| Previous changeset 1:5112462f2dd3 (2025-01-15) Next changeset 3:33d53eb476fd (2025-01-20) |
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Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/tabpfn commit e87b82b59dced736af2f0d9de045c916400b7bc2 |
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modified:
main.py tabpfn.xml test-data/pr_curve.png |
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added:
test-data/classification_local_test_rows.tabular test-data/classification_local_test_rows_labels.tabular test-data/classification_local_train_rows.tabular test-data/r2_curve.png test-data/regression_local_test_rows.tabular test-data/regression_local_test_rows_labels.tabular test-data/regression_local_train_rows.tabular |
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removed:
test-data/local_test_rows.tabular test-data/local_train_rows.tabular |
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| diff -r 5112462f2dd3 -r c081e5e1d7ce main.py --- a/main.py Wed Jan 15 20:34:13 2025 +0000 +++ b/main.py Fri Jan 17 22:23:34 2025 +0000 |
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| @@ -5,9 +5,15 @@ import time import matplotlib.pyplot as plt +import numpy as np import pandas as pd -from sklearn.metrics import accuracy_score, average_precision_score, precision_recall_curve -from tabpfn import TabPFNClassifier +from sklearn.metrics import ( + average_precision_score, + precision_recall_curve, + r2_score, + root_mean_squared_error +) +from tabpfn import TabPFNClassifier, TabPFNRegressor def separate_features_labels(data): @@ -17,38 +23,103 @@ return features, labels +def classification_plot(xval, yval, leg_label, title, xlabel, ylabel): + plt.figure(figsize=(8, 6)) + plt.plot(xval, yval, label=leg_label) + plt.xlabel(xlabel) + plt.ylabel(ylabel) + plt.title(title) + plt.legend(loc="lower left") + plt.grid(True) + plt.savefig("output_plot.png") + + +def regression_plot(xval, yval, title, xlabel, ylabel): + plt.figure(figsize=(8, 6)) + plt.xlabel(xlabel) + plt.ylabel(ylabel) + plt.title(title) + plt.legend(loc="lower left") + plt.grid(True) + plt.scatter(xval, yval, alpha=0.8) + xticks = np.arange(len(xval)) + plt.plot(xticks, xticks, color="red", linestyle="--", label="y = x") + plt.savefig("output_plot.png") + + def train_evaluate(args): """ - Train TabPFN + Train TabPFN and predict """ + # prepare train data tr_features, tr_labels = separate_features_labels(args["train_data"]) - te_features, te_labels = separate_features_labels(args["test_data"]) - classifier = TabPFNClassifier(device='cpu') + # prepare test data + if args["testhaslabels"] == "haslabels": + te_features, te_labels = separate_features_labels(args["test_data"]) + else: + te_features = pd.read_csv(args["test_data"], sep="\t") + te_labels = [] s_time = time.time() - classifier.fit(tr_features, tr_labels) + if args["selected_task"] == "Classification": + classifier = TabPFNClassifier(device="cpu") + classifier.fit(tr_features, tr_labels) + 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", + ) + else: + regressor = TabPFNRegressor(device="cpu") + regressor.fit(tr_features, tr_labels) + y_eval = regressor.predict(te_features) + if len(te_labels) > 0: + score = root_mean_squared_error(te_labels, y_eval) + r2_metric_score = r2_score(te_labels, y_eval) + regression_plot( + te_labels, + y_eval, + f"Scatter plot: True vs predicted values. RMSE={score:.2f}, R2={r2_metric_score:.2f}", + "True values", + "Predicted values", + ) e_time = time.time() - print("Time taken by TabPFN for training: {} seconds".format(e_time - s_time)) - y_eval = classifier.predict(te_features) - print('Accuracy', accuracy_score(te_labels, y_eval)) - pred_probas_test = classifier.predict_proba(te_features) + print( + "Time taken by TabPFN for training and prediction: {} seconds".format( + e_time - s_time + ) + ) te_features["predicted_labels"] = y_eval te_features.to_csv("output_predicted_data", sep="\t", index=None) - precision, recall, thresholds = precision_recall_curve(te_labels, pred_probas_test[:, 1]) - average_precision = average_precision_score(te_labels, pred_probas_test[:, 1]) - plt.figure(figsize=(8, 6)) - plt.plot(recall, precision, label=f'Precision-Recall Curve (AP={average_precision:.2f})') - plt.xlabel('Recall') - plt.ylabel('Precision') - plt.title('Precision-Recall Curve') - plt.legend(loc='lower left') - plt.grid(True) - plt.savefig("output_prec_recall_curve.png") if __name__ == "__main__": arg_parser = argparse.ArgumentParser() arg_parser.add_argument("-trdata", "--train_data", required=True, help="Train data") arg_parser.add_argument("-tedata", "--test_data", required=True, help="Test data") + arg_parser.add_argument( + "-testhaslabels", + "--testhaslabels", + required=True, + help="if test data contain labels", + ) + arg_parser.add_argument( + "-selectedtask", + "--selected_task", + required=True, + help="Type of machine learning task", + ) # get argument values args = vars(arg_parser.parse_args()) train_evaluate(args) |
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| diff -r 5112462f2dd3 -r c081e5e1d7ce tabpfn.xml --- a/tabpfn.xml Wed Jan 15 20:34:13 2025 +0000 +++ b/tabpfn.xml Fri Jan 17 22:23:34 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@">0</token> + <token name="@VERSION_SUFFIX@">1</token> </macros> <creator> <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/" /> @@ -17,23 +17,34 @@ <version_command>echo "@VERSION@"</version_command> <command detect_errors="aggressive"> <![CDATA[ - python '$__tool_directory__/main.py' + python '$__tool_directory__/main.py' + --selected_task '$selected_task' --train_data '$train_data' + --testhaslabels '$testhaslabels' --test_data '$test_data' ]]> </command> <inputs> - <param name="train_data" type="data" format="tabular" label="Train data" help="Please provide training data for training model."/> - <param name="test_data" type="data" format="tabular" label="Test data" help="Please provide test data for evaluating model."/> + <param name="selected_task" type="select" label="Select a machine learning task"> + <option value="Classification" selected="true"></option> + <option value="Regression" selected="false"></option> + </param> + <param name="train_data" type="data" format="tabular" label="Train data" help="Please provide training data for training model. It should contain labels/class/target in the last column" /> + <param name="test_data" type="data" format="tabular" label="Test data" help="Please provide test data for evaluating model. It may or may not contain labels/class/target in the last column" /> + <param name="testhaslabels" type="boolean" truevalue="haslabels" falsevalue="" checked="false" label="Does test data contain labels?" help="Set this parameter when test data contains labels" /> </inputs> <outputs> <data format="tabular" name="output_predicted_data" from_work_dir="output_predicted_data" label="Predicted data"></data> - <data format="png" name="output_prec_recall_curve" from_work_dir="output_prec_recall_curve.png" label="Precision-recall curve"></data> + <data format="png" name="output_plot" from_work_dir="output_plot.png" label="Prediction plot on test data"> + <filter>testhaslabels is True</filter> + </data> </outputs> <tests> - <test> - <param name="train_data" value="local_train_rows.tabular" ftype="tabular" /> - <param name="test_data" value="local_test_rows.tabular" ftype="tabular" /> + <test expect_num_outputs="1"> + <param name="selected_task" value="Classification" /> + <param name="train_data" value="classification_local_train_rows.tabular" ftype="tabular" /> + <param name="test_data" value="classification_local_test_rows.tabular" ftype="tabular" /> + <param name="testhaslabels" value="" /> <output name="output_predicted_data"> <assert_contents> <has_n_columns n="42" /> @@ -41,24 +52,46 @@ </assert_contents> </output> </test> - <test> - <param name="train_data" value="local_train_rows.tabular" ftype="tabular" /> - <param name="test_data" value="local_test_rows.tabular" ftype="tabular" /> - <output name="output_prec_recall_curve" file="pr_curve.png" compare="sim_size" /> + <test expect_num_outputs="2"> + <param name="selected_task" value="Classification" /> + <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" /> + </test> + <test expect_num_outputs="2"> + <param name="selected_task" value="Regression" /> + <param name="train_data" value="regression_local_train_rows.tabular" ftype="tabular" /> + <param name="test_data" value="regression_local_test_rows_labels.tabular" ftype="tabular" /> + <param name="testhaslabels" value="haslabels" /> + <output name="output_plot" file="r2_curve.png" compare="sim_size" /> + </test> + <test expect_num_outputs="1"> + <param name="selected_task" value="Regression" /> + <param name="train_data" value="regression_local_train_rows.tabular" ftype="tabular" /> + <param name="test_data" value="regression_local_test_rows.tabular" ftype="tabular" /> + <param name="testhaslabels" value="" /> + <output name="output_predicted_data"> + <assert_contents> + <has_n_columns n="14" /> + <has_n_lines n="105" /> + </assert_contents> + </output> </test> </tests> <help> <![CDATA[ **What it does** - Classification on tabular data by TabPFN + Classification and Regression on tabular data by TabPFN **Input files** - - Training data: the training data should contain features and the last column should be the class labels. It could either be tabular or in CSV format. - - Test data: the test data should also contain the same features as the training data and the last column should be the class labels. It could either be tabular or in CSV format. + - Training data: the training data should contain features and the last column should be the class labels. It should be in tabular format. + - Test data: the test data should also contain the same features as the training data and the last column should be the class labels if labels are avaialble. It should be in tabular format. It is not required for the test data to have labels. **Output files** - - Predicted data along with predicted labels + - Predicted data along with predicted labels. + - Prediction plot (when test data has labels available). ]]> </help> <citations> |
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| diff -r 5112462f2dd3 -r c081e5e1d7ce test-data/classification_local_test_rows.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/classification_local_test_rows.tabular Fri Jan 17 22:23:34 2025 +0000 |
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| @@ -0,0 +1,3 @@ +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 +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 +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 |
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| diff -r 5112462f2dd3 -r c081e5e1d7ce test-data/classification_local_test_rows_labels.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/classification_local_test_rows_labels.tabular Fri Jan 17 22:23:34 2025 +0000 |
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| @@ -0,0 +1,3 @@ +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 |
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| diff -r 5112462f2dd3 -r c081e5e1d7ce test-data/classification_local_train_rows.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/classification_local_train_rows.tabular Fri Jan 17 22:23:34 2025 +0000 |
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| b'@@ -0,0 +1,838 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t2.288\t-0.008\t0\t20.772\t1.602\t0\t1\t2\t0\t3.853\t2.803\t1\t8.521\t0\t0\t0\n+5.418\t3.1768\t10\t0\t0\t0\t12\t54.5\t0\t0\t0\t0\t4.608\t0.72\t11.151\t0\t1.121\t1.069\t0\t0\t0\t1.413\t2\t0\t1\t0\t2.532\t0\t0\t0\t2.359\t0\t10\t0\t0\t4.361\t2.778\t0\t10.237\t0\t10\t0\n' |
| b |
| diff -r 5112462f2dd3 -r c081e5e1d7ce test-data/local_test_rows.tabular --- a/test-data/local_test_rows.tabular Wed Jan 15 20:34:13 2025 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| @@ -1,3 +0,0 @@ -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 predicted_labels -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 |
| b |
| diff -r 5112462f2dd3 -r c081e5e1d7ce test-data/local_train_rows.tabular --- a/test-data/local_train_rows.tabular Wed Jan 15 20:34:13 2025 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b'@@ -1,838 +0,0 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| b |
| b'@@ -0,0 +1,105 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|
| b |
| diff -r 5112462f2dd3 -r c081e5e1d7ce test-data/regression_local_test_rows_labels.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/regression_local_test_rows_labels.tabular Fri Jan 17 22:23:34 2025 +0000 |
| b |
| b'@@ -0,0 +1,105 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|
| b |
| diff -r 5112462f2dd3 -r c081e5e1d7ce test-data/regression_local_train_rows.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/regression_local_train_rows.tabular Fri Jan 17 22:23:34 2025 +0000 |
| b |
| b'@@ -0,0 +1,209 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|
