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symfony.sql
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-- phpMyAdmin SQL Dump
-- version 4.1.14
-- http://www.phpmyadmin.net
--
-- Host: 127.0.0.1
-- Generation Time: May 22, 2015 at 05:16 PM
-- Server version: 5.6.17
-- PHP Version: 5.5.12
SET SQL_MODE = "NO_AUTO_VALUE_ON_ZERO";
SET time_zone = "+00:00";
/*!40101 SET @OLD_CHARACTER_SET_CLIENT=@@CHARACTER_SET_CLIENT */;
/*!40101 SET @OLD_CHARACTER_SET_RESULTS=@@CHARACTER_SET_RESULTS */;
/*!40101 SET @OLD_COLLATION_CONNECTION=@@COLLATION_CONNECTION */;
/*!40101 SET NAMES utf8 */;
--
-- Database: `symfony`
--
-- --------------------------------------------------------
--
-- Table structure for table `ode_dataset`
--
CREATE TABLE IF NOT EXISTS `ode_dataset` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`path` varchar(100) COLLATE utf8_unicode_ci,
`name` varchar(100) COLLATE utf8_unicode_ci NOT NULL,
`description` longtext COLLATE utf8_unicode_ci NOT NULL,
`num_instances` int(11) NOT NULL,
`num_features` int(11) NOT NULL,
`filetype` longtext COLLATE utf8_unicode_ci NOT NULL,
`filesize` bigint(20) NOT NULL,
`filename` longtext COLLATE utf8_unicode_ci NOT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COLLATE=utf8_unicode_ci AUTO_INCREMENT=4 ;
-- --------------------------------------------------------
--
-- Table structure for table `ode_models`
--
CREATE TABLE IF NOT EXISTS `ode_models` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`type` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`parameters` longtext COLLATE utf8_unicode_ci NOT NULL COMMENT '(DC2Type:json_array)',
`name` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`form` longtext COLLATE utf8_unicode_ci NOT NULL,
`description` longtext COLLATE utf8_unicode_ci NOT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COLLATE=utf8_unicode_ci AUTO_INCREMENT=10 ;
--
-- Dumping data for table `ode_models`
--
INSERT INTO `ode_models` (`id`, `type`, `parameters`, `name`, `form`, `description`) VALUES
(1, 'classification', '{"criterion":"string","splitter":"string","max_features":"int","max_leaf_nodes":"int","min_samples_split":"int","min_samples_leaf":"int","max_depth":"int"}', 'Decision Tree', '<div class="row">\n <div class="col-lg-2">\n <label>Criterion: <i class="fa fa-question-circle" style="color:#676a6c" id="q9"></i></label>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="gini" name="criterion" checked="" class="required"> Gini</label>\n </div>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="entropy" name="criterion" class="required"> Entropy</label>\n </div>\n </div>\n <div class="col-lg-2">\n <label>Splitter: <i class="fa fa-question-circle" style="color:#676a6c" id="q10"></i></label>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="best" name="splitter" checked="" class="required"> Best</label>\n </div>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="random" name="splitter" class="required"> Random</label>\n </div>\n </div>\n <div class="col-lg-4 text-center">\n <label>max_leaf_nodes = <span class="example-val" style="color:#1AB394" id="max_leaf_nodes-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q11"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="max_leaf_nodes-slider"></div>\n <input type="hidden" name="max_leaf_nodes" id="max_leaf_nodes" value="">\n <br/>\n </div>\n </div>\n <div class="col-lg-4 text-center">\n <label>min_samples_leaf = <span class="example-val" style="color:#1AB394" id="min_samples_leaf-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q12"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="min_samples_leaf-slider"></div>\n <input type="hidden" name="min_samples_leaf" id="min_samples_leaf" value="">\n </div>\n </div>\n</div>\n<div class="hr-line-dashed"></div>\n<div class="row">\n <div class="col-lg-4 text-center">\n <label>max_features = <span class="example-val" style="color:#1AB394" id="max_features-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q13"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="max_features-slider"></div>\n <input type="hidden" name="max_features" id="max_features" value="">\n <br/>\n </div>\n </div>\n <div class="col-lg-4 text-center">\n <label>min_samples_split = <span class="example-val" style="color:#1AB394" id="min_samples_split-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q14"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="min_samples_split-slider"></div>\n <input type="hidden" name="min_samples_split" id="min_samples_split" value="">\n </div>\n </div>\n <div class="col-lg-4 text-center" style="display:inline-block; vertical-align: middle; float: none; margin-right:-4px;">\n <label>max_depth = <span class="example-val" style="color:#1AB394" id="max_depth-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q15"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="max_depth-slider"></div>\n <input type="hidden" name="max_depth" id="max_depth" value="">\n </div>\n </div>\n</div>\n<div class="hr-line-dashed"></div>\n<script>\n $(document).ready(function() {\n $(''.i-checks'').iCheck({\n checkboxClass: ''icheckbox_square-green'',\n radioClass: ''iradio_square-green'',\n });\n $(''#max_features-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [NUM_FEATURES]\n }\n });\n $(''#max_features-slider'').Link(''lower'').to($(''#max_features-value''));\n $(''#max_features-slider'').Link(''lower'').to($(''#max_features''));\n $(''#min_samples_split-slider'').noUiSlider({\n start: [2],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [NUM_INSTANCES]\n }\n });\n $(''#min_samples_split-slider'').Link(''lower'').to($(''#min_samples_split-value''));\n $(''#min_samples_split-slider'').Link(''lower'').to($(''#min_samples_split''));\n $(''#max_leaf_nodes-slider'').noUiSlider({\n start: [2],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [2],\n ''max'': [20]\n }\n });\n $(''#max_leaf_nodes-slider'').Link(''lower'').to($(''#max_leaf_nodes-value''));\n $(''#max_leaf_nodes-slider'').Link(''lower'').to($(''#max_leaf_nodes''));\n $(''#min_samples_leaf-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [NUM_FEATURES]\n }\n });\n $(''#min_samples_leaf-slider'').Link(''lower'').to($(''#min_samples_leaf-value''));\n $(''#min_samples_leaf-slider'').Link(''lower'').to($(''#min_samples_leaf''));\n $(''#max_depth-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [20]\n }\n });\n $(''#max_depth-slider'').Link(''lower'').to($(''#max_depth-value''));\n $(''#max_depth-slider'').Link(''lower'').to($(''#max_depth''));\n\n $("#q9").qtip({ \n content: {\n text: ''The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.'',\n title:''<i>criterion </i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q10").qtip({ \n content: {\n text: ''The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.'',\n title:''<i>splitter</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q11").qtip({ \n content: {\n text: ''Grow a tree with <i>max_leaf_nodes</i> in best-first fashion. Best nodes are defined as relative reduction in impurity. If <strong>0</strong> then unlimited number of leaf nodes. If not <strong>0</strong> then <i>max_depth</i> will be ignored.'',\n title:''<i>max_leaf_nodes</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q12").qtip({ \n content: {\n text: ''The minimum number of samples required to be at a leaf node.'',\n title:''<i>min_samples_leaf</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q13").qtip({ \n content: {\n text: ''The number of features to consider when looking for the best split.'',\n title:''<i>max_features</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q14").qtip({ \n content: {\n text: ''The minimum number of samples required to split an internal node.'',\n title:''<i>min_samples_split</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q15").qtip({ \n content: {\n text: ''The maximum depth of the tree. If <strong>0</strong>, then nodes are expanded until all leaves are pure or until all leaves contain less than <i>min_samples_split</i> samples. Ignored if <i>max_leaf_nodes</i> is not <strong>0</strong>.'',\n title:''<i>max_depth</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n })\n });\n</script>', '<h4 class="m-b-xxs text-center">Decision Tree Classifier <i class="fa fa-tree"></i></h4><p>A decision tree is a decision support tool that uses a tree-like graph ormodel of decisions and their possible consequences, including chance eventoutcomes, resource costs, and utility. It is one way to display an algorithm.<a href="http://en.wikipedia.org/wiki/Decision_tree_learning" target="_blank"> [ref]</a></p><div class="col-xs-6"> <div> <span>Complexity</span> <small class="pull-right">5/10</small> </div> <div class="progress progress-small"> <div class="progress-bar progress-bar-warning" style="width: 50%;"> </div> </div></div><div class="col-xs-6"> <div> <span>Interpretability</span> <small class="pull-right">9/10</small> </div> <div class="progress progress-small"> <div class="progress-bar" style="width: 90%;"> </div> </div></div>');
INSERT INTO `ode_models` (`id`, `type`, `parameters`, `name`, `form`, `description`) VALUES
