commit 2b00bb382463038a184a858a4db7ca118942525f
parent 2db11f21757ae1d50f8bbbb16a1fc48de979d51d
Author: Andrew <andrewlaack1@gmail.com>
Date: Mon, 28 Oct 2024 15:58:28 -0500
Wrote basic code to do general decision tree related stuff.
Diffstat:
1 file changed, 463 insertions(+), 21 deletions(-)
diff --git a/decisionTreeClassifierFromScratch/DecisionTreeClassifier.ipynb b/decisionTreeClassifierFromScratch/DecisionTreeClassifier.ipynb
@@ -6,14 +6,14 @@
"source": [
"10.28 Stream:\n",
"\n",
- "1. Create some data\n",
- "2. Create method calculate gini impurity\n",
- "3. Create node class\n",
- "4. Create recursive splitting method\n",
+ "- [x] Create some data\n",
+ "- [x] Create method calculate gini impurity\n",
+ "- [x] Create node class\n",
+ "- [x] Create recursive splitting method\n",
+ "- [x] Print Tree\n",
"\n",
"LATER:\n",
"\n",
- "5. Print Tree\n",
"6. Create prediction method"
]
},
@@ -32,27 +32,55 @@
"| 2 | 20 | 1 |\n",
"| 3 | 15 | 1 |\n",
"| 3.2 | 20 | 1 |\n",
- "| 3.5 | 20 | 1 |"
+ "| 3.5 | 30 | 1 |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "| Class | Count |\n",
+ "| - | - |\n",
+ "| 0 | 3 |\n",
+ "| 1 | 5 |\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "gini = 1 - sum(p^2 of each classification)\n",
+ "\n",
+ "5 + 3 = 8\n",
+ "\n",
+ "5 / 8 = .625\n",
+ "3 / 8 = .375\n",
+ "\n",
+ "1- prob(0)^2 + prob(1)^2\n",
+ "\n",
+ "1 - .375^2 + .625^2\n",
+ "= 0.46875"
]
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 140,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.collections.PathCollection at 0x7fe6090860d0>"
+ "<matplotlib.collections.PathCollection at 0x7f037486e2d0>"
]
},
- "execution_count": 26,
+ "execution_count": 140,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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",
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -68,12 +96,15 @@
"values = [\n",
" [1 , 10 , 0],\n",
" [2 , 10 , 0],\n",
- " [3 , 10 , 0],\n",
+ " [3 , 12 , 0],\n",
+ " [3 , 9 , 1],\n",
+ " [1 , 7 , 1],\n",
+ " [2 , 9 , 1],\n",
" [1 , 15 , 1],\n",
" [2 , 20 , 1],\n",
" [3 , 15 , 1],\n",
" [3.2 , 20 , 1],\n",
- " [3.5 , 20 , 1],\n",
+ " [3.5 , 30 , 1],\n",
"]\n",
"\n",
"values = np.array(values)\n",
@@ -83,26 +114,437 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 141,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
"text/plain": [
- "<Figure size 640x480 with 1 Axes>"
+ "array([[ 1. , 10. , 0. ],\n",
+ " [ 2. , 10. , 0. ],\n",
+ " [ 3. , 12. , 0. ],\n",
+ " [ 3. , 9. , 1. ],\n",
+ " [ 1. , 7. , 1. ],\n",
+ " [ 2. , 9. , 1. ],\n",
+ " [ 1. , 15. , 1. ],\n",
+ " [ 2. , 20. , 1. ],\n",
+ " [ 3. , 15. , 1. ],\n",
+ " [ 3.2, 20. , 1. ],\n",
+ " [ 3.5, 30. , 1. ]])"
]
},
+ "execution_count": 141,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 142,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.39669421487603307"
+ ]
+ },
+ "execution_count": 142,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# 1D list of classifications\n",
+ "# elements is a numpy array\n",
+ "# assume final element is label\n",
+ "\n",
+ "def giniImpurity(elements):\n",
+ "\n",
+ " counts = {}\n",
+ "\n",
