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Finish the scipy-doctest migration by dropping numeric ellipses #1051

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26 changes: 13 additions & 13 deletions econml/_ortho_learner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -192,8 +192,8 @@ def predict(self, X, y, W=None):
X, y,W=y, Z=None)

>>> nuisance
(array([-1.105728... , -1.537566..., -2.451827... , ..., 1.106287...,
-1.829662..., -1.782273...], shape=(5000,)),)
(array([-1.1057289 , -1.53756637, -2.4518278 , ..., 1.10628792,
-1.82966233, -1.78227335], shape=(5000,)),)
>>> model_list
[<Wrapper object at 0x...>, <Wrapper object at 0x...>]
>>> fitted_inds
Expand Down Expand Up @@ -475,19 +475,19 @@ def _gen_ortho_learner_model_final(self):
est.fit(y, X[:, 0], W=X[:, 1:])

>>> est.score_
np.float64(0.00756830...)
np.float64(0.00756830)
>>> est.const_marginal_effect()
np.float64(1.02364992...)
np.float64(1.02364992)
>>> est.effect()
array([1.023649...])
array([1.023649])
>>> est.effect(T0=0, T1=10)
array([10.236499...])
array([10.236499])
>>> est.score(y, X[:, 0], W=X[:, 1:])
np.float64(0.00727995...)
np.float64(0.00727995)
>>> est.ortho_learner_model_final_.model
LinearRegression(fit_intercept=False)
>>> est.ortho_learner_model_final_.model.coef_
array([1.023649...])
array([1.023649])

The following example shows how to do double machine learning with discrete treatments, using
the _OrthoLearner:
Expand Down Expand Up @@ -533,15 +533,15 @@ def _gen_ortho_learner_model_final(self):
est.fit(y, T, W=W)

>>> est.score_
np.float64(0.00672978...)
np.float64(0.00672978)
>>> est.const_marginal_effect()
array([[1.008402...]])
array([[1.008402]])
>>> est.effect()
array([1.008402...])
array([1.008402])
>>> est.score(y, T, W=W)
np.float64(0.00310431...)
np.float64(0.00310431)
>>> est.ortho_learner_model_final_.model.coef_[0]
np.float64(1.00840240...)
np.float64(1.00840240)

Attributes
----------
Expand Down
10 changes: 5 additions & 5 deletions econml/dml/_rlearner.py
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Expand Up @@ -332,17 +332,17 @@ def _gen_rlearner_model_final(self):
est.fit(y, X[:, 0], X=np.ones((X.shape[0], 1)), W=X[:, 1:])

>>> est.const_marginal_effect(np.ones((1,1)))
array([0.999631...])
array([0.999631])
>>> est.effect(np.ones((1,1)), T0=0, T1=10)
array([9.996314...])
array([9.996314])
>>> est.score(y, X[:, 0], X=np.ones((X.shape[0], 1)), W=X[:, 1:])
9.73638006...e-05
9.73638006e-05
>>> est.rlearner_model_final_.model
LinearRegression(fit_intercept=False)
>>> est.rlearner_model_final_.model.coef_
array([0.999631...])
array([0.999631])
>>> est.score_
9.82623204...e-05
9.82623204e-05
>>> [mdl._model for mdls in est.models_y for mdl in mdls]
[LinearRegression(), LinearRegression()]
>>> [mdl._model for mdls in est.models_t for mdl in mdls]
Expand Down
6 changes: 3 additions & 3 deletions econml/dml/causal_forest.py
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Expand Up @@ -561,10 +561,10 @@ class CausalForestDML(_BaseDML):
est.fit(y, T, X=X, W=None)

>>> est.effect(X[:3])
array([0.62769..., 1.64575..., 0.68497...])
array([0.62769, 1.64575, 0.68497])
>>> est.effect_interval(X[:3])
(array([0.18798..., 1.17144..., 0.10788...]),
array([1.06739..., 2.12006..., 1.26205...]))
(array([0.18798, 1.17144, 0.10788]),
array([1.06739, 2.12006, 1.26205]))

Attributes
----------
Expand Down
52 changes: 26 additions & 26 deletions econml/dml/dml.py
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Original file line number Diff line number Diff line change
Expand Up @@ -500,19 +500,19 @@ class takes as input the parameter `model_t`, which is an arbitrary scikit-learn
est.fit(y, T, X=X, W=None)

