small omission in code
This commit is contained in:
Morten Hjorth-Jensen
@@ -187,7 +187,7 @@ MathJax.Hub.Config({
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</center>
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<br>
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<center>
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<h4>Nov 8, 2022</h4>
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<h4>Nov 10, 2022</h4>
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</center> <!-- date -->
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<br>
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@@ -194,6 +194,7 @@ MathJax.Hub.Config({
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> load_breast_cancer
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.svm</span> <span style="color: #008000; font-weight: bold">import</span> SVC
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.linear_model</span> <span style="color: #008000; font-weight: bold">import</span> LogisticRegression
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeClassifier
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> RandomForestClassifier
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> LabelEncoder
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> cross_validate
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@@ -187,7 +187,7 @@ MathJax.Hub.Config({
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</center>
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<br>
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<center>
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<h4>Nov 8, 2022</h4>
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<h4>Nov 10, 2022</h4>
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</center> <!-- date -->
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<br>
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@@ -184,7 +184,7 @@ MathJax.Hub.Config({
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</center>
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<br>
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<center>
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<h4>Nov 8, 2022</h4>
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<h4>Nov 10, 2022</h4>
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</center> <!-- date -->
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<br>
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@@ -249,6 +249,7 @@ MathJax.Hub.Config({
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_breast_cancer
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.svm</span> <span style="color: #8B008B; font-weight: bold">import</span> SVC
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.linear_model</span> <span style="color: #8B008B; font-weight: bold">import</span> LogisticRegression
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeClassifier
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.ensemble</span> <span style="color: #8B008B; font-weight: bold">import</span> RandomForestClassifier
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> LabelEncoder
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> cross_validate
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@@ -167,7 +167,7 @@ MathJax.Hub.Config({
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</center>
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<br>
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<center>
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<h4>Nov 8, 2022</h4>
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<h4>Nov 10, 2022</h4>
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</center> <!-- date -->
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<br>
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@@ -225,6 +225,7 @@ MathJax.Hub.Config({
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.datasets</span> <span style="color: #8B008B; font-weight: bold">import</span> load_breast_cancer
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.svm</span> <span style="color: #8B008B; font-weight: bold">import</span> SVC
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.linear_model</span> <span style="color: #8B008B; font-weight: bold">import</span> LogisticRegression
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.tree</span> <span style="color: #8B008B; font-weight: bold">import</span> DecisionTreeClassifier
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.ensemble</span> <span style="color: #8B008B; font-weight: bold">import</span> RandomForestClassifier
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.preprocessing</span> <span style="color: #8B008B; font-weight: bold">import</span> LabelEncoder
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<span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.model_selection</span> <span style="color: #8B008B; font-weight: bold">import</span> cross_validate
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@@ -244,7 +244,7 @@ MathJax.Hub.Config({
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</center>
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<br>
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<center>
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<h4>Nov 8, 2022</h4>
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<h4>Nov 10, 2022</h4>
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</center> <!-- date -->
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<br>
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@@ -302,6 +302,7 @@ MathJax.Hub.Config({
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.datasets</span> <span style="color: #008000; font-weight: bold">import</span> load_breast_cancer
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.svm</span> <span style="color: #008000; font-weight: bold">import</span> SVC
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.linear_model</span> <span style="color: #008000; font-weight: bold">import</span> LogisticRegression
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.tree</span> <span style="color: #008000; font-weight: bold">import</span> DecisionTreeClassifier
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.ensemble</span> <span style="color: #008000; font-weight: bold">import</span> RandomForestClassifier
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.preprocessing</span> <span style="color: #008000; font-weight: bold">import</span> LabelEncoder
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<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.model_selection</span> <span style="color: #008000; font-weight: bold">import</span> cross_validate
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@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "markdown",
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"id": "69ffa59c",
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"id": "7a978d39",
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"metadata": {
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"editable": true
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},
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@@ -14,7 +14,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "22893fe1",
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"id": "e0283e6d",
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"metadata": {
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"editable": true
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},
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@@ -22,14 +22,14 @@
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"# Week 45: Decisions Trees, Random Forests, Bagging and Boosting\n",
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"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
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"\n",
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"Date: **Nov 8, 2022**\n",
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"Date: **Nov 10, 2022**\n",
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"\n",
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"Copyright 1999-2022, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6ed587a5",
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"id": "a3136428",
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"metadata": {
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"editable": true
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},
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@@ -57,7 +57,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "20a33b84",
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"id": "e2e0134a",
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"metadata": {
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"editable": true
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},
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@@ -68,7 +68,7 @@
