Fix misc. errors flagged by flake8

Signed-off-by: Keith Battocchi <kebatt@microsoft.com>

econml/_ortho_learner.py

@@ -853,7 +853,7 @@ class _OrthoLearner(TreatmentExpansionMixin, LinearCateEstimator):
                                       for nuisance_mc_variants in zip(*all_nuisances))
                 else:
                     raise ValueError(
-                        "Parameter `mc_agg` must be one of {'mean', 'median'}. Got {}".format(self.mc_agg))
+                        f"Parameter `mc_agg` must be one of {{'mean', 'median'}}. Got {self.mc_agg}")
 
             Y, T, X, W, Z, sample_weight, freq_weight, sample_var = (self._subinds_check_none(arr, fitted_inds)
                                                                      for arr in (Y, T, X, W, Z, sample_weight,

econml/tests/test_cate_interpreter.py

@@ -4,6 +4,7 @@
 import numpy as np
 import unittest
 import pytest
+import matplotlib
 from econml.cate_interpreter import SingleTreeCateInterpreter, SingleTreePolicyInterpreter
 from econml.dml import LinearDML
 from sklearn.linear_model import LinearRegression, LogisticRegression
@@ -16,7 +17,6 @@ try:
 except Exception:
     graphviz_works = False
 
-import matplotlib
 matplotlib.use('Agg')
 
 

econml/tests/test_ortho_learner.py

@@ -3,7 +3,6 @@
 
 from sklearn.datasets import make_regression
 from econml._ortho_learner import _OrthoLearner, _crossfit
-from sklearn.linear_model import LinearRegression
 from sklearn.preprocessing import PolynomialFeatures
 from sklearn.linear_model import LinearRegression, LassoCV, Lasso
 from sklearn.model_selection import KFold

econml/tests/test_shap.py

@@ -4,10 +4,10 @@
 import numpy as np
 import unittest
 import shap
-from econml.dml import *
-from econml.orf import *
-from econml.dr import *
-from econml.metalearners import *
+from econml.dml import LinearDML, CausalForestDML, NonParamDML
+from econml.orf import DMLOrthoForest, DROrthoForest
+from econml.dr import DRLearner, ForestDRLearner
+from econml.metalearners import TLearner, SLearner, XLearner, DomainAdaptationLearner
 from sklearn.linear_model import LinearRegression, LogisticRegression, Lasso
 from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
 from sklearn.preprocessing import PolynomialFeatures

econml/tests/test_statsmodels.py

@@ -416,7 +416,7 @@ class TestStatsModels(unittest.TestCase):
                 "{}, {}".format(est.coef__interval()[1][t],
                                 np.array([scipy.stats.norm.ppf(.975, loc=1, scale=1)] +
                                          [scipy.stats.norm.ppf(.975, loc=0, scale=1)] * (d - 1)))
-            assert np.all(np.abs(est.intercept_[t]) <= 1e-12), "{}, {}".format(est.intercept_[t])
+            assert np.all(np.abs(est.intercept_[t]) <= 1e-12), "{}".format(est.intercept_[t])
             assert np.all(np.abs(est.intercept_stderr_[t]) <= 1e-12), "{}".format(est.intercept_stderr_[t])
             assert np.all(np.abs(est.intercept__interval()[0][t]) <=
                           1e-12), "{}".format(est.intercept__interval()[0][t])
@@ -446,7 +446,7 @@ class TestStatsModels(unittest.TestCase):
                 "{}, {}".format(est.coef__interval()[1][t],
                                 np.array([scipy.stats.norm.ppf(.975, loc=1, scale=np.sqrt(2))] +
                                          [scipy.stats.norm.ppf(.975, loc=0, scale=np.sqrt(2))] * (d - 1)))
-            assert np.all(np.abs(est.intercept_[t]) <= 1e-12), "{}, {}".format(est.intercept_[t])
+            assert np.all(np.abs(est.intercept_[t]) <= 1e-12), "{}".format(est.intercept_[t])
             assert np.all(np.abs(est.intercept_stderr_[t] - 1) <= 1e-12), "{}".format(est.intercept_stderr_[t])
             assert np.all(np.abs(est.intercept__interval()[0][t] -
                                  scipy.stats.norm.ppf(.025, loc=0, scale=1)) <= 1e-12), \

monte_carlo_tests/monte_carlo_statsmodels.py

@@ -14,8 +14,6 @@ import argparse
 import warnings
 import joblib
 from sklearn.model_selection import GridSearchCV
-from statsmodels.tools.tools import add_constant
-from econml.utilities import cross_product
 from sklearn.multioutput import MultiOutputRegressor
 
 
@@ -251,7 +249,7 @@ def run_all_mc(first_stage, folder, n_list, n_exp, hetero_coef_list, d_list,
                                     (hetero_coef * X[:, [0]] + 1) * np.random.normal(0, 1, size=(n, p))
 
                                 XT = np.hstack([X, T])
-                                X1, X2, y1, y2, X_final_first, X_final_sec, y_sum_first, y_sum_sec,\
+                                X1, X2, y1, y2, X_final_first, X_final_sec, y_sum_first, y_sum_sec, \
                                     n_sum_first, n_sum_sec, var_first, var_sec = _summarize(XT, y)
                                 X = np.vstack([X1, X2])
                                 y = np.concatenate((y1, y2))
@@ -420,7 +418,8 @@ def monte_carlo_gcv(folder='gcv'):
                                                        min_samples_leaf=10, random_state=123),
                                  MultiOutputRegressor(GradientBoostingRegressor(n_estimators=20,
                                                                                 max_depth=3,
-                                                                                min_samples_leaf=10, random_state=123))],
+                                                                                min_samples_leaf=10,
+                                                                                random_state=123))],
                                 param_grid_list=[{},
                                                  {},
                                                  {},