(2, 'classification', '{"n_estimators":"int","criterion":"string","max_features":"int","max_depth":"int","min_samples_split":"int","min_samples_leaf":"int","min_weight_fraction_leaf":"float","max_leaf_nodes":"int","bootstrap":"bool","oob_score":"bool","random_state":"int","warm_start":"bool"}', 'Extremely Randomized Trees', '<div class="row">\r\n <div class="col-lg-2">\r\n <label>Criterion: <i class="fa fa-question-circle" style="color:#676a6c" id="q9"></i></label>\r\n <div class="radio i-checks">\r\n <label>\r\n <input type="radio" value="gini" name="criterion" checked="" class="required"> Gini</label>\r\n </div>\r\n <div class="radio i-checks">\r\n <label>\r\n <input type="radio" value="entropy" name="criterion" class="required"> Entropy</label>\r\n </div>\r\n <br>\r\n\r\n <label>Boostrap: <i class="fa fa-question-circle" style="color:#676a6c" id="q10"></i></label>\r\n <div class="radio i-checks">\r\n <label>\r\n <input type="radio" value="1" name="bootstrap" checked="" class="required"> True</label>\r\n </div>\r\n <div class="radio i-checks">\r\n <label>\r\n <input type="radio" value="0" name="bootstrap" class="required"> False</label>\r\n </div>\r\n </div>\r\n\r\n <div class="col-lg-2">\r\n <label>Out-of-bag score: <i class="fa fa-question-circle" style="color:#676a6c" id="q11"></i></label>\r\n <div class="radio i-checks">\r\n <label>\r\n <input type="radio" value="1" name="oob_score" checked="" class="required"> True</label>\r\n </div>\r\n <div class="radio i-checks">\r\n <label>\r\n <input type="radio" value="0" name="oob_score" class="required"> False</label>\r\n </div>\r\n <br>\r\n\r\n <label>Warm start: <i class="fa fa-question-circle" style="color:#676a6c" id="q12"></i></label>\r\n <div class="radio i-checks">\r\n <label>\r\n <input type="radio" value="1" name="warm_start" checked="" class="required"> True</label>\r\n </div>\r\n <div class="radio i-checks">\r\n <label>\r\n <input type="radio" value="0" name="warm_start" class="required"> False</label>\r\n </div>\r\n </div>\r\n \r\n <div class="col-lg-4 text-center">\r\n <div class="row text-center">\r\n <label>max_leaf_nodes = <span class="example-val" style="color:#1AB394" id="max_leaf_nodes-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q13"></i></label>\r\n <div class="col-lg-10 col-lg-offset-1">\r\n <div id="max_leaf_nodes-slider"></div>\r\n <input type="hidden" name="max_leaf_nodes" id="max_leaf_nodes" value=""> \r\n </div>\r\n </div>\r\n <br>\r\n\r\n <div class="row text-center">\r\n <label>min_samples_leaf = <span class="example-val" style="color:#1AB394" id="min_samples_leaf-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q14"></i></label>\r\n <div class="col-lg-10 col-lg-offset-1">\r\n <div id="min_samples_leaf-slider"></div>\r\n <input type="hidden" name="min_samples_leaf" id="min_samples_leaf" value="">\r\n </div>\r\n </div>\r\n <br>\r\n\r\n <div class="row text-center">\r\n <label>max_features = <span class="example-val" style="color:#1AB394" id="max_features-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q15"></i></label>\r\n <div class="col-lg-10 col-lg-offset-1">\r\n <div id="max_features-slider"></div>\r\n <input type="hidden" name="max_features" id="max_features" value="">\r\n </div>\r\n </div>\r\n <br>\r\n\r\n <div class="row text-center">\r\n <label>num_estimators = <span class="example-val" style="color:#1AB394" id="n_estimators-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q16"></i></label>\r\n <div class="col-lg-10 col-lg-offset-1">\r\n <div id="n_estimators-slider"></div>\r\n <input type="hidden" name="n_estimators" id="n_estimators" value="">\r\n </div>\r\n </div>\r\n </div>\r\n \r\n\r\n\r\n <div class="col-lg-4 text-center">\r\n <div class="row text-center">\r\n <label>min_samples_split = <span class="example-val" style="color:#1AB394" id="min_samples_split-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q17"></i></label>\r\n <div class="col-lg-10 col-lg-offset-1">\r\n <div id="min_samples_split-slider"></div>\r\n <input type="hidden" name="min_samples_split" id="min_samples_split" value="">\r\n </div>\r\n </div>\r\n <br>\r\n\r\n <div class="row text-center">\r\n <label>max_depth = <span class="example-val" style="color:#1AB394" id="max_depth-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q18"></i></label>\r\n <div class="col-lg-10 col-lg-offset-1">\r\n <div id="max_depth-slider"></div>\r\n <input type="hidden" name="max_depth" id="max_depth" value="">\r\n </div>\r\n </div>\r\n <br>\r\n\r\n <div class="row text-center">\r\n <label>random_state = <span class="example-val" id=\r\n "random_state-value" style="color:#1AB394"></span> <i class=\r\n "fa fa-question-circle" id="q19" style=\r\n "color:#676a6c"></i></label>\r\n\r\n <div class="col-lg-10 col-lg-offset-1">\r\n <div id="random_state-slider"></div><input id=\r\n "random_state" name="random_state" type="hidden" value="">\r\n </div>\r\n </div>\r\n </div>\r\n</div>\r\n\r\n<script>\r\n $(document).ready(function() {\r\n $(''.i-checks'').iCheck({\r\n checkboxClass: ''icheckbox_square-green'',\r\n radioClass: ''iradio_square-green'',\r\n });\r\n $(''#max_features-slider'').noUiSlider({\r\n start: [1],\r\n step: 1,\r\n connect: "lower",\r\n range: {\r\n ''min'': [1],\r\n ''max'': [NUM_FEATURES]\r\n }\r\n });\r\n $(''#max_features-slider'').Link(''lower'').to($(''#max_features-value''));\r\n $(''#max_features-slider'').Link(''lower'').to($(''#max_features''));\r\n $(''#min_samples_split-slider'').noUiSlider({\r\n start: [1],\r\n step: 1,\r\n connect: "lower",\r\n range: {\r\n ''min'': [1],\r\n ''max'': [NUM_INSTANCES]\r\n }\r\n });\r\n $(''#min_samples_split-slider'').Link(''lower'').to($(''#min_samples_split-value''));\r\n $(''#min_samples_split-slider'').Link(''lower'').to($(''#min_samples_split''));\r\n $(''#max_leaf_nodes-slider'').noUiSlider({\r\n start: [2],\r\n step: 1,\r\n connect: "lower",\r\n range: {\r\n ''min'': [2],\r\n ''max'': [20]\r\n }\r\n });\r\n $(''#max_leaf_nodes-slider'').Link(''lower'').to($(''#max_leaf_nodes-value''));\r\n $(''#max_leaf_nodes-slider'').Link(''lower'').to($(''#max_leaf_nodes''));\r\n $(''#min_samples_leaf-slider'').noUiSlider({\r\n start: [1],\r\n step: 1,\r\n connect: "lower",\r\n range: {\r\n ''min'': [1],\r\n ''max'': [NUM_FEATURES]\r\n }\r\n });\r\n $(''#min_samples_leaf-slider'').Link(''lower'').to($(''#min_samples_leaf-value''));\r\n $(''#min_samples_leaf-slider'').Link(''lower'').to($(''#min_samples_leaf''));\r\n $(''#max_depth-slider'').noUiSlider({\r\n start: [1],\r\n step: 1,\r\n connect: "lower",\r\n range: {\r\n ''min'': [1],\r\n ''max'': [20]\r\n }\r\n });\r\n $(''#max_depth-slider'').Link(''lower'').to($(''#max_depth-value''));\r\n $(''#max_depth-slider'').Link(''lower'').to($(''#max_depth''));\r\n\r\n $(''#n_estimators-slider'').noUiSlider({\r\n start: [10],\r\n step: 10,\r\n connect: "lower",\r\n range: {\r\n ''min'': [10],\r\n ''max'': [1000]\r\n }\r\n });\r\n $(''#n_estimators-slider'').Link(''lower'').to($(''#n_estimators-value''));\r\n $(''#n_estimators-slider'').Link(''lower'').to($(''#n_estimators''));\r\n\r\n $(''#random_state-slider'').noUiSlider({\r\n start: [1],\r\n step: 1,\r\n connect: "lower",\r\n range: {\r\n ''min'': [1],\r\n ''max'': [100]\r\n }\r\n });\r\n $(''#random_state-slider'').Link(''lower'').to($(''#random_state-value''));\r\n $(''#random_state-slider'').Link(''lower'').to($(''#random_state''));\r\n\r\n $("#q9").qtip({ \r\n content: {\r\n text: ''The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.'',\r\n title:''<i>criterion </i>''\r\n },\r\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\r\n });\r\n $("#q10").qtip({ \r\n content: {\r\n text: ''Whether bootstrap samples are used when building trees.'',\r\n title:''<i>boostrap</i>''\r\n },\r\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\r\n });\r\n $("#q11").qtip({ \r\n content: {\r\n text: ''Whether to use out-of-bag samples to estimate the generalization error.'',\r\n title:''<i>oob_score</i>''\r\n },\r\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\r\n });\r\n $("#q12").qtip({ \r\n content: {\r\n text: ''When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest.'',\r\n title:''<i>warm_start</i>''\r\n },\r\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\r\n });\r\n $("#q13").qtip({ \r\n content: {\r\n text: ''Grow a tree with <i>max_leaf_nodes</i> in best-first fashion. Best nodes are defined as relative reduction in impurity. If <strong>0</strong> then unlimited number of leaf nodes. If not <strong>0</strong> then <i>max_depth</i> will be ignored.'',\r\n title:''<i>max_leaf_nodes</i>''\r\n },\r\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\r\n });\r\n $("#q14").qtip({ \r\n content: {\r\n text: ''The minimum number of samples required to be at a leaf node.'',\r\n title:''<i>min_samples_leaf</i>''\r\n },\r\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\r\n });\r\n $("#q15").qtip({ \r\n content: {\r\n text: ''The number of features to consider when looking for the best split.'',\r\n title:''<i>max_features</i>''\r\n },\r\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\r\n });\r\n $("#q16").qtip({ \r\n content: {\r\n text: ''The number of trees in the forest.'',\r\n title:''<i>num_estimators</i>''\r\n },\r\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\r\n });\r\n $("#q17").qtip({ \r\n content: {\r\n text: ''The minimum number of samples required to split an internal node.'',\r\n title:''<i>min_samples_split</i>''\r\n },\r\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\r\n });\r\n $("#q18").qtip({ \r\n content: {\r\n text: ''The maximum depth of the tree. If <strong>0</strong>, then nodes are expanded until all leaves are pure or until all leaves contain less than <i>min_samples_split</i> samples. Ignored if <i>max_leaf_nodes</i> is not <strong>0</strong>.'',\r\n title:''<i>max_depth</i>''\r\n },\r\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\r\n })\r\n $("#q19").qtip({\r\n content: {\r\n text: ''The seed of the pseudo random number generator to use when shuffling the data.'',\r\n title:''<i>random_state<\\/i>''\r\n },\r\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\r\n });\r\n });\r\n</script>', '<h4 class="m-b-xxs text-center">Extremely Randomized Trees <i class="fa fa-tree"></i><i class="fa fa-tree"></i><i class="fa fa-tree"></i></h4>\n\n\n<p>This classifier fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. <a href=\n"http://www.montefiore.ulg.ac.be/~ernst/uploads/news/id63/extremely-randomized-trees.pdf" target=\n"_blank">[ref]</a></p>\n\n\n<div class="col-xs-6">\n <div>\n <span>Complexity</span> <small class="pull-right">7/10</small>\n </div>\n\n\n <div class="progress progress-small">\n <div class="progress-bar progress-bar-warning" style="width: 70%;">\n </div>\n </div>\n</div>\n\n\n<div class="col-xs-6">\n <div>\n <span>Interpretability</span> <small class="pull-right">7/10</small>\n </div>\n\n\n <div class="progress progress-small">\n <div class="progress-bar progress-bar-warning" style="width: 70%;">\n </div>\n </div>\n</div>');
INSERT INTO `ode_models` (`id`, `type`, `parameters`, `name`, `form`, `description`) VALUES
(3, 'classification', '("alpha", "fit_prior", "class_prior")', 'Gaussian Naive Bayes', '<h4><b>There are no configurable parameters for the selected model.</b></h4>', '<h4 class="m-b-xxs text-center">Naive Bayes Classifier <i class="fa fa-bars"></i></h4><p>Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of independence between every pair of features.<a href="http://en.wikipedia.org/wiki/Naive_Bayes_classifier" target="_blank"> [ref]</a></p><div class="col-xs-6"> <div> <span>Complexity</span> <small class="pull-right">3/10</small> </div> <div class="progress progress-small"> <div class="progress-bar" style="width: 30%;"> </div> </div></div><div class="col-xs-6"> <div> <span>Interpretability</span> <small class="pull-right">5/10</small> </div> <div class="progress progress-small"> <div class="progress-bar progress-bar-warning" style="width: 50%;"> </div> </div></div>');
INSERT INTO `ode_models` (`id`, `type`, `parameters`, `name`, `form`, `description`) VALUES
(4, 'classification', '{"n_estimators":"int","learning_rate":"float","max_depth":"int","min_samples_split":"int","min_samples_leaf":"int","min_weight_fraction_leaf":"int","max_features":"int","max_leaf_nodes":"int","warm_start":"bool"}', 'Gradient Boosting', '<div class="row">\n <div class="col-lg-3">\n <label>Warm start: <i class="fa fa-question-circle" style="color:#676a6c" id="q9"></i></label>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="1" name="warm_start" checked="" class="required"> True</label>\n </div>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="0" name="warm_start" class="required"> False</label>\n </div>\n </div>\n\n <div class="col-lg-3 text-center">\n <div class="row text-center">\n <label>max_leaf_nodes = <span class="example-val" style="color:#1AB394" id="max_leaf_nodes-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q10"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="max_leaf_nodes-slider"></div>\n <input type="hidden" name="max_leaf_nodes" id="max_leaf_nodes" value=""> \n </div>\n </div>\n <br>\n\n <div class="row text-center">\n <label>min_samples_leaf = <span class="example-val" style="color:#1AB394" id="min_samples_leaf-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q11"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="min_samples_leaf-slider"></div>\n <input type="hidden" name="min_samples_leaf" id="min_samples_leaf" value="">\n </div>\n </div>\n <br>\n\n <div class="row text-center">\n <label>max_features = <span class="example-val" style="color:#1AB394" id="max_features-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q12"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="max_features-slider"></div>\n <input type="hidden" name="max_features" id="max_features" value="">\n </div>\n </div>\n </div>\n \n <div class="col-lg-3 text-center">\n <div class="row text-center">\n <label>num_estimators = <span class="example-val" style="color:#1AB394" id="n_estimators-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q13"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="n_estimators-slider"></div>\n <input type="hidden" name="n_estimators" id="n_estimators" value="">\n </div>\n </div>\n <br>\n\n <div class="row text-center">\n <label>min_samples_split = <span class="example-val" style="color:#1AB394" id="min_samples_split-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q14"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="min_samples_split-slider"></div>\n <input type="hidden" name="min_samples_split" id="min_samples_split" value="">\n </div>\n </div>\n </div>\n \n\n\n <div class="col-lg-3 text-center">\n <div class="row text-center">\n <label>max_depth = <span class="example-val" style="color:#1AB394" id="max_depth-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q15"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="max_depth-slider"></div>\n <input type="hidden" name="max_depth" id="max_depth" value="">\n </div>\n </div>\n <br>\n\n <div class="row text-center">\n <label>learning_rate = <span class="example-val" id=\n "learning_rate-value" style="color:#1AB394"></span> <i class=\n "fa fa-question-circle" id="q16" style=\n "color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="learning_rate-slider"></div><input id=\n "learning_rate" name="learning_rate" type="hidden" value="">\n </div>\n </div>\n </div>\n</div>\n\n<script>\n $(document).ready(function() {\n $(''.i-checks'').iCheck({\n checkboxClass: ''icheckbox_square-green'',\n radioClass: ''iradio_square-green'',\n });\n $(''#max_features-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [NUM_FEATURES]\n }\n });\n $(''#max_features-slider'').Link(''lower'').to($(''#max_features-value''));\n $(''#max_features-slider'').Link(''lower'').to($(''#max_features''));\n $(''#min_samples_split-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [NUM_INSTANCES]\n }\n });\n $(''#min_samples_split-slider'').Link(''lower'').to($(''#min_samples_split-value''));\n $(''#min_samples_split-slider'').Link(''lower'').to($(''#min_samples_split''));\n $(''#max_leaf_nodes-slider'').noUiSlider({\n start: [2],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [2],\n ''max'': [20]\n }\n });\n $(''#max_leaf_nodes-slider'').Link(''lower'').to($(''#max_leaf_nodes-value''));\n $(''#max_leaf_nodes-slider'').Link(''lower'').to($(''#max_leaf_nodes''));\n $(''#min_samples_leaf-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [NUM_FEATURES]\n }\n });\n $(''#min_samples_leaf-slider'').Link(''lower'').to($(''#min_samples_leaf-value''));\n $(''#min_samples_leaf-slider'').Link(''lower'').to($(''#min_samples_leaf''));\n $(''#max_depth-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [20]\n }\n });\n $(''#max_depth-slider'').Link(''lower'').to($(''#max_depth-value''));\n $(''#max_depth-slider'').Link(''lower'').to($(''#max_depth''));\n\n $(''#n_estimators-slider'').noUiSlider({\n start: [10],\n step: 10,\n connect: "lower",\n range: {\n ''min'': [10],\n ''max'': [1000]\n }\n });\n $(''#n_estimators-slider'').Link(''lower'').to($(''#n_estimators-value''));\n $(''#n_estimators-slider'').Link(''lower'').to($(''#n_estimators''));\n\n $(''#learning_rate-slider'').noUiSlider({\n start: [0.1],\n step: 0.05,\n connect: "lower",\n range: {\n ''min'': [0.05],\n ''max'': [1]\n }\n });\n $(''#learning_rate-slider'').Link(''lower'').to($(''#learning_rate-value''));\n $(''#learning_rate-slider'').Link(''lower'').to($(''#learning_rate''));\n\n $("#q9").qtip({ \n content: {\n text: ''When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest.'',\n title:''<i>warm_start</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q10").qtip({ \n content: {\n text: ''Grow a tree with <i>max_leaf_nodes</i> in best-first fashion. Best nodes are defined as relative reduction in impurity. If <strong>0</strong> then unlimited number of leaf nodes. If not <strong>0</strong> then <i>max_depth</i> will be ignored.'',\n title:''<i>max_leaf_nodes</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q11").qtip({ \n content: {\n text: ''The minimum number of samples required to be at a leaf node.'',\n title:''<i>min_samples_leaf</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q12").qtip({ \n content: {\n text: ''The number of features to consider when looking for the best split.'',\n title:''<i>max_features</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q13").qtip({ \n content: {\n text: ''The number of trees in the forest.'',\n title:''<i>num_estimators</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q14").qtip({ \n content: {\n text: ''The minimum number of samples required to split an internal node.'',\n title:''<i>min_samples_split</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q15").qtip({ \n content: {\n text: ''The maximum depth of the tree. If <strong>0</strong>, then nodes are expanded until all leaves are pure or until all leaves contain less than <i>min_samples_split</i> samples. Ignored if <i>max_leaf_nodes</i> is not <strong>0</strong>.'',\n title:''<i>max_depth</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n })\n $("#q16").qtip({\n content: {\n text: ''learning rate shrinks the contribution of each tree by learning_rate. There is a trade-off between learning_rate and n_estimators.'',\n title:''<i>learning_rate<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n });\n</script>', '<h4 class="m-b-xxs text-center">Gradient Boosting <i class="fa fa-tree"></i><i class="fa fa-tree"></i><i class="fa fa-tree"></i></h4>\n\n\n<p>Gradient Boosting builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage, regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. <a href=\n"http://en.wikipedia.org/wiki/Gradient_boosting" target=\n"_blank">[ref]</a></p>\n\n\n<div class="col-xs-6">\n <div>\n <span>Complexity</span> <small class="pull-right">8/10</small>\n </div>\n\n\n <div class="progress progress-small">\n <div class="progress-bar progress-bar-danger" style="width: 70%;">\n </div>\n </div>\n</div>\n\n\n<div class="col-xs-6">\n <div>\n <span>Interpretability</span> <small class="pull-right">8/10</small>\n </div>\n\n\n <div class="progress progress-small">\n <div class="progress-bar progress-bar-danger" style="width: 70%;">\n </div>\n </div>\n</div>');
INSERT INTO `ode_models` (`id`, `type`, `parameters`, `name`, `form`, `description`) VALUES
(5, 'classification', '{"n_neighbors":"int","weights":"string","algorithm":"string","leaf_size":"int","metric":"string"}', 'K-Nearest Neighbors', '<div class="row">\n <div class="col-lg-2">\n <label>Algorithm: <i class="fa fa-question-circle" id="q9" style=\n "color:#676a6c"></i></label>\n\n <div class="radio i-checks">\n <label><input checked class="required" name="algorithm" type=\n "radio" value="auto"> Auto</label>\n </div>\n\n <div class="radio i-checks">\n <label><input class="required" name="algorithm" type="radio"\n value="ball_tree"> ball_tree</label>\n </div>\n\n <div class="radio i-checks">\n <label><input class="required" name="algorithm" type="radio"\n value="kd_tree"> kd_tree</label>\n </div>\n\n <div class="radio i-checks">\n <label><input class="required" name="algorithm" type="radio"\n value="brute"> Brute-force</label>\n </div>\n </div>\n\n <div class="col-lg-2">\n <label>Weights: <i class="fa fa-question-circle" id="q10" style=\n "color:#676a6c"></i></label>\n\n <div class="radio i-checks">\n <label><input checked class="required" name="weights" type=\n "radio" value="uniform"> Uniform</label>\n </div>\n\n <div class="radio i-checks">\n <label><input class="required" name="weights" type="radio"\n value="distance"> Distance</label>\n </div>\n </div>\n\n <div class="col-lg-4 text-center">\n <div class="row text-center">\n <label>n_neighbors = <span class="example-val" id=\n "n_neighbors-value" style="color:#1AB394"></span> <i class=\n "fa fa-question-circle" id="q11" style=\n "color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="n_neighbors-slider"></div><input id="n_neighbors"\n name="n_neighbors" type="hidden" value="">\n </div>\n </div><br>\n <label>metric: <i class="fa fa-question-circle" id="q12" style=\n "color:#676a6c"></i></label> <select class="form-control required"\n id="metric" name="metric">\n <option value="minkowski">\n Minkowski Distance\n </option>\n\n <option value="euclidean">\n Euclidean Distance\n </option>\n\n <option value="manhattan">\n Manhattan Distance\n </option>\n\n <option value="chebyshev">\n Chebyshev Distance\n </option>\n </select>\n </div>\n\n <div class="col-lg-4 text-center">\n <div class="row text-center">\n <label>leaf_size = <span class="example-val" id=\n "leaf_size-value" style="color:#1AB394"></span> <i class=\n "fa fa-question-circle" id="q13" style=\n "color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="leaf_size-slider"></div><input id="leaf_size"\n name="leaf_size" type="hidden" value="">\n </div>\n </div>\n </div>\n</div>\n<script>\n $(document).ready(function() {\n $(''.i-checks'').iCheck({\n checkboxClass: ''icheckbox_square-green'',\n radioClass: ''iradio_square-green'',\n });\n\n $(''#n_neighbors-slider'').noUiSlider({\n start: [5],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [20]\n }\n });\n $(''#n_neighbors-slider'').Link(''lower'').to($(''#n_neighbors-value''));\n $(''#n_neighbors-slider'').Link(''lower'').to($(''#n_neighbors''));\n\n $(''#leaf_size-slider'').noUiSlider({\n start: [30],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [50]\n }\n });\n $(''#leaf_size-slider'').Link(''lower'').to($(''#leaf_size-value''));\n $(''#leaf_size-slider'').Link(''lower'').to($(''#leaf_size''));\n\n\n $("#q9").qtip({\n content: {\n text: ''Algorithm used to compute the nearest neighbors.'',\n title:''<i>algorithm</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q10").qtip({\n content: {\n text: ''Weight function used in prediction.'',\n title:''<i>weights</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q11").qtip({\n content: {\n text: ''Number of neighbors to use by default for k_neighbors queries.'',\n title:''<i>n_neighbors</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q12").qtip({\n content: {\n text: ''The distance metric to use for computing closest neighbors. The default metric is minkowski, and with p=2 is equivalent to the standard Euclidean metric.'',\n title:''<i>metric</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q13").qtip({\n content: {\n text: ''Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.'',\n title:''<i>leaf_size</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n });\n</script>', '<h4 class="m-b-xxs text-center">K-Nearest Neighbor Classifier <i class=\n "fa fa-connectdevelop"></i></h4>\n\n <p>In k-NN classification, an instance is classified by a majority vote of\n its neighbors, being assigned to the class most common\n among its <i>k</i> nearest neighbors .<a href=\n "http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm" target=\n "_blank">[ref]</a></p>\n\n <div class="col-xs-6">\n <div>\n <span>Complexity</span> <small class="pull-right">2/10</small>\n </div>\n\n <div class="progress progress-small">\n <div class="progress-bar" style="width: 20%;">\n </div>\n </div>\n </div>\n\n <div class="col-xs-6">\n <div>\n <span>Interpretability</span> <small class=\n "pull-right">9/10</small>\n </div>\n\n <div class="progress progress-small">\n <div class="progress-bar" style="width: 90%;"></div>\n </div>\n </div>');
INSERT INTO `ode_models` (`id`, `type`, `parameters`, `name`, `form`, `description`) VALUES
(6, 'classification', '{"penalty":"string","C":"float","fit_intercept":"bool","intercept_scaling":"float","max_iter":"int", "random_state":"int","solver":"string","tol":"float"}', 'Logistic Regression', '<div class="row">\n <div class="col-lg-2">\n <label>Penalty: <i class="fa fa-question-circle" id="q9" style=\n "color:#676a6c"></i></label>\n\n <div class="radio i-checks">\n <label><input checked class="required" name="penalty" type=\n "radio" value="l1"> L1</label>\n </div>\n\n <div class="radio i-checks">\n <label><input class="required" name="penalty" type="radio"\n value="l2"> L2</label>\n </div>\n <br>\n <label>fit_intercept: <i class="fa fa-question-circle" id="q10" style=\n "color:#676a6c"></i></label>\n\n <div class="radio i-checks">\n <label><input checked class="required" name="fit_intercept" type=\n "radio" value="1"> True</label>\n </div>\n\n <div class="radio i-checks">\n <label><input class="required" name="fit_intercept" type="radio"\n value="0"> False</label>\n </div>\n </div>\n\n <div class="col-lg-2">\n <label>Solver: <i class="fa fa-question-circle" id="q11" style=\n "color:#676a6c"></i></label>\n\n <div class="radio i-checks">\n <label><input checked class="required" name="solver" type="radio"\n value="newton-cg"> newton-cg</label>\n </div>\n\n\n <div class="radio i-checks">\n <label><input class="required" name="solver" type="radio" value=\n "lbfgs"> lbfgs</label>\n </div>\n\n\n <div class="radio i-checks">\n <label><input class="required" name="solver" type="radio" value=\n "liblinear"> liblinear</label>\n </div>\n </div>\n\n <div class="col-lg-4 text-center">\n <div class="row text-center">\n <label>C = <span class="example-val" id="C-value" style=\n "color:#1AB394"></span> <i class="fa fa-question-circle" id="q12"\n style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="C-slider">\n </div>\n <input id="C" name="C" type="hidden" value="">\n </div>\n </div>\n <br>\n\n\n <div class="row text-center">\n <label>intercept_scaling = <span class="example-val" id=\n "intercept_scaling-value" style="color:#1AB394"></span> <i class=\n "fa