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Update doctest contents
Signed-off-by: Keith Battocchi <kebatt@microsoft.com>
1 parent 73d6b3a commit 641c1ac

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9 files changed

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-116
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‎econml/_ortho_learner.py‎

Lines changed: 10 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -190,11 +190,11 @@ def predict(self, X, y, W=None):
190190
191191
>>> nuisance
192192
(array([-1.105728... , -1.537566..., -2.451827... , ..., 1.106287...,
193-
-1.829662..., -1.782273...]),)
193+
-1.829662..., -1.782273...], shape=(5000,)),)
194194
>>> model_list
195195
[<Wrapper object at 0x...>, <Wrapper object at 0x...>]
196196
>>> fitted_inds
197-
array([ 0, 1, 2, ..., 4997, 4998, 4999])
197+
array([ 0, 1, 2, ..., 4997, 4998, 4999], shape=(5000,))
198198
199199
"""
200200
model_list = []
@@ -472,15 +472,15 @@ def _gen_ortho_learner_model_final(self):
472472
est.fit(y, X[:, 0], W=X[:, 1:])
473473
474474
>>> est.score_
475-
0.00756830...
475+
np.float64(0.00756830...)
476476
>>> est.const_marginal_effect()
477-
1.02364992...
477+
np.float64(1.02364992...)
478478
>>> est.effect()
479479
array([1.023649...])
480480
>>> est.effect(T0=0, T1=10)
481481
array([10.236499...])
482482
>>> est.score(y, X[:, 0], W=X[:, 1:])
483-
0.00727995...
483+
np.float64(0.00727995...)
484484
>>> est.ortho_learner_model_final_.model
485485
LinearRegression(fit_intercept=False)
486486
>>> est.ortho_learner_model_final_.model.coef_
@@ -530,15 +530,15 @@ def _gen_ortho_learner_model_final(self):
530530
est.fit(y, T, W=W)
531531
532532
>>> est.score_
533-
0.00673015...
533+
np.float64(0.00672978...)
534534
>>> est.const_marginal_effect()
535-
array([[1.008401...]])
535+
array([[1.008402...]])
536536
>>> est.effect()
537-
array([1.008401...])
537+
array([1.008402...])
538538
>>> est.score(y, T, W=W)
539-
0.00310431...
539+
np.float64(0.00310431...)
540540
>>> est.ortho_learner_model_final_.model.coef_[0]
541-
1.00840170...
541+
np.float64(1.00840240...)
542542
543543
Attributes
544544
----------

‎econml/dml/_rlearner.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -255,13 +255,13 @@ def _gen_rlearner_model_final(self):
255255
>>> est.effect(np.ones((1,1)), T0=0, T1=10)
256256
array([9.996314...])
257257
>>> est.score(y, X[:, 0], X=np.ones((X.shape[0], 1)), W=X[:, 1:])
258-
9.73638006...e-05
258+
np.float64(9.73638006...e-05)
259259
>>> est.rlearner_model_final_.model
260260
LinearRegression(fit_intercept=False)
261261
>>> est.rlearner_model_final_.model.coef_
262262
array([0.999631...])
263263
>>> est.score_
264-
9.82623204...e-05
264+
np.float64(9.82623204...e-05)
265265
>>> [mdl._model for mdls in est.models_y for mdl in mdls]
266266
[LinearRegression(), LinearRegression()]
267267
>>> [mdl._model for mdls in est.models_t for mdl in mdls]

‎econml/dml/causal_forest.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -558,10 +558,10 @@ class CausalForestDML(_BaseDML):
558558
est.fit(y, T, X=X, W=None)
559559
560560
>>> est.effect(X[:3])
561-
array([0.62947..., 1.64576..., 0.68496... ])
561+
array([0.62769..., 1.64575..., 0.68497...])
562562
>>> est.effect_interval(X[:3])
563-
(array([0.19136... , 1.17143..., 0.10789...]),
564-
array([1.06758..., 2.12009..., 1.26203...]))
563+
(array([0.18798..., 1.17144..., 0.10788...]),
564+
array([1.06739..., 2.12006..., 1.26205...]))
565565
566566
Attributes
567567
----------