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "4cddf972",
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"id": "2fdb28db",
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"metadata": {
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"collapsed": false,
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"editable": true
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@@ -93,6 +93,7 @@
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"from sklearn.datasets import load_breast_cancer\n",
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"from sklearn.svm import SVC\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.model_selection import cross_validate\n",
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@@ -167,7 +168,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "3f3b4ec0",
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"id": "eb3a5ee9",
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"metadata": {
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"editable": true
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},
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@@ -187,7 +188,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "1d2fa07b",
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"id": "b793724a",
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"metadata": {
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"editable": true
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},
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@@ -201,7 +202,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "ae378fc0",
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"id": "f2c22ac4",
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"metadata": {
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"editable": true
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},
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@@ -213,7 +214,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "b659b768",
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"id": "cbaa4a6f",
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"metadata": {
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"editable": true
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},
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@@ -230,7 +231,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "25f72330",
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"id": "c0ea84be",
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"metadata": {
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"editable": true
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},
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@@ -242,7 +243,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "45aa3f32",
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"id": "94736458",
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"metadata": {
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"editable": true
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},
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@@ -256,7 +257,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "b4c3c38c",
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"id": "b41565d5",
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"metadata": {
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"editable": true
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},
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@@ -268,7 +269,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "3e6e9b65",
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"id": "c1b8efd9",
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"metadata": {
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"editable": true
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},
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},
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{
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"cell_type": "markdown",
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"id": "05fbdabe",
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"id": "7f7e8a84",
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"metadata": {
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"editable": true
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},
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@@ -293,7 +294,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "1ed2d372",
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"id": "ed8d7b77",
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"metadata": {
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"editable": true
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},
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@@ -303,7 +304,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "551e69df",
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"id": "4246f00d",
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"metadata": {
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"editable": true
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},
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},
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{
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"cell_type": "markdown",
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"id": "a9173c39",
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"id": "6ccbc0e9",
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"metadata": {
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"editable": true
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},
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@@ -347,7 +348,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "51535a0f",
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"id": "4b4f4548",
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"metadata": {
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"editable": true
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},
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@@ -359,7 +360,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "ddad2f59",
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"id": "f5434423",
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"metadata": {
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"editable": true
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},
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@@ -370,7 +371,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "96d2aee2",
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"id": "a4c33651",
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"metadata": {
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"editable": true
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},
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@@ -382,7 +383,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "ed53f899",
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"id": "d25fd438",
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"metadata": {
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"editable": true
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},
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{
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"cell_type": "markdown",
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"id": "573671e9",
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"id": "261f7514",
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"metadata": {
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"editable": true
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},
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},
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{
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"cell_type": "markdown",
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"id": "4e261b39",
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"id": "ee46c495",
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"metadata": {
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"editable": true
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},
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},
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{
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"cell_type": "markdown",
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"id": "88f94be1",
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"id": "4b6cf657",
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"metadata": {
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"editable": true
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},
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{
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"cell_type": "markdown",
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"id": "8d5732c1",
