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Add DecisionTreeClassifier C examples
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nok committed Oct 30, 2017
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181 changes: 181 additions & 0 deletions examples/estimator/classifier/DecisionTreeClassifier/c/basics.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# sklearn-porter\n",
"\n",
"Repository: https://github.com/nok/sklearn-porter\n",
"\n",
"## DecisionTreeClassifier\n",
"\n",
"Documentation: [sklearn.tree.DecisionTreeClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Loading data:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"((150, 4), (150,))\n"
]
}
],
"source": [
"from sklearn.datasets import load_iris\n",
"\n",
"iris_data = load_iris()\n",
"X = iris_data.data\n",
"y = iris_data.target\n",
"\n",
"print(X.shape, y.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train classifier:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
" splitter='best')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.tree import tree\n",
"\n",
"clf = tree.DecisionTreeClassifier()\n",
"clf.fit(X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Transpile classifier:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#include <stdlib.h>\n",
"#include <stdio.h>\n",
"#include <math.h>\n",
"\n",
"#define N_FEATURES 4\n",
"#define N_CLASSES 3\n",
"\n",
"int lChilds[17] = {1, -1, 3, 4, 5, -1, -1, 8, -1, 10, -1, -1, 13, 14, -1, -1, -1};\n",
"int rChilds[17] = {2, -1, 12, 7, 6, -1, -1, 9, -1, 11, -1, -1, 16, 15, -1, -1, -1};\n",
"double thresholds[17] = {2.45000004768, -2.0, 1.75, 4.94999980927, 1.65000009537, -2.0, -2.0, 1.54999995232, -2.0, 5.44999980927, -2.0, -2.0, 4.85000038147, 5.94999980927, -2.0, -2.0, -2.0};\n",
"int indices[17] = {2, 2, 3, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 0, 2, 2, 2};\n",
"int classes[17][3] = {{50, 50, 50}, {50, 0, 0}, {0, 50, 50}, {0, 49, 5}, {0, 47, 1}, {0, 47, 0}, {0, 0, 1}, {0, 2, 4}, {0, 0, 3}, {0, 2, 1}, {0, 2, 0}, {0, 0, 1}, {0, 1, 45}, {0, 1, 2}, {0, 1, 0}, {0, 0, 2}, {0, 0, 43}};\n",
"\n",
"int findMax(int nums[N_CLASSES]) {\n",
" int index = 0;\n",
" for (int i = 0; i < N_CLASSES; i++) {\n",
" index = nums[i] > nums[index] ? i : index;\n",
" }\n",
" return index;\n",
"}\n",
"\n",
"int predict(double features[N_FEATURES], int node) {\n",
" if (thresholds[node] != -2) {\n",
" if (features[indices[node]] <= thresholds[node]) {\n",
" return predict(features, lChilds[node]);\n",
" } else {\n",
" return predict(features, rChilds[node]);\n",
" }\n",
" }\n",
" return findMax(classes[node]);\n",
"}\n",
"\n",
"int main(int argc, const char * argv[]) {\n",
"\n",
" /* Features: */\n",
" double features[argc-1];\n",
" int i;\n",
" for (i = 1; i < argc; i++) {\n",
" features[i-1] = atof(argv[i]);\n",
" }\n",
"\n",
" /* Prediction: */\n",
" printf(\"%d\", predict(features, 0));\n",
" return 0;\n",
"\n",
"}\n",
"\n"
]
}
],
"source": [
"from sklearn_porter import Porter\n",
"\n",
"porter = Porter(clf, language='c')\n",
"output = porter.export()\n",
"\n",
"print(output)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
66 changes: 66 additions & 0 deletions examples/estimator/classifier/DecisionTreeClassifier/c/basics.py
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# -*- coding: utf-8 -*-

from sklearn.tree import tree
from sklearn.datasets import load_iris
from sklearn_porter import Porter


iris_data = load_iris()
X = iris_data.data
y = iris_data.target

clf = tree.DecisionTreeClassifier()
clf.fit(X, y)

porter = Porter(clf, language='c')
output = porter.export()
print(output)

"""
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#define N_FEATURES 4
#define N_CLASSES 3
int lChilds[17] = {1, -1, 3, 4, 5, -1, -1, 8, -1, 10, -1, -1, 13, 14, -1, -1, -1};
int rChilds[17] = {2, -1, 12, 7, 6, -1, -1, 9, -1, 11, -1, -1, 16, 15, -1, -1, -1};
double thresholds[17] = {2.45000004768, -2.0, 1.75, 4.94999980927, 1.65000009537, -2.0, -2.0, 1.54999995232, -2.0, 6.94999980927, -2.0, -2.0, 4.85000038147, 3.09999990463, -2.0, -2.0, -2.0};
int indices[17] = {2, 2, 3, 2, 3, 2, 2, 3, 2, 0, 2, 2, 2, 1, 2, 2, 2};
int classes[17][3] = {{50, 50, 50}, {50, 0, 0}, {0, 50, 50}, {0, 49, 5}, {0, 47, 1}, {0, 47, 0}, {0, 0, 1}, {0, 2, 4}, {0, 0, 3}, {0, 2, 1}, {0, 2, 0}, {0, 0, 1}, {0, 1, 45}, {0, 1, 2}, {0, 0, 2}, {0, 1, 0}, {0, 0, 43}};
int findMax(int nums[N_CLASSES]) {
int index = 0;
for (int i = 0; i < N_CLASSES; i++) {
index = nums[i] > nums[index] ? i : index;
}
return index;
}
int predict(double features[N_FEATURES], int node) {
if (thresholds[node] != -2) {
if (features[indices[node]] <= thresholds[node]) {
return predict(features, lChilds[node]);
} else {
return predict(features, rChilds[node]);
}
}
return findMax(classes[node]);
}
int main(int argc, const char * argv[]) {
/* Features: */
double features[argc-1];
int i;
for (i = 1; i < argc; i++) {
features[i-1] = atof(argv[i]);
}
/* Prediction: */
printf("%d", predict(features, 0));
return 0;
}
"""

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