add multidimensional full interaction detection

R/src/Makevars

@@ -35,6 +35,7 @@ OBJECTS = \
    $(NATIVEDIR)/PartitionOneDimensionalBoosting.o \
    $(NATIVEDIR)/PartitionRandomBoosting.o \
    $(NATIVEDIR)/PartitionMultiDimensionalCorner.o \
+   $(NATIVEDIR)/PartitionMultiDimensionalFull.o \
    $(NATIVEDIR)/PartitionMultiDimensionalTree.o \
    $(NATIVEDIR)/PartitionMultiDimensionalStraight.o \
    $(NATIVEDIR)/Purify.o \

R/src/Makevars.interpret

@@ -35,6 +35,7 @@ OBJECTS = \
    $(NATIVEDIR)/PartitionOneDimensionalBoosting.o \
    $(NATIVEDIR)/PartitionRandomBoosting.o \
    $(NATIVEDIR)/PartitionMultiDimensionalCorner.o \
+   $(NATIVEDIR)/PartitionMultiDimensionalFull.o \
    $(NATIVEDIR)/PartitionMultiDimensionalTree.o \
    $(NATIVEDIR)/PartitionMultiDimensionalStraight.o \
    $(NATIVEDIR)/Purify.o \

README.md

@@ -658,6 +658,7 @@ We also build on top of many great packages. Please check them out!
 - [Proxy endpoints - bridging clinical trials and real world data](https://pdf.sciencedirectassets.com/272371/1-s2.0-S1532046424X00064/1-s2.0-S1532046424001412/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEIn%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIBYgAN6aOVrDnvQ1932tPndUyJ0Dm1nHdMVLiekPVduQAiAzbYe7W%2Bd6Dj8ee42ZeZnQxJwEjEjuGdiUEPx0a2G43SqyBQgSEAUaDDA1OTAwMzU0Njg2NSIMyMkCUNFeDTCUCppMKo8FiVShykb8phR%2F8aWUGE9gfnE5y7X3Jj1ZA2CVldH13T67s536bdTBhjIMF18rV0YP9iMi6B5aGr%2F286ovIJl332fxZ6iQNBIOPTm8kXQDUqvZbknYldiZqUPs69kuC%2FcKnJd1BWnv2SEZwbRuX94rWnRDPDaSoJx%2FVS6o4qsbFjp9%2BMYZr%2BvJzWHKrXAI4W%2Fh9%2BsIa0yvlac3IMWzAeD23HzDNmF0nqjJ6BSZzmDNW4HRIGBTrTUTO40TzQzhaOY7wyGA0Zv8SpWIULI%2FrY8z8EOX%2FU6OhqgyIMKv%2FSx3rUpMi5CrC1WcpnL97j%2FDAijNi4vMfG1b%2BBQIFRu2EmUky76k4w3FYxkCpYj4n4mk9H%2B%2Bc9C%2BdjKjUiayi%2FisIZUD7ISNhQ9oov0kXI1IVTCGKKQC9jqHOvdiA8YbVuMdEzy1Lkx%2B1kiEo79qvSlpTe2BtWAOm2Iequ01XoaMv%2FQb4ajhWKKSkTafzDAxc58aayP1YH49UzQ68Me7ecdHpx3JUHyYnxJGQ82wRpPkfZJA5wCmOUVI%2FBLuwFJyczG0LpALN5IpIqZz%2B8DvDR0xjRoN49dVwhrTSQ9BesvXbi2LKVm1ptacaaKqyx0PwLjQYKOd%2BPI3zCvRxEiM3IKSNFRLsUTyPNEE4E8pMFNxfyEX59yvTQrHwM62P7hvxHs%2BY6CxUGZTKBQwDAgxttJmiO%2BvjCRbTBXZg1WrQdXCkxntBXb15Mnqxo4lyPzUUkLdLAFK%2BLSwzBIcvSw2qG81Y8qhWmBgBT9vfAoSrjxsILFrB3nnz7u9XNNpRxb5Z9NuNG92%2Fpd%2F%2F5VespMY8Q0iwsNqazZ4M4H8UB34JgtrUEY27WrIsDWzLR%2FAYAxU%2BZHrFzCrsae5BjqyASqDBsNqjEkho%2FbuQDT%2F0vGx%2BgAqrksvVX0GrzNgvqnuPyvw6%2F%2B40ZJP5EA4axfltOYb2tNjd18Ngy2A3cd6J57v1G7wYyuSFIUfHGN5LA8BXK7p0x1mNcwN3pKHtAf260gjpsWMG7anvpK%2F3YupTz498C1lAmurJD%2BLN41lq05wBr403cchE41yzqAKHVKVpNq9s6oGHJmq0KJRvk%2FfjZr8oLhod5gtrwLKvLGqULf50L0%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241105T092058Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYUKUJCDYI%2F20241105%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=15f8e40964e2c750ae15e43aa8e7f7c76eef6a76b792e41434d14bed42b31432&hash=d4a3e49b29443e5eea9e5a44c0dc11b3f30b21addbe6d6d20d523c68db23cd23&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S1532046424001412&tid=spdf-4fbabbd8-becb-4526-98d3-c7517914e457&sid=8ab2a095350fc74edc4b8765ecd8c0260edcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=0f165f0b050207505b0151&rr=8ddbc55a0d60a380&cc=us)