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"id": "07fd8905",
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"metadata": {
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"editable": true
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},
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"cell_type": "markdown",
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"id": "17699be3",
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"metadata": {
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},
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{
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"cell_type": "markdown",
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"id": "15a2125d",
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"id": "91046f02",
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"metadata": {
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{
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"cell_type": "markdown",
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"id": "8f34d330",
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"id": "b886c010",
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"metadata": {
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},
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{
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"cell_type": "markdown",
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"id": "b58de40d",
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"id": "b5f82299",
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"metadata": {
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"editable": true
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},
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{
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"cell_type": "markdown",
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"id": "630a85ef",
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"id": "9843cd00",
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"metadata": {
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"editable": true
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},
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{
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"cell_type": "markdown",
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"id": "e19f8094",
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"id": "1090524c",
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"metadata": {
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"editable": true
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},
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},
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{
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"cell_type": "markdown",
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"id": "1bd1ca26",
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"id": "f3088911",
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"metadata": {
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"editable": true
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},
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},
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{
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"cell_type": "markdown",
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"id": "28c94b01",
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"id": "4ba3fedb",
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"metadata": {
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"editable": true
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},
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},
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{
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"cell_type": "markdown",
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"id": "cd5a9b54",
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"id": "6e447640",
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"metadata": {
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"editable": true
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},
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{
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"cell_type": "markdown",
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"id": "c80e3529",
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"id": "a114cfe7",
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"metadata": {
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"editable": true
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},
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{
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"cell_type": "markdown",
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"id": "9836bef1",
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||||
"id": "a00f5ba6",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -575,7 +576,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0118af58",
|
||||
"id": "ea0be85b",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -587,7 +588,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c12b4a0d",
|
||||
"id": "a65ab9b7",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -598,7 +599,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ed7915b4",
|
||||
"id": "b7cffa99",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -610,7 +611,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "bed73666",
|
||||
"id": "081d507c",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -620,7 +621,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5cf114dd",
|
||||
"id": "6e734ee3",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -632,7 +633,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "99c625b1",
|
||||
"id": "ee564d2c",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -644,7 +645,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d33941de",
|
||||
"id": "a9cc2890",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -656,7 +657,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "8c90eae5",
|
||||
"id": "3a89610f",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -668,7 +669,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "7d0065c2",
|
||||
"id": "a2b97b3f",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -678,7 +679,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "30537f2c",
|
||||
"id": "edd6b448",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -690,7 +691,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d37b9ab1",
|
||||
"id": "6e5b95b5",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -700,7 +701,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c972723b",
|
||||
"id": "3e656f64",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -712,7 +713,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a4f86aa7",
|
||||
"id": "871fb2d1",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -722,7 +723,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "25115dd1",
|
||||
"id": "80d6e316",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -734,7 +735,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1b3ec70a",
|
||||
"id": "939363d6",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -744,7 +745,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d99f2c28",
|
||||
"id": "70e5b39d",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -756,7 +757,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "784d5cf1",
|
||||
"id": "1394fd74",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -766,7 +767,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3f2bfe8f",
|
||||
"id": "6909eec6",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -778,7 +779,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ff895053",
|
||||
"id": "d7b56142",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -798,7 +799,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2a56a0f2",
|
||||
"id": "a31c3041",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -810,7 +811,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e9df78f4",
|
||||
"id": "a771e5f4",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -820,7 +821,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "dbd929f5",
|
||||
"id": "9a9c96d1",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -836,7 +837,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c2afd475",
|
||||
"id": "07323be9",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -848,7 +849,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a7c172ee",
|
||||
"id": "3c996e3d",
|
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"metadata": {
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"editable": true
|
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},
|
||||
@@ -876,7 +877,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a8b51916",
|
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"id": "39637844",
|
