mirror of
https://github.com/Doctorado-ML/FImdlp.git
synced 2025-08-17 00:15:52 +00:00
First try new cutPoints algorithm
This commit is contained in:
4
Makefile
4
Makefile
@@ -24,7 +24,9 @@ buildext: ## Build extension
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rm -fr build/*
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make clean
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python setup.py build_ext
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echo "Build extension success"; mv build/lib.macosx-12-x86_64-cpython-310/cppfimdlp.cpython-310-darwin.so fimdlp;
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echo "Build extension success"
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if [ -f build/lib.macosx-12-x86_64-cpython-310/cppfimdlp.cpython-310-darwin.so ] ; then mv build/lib.macosx-12-x86_64-cpython-310/cppfimdlp.cpython-310-darwin.so fimdlp; fi
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if [ -f build/lib.macosx-10.9-universal2-3.10/cppfimdlp.cpython-310-darwin.so ] ; then mv build/lib.macosx-10.9-universal2-3.10/cppfimdlp.cpython-310-darwin.so fimdlp; fi
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audit: ## Audit pip
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pip-audit
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@@ -18,6 +18,55 @@ namespace CPPFImdlp
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{
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}
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std::vector<float> CPPFImdlp::cutPoints(std::vector<float> &X, std::vector<int> &y)
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{
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std::vector<float> cutPts;
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std::vector<size_t> cutIdx;
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float xPrev, cutPoint, curx;
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int yPrev, cury;
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size_t idxPrev, idx;
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bool first = true;
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std::vector<size_t> indices = sortIndices(X);
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xPrev = X.at(indices.at(0));
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yPrev = y.at(indices.at(0));
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idxPrev = indices.at(0);
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idx = 0;
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while (idx < indices.size() - 1)
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{
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if (first)
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{
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first = false;
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curx = X.at(indices.at(idx));
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cury = y.at(indices.at(idx));
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}
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if (debug)
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printf("<idx=%lu -> (%3.1f, %d) Prev(%3.1f, %d)\n", idx, curx, cury, xPrev, yPrev);
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// Read the same values and check class changes
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while (idx < indices.size() - 1 && curx == xPrev)
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{
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idx++;
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curx = X.at(indices.at(idx));
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cury = y.at(indices.at(idx));
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if (cury != yPrev && curx == xPrev)
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{
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yPrev = -1;
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}
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if (debug)
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printf(">idx=%lu -> (%3.1f, %d) Prev(%3.1f, %d)\n", idx, curx, cury, xPrev, yPrev);
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}
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if (yPrev == -1 || yPrev != cury)
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{
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cutPoint = (xPrev + curx) / 2;
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printf("Cutpoint (%3.1f, %d) -> (%3.1f, %d) = %3.1f", xPrev, yPrev, curx, cury, cutPoint);
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cutPts.push_back(cutPoint);
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cutIdx.push_back(idxPrev);
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}
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yPrev = cury;
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xPrev = curx;
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idxPrev = indices.at(idx);
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}
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return cutPts;
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}
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std::vector<float> CPPFImdlp::cutPointsAnt(std::vector<float> &X, std::vector<int> &y)
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{
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std::vector<float> cutPts;
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std::vector<int> cutIdx;
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@@ -19,6 +19,7 @@ namespace CPPFImdlp
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CPPFImdlp(int, bool debug = false);
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~CPPFImdlp();
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std::vector<float> cutPoints(std::vector<float> &, std::vector<int> &);
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std::vector<float> cutPointsAnt(std::vector<float> &, std::vector<int> &);
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};
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}
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#endif
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@@ -8,6 +8,7 @@ cdef extern from "CPPFImdlp.h" namespace "CPPFImdlp":
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CPPFImdlp() except +
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CPPFImdlp(int, bool) except +
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vector[float] cutPoints(vector[float]&, vector[int]&)
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vector[float] cutPointsAnt(vector[float]&, vector[int]&)
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cdef class CFImdlp:
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cdef CPPFImdlp *thisptr
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@@ -17,3 +18,5 @@ cdef class CFImdlp:
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del self.thisptr
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def cut_points(self, X, y):
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return self.thisptr.cutPoints(X, y)
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def cut_points_ant(self, X, y):
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return self.thisptr.cutPointsAnt(X, y)
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Binary file not shown.
@@ -61,7 +61,7 @@ class FImdlp(TransformerMixin, BaseEstimator):
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self.n_features_ = X.shape[1]
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self.X_ = X
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self.y_ = y
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self.discretizer_ = CFImdlp()
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self.discretizer_ = CFImdlp(debug=True)
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return self
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def transform(self, X):
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@@ -108,5 +108,7 @@ class FImdlp(TransformerMixin, BaseEstimator):
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datax = self.X_[np.argsort(self.X_[:, i]), i]
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y_ = self.y_[np.argsort(self.X_[:, i])]
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Xcutpoints = self.discretizer_.cut_points(datax, y_)
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print(f"{self.features_[i]:20s}: {Xcutpoints}")
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print(f"New:{self.features_[i]:20s}: {Xcutpoints}")
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Xcutpoints = self.discretizer_.cut_points_ant(datax, y_)
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print(f"Ant:{self.features_[i]:20s}: {Xcutpoints}")
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return X
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14
sample.py
14
sample.py
@@ -1,10 +1,22 @@
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from sklearn.datasets import load_iris
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from fimdlp.mdlp import FImdlp
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from fimdlp.cppfimdlp import CFImdlp
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import numpy as np
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data = load_iris()
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X = data.data
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y = data.target
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features = data.feature_names
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test = FImdlp()
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test.fit(X, y, features=features).transform(X)
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# test.fit(X, y, features=features).transform(X)
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X = np.array(
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[
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[5.1, 3.5, 1.4, 0.2],
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[5.2, 3.0, 1.4, 0.2],
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[5.3, 3.2, 1.3, 0.2],
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[5.3, 3.1, 1.5, 0.2],
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]
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)
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y = np.array([0, 0, 0, 1])
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test.fit(X, y).transform(X)
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