 - [Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentations](https://onlinelibrary.wiley.com/doi/epdf/10.1111/inm.13402)
 - [Towards Cleaner Cities: Estimating Vehicle-Induced PM2.5 with Hybrid EBM-CMA-ES Modeling](https://www.mdpi.com/2305-6304/12/11/827)
+- [Predicting Robotic Hysterectomy Incision Time: Optimizing Surgical Scheduling with Machine Learning](https://pmc.ncbi.nlm.nih.gov/articles/PMC11741200/pdf/e2024.00040.pdf)
 - [Using machine learning to assist decision making in the assessment of mental health patients presenting to emergency departments](https://journals.sagepub.com/doi/full/10.1177/20552076241287364)
 - [Proposing an inherently interpretable machine learning model for shear strength prediction of reinforced concrete beams with stirrups](https://pdf.sciencedirectassets.com/287527/1-s2.0-S2214509523X00035/1-s2.0-S2214509524005011/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjECUaCXVzLWVhc3QtMSJGMEQCIB0r0KsYBZufOjbCVtUtozwn1QKMdLt2tbbfhuJKjWlXAiB5Dfr7p0yyj%2FSfypTLmjPL8WbjGAB3tRACFjyyqQbbfiq8BQiu%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAUaDDA1OTAwMzU0Njg2NSIMqBpZ2HmN91c%2BJPqpKpAFZtvqQjCScZa4FN%2FeubsPzOk5c%2B58LliO4Zr%2Bn1pm3vtW4I9I1vA29pkhT5was1N3ccPPIm2jNLwJ%2FHiZej7A2SmFv13Ro3sTvhqG%2F6A9Xx70Nx9jOlDPJUmCypKadKp0FGfuhZQuxeN0b%2F1QUUQZG4RpxC%2FXorRRHmb%2FrXcOWBwu4PmLZAkWmTKpncjDI7oj8eh8yBe6%2FA3JkJ14ZyBgR7JnPzR2ZqMdIhvlKoyMn6EnL1Azq2y3qwEMdzSCvz3wH3sT4pClc2vPs6ruQS4CdT3E7BHrf42Q0VnUXWjuy7gt9iRr0vaWR3tD%2FxyrrEKw7XuMHO9L4rQ4Pfn1dhGZ2J8H5ocwJGSh13U5fY6noyaTNViqvHx1oHNMWL03QpkJxmUxYquBWepcDjxEc32V6eGF7Ecm8Vij3s20wdRNcHqxGFKlUCgph48CKUA79iwSGQCkWQh7bq%2FTtowTbSPud7l8xeG1MvfIVy%2B6yzrjqygvPBQs3qkvdoWUrKXe57bhr2jEkKlSdYyp2TJMD6yoYRdTPyFx5xb0KgIt6KQTPmfbqYXkd3FFz3uc0HmWC5NQz6qP9UzNcBhcK8dXo3Dw042pl0HLO1njFaa%2BBfbT89VUVUIqjrAcmHweIl1v7Eyldzr%2BGBXIlsxPO3gPzyPLF2LTggc6dA%2Bswxmgmkv%2B7n5pU5%2F5sxvEhemb%2Fqu%2B8d47O%2Bn6RH8fL4eLGGL2d0dvFvyE7gEwt%2BaU9HsIN0IHqyH5VmaTF5zaKy%2Fn%2BhkF8yGpe5Hq5yNOUGrfQgfyFn4Kqd%2FTVajxIFzk8DEY%2F%2FFtyGJ%2B8BrHV4P%2FYs8R4XcBzPQtyrTuUC1CGmF01Tc2gnnEo4pVPaIjfBk9B%2BXVMc3Mu4Ywy4L%2BsgY6sgFK3hFIXjIfoVjqrIlBvsGYaFiZB1bVKBVy3DRiBgozzYmIVhipN%2FS%2BPok1oETqvYVvLqEVkGcb5W7nUIK16lFgjwDq6ePuxdqSafgOw5jVQroNsDCPRz8B%2F4fg7kv6gs4R9SX7gCaQ2V7L6NxqJDUUqsCMtIYq05Qx43dGByqLoVEz9USpRBmTLQwpGvOmUaGNNwTsCwmt5gRP8UX3CnkwI%2FydxmhrXLEdaUIFVwJbIor9&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240604T221639Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY4E2DAHPF%2F20240604%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=eece32da8855b55208baecc0ce041e79aa03be1c292b58c67ce0215de36cbdb4&hash=46dd1da122f4cea242c6444a811fb16dde5cb8465e88552ac3eaeee97b975e9b&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S2214509524005011&tid=spdf-45c1c4d1-dd97-4c0d-a04f-c30843a79e78&sid=1fea53ed2d5cf1443e4a7c4-33f4bf6475e1gxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=0f155c5f060d565b01055d&rr=88eb49dd2a5f7688&cc=us)