>>> est.effect(X[:3])
array([0.63382..., 1.78225..., 0.71859...])
array([0.63382, 1.78225, 0.71859])
>>> est.effect_interval(X[:3])
(array([0.27937..., 1.27619..., 0.42091...]),
array([0.98827... , 2.28831..., 1.01628...]))
(array([0.27937, 1.27619, 0.42091]),
array([0.98827 , 2.28831, 1.01628]))
>>> est.coef_
array([ 0.42857..., 0.04488..., -0.03317..., 0.02258..., -0.14875...])
array([ 0.42857, 0.04488, -0.03317, 0.02258, -0.14875])
>>> est.coef__interval()
(array([ 0.25179..., -0.10558..., -0.16723... , -0.11916..., -0.28759...]),
array([ 0.60535..., 0.19536..., 0.10088..., 0.16434..., -0.00990...]))
(array([ 0.25179, -0.10558, -0.16723 , -0.11916, -0.28759]),
array([ 0.60535, 0.19536, 0.10088, 0.16434, -0.00990]))
>>> est.intercept_
1.01166...
1.01166
>>> est.intercept__interval()
(0.87125..., 1.15207...)
(0.87125, 1.15207)
"""

def __init__(self, *,
Expand Down Expand Up @@ -854,19 +854,19 @@ class LinearDML(StatsModelsCateEstimatorMixin, DML):
est.fit(y, T, X=X, W=None)

>>> est.effect(X[:3])
array([0.49976..., 1.91673..., 0.70800...])
array([0.49976, 1.91673, 0.70800])
>>> est.effect_interval(X[:3])
(array([0.15122..., 1.40182..., 0.40956...]),
array([0.84831..., 2.43164..., 1.00645...]))
(array([0.15122, 1.40182, 0.40956]),
array([0.84831, 2.43164, 1.00645]))
>>> est.coef_
array([ 0.48825..., 0.00104..., 0.00244..., 0.02217..., -0.08472...])
array([ 0.48825, 0.00104, 0.00244, 0.02217, -0.08472])
>>> est.coef__interval()
(array([ 0.30470... , -0.13905..., -0.12789..., -0.11513..., -0.22506...]),
array([0.67180..., 0.14114..., 0.13278..., 0.15949..., 0.05561...]))
(array([ 0.30470 , -0.13905, -0.12789, -0.11513, -0.22506]),
array([0.67180, 0.14114, 0.13278, 0.15949, 0.05561]))
>>> est.intercept_
1.01248...
1.01248
>>> est.intercept__interval()
(0.87481..., 1.15015...)
(0.87481, 1.15015)
"""

def __init__(self, *,
Expand Down Expand Up @@ -1115,19 +1115,19 @@ class SparseLinearDML(DebiasedLassoCateEstimatorMixin, DML):
est.fit(y, T, X=X, W=None)

>>> est.effect(X[:3])
array([0.50083..., 1.91668..., 0.70388...])
array([0.50083, 1.91668, 0.70388])
>>> est.effect_interval(X[:3])
(array([0.14616..., 1.40370..., 0.40675...]),
array([0.85550..., 2.42966..., 1.00101...]))
(array([0.14616, 1.40370, 0.40675]),
array([0.85550, 2.42966, 1.00101]))
>>> est.coef_
array([ 0.49123..., 0.00493... , 0.00007..., 0.02302..., -0.08484...])
array([ 0.49123452, 0.0049377 , 0.00007303, 0.02302108, -0.08484665])
>>> est.coef__interval()
(array([ 0.31323..., -0.13850..., -0.13720..., -0.11141..., -0.22962...]),
array([0.66923..., 0.14837..., 0.13735..., 0.15745..., 0.05992...]))
(array([ 0.31323, -0.13850, -0.13720, -0.11141, -0.22962]),
array([0.66923, 0.14837, 0.13735, 0.15745, 0.05992]))
>>> est.intercept_
1.01477...
1.01477
>>> est.intercept__interval()
(0.87621..., 1.15333...)
(0.87621, 1.15333)
"""

def __init__(self, *,
Expand Down Expand Up @@ -1373,7 +1373,7 @@ class KernelDML(DML):
est.fit(y, T, X=X, W=None)

>>> est.effect(X[:3])
array([0.63042..., 1.86101..., 0.74220...])
array([0.63042, 1.86101, 0.74220])
"""

def __init__(self, model_y='auto', model_t='auto',
Expand Down Expand Up @@ -1597,7 +1597,7 @@ class NonParamDML(_BaseDML):
est.fit(y, T, X=X, W=None)