+ " for i in elements:\n",
+ " try:\n",
+ " counts[i[-1]] = counts[i[-1]] + 1\n",
+ " except:\n",
+ " counts[i[-1]] = 1\n",
+ " \n",
+ " # 1. Iterate through all element of counts\n",
+ " # 2. for each of these calculate probability \n",
+ " # 3. add this to the sum\n",
+ " # 4. return sum\n",
+ "\n",
+ " sum = 0\n",
+ " for i in counts.keys():\n",
+ " # cur = instances of current class\n",
+ " cur = counts[i] \n",
+ " sum += (cur/len(elements)) ** 2\n",
+ " return 1 - sum\n",
+ "\n",
+ "\n",
+ "giniImpurity(values)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 143,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import graphviz\n",
+ "\n",
+ "class SplitNode:\n",
+ " def __init__(self, colNum=None, criteria=None, leftNode=None, rightNode=None):\n",
+ " self.colNum = colNum\n",
+ " self.criteria = criteria\n",
+ " self.leftNode = leftNode\n",
+ " self.rightNode = rightNode\n",
+ " \n",
+ " # Column number we are splitting on\n",
+ " colNum = None\n",
+ " # Value to split on (<=)\n",
+ " criteria = None\n",
+ "\n",
+ " # Iff leftNode = None and rightNode = None then leaf\n",
+ " leftNode = None\n",
+ " rightNode = None\n",
+ "\n",
+ " def __str__(self, ):\n",
+ " retStr = \"Split on: \" + str(self.colNum)\n",
+ " retStr += \" Split Value: <= \" + str(self.criteria)\n",
+ " retStr += \"\\n\"\n",
+ " if self.leftNode != None:\n",
+ " retStr += self.leftNode.__str__()\n",
+ " if self.rightNode != None:\n",
+ " retStr += self.rightNode.__str__()\n",
+ " return retStr\n",
+ " \n",
+ " def graph(self, columnNames=[]):\n",
+ "\n",
+ " diGraph = graphviz.Digraph()\n",
+ " helperGraph(\"0\",self, diGraph, columnNames)\n",
+ " return diGraph\n",
+ "\n",
+ "\n",
+ "# Recursive method to build visualization of tree\n",
+ "def helperGraph(prior, curNode, diGraph, columnNames):\n",
+ "\n",
+ " if curNode.colNum == None:\n",
+ " # Maybe add impurity stats later\n",
+ " diGraph.node(str(id(curNode)), f\"Leaf\")\n",
+ " if prior != \"0\":\n",
+ " diGraph.edge(prior, str(id(curNode)))\n",
+ " return\n",
+ "\n",
+ " splitOn = str(curNode.colNum)\n",
+ " if(len(columnNames) > curNode.colNum):\n",
+ " splitOn = columnNames[curNode.colNum]\n",
+ " \n",
+ " diGraph.node(str(id(curNode)), f\"Split on: {splitOn} \\n Split Value: <= {str(curNode.criteria)}\")\n",
+ " if curNode.leftNode != None:\n",
+ " helperGraph(str(id(curNode)), curNode.leftNode, diGraph, columnNames)\n",
+ "\n",
+ " if curNode.rightNode != None:\n",
+ " helperGraph(str(id(curNode)), curNode.rightNode, diGraph, columnNames)\n",
+ " \n",
+ " if prior != \"0\":\n",
+ " diGraph.edge(prior, str(id(curNode)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Recursive find <= on left and > on right"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 144,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1. , 10. , 0. ],\n",
+ " [ 2. , 10. , 0. ],\n",
+ " [ 3. , 12. , 0. ],\n",
+ " [ 3. , 9. , 1. ],\n",
+ " [ 1. , 7. , 1. ],\n",
+ " [ 2. , 9. , 1. ],\n",
+ " [ 1. , 15. , 1. ],\n",
+ " [ 2. , 20. , 1. ],\n",
+ " [ 3. , 15. , 1. ],\n",
+ " [ 3.2, 20. , 1. ],\n",
+ " [ 3.5, 30. , 1. ]])"
+ ]
+ },
