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Adds logistic regression hyperparams
Fixes #5
1 parent a6ef238 commit 553bcc2

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+268
-18
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2 files changed

+268
-18
lines changed

‎static/js/dataspace.js

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@@ -122,6 +122,16 @@ class Dataspace {
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$("#metrics").removeClass("visible").addClass("invisible");
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}
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// Count the number of classes in the dataset
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classes() {
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return this.dataset.reduce(function(acc, val) {
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if (!acc.includes(val.c)) {
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acc.push(val.c);
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}
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return acc;
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}, []);
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}
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}
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$(document).ready(function() {
@@ -270,4 +280,82 @@ $(document).ready(function() {
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});
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// Enable the correct hyperparameters based on the selected logistic solver
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$('#logit select[name="solver"]').change(function(e) {
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// Disable all the penalties
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$('#logit input[name="penalty"]').each(function(_, elem) {
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$(elem).prop("disabled", true);
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});
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// Disable dual -- this only works with liblinear and l2
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$('#logit input[name="dual"]').prop("disabled", true);
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// Disable intercept_scaling -- this only works with liblinear and fit_intercept=True
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$('#logit input[name="intercept_scaling"]').prop("disabled", true);
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// Disable l1_ratio -- this only works with SAGA and penalty=elasticnet
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$('#logit input[name="l1_ratio"]').prop("disabled", true);
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// Enable/Disable based on the selected penalty
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switch ($(this).val()) {
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case "newton-cg":
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case "sag":
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case "lbfgs":
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$('#logit input[name="penalty"]#penalty2').prop("disabled", false);
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$('#logit input[name="penalty"]#penalty4').prop("disabled", false);
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break;
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case "liblinear":
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$('#logit input[name="penalty"]').each(function (_, elem) {
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elem = $(elem);
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if (elem.val() != 'none' && elem.val() != 'elasticnet') {
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elem.prop("disabled", false);
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}
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});
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if ($('#logit input[name="penalty"]:checked').val() == 'l2') {
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$('#logit input[name="dual"]').prop("disabled", false);
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}
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if ($('#logit input[name="penalty"]:checked').val() == 'elasticnet') {
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$('#logit input[name="l1_ratio"]').prop("disabled", false);
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}
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$('#logit input[name="intercept_scaling"]').prop("disabled", false);
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break;
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case "saga":
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$('#logit input[name="penalty"]').each(function (_, elem) {
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$(elem).prop("disabled", false);
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});
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break;
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default:
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console.log("unknown solver selected, cannot enable form!");
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}
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})
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// Enable the correct hyperparameters based on the selected logistic penalty
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$('#logit input[name="penalty"]').change(function(e) {
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// Disable dual -- this only works with liblinear and l2
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$('#logit input[name="dual"]').prop("disabled", true);
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// Disable l1_ratio -- this only works with elasticnet
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$('#logit input[name="l1_ratio"]').prop("disabled", true);
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// Enable/Disable based on the selected kernel
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switch ($(this).val()) {
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case "l1":
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break
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case "l2":
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if ($('#logit select[name="solver"]').val() == "liblinear") {
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$('#logit input[name="dual"]').prop("disabled", false);
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}
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break
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case "elasticnet":
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$('#logit input[name="l1_ratio"]').prop("disabled", false);
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case "none":
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break
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default:
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console.log($(this).val());
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console.log("unknown penalty, cannot correctly enable form!");
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}
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});
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});