>>> est.effect(X[:3])
array([0.35318..., 1.28760..., 0.83506...])
array([0.35318, 1.28760, 0.83506])
"""

def __init__(self, *,
Expand Down
58 changes: 29 additions & 29 deletions econml/dr/_drlearner.py
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Expand Up @@ -520,18 +520,18 @@ class takes as input the parameter ``model_regressor``, which is an arbitrary sc
est.fit(y, T, X=X, W=None)

>>> est.const_marginal_effect(X[:2])
array([[0.516888..., 0.995747...],
[0.356386..., 0.671889...]])
array([[0.516888, 0.995747],
[0.356386, 0.671889]])
>>> est.effect(X[:2], T0=0, T1=1)
array([0.516888..., 0.356386...])
array([0.516888, 0.356386])
>>> est.score_
np.float64(2.845660...)
np.float64(2.845660)
>>> est.score(y, T, X=X)
np.float64(1.062668...)
np.float64(1.062668)
>>> est.model_cate(T=1).coef_
array([ 0.447146..., -0.001025..., 0.018984...])
array([ 0.447146, -0.001025, 0.018984])
>>> est.model_cate(T=2).coef_
array([ 0.925064..., -0.012351..., 0.033480...])
array([ 0.925064, -0.012351, 0.033480])
>>> est.cate_feature_names()
['X0', 'X1', 'X2']

Expand All @@ -555,19 +555,19 @@ class takes as input the parameter ``model_regressor``, which is an arbitrary sc
est.fit(y, T, X=X, W=None)

>>> est.score_
np.float64(1.73...)
np.float64(1.73)
>>> est.const_marginal_effect(X[:3])
array([[0.68..., 1.10...],
[0.56..., 0.79... ],
[0.34..., 0.10... ]])
array([[0.68151771, 1.10936267],
[0.56679957, 0.7941794 ],
[0.34171272, 0.1045798 ]])
>>> est.model_cate(T=2).coef_
array([0.74..., 0. , 0. ])
array([0.74925162, 0. , 0. ])
>>> est.model_cate(T=2).intercept_
np.float64(1.9...)
np.float64(1.9227731541272008)
>>> est.model_cate(T=1).coef_
array([0.24..., 0.00..., 0. ])
array([0.24572861, 0.00720134, 0. ])
>>> est.model_cate(T=1).intercept_
np.float64(0.94...)
np.float64(0.94)

Attributes
----------
Expand Down Expand Up @@ -1245,19 +1245,19 @@ class LinearDRLearner(StatsModelsCateEstimatorDiscreteMixin, DRLearner):
est.fit(y, T, X=X, W=None)

>>> est.effect(X[:3])
array([ 0.432365..., 0.359694..., -0.085428...])
array([ 0.432365, 0.359694, -0.085428])
>>> est.effect_interval(X[:3])
(array([ 0.084048..., -0.177951... , -0.734747...]),
array([0.780683..., 0.897341..., 0.563889...]))
(array([ 0.084048, -0.177951 , -0.734747]),
array([0.780683, 0.897341, 0.563889]))
>>> est.coef_(T=1)
array([ 0.450666..., -0.008821..., 0.075271...])
array([ 0.450666, -0.008821, 0.075271])
>>> est.coef__interval(T=1)
(array([ 0.156245..., -0.252216..., -0.159709...]),
array([0.745086..., 0.234572..., 0.310252...]))
(array([ 0.156245, -0.252216, -0.159709]),
array([0.745086, 0.234572, 0.310252]))
>>> est.intercept_(T=1)
np.float64(0.909121...)
np.float64(0.909121)
>>> est.intercept__interval(T=1)
(np.float64(0.668518...), np.float64(1.149723...))
(np.float64(0.668518), np.float64(1.149723))

Attributes
----------
Expand Down Expand Up @@ -1582,17 +1582,17 @@ class SparseLinearDRLearner(DebiasedLassoCateEstimatorDiscreteMixin, DRLearner):
est.fit(y, T, X=X, W=None)

