Deal with scipy unceremoniously removing wrap_function (#184)

* Deal with scipy unceremoniously removing `wrap_function`

Change-Id: I9072119de859a36bfa19ccf0e3e75674090d7244

* License

Change-Id: I09bc51a8f5b05e474cc201574807c8a112669bc0

* Move to own file, docstring

Change-Id: I6b0c01943089170177ed133eafc310de4328ef70

recirq/optimize/_util.py

@@ -0,0 +1,64 @@
+# Copyright 2021 Google
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# `wrap_function` is re-used under the following license:
+
+# Copyright (c) 2001-2002 Enthought, Inc.  2003-2019, SciPy Developers.
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+# 1. Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above
+#    copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided
+#    with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+def wrap_function(function, args):
+    """Count the number of times a function is called and close around `args`.
+
+    This function existed in scipy prior to 1.7
+    It must not have been considered part of the public API, as it has
+    disappeared without a trace.
+    """
+    ncalls = [0]
+    if function is None:
+        return ncalls, None
+
+    def function_wrapper(*wrapper_args):
+        ncalls[0] += 1
+        return function(*(wrapper_args + args))
+
+    return ncalls, function_wrapper

recirq/optimize/mgd.py

@@ -16,12 +16,13 @@
 
 import numpy as np
 import scipy
-from scipy.optimize.optimize import wrap_function
 from scipy.optimize import OptimizeResult
 from sklearn.linear_model import LinearRegression
 from sklearn.preprocessing import PolynomialFeatures
 from sklearn.pipeline import Pipeline
 
+from recirq.optimize._util import wrap_function
+
 
 def _get_least_squares_model_gradient(
         xs: List[np.ndarray],

recirq/optimize/mpg.py

@@ -18,13 +18,14 @@
 import numpy as np
 import scipy
 from scipy.optimize import OptimizeResult
-from scipy.optimize.optimize import wrap_function
 from sklearn.linear_model import LinearRegression
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import PolynomialFeatures
 
 from cirq import value
 
+from recirq.optimize._util import wrap_function
+
 if TYPE_CHECKING:
     import cirq
 
@@ -98,6 +99,7 @@ def value(self, t):
 
         return self.learning_rate * self.decay_rate ** m
 
+
 def _adam_update(
     grad: np.ndarray,
     x: np.ndarray,
@@ -167,7 +169,6 @@ def model_policy_gradient(
     known_values: Optional[Tuple[List[np.ndarray], List[float]]] = None,
     max_evaluations: Optional[int] = None
 ) -> scipy.optimize.OptimizeResult:
-
     """Model policy gradient algorithm for black-box optimization.
 
     The idea of this algorithm is to perform policy gradient, but estimate