‎templates/index.html

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@@ -284,7 +284,7 @@
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</div>
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</div>
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<div class="form-group form-row mb-1">
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<label for="decision_function_shape" class="col-form-label col-md-4">Decision Shape</label>
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<label for="decision_function_shape" class="col-form-label col-md-4 my-1">Decision Shape</label>
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<div class="col-md-8">
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<select class="custom-select my-1 mr-sm-2" name="decision_function_shape" id="decision_function_shape">
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<option value="ovr" selected>One v. Rest</option>
@@ -297,9 +297,118 @@
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</form>
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</div>
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<div class="tab-pane fade" id="logit" role="tabpanel">
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<p>Logistic Regression</p>
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<form class="form">
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<input type="hidden" name="model" value="logit" />
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<div class="row">
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<div class="col-md-3">
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<div class="form-group form-row mb-1">
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<label for="solver" class="col-form-label col-md-4">Solver</label>
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<div class="col-md-8">
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<select class="custom-select my-1 mr-sm-2" name="solver" id="solver">
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<option value="newton-cg">Newton-CG</option>
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<option value="lbfgs">LBFGS</option>
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<option value="liblinear" selected>LibLinear</option>
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<option value="sag">SAG</option>
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<option value="saga">SAGA</option>
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</select>
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</div>
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</div>
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<div class="form-row">
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<label for="penalty" class="col-form-label col-md-4">Penalty</label>
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<div class="col-md-8">
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<div class="form-check">
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<input class="form-check-input" type="radio" name="penalty" id="penalty1" value="l1">
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<label class="form-check-label" for="penalty1">
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L1
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</label>
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</div>
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<div class="form-check">
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<input class="form-check-input" type="radio" name="penalty" id="penalty2" value="l2" checked>
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<label class="form-check-label" for="penalty2">
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L2
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</label>
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</div>
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<div class="form-check">
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<input class="form-check-input" type="radio" name="penalty" id="penalty3" value="elasticnet" disabled>
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<label class="form-check-label" for="penalty3">
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ElasticNet
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</label>
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</div>
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<div class="form-check">
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<input class="form-check-input" type="radio" name="penalty" id="penalty4" value="none" disabled>
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<label class="form-check-label" for="penalty4">
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None
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</label>
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</div>
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</div>
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</div>
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</div>
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<div class="col-md-3">
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<div class="form-group form-row mb-1">
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<label for="C" class="col-form-label col-md-5">C</label>
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<div class="col-md-7">
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<input class="form-control" type="text" name="C" id="C" value="1.0" />
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</div>
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</div>
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<div class="form-group form-row mb-1">
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<label for="intercept_scaling" class="col-form-label col-md-5">Scale Intercept</label>
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<div class="col-md-7">
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<input class="form-control" type="text" name="intercept_scaling" id="intercept_scaling" value="1.0" />
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</div>
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</div>
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<div class="form-group form-row mb-1">
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<label for="l1_ratio" class="col-form-label col-md-5">L1 Ratio</label>
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<div class="col-md-7">
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<input class="form-control" type="text" name="l1_ratio" id="l1_ratio" value="0.5" disabled />
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</div>
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</div>
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</div>
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<div class="col-md-3">
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<div class="row">
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<div class="col-md-4"></div>
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<div class="col-md-8">
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<div class="form-check">