fa-question-circle" id="q13" style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="intercept_scaling-slider">\n </div>\n <input id="intercept_scaling" name="intercept_scaling" type=\n "hidden" value="">\n </div>\n </div>\n <br>\n\n <div class="row text-center">\n <label>max_iter = <span class="example-val" id=\n "max_iter-value" style="color:#1AB394"></span> <i class=\n "fa fa-question-circle" id="q14" style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="max_iter-slider">\n </div>\n <input id="max_iter" name="max_iter" type=\n "hidden" value="">\n </div>\n </div>\n </div>\n\n\n <div class="col-lg-4 text-center">\n <div class="row text-center">\n <label>random_state = <span class="example-val" id="random_state-value"\n style="color:#1AB394"></span> <i class="fa fa-question-circle" id=\n "q15" style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="random_state-slider">\n </div>\n <input id="random_state" name="random_state" type="hidden" value="">\n </div>\n </div>\n <br>\n\n <div class="row text-center">\n <label>tol = <span class="example-val" id="tol-value"\n style="color:#1AB394"></span> <i class="fa fa-question-circle" id=\n "q16" style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="tol-slider">\n </div>\n <input id="tol" name="tol" type="hidden" value="">\n </div>\n </div>\n </div>\n</div>\n<script>\n $(document).ready(function() {\n $(''.i-checks'').iCheck({\n checkboxClass: ''icheckbox_square-green'',\n radioClass: ''iradio_square-green'',\n });\n\n $(''#C-slider'').noUiSlider({\n start: [1],\n step: 0.1,\n connect: "lower",\n range: {\n ''min'': [0.1],\n ''max'': [10]\n }\n });\n $(''#C-slider'').Link(''lower'').to($(''#C-value''));\n $(''#C-slider'').Link(''lower'').to($(''#C''));\n\n $(''#intercept_scaling-slider'').noUiSlider({\n start: [1],\n step: 0.1,\n connect: "lower",\n range: {\n ''min'': [0.1],\n ''max'': [10]\n }\n });\n $(''#intercept_scaling-slider'').Link(''lower'').to($(''#intercept_scaling-value''));\n $(''#intercept_scaling-slider'').Link(''lower'').to($(''#intercept_scaling''));\n\n $(''#max_iter-slider'').noUiSlider({\n start: [100],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [1000]\n }\n });\n $(''#max_iter-slider'').Link(''lower'').to($(''#max_iter-value''));\n $(''#max_iter-slider'').Link(''lower'').to($(''#max_iter''));\n\n $(''#random_state-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [100]\n }\n });\n $(''#random_state-slider'').Link(''lower'').to($(''#random_state-value''));\n $(''#random_state-slider'').Link(''lower'').to($(''#random_state''));\n\n $(''#tol-slider'').noUiSlider({\n start: [1],\n step: 0.1,\n connect: "lower",\n range: {\n ''min'': [0.1],\n ''max'': [10]\n }\n });\n $(''#tol-slider'').Link(''lower'').to($(''#tol-value''));\n $(''#tol-slider'').Link(''lower'').to($(''#tol''));\n\n\n $("#q9").qtip({\n content: {\n text: ''Used to specify the norm used in the penalization. The newton-cg and lbfgs solvers support only l2 penalties.'',\n title:''<i>Penalty</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q10").qtip({\n content: {\n text: ''Specifies if a constant (a.k.a. bias or intercept) should be added the decision function.'',\n title:''<i>fit_intercept</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q11").qtip({\n content: {\n text: ''Algorithm to use in the optimization problem.'',\n title:''<i>Solver</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q12").qtip({\n content: {\n text: ''Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization.'',\n title:''<i>c</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q13").qtip({\n content: {\n text: ''Useful only if solver is liblinear. when self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.'',\n title:''<i>intercept_scaling</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q14").qtip({\n content: {\n text: ''Useful only for the newton-cg and lbfgs solvers. Maximum number of iterations taken for the solvers to converge.'',\n title:''<i>max_iter</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q15").qtip({\n content: {\n text: ''The seed of the pseudo random number generator to use when shuffling the data.'',\n title:''<i>random_state</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q16").qtip({\n content: {\n text: ''Tolerance for stopping criteria.'',\n title:''<i>tol</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n });\n</script>', '<h4 class="m-b-xxs text-center">Logitic Regression <i class=\n"fa fa-superscript"></i></h4>\n\n\n<p>Logistic regression measures the relationship between the categorical\ndependent variable and one or more independent variables, which are usually\n(but not necessarily) continuous, by estimating probabilities. <a href=\n"http://en.wikipedia.org/wiki/Logistic_regression" target=\n"_blank">[ref]</a></p>\n\n\n<div class="col-xs-6">\n <div>\n <span>Complexity</span> <small class="pull-right">4/10</small>\n </div>\n\n\n <div class="progress progress-small">\n <div class="progress-bar progress-bar-warning" style="width: 40%;">\n </div>\n </div>\n</div>\n\n\n<div class="col-xs-6">\n <div>\n <span>Interpretability</span> <small class="pull-right">8/10</small>\n </div>\n\n\n <div class="progress progress-small">\n <div class="progress-bar" style="width: 80%;">\n </div>\n </div>\n</div>');
INSERT INTO `ode_models` (`id`, `type`, `parameters`, `name`, `form`, `description`) VALUES
(7, 'classification', '{"n_estimators":"int","criterion":"string","max_features":"int","max_depth":"int","min_samples_split":"int","min_samples_leaf":"int","min_weight_fraction_leaf":"float","max_leaf_nodes":"int","bootstrap":"bool","oob_score":"bool","random_state":"int","warm_start":"bool"}', 'Random Forest', '<div class="row">\n <div class="col-lg-2">\n <label>Criterion: <i class="fa fa-question-circle" style="color:#676a6c" id="q9"></i></label>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="gini" name="criterion" checked="" class="required"> Gini</label>\n </div>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="entropy" name="criterion" class="required"> Entropy</label>\n </div>\n <br>\n\n <label>Boostrap: <i class="fa fa-question-circle" style="color:#676a6c" id="q10"></i></label>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="1" name="bootstrap" checked="" class="required"> True</label>\n </div>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="0" name="bootstrap" class="required"> False</label>\n </div>\n </div>\n\n <div class="col-lg-2">\n <label>Out-of-bag score: <i class="fa fa-question-circle" style="color:#676a6c" id="q11"></i></label>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="1" name="oob_score" checked="" class="required"> True</label>\n </div>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="0" name="oob_score" class="required"> False</label>\n </div>\n <br>\n\n <label>Warm start: <i class="fa fa-question-circle" style="color:#676a6c" id="q12"></i></label>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="1" name="warm_start" checked="" class="required"> True</label>\n </div>\n <div class="radio i-checks">\n <label>\n <input type="radio" value="0" name="warm_start" class="required"> False</label>\n </div>\n </div>\n \n <div class="col-lg-4 text-center">\n <div class="row text-center">\n <label>max_leaf_nodes = <span class="example-val" style="color:#1AB394" id="max_leaf_nodes-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q13"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="max_leaf_nodes-slider"></div>\n <input type="hidden" name="max_leaf_nodes" id="max_leaf_nodes" value=""> \n </div>\n </div>\n <br>\n\n <div class="row text-center">\n <label>min_samples_leaf = <span class="example-val" style="color:#1AB394" id="min_samples_leaf-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q14"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="min_samples_leaf-slider"></div>\n <input type="hidden" name="min_samples_leaf" id="min_samples_leaf" value="">\n </div>\n </div>\n <br>\n\n <div class="row text-center">\n <label>max_features = <span class="example-val" style="color:#1AB394" id="max_features-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q15"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="max_features-slider"></div>\n <input type="hidden" name="max_features" id="max_features" value="">\n </div>\n </div>\n <br>\n\n <div class="row text-center">\n <label>num_estimators = <span class="example-val" style="color:#1AB394" id="n_estimators-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q16"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="n_estimators-slider"></div>\n <input type="hidden" name="n_estimators" id="n_estimators" value="">\n </div>\n </div>\n </div>\n \n\n\n <div class="col-lg-4 text-center">\n <div class="row text-center">\n <label>min_samples_split = <span class="example-val" style="color:#1AB394" id="min_samples_split-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q17"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="min_samples_split-slider"></div>\n <input type="hidden" name="min_samples_split" id="min_samples_split" value="">\n </div>\n </div>\n <br>\n\n <div class="row text-center">\n <label>max_depth = <span class="example-val" style="color:#1AB394" id="max_depth-value"></span> <i class="fa