Last active
June 8, 2019 13:01
-
-
Save sdwfrost/450cd038405a20970bce307cc44403b8 to your computer and use it in GitHub Desktop.
Jupyter notebook for MLJ using Iris data and DecisionTree
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<table class=\"data-frame\"><thead><tr><th></th><th>SepalLength</th><th>SepalWidth</th><th>PetalLength</th><th>PetalWidth</th><th>Species</th></tr><tr><th></th><th>Float64</th><th>Float64</th><th>Float64</th><th>Float64</th><th>Categorical…</th></tr></thead><tbody><p>150 rows × 5 columns</p><tr><th>1</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>2</th><td>4.9</td><td>3.0</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>3</th><td>4.7</td><td>3.2</td><td>1.3</td><td>0.2</td><td>setosa</td></tr><tr><th>4</th><td>4.6</td><td>3.1</td><td>1.5</td><td>0.2</td><td>setosa</td></tr><tr><th>5</th><td>5.0</td><td>3.6</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>6</th><td>5.4</td><td>3.9</td><td>1.7</td><td>0.4</td><td>setosa</td></tr><tr><th>7</th><td>4.6</td><td>3.4</td><td>1.4</td><td>0.3</td><td>setosa</td></tr><tr><th>8</th><td>5.0</td><td>3.4</td><td>1.5</td><td>0.2</td><td>setosa</td></tr><tr><th>9</th><td>4.4</td><td>2.9</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>10</th><td>4.9</td><td>3.1</td><td>1.5</td><td>0.1</td><td>setosa</td></tr><tr><th>11</th><td>5.4</td><td>3.7</td><td>1.5</td><td>0.2</td><td>setosa</td></tr><tr><th>12</th><td>4.8</td><td>3.4</td><td>1.6</td><td>0.2</td><td>setosa</td></tr><tr><th>13</th><td>4.8</td><td>3.0</td><td>1.4</td><td>0.1</td><td>setosa</td></tr><tr><th>14</th><td>4.3</td><td>3.0</td><td>1.1</td><td>0.1</td><td>setosa</td></tr><tr><th>15</th><td>5.8</td><td>4.0</td><td>1.2</td><td>0.2</td><td>setosa</td></tr><tr><th>16</th><td>5.7</td><td>4.4</td><td>1.5</td><td>0.4</td><td>setosa</td></tr><tr><th>17</th><td>5.4</td><td>3.9</td><td>1.3</td><td>0.4</td><td>setosa</td></tr><tr><th>18</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.3</td><td>setosa</td></tr><tr><th>19</th><td>5.7</td><td>3.8</td><td>1.7</td><td>0.3</td><td>setosa</td></tr><tr><th>20</th><td>5.1</td><td>3.8</td><td>1.5</td><td>0.3</td><td>setosa</td></tr><tr><th>21</th><td>5.4</td><td>3.4</td><td>1.7</td><td>0.2</td><td>setosa</td></tr><tr><th>22</th><td>5.1</td><td>3.7</td><td>1.5</td><td>0.4</td><td>setosa</td></tr><tr><th>23</th><td>4.6</td><td>3.6</td><td>1.0</td><td>0.2</td><td>setosa</td></tr><tr><th>24</th><td>5.1</td><td>3.3</td><td>1.7</td><td>0.5</td><td>setosa</td></tr><tr><th>25</th><td>4.8</td><td>3.4</td><td>1.9</td><td>0.2</td><td>setosa</td></tr><tr><th>26</th><td>5.0</td><td>3.0</td><td>1.6</td><td>0.2</td><td>setosa</td></tr><tr><th>27</th><td>5.0</td><td>3.4</td><td>1.6</td><td>0.4</td><td>setosa</td></tr><tr><th>28</th><td>5.2</td><td>3.5</td><td>1.5</td><td>0.2</td><td>setosa</td></tr><tr><th>29</th><td>5.2</td><td>3.4</td><td>1.4</td><td>0.2</td><td>setosa</td></tr><tr><th>30</th><td>4.7</td><td>3.2</td><td>1.6</td><td>0.2</td><td>setosa</td></tr><tr><th>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr></tbody></table>" | |