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<input class="form-check-input" type="checkbox" name="fit_intercept" id="fit_intercept" checked>
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<label class="form-check-label" for="fit_intercept">Fit Intercept</label>
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</div>
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<div class="form-check">
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<input class="form-check-input" type="checkbox" name="dual" id="dual">
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<label class="form-check-label" for="dual">Dual</label>
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</div>
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</div>
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</div>
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<div class="form-group form-row mb-1">
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<label for="multi_class" class="col-form-label col-md-4 my-1">Multi-Class</label>
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<div class="col-md-8">
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<select class="custom-select my-1 mr-sm-2" name="multi_class" id="multi_class">
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<option value="auto" selected>Auto</option>
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<option value="ovr">One v. Rest</option>
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<option value="multinomial">Multinomial</option>
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</select>
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</div>
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</div>
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</div>
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<div class="col-md-3">
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<div class="form-group form-row mb-1">
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<label for="class_weight" class="col-form-label col-md-5">Class Weight</label>
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<div class="col-md-7">
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<input class="form-control" type="text" name="class_weight" id="class_weight" value="null" />
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</div>
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</div>
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<div class="form-group form-row mb-1">
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<label for="tol" class="col-form-label col-md-5">Tol</label>
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<div class="col-md-7">
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<input class="form-control" type="text" name="tol" id="tol" value="0.0001" />
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</div>
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</div>
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<div class="form-group form-row mb-1">
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<label for="max_iter" class="col-form-label col-md-5">Max Iter</label>
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<div class="col-md-7">
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<input class="form-control" type="number" name="max_iter" id="max_iter" value="100" step="10" min="10" max="10000" />
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</div>
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</div>
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</div>
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</div>
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</form>
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</div>
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</div><!-- model tabs ends -->
@@ -441,35 +550,35 @@ <h5 class="modal-title" id="svmInfoModalLabel">Support Vector Machines</h5>
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</p>
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<h6>Hyperparameters</h6>
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<dl>
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<dt>C <code>float</code></dt>
553+
<dt>C &middot; <code>float</code></dt>
445554
<dd>Penalty parameter C of the error term.</dd>
446-
<dt>kernel <code>string</code></dt>
555+
<dt>kernel &middot; <code>{'linear', 'poly', 'rbf', 'sigmoid', 'precomputed', None}</code></dt>
447556
<dd>
448-
Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’,
449-
‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a
450-
callable is given it is used to pre-compute the kernel matrix from data matrices; that
557+
Specifies the kernel type to be used in the algorithm. It must be one of the string
558+
choices or a callable. If None is given, ‘rbf’ will be used. If a callable is given
559+
it is used to pre-compute the kernel matrix from data matrices; that
451560
matrix should be an array of shape <code>(n_samples, n_samples)</code>.
452561
</dd>
453-
<dt>degree <code>int</code></dt>
562+
<dt>degree &middot; <code>int</code></dt>
454563
<dd>Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.</dd>
455-
<dt>gamma <code>float</code></dt>
564+
<dt>gamma &middot; <code>float</code></dt>
456565
<dd>Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.</dd>
457-
<dt>coef0 <code>float</code></dt>
566+
<dt>coef0 &middot; <code>float</code></dt>
458567
<dd>Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.</dd>
459-
<dt>shrinking <code>boolean</code></dt>
568+
<dt>shrinking &middot; <code>boolean</code></dt>
460569
<dd>Whether to use the shrinking heuristic.</dd>
461-
<dt>tol <code>float</code></dt>
570+
<dt>tol &middot; <code>float</code></dt>
462571
<dd>Tolerance for stopping criterion.</dd>
463-
<dt>class_weight <code>{dict, 'balanced'}</code></dt>
572+
<dt>class_weight &middot; <code>{dict, 'balanced'}</code></dt>
464573
<dd>
465574
Set the parameter C of class i to class_weight[i]*C for SVC. If not given, all classes are
466575
supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust
467576
weights inversely proportional to class frequencies in the input data as
468577
<code>n_samples / (n_classes * np.bincount(y))</code>
469578
</dd>
470-
<dt>max_iter <code>int</code></dt>
579+
<dt>max_iter &middot; <code>int</code></dt>
471580
<dd>Hard limit on iterations within solver, or -1 for no limit.</dd>
472-
<dt>decision_function_shape <code>{‘ovo’, ‘ovr’}</code></dt>
581+
<dt>decision_function_shape &middot; <code>{‘ovo’, ‘ovr’}</code></dt>
473582
<dd>
474583
Whether to return a one-vs-rest (‘ovr’) decision function of shape <code>(n_samples, n_classes)</code>
475584
as all other classifiers, or the original one-vs-one (‘ovo’) decision function of libsvm which has shape
@@ -496,12 +605,65 @@ <h5 class="modal-title" id="logitInfoModalLabel">Logistic Regression</h5>
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</button>
497606
</div>
498607
<div class="modal-body">
499-
<p>TODO</p>
608+
<p>
609+
<a href="https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression" target="_blank">
610+
Logistic Regression</a> is a supervised classification algorithm that models the probabilities
611+
describing the possible outcome (class) of a single trial using a logistic function. This method
612+
is also known as a logit regression, maximum-entropy classifier, or log-linear classifier.
613+
</p>
500614

