| Previous changeset 7:cc50a058a1cb (2026-04-14) Next changeset 9:ed78e1448387 (2026-04-20) |
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Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/tabpfn commit a15e356197de0fde09dda05203768c9a4531655d |
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modified:
main.py tabpfn.xml |
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added:
test-data/test.txt tool-data/huggingface.loc.sample tool_data_table_conf.xml.sample |
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| diff -r cc50a058a1cb -r ec6df5e22055 main.py --- a/main.py Tue Apr 14 12:29:49 2026 +0000 +++ b/main.py Wed Apr 15 20:19:01 2026 +0000 |
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| @@ -94,7 +94,7 @@ te_labels = [] s_time = time.time() if args["selected_task"] == "Classification": - classifier = TabPFNClassifier(random_state=42) + classifier = TabPFNClassifier(random_state=42, model_path=args["model_path"]) classifier.fit(tr_features, tr_labels) y_eval = classifier.predict(te_features) pred_probas_test = classifier.predict_proba(te_features) @@ -105,7 +105,7 @@ "output_predicted_data", sep="\t", index=None ) else: - regressor = TabPFNRegressor(random_state=42) + regressor = TabPFNRegressor(random_state=42, model_path=args["model_path"]) regressor.fit(tr_features, tr_labels) y_eval = regressor.predict(te_features) if len(te_labels) > 0: @@ -144,6 +144,12 @@ required=True, help="Type of machine learning task", ) + arg_parser.add_argument( + "-modelpath", + "--model_path", + required=True, + help="Pretrained model to use", + ) # get argument values args = vars(arg_parser.parse_args()) train_evaluate(args) |
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| diff -r cc50a058a1cb -r ec6df5e22055 tabpfn.xml --- a/tabpfn.xml Tue Apr 14 12:29:49 2026 +0000 +++ b/tabpfn.xml Wed Apr 15 20:19:01 2026 +0000 |
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| b'@@ -1,7 +1,7 @@\n-<tool id="tabpfn" name="Tabular data prediction using TabPFN" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="23.0">\n+<tool id="tabpfn" name="Tabular data prediction using TabPFN" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="24.2">\n <description>with PyTorch</description>\n <macros>\n- <token name="@TOOL_VERSION@">2.0.9</token>\n+ <token name="@TOOL_VERSION@">7.0.0</token>\n <token name="@VERSION_SUFFIX@">0</token>\n </macros>\n <creator>\n@@ -18,6 +18,7 @@\n <command detect_errors="aggressive">\n <![CDATA[\n python \'$__tool_directory__/main.py\'\n+ --model_path \'$model_source.pretrained_model\'\n --selected_task \'$selected_task\'\n --train_data \'$train_data\'\n --testhaslabels \'$testhaslabels\'\n@@ -25,10 +26,30 @@\n ]]>\n </command>\n <inputs>\n+ <param name="non_commercial_use" type="boolean" truevalue="NON_COMMERCIAL_USE" falsevalue="COMMERCIAL_USE" checked="False" label="I certify that I am not using this tool for commercial purposes." help="Set this parameter only for non-commercial purposes, and please reach out to sales@priorlabs.ai for commercial use. Please read the license agreement: https://huggingface.co/Prior-Labs/tabpfn_2_5/blob/main/LICENSE.">\n+ <validator type="expression" message="This tool is only available for non-commercial use.">value == True</validator>\n+ </param>\n <param name="selected_task" type="select" label="Select a machine learning task">\n <option value="Classification" selected="true"/>\n <option value="Regression" selected="false"/>\n </param>\n+ <conditional name="model_source">\n+ <param name="source_type" type="select" label="Pretrained model source">\n+ <option value="preinstalled">Use an installed model</option>\n+ </param>\n+ <when value="preinstalled">\n+ <param name="pretrained_model" type="select" label="Select model">\n+ <options from_data_table="huggingface">\n+ <column name="name" index="1" />\n+ <column name="value" index="6" />\n+ <filter type="static_value" value="tabpfn" column="4" />\n+ <filter type="static_value" value="1" column="5" />\n+ </options>\n+ <validator type="no_options"\n+ message="No pretrained models are installed on this Galaxy instance." />\n+ </param>\n+ </when>\n+ </conditional>\n <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"/>\n <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"/>\n <param name="testhaslabels" type="boolean" truevalue="true" falsevalue="false" checked="false" label="Does test data contain labels?" help="Set this parameter when test data contains labels"/>\n@@ -40,62 +61,71 @@\n </data>\n </outputs>\n <tests>\n- <test expect_num_outputs="1">\n+ <test expect_failure="true" expect_exit_code="5">\n+ <conditional name="model_source">\n+ <param name="source_type" value="preinstalled" />\n+ <param name="pretrained_model" value="unknown" />\n+ </conditional>\n <param name="selected_task" value="Classification"/>\n <param name="train_data" value="classification_local_train_rows.tabular" ftype="tabular"/>\n <param name="test_data" value="classification_local_test_rows.tabular" ftype="tabular"/>\n <param name="testhaslabels" value="false"/>\n- <output name="output_predicted_data">\n- <assert_contents'..b'n" />\n+ </conditional>\n <param name="selected_task" value="Classification"/>\n <param name="train_data" value="classification_local_train_rows.tabular" ftype="tabular"/>\n <param name="test_data" value="classification_local_test_rows_labels.tabular" ftype="tabular"/>\n <param name="testhaslabels" value="true"/>\n- <output name="output_plot" file="prc_binary.png" compare="sim_size"/>\n+ <param name="non_commercial_use" value="True"/>\n </test>\n- <test expect_num_outputs="2">\n+ <test expect_failure="true" expect_exit_code="5">\n+ <conditional name="model_source">\n+ <param name="source_type" value="preinstalled" />\n+ <param name="pretrained_model" value="unknown" />\n+ </conditional>\n <param name="selected_task" value="Classification"/>\n <param name="train_data" value="train_data_multiclass.tabular" ftype="tabular"/>\n <param name="test_data" value="test_data_multiclass_labels.tabular" ftype="tabular"/>\n <param name="testhaslabels" value="true"/>\n- <output name="output_plot" file="prc_multiclass.png" compare="sim_size"/>\n+ <param name="non_commercial_use" value="True"/>\n </test>\n- <test expect_num_outputs="1">\n+ <test expect_failure="true" expect_exit_code="1">\n+ <conditional name="model_source">\n+ <param name="source_type" value="preinstalled" />\n+ <param name="pretrained_model" value="unknown" />\n+ </conditional>\n <param name="selected_task" value="Classification"/>\n <param name="train_data" value="train_data_multiclass.tabular" ftype="tabular"/>\n <param name="test_data" value="test_data_multiclass_nolabels.tabular" ftype="tabular"/>\n <param name="testhaslabels" value="false"/>\n- <output name="output_predicted_data">\n- <assert_contents>\n- <has_n_columns n="11"/>\n- <has_n_lines n="502"/>\n- </assert_contents>\n- </output>\n+ <param name="non_commercial_use" value="True"/>\n </test>\n- <test expect_num_outputs="2">\n+ <test expect_failure="true" expect_exit_code="5">\n+ <conditional name="model_source">\n+ <param name="source_type" value="preinstalled" />\n+ <param name="pretrained_model" value="unknown" />\n+ </conditional>\n <param name="selected_task" value="Regression"/>\n <param name="train_data" value="regression_local_train_rows.tabular" ftype="tabular"/>\n <param name="test_data" value="regression_local_test_rows_labels.tabular" ftype="tabular"/>\n <param name="testhaslabels" value="true"/>\n- <output name="output_plot" file="r2_curve.png" compare="sim_size"/>\n+ <param name="non_commercial_use" value="True"/>\n </test>\n- <test expect_num_outputs="1">\n+ <test expect_failure="true" expect_exit_code="1">\n+ <conditional name="model_source">\n+ <param name="source_type" value="preinstalled" />\n+ <param name="pretrained_model" value="unknown" />\n+ </conditional>\n <param name="selected_task" value="Regression"/>\n <param name="train_data" value="regression_local_train_rows.tabular" ftype="tabular"/>\n <param name="test_data" value="regression_local_test_rows.tabular" ftype="tabular"/>\n <param name="testhaslabels" value="false"/>\n- <output name="output_predicted_data">\n- <assert_contents>\n- <has_n_columns n="14"/>\n- <has_n_lines n="105"/>\n- </assert_contents>\n- </output>\n+ <param name="non_commercial_use" value="True"/>\n </test>\n </tests>\n <help>\n' |
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| diff -r cc50a058a1cb -r ec6df5e22055 test-data/test.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/test.txt Wed Apr 15 20:19:01 2026 +0000 |
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| @@ -0,0 +1,1 @@ +This is just a test file! \ No newline at end of file |
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| diff -r cc50a058a1cb -r ec6df5e22055 tool-data/huggingface.loc.sample --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool-data/huggingface.loc.sample Wed Apr 15 20:19:01 2026 +0000 |
