Mercurial > repos > goeckslab > pycaret_predict

annotate pycaret_classification.py @ 16:4fee4504646e draft default tip

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planemo upload for repository https://github.com/goeckslab/gleam commit 2b826699ef9518d4610f5cfb6468ce719ec8039d
author goeckslab
date Fri, 28 Nov 2025 22:28:26 +0000
parents a2aeeb754d76
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1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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diff changeset
1 import logging
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2 import types
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3 from typing import Dict
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4
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5 import numpy as np
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6 import pandas as pd
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7 import plotly.graph_objects as go
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8 from base_model_trainer import BaseModelTrainer
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9 from dashboard import generate_classifier_explainer_dashboard
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10 from pycaret.classification import ClassificationExperiment
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11 from sklearn.metrics import auc, confusion_matrix, precision_recall_curve, roc_curve
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12 from utils import predict_proba
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13
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14 LOG = logging.getLogger(__name__)
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16
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17 def _apply_report_layout(fig: go.Figure) -> go.Figure:
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18 # Give the left side more space for y-axis title/ticks and let axes auto-reserve room
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19 fig.update_xaxes(automargin=True, title_standoff=12)
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20 fig.update_yaxes(automargin=True, title_standoff=12)
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21 fig.update_layout(
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22 plot_bgcolor="#ffffff",
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23 paper_bgcolor="#ffffff",
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24 )
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25 fig.update_xaxes(gridcolor="#e8e8e8")
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26 fig.update_yaxes(gridcolor="#e8e8e8")
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27 fig.update_layout(
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28 autosize=True,
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29 margin=dict(l=120, r=40, t=60, b=60), # bump 'l' if you still see clipping
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30 )
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31 return fig
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33
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34 class ClassificationModelTrainer(BaseModelTrainer):
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35 def __init__(
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36 self,
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37 input_file,
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38 target_col,
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39 output_dir,
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40 task_type,
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41 random_seed,
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42 test_file=None,
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43 **kwargs,
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44 ):
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45 super().__init__(
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46 input_file,
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47 target_col,
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48 output_dir,
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49 task_type,
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50 random_seed,
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51 test_file,
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52 **kwargs,
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53 )
0
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54 self.exp = ClassificationExperiment()
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55
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56 def save_dashboard(self):
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57 LOG.info("Saving explainer dashboard")
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58 dashboard = generate_classifier_explainer_dashboard(self.exp, self.best_model)
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59 dashboard.save_html("dashboard.html")
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60
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61 def generate_plots(self):
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62 LOG.info("Generating and saving plots")
2
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63
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64 if not hasattr(self.best_model, "predict_proba"):
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65 self.best_model.predict_proba = types.MethodType(
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66 predict_proba, self.best_model
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67 )
2
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68 LOG.warning(
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69 f"The model {type(self.best_model).__name__} does not support `predict_proba`. Applying monkey patch."
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70 )
2
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71
8
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72 plots = [
12
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73 "auc",
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74 "threshold",
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75 "pr",
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76 "error",
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77 "class_report",
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78 "learning",
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79 "calibration",
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80 "vc",
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81 "dimension",
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82 "manifold",
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83 "rfe",
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84 "feature",
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85 "feature_all",
8
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86 ]
0
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87 for plot_name in plots:
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88 try:
8
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89 if plot_name == "threshold":
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90 plot_path = self.exp.plot_model(
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91 self.best_model,
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92 plot=plot_name,
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93 save=True,
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94 plot_kwargs={"binary": True, "percentage": True},
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95 )
0
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96 self.plots[plot_name] = plot_path
8
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97 elif plot_name == "auc" and not self.exp.is_multiclass:
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98 plot_path = self.exp.plot_model(
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99 self.best_model,
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100 plot=plot_name,
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101 save=True,
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102 plot_kwargs={
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103 "micro": False,
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104 "macro": False,
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105 "per_class": False,
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106 "binary": True,
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107 },
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108 )
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109 self.plots[plot_name] = plot_path
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110 else:
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111 plot_path = self.exp.plot_model(
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112 self.best_model, plot=plot_name, save=True
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113 )
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114 self.plots[plot_name] = plot_path
0
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115 except Exception as e:
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116 LOG.error(f"Error generating plot {plot_name}: {e}")
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117 continue
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118
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119 def generate_plots_explainer(self):
8
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120 from explainerdashboard import ClassifierExplainer
0
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121
8
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122 LOG.info("Generating explainer plots")
0
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123
12
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124 # Ensure predict_proba is available here too
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125 if not hasattr(self.best_model, "predict_proba"):
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126 self.best_model.predict_proba = types.MethodType(
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127 predict_proba, self.best_model
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128 )
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129 LOG.warning(
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130 f"The model {type(self.best_model).__name__} does not support `predict_proba`. Applying monkey patch."