 - [A hybrid machine learning approach for predicting fiber-reinforced polymer-concrete interface bond strength](https://download.ssrn.com/eaai/e646e179-ec4a-4987-80b5-8d6bbf43ceda-meca.pdf?response-content-disposition=inline&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBMaCXVzLWVhc3QtMSJHMEUCIFVH%2Ba5TT2NOEqgCl7GMhXBXBZWE9VzzcRFT6kYXzdxYAiEA4yvXsrzNQnNq%2BkJRB0rw1d2p35f418pIO%2FT3PHKoZ%2BoqxgUI%2B%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARAEGgwzMDg0NzUzMDEyNTciDCD0kCrKAqamcwb9LCqaBb4zlqjDhNBhf%2Frbe%2FX3lzSjvS58HiJQtbOHmzaM7putg93e7Wk8nPesoiupTH8uB5ejDC7stGJElRZp5ulT5M6CokoMu82ERn15kMpkgptj3MVEmsY9VTCP%2BCbROJ6v4YcAttOOAEzOc2M6li6o0w4IsF8DNXEIJr%2FJvjB3IDYPkrmpIiHl25h3AzfxPuOF01E2rgucLnY0xTyKGnPBBDZ%2FPtcuqlk2NKun3Q9HbcKj8EPJP%2FPupMW3IQvMnhcdJqqLHXs6wL1P42NTw5vtZO2W5WiEC1CNGDFUTSFRdb9hjhpH4JsYl8X%2BSFT6mZ31K2HTWeuigs5nXp1JN8r8r4O021yiVxHAJ6Chnddr0Z19iM5yOZA4H1EhO1rxxL0VF%2F%2F8Ac3GxuEfkBiug5wuL7aNlBNX6720pYfHH%2FgyrqdU5KSDIp8VYw3KgEij0LkizBHQIoolC48VAEMNc%2F8iWOdZpAVYprhEbABbff8%2BW6c4y1N9vmLTkjZkJtZODpzpQVjrHkL9hAOvmXZocEEN6maRoVJx3DlcTHrfQr8%2BQnPQnmajb5x0FHo44xxBIUt7UB4FOc6beDprle%2F7BO2SNEPLw6rJ9e3WJeVaYch46iqk2tiWFroNHDXlQ73CbzV59AEVtLAR29eIf7uyz%2BU0fOAXG5oAsJyB7YXUjH%2Bh79sxJgBq3%2FoqkEja06CFPRhWeqxixc8y9bEU%2FvvjhfbcWcxGY%2Be%2FwnXbemUbSyr26Y5xvADyicKIMexZNjeHBJ9MKMifQ9oh%2FjmudjxtMLbTpA6EAxMelLjhWcoURF0XeTttMEzEuTjO1OXUwMeXSPZ9roJqH3DB4PHi%2B8UIUG1JoVocv7wDu5ZVlMzgmDr0ti1BShKr9szxagq34jCEkJe8BjqxAbm7bsef33J3AImECx0GZeL0R2tFJZ7ctogL261zP7RqJ4T71rDMbpyfX6HfGuNEbWVROKHUexpuH8FZBodmn%2FjDjZSviK1oxQ1L5TDA2rwMsodnThreIad8vSXqxAzx9qng%2BeN2llXkNdIB7WEnkttzcJ24pZqwYnPI%2FsOznTq%2BDJ88mdNPtzph%2FGdVQcR99tV3waapotTEnUjjoqTTSh9aMgi1jIYMGMrJj6Jb4N%2FhWA%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20250114T024208Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAUPUUPRWEXKDDLJZE%2F20250114%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=11c6f325f84736d5324ab155663c94231696de52be8910a03bb5e9c18f0d1689&abstractId=5055231)
@@ -715,6 +716,7 @@ We also build on top of many great packages. Please check them out!
 - [Improving Neural Additive Models with Bayesian Principles](https://arxiv.org/pdf/2305.16905.pdf)
 - [NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning](https://arxiv.org/pdf/2106.01613.pdf)
 - [Scalable Interpretability via Polynomials](https://arxiv.org/pdf/2205.14108v1.pdf)
+- [Polynomial Threshold Functions of Bounded Tree-Width: Some Explainability and Complexity Aspects](https://arxiv.org/pdf/2501.08297)
 - [Neural Basis Models for Interpretability](https://arxiv.org/pdf/2205.14120.pdf)
 - [ILMART: Interpretable Ranking with Constrained LambdaMART](https://arxiv.org/pdf/2206.00473.pdf)
 - [Integrating Co-Clustering and Interpretable Machine Learning for the Prediction of Intravenous Immunoglobulin Resistance in Kawasaki Disease](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9097874)

