Implement linear extrapolation

pyproject.toml

@@ -231,6 +231,7 @@ extend-ignore = [
 "explanations/*" = ["INP001", "B018", "T201", "E402", "PD008"]
 "scripts/*" = ["INP001", "D101", "RET503"]
 "**/*.ipynb" = ["FBT003", "E402", "D101"]
+"src/lcm/ndimage.py" = ["A002"]  # Argument `input` is shadowing a Python builtin
 
 [tool.ruff.lint.pydocstyle]
 convention = "google"

src/lcm/entry_point.py

@@ -15,7 +15,6 @@
     get_utility_and_feasibility_function,
 )
 from lcm.next_state import get_next_state_function
-from lcm.options import DefaultMapCoordinatesOptions
 from lcm.simulate import simulate
 from lcm.solve_brute import solve
 from lcm.state_space import create_state_choice_space
@@ -29,7 +28,6 @@ def get_lcm_function(
     *,
     debug_mode: bool = True,
     jit: bool = True,
-    interpolation_options: dict | None = None,
 ) -> tuple[Callable, ParamsDict]:
     """Entry point for users to get high level functions generated by lcm.
 
@@ -51,8 +49,6 @@ def get_lcm_function(
             "solve_and_simulate" are supported.
         debug_mode: Whether to log debug messages.
         jit: Whether to jit the returned function.
-        interpolation_options: Dictionary of keyword arguments for interpolation
-            via map_coordinates. If None, the default options are used.
 
     Returns:
         - A function that takes params (and possibly other arguments, such as initial
@@ -68,7 +64,6 @@ def get_lcm_function(
 
     _mod = process_model(model)
     last_period = _mod.n_periods - 1
-    interpolation_options = DefaultMapCoordinatesOptions | (interpolation_options or {})
 
     logger = get_logger(debug_mode)
 
@@ -133,7 +128,6 @@ def get_lcm_function(
             model=_mod,
             space_info=space_infos[period],
             name_of_values_on_grid="vf_arr",
-            interpolation_options=interpolation_options,
             period=period,
             is_last_period=is_last_period,
         )

src/lcm/function_representation.py

@@ -1,23 +1,20 @@
 from collections.abc import Callable
-from functools import partial
 
 import jax.numpy as jnp
 from dags import concatenate_functions
 from dags.signature import with_signature
 from jax import Array
-from jax.scipy.ndimage import map_coordinates
 
 from lcm.functools import all_as_kwargs
 from lcm.grids import ContinuousGrid
 from lcm.interfaces import SpaceInfo
-from lcm.typing import MapCoordinatesOptions
+from lcm.ndimage import map_coordinates
 
 
 def get_function_representation(
     space_info: SpaceInfo,
     name_of_values_on_grid: str,
     *,
-    interpolation_options: MapCoordinatesOptions,
     input_prefix: str = "",
 ) -> Callable[..., Array]:
     """Create a function representation of pre-calculated values on a grid.
@@ -61,9 +58,6 @@ def get_function_representation(
             into the resulting function. In the value function case, this could be
             'vf_arr', in which case, one would partial in 'vf_arr' into the
             representation.
-        interpolation_options: Dictionary of interpolation options that will be passed
-            to jax.scipy.ndimage.map_coordinates. If None, DefaultMapCoordinatesOptions
-            will be used.
         input_prefix: Prefix that will be added to all argument names of the resulting
             function, except for the helpers arguments such as indexers or value arrays.
             Default is the empty string. The prefix needs to contain the separator. E.g.
@@ -134,7 +128,6 @@ def get_function_representation(
         funcs["__fval__"] = _get_interpolator(
             name_of_values_on_grid="__interpolation_data__",
             axis_names=_interpolation_axes,
-            map_coordinates_options=interpolation_options,
         )
 
     return concatenate_functions(
@@ -228,7 +221,6 @@ def find_coordinate(*args, **kwargs):
 def _get_interpolator(
     name_of_values_on_grid: str,
     axis_names: list[str],
-    map_coordinates_options: MapCoordinatesOptions,
 ) -> Callable[..., Array]:
     """Create a function interpolator via named axes.
 
@@ -237,22 +229,18 @@ def _get_interpolator(
             values, that have been evaluated on a grid, will be passed into the
             resulting function.
         axis_names: Names of the axes in the data array.
-        map_coordinates_options: Dictionary of interpolation options that will be passed
-            to jax.scipy.ndimage.map_coordinates.
 
     Returns:
         callable: A callable that interpolates a function via named axes.
 