| ], | |
| "text/latex": [ | |
| "\\begin{tabular}{r|ccccc}\n", | |
| "\t& SepalLength & SepalWidth & PetalLength & PetalWidth & Species\\\\\n", | |
| "\t\\hline\n", | |
| "\t& Float64 & Float64 & Float64 & Float64 & Categorical…\\\\\n", | |
| "\t\\hline\n", | |
| "\t1 & 5.1 & 3.5 & 1.4 & 0.2 & setosa \\\\\n", | |
| "\t2 & 4.9 & 3.0 & 1.4 & 0.2 & setosa \\\\\n", | |
| "\t3 & 4.7 & 3.2 & 1.3 & 0.2 & setosa \\\\\n", | |
| "\t4 & 4.6 & 3.1 & 1.5 & 0.2 & setosa \\\\\n", | |
| "\t5 & 5.0 & 3.6 & 1.4 & 0.2 & setosa \\\\\n", | |
| "\t6 & 5.4 & 3.9 & 1.7 & 0.4 & setosa \\\\\n", | |
| "\t7 & 4.6 & 3.4 & 1.4 & 0.3 & setosa \\\\\n", | |
| "\t8 & 5.0 & 3.4 & 1.5 & 0.2 & setosa \\\\\n", | |
| "\t9 & 4.4 & 2.9 & 1.4 & 0.2 & setosa \\\\\n", | |
| "\t10 & 4.9 & 3.1 & 1.5 & 0.1 & setosa \\\\\n", | |
| "\t11 & 5.4 & 3.7 & 1.5 & 0.2 & setosa \\\\\n", | |
| "\t12 & 4.8 & 3.4 & 1.6 & 0.2 & setosa \\\\\n", | |
| "\t13 & 4.8 & 3.0 & 1.4 & 0.1 & setosa \\\\\n", | |
| "\t14 & 4.3 & 3.0 & 1.1 & 0.1 & setosa \\\\\n", | |
| "\t15 & 5.8 & 4.0 & 1.2 & 0.2 & setosa \\\\\n", | |
| "\t16 & 5.7 & 4.4 & 1.5 & 0.4 & setosa \\\\\n", | |
| "\t17 & 5.4 & 3.9 & 1.3 & 0.4 & setosa \\\\\n", | |
| "\t18 & 5.1 & 3.5 & 1.4 & 0.3 & setosa \\\\\n", | |
| "\t19 & 5.7 & 3.8 & 1.7 & 0.3 & setosa \\\\\n", | |
| "\t20 & 5.1 & 3.8 & 1.5 & 0.3 & setosa \\\\\n", | |
| "\t21 & 5.4 & 3.4 & 1.7 & 0.2 & setosa \\\\\n", | |
| "\t22 & 5.1 & 3.7 & 1.5 & 0.4 & setosa \\\\\n", | |
| "\t23 & 4.6 & 3.6 & 1.0 & 0.2 & setosa \\\\\n", | |
| "\t24 & 5.1 & 3.3 & 1.7 & 0.5 & setosa \\\\\n", | |
| "\t25 & 4.8 & 3.4 & 1.9 & 0.2 & setosa \\\\\n", | |
| "\t26 & 5.0 & 3.0 & 1.6 & 0.2 & setosa \\\\\n", | |
| "\t27 & 5.0 & 3.4 & 1.6 & 0.4 & setosa \\\\\n", | |
| "\t28 & 5.2 & 3.5 & 1.5 & 0.2 & setosa \\\\\n", | |
| "\t29 & 5.2 & 3.4 & 1.4 & 0.2 & setosa \\\\\n", | |
| "\t30 & 4.7 & 3.2 & 1.6 & 0.2 & setosa \\\\\n", | |
| "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n", | |
| "\\end{tabular}\n" | |
| ], | |
| "text/plain": [ | |
| "150×5 DataFrame\n", | |
| "│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │\n", | |
| "│ │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mFloat64\u001b[39m │ \u001b[90mCategorical…\u001b[39m │\n", | |
| "├─────┼─────────────┼────────────┼─────────────┼────────────┼──────────────┤\n", | |
| "│ 1 │ 5.1 │ 3.5 │ 1.4 │ 0.2 │ setosa │\n", | |