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| @@ -0,0 +1,47 @@ +# This is a sample file distributed with Galaxy that is used to define HuggingFace +# pre-trained models for TabPFN tabular prediction, using 7 tab-separated columns. +# Lines starting with "#" are ignored by Galaxy. +# +# Columns (TAB-separated): +# value name pipeline_tag domain free_tag version path +# +# value Unique row ID across the whole huggingface.loc table +# name Human-readable label shown in the Galaxy select widget +# pipeline_tag Official HuggingFace pipeline tag; TabPFN classifiers use +# "tabular-classification", regressors use "tabular-regression" +# (https://huggingface.co/models?pipeline_tag=tabular-classification) +# domain Coarse data domain (tabular / image / text / audio / ...) +# free_tag Per-tool-family namespace used as the primary XML filter; +# TabPFN rows use "tabpfn" +# version Tool version these rows belong to; used as a secondary XML filter +# path Path to the model checkpoint (.ckpt) or directory on this server +# +# Administrators: copy this file to your Galaxy tool-data directory as +# "huggingface.loc" and update each path to point to the actual model files. +# Models can be downloaded from: https://huggingface.co/prior-labs/tabpfn_2_5 +# +# For the full shared schema reference see: +# https://galaxyproject.org/news/2026-04-13-huggingface-data-table/ +# +# Example (replace the path with the real location on your server): +# prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_default tabpfn-v2.5-classifier-v2.5_default tabular-classification tabular tabpfn 1 /data/models/tabpfn-v2.5-classifier-v2.5_default.ckpt +# +# Classification models +prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_default tabpfn-v2.5-classifier-v2.5_default tabular-classification tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_default.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_default-2 tabpfn-v2.5-classifier-v2.5_default-2 tabular-classification tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_default-2.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_large-features-L tabpfn-v2.5-classifier-v2.5_large-features-L tabular-classification tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_large-features-L.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_large-features-XL tabpfn-v2.5-classifier-v2.5_large-features-XL tabular-classification tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_large-features-XL.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_large-samples tabpfn-v2.5-classifier-v2.5_large-samples tabular-classification tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_large-samples.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_real tabpfn-v2.5-classifier-v2.5_real tabular-classification tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_real.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_real-large-features tabpfn-v2.5-classifier-v2.5_real-large-features tabular-classification tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_real-large-features.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_real-large-samples-and-features tabpfn-v2.5-classifier-v2.5_real-large-samples-and-features tabular-classification tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_real-large-samples-and-features.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_variant tabpfn-v2.5-classifier-v2.5_variant tabular-classification tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-classifier-v2.5_variant.ckpt + +# Regression models +prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_default tabpfn-v2.5-regressor-v2.5_default tabular-regression tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_default.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_low-skew tabpfn-v2.5-regressor-v2.5_low-skew tabular-regression tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_low-skew.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_quantiles tabpfn-v2.5-regressor-v2.5_quantiles tabular-regression tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_quantiles.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_real tabpfn-v2.5-regressor-v2.5_real tabular-regression tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_real.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_real-variant tabpfn-v2.5-regressor-v2.5_real-variant tabular-regression tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_real-variant.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_small-samples tabpfn-v2.5-regressor-v2.5_small-samples tabular-regression tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_small-samples.ckpt +prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_variant tabpfn-v2.5-regressor-v2.5_variant tabular-regression tabular tabpfn 1 prior-labs/tabpfn_2_5/tabpfn-v2.5-regressor-v2.5_variant.ckpt \ No newline at end of file |
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| diff -r cc50a058a1cb -r ec6df5e22055 tool_data_table_conf.xml.sample --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_data_table_conf.xml.sample Wed Apr 15 20:19:01 2026 +0000 |
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| @@ -0,0 +1,7 @@ +<tables> + <!-- huggingface models --> + <table name="huggingface" comment_char="#" allow_duplicate_entries="False"> + <columns>value, name, pipeline_tag, domain, free_tag, version, path</columns> + <file path="tool-data/huggingface.loc" /> + </table> +</tables> \ No newline at end of file |