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131 )
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132
0
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133 X_test = self.exp.X_test_transformed.copy()
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134 y_test = self.exp.y_test_transformed
8
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135 explainer = ClassifierExplainer(self.best_model, X_test, y_test)
0
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136
8
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137 # a dict to hold the raw Figure objects or callables
12
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138 self.explainer_plots: Dict[str, go.Figure] = {}
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139
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140 # --- Threshold-aware overrides for CM / ROC / PR ---
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141 prob_thresh = getattr(self, "probability_threshold", None)
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142
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143 # Only for binary classification and when threshold is provided
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144 if (prob_thresh is not None) and (not self.exp.is_multiclass):
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145 X = self.exp.X_test_transformed
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146 y = pd.Series(self.exp.y_test_transformed).reset_index(drop=True)
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147
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148 # Get positive-class scores (robust defaults)
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149 classes = list(getattr(self.best_model, "classes_", [0, 1]))
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150 try:
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151 pos_idx = classes.index(1) if 1 in classes else 1
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152 except Exception:
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diff changeset
153 pos_idx = 1
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diff changeset
154
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155 proba = self.best_model.predict_proba(X)
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156 y_scores = proba[:, pos_idx]
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diff changeset
157
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158 # Derive label names consistently
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159 pos_label = classes[pos_idx] if len(classes) > pos_idx else 1
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160 neg_label = classes[1 - pos_idx] if len(classes) > 1 else 0
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diff changeset
161
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162 # ---- Confusion Matrix @ threshold ----
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163 try:
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164 y_pred = np.where(y_scores >= prob_thresh, pos_label, neg_label)
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165 cm = confusion_matrix(y, y_pred, labels=[neg_label, pos_label])
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166 fig_cm = go.Figure(
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167 data=go.Heatmap(
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168 z=cm,
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169 x=[f"Pred {neg_label}", f"Pred {pos_label}"],
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170 y=[f"True {neg_label}", f"True {pos_label}"],
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171 text=cm,
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172 texttemplate="%{text}",
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173 colorscale="Blues",
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174 showscale=False,
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175 )
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176 )
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177 fig_cm.update_layout(
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178 title=f"Confusion Matrix @ threshold={prob_thresh:.2f}",
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179 xaxis_title="Predicted label",
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180 yaxis_title="True label",
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181 )
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182 _apply_report_layout(fig_cm)
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183 self.explainer_plots["confusion_matrix"] = fig_cm
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184 except Exception as e:
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185 LOG.warning(
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186 f"Threshold-aware confusion matrix failed; falling back: {e}"
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187 )
0
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188
12
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189 # ---- ROC with threshold marker ----
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190 try:
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191 fpr, tpr, thr = roc_curve(y, y_scores)
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192 roc_auc = auc(fpr, tpr)
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193 fig_roc = go.Figure()
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194 fig_roc.add_scatter(
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195 x=fpr, y=tpr, mode="lines", name=f"ROC (AUC={roc_auc:.3f})"
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196 )
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197 if len(thr):
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198 mask = np.isfinite(thr)
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199 if mask.any():
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200 idx_local = int(np.argmin(np.abs(thr[mask] - prob_thresh)))
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201 idx = np.where(mask)[0][idx_local]
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202 if 0 <= idx < len(fpr):
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203 fig_roc.add_scatter(
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204 x=[fpr[idx]],
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205 y=[tpr[idx]],
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206 mode="markers",
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207 name=f"@ {prob_thresh:.2f}",
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208 marker=dict(size=10),
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209 )
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210 fig_roc.update_layout(
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211 title=f"ROC Curve (marker at threshold={prob_thresh:.2f})",
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212 xaxis_title="False Positive Rate",
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213 yaxis_title="True Positive Rate",
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214 )
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215 _apply_report_layout(fig_roc)
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216 self.explainer_plots["roc_auc"] = fig_roc
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217 except Exception as e:
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218 LOG.warning(f"Threshold marker on ROC failed; falling back: {e}")
