Improve pylint

.pylintrc

@@ -63,66 +63,14 @@ confidence=
 # --enable=similarities". If you want to run only the classes checker, but have
 # no Warning level messages displayed, use "--disable=all --enable=classes
 # --disable=W".
-disable=old-raise-syntax,
-        import-star-module-level,
-        raw-checker-failed,
+disable=raw-checker-failed,
         bad-inline-option,
         locally-disabled,
         file-ignored,
         suppressed-message,
         useless-suppression,
         deprecated-pragma,
         use-symbolic-message-instead,
-        apply-builtin,
-        basestring-builtin,
-        buffer-builtin,
-        cmp-builtin,
-        coerce-builtin,
-        execfile-builtin,
-        file-builtin,
-        long-builtin,
-        raw_input-builtin,
-        reduce-builtin,
-        standarderror-builtin,
-        unicode-builtin,
-        xrange-builtin,
-        coerce-method,
-        no-absolute-import,
-        old-division,
-        dict-iter-method,
-        dict-view-method,
-        next-method-called,
-        metaclass-assignment,
-        indexing-exception,
-        reload-builtin,
-        nonzero-method,
-        cmp-method,
-        input-builtin,
-        round-builtin,
-        intern-builtin,
-        unichr-builtin,
-        map-builtin-not-iterating,
-        zip-builtin-not-iterating,
-        range-builtin-not-iterating,
-        filter-builtin-not-iterating,
-        using-cmp-argument,
-        exception-message-attribute,
-        invalid-str-codec,
-        sys-max-int,
-        bad-python3-import,
-        deprecated-string-function,
-        deprecated-str-translate-call,
-        deprecated-itertools-function,
-        deprecated-types-field,
-        next-method-defined,
-        dict-items-not-iterating,
-        dict-keys-not-iterating,
-        dict-values-not-iterating,
-        deprecated-operator-function,
-        deprecated-urllib-function,
-        xreadlines-attribute,
-        exception-escape,
-        comprehension-escape,
         import-outside-toplevel,
         bare-except
 

bayeso/bo/bo_w_gp.py

@@ -509,9 +509,12 @@ def optimize(
         time_end_surrogate = time.time()
 
         time_start_acq = time.time()
-        fun_negative_acquisition = lambda X_test: -1.0 * self.compute_acquisitions(
-            X_test, X_train, Y_train, cov_X_X, inv_cov_X_X, hyps
-        )
+
+        def fun_negative_acquisition(X_test):
+            return -1.0 * self.compute_acquisitions(
+                X_test, X_train, Y_train, cov_X_X, inv_cov_X_X, hyps
+            )
+
         next_point, next_points = self._optimize(
             fun_negative_acquisition,
             str_sampling_method=str_sampling_method,

bayeso/gp/gp_kernel.py

@@ -91,28 +91,32 @@ def get_optimized_kernel(
         use_gradient = False
 
     if str_modelselection_method == "ml":
-        neg_log_ml_ = lambda hyps: gp_likelihood.neg_log_ml(
-            X_train,
-            Y_train,
-            hyps,
-            str_cov,
-            prior_mu_train,
-            use_ard=use_ard,
-            fix_noise=fix_noise,
-            use_gradient=use_gradient,
-            debug=debug,
-        )
+        def neg_log_ml_(hyps):
+            return gp_likelihood.neg_log_ml(
+                X_train,
+                Y_train,
+                hyps,
+                str_cov,
+                prior_mu_train,
+                use_ard=use_ard,
+                fix_noise=fix_noise,
+                use_gradient=use_gradient,
+                debug=debug,
+            )
+
     elif str_modelselection_method == "loocv":
         # TODO: add use_ard.
-        neg_log_ml_ = lambda hyps: gp_likelihood.neg_log_pseudo_l_loocv(
-            X_train,
-            Y_train,
-            hyps,
-            str_cov,
-            prior_mu_train,
-            fix_noise=fix_noise,
-            debug=debug,
-        )
+        def neg_log_ml_(hyps):
+            return gp_likelihood.neg_log_pseudo_l_loocv(
+                X_train,
+                Y_train,
+                hyps,
+                str_cov,
+                prior_mu_train,
+                fix_noise=fix_noise,
+                debug=debug,
+            )
+
         use_gradient = False
     else:  # pragma: no cover
         raise ValueError(

bayeso/tp/tp_kernel.py

@@ -83,17 +83,18 @@ def get_optimized_kernel(
         num_dim = X_train.shape[2]
         use_gradient = False
 
-    neg_log_ml_ = lambda hyps: tp_likelihood.neg_log_ml(
-        X_train,
-        Y_train,
-        hyps,
-        str_cov,
-        prior_mu_train,
-        fix_noise=fix_noise,
-        use_gradient=use_gradient,
-        use_ard=use_ard,
-        debug=debug,
-    )
+    def neg_log_ml_(hyps):
+        return tp_likelihood.neg_log_ml(
+            X_train,
+            Y_train,
+            hyps,
+            str_cov,
+            prior_mu_train,
+            fix_noise=fix_noise,
+            use_gradient=use_gradient,
+            use_ard=use_ard,
+            debug=debug,
+        )
 
     hyps_converted = utils_covariance.convert_hyps(
         str_cov,