| Previous changeset 3:33d53eb476fd (2025-01-20) Next changeset 5:49b4ee0d0965 (2025-03-26) |
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Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/tabpfn commit f514c9038f1aac1ef0ca40a9f4866b1ad0fc7747 |
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modified:
main.py tabpfn.xml test-data/classification_local_test_rows_labels.tabular |
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added:
test-data/prc_binary.png test-data/prc_multiclass.png test-data/test_data_multiclass_labels.tabular test-data/test_data_multiclass_nolabels.tabular test-data/train_data_multiclass.tabular |
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removed:
test-data/pr_curve.png |
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| diff -r 33d53eb476fd -r e7b4afedc471 main.py --- a/main.py Mon Jan 20 15:45:17 2025 +0000 +++ b/main.py Tue Feb 11 10:14:12 2025 +0000 |
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| @@ -11,8 +11,9 @@ average_precision_score, precision_recall_curve, r2_score, - root_mean_squared_error + root_mean_squared_error, ) +from sklearn.preprocessing import label_binarize from tabpfn import TabPFNClassifier, TabPFNRegressor @@ -23,12 +24,42 @@ return features, labels -def classification_plot(xval, yval, leg_label, title, xlabel, ylabel): +def classification_plot(y_true, y_scores): plt.figure(figsize=(8, 6)) - plt.plot(xval, yval, label=leg_label) - plt.xlabel(xlabel) - plt.ylabel(ylabel) - plt.title(title) + is_binary = len(np.unique(y_true)) == 2 + if is_binary: + # Compute precision-recall curve + precision, recall, _ = precision_recall_curve(y_true, y_scores[:, 1]) + average_precision = average_precision_score(y_true, y_scores[:, 1]) + plt.plot( + recall, + precision, + label=f"Precision-Recall Curve (AP={average_precision:.2f})", + ) + plt.title("Precision-Recall Curve (binary classification)") + else: + y_true_bin = label_binarize(y_true, classes=np.unique(y_true)) + n_classes = y_true_bin.shape[1] + class_labels = [f"Class {i}" for i in range(n_classes)] + # Plot PR curve for each class + for i in range(n_classes): + precision, recall, _ = precision_recall_curve( + y_true_bin[:, i], y_scores[:, i] + ) + ap_score = average_precision_score(y_true_bin[:, i], y_scores[:, i]) + plt.plot( + recall, precision, label=f"{class_labels[i]} (AP = {ap_score:.2f})" + ) + # Compute micro-average PR curve + precision, recall, _ = precision_recall_curve( + y_true_bin.ravel(), y_scores.ravel() + ) + plt.plot( + recall, precision, linestyle="--", color="black", label="Micro-average" + ) + plt.title("Precision-Recall Curve (Multiclass Classification)") + plt.xlabel("Recall") + plt.ylabel("Precision") plt.legend(loc="lower left") plt.grid(True) plt.savefig("output_plot.png") @@ -66,20 +97,7 @@ y_eval = classifier.predict(te_features) pred_probas_test = classifier.predict_proba(te_features) if len(te_labels) > 0: - precision, recall, thresholds = precision_recall_curve( - te_labels, pred_probas_test[:, 1] - ) - average_precision = average_precision_score( - te_labels, pred_probas_test[:, 1] - ) - classification_plot( - recall, - precision, - f"Precision-Recall Curve (AP={average_precision:.2f})", - "Precision-Recall Curve", - "Recall", - "Precision", - ) + classification_plot(te_labels, pred_probas_test) else: regressor = TabPFNRegressor() regressor.fit(tr_features, tr_labels) |
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| diff -r 33d53eb476fd -r e7b4afedc471 tabpfn.xml --- a/tabpfn.xml Mon Jan 20 15:45:17 2025 +0000 +++ b/tabpfn.xml Tue Feb 11 10:14:12 2025 +0000 |