501615
<h6>Hyperparameters</h6>
502616
<dl>
503-
<dt>param <code>type</code></dt>
504-
<dd>description</dd>
617+
<dt>penalty &middot; <code>{'l1', 'l2', 'elasticnet', 'none'}</code></dt>
618+
<dd>
619+
Used to specify the norm used in the penalization. The ‘newton-cg’, ‘sag’ and ‘lbfgs’ solvers
620+
support only l2 penalties. ‘elasticnet’ is only supported by the ‘saga’ solver. If ‘none’ (not
621+
supported by the liblinear solver), no regularization is applied.
622+
</dd>
623+
<dt>dual &middot; <code>bool</code></dt>
624+
<dd>
625+
Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear
626+
solver. Prefer <code>dual=False</code> when <code>n_samples > n_features</code>.
627+
</dd>
628+
<dt>tol &middot; <code>float</code></dt>
629+
<dd>Tolerance for stopping criteria.</dd>
630+
<dt>C &middot; <code>float</code></dt>
631+
<dd>
632+
Inverse of regularization strength; must be a positive float. Like in support vector machines,
633+
smaller values specify stronger regularization.
634+
</dd>
635+
<dt>fit_intercept &middot; <code>bool</code></dt>
636+
<dd>Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.</dd>
637+
<dt>intercept_scaling &middot; <code>float</code></dt>
638+
<dd>
639+
Useful only when the solver ‘liblinear’ is used and self.fit_intercept is set to True. In this case,
640+
x becomes <code>[x, self.intercept_scaling]</code>, i.e. a “synthetic” feature with constant value
641+
equal to intercept_scaling is appended to the instance vector.
642+
</dd>
643+
<dt>class_weight &middot; <code>{dict, 'balanced'}</code></dt>
644+
<dd>
645+
Weights associated with classes in the form {class_label: weight}. If not given, all classes are
646+
supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights
647+
inversely proportional to class frequencies in the input data as
648+
<code>n_samples / (n_classes * np.bincount(y)).</code>
649+
</dd>
650+
<dt>solver &middot; <code>{'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'}</code></dt>
651+
<dd>Algorithm to use in the optimization problem.</dd>
652+
<dt>max_iter &middot; <code>int</code></dt>
653+
<dd>Maximum number of iterations taken for the solvers to converge.</dd>
654+
<dt>multi_class &middot; <code>{'ovr', 'multinomial', 'auto'}</code></dt>
655+
<dd>
656+
If the option chosen is ‘ovr’, then a binary problem is fit for each label. For ‘multinomial’ the
657+
loss minimised is the multinomial loss fit across the entire probability distribution, even when the
658+
data is binary. ‘multinomial’ is unavailable when solver=’liblinear’. ‘auto’ selects ‘ovr’ if the
659+
data is binary, or if solver=’liblinear’, and otherwise selects ‘multinomial’.
660+
</dd>
661+
<dt>l1_ratio &middot; <code>float</code></dt>
662+
<dd>
663+
The Elastic-Net mixing parameter, with <code>0 <= l1_ratio <=1</code>. Only used if <code>penalty='elasticnet'</code>.
664+
Setting <code>l1_ratio=0</code> is equivalent to using <code>penalty='l2'</code>, while setting <code>l1_ratio=1</code>
665+
is equivalent to using <code>penalty='l1'</code>. For <code>0 < l1_ratio < 1</code>, the penalty is a combination of L1 and L2.
666+
</dd>
505667
</dl>
506668
</div>
507669
<div class="modal-footer">

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