feat: support from_sklearn for trees

Support `from_sklearn` for tree based models.

Two options:
- Quantization from thresholds: the main idea is to consider the
  thresholds of the nodes of the trees for quantization to not have
  to use data.
- Quantization from data: build a quantizer from the data provided by
  the user and quantize the thresholds based on that.

This also raises the question of non-uniform input quantization.
We could quantize the data based on the thresholds thus reducing the
number of bits required to log2(max_{feature}(node_{feature})).

That would leak the thresholds used in the model per feature but not the
structure of the tree itself while increasing significantly the number
of bits required.

We could try to automatically determine the n-bits to use to
properly represent all thresholds but this might result in a very high
bit-with.

.gitleaksignore

@@ -8,3 +8,4 @@ a99389ee01cbb972e46a892d3d0e9c7f8ee23f59:use_case_examples/training/analyze.ipyn
 a99389ee01cbb972e46a892d3d0e9c7f8ee23f59:use_case_examples/training/analyze.ipynb:aws-access-token:18379
 f41de03048a9ed27946b875e81b34138bb4bb17b:use_case_examples/training/analyze.ipynb:aws-access-token:6404
 e2904473898ddd325f245f4faca526a0e9520f49:builders/Dockerfile.zamalang-env:generic-api-key:5
+7d5e885816f1f1e432dd94da38c5c8267292056a:docs/advanced_examples/XGBRegressor.ipynb:aws-access-token:1026

deps_licenses/licenses_mac_silicon_user.txt

@@ -1,28 +1,28 @@
 Name, Version, License
 PyYAML, 6.0.1, MIT License
 brevitas, 0.8.0, UNKNOWN
-certifi, 2024.2.2, Mozilla Public License 2.0 (MPL 2.0)
+certifi, 2023.7.22, Mozilla Public License 2.0 (MPL 2.0)
 charset-normalizer, 3.3.2, MIT License
 coloredlogs, 15.0.1, MIT License
 concrete-python, 2024.4.19, BSD-3-Clause
 dependencies, 2.0.1, BSD License
 dill, 0.3.8, BSD License
-filelock, 3.14.0, The Unlicense (Unlicense)
+filelock, 3.13.4, The Unlicense (Unlicense)
 flatbuffers, 24.3.25, Apache Software License
 fsspec, 2024.3.1, BSD License
 huggingface-hub, 0.22.2, Apache Software License
 humanfriendly, 10.0, MIT License
-hummingbird-ml, 0.4.11, MIT License
+hummingbird-ml, 0.4.8, MIT License
 idna, 3.7, BSD License
 importlib_resources, 6.4.0, Apache Software License
 joblib, 1.4.0, BSD License
 jsonpickle, 3.0.4, BSD License
 mpmath, 1.3.0, BSD License
 networkx, 3.1, BSD License
-numpy, 1.24.3, BSD License
-onnx, 1.16.0, Apache License v2.0
+numpy, 1.23.5, BSD License
+onnx, 1.15.0, Apache License v2.0
 onnxconverter-common, 1.13.0, MIT License
-onnxmltools, 1.12.0, Apache Software License
+onnxmltools, 1.11.0, Apache Software License
 onnxoptimizer, 0.3.13, Apache License v2.0
 onnxruntime, 1.17.3, MIT License
 packaging, 24.0, Apache Software License; BSD License
@@ -32,19 +32,20 @@ psutil, 5.9.8, BSD License
 python-dateutil, 2.9.0.post0, Apache Software License; BSD License
 pytz, 2024.1, MIT License
 requests, 2.31.0, Apache Software License
-scikit-learn, 1.3.2, BSD License
+scikit-learn, 1.1.3, BSD License
 scipy, 1.10.1, BSD License
 six, 1.16.0, MIT License
-skl2onnx, 1.16.0, Apache Software License
+skl2onnx, 1.12, Apache Software License
 skops, 0.5.0, MIT
 skorch, 0.11.0, new BSD 3-Clause
 sympy, 1.12, BSD License
 tabulate, 0.8.10, MIT License
-threadpoolctl, 3.5.0, BSD License
+threadpoolctl, 3.4.0, BSD License
 torch, 1.13.1, BSD License
 tqdm, 4.66.2, MIT License; Mozilla Public License 2.0 (MPL 2.0)
 typing_extensions, 4.5.0, Python Software Foundation License
 tzdata, 2024.1, Apache Software License
 urllib3, 2.2.1, MIT License
-xgboost, 1.7.6, Apache Software License
+xgboost, 1.6.2, Apache Software License
 z3-solver, 4.13.0.0, MIT License
+zipp, 3.18.1, MIT License