>>> est.effect(X[:3])
array([ 0.43..., 0.35..., -0.08...])
array([ 0.43296409, 0.35952902, -0.08460643])
>>> est.effect_interval(X[:3])
(array([-0.01..., -0.26..., -0.81...]), array([0.87..., 0.98..., 0.65...]))
(array([-0.01363264, -0.26369244, -0.81995536]), array([0.87956083, 0.98275048, 0.65074251]))
>>> est.coef_(T=1)
array([ 0.44..., -0.00..., 0.07...])
array([ 0.44991911, -0.00881137, 0.07449517])
>>> est.coef__interval(T=1)
(array([ 0.19... , -0.24..., -0.17...]), array([0.70..., 0.22..., 0.32...]))
(array([ 0.1972161 , -0.24730627, -0.17104381]), array([0.70262212, 0.22968353, 0.32003415]))
>>> est.intercept_(T=1)
np.float64(0.90...)
np.float64(0.9091174878444385)
>>> est.intercept__interval(T=1)
(np.float64(0.66...), np.float64(1.14...))
(np.float64(0.6687723462464192), np.float64(1.1494626294424575))

Attributes
----------
Expand Down
6 changes: 3 additions & 3 deletions econml/grf/classes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -949,7 +949,7 @@ class RegressionForest(BaseGRF):

np.set_printoptions(suppress=True)
np.random.seed(123)
X, y = make_regression(n_samples=1000, n_features=4, n_informative=2,
X, y = make_regression(n_samples=1000, n_features=4, n_informative=4,
random_state=0, shuffle=False)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5)
regr = RegressionForest(max_depth=None, random_state=0,
Expand All @@ -958,9 +958,9 @@ class RegressionForest(BaseGRF):
>>> regr.fit(X_train, y_train)
RegressionForest(n_estimators=1000, random_state=0)
>>> regr.feature_importances_
array([0.88..., 0.11..., 0.00..., 0.00...])
array([0.2869952 , 0.03651571, 0.59767877, 0.07881031])
>>> regr.predict(np.ones((1, 4)), interval=True, alpha=.05)
(array([[121.0...]]), array([[103.6...]]), array([[138.3...]]))
(array([[194.5075591]]), array([[159.10510002]]), array([[229.91001818]]))

References
----------
Expand Down
24 changes: 12 additions & 12 deletions econml/iv/dml/_dml.py
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Expand Up @@ -344,19 +344,19 @@ def true_heterogeneity_function(X):
est.fit(Y=y, T=T, Z=Z, X=X)

>>> est.effect(X[:3])
array([-4.28039..., 6.02945..., -2.86845...])
array([-4.28039, 6.02945, -2.86845])
>>> est.effect_interval(X[:3])
(array([-7.20723... , 1.75413..., -5.20891...]),
array([-1.35355..., 10.30477..., -0.52799...]))
(array([-7.20723 , 1.75413, -5.20891]),
array([-1.35355, 10.30477, -0.52799]))
>>> est.coef_
array([ 4.51656..., 0.78512..., 0.23706..., 0.24127..., -0.47167...])
array([ 4.51656, 0.78512, 0.23706, 0.24127, -0.47167])
>>> est.coef__interval()
(array([ 3.15599..., -0.35785..., -0.89798..., -0.90529..., -1.62444...]),
array([5.87712..., 1.92810..., 1.37211..., 1.38783..., 0.68110...]))
(array([ 3.15599, -0.35785, -0.89798, -0.90529, -1.62444]),
array([5.87712, 1.92810, 1.37211, 1.38783, 0.68110]))
>>> est.intercept_
-0.13668...
-0.13668
>>> est.intercept__interval()
(-1.27031..., 0.99694...)
(-1.27031, 0.99694)
"""

def __init__(self, *,
Expand Down Expand Up @@ -1143,11 +1143,11 @@ def true_heterogeneity_function(X):
est.fit(Y=y, T=T, Z=Z, X=X)

>>> est.effect(X[:3])
array([-3.83456..., 5.32010..., -2.78127...])
array([-3.83456, 5.32010, -2.78127])
>>> est.coef_
array([ 4.03977..., 0.89360..., 0.12047..., 0.37971..., -0.66072...])
array([ 4.03977, 0.89360, 0.12047, 0.37971, -0.66072])
>>> est.intercept_
-0.18452...
-0.18452

"""

Expand Down Expand Up @@ -1536,7 +1536,7 @@ def true_heterogeneity_function(X):
est.fit(Y=y, T=T, Z=Z, X=X)

>>> est.effect(X[:3])
array([-5.98684..., 9.03751..., -3.56817...])
array([-5.98684, 9.03751, -3.56817])

"""

Expand Down
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