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Optimizing NormalInferenceResults confidence interval method speed #879

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merged 9 commits into from
May 7, 2024
Merged

Optimizing NormalInferenceResults confidence interval method speed #879

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f613453
Fixed normal inference results confidence interval unnecessary loop
gdaiha Apr 30, 2024
449cbe4
Improved conf_int_mean method from population summary results
gdaiha May 2, 2024
df484a4
Fixed stat_norm_ppf references in linear_model.py
gdaiha May 2, 2024
81f4515
Fixed linting claims in _inference.py
gdaiha May 2, 2024
d3bc269
Fixed linting claims in linear_model.py
gdaiha May 2, 2024
2c8185e
Fixed line too long linting claim at _inference.py
gdaiha May 3, 2024
3896387
Fixed line too long linting claim at linear_model.py
gdaiha May 3, 2024
ce2ad24
removed trailing whitespaces
gdaiha May 3, 2024
efeb09b
Merge branch 'main' into fix-conf-int
kbattocchi May 4, 2024
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17 changes: 3 additions & 14 deletions econml/inference/_inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1066,14 +1066,9 @@ def conf_int(self, alpha=0.05):
"""
if self.stderr is None:
raise AttributeError("Only point estimates are available!")
if np.isscalar(self.point_estimate):
else:
return _safe_norm_ppf(alpha / 2, loc=self.point_estimate, scale=self.stderr), \
_safe_norm_ppf(1 - alpha / 2, loc=self.point_estimate, scale=self.stderr)
else:
return np.array([_safe_norm_ppf(alpha / 2, loc=p, scale=err)
for p, err in zip(self.point_estimate, self.stderr)]), \
np.array([_safe_norm_ppf(1 - alpha / 2, loc=p, scale=err)
for p, err in zip(self.point_estimate, self.stderr)])

def pvalue(self, value=0):
"""
Expand Down Expand Up @@ -1398,14 +1393,8 @@ def conf_int_mean(self, *, alpha=None):
alpha = self.alpha if alpha is None else alpha
mean_point = self.mean_point
stderr_mean = self.stderr_mean
if np.isscalar(mean_point):
return (_safe_norm_ppf(alpha / 2, loc=mean_point, scale=stderr_mean),
_safe_norm_ppf(1 - alpha / 2, loc=mean_point, scale=stderr_mean))
else:
return np.array([_safe_norm_ppf(alpha / 2, loc=p, scale=err)
for p, err in zip(mean_point, stderr_mean)]), \
np.array([_safe_norm_ppf(1 - alpha / 2, loc=p, scale=err)
for p, err in zip(mean_point, stderr_mean)])
return (_safe_norm_ppf(alpha / 2, loc=mean_point, scale=stderr_mean),
_safe_norm_ppf(1 - alpha / 2, loc=mean_point, scale=stderr_mean))

@property
def std_point(self):
Expand Down
26 changes: 10 additions & 16 deletions econml/sklearn_extensions/linear_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1627,10 +1627,8 @@ def coef__interval(self, alpha=0.05):
coef__interval : {tuple ((p, d) array, (p,d) array), tuple ((d,) array, (d,) array)}
The lower and upper bounds of the confidence interval of the coefficients
"""
return np.array([_safe_norm_ppf(alpha / 2, loc=p, scale=err)
for p, err in zip(self.coef_, self.coef_stderr_)]), \
np.array([_safe_norm_ppf(1 - alpha / 2, loc=p, scale=err)
for p, err in zip(self.coef_, self.coef_stderr_)])
return (_safe_norm_ppf(alpha / 2, loc=self.coef_, scale=self.coef_stderr_),
_safe_norm_ppf(1 - alpha / 2, loc=self.coef_, scale=self.coef_stderr_))

def intercept__interval(self, alpha=0.05):
"""
Expand All @@ -1651,14 +1649,8 @@ def intercept__interval(self, alpha=0.05):
return (0 if self._n_out == 0 else np.zeros(self._n_out)), \
(0 if self._n_out == 0 else np.zeros(self._n_out))

if self._n_out == 0:
return _safe_norm_ppf(alpha / 2, loc=self.intercept_, scale=self.intercept_stderr_), \
_safe_norm_ppf(1 - alpha / 2, loc=self.intercept_, scale=self.intercept_stderr_)
else:
return np.array([_safe_norm_ppf(alpha / 2, loc=p, scale=err)
for p, err in zip(self.intercept_, self.intercept_stderr_)]), \
np.array([_safe_norm_ppf(1 - alpha / 2, loc=p, scale=err)
for p, err in zip(self.intercept_, self.intercept_stderr_)])
return (_safe_norm_ppf(alpha / 2, loc=self.intercept_, scale=self.intercept_stderr_),
_safe_norm_ppf(1 - alpha / 2, loc=self.intercept_, scale=self.intercept_stderr_))

def predict_interval(self, X, alpha=0.05):
"""
Expand All @@ -1677,10 +1669,12 @@ def predict_interval(self, X, alpha=0.05):
prediction_intervals : {tuple ((n,) array, (n,) array), tuple ((n,p) array, (n,p) array)}
The lower and upper bounds of the confidence intervals of the predicted mean outcomes
"""
return np.array([_safe_norm_ppf(alpha / 2, loc=p, scale=err)
for p, err in zip(self.predict(X), self.prediction_stderr(X))]), \
np.array([_safe_norm_ppf(1 - alpha / 2, loc=p, scale=err)
for p, err in zip(self.predict(X), self.prediction_stderr(X))])

pred = self.predict(X)
pred_stderr = self.prediction_stderr(X)

return (_safe_norm_ppf(alpha / 2, loc=pred, scale=pred_stderr),
_safe_norm_ppf(1 - alpha / 2, loc=pred, scale=pred_stderr))


class StatsModelsLinearRegression(_StatsModelsWrapper):
Expand Down
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