(self.n_sfv_, self.n_features_)

clr = LogisticRegression(C=self.C, penalty='l2',
Expand All @@ -232,7 +232,7 @@ def init_coef(self, itype, X, y, s):
elif itype == 3:
# learned by standard LR
self.coef_ = np.empty(self.n_sfv_ * self.n_features_,
dtype=np.float)
dtype=float)
coef = self.coef_.reshape(self.n_sfv_, self.n_features_)

for i in range(self.n_sfv_):
Expand Down Expand Up @@ -276,7 +276,7 @@ def fit(self, X, y, ns=N_S, itype=0, **kwargs):
# set instance variables
self.n_s_ = ns
self.n_sfv_ = np.max(s) + 1
self.c_s_ = np.array([np.sum(s == si).astype(np.float)
self.c_s_ = np.array([np.sum(s == si).astype(float)
for si in range(self.n_sfv_)])
self.n_features_ = X.shape[1]
self.n_samples_ = X.shape[0]
Expand Down
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