+ "execution_count": 144,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def split(samples, criteria, column):\n",
+ " leftArr = []\n",
+ " rightArr = []\n",
+ "\n",
+ " for sample in samples:\n",
+ " if sample[column] <= criteria:\n",
+ " leftArr.append(sample)\n",
+ " else:\n",
+ " rightArr.append(sample)\n",
+ " \n",
+ " leftArr = np.array(leftArr)\n",
+ " rightArr = np.array(rightArr)\n",
+ " return leftArr, rightArr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# take input of samples as matrix (np.array)\n",
+ "# return splitting node\n",
+ "def recurse(samples):\n",
+ "\n",
+ " if giniImpurity(samples) == 0:\n",
+ " return SplitNode()\n",
+ " \n",
+ " # Track best choice so far\n",
+ " bestGini = 1\n",
+ " colNum = 0\n",
+ " criteria = 0\n",
+ "\n",
+ " for i in range(len(samples)):\n",
+ " for x in range(0,len(samples[i])-1):\n",
+ "\n",
+ " # i = current row\n",
+ " # x = current column\n",
+ " \n",
+ " leftArr, rightArr = split(samples, samples[i][x], x)\n",
+ " gini = giniImpurity(leftArr)\n",
+ " gini += giniImpurity(rightArr)\n",
+ " if gini < bestGini:\n",
+ " bestGini = gini\n",
+ " colNum = x\n",
+ " criteria = samples[i][x]\n",
+ "\n",
+ " leftArr, rightArr = split(samples, criteria, colNum)\n",
+ " # colNum, criteria, leftNode, rightNode\n",
+ " splitNode = SplitNode(colNum, criteria, recurse(leftArr), recurse(rightArr))\n",
+ " return splitNode\n",
+ "\n",
+ "rootNode = recurse(values)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Split on: 1 Split Value: <= 7.0\n",
+ "Split on: None Split Value: <= None\n",
+ "Split on: 1 Split Value: <= 20.0\n",
+ "Split on: 0 Split Value: <= 3.0\n",
+ "Split on: 1 Split Value: <= 12.0\n",
+ "Split on: 1 Split Value: <= 9.0\n",
+ "Split on: None Split Value: <= None\n",
+ "Split on: None Split Value: <= None\n",
+ "Split on: None Split Value: <= None\n",
+ "Split on: None Split Value: <= None\n",
+ "Split on: None Split Value: <= None\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(rootNode)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
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+ "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 488.7)\">\n",
+ "<title>%3</title>\n",
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+ "<text text-anchor=\"middle\" x=\"274.72\" y=\"-267.15\" font-family=\"Times,serif\" font-size=\"14.00\"> Split Value: <= 3.0</text>\n",
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+ "<text text-anchor=\"middle\" x=\"191.72\" y=\"-192.41\" font-family=\"Times,serif\" font-size=\"14.00\">Split on: y </text>\n",
+ "<text text-anchor=\"middle\" x=\"191.72\" y=\"-177.41\" font-family=\"Times,serif\" font-size=\"14.00\"> Split Value: <= 12.0</text>\n",
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}
],
"source": [
- "plt.scatter(x = values[:,0], y = values[:,1], c= values[:,2], cmap=\"jet\")\n",
- "plt.axhline(y=15, color='r')\n",
- "plt.fill_between(x=np.linspace(0, 3.5, 100), y1=0, y2=15, color='lightblue', alpha=0.3)\n",
- "plt.fill_between(x=np.linspace(0, 3.5, 100), y1=15, y2=20, color='red', alpha=0.1)\n",
- "plt.show()"
+ "rootNode.graph(columnNames=['x', 'y'])"
]
}
],