fa-question-circle" style="color:#676a6c" id="q18"></i></label>\n <div class="col-lg-10 col-lg-offset-1">\n <div id="max_depth-slider"></div>\n <input type="hidden" name="max_depth" id="max_depth" value="">\n </div>\n </div>\n <br>\n\n <div class="row text-center">\n <label>random_state = <span class="example-val" id=\n "random_state-value" style="color:#1AB394"></span> <i class=\n "fa fa-question-circle" id="q19" style=\n "color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="random_state-slider"></div><input id=\n "random_state" name="random_state" type="hidden" value="">\n </div>\n </div>\n </div>\n</div>\n\n<script>\n $(document).ready(function() {\n $(''.i-checks'').iCheck({\n checkboxClass: ''icheckbox_square-green'',\n radioClass: ''iradio_square-green'',\n });\n $(''#max_features-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [NUM_FEATURES]\n }\n });\n $(''#max_features-slider'').Link(''lower'').to($(''#max_features-value''));\n $(''#max_features-slider'').Link(''lower'').to($(''#max_features''));\n $(''#min_samples_split-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [NUM_INSTANCES]\n }\n });\n $(''#min_samples_split-slider'').Link(''lower'').to($(''#min_samples_split-value''));\n $(''#min_samples_split-slider'').Link(''lower'').to($(''#min_samples_split''));\n $(''#max_leaf_nodes-slider'').noUiSlider({\n start: [2],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [2],\n ''max'': [20]\n }\n });\n $(''#max_leaf_nodes-slider'').Link(''lower'').to($(''#max_leaf_nodes-value''));\n $(''#max_leaf_nodes-slider'').Link(''lower'').to($(''#max_leaf_nodes''));\n $(''#min_samples_leaf-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [NUM_FEATURES]\n }\n });\n $(''#min_samples_leaf-slider'').Link(''lower'').to($(''#min_samples_leaf-value''));\n $(''#min_samples_leaf-slider'').Link(''lower'').to($(''#min_samples_leaf''));\n $(''#max_depth-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [20]\n }\n });\n $(''#max_depth-slider'').Link(''lower'').to($(''#max_depth-value''));\n $(''#max_depth-slider'').Link(''lower'').to($(''#max_depth''));\n\n $(''#n_estimators-slider'').noUiSlider({\n start: [10],\n step: 10,\n connect: "lower",\n range: {\n ''min'': [10],\n ''max'': [1000]\n }\n });\n $(''#n_estimators-slider'').Link(''lower'').to($(''#n_estimators-value''));\n $(''#n_estimators-slider'').Link(''lower'').to($(''#n_estimators''));\n\n $(''#random_state-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [100]\n }\n });\n $(''#random_state-slider'').Link(''lower'').to($(''#random_state-value''));\n $(''#random_state-slider'').Link(''lower'').to($(''#random_state''));\n\n $("#q9").qtip({ \n content: {\n text: ''The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.'',\n title:''<i>criterion </i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q10").qtip({ \n content: {\n text: ''Whether bootstrap samples are used when building trees.'',\n title:''<i>boostrap</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q11").qtip({ \n content: {\n text: ''Whether to use out-of-bag samples to estimate the generalization error.'',\n title:''<i>oob_score</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q12").qtip({ \n content: {\n text: ''When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest.'',\n title:''<i>warm_start</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q13").qtip({ \n content: {\n text: ''Grow a tree with <i>max_leaf_nodes</i> in best-first fashion. Best nodes are defined as relative reduction in impurity. If <strong>0</strong> then unlimited number of leaf nodes. If not <strong>0</strong> then <i>max_depth</i> will be ignored.'',\n title:''<i>max_leaf_nodes</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q14").qtip({ \n content: {\n text: ''The minimum number of samples required to be at a leaf node.'',\n title:''<i>min_samples_leaf</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q15").qtip({ \n content: {\n text: ''The number of features to consider when looking for the best split.'',\n title:''<i>max_features</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q16").qtip({ \n content: {\n text: ''The number of trees in the forest.'',\n title:''<i>num_estimators</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q17").qtip({ \n content: {\n text: ''The minimum number of samples required to split an internal node.'',\n title:''<i>min_samples_split</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q18").qtip({ \n content: {\n text: ''The maximum depth of the tree. If <strong>0</strong>, then nodes are expanded until all leaves are pure or until all leaves contain less than <i>min_samples_split</i> samples. Ignored if <i>max_leaf_nodes</i> is not <strong>0</strong>.'',\n title:''<i>max_depth</i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n })\n $("#q19").qtip({\n content: {\n text: ''The seed of the pseudo random number generator to use when shuffling the data.'',\n title:''<i>random_state<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n });\n</script>', '<h4 class="m-b-xxs text-center">Random Forest <i class="fa fa-tree"></i><i class="fa fa-tree"></i><i class="fa fa-tree"></i></h4>\n\n\n<p>Random forests are an ensemble learning method that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes of the individual trees. Random forests correct for decision trees'' habit of overfitting to their training set. <a href=\n"http://en.wikipedia.org/wiki/Random_forest" target=\n"_blank">[ref]</a></p>\n\n\n<div class="col-xs-6">\n <div>\n <span>Complexity</span> <small class="pull-right">7/10</small>\n </div>\n\n\n <div class="progress progress-small">\n <div class="progress-bar progress-bar-warning" style="width: 70%;">\n </div>\n </div>\n</div>\n\n\n<div class="col-xs-6">\n <div>\n <span>Interpretability</span> <small class="pull-right">7/10</small>\n </div>\n\n\n <div class="progress progress-small">\n <div class="progress-bar progress-bar-warning" style="width: 70%;">\n </div>\n </div>\n</div>');
INSERT INTO `ode_models` (`id`, `type`, `parameters`, `name`, `form`, `description`) VALUES
(8, 'classification', '{"loss":"string","penalty":"string","alpha":"float","l1_ratio":"float","fit_intercept":"bool","n_iter":"int","shuffle":"bool","random_state":"int","epsilon":"float","learning_rate":"string","eta0":"float","power_t":"float","warm_start":"bool","average":"bool"}', 'Stochastic Gradient Descent', '<div class="row">\n <div class="col-lg-2">\n <label>Loss function: <i class="fa fa-question-circle" id="q9" style=\n "color:#676a6c"></i></label> <select class="form-control required"\n id="loss" name="loss">\n <option value="log">\n log\n </option>\n <option value="modified_huber">\n modified_huber\n </option>\n </select>\n <br>\n <label>Penalty: <i class="fa fa-question-circle" id="q10" style=\n "color:#676a6c"></i></label> <select class="form-control required"\n id="penalty" name="penalty">\n <option value="l2">\n L2\n </option>\n <option value="none">\n none\n </option>\n <option value="l1">\n L1\n </option>\n <option value="elasticnet">\n elasticnet\n </option> \n </select>\n <br>\n <label>Learning rate: <i class="fa fa-question-circle" id="q11" style=\n "color:#676a6c"></i></label> <select class="form-control required"\n id="learning_rate" name="learning_rate">\n <option value="constant">\n constant\n </option>\n <option value="invscaling">\n invscaling\n </option> \n </select>\n </div>\n\n <div class="col-lg-2">\n <label>Fit intercept: <i class="fa fa-question-circle" id="q12" style=\n "color:#676a6c"></i></label> <select class="form-control required"\n id="fit_intercept" name="fit_intercept">\n <option value="1">\n True\n </option>\n <option value="0">\n False\n </option> \n </select>\n <br>\n <label>Warm start: <i class="fa fa-question-circle" id="q13" style=\n "color:#676a6c"></i></label> <select class="form-control required"\n id="warm_start" name="warm_start">\n <option value="1">\n True\n </option>\n <option value="0">\n False\n </option> \n </select>\n <br>\n <label>Shuffle: <i class="fa fa-question-circle" id="q14" style=\n "color:#676a6c"></i></label> <select class="form-control required"\n id="shuffle" name="shuffle">\n <option value="1">\n True\n </option>\n <option value="0">\n False\n </option> \n </select>\n <label>Average: <i class="fa fa-question-circle" id="q15" style=\n "color:#676a6c"></i></label> <select class="form-control required"\n id="average" name="average">\n <option value="1">\n True\n </option>\n <option value="0">\n False\n </option> \n </select>\n </div>\n\n <div class="col-lg-4 text-center">\n <div class="row text-center">\n <span><label>Alpha = <span id="alpha-value" style=\n "color:#1AB394"></span> <i class="fa fa-question-circle" id=\n "q16" style="color:#676a6c"></i>\n <input type="number" min="-1" max="1" step="0.0001" id="input-alpha"></label></span>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="alpha-slider"></div><input id="alpha" name="alpha" type=\n "hidden" value="">\n </div>\n </div><br>\n\n <div class="row text-center">\n <label>n_iter = <span class="example-val" id="n_iter-value"\n style="color:#1AB394"></span> <i class="fa fa-question-circle"\n id="q17" style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="n_iter-slider"></div><input