‎econml/dml/dml.py‎

Lines changed: 17 additions & 17 deletions
Original file line numberDiff line numberDiff line change
@@ -831,19 +831,19 @@ class LinearDML(StatsModelsCateEstimatorMixin, DML):
831831
est.fit(y, T, X=X, W=None)
832832
833833
>>> est.effect(X[:3])
834-
array([0.49977..., 1.91668..., 0.70799...])
834+
array([0.49976..., 1.91673..., 0.70800...])
835835
>>> est.effect_interval(X[:3])
836-
(array([0.15122..., 1.40176..., 0.40954...]),
837-
array([0.84831..., 2.43159..., 1.00644...]))
836+
(array([0.15122..., 1.40182..., 0.40956...]),
837+
array([0.84831..., 2.43164..., 1.00645...]))
838838
>>> est.coef_
839-
array([ 0.48825..., 0.00105..., 0.00244..., 0.02217..., -0.08471...])
839+
array([ 0.48825..., 0.00104..., 0.00244..., 0.02217..., -0.08472...])
840840
>>> est.coef__interval()
841-
(array([ 0.30469..., -0.13904..., -0.12790..., -0.11514..., -0.22505... ]),
842-
array([0.67180..., 0.14116..., 0.13278..., 0.15949..., 0.05562...]))
841+
(array([ 0.30470... , -0.13905..., -0.12789..., -0.11513..., -0.22506...]),
842+
array([0.67180..., 0.14114..., 0.13278..., 0.15949..., 0.05561...]))
843843
>>> est.intercept_
844-
1.01247...
844+
1.01248...
845845
>>> est.intercept__interval()
846-
(0.87480..., 1.15015...)
846+
(0.87481..., 1.15015...)
847847
"""
848848

849849
def __init__(self, *,
@@ -1092,19 +1092,19 @@ class SparseLinearDML(DebiasedLassoCateEstimatorMixin, DML):
10921092
est.fit(y, T, X=X, W=None)
10931093
10941094
>>> est.effect(X[:3])
1095-
array([0.50083..., 1.91663..., 0.70386...])
1095+
array([0.50083..., 1.91668..., 0.70388...])
10961096
>>> est.effect_interval(X[:3])
1097-
(array([0.14616..., 1.40364..., 0.40674...]),
1098-
array([0.85550... , 2.42962... , 1.00099...]))
1097+
(array([0.14616..., 1.40370..., 0.40675...]),
1098+
array([0.85550..., 2.42966..., 1.00101...]))
10991099
>>> est.coef_
1100-
array([ 0.49123..., 0.00495..., 0.00007..., 0.02302..., -0.08483...])
1100+
array([ 0.49123..., 0.00493... , 0.00007..., 0.02302..., -0.08484...])
11011101
>>> est.coef__interval()
1102-
(array([ 0.31323..., -0.13848..., -0.13721..., -0.11141..., -0.22961...]),
1103-
array([0.66923..., 0.14839... , 0.13735..., 0.15745..., 0.05993...]))
1102+
(array([ 0.31323..., -0.13850..., -0.13720..., -0.11141..., -0.22962...]),
1103+
array([0.66923..., 0.14837..., 0.13735..., 0.15745..., 0.05992...]))
11041104
>>> est.intercept_
1105-
1.01476...
1105+
1.01477...
11061106
>>> est.intercept__interval()
1107-
(0.87620..., 1.15332...)
1107+
(0.87621..., 1.15333...)
11081108
"""
11091109

11101110
def __init__(self, *,
@@ -1350,7 +1350,7 @@ class KernelDML(DML):
13501350
est.fit(y, T, X=X, W=None)
13511351
13521352
>>> est.effect(X[:3])
1353-
array([0.63041..., 1.86098..., 0.74218...])
1353+
array([0.63042..., 1.86101..., 0.74220...])
13541354
"""
13551355

