| Previous changeset 17:a98b60c639af (2023-08-09) Next changeset 19:305c1229ac99 (2023-10-02) |
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Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 3c1e6c72303cfd8a5fd014734f18402b97f8ecb5 |
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modified:
keras_batch_models.xml ml_visualization_ex.py |
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removed:
pdb70_cs219.ffdata |
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| diff -r a98b60c639af -r 9d77b259ef27 keras_batch_models.xml --- a/keras_batch_models.xml Wed Aug 09 14:02:18 2023 +0000 +++ b/keras_batch_models.xml Fri Sep 22 16:59:40 2023 +0000 |
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| @@ -176,7 +176,7 @@ - compile parameters, are mainly composed of loss function and optimizer. -- fit parameters, a group of variables that control the training process, referring to `galaxy_ml.keras_galaxy_model.KerasGBatchClassifier`_ and `keras.io`_. +- fit parameters, a group of variables that control the training process, referring to `galaxy_ml.keras_galaxy_model.KerasGBatchClassifier`_ and Keras. - other parameters, including `class_positive_factor`, `prediction_steps`, `seed` (random seed) and so on. @@ -195,8 +195,6 @@ .. _`galaxy_ml.preprocessors.GenomicIntervalBatchGenerator`: https://goeckslab.github.io/Galaxy-ML/APIs/keras-galaxy-models/#GenomicIntervalBatchGenerator -.. _`keras.io`: https://keras.io/models/model/#fit_generator - ]]> </help> <citations> |
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| diff -r a98b60c639af -r 9d77b259ef27 ml_visualization_ex.py --- a/ml_visualization_ex.py Wed Aug 09 14:02:18 2023 +0000 +++ b/ml_visualization_ex.py Fri Sep 22 16:59:40 2023 +0000 |
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| @@ -15,6 +15,7 @@ from sklearn.metrics import ( auc, average_precision_score, + confusion_matrix, precision_recall_curve, roc_curve, ) @@ -258,6 +259,30 @@ os.rename(os.path.join(folder, "output.svg"), os.path.join(folder, "output")) +def get_dataframe(file_path, plot_selection, header_name, column_name): + header = "infer" if plot_selection[header_name] else None + column_option = plot_selection[column_name]["selected_column_selector_option"] + if column_option in [ + "by_index_number", + "all_but_by_index_number", + "by_header_name", + "all_but_by_header_name", + ]: + col = plot_selection[column_name]["col1"] + else: + col = None + _, input_df = read_columns( + file_path, + c=col, + c_option=column_option, + return_df=True, + sep="\t", + header=header, + parse_dates=True, + ) + return input_df + + def main( inputs, infile_estimator=None, @@ -271,6 +296,10 @@ targets=None, fasta_path=None, model_config=None, + true_labels=None, + predicted_labels=None, + plot_color=None, + title=None, ): """ Parameter @@ -311,6 +340,18 @@ model_config : str, default is None File path to dataset containing JSON config for neural networks + + true_labels : str, default is None + File path to dataset containing true labels + + predicted_labels : str, default is None + File path to dataset containing true predicted labels + + plot_color : str, default is None + Color of the confusion matrix heatmap + + title : str, default is None + Title of the confusion matrix heatmap """ warnings.simplefilter("ignore") @@ -534,6 +575,36 @@ return 0 + elif plot_type == "classification_confusion_matrix": + plot_selection = params["plotting_selection"] + input_true = get_dataframe( + true_labels, plot_selection, "header_true", "column_selector_options_true" + ) + header_predicted = "infer" if plot_selection["header_predicted"] else None + input_predicted = pd.read_csv( + predicted_labels, sep="\t", parse_dates=True, header=header_predicted + ) + true_classes = input_true.iloc[:, -1].copy() + predicted_classes = input_predicted.iloc[:, -1].copy() + axis_labels = list(set(true_classes)) + c_matrix = confusion_matrix(true_classes, predicted_classes) + fig, ax = plt.subplots(figsize=(7, 7)) + im = plt.imshow(c_matrix, cmap=plot_color) + for i in range(len(c_matrix)): + for j in range(len(c_matrix)): + ax.text(j, i, c_matrix[i, j], ha="center", va="center", color="k") + ax.set_ylabel("True class labels") + ax.set_xlabel("Predicted class labels") + ax.set_title(title) + ax.set_xticks(axis_labels) + ax.set_yticks(axis_labels) + fig.colorbar(im, ax=ax) + fig.tight_layout() + plt.savefig("output.png", dpi=125) + os.rename("output.png", "output") + + return 0 + # save pdf file to disk # fig.write_image("image.pdf", format='pdf') # fig.write_image("image.pdf", format='pdf', width=340*2, height=226*2) @@ -553,6 +624,10 @@ aparser.add_argument("-t", "--targets", dest="targets") aparser.add_argument("-f", "--fasta_path", dest="fasta_path") aparser.add_argument("-c", "--model_config", dest="model_config") + aparser.add_argument("-tl", "--true_labels", dest="true_labels") + aparser.add_argument("-pl", "--predicted_labels", dest="predicted_labels") + aparser.add_argument("-pc", "--plot_color", dest="plot_color") + aparser.add_argument("-pt", "--title", dest="title") args = aparser.parse_args() main( @@ -568,4 +643,8 @@ targets=args.targets, fasta_path=args.fasta_path, model_config=args.model_config, + true_labels=args.true_labels, + predicted_labels=args.predicted_labels, + plot_color=args.plot_color, + title=args.title, ) |
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| diff -r a98b60c639af -r 9d77b259ef27 pdb70_cs219.ffdata --- a/pdb70_cs219.ffdata Wed Aug 09 14:02:18 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| @@ -1,189 +0,0 @@ - - - - -<!DOCTYPE HTML> -<html> - <!--base.mako--> - - - - <head> - <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> - <meta name = "viewport" content = "maximum-scale=1.0"> - <meta http-equiv="X-UA-Compatible" content="IE=Edge,chrome=1"> - - <title> - Galaxy - | Europe - | - </title> - - <link rel="index" href="/"/> - - - - <link href="/static/style/bootstrap-tour.css?v=1618364054" media="screen" rel="stylesheet" type="text/css" /> - <link href="/static/dist/base.css?v=1618364054" media="screen" rel="stylesheet" type="text/css" /> - - - <script src="/static/dist/libs.chunk.js?v=1618364054" type="text/javascript"></script> -<script src="/static/dist/base.chunk.js?v=1618364054" type="text/javascript"></script> -<script src="/static/dist/generic.bundled.js?v=1618364054" type="text/javascript"></script> - - - <!-- message.mako javascript_app() --> - - - - <script type="text/javascript"> - // galaxy_client_app.mako, load - - var bootstrapped; - try { - bootstrapped = -{} -; - } catch(err) { - console.warn("Unable to parse bootstrapped variable", err); - bootstrapped = {}; - } - - var options = { - root: '/', - config: - -{ -"display_galaxy_brand": true, -"chunk_upload_size": 104857600, -"use_remote_user": null, -"enable_oidc": true, -"mailing