build.sh

@@ -343,6 +343,7 @@ if [ $is_conda -eq 1 ]; then
    ${CXX} -c ${CPPFLAGS} ${CXXFLAGS} ${extras} "$code_path/PartitionOneDimensionalBoosting.cpp" -o "$tmp_path/PartitionOneDimensionalBoosting.o"
    ${CXX} -c ${CPPFLAGS} ${CXXFLAGS} ${extras} "$code_path/PartitionRandomBoosting.cpp" -o "$tmp_path/PartitionRandomBoosting.o"
    ${CXX} -c ${CPPFLAGS} ${CXXFLAGS} ${extras} "$code_path/PartitionMultiDimensionalCorner.cpp" -o "$tmp_path/PartitionMultiDimensionalCorner.o"
+   ${CXX} -c ${CPPFLAGS} ${CXXFLAGS} ${extras} "$code_path/PartitionMultiDimensionalFull.cpp" -o "$tmp_path/PartitionMultiDimensionalFull.o"
    ${CXX} -c ${CPPFLAGS} ${CXXFLAGS} ${extras} "$code_path/PartitionMultiDimensionalTree.cpp" -o "$tmp_path/PartitionMultiDimensionalTree.o"
    ${CXX} -c ${CPPFLAGS} ${CXXFLAGS} ${extras} "$code_path/PartitionMultiDimensionalStraight.cpp" -o "$tmp_path/PartitionMultiDimensionalStraight.o"
    ${CXX} -c ${CPPFLAGS} ${CXXFLAGS} ${extras} "$code_path/Purify.cpp" -o "$tmp_path/Purify.o"
@@ -383,6 +384,7 @@ if [ $is_conda -eq 1 ]; then
    "$tmp_path/PartitionOneDimensionalBoosting.o" \
    "$tmp_path/PartitionRandomBoosting.o" \
    "$tmp_path/PartitionMultiDimensionalCorner.o" \
+   "$tmp_path/PartitionMultiDimensionalFull.o" \
    "$tmp_path/PartitionMultiDimensionalTree.o" \
    "$tmp_path/PartitionMultiDimensionalStraight.o" \
    "$tmp_path/Purify.o" \

