Merge pull request #13 from NREL/ndr/phev-multiple-target

Multi-energy target for PHEV

.github/workflows/test.yaml

@@ -37,7 +37,7 @@ jobs:
           pip install ".[dev]"
 
       - name: Run mypy
-        run: mypy .
+        run: mypy . 
 
       - name: Run black
         run: |

nrel/routee/powertrain/core/powertrain_type.py

@@ -9,6 +9,8 @@ class PowertrainType(Enum):
     ICE = 1
     HEV = 2
     BEV = 3
+    PHEV_EV_MODE = 4
+    PHEV_HEV_MODE = 5
 
     @classmethod
     def from_string(cls, s: Optional[str]) -> PowertrainType:

nrel/routee/powertrain/core/real_world_adjustments.py

@@ -7,4 +7,6 @@
     PowertrainType.ICE: 1.166,
     PowertrainType.HEV: 1.1252,
     PowertrainType.BEV: 1.3958,
+    PowertrainType.PHEV_EV_MODE: 1.3958,
+    PowertrainType.PHEV_HEV_MODE: 1.1252,
 }

nrel/routee/powertrain/resources/default_models/external_model_links.json

@@ -4,19 +4,19 @@
     "2016_AUDI_A3_4cyl_2WD": "https://app.box.com/shared/static/msdkke3xtdpknmu6a26j08ol8vxiw0hu.json",
     "2016_BMW_328d_4cyl_2WD": "https://app.box.com/shared/static/1710cj655y0t5rtclh1ogo0eqt8subr7.json",
     "2016_BMW_i3_REx_PHEV_Charge_Depleting": "https://app.box.com/shared/static/oe6tbw2mlx35ak3m39ms6anxaeulheph.json",
-    "2016_BMW_i3_REx_PHEV_Charge_Sustaining": "https://app.box.com/shared/static/wjrmdj9ec6vb9mhtovsqe5bhi67ot4h9.json",
+    "2016_BMW_i3_REx_PHEV_Charge_Sustaining": "https://app.box.com/shared/static/tzhyb99ch98s3wofwld6vhu4ylbfuq8j.json",
     "2016_CHEVROLET_Malibu_4cyl_2WD": "https://app.box.com/shared/static/hi0k3dsnliru8bf3ho4tygnz7o2bbc39.json",
     "2016_CHEVROLET_Spark_EV": "https://app.box.com/shared/static/p3w6sifsaf924hm4xr0fcnyvq5f735sb.json",
     "2016_CHEVROLET_Volt_Charge_Depleting": "https://app.box.com/shared/static/refg47ekep5anyq41cvd5e347w5bglv4.json",
-    "2016_CHEVROLET_Volt_Charge_Sustaining": "https://app.box.com/shared/static/gtduyd1vhfm55lk3je18zsjb5e75dxcb.json",
+    "2016_CHEVROLET_Volt_Charge_Sustaining": "https://app.box.com/shared/static/v90zippsq2gbenzevk6tktjh20skm5rb.json",
     "2016_FORD_C-MAX_(PHEV)_Charge_Depleting": "https://app.box.com/shared/static/8xzw1joi4z75146wosob55z0v4t56s0t.json",
-    "2016_FORD_C-MAX_(PHEV)_Charge_Sustaining": "https://app.box.com/shared/static/5e2tbg1lxhbb8xpgg9xpjee8po0vuile.json",
+    "2016_FORD_C-MAX_(PHEV)_Charge_Sustaining": "https://app.box.com/shared/static/ejgyl2ye0j0d88p99t8tjs7xqpmfute9.json",
     "2016_FORD_C-MAX_HEV": "https://app.box.com/shared/static/1g0ut3e8dux98wupucrqnlhcrqb4pg2q.json",
     "2016_FORD_Escape_4cyl_2WD": "https://app.box.com/shared/static/co9prrqzfqk4lj8860zwpah1n6lmethz.json",
     "2016_FORD_Explorer_4cyl_2WD": "https://app.box.com/shared/static/odlr0di4avsyk09sgt98oifuekemkluw.json",
     "2016_HYUNDAI_Elantra_4cyl_2WD": "https://app.box.com/shared/static/gou1vtt6ii5npr65o834iz1xsn1wxx1h.json",
     "2016_HYUNDAI_Sonata_PHEV_Charge_Depleting": "https://app.box.com/shared/static/s802i81gce7zgue9bqksx7qhupbuq30i.json",
-    "2016_HYUNDAI_Sonata_PHEV_Charge_Sustaining": "https://app.box.com/shared/static/4clk59eyyxebg4jcrok5xhyfbykzjee4.json",
+    "2016_HYUNDAI_Sonata_PHEV_Charge_Sustaining": "https://app.box.com/shared/static/m7cskbeppcug6ekwx0dpt7btvza01d8m.json",
     "2016_KIA_Optima_Hybrid": "https://app.box.com/shared/static/iavjn7x93umljvtmuwtbplsbxd1nrjbm.json",
     "2016_Leaf_24_kWh": "https://app.box.com/shared/static/gl7ap1czp6cihsil29wwaktindqyfzk4.json",
     "2016_MITSUBISHI_i-MiEV": "https://app.box.com/shared/static/93khsx238l3b3emq36ui8yj0rnmf737w.json",
@@ -29,7 +29,7 @@
     "2017_CHEVROLET_Bolt": "https://app.box.com/shared/static/6yg9ed4gixrp7q5irze2w4ud5h3nthme.json",
     "2017_Maruti_Dzire_VDI": "https://app.box.com/shared/static/luyzs74knk7yowjbuer5zb88getfstjx.json",
     "2017_Prius_Prime_Charge_Depleting": "https://app.box.com/shared/static/5m6mnxtg0bpb8to19570nikcxa7mryn3.json",
-    "2017_Prius_Prime_Charge_Sustaining": "https://app.box.com/shared/static/84yx66loytq79of7w528m68ehibs16ze.json",
+    "2017_Prius_Prime_Charge_Sustaining": "https://app.box.com/shared/static/cxtts7pj9hi2n8om2xcx2d1i0rpsa6pw.json",
     "2017_Toyota_Highlander_3": "https://app.box.com/shared/static/ihmhqsy2myfceh25xhhld8jhv995np2d.json",
     "2020_Chevrolet_Colorado_2WD_Diesel": "https://app.box.com/shared/static/i969w1bc6p1uvvubtxtiugvnh56ib1dq.json",
     "2020_VW_Golf_1": "https://app.box.com/shared/static/u3ya0kc5ehol7qcv7ecov6992839cn9g.json",