     """
-    partialled_map_coordinates = partial(map_coordinates, **map_coordinates_options)
-
     arg_names = [name_of_values_on_grid, *axis_names]
 
     @with_signature(args=arg_names)
     def interpolate(*args, **kwargs):
         kwargs = all_as_kwargs(args, kwargs, arg_names=arg_names)
         coordinates = jnp.array([kwargs[var] for var in axis_names])
-        return partialled_map_coordinates(
+        return map_coordinates(
             input=kwargs[name_of_values_on_grid],
             coordinates=coordinates,
         )

src/lcm/model_functions.py

@@ -19,7 +19,6 @@ def get_utility_and_feasibility_function(
     model: InternalModel,
     space_info,
     name_of_values_on_grid,
-    interpolation_options,
     period,
     is_last_period,
 ):
@@ -45,7 +44,6 @@ def get_utility_and_feasibility_function(
         scalar_value_function = get_function_representation(
             space_info=space_info,
             name_of_values_on_grid=name_of_values_on_grid,
-            interpolation_options=interpolation_options,
             input_prefix="next_",
         )
 

src/lcm/ndimage.py

@@ -0,0 +1,94 @@
+# Copyright 2019 The JAX Authors.
+#
+# 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.
+#
+# Modifications made by Tim Mensinger, 2024
+
+import functools
+import itertools
+import operator
+from collections.abc import Sequence
+
+import jax.numpy as jnp
+from jax import Array, lax, util
+
+
+def map_coordinates(
+    input: Array,
+    coordinates: Sequence[Array],
+) -> Array:
+    """Map the input array to new coordinates using linear interpolation.
+
+    Modified from JAX implementation of :func:`scipy.ndimage.map_coordinates`.
+
+    Given an input array and a set of coordinates, this function returns the
+    interpolated values of the input array at those coordinates. For coordinates outside
+    the input array, linear extrapolation is used.
+
+    Args:
+      input: N-dimensional input array from which values are interpolated.
+      coordinates: length-N sequence of arrays specifying the coordinates
+        at which to evaluate the interpolated values
+
+    Returns:
+      The interpolated (extrapolated) values at the specified coordinates.
+
+    """
+    if len(coordinates) != input.ndim:
+        raise ValueError(
+            "coordinates must be a sequence of length input.ndim, but "
+            f"{len(coordinates)} != {input.ndim}"
+        )
+
+    interpolation_data = [
+        _compute_indices_and_weights(coordinate, size)
+        for coordinate, size in util.safe_zip(coordinates, input.shape)
+    ]
+
+    interpolation_values = []
+    for indices_and_weights in itertools.product(*interpolation_data):
+        indices, weights = util.unzip2(indices_and_weights)
+        contribution = input[indices]
+        weighted_value = _multiply_all(weights) * contribution
+        interpolation_values.append(weighted_value)
+
+    result = _sum_all(interpolation_values)
+
+    if jnp.issubdtype(input.dtype, jnp.integer):
+        result = _round_half_away_from_zero(result)
+
+    return result.astype(input.dtype)
+
+
+def _compute_indices_and_weights(
+    coordinate: Array, input_size: int
+) -> list[tuple[Array, Array]]:
+    """Compute indices and weights for linear interpolation."""
+    lower_index = jnp.clip(jnp.floor(coordinate), 0, input_size - 2).astype(jnp.int32)
+    upper_weight = coordinate - lower_index
+    lower_weight = 1 - upper_weight
+    return [(lower_index, lower_weight), (lower_index + 1, upper_weight)]
+
+
+def _multiply_all(arrs: Sequence[Array]) -> Array:
+    """Multiply all arrays in the sequence."""
+    return functools.reduce(operator.mul, arrs)
+
+
+def _sum_all(arrs: Sequence[Array]) -> Array:
+    """Sum all arrays in the sequence."""
+    return functools.reduce(operator.add, arrs)
+
+
+def _round_half_away_from_zero(a: Array) -> Array:
+    return a if jnp.issubdtype(a.dtype, jnp.integer) else lax.round(a)

src/lcm/typing.py

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Any, Literal, TypedDict
+from typing import Any, TypedDict
 
 from jax import Array
 
@@ -31,31 +31,3 @@ class SegmentInfo(TypedDict):
 
