Scarliles/defuse partitioner

asv_benchmarks/benchmarks/ensemble.py

@@ -2,15 +2,58 @@
  GradientBoostingClassifier,
  HistGradientBoostingClassifier,
  RandomForestClassifier,
+ RandomForestRegressor,
 )
 
 from .common import Benchmark, Estimator, Predictor
 from .datasets import (
  _20newsgroups_highdim_dataset,
  _20newsgroups_lowdim_dataset,
  _synth_classification_dataset,
+ _synth_regression_dataset,
+ _synth_regression_sparse_dataset,
 )
-from .utils import make_gen_classif_scorers
+from .utils import make_gen_classif_scorers, make_gen_reg_scorers
+
+
+class RandomForestRegressorBenchmark(Predictor, Estimator, Benchmark):
+ """
+ Benchmarks for RandomForestRegressor.
+ """
+
+ param_names = ["representation", "n_jobs"]
+ params = (["dense", "sparse"], Benchmark.n_jobs_vals)
+
+ def setup_cache(self):
+ super().setup_cache()
+
+ def make_data(self, params):
+ representation, n_jobs = params
+
+ if representation == "sparse":
+ data = _synth_regression_sparse_dataset()
+ else:
+ data = _synth_regression_dataset()
+
+ return data
+
+ def make_estimator(self, params):
+ representation, n_jobs = params
+
+ n_estimators = 500 if Benchmark.data_size == "large" else 100
+
+ estimator = RandomForestRegressor(
+ n_estimators=n_estimators,
+ min_samples_split=10,
+ max_features="log2",
+ n_jobs=n_jobs,
+ random_state=0,
+ )
+
+ return estimator
+
+ def make_scorers(self):
+ make_gen_reg_scorers(self)
 
 
 class RandomForestClassifierBenchmark(Predictor, Estimator, Benchmark):

sklearn/tree/_partitioner.pxd

@@ -0,0 +1,114 @@
+from ..utils._typedefs cimport float32_t, float64_t, intp_t, int8_t, int32_t, uint32_t
+
+# Constant to switch between algorithm non zero value extract algorithm
+# in SparsePartitioner
+cdef float32_t EXTRACT_NNZ_SWITCH = 0.1
+
+
+# Introduce a fused-class to make it possible to share the split implementation
+# between the dense and sparse cases in the node_split_best and node_split_random
+# functions. The alternative would have been to use inheritance-based polymorphism
+# but it would have resulted in a ~10% overall tree fitting performance
+# degradation caused by the overhead frequent virtual method lookups.
+#ctypedef fused Partitioner:
+# DensePartitioner
+# SparsePartitioner
+
+
+ctypedef void (*InitNodeSplitFunction)(
+ Partitioner partitioner, intp_t start, intp_t end
+) noexcept nogil
+
+ctypedef void (*SortSamplesAndFeatureValuesFunction)(
+ Partitioner partitioner, intp_t current_feature
+) noexcept nogil
+
+ctypedef void (*FindMinMaxFunction)(
+ Partitioner partitioner,
+ intp_t current_feature,
+ float32_t* min_feature_value_out,
+ float32_t* max_feature_value_out,
+) noexcept nogil
+
+ctypedef void (*NextPFunction)(
+ Partitioner partitioner, intp_t* p_prev, intp_t* p
+) noexcept nogil
+
+ctypedef intp_t (*PartitionSamplesFunction)(
+ Partitioner partitioner, float64_t current_threshold
+) noexcept nogil
+
+ctypedef void (*PartitionSamplesFinalFunction)(
+ Partitioner partitioner,
+ intp_t best_pos,
+ float64_t best_threshold,
+ intp_t best_feature,
+ intp_t best_n_missing,
+) noexcept nogil
+
+
+cdef class Partitioner:
+ cdef:
+ intp_t[::1] samples
+ float32_t[::1] feature_values
+ intp_t start
+ intp_t end
+ intp_t n_missing
+ const unsigned char[::1] missing_values_in_feature_mask
+
+ inline void init_node_split(self, intp_t start, intp_t end) noexcept nogil
+ inline void sort_samples_and_feature_values(
+ self,
+ intp_t current_feature
+ ) noexcept nogil
+ inline void find_min_max(
+ self,
+ intp_t current_feature,
+ float32_t* min_feature_value_out,
+ float32_t* max_feature_value_out,
+ ) noexcept nogil
+ inline void next_p(self, intp_t* p_prev, intp_t* p) noexcept nogil
+ inline intp_t partition_samples(self, float64_t current_threshold) noexcept nogil 
+ inline void partition_samples_final(
+ self,
+ intp_t best_pos,
+ float64_t best_threshold,
+ intp_t best_feature,
+ intp_t best_n_missing,
+ ) noexcept nogil
+
+ InitNodeSplitFunction _init_node_split
+ SortSamplesAndFeatureValuesFunction _sort_samples_and_feature_values
+ FindMinMaxFunction _find_min_max
+ NextPFunction _next_p
+ PartitionSamplesFunction _partition_samples
+ PartitionSamplesFinalFunction _partition_samples_final
+
+
+cdef class DensePartitioner(Partitioner):
+ """Partitioner specialized for dense data.
+
+ Note that this partitioner is agnostic to the splitting strategy (best vs. random).
+ """
+ cdef:
+ const float32_t[:, :] X
+
+
+cdef class SparsePartitioner(Partitioner):
+ """Partitioner specialized for sparse CSC data.
+
+ Note that this partitioner is agnostic to the splitting strategy (best vs. random).
+ """
+ cdef:
+ const float32_t[::1] X_data
+ const int32_t[::1] X_indices
+ const int32_t[::1] X_indptr
+
+ intp_t n_total_samples
+
+ intp_t[::1] index_to_samples
+ intp_t[::1] sorted_samples
+
+ intp_t start_positive
+ intp_t end_negative
+ bint is_samples_sorted