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"metadata": {
|
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"editable": true
|
||||
},
|
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@@ -889,7 +890,7 @@
|
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{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
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"id": "6cb3eb3c",
|
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"id": "76903ecb",
|
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"metadata": {
|
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"collapsed": false,
|
||||
"editable": true
|
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@@ -921,7 +922,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "dd395b89",
|
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"id": "ca8755d2",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -939,7 +940,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1ed1052e",
|
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"id": "f0463b3c",
|
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"metadata": {
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"editable": true
|
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},
|
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@@ -952,7 +953,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "323f5865",
|
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"id": "8b514ec6",
|
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"metadata": {
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"editable": true
|
||||
},
|
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@@ -964,7 +965,7 @@
|
||||
},
|
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{
|
||||
"cell_type": "markdown",
|
||||
"id": "4e5c8544",
|
||||
"id": "423db9ff",
|
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"metadata": {
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"editable": true
|
||||
},
|
||||
@@ -974,7 +975,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e9b4fb88",
|
||||
"id": "06b275d6",
|
||||
"metadata": {
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||||
"editable": true
|
||||
},
|
||||
@@ -986,7 +987,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ab01f1bc",
|
||||
"id": "4ca06fe0",
|
||||
"metadata": {
|
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"editable": true
|
||||
},
|
||||
@@ -996,7 +997,7 @@
|
||||
},
|
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{
|
||||
"cell_type": "markdown",
|
||||
"id": "320732d2",
|
||||
"id": "da5754bd",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -1008,7 +1009,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b7977014",
|
||||
"id": "c0f54e05",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -1021,7 +1022,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0fe86cf6",
|
||||
"id": "9152ac2b",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -1033,7 +1034,7 @@
|
||||
},
|
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{
|
||||
"cell_type": "markdown",
|
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"id": "8a51677a",
|
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"id": "a4d9623f",
|
||||
"metadata": {
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"editable": true
|
||||
},
|
||||
@@ -1045,7 +1046,7 @@
|
||||
},
|
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{
|
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"cell_type": "markdown",
|
||||
"id": "2ebafed4",
|
||||
"id": "5c95acd3",
|
||||
"metadata": {
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"editable": true
|
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},
|
||||
@@ -1057,7 +1058,7 @@
|
||||
},
|
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{
|
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"cell_type": "markdown",
|
||||
"id": "afde236a",
|
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"id": "08667505",
|
||||
"metadata": {
|
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"editable": true
|
||||
},
|
||||
@@ -1067,7 +1068,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c529803e",
|
||||
"id": "95940c93",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -1079,7 +1080,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "58eb0e6c",
|
||||
"id": "888345e6",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -1089,7 +1090,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0c3a10a0",
|
||||
"id": "3aa4d471",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -1105,7 +1106,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "6ebff1c2",
|
||||
"id": "7778dcfd",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -1117,7 +1118,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cea9a34e",
|
||||
"id": "70b44d33",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -1138,7 +1139,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d226041b",
|
||||
"id": "4ca117e7",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -1149,7 +1150,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "bcf8516d",
|
||||
"id": "2039b018",
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
@@ -1201,7 +1202,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b96fef32",
|
||||
"id": "6db0b31b",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -1212,7 +1213,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "4f8ce2c3",
|
||||
"id": "de26bfe7",
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
@@ -1263,7 +1264,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "35426902",
|
||||
"id": "98629401",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -1286,7 +1287,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "bd80e051",
|
||||
"id": "70f7d4cc",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -1297,7 +1298,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "0f0e552d",
|
||||
"id": "01dab946",
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
@@ -1349,7 +1350,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "70eaee5f",
|
||||
"id": "f132426b",
|
||||
"metadata": {
|
||||
"editable": true
|
||||
},
|
||||
@@ -1362,7 +1363,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "dbe4ac22",
|
||||
"id": "7bed8a59",
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"editable": true
|
||||
|
||||
@@ -0,0 +1,75 @@
|
||||
"""
|
||||
Code to test Ridge and NNs using Scikit-Learn only
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn import linear_model
|
||||
from sklearn.neural_network import MLPRegressor
|
||||
from sklearn.metrics import accuracy_score
|
||||
import seaborn as sns
|
||||
|
||||
|
||||
def MSE(y_data,y_model):
|
||||
n = np.size(y_model)
|
||||
return np.sum((y_data-y_model)**2)/n
|
||||
# A seed just to ensure that the random numbers are the same for every run.
|
||||
# Useful for eventual debugging.
|
||||
np.random.seed(315)
|
||||
|
||||
n = 1000
|
||||
x = np.random.rand(n)
|
||||
y = x+x*x
|
||||
X = np.zeros((n,1))
|
||||
X[:,0] = x
|
||||
|
||||
|
||||
# We split the data in test and training data
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
|
||||
|
||||
# Decide which values of lambda to use
|
||||
|
||||
nlambdas = 5
|
||||
lmbd_vals = np.logspace(-4, 0, nlambdas)
|
||||
MSERidgePredict = np.zeros(nlambdas)
|
||||
for i in range(nlambdas):
|
||||
lmb = lmbd_vals[i]
|
||||
RegRidge = linear_model.Ridge(lmb)
|
||||
RegRidge.fit(X_train,y_train)
|
||||
ypredictRidge = RegRidge.predict(X_test)
|
||||
MSERidgePredict[i] = MSE(y_test,ypredictRidge)
|
||||
|
||||
plt.figure()
|
||||
plt.plot(np.log10(lmbd_vals), MSERidgePredict, 'g--', label = 'MSE SL Ridge Test')
|
||||
plt.xlabel('log10(lambda)')
|
||||
plt.ylabel('MSE')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
# Neural Network part
|
||||
|
||||
n_hidden_neurons = 100
|
||||
epochs = 100
|
||||
# store models for later use
|
||||
eta_vals = np.logspace(-4, 0, 5)
|
||||
# store the models for later use
|
||||
DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
|
||||
test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
|
||||
sns.set()
|
||||
for i, eta in enumerate(eta_vals):
|
||||
for j, lmbd in enumerate(lmbd_vals):
|
||||
dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation='logistic',
|
||||
alpha=lmbd, learning_rate_init=eta, max_iter=epochs)
|
||||
dnn.fit(X_train, y_train)
|
||||
ypredictMLP = dnn.predict(X_test)
|
||||
test_accuracy[i][j] = MSE(ypredictMLP, y_test)
|
||||
|
||||
fig, ax = plt.subplots(figsize = (10, 10))
|
||||
sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
|
||||
ax.set_title("Test Accuracy")
|
||||
ax.set_ylabel("$\eta$")
|
||||
ax.set_xlabel("$\lambda$")
|
||||
plt.show()
|
||||
|
||||
@@ -0,0 +1,104 @@
|
||||
"""
|
||||
Code to test Ridge and NNs using Scikit-Learn only
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn import linear_model
|
||||
from sklearn.neural_network import MLPRegressor
|
||||
from sklearn.metrics import accuracy_score
|
||||
import seaborn as sns
|
||||
|
||||
|
||||
def MSE(y_data,y_model):
|
||||
n = np.size(y_model)