13561356
def __init__(self, model_y='auto', model_t='auto',

‎econml/dr/_drlearner.py‎

Lines changed: 21 additions & 19 deletions
Original file line numberDiff line numberDiff line change
@@ -364,18 +364,18 @@ class takes as input the parameter ``model_regressor``, which is an arbitrary sc
364364
est.fit(y, T, X=X, W=None)
365365
366366
>>> est.const_marginal_effect(X[:2])
367-
array([[0.516931..., 0.995704...],
368-
[0.356427..., 0.671870...]])
367+
array([[0.516888..., 0.995747...],
368+
[0.356386..., 0.671889...]])
369369
>>> est.effect(X[:2], T0=0, T1=1)
370-
array([0.516931..., 0.356427...])
370+
array([0.516888..., 0.356386...])
371371
>>> est.score_
372-
2.84365756...
372+
np.float64(2.845660...)
373373
>>> est.score(y, T, X=X)
374-
1.06259465...
374+
np.float64(1.062668...)
375375
>>> est.model_cate(T=1).coef_
376-
array([ 0.447095..., -0.001013... , 0.018982...])
376+
array([ 0.447146..., -0.001025..., 0.018984...])
377377
>>> est.model_cate(T=2).coef_
378-
array([ 0.925055..., -0.012357... , 0.033489...])
378+
array([ 0.925064..., -0.012351..., 0.033480...])
379379
>>> est.cate_feature_names()
380380
['X0', 'X1', 'X2']
381381
@@ -399,19 +399,19 @@ class takes as input the parameter ``model_regressor``, which is an arbitrary sc
399399
est.fit(y, T, X=X, W=None)
400400
401401
>>> est.score_
402-
1.7...
402+
np.float64(1.73...)
403403
>>> est.const_marginal_effect(X[:3])
404404
array([[0.68..., 1.10...],
405405
[0.56..., 0.79... ],
406406
[0.34..., 0.10... ]])
407407
>>> est.model_cate(T=2).coef_
408408
array([0.74..., 0. , 0. ])
409409
>>> est.model_cate(T=2).intercept_
410-
1.9...
410+
np.float64(1.9...)
411411
>>> est.model_cate(T=1).coef_
412412
array([0.24..., 0.00..., 0. ])
413413
>>> est.model_cate(T=1).intercept_
414-
0.94...
414+
np.float64(0.94...)
415415
416416
Attributes
417417
----------
@@ -1015,17 +1015,19 @@ class LinearDRLearner(StatsModelsCateEstimatorDiscreteMixin, DRLearner):
10151015
est.fit(y, T, X=X, W=None)
10161016
10171017
>>> est.effect(X[:3])
1018-
array([ 0.432476..., 0.359739..., -0.085326...])
1018+
array([ 0.432365..., 0.359694..., -0.085428...])
10191019
>>> est.effect_interval(X[:3])
1020-
(array([ 0.084145... , -0.178020..., -0.734688...]), array([0.780807..., 0.897500..., 0.564035...]))
1020+
(array([ 0.084048..., -0.177951... , -0.734747...]),
1021+
array([0.780683..., 0.897341..., 0.563889...]))
10211022
>>> est.coef_(T=1)
1022-
array([ 0.450620..., -0.008792..., 0.075242...])
1023+
array([ 0.450666..., -0.008821..., 0.075271...])
10231024
>>> est.coef__interval(T=1)
1024-
(array([ 0.156233... , -0.252177..., -0.159805...]), array([0.745007..., 0.234592..., 0.310290...]))
1025+
(array([ 0.156245..., -0.252216..., -0.159709...]),
1026+
array([0.745086..., 0.234572..., 0.310252...]))
10251027
>>> est.intercept_(T=1)
1026-
0.90916103...
1028+
np.float64(0.909121...)
10271029
>>> est.intercept__interval(T=1)
1028-
(0.66855287..., 1.14976919...)
1030+
(np.float64(0.668518...), np.float64(1.149723...))
10291031
10301032
Attributes
10311033
----------
@@ -1321,17 +1323,17 @@ class SparseLinearDRLearner(DebiasedLassoCateEstimatorDiscreteMixin, DRLearner):
13211323
est.fit(y, T, X=X, W=None)
13221324
13231325
>>> est.effect(X[:3])
1324-
array([ 0.43..., 0.35..., -0.08... ])
1326+
array([ 0.43..., 0.35..., -0.08...])
13251327
>>> est.effect_interval(X[:3])
13261328
(array([-0.01..., -0.26..., -0.81...]), array([0.87..., 0.98..., 0.65...]))
13271329
>>> est.coef_(T=1)
13281330
array([ 0.44..., -0.00..., 0.07...])
13291331
>>> est.coef__interval(T=1)
13301332
(array([ 0.19... , -0.24..., -0.17...]), array([0.70..., 0.22..., 0.32...]))
13311333
>>> est.intercept_(T=1)
1332-
0.90...
1334+
np.float64(0.90...)
13331335
>>> est.intercept__interval(T=1)
1334-
(0.66..., 1.14...)
1336+
(np.float64(0.66...), np.float64(1.14...))
13351337
13361338
Attributes
13371339
----------

‎econml/iv/dml/_dml.py‎

Lines changed: 12 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -344,19 +344,19 @@ def true_heterogeneity_function(X):
344344
est.fit(Y=y, T=T, Z=Z, X=X)
345345
346346
>>> est.effect(X[:3])
347-
array([-4.28045... , 6.02945..., -2.86851...])
347+
array([-4.28039..., 6.02945..., -2.86845...])
348348
>>> est.effect_interval(X[:3])
349-
(array([-7.20729..., 1.75412..., -5.20897...]),
350-
array([-1.35361..., 10.30478..., -0.52805...]))
349+
(array([-7.20723... , 1.75413..., -5.20891...]),
350+
array([-1.35355..., 10.30477..., -0.52799...]))
351351
>>> est.coef_
352-
array([ 4.51659..., 0.78512..., 0.23706..., 0.24126... , -0.47167...])
352+
array([ 4.51656..., 0.78512..., 0.23706..., 0.24127..., -0.47167...])
353353
>>> est.coef__interval()
354-
(array([ 3.15602..., -0.35785..., -0.89798..., -0.90530..., -1.62445...]),
355-
array([5.87715... , 1.92810... , 1.37211..., 1.38783..., 0.68110...]))
354+
(array([ 3.15599..., -0.35785..., -0.89798..., -0.90529..., -1.62444...]),
355+
array([5.87712..., 1.92810..., 1.37211..., 1.38783..., 0.68110...]))
356356
>>> est.intercept_
357-
-0.13672...
357+
-0.13668...
358358
>>> est.intercept__interval()
359-
(-1.27036..., 0.99690...)
359+
(-1.27031..., 0.99694...)
360360
"""
361361

362362
def __init__(self, *,
@@ -1143,11 +1143,11 @@ def true_heterogeneity_function(X):
11431143
est.fit(Y=y, T=T, Z=Z, X=X)
11441144
11451145
>>> est.effect(X[:3])
1146-
array([-3.83383..., 5.31902..., -2.78082...])
1146+
array([-3.83456..., 5.32010..., -2.78127...])
11471147
>>> est.coef_
1148-
array([ 4.03889..., 0.89335..., 0.12043..., 0.37958..., -0.66097...])
1148+
array([ 4.03977..., 0.89360..., 0.12047..., 0.37971..., -0.66072...])
11491149
>>> est.intercept_
1150-
-0.18482...
1150+
-0.18452...
11511151
11521152
"""
11531153

@@ -1536,7 +1536,7 @@ def true_heterogeneity_function(X):
15361536
est.fit(Y=y, T=T, Z=Z, X=X)
15371537
15381538
>>> est.effect(X[:3])
1539-
array([-5.98517..., 9.03610..., -3.56684...])
1539+
array([-5.98684..., 9.03751..., -3.56817...])
15401540
15411541
"""
15421542

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