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annotate feature_help_modal.py @ 8:1aed7d47c5ec draft default tip

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planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
author goeckslab
date Fri, 25 Jul 2025 19:02:32 +0000
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1 def get_feature_metrics_help_modal() -> str:
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2 modal_html = """
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3 <div id="metricsHelpModal" class="modal">
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4 <div class="modal-content">
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5 <span class="close">&times;</span>
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6 <h2>Help Guide: Common Model Metrics</h2>
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7 <div class="metrics-guide">
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9 <!-- Classification Metrics -->
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10 <h3>1) Classification Metrics</h3>
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12 <p><strong>Accuracy:</strong>
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13 The proportion of correct predictions over all predictions:<br>
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14 <code>(TP + TN) / (TP + TN + FP + FN)</code>.
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15 <em>Use when</em> classes are balanced and you want a single easy‐to‐interpret number.</p>
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17 <p><strong>Precision:</strong>
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18 The fraction of positive predictions that are actually positive:<br>
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19 <code>TP / (TP + FP)</code>.
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20 <em>Use when</em> false positives are costly (e.g. spam filter—better to miss some spam than flag good mail).</p>
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22 <p><strong>Recall (Sensitivity):</strong>
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23 The fraction of actual positives correctly identified:<br>
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24 <code>TP / (TP + FN)</code>
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25 <em>Use when</em> false negatives are costly (e.g. disease screening—don’t miss sick patients).</p>
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27 <p><strong>F1 Score:</strong>
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28 The harmonic mean of Precision and Recall:<br>
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29 <code>2·(Precision·Recall)/(Precision+Recall)</code>
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30 <em>Use when</em> you need a balance between Precision & Recall on an imbalanced dataset.</p>
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32 <p><strong>ROC-AUC (Area Under ROC Curve):</strong>
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33 Measures ability to distinguish classes across all thresholds.
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34 Ranges from 0.5 (random) to 1 (perfect).
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35 <em>Use when</em> you care about ranking positives above negatives.</p>
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37 <p><strong>PR-AUC (Area Under Precision-Recall Curve):</strong>
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38 Summarizes Precision vs. Recall trade-off.
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39 More informative than ROC-AUC when positives are rare.
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40 <em>Use when</em> dealing with highly imbalanced data.</p>
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41
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42 <p><strong>Log Loss:</strong>
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43 Penalizes confident wrong predictions via negative log-likelihood.
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44 Lower is better.
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45 <em>Use when</em> you need well-calibrated probability estimates.</p>
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47 <p><strong>Cohen’s Kappa:</strong>
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48 Measures agreement between predictions and true labels accounting for chance.
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49 1 is perfect, 0 is random.
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50 <em>Use when</em> you want to factor out chance agreement.</p>
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51
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52 <hr>
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54 <!-- Regression Metrics -->
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55 <h3>2) Regression Metrics</h3>
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57 <p><strong>R² (Coefficient of Determination):</strong>
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58 Proportion of variance in the target explained by features:<br>
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59 1 is perfect, 0 means no better than predicting the mean, negative is worse than mean.
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60 <em>Use when</em> you want a normalized measure of fit.</p>
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62 <p><strong>MAE (Mean Absolute Error):</strong>
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63 Average absolute difference between predictions and actual values:<br>
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64 <code>mean(|y_pred − y_true|)</code>
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65 <em>Use when</em> you need an interpretable “average” error and want to downweight outliers.</p>
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67 <p><strong>RMSE (Root Mean Squared Error):</strong>
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68 Square root of the average squared errors:<br>
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69 <code>√mean((y_pred − y_true)²)</code>.
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70 Penalizes large errors more heavily.
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71 <em>Use when</em> large deviations are especially undesirable.</p>
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73 <p><strong>MSE (Mean Squared Error):</strong>
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74 The average squared error:<br>
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75 <code>mean((y_pred − y_true)²)</code>.
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76 Similar to RMSE but in squared units; often used in optimization.</p>
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78 <p><strong>RMSLE (Root Mean Squared Log Error):</strong>
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79 <code>√mean((log(1+y_pred) − log(1+y_true))²)</code>.
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80 Less sensitive to large differences when both true and predicted are large.
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81 <em>Use when</em> target spans several orders of magnitude.</p>
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83 <p><strong>MAPE (Mean Absolute Percentage Error):</strong>
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84 <code>mean(|(y_true − y_pred)/y_true|)·100</code>.
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85 Expresses error as a percentage.
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86 <em>Use when</em> relative error matters—but avoid if y_true≈0.</p>
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87
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88 </div>
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89 </div>
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90 </div>
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91 """
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92
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93 modal_css = """
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94 <style>
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95 .modal {
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96 display: none;
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97 position: fixed;
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98 z-index: 1;
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99 left: 0;
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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100 top: 0;
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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101 width: 100%;
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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102 height: 100%;
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103 overflow: auto;
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104 background-color: rgba(0,0,0,0.4);
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105 }
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106 .modal-content {
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107 background-color: #fefefe;
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108 margin: 15% auto;
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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109 padding: 20px;
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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110 border: 1px solid #888;
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111 width: 80%;
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112 max-width: 800px;
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113 }
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114 .close {
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115 color: #aaa;
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116 float: right;
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117 font-size: 28px;
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118 font-weight: bold;
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119 }
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diff changeset
120 .close:hover,
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121 .close:focus {
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122 color: black;
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123 text-decoration: none;
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124 cursor: pointer;
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125 }
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126 .metrics-guide h3 {
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127 margin-top: 20px;
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128 }
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129 .metrics-guide p {
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130 margin: 5px 0;
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131 }
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132 .metrics-guide ul {
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133 margin: 10px 0;
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134 padding-left: 20px;
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135 }
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136 </style>
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137 """
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138 modal_js = """
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139 <script>
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140 document.addEventListener("DOMContentLoaded", function() {
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141 var modal = document.getElementById("metricsHelpModal");
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142 var openBtn = document.getElementById("openMetricsHelp");
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143 var span = document.getElementsByClassName("close")[0];
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144 if (openBtn && modal) {
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145 openBtn.onclick = function() {
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146 modal.style.display = "block";
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147 };
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148 }
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149 if (span && modal) {
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150 span.onclick = function() {
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151 modal.style.display = "none";
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152 };
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153 }
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154 window.onclick = function(event) {
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155 if (event.target == modal) {
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156 modal.style.display = "none";
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157 }
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158 }
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159 });
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160 </script>
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161 """
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162 return modal_css + modal_html + modal_js