docs/benchmarks/ebm-benchmark.ipynb

@@ -1277,6 +1277,40 @@
     "\n",
     "    plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2e25dfd-02e7-4e14-9187-fc5c7db3ca89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "iterations_df = results_df[results_df['name'] == 'iterations'].copy()\n",
+    "iterations_df['iterations_array'] = iterations_df['str_val'].apply(lambda x: np.array(eval(x.replace('][', '],[').replace(' ', ','))))\n",
+    "iterations_df = iterations_df.groupby(['task', 'method', 'meta'])['iterations_array'].apply(lambda x: np.mean(np.stack(x), axis=0)).reset_index()\n",
+    "iterations_df['iterations_array'] = iterations_df['iterations_array'].apply(lambda x: x.mean(axis=1))\n",
+    "iterations_overall_df = iterations_df.groupby(['method', 'meta'])['iterations_array'].apply(\n",
+    "    lambda x: pd.Series({\n",
+    "        'avg_index_0': np.nanmean([arr[0] for arr in x if len(arr) > 0]) if any(len(arr) > 0 for arr in x) else np.nan,\n",
+    "        'avg_index_1': np.nanmean([arr[1] for arr in x if len(arr) > 1]) if any(len(arr) > 1 for arr in x) else np.nan\n",
+    "    })\n",
+    ").reset_index()\n",
+    "iterations_overall_df = iterations_overall_df.pivot_table(columns='level_2', index=['method', 'meta'], values='iterations_array').reset_index()\n",
+    "print(\"EBM ITERATIONS:\\n\")\n",
+    "print(iterations_overall_df.to_string(index=False))\n",
+    "\n",
+    "iterations_df = iterations_df.groupby(['task', 'method', 'meta'])['iterations_array'].apply(\n",
+    "    lambda x: pd.Series({\n",
+    "        'avg_index_0': np.nanmean([arr[0] for arr in x if len(arr) > 0]) if any(len(arr) > 0 for arr in x) else np.nan,\n",
+    "        'avg_index_1': np.nanmean([arr[1] for arr in x if len(arr) > 1]) if any(len(arr) > 1 for arr in x) else np.nan\n",
+    "    })\n",
+    ").reset_index()\n",
+    "iterations_df = iterations_df.pivot_table(columns='level_3', index=['task', 'method', 'meta'], values='iterations_array').reset_index()\n",
+    "iterations_df['ratio'] = iterations_df['avg_index_0'] / iterations_df['avg_index_1']\n",
+    "print(\"\\nEBM ITERATIONS per dataset:\\n\")\n",
+    "print(iterations_df.to_string(index=False))"
+   ]
   }
  ],
  "metadata": {

python/interpret-core/interpret/develop.py

@@ -19,6 +19,7 @@
     "learning_rate_scale": 1.0,
     "max_cat_threshold": 9223372036854775807,
     "cat_include": 1.0,
+    "full_interaction": False,
     "boost_corners": False,
     "purify_boosting": False,
     "purify_result": False,

python/interpret-core/interpret/glassbox/_ebm/_ebm.py

@@ -951,6 +951,7 @@ def fit(self, X, y, sample_weight=None, bags=None, init_score=None):
             interactions = 0
             monotone_constraints = None
             n_intercept_rounds = 0
+            interaction_flags = Native.CalcInteractionFlags_Default
         else:
             noise_scale_boosting = None
             bin_data_weights = None
@@ -977,6 +978,9 @@ def fit(self, X, y, sample_weight=None, bags=None, init_score=None):
             interactions = self.interactions
             monotone_constraints = self.monotone_constraints
             n_intercept_rounds = develop.get_option("n_intercept_rounds_initial")
+            interaction_flags = Native.CalcInteractionFlags_Default
+            if develop.get_option("full_interaction"):
+                interaction_flags |= Native.CalcInteractionFlags_Full
 
         exclude_features = set()
         if monotone_constraints is not None:
@@ -1233,7 +1237,7 @@ def fit(self, X, y, sample_weight=None, bags=None, init_score=None):
                             combinations(range(n_features_in), 2),
                             exclude,
                             exclude_features,
-                            Native.CalcInteractionFlags_Default,
+                            interaction_flags,
                             max_cardinality,
                             min_samples_leaf,
                             min_hessian,

python/interpret-core/interpret/utils/_measure_interactions.py

@@ -250,6 +250,10 @@ def measure_interactions(
         feature_types_in=feature_types_in,
     )
 
+    interaction_flags = Native.CalcInteractionFlags_Default
+    if develop.get_option("full_interaction"):
+        interaction_flags |= Native.CalcInteractionFlags_Full
+
     if isinstance(interactions, int):
         n_output_interactions = interactions
         iter_term_features = combinations(range(n_features_in), 2)
@@ -268,7 +272,7 @@ def measure_interactions(
         iter_term_features=iter_term_features,
         exclude=set(),
         exclude_features=set(),
-        calc_interaction_flags=Native.CalcInteractionFlags_Default,
+        calc_interaction_flags=interaction_flags,
         max_cardinality=max_cardinality,
         min_samples_leaf=min_samples_leaf,
         min_hessian=min_hessian,

python/interpret-core/interpret/utils/_native.py

@@ -52,6 +52,7 @@ class Native:
     CalcInteractionFlags_Default = 0x00000000
     CalcInteractionFlags_Purify = 0x00000001
     CalcInteractionFlags_DisableNewton = 0x00000002
+    CalcInteractionFlags_Full = 0x00000004
 
     # AccelerationFlags
     AccelerationFlags_NONE = 0x00000000