deps_licenses/licenses_mac_silicon_user.txt.md5

@@ -1 +1 @@
-7be80ba54850fbc203015560c8acb9a8
+9b8316c2a6c823884676b39f52eb018a

docs/advanced_examples/XGBClassifier.ipynb

@@ -587,5 +587,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

pyproject.toml

@@ -64,18 +64,18 @@ pylint = "^2.13.0"
 # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/2541
 pytest = "7.4.1"
 pytest-cov = "^4.1.0"
+pytest-xdist = "^3.3.1"
+pytest-randomly = "^3.11.0"
+pytest-repeat = "^0.9.1"
+pytest-subtests = "^0.11.0"
 pytest_codeblocks = "^0.14.0"
 mypy = "^1.8.0"
 pydocstyle = "^6.1.1"
 python-semantic-release = "^7.27.0"
 semver = "^2.13.0"
 tomlkit = "^0.7.0"
-pytest-json-report = "^1.5.0"
-pytest-xdist = "^3.3.1"
-pytest-randomly = "^3.11.0"
 nbmake = "^1.3.0"
 pygments-style-tomorrow = "^1.0.0"
-pytest-repeat = "^0.9.1"
 mdformat = "^0.7.14"
 mdformat_myst = "^0.1.4"
 mdformat-toc = "^0.3.0"

src/concrete/ml/onnx/onnx_impl_utils.py

@@ -5,7 +5,7 @@
 import numpy
 from concrete.fhe import conv as fhe_conv
 from concrete.fhe import ones as fhe_ones
-from concrete.fhe import round_bit_pattern
+from concrete.fhe import truncate_bit_pattern
 from concrete.fhe.tracing import Tracer
 
 from ..common.debugging import assert_true
@@ -265,9 +265,16 @@ def rounded_comparison(
     # Workaround: in this context, `round_bit_pattern` is used as a truncate operation.
     # Consequently, we subtract a term, called `half` that will subsequently be re-added during the
     # `round_bit_pattern` process.
-    half = 1 << (lsbs_to_remove - 1)
+    # half = 1 << (lsbs_to_remove - 1)
 
     # To determine if 'x' 'operation' 'y' (operation being <, >, >=, <=), we evaluate 'x - y'
-    rounded_subtraction = round_bit_pattern((x - y) - half, lsbs_to_remove=lsbs_to_remove)
+    # We cast to int because if half is too high the result might be float
+    # intermediate = ((x - y) - half)
+    # intermediate_as_int = intermediate.astype(numpy.int64)
+    #
+    # if not isinstance(intermediate, Tracer):
+    #     assert (intermediate == intermediate_as_int).all()
+
+    rounded_subtraction = truncate_bit_pattern(x - y, lsbs_to_remove=lsbs_to_remove)
 
     return (operation(rounded_subtraction),)

src/concrete/ml/quantization/quantizers.py

@@ -797,7 +797,7 @@ def dequant(self, qvalues: numpy.ndarray) -> Union[numpy.ndarray, Tracer]:
 
         values = self.scale * (qvalues - numpy.asarray(self.zero_point, dtype=numpy.float64))
 
-        assert isinstance(values, (numpy.ndarray, Tracer))
+        assert isinstance(values, (float, int, numpy.ndarray, Tracer))
         return values