id="n_iter" name=\n "n_iter" type="hidden" value="">\n </div>\n </div><br>\n\n <div class="row text-center">\n <label>random_state = <span class="example-val" id=\n "random_state-value" style="color:#1AB394"></span> <i class=\n "fa fa-question-circle" id="q18" style=\n "color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="random_state-slider"></div><input id=\n "random_state" name="random_state" type="hidden" value="">\n </div>\n </div>\n </div>\n\n <div class="col-lg-4 text-center">\n <div class="row text-center">\n <label>l1_ratio = <span class="parameter_value" id=\n "l1_ratio-value"></span> <i class="fa fa-question-circle" id=\n "q19" style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="l1_ratio-slider"></div><input id="l1_ratio" name=\n "l1_ratio" type="hidden" value="">\n </div>\n </div><br>\n\n <div class="row text-center">\n <label>epsilon = <span class="parameter_value_off" id=\n "epsilon-value"></span> <i class="fa fa-question-circle" id="q20"\n style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="epsilon-slider"></div><input id="epsilon" name="epsilon"\n type="hidden" value="">\n </div>\n </div><br>\n\n <div class="row text-center">\n <label>eta0 = <span class="parameter_value_off" id=\n "eta0-value"></span> <i class="fa fa-question-circle" id="q21"\n style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="eta0-slider"></div><input id="eta0" name="eta0"\n type="hidden" value="">\n </div>\n </div><br>\n\n <div class="row text-center">\n <label>power_t = <span class="parameter_value_off" id=\n "power_t-value"></span> <i class="fa fa-question-circle" id="q22"\n style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="power_t-slider"></div><input id="power_t" name="power_t"\n type="hidden" value="">\n </div>\n </div>\n </div>\n</div><script>\n$(document).ready(function() {\n $(''.i-checks'').iCheck({\n checkboxClass: ''icheckbox_square-green'',\n radioClass: ''iradio_square-green'',\n });\n\n $(''#alpha-slider'').noUiSlider({\n start: [0.0001],\n step: 0.0001,\n connect: "lower",\n range: {\n ''min'': [0],\n ''max'': [1]\n }\n });\n $(''#alpha-slider'').Link(''lower'').to($(''#alpha-value''));\n $(''#alpha-slider'').Link(''lower'').to($(''#alpha''));\n $(''#alpha-slider'').Link(''lower'').to($(''#input-alpha''));\n\n $(''#n_iter-slider'').noUiSlider({\n start: [5],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [1000]\n }\n });\n $(''#n_iter-slider'').Link(''lower'').to($(''#n_iter-value''));\n $(''#n_iter-slider'').Link(''lower'').to($(''#n_iter''));\n\n $(''#random_state-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [100]\n }\n });\n $(''#random_state-slider'').Link(''lower'').to($(''#random_state-value''));\n $(''#random_state-slider'').Link(''lower'').to($(''#random_state''));\n\n $(''#l1_ratio-slider'').noUiSlider({\n start: [0.15],\n step: 0.1,\n connect: "lower",\n range: {\n ''min'': [0],\n ''max'': [1]\n }\n });\n $(''#l1_ratio-slider'').Link(''lower'').to($(''#l1_ratio-value''));\n $(''#l1_ratio-slider'').Link(''lower'').to($(''#l1_ratio''));\n\n $(''#epsilon-slider'').noUiSlider({\n start: [0.05],\n step: 0.01,\n connect: "lower",\n range: {\n ''min'': [0],\n ''max'': [1]\n }\n });\n $(''#epsilon-slider'').Link(''lower'').to($(''#epsilon-value''));\n $(''#epsilon-slider'').Link(''lower'').to($(''#epsilon''));\n\n $(''#eta0-slider'').noUiSlider({\n start: [0.1],\n step: 0.05,\n connect: "lower",\n range: {\n ''min'': [0.05],\n ''max'': [1]\n }\n });\n $(''#eta0-slider'').Link(''lower'').to($(''#eta0-value''));\n $(''#eta0-slider'').Link(''lower'').to($(''#eta0''));\n\n $(''#power_t-slider'').noUiSlider({\n start: [0.5],\n step: 0.05,\n connect: "lower",\n range: {\n ''min'': [0],\n ''max'': [1]\n }\n });\n $(''#power_t-slider'').Link(''lower'').to($(''#power_t-value''));\n $(''#power_t-slider'').Link(''lower'').to($(''#power_t''));\n\n\n $("#q9").qtip({\n content: {\n text: ''The loss function to be used. Defaults to ‘hinge’, which gives a linear SVM. The ‘log’ loss gives logistic regression, a probabilistic classifier. ‘modified_huber’ is another smooth loss that brings tolerance to outliers as well as probability estimates. ‘squared_hinge’ is like hinge but is quadratically penalized. ‘perceptron’ is the linear loss used by the perceptron algorithm. The other losses are designed for regression but can be useful in classification as well.'',\n title:''<i>loss Function<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q10").qtip({\n content: {\n text: ''The penalty (aka regularization term) to be used. Defaults to ‘l2’ which is the standard regularizer for linear SVM models. ‘l1’ and ‘elasticnet’ might bring sparsity to the model (feature selection) not achievable with ‘l2’.'',\n title:''<i>penalty<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q11").qtip({\n content: {\n text: ''The learning rate schedule: constant: eta = eta0 optimal: eta = 1.0 / (t + t0) [default] invscaling: eta = eta0 / pow(t, power_t) where t0 is chosen by a heuristic proposed by Leon Bottou.'',\n title:''<i>learning_rate<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q12").qtip({\n content: {\n text: ''Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True.'',\n title:''<i>fit_intercept<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q13").qtip({\n content: {\n text: ''When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.'',\n title:''<i>warm_start<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q14").qtip({\n content: {\n text: ''Whether or not the training data should be shuffled after each epoch. Defaults to True.'',\n title:''<i>shuffle<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q15").qtip({\n content: {\n text: ''When set to True, computes the averaged SGD weights and stores the result in the coef_ attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples.'',\n title:''<i>average<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q16").qtip({\n content: {\n text: ''Constant that multiplies the regularization term. Defaults to 0.0001'',\n title:''<i>alpha<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q17").qtip({\n content: {\n text: ''The number of passes over the training data (aka epochs). The number of iterations is set to 1 if using partial_fit. Defaults to 5.'',\n title:''<i>n_iter<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q18").qtip({\n content: {\n text: ''The seed of the pseudo random number generator to use when shuffling the data.'',\n title:''<i>random_state<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q19").qtip({\n content: {\n text: ''The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Defaults to 0.15.'',\n title:''<i>l1_ratio<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q20").qtip({\n content: {\n text: ''Epsilon in the epsilon-insensitive loss functions; only if loss is ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’. For ‘huber’, determines the threshold at which it becomes less important to get the prediction exactly right. For epsilon-insensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold.'',\n title:''<i>epsilon<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q21").qtip({\n content: {\n text: ''The initial learning rate for the ‘constant’ or ‘invscaling’ schedules. The default value is 0.0 as eta0 is not used by the default schedule ‘optimal’.'',\n title:''<i>eta0<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q22").qtip({\n content: {\n text: ''The exponent for inverse scaling learning rate [default 0.5].'',\n title:''<i>power_t<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n});\n\n</script>', '<h4 class="m-b-xxs text-center">Stochastic Gradient Descent <i class=\n"fa fa-line-chart"></i></h4>\n\n\n<p>This classifier implements a plain stochastic gradient descent learning routine which supports different loss functions and penalties for classification. <a href=\n"http://en.wikipedia.org/wiki/Stochastic_gradient_descent" target=\n"_blank">[ref]</a></p>\n\n\n<div class="col-xs-6">\n <div>\n <span>Complexity</span> <small class="pull-right">8/10</small>\n </div>\n\n\n <div class="progress progress-small">\n <div class="progress-bar progress-bar-danger" style="width: 80%;">\n </div>\n </div>\n</div>\n\n\n<div class="col-xs-6">\n <div>\n <span>Interpretability</span> <small class="pull-right">7/10</small>\n </div>\n\n\n <div class="progress progress-small">\n <div class="progress-bar" style="width: 80%;">\n </div>\n </div>\n</div>');
INSERT INTO `ode_models` (`id`, `type`, `parameters`, `name`, `form`, `description`) VALUES