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219
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220 # ---- PR with threshold marker ----
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221 try:
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222 precision, recall, thr_pr = precision_recall_curve(y, y_scores)
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223 pr_auc = auc(recall, precision)
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224 fig_pr = go.Figure()
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225 fig_pr.add_scatter(
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226 x=recall, y=precision, mode="lines", name=f"PR (AUC={pr_auc:.3f})"
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227 )
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228 if len(thr_pr):
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229 idx_pr = int(np.argmin(np.abs(thr_pr - prob_thresh)))
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230 # note: thr_pr has length = len(precision) - 1
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231 idx_pr = max(0, min(idx_pr, len(recall) - 1))
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232 fig_pr.add_scatter(
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233 x=[recall[idx_pr]],
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234 y=[precision[idx_pr]],
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235 mode="markers",
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236 name=f"@ {prob_thresh:.2f}",
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237 marker=dict(size=10),
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238 )
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239 fig_pr.update_layout(
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240 title=f"Precision–Recall (marker at threshold={prob_thresh:.2f})",
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241 xaxis_title="Recall",
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242 yaxis_title="Precision",
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243 )
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244 _apply_report_layout(fig_pr)
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245 self.explainer_plots["pr_auc"] = fig_pr
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246 except Exception as e:
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247 LOG.warning(f"Threshold marker on PR failed; falling back: {e}")
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248
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249 # these go into the Test tab (don't overwrite overrides)
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250 for key, fn in [
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251 ("roc_auc", explainer.plot_roc_auc),
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252 ("pr_auc", explainer.plot_pr_auc),
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253 ("lift_curve", explainer.plot_lift_curve),
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254 ("confusion_matrix", explainer.plot_confusion_matrix),
12
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255 ("threshold", explainer.plot_precision), # percentage vs probability
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256 ("cumulative_precision", explainer.plot_cumulative_precision),
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257 ]:
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258 if key in self.explainer_plots:
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259 continue
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260 try:
12
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261 fig = fn()
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262 if fig is not None:
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263 self.explainer_plots[key] = fig
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264 except Exception as e:
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265 LOG.error(f"Error generating explainer plot {key}: {e}")
0
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266
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267 # mean SHAP importances
0
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268 try:
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269 self.explainer_plots["shap_mean"] = explainer.plot_importances()
0
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270 except Exception as e:
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271 LOG.warning(f"Could not generate shap_mean: {e}")
0
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272
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273 # permutation importances
0
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274 try:
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275 self.explainer_plots["shap_perm"] = lambda: explainer.plot_importances(
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276 kind="permutation"
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277 )
0
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278 except Exception as e:
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279 LOG.warning(f"Could not generate shap_perm: {e}")
0
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280
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281 # PDPs for each feature (appended last)
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282 valid_feats = []
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283 for feat in self.features_name:
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284 if feat in explainer.X.columns or feat in explainer.onehot_cols:
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285 valid_feats.append(feat)
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286 else:
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287 LOG.warning(
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288 f"Skipping PDP for feature {feat!r}: not found in explainer data"
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289 )
0
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290
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291 for feat in valid_feats:
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292 # wrap each PDP call to catch any unexpected AssertionErrors
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293 def make_pdp_plotter(f):
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294 def _plot():
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295 try:
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296 return explainer.plot_pdp(f)
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297 except AssertionError as ae:
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298 LOG.warning(f"PDP AssertionError for {f!r}: {ae}")
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299 return None
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300 except Exception as e:
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301 LOG.error(f"Unexpected error plotting PDP for {f!r}: {e}")
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302 return None
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303
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304 return _plot
0
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305
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1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
goeckslab
parents: 3
diff changeset
306 self.explainer_plots[f"pdp__{feat}"] = make_pdp_plotter(feat)