|
||||
return np.sum((y_data-y_model)**2)/n
|
||||
# A seed just to ensure that the random numbers are the same for every run.
|
||||
# Useful for eventual debugging.
|
||||
np.random.seed(315)
|
||||
|
||||
n = 100
|
||||
x = np.random.rand(n)
|
||||
y = x+x*x#np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)
|
||||
|
||||
Maxpolydegree = 2
|
||||
X = np.zeros((n,Maxpolydegree-1))
|
||||
|
||||
for degree in range(1,Maxpolydegree): #No intercept column
|
||||
X[:,degree-1] = x**(degree)
|
||||
|
||||
# We split the data in test and training data
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
|
||||
|
||||
# Decide which values of lambda to use
|
||||
|
||||
nlambdas = 10
|
||||
lmbd_vals = np.logspace(-4, 0, nlambdas)
|
||||
MSERidgePredict = np.zeros(nlambdas)
|
||||
for i in range(nlambdas):
|
||||
lmb = lmbd_vals[i]
|
||||
RegRidge = linear_model.Ridge(lmb)
|
||||
RegRidge.fit(X_train,y_train)
|
||||
ypredictRidge = RegRidge.predict(X_test)
|
||||
MSERidgePredict[i] = MSE(y_test,ypredictRidge)
|
||||
|
||||
plt.figure()
|
||||
plt.plot(np.log10(lmbd_vals), MSERidgePredict, 'g--', label = 'MSE SL Ridge Test')
|
||||
plt.xlabel('log10(lambda)')
|
||||
plt.ylabel('MSE')
|
||||
plt.legend()
|
||||
plt.show()
|
||||
|
||||
# Neural Network part
|
||||
|
||||
n_hidden_neurons = 50
|
||||
epochs = 100
|
||||
# store models for later use
|
||||
eta_vals = np.logspace(-4, 0, 10)
|
||||
# store the models for later use
|
||||
DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
|
||||
test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
|
||||
sns.set()
|
||||
for i, eta in enumerate(eta_vals):
|
||||
for j, lmbd in enumerate(lmbd_vals):
|
||||
dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation='logistic',
|
||||
alpha=lmbd, learning_rate_init=eta, max_iter=epochs)
|
||||
dnn.fit(X_train, y_train)
|
||||
ypredictMLP = dnn.predict(X_test)
|
||||
test_accuracy[i][j] = MSE(ypredictMLP, y_test)
|
||||
|
||||
fig, ax = plt.subplots(figsize = (10, 10))
|
||||
sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
|
||||
ax.set_title("Training Accuracy")
|
||||
ax.set_ylabel("$\eta$")
|
||||
ax.set_xlabel("$\lambda$")
|
||||
plt.show()
|
||||
|
||||
# Now we redefine our design matrix to include only the x-values and try out our NN
|
||||
|
||||
X = np.zeros((n,1))
|
||||
X[:,0] = x
|
||||
|
||||
# We split the data in test and training data again
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
|
||||
# Repeat the NN calculation
|
||||
DNN_scikit = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
|
||||
test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
|
||||
sns.set()
|
||||
for i, eta in enumerate(eta_vals):
|
||||
for j, lmbd in enumerate(lmbd_vals):
|
||||
dnn = MLPRegressor(hidden_layer_sizes=(n_hidden_neurons), activation='logistic',
|
||||
alpha=lmbd, learning_rate_init=eta, max_iter=epochs)
|
||||
dnn.fit(X_train, y_train)
|
||||
ypredictMLP = dnn.predict(X_test)
|
||||
test_accuracy[i][j] = MSE(ypredictMLP, y_test)
|
||||
|
||||
fig, ax = plt.subplots(figsize = (10, 10))
|
||||
sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
|
||||
ax.set_title("Training Accuracy")
|
||||
ax.set_ylabel("$\eta$")
|
||||
ax.set_xlabel("$\lambda$")
|
||||
plt.show()
|
||||
|
||||
@@ -40,6 +40,7 @@ from pydot import graph_from_dot_data
|
||||
from sklearn.datasets import load_breast_cancer
|
||||
from sklearn.svm import SVC
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.tree import DecisionTreeClassifier
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
from sklearn.preprocessing import LabelEncoder
|
||||
from sklearn.model_selection import cross_validate
|
||||
|
||||
Reference in New Issue
Block a user