     segment_ids: Array
     num_segments: int
-
-
-class MapCoordinatesOptions(TypedDict):
-    """Options passed to  `jax.scipy.ndimage.map_coordinates`.
-
-    From the JAX documentation (as of 2024-06-16):
-
-    - "order": The order of interpolation. JAX supports the following:
-      - 0: Nearest-neighbor
-      - 1: Linear
-
-    - "mode": Points outside the boundaries of the input are filled according to the
-      given mode. JAX supports one of ('constant', 'nearest', 'mirror', 'wrap',
-      'reflect'). Note the 'wrap' mode in JAX behaves as 'grid-wrap' mode in SciPy, and
-      'constant' mode in JAX behaves as 'grid-constant' mode in SciPy. This discrepancy
-      was caused by a former bug in those modes in SciPy (scipy/scipy#2640), which was
-      first fixed in JAX by changing the behavior of the existing modes, and later on
-      fixed in SciPy, by adding modes with new names, rather than fixing the existing
-      ones, for backwards compatibility reasons.
-
-    - "cval": Value used for points outside the boundaries of the input if
-      mode='constant'.
-
-    """
-
-    order: Literal[0, 1]
-    mode: Literal["constant", "nearest", "mirror", "wrap", "reflect"]
-    cval: Scalar

tests/test_entry_point.py

@@ -193,7 +193,6 @@ def test_create_compute_conditional_continuation_value():
         model=model,
         space_info=space_info,
         name_of_values_on_grid="vf_arr",
-        interpolation_options={},
         period=model.n_periods - 1,
         is_last_period=True,
     )
@@ -242,7 +241,6 @@ def test_create_compute_conditional_continuation_value_with_discrete_model():
         model=model,
         space_info=space_info,
         name_of_values_on_grid="vf_arr",
-        interpolation_options={},
         period=model.n_periods - 1,
         is_last_period=True,
     )
@@ -296,7 +294,6 @@ def test_create_compute_conditional_continuation_policy():
         model=model,
         space_info=space_info,
         name_of_values_on_grid="vf_arr",
-        interpolation_options={},
         period=model.n_periods - 1,
         is_last_period=True,
     )
@@ -346,7 +343,6 @@ def test_create_compute_conditional_continuation_policy_with_discrete_model():
         model=model,
         space_info=space_info,
         name_of_values_on_grid="vf_arr",
-        interpolation_options={},
         period=model.n_periods - 1,
         is_last_period=True,
     )

tests/test_function_representation.py

@@ -18,7 +18,6 @@
     IndexerInfo,
     SpaceInfo,
 )
-from lcm.options import DefaultMapCoordinatesOptions
 
 
 def test_function_evaluator_with_one_continuous_variable():
@@ -40,7 +39,6 @@ def test_function_evaluator_with_one_continuous_variable():
         space_info=space_info,
         name_of_values_on_grid="vf_arr",
         input_prefix="next_",
-        interpolation_options=DefaultMapCoordinatesOptions,
     )
 
     # partial the function values into the evaluator
@@ -67,7 +65,6 @@ def test_function_evaluator_with_one_discrete_variable():
         space_info=space_info,
         name_of_values_on_grid="vf_arr",
         input_prefix="next_",
-        interpolation_options=DefaultMapCoordinatesOptions,
     )
 
     # partial the function values into the evaluator
@@ -139,7 +136,6 @@ def test_function_evaluator():
     evaluator = get_function_representation(
         space_info=space_info,
         name_of_values_on_grid="vf_arr",
-        interpolation_options=DefaultMapCoordinatesOptions,
     )
 
     # test the evaluator
@@ -217,7 +213,6 @@ def test_function_evaluator_longer_indexer():
     evaluator = get_function_representation(
         space_info=space_info,
         name_of_values_on_grid="vf_arr",
-        interpolation_options=DefaultMapCoordinatesOptions,
     )
 
     # test the evaluator
@@ -282,7 +277,6 @@ def test_get_interpolator():
     interpolate = _get_interpolator(
         name_of_values_on_grid="vf",
         axis_names=["wealth", "working"],
-        map_coordinates_options=DefaultMapCoordinatesOptions,
     )
 
     def _utility(wealth, working):
@@ -325,7 +319,6 @@ def test_get_function_evaluator_illustrative():
         space_info=space_info,
         name_of_values_on_grid="values_name",
         input_prefix="prefix_",
-        interpolation_options=DefaultMapCoordinatesOptions,
     )
 
     # partial the function values into the evaluator
@@ -364,7 +357,6 @@ def test_get_interpolator_illustrative():
     interpolate = _get_interpolator(
         name_of_values_on_grid="test_name",
         axis_names=["a", "b"],
-        map_coordinates_options=DefaultMapCoordinatesOptions,
     )
 
     def f(a, b):