(9, 'classification', '{"C":"float","kernel":"string","degree":"int","gamma":"float","probability":"bool","shrinking":"bool","tol":"float","max_iter":"int","random_state":"int"}', 'Support Vector Machine', '<input name="probability" type="hidden" value="1">\n<div class="row">\n <div class="col-lg-2">\n <label>Kernel: <i class="fa fa-question-circle" id="q9" style=\n "color:#676a6c"></i></label> <select class="form-control required"\n id="kernel" name="kernel">\n <option value="linear">\n Linear\n </option>\n\n <option value="poly">\n Poly\n </option>\n\n <option value="rbf">\n Rbf\n </option>\n\n <option value="sigmoid">\n Sigmoid\n </option>\n </select>\n </div>\n\n <div class="col-lg-2">\n <label>Shrinking: <i class="fa fa-question-circle" id="q10" style=\n "color:#676a6c"></i></label>\n\n <div class="radio i-checks">\n <label><input checked class="required" name="shrinking" type=\n "radio" value="1"> True</label>\n </div>\n\n <div class="radio i-checks">\n <label><input class="required" name="shrinking" type="radio"\n value="0"> False</label>\n </div>\n </div>\n\n <div class="col-lg-4 text-center">\n <div class="row text-center">\n <label>C = <span class="example-val" id="C-value" style=\n "color:#1AB394"></span> <i class="fa fa-question-circle" id=\n "q11" style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="C-slider"></div><input id="C" name="C" type=\n "hidden" value="">\n </div>\n </div><br>\n\n <div class="row text-center">\n <label>max_iter = <span class="example-val" id="max_iter-value"\n style="color:#1AB394"></span> <i class="fa fa-question-circle"\n id="q12" style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="max_iter-slider"></div><input id="max_iter" name=\n "max_iter" type="hidden" value="">\n </div>\n </div><br>\n\n <div class="row text-center">\n <label>random_state = <span class="example-val" id=\n "random_state-value" style="color:#1AB394"></span> <i class=\n "fa fa-question-circle" id="q13" style=\n "color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="random_state-slider"></div><input id=\n "random_state" name="random_state" type="hidden" value="">\n </div>\n </div>\n </div>\n\n <div class="col-lg-4 text-center">\n <div class="row text-center">\n <label>degree = <span class="parameter_value" id=\n "degree-value"></span> <i class="fa fa-question-circle" id=\n "q14" style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="degree-slider"></div><input id="degree" name=\n "degree" type="hidden" value="">\n </div>\n </div><br>\n\n <div class="row text-center">\n <label>gamma = <span class="parameter_value_off" id=\n "gamma-value"></span> <i class="fa fa-question-circle" id="q15"\n style="color:#676a6c"></i></label>\n\n <div class="col-lg-10 col-lg-offset-1">\n <div id="gamma-slider"></div><input id="gamma" name="gamma"\n type="hidden" value="">\n </div>\n </div>\n </div>\n</div><script>\n$(document).ready(function() {\n $(''.i-checks'').iCheck({\n checkboxClass: ''icheckbox_square-green'',\n radioClass: ''iradio_square-green'',\n });\n\n $(''#C-slider'').noUiSlider({\n start: [1],\n step: 0.1,\n connect: "lower",\n range: {\n ''min'': [0.1],\n ''max'': [10]\n }\n });\n $(''#C-slider'').Link(''lower'').to($(''#C-value''));\n $(''#C-slider'').Link(''lower'').to($(''#C''));\n\n $(''#max_iter-slider'').noUiSlider({\n start: [100],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [1000]\n }\n });\n $(''#max_iter-slider'').Link(''lower'').to($(''#max_iter-value''));\n $(''#max_iter-slider'').Link(''lower'').to($(''#max_iter''));\n\n $(''#random_state-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [100]\n }\n });\n $(''#random_state-slider'').Link(''lower'').to($(''#random_state-value''));\n $(''#random_state-slider'').Link(''lower'').to($(''#random_state''));\n\n $(''#degree-slider'').noUiSlider({\n start: [1],\n step: 1,\n connect: "lower",\n range: {\n ''min'': [1],\n ''max'': [10]\n }\n });\n $(''#degree-slider'').Link(''lower'').to($(''#degree-value''));\n $(''#degree-slider'').Link(''lower'').to($(''#degree''));\n\n $(''#gamma-slider'').noUiSlider({\n start: [1],\n step: 0.1,\n connect: "lower",\n range: {\n ''min'': [0.1],\n ''max'': [10]\n }\n });\n $(''#gamma-slider'').Link(''lower'').to($(''#gamma-value''));\n $(''#gamma-slider'').Link(''lower'').to($(''#gamma''));\n $(''#gamma-slider'').attr(''disabled'', ''disabled'');\n\n\n $("#q9").qtip({\n content: {\n text: ''Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.'',\n title:''<i>Kernel<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q10").qtip({\n content: {\n text: ''Whether to use the shrinking heuristic.'',\n title:''<i>shrinking<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q11").qtip({\n content: {\n text: ''Penalty parameter C of the error term.'',\n title:''<i>c<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q12").qtip({\n content: {\n text: ''Hard limit on iterations within solver.'',\n title:''<i>max_iter<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q13").qtip({\n content: {\n text: ''The seed of the pseudo random number generator to use when shuffling the data for probability estimation.'',\n title:''<i>random_state<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q14").qtip({\n content: {\n text: ''Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.'',\n title:''<i>degree<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n $("#q15").qtip({\n content: {\n text: ''Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is 0.0 then 1/n_features will be used instead.'',\n title:''<i>gamma<\\/i>''\n },\n style: { classes: ''qtip-rounded qtip-shadow qtip-cream'' }\n });\n\n\n $( "#kernel" ).change(function () {\n var kernel_choice = "";\n $( "select option:selected" ).each(function() {\n kernel_choice = $( this ).val();\n });\n \n if (kernel_choice == ''linear'') {\n $(''#degree-slider'').removeAttr(''disabled'');\n $(''#degree-value'').attr(''class'', "parameter_value");\n }\n else {\n $(''#degree-slider'').attr(''disabled'', ''disabled''); \n $(''#degree-value'').attr(''class'', "parameter_value_off");\n }\n\n if (kernel_choice == ''rbf'' || kernel_choice == ''poly'' || kernel_choice == ''sigmoid'') {\n $(''#gamma-slider'').removeAttr(''disabled'');\n $(''#gamma-value'').attr(''class'', "parameter_value");\n }\n else {\n $(''#gamma-slider'').attr(''disabled'', ''disabled''); \n $(''#gamma-value'').attr(''class'', "parameter_value_off");\n }\n \n })\n .change();\n});\n\n</script>', '<h4 class="m-b-xxs text-center">Suport Vector Machine <i class=\n"fa fa-line-chart"></i></h4>\n\n\n<p>Support vector machines are supervised learning models with associated learning algorithms that analyze data and recognize patterns. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. <a href=\n"http://en.wikipedia.org/wiki/Support_vector_machine" target=\n"_blank">[ref]</a></p>\n\n\n<div class="col-xs-6">\n <div>\n <span>Complexity</span> <small class="pull-right">8/10</small>\n </div>\n\n\n <div class="progress progress-small">\n <div class="progress-bar progress-bar-danger" style="width: 80%;">\n </div>\n </div>\n</div>\n\n\n<div class="col-xs-6">\n <div>\n <span>Interpretability</span> <small class="pull-right">7/10</small>\n </div>\n\n\n <div class="progress progress-small">\n <div class="progress-bar" style="width: 80%;">\n </div>\n </div>\n</div>');
-- --------------------------------------------------------
--
-- Table structure for table `ode_results`
--
CREATE TABLE IF NOT EXISTS `ode_results` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`model_id` int(11) DEFAULT NULL,
`dataset_id` int(11) DEFAULT NULL,
`params` longtext COLLATE utf8_unicode_ci COMMENT '(DC2Type:json_array)',
`runtime` double DEFAULT NULL,
`finished` tinyint(1) NOT NULL,
`auroc` double DEFAULT NULL,
`aupr` double DEFAULT NULL,
`accuracy` double DEFAULT NULL,
`report_data` longtext COLLATE utf8_unicode_ci COMMENT '(DC2Type:json_array)',
`date` varchar(255) COLLATE utf8_unicode_ci DEFAULT NULL,
`user_id` int(11) DEFAULT NULL,
`preprocessing_params` longtext COLLATE utf8_unicode_ci COMMENT '(DC2Type:json_array)',
`precision_score` double DEFAULT NULL,
`recall_score` double DEFAULT NULL,
`f1_score` double DEFAULT NULL,
PRIMARY KEY (`id`),
KEY `IDX_E89A69F37975B7E7` (`model_id`),
KEY `IDX_E89A69F3D47C2D1B` (`dataset_id`),
KEY `IDX_E89A69F3A76ED395` (`user_id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COLLATE=utf8_unicode_ci AUTO_INCREMENT=1 ;
-- --------------------------------------------------------
--
-- Table structure for table `ode_users`
--
CREATE TABLE IF NOT EXISTS `ode_users` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`username` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`username_canonical` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`email` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`email_canonical` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`enabled` tinyint(1) NOT NULL,
`salt` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`password` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`last_login` datetime DEFAULT NULL,
`locked` tinyint(1) NOT NULL,
`expired` tinyint(1) NOT NULL,
`expires_at` datetime DEFAULT NULL,
`confirmation_token` varchar(255) COLLATE utf8_unicode_ci DEFAULT NULL,
`password_requested_at` datetime DEFAULT NULL,
`roles` longtext COLLATE utf8_unicode_ci NOT NULL COMMENT '(DC2Type:array)',
`credentials_expired` tinyint(1) NOT NULL,
`credentials_expire_at` datetime DEFAULT NULL,
`firstname` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`lastname` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`affiliation` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`member_since` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`profilePicturePath` varchar(255) COLLATE utf8_unicode_ci DEFAULT NULL,
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