| "│ 2 │ 4.9 │ 3.0 │ 1.4 │ 0.2 │ setosa │\n", | |
| "│ 3 │ 4.7 │ 3.2 │ 1.3 │ 0.2 │ setosa │\n", | |
| "│ 4 │ 4.6 │ 3.1 │ 1.5 │ 0.2 │ setosa │\n", | |
| "│ 5 │ 5.0 │ 3.6 │ 1.4 │ 0.2 │ setosa │\n", | |
| "│ 6 │ 5.4 │ 3.9 │ 1.7 │ 0.4 │ setosa │\n", | |
| "│ 7 │ 4.6 │ 3.4 │ 1.4 │ 0.3 │ setosa │\n", | |
| "│ 8 │ 5.0 │ 3.4 │ 1.5 │ 0.2 │ setosa │\n", | |
| "│ 9 │ 4.4 │ 2.9 │ 1.4 │ 0.2 │ setosa │\n", | |
| "│ 10 │ 4.9 │ 3.1 │ 1.5 │ 0.1 │ setosa │\n", | |
| "⋮\n", | |
| "│ 140 │ 6.9 │ 3.1 │ 5.4 │ 2.1 │ virginica │\n", | |
| "│ 141 │ 6.7 │ 3.1 │ 5.6 │ 2.4 │ virginica │\n", | |
| "│ 142 │ 6.9 │ 3.1 │ 5.1 │ 2.3 │ virginica │\n", | |
| "│ 143 │ 5.8 │ 2.7 │ 5.1 │ 1.9 │ virginica │\n", | |
| "│ 144 │ 6.8 │ 3.2 │ 5.9 │ 2.3 │ virginica │\n", | |
| "│ 145 │ 6.7 │ 3.3 │ 5.7 │ 2.5 │ virginica │\n", | |
| "│ 146 │ 6.7 │ 3.0 │ 5.2 │ 2.3 │ virginica │\n", | |
| "│ 147 │ 6.3 │ 2.5 │ 5.0 │ 1.9 │ virginica │\n", | |
| "│ 148 │ 6.5 │ 3.0 │ 5.2 │ 2.0 │ virginica │\n", | |
| "│ 149 │ 6.2 │ 3.4 │ 5.4 │ 2.3 │ virginica │\n", | |
| "│ 150 │ 5.9 │ 3.0 │ 5.1 │ 1.8 │ virginica │" | |
| ] | |
| }, | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "using MLJ\n", | |
| "using RDatasets\n", | |
| "iris = dataset(\"datasets\", \"iris\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "const X = iris[:, 1:4];\n", | |
| "const y = iris[:, 5];" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "import MLJModels ✔\n", | |
| "import DecisionTree ✔\n", | |
| "import MLJModels.DecisionTree_.DecisionTreeClassifier ✔\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "@load DecisionTreeClassifier" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "DecisionTreeClassifier(pruning_purity = 1.0,\n", | |
| " max_depth = 2,\n", | |
| " min_samples_leaf = 1,\n", | |
| " min_samples_split = 2,\n", | |
| " min_purity_increase = 0.0,\n", | |
| " n_subfeatures = 0.0,\n", | |
| " display_depth = 5,\n", | |
| " post_prune = false,\n", | |
| " merge_purity_threshold = 0.9,)\u001b[34m @ 1…35\u001b[39m" | |
| ] | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "tree_model = DecisionTreeClassifier(max_depth=2)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "\u001b[34mMachine{DecisionTreeClassifier} @ 6…34\u001b[39m\n" | |
| ] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "tree = machine(tree_model, X, y)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "┌ Info: Training \u001b[34mMachine{DecisionTreeClassifier} @ 6…34\u001b[39m.\n", | |
| "└ @ MLJ /home/simon/.julia/dev/MLJ/src/machines.jl:130\n" | |
| ] | |
| }, | |
| { | |
| "ename": "MethodError", | |