nrel/routee/powertrain/trainers/sklearn_random_forest.py

@@ -1,6 +1,6 @@
 from enum import Enum
 import pandas as pd
-from skl2onnx import to_onnx
+from skl2onnx import convert_sklearn
 from skl2onnx.common.data_types import FloatTensorType
 from sklearn.ensemble import RandomForestRegressor
 
@@ -55,10 +55,19 @@ def inner_train(
         if self.output_type == RandomForestTrainerOutput.ONNX:
             # convert to ONNX
             n_features = len(features.columns)
+            n_targets = len(target.columns)
+
+            # explicity specify the output shape since skl2onnx was not able to infer it
+            def custom_transform_shape_calculator(operator):
+                operator.outputs[0].type = FloatTensorType([None, n_targets])
+
             initial_type = [(ONNX_INPUT_NAME, FloatTensorType([None, n_features]))]
-            onnx_model = to_onnx(
+            onnx_model = convert_sklearn(
                 rf,
                 initial_types=initial_type,
+                custom_shape_calculators={
+                    rf.__class__: custom_transform_shape_calculator
+                },
             )
 
             estimator = ONNXEstimator(onnx_model)

nrel/routee/powertrain/validation/errors.py

@@ -28,22 +28,18 @@
 }
 
 
-def mean_squared_error(
-    A: np.ndarray, B: np.ndarray, axis: Optional[int] = None
-) -> float:
+def mean_squared_error(A, B, axis: Optional[int] = None) -> float:
     return np.square(A - B).mean(axis=axis)
 
 
-def net_energy_error(target: np.ndarray, target_pred: np.ndarray) -> float:
+def net_energy_error(target, target_pred) -> float:
     net_e = np.sum(target)
     net_e_pred = np.sum(target_pred)
     net_error = (net_e_pred - net_e) / net_e
     return net_error
 
 
-def weighted_relative_percent_difference(
-    target: np.ndarray, target_pred: np.ndarray
-) -> float:
+def weighted_relative_percent_difference(target, target_pred) -> float:
     epsilon = np.finfo(np.float64).eps
 
     w = np.array(np.abs(target) / np.sum(np.abs(target)))
@@ -61,7 +57,7 @@ def weighted_relative_percent_difference(
     return mean_error
 
 
-def relative_percent_difference(target: np.ndarray, target_pred: np.ndarray) -> float:
+def relative_percent_difference(target, target_pred) -> float:
     epsilon = np.finfo(np.float64).eps
 
     error_norm = np.abs(

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "nrel.routee.powertrain"
-version = "1.0.2"
+version = "1.0.3"
 description = "RouteE-Powertrain is a tool for predicting energy usage over a set of road links."
 readme = "README.md"
 authors = [{ name = "National Renewable Energy Laboratory" }]
@@ -39,7 +39,8 @@ dev = [
     "types-protobuf",
     "types-python-dateutil",
     "types-redis",
-    "types-requests"
+    "types-requests",
+    "pandas-stubs",
 ]
 
 [project.urls]
@@ -56,3 +57,9 @@ include = [
 
 [tool.ruff.per-file-ignores]
 "__init__.py" = ["F401"]
+
+[tool.mypy]
+ignore_missing_imports = true
+namespace_packages = true
+explicit_package_bases = true
+exclude = ["docs/", "build/", "dist/"]

scripts/developers/train_model_catalog.py

@@ -312,25 +312,25 @@ def load_all_files(files, file_limit=FILE_LIMIT):
     pt.DataColumn(name="grade_dec", units="decimal"),
     pt.DataColumn(name="entry_angle", units="degrees"),
 ]
-features = [ 
+features = [
     [pt.DataColumn(name="speed_mph", units="mph")],
     [
         pt.DataColumn(name="speed_mph", units="mph"),
-        pt.DataColumn(name="grade_dec", units="decimal")
+        pt.DataColumn(name="grade_dec", units="decimal"),
     ],
     [
         pt.DataColumn(name="speed_mph", units="mph"),
         pt.DataColumn(name="previous_speed_mph", units="mph"),
         pt.DataColumn(name="grade_dec", units="decimal"),
-        pt.DataColumn(name="previous_grade_dec", units="decimal")
+        pt.DataColumn(name="previous_grade_dec", units="decimal"),
     ],
     [
         pt.DataColumn(name="previous_speed_mph", units="mph"),
         pt.DataColumn(name="speed_mph", units="mph"),
         pt.DataColumn(name="previous_grade_dec", units="decimal"),
         pt.DataColumn(name="grade_dec", units="decimal"),
         pt.DataColumn(name="entry_angle", units="degrees"),
-        pt.DataColumn(name="exit_angle", units="degrees")
+        pt.DataColumn(name="exit_angle", units="degrees"),
     ],
 ]