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| @@ -2,7 +2,7 @@ <description>with PyTorch</description> <macros> <token name="@TOOL_VERSION@">2.0.3</token> - <token name="@VERSION_SUFFIX@">1</token> + <token name="@VERSION_SUFFIX@">1.1</token> </macros> <creator> <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/" /> @@ -57,7 +57,26 @@ <param name="train_data" value="classification_local_train_rows.tabular" ftype="tabular" /> <param name="test_data" value="classification_local_test_rows_labels.tabular" ftype="tabular" /> <param name="testhaslabels" value="haslabels" /> - <output name="output_plot" file="pr_curve.png" compare="sim_size" /> + <output name="output_plot" file="prc_binary.png" compare="sim_size" /> + </test> + <test expect_num_outputs="2"> + <param name="selected_task" value="Classification" /> + <param name="train_data" value="train_data_multiclass.tabular" ftype="tabular" /> + <param name="test_data" value="test_data_multiclass_labels.tabular" ftype="tabular" /> + <param name="testhaslabels" value="haslabels" /> + <output name="output_plot" file="prc_multiclass.png" compare="sim_size" /> + </test> + <test expect_num_outputs="1"> + <param name="selected_task" value="Classification" /> + <param name="train_data" value="train_data_multiclass.tabular" ftype="tabular" /> + <param name="test_data" value="test_data_multiclass_nolabels.tabular" ftype="tabular" /> + <param name="testhaslabels" value="" /> + <output name="output_predicted_data"> + <assert_contents> + <has_n_columns n="11" /> + <has_n_lines n="502" /> + </assert_contents> + </output> </test> <test expect_num_outputs="2"> <param name="selected_task" value="Regression" /> |
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| diff -r 33d53eb476fd -r e7b4afedc471 test-data/classification_local_test_rows_labels.tabular --- a/test-data/classification_local_test_rows_labels.tabular Mon Jan 20 15:45:17 2025 +0000 +++ b/test-data/classification_local_test_rows_labels.tabular Tue Feb 11 10:14:12 2025 +0000 |
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| @@ -1,3 +1,5 @@ SpMax_L J_Dz(e) nHM F01[N-N] F04[C-N] NssssC nCb- C% nCp nO F03[C-N] SdssC HyWi_B(m) LOC SM6_L F03[C-O] Me Mi nN-N nArNO2 nCRX3 SpPosA_B(p) nCIR B01[C-Br] B03[C-Cl] N-073 SpMax_A Psi_i_1d B04[C-Br] SdO TI2_L nCrt C-026 F02[C-N] nHDon SpMax_B(m) Psi_i_A nN SM6_B(m) nArCOOR nX Class 3.919 2.6909 0 0 0 0 0 31.4 2 0 0 0 3.106 2.55 9.002 0 0.96 1.142 0 0 0 1.201 0 0 0 0 1.932 0.011 0 0 4.489 0 0 0 0 2.949 1.591 0 7.253 0 0 1 4.17 2.1144 0 0 0 0 0 30.8 1 1 0 0 2.461 1.393 8.723 1 0.989 1.144 0 0 0 1.104 1 0 0 0 2.214 -0.204 0 0 1.542 0 0 0 0 3.315 1.967 0 7.257 0 0 1 +3.919 2.6909 0 0 0 0 0 31.4 2 0 0 0 3.106 2.55 9.002 0 0.96 1.142 0 0 0 1.201 0 0 0 0 1.932 0.011 0 0 4.489 0 0 0 0 2.949 1.591 0 7.253 0 0 0 +4.17 2.1144 0 0 0 0 0 30.8 1 1 0 0 2.461 1.393 8.723 1 0.989 1.144 0 0 0 1.104 1 0 0 0 2.214 -0.204 0 0 1.542 0 0 0 0 3.315 1.967 0 7.257 0 0 0 |
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| diff -r 33d53eb476fd -r e7b4afedc471 test-data/pr_curve.png |
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| Binary file test-data/pr_curve.png has changed |
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| diff -r 33d53eb476fd -r e7b4afedc471 test-data/prc_binary.png |
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| Binary file test-data/prc_binary.png has changed |
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| diff -r 33d53eb476fd -r e7b4afedc471 test-data/prc_multiclass.png |
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| Binary file test-data/prc_multiclass.png has changed |
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| diff -r 33d53eb476fd -r e7b4afedc471 test-data/test_data_multiclass_labels.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/test_data_multiclass_labels.tabular Tue Feb 11 10:14:12 2025 +0000 |
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| b'@@ -0,0 +1,502 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| diff -r 33d53eb476fd -r e7b4afedc471 test-data/test_data_multiclass_nolabels.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/test_data_multiclass_nolabels.tabular Tue Feb 11 10:14:12 2025 +0000 |
| b |
| b'@@ -0,0 +1,502 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|
| b |
| diff -r 33d53eb476fd -r e7b4afedc471 test-data/train_data_multiclass.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/train_data_multiclass.tabular Tue Feb 11 10:14:12 2025 +0000 |
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