| "evalue": "MethodError: no method matching build_tree(::CategoricalArray{String,1,UInt8,String,CategoricalString{UInt8},Union{}}, ::Array{Float64,2}, ::Float64, ::Int64, ::Int64, ::Int64, ::Float64)\nClosest candidates are:\n build_tree(!Matched::Array{T<:Float64,1}, ::Array{S,2}, ::Any, ::Any, ::Any, ::Any, ::Any; rng) where {S, T<:Float64} at /home/simon/.julia/dev/DecisionTree/src/regression/main.jl:27\n build_tree(!Matched::Array{T,1}, ::Array{S,2}, ::Any, ::Any, ::Any, ::Any, ::Any; rng) where {S, T} at /home/simon/.julia/dev/DecisionTree/src/classification/main.jl:83\n build_tree(!Matched::Array{T<:Float64,1}, ::Array{S,2}, ::Any, ::Any, ::Any, ::Any) where {S, T<:Float64} at /home/simon/.julia/dev/DecisionTree/src/regression/main.jl:27\n ...", | |
| "output_type": "error", | |
| "traceback": [ | |
| "MethodError: no method matching build_tree(::CategoricalArray{String,1,UInt8,String,CategoricalString{UInt8},Union{}}, ::Array{Float64,2}, ::Float64, ::Int64, ::Int64, ::Int64, ::Float64)\nClosest candidates are:\n build_tree(!Matched::Array{T<:Float64,1}, ::Array{S,2}, ::Any, ::Any, ::Any, ::Any, ::Any; rng) where {S, T<:Float64} at /home/simon/.julia/dev/DecisionTree/src/regression/main.jl:27\n build_tree(!Matched::Array{T,1}, ::Array{S,2}, ::Any, ::Any, ::Any, ::Any, ::Any; rng) where {S, T} at /home/simon/.julia/dev/DecisionTree/src/classification/main.jl:83\n build_tree(!Matched::Array{T<:Float64,1}, ::Array{S,2}, ::Any, ::Any, ::Any, ::Any) where {S, T<:Float64} at /home/simon/.julia/dev/DecisionTree/src/regression/main.jl:27\n ...", | |
| "", | |
| "Stacktrace:", | |
| " [1] fit(::DecisionTreeClassifier, ::Int64, ::DataFrame, ::CategoricalArray{String,1,UInt8,String,CategoricalString{UInt8},Union{}}) at /home/simon/.julia/dev/MLJModels/src/DecisionTree.jl:110", | |
| " [2] #fit!#3(::Array{Int64,1}, ::Int64, ::Bool, ::Function, ::Machine{DecisionTreeClassifier}) at /home/simon/.julia/dev/MLJ/src/machines.jl:131", | |
| " [3] (::getfield(StatsBase, Symbol(\"#kw##fit!\")))(::NamedTuple{(:rows,),Tuple{Array{Int64,1}}}, ::typeof(fit!), ::Machine{DecisionTreeClassifier}) at ./none:0", | |
| " [4] top-level scope at In[6]:2" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "train, test = partition(eachindex(y), 0.7, shuffle=true); # 70:30 split\n", | |
| "fit!(tree, rows=train)\n", | |
| "yhat = predict(tree, X[test,:]);\n", | |
| "misclassification_rate(yhat, y[test])" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "@webio": { | |
| "lastCommId": null, | |
| "lastKernelId": null | |
| }, | |
| "kernelspec": { | |
| "display_name": "Julia 1.1.0", | |
| "language": "julia", | |
| "name": "julia-1.1" | |
| }, | |
| "language_info": { | |
| "file_extension": ".jl", | |
| "mimetype": "application/julia", | |
| "name": "julia", | |
| "version": "1.1.0" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment