| Next changeset 1:5112462f2dd3 (2025-01-15) |
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Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/tabpfn commit bce8b0297bff54e7e29a6106a7f385fd1318c0aa |
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added:
main.py tabpfn.xml test-data/local_test_rows.tabular test-data/local_train_rows.tabular |
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| diff -r 000000000000 -r 3dc3c7443c8e main.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/main.py Wed Jan 15 12:33:49 2025 +0000 |
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| @@ -0,0 +1,54 @@ +""" +Tabular data prediction using TabPFN +""" +import argparse +import time + +import matplotlib.pyplot as plt +import pandas as pd +from sklearn.metrics import accuracy_score, average_precision_score, precision_recall_curve +from tabpfn import TabPFNClassifier + + +def separate_features_labels(data): + df = pd.read_csv(data, sep="\t") + labels = df.iloc[:, -1] + features = df.iloc[:, :-1] + return features, labels + + +def train_evaluate(args): + """ + Train TabPFN + """ + tr_features, tr_labels = separate_features_labels(args["train_data"]) + te_features, te_labels = separate_features_labels(args["test_data"]) + classifier = TabPFNClassifier(device='cpu') + s_time = time.time() + classifier.fit(tr_features, tr_labels) + 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) + 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") + # get argument values + args = vars(arg_parser.parse_args()) + train_evaluate(args) |
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| diff -r 000000000000 -r 3dc3c7443c8e tabpfn.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tabpfn.xml Wed Jan 15 12:33:49 2025 +0000 |
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| @@ -0,0 +1,62 @@ +<tool id="tabpfn" name="Tabular data prediction using TabPFN" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="23.0"> + <description>with PyTorch</description> + <macros> + <token name="@TOOL_VERSION@">2.0.3</token> + <token name="@VERSION_SUFFIX@">0</token> + </macros> + <creator> + <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/" /> + <person givenName="Anup" familyName="Kumar" email="kumara@informatik.uni-freiburg.de" /> + <person givenName="Frank" familyName="Hutter" email="fh@cs.uni-freiburg.de" /> + </creator> + <requirements> + <requirement type="package" version="@TOOL_VERSION@">tabpfn</requirement> + <requirement type="package" version="2.2.2">pandas</requirement> + <requirement type="package" version="3.9.2">matplotlib</requirement> + </requirements> + <version_command>echo "@VERSION@"</version_command> + <command detect_errors="aggressive"> + <![CDATA[ + python '$__tool_directory__/main.py' + --train_data '$train_data' + --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."/> + </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" label="Precision-recall curve"></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" /> + <output name="output_predicted_data"> + <assert_contents> + <has_n_columns n="42" /> + <has_n_lines n="3" /> + </assert_contents> + </output> + </test> + </tests> + <help> + <![CDATA[ + **What it does** + + Classification 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. + + **Output files** + - Predicted data along with predicted labels + ]]> + </help> + <citations> + <citation type="doi">10.1038/s41586-024-08328-6</citation> + </citations> +</tool> |
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| diff -r 000000000000 -r 3dc3c7443c8e test-data/local_test_rows.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/local_test_rows.tabular Wed Jan 15 12:33:49 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 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 |
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| diff -r 000000000000 -r 3dc3c7443c8e test-data/local_train_rows.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/local_train_rows.tabular Wed Jan 15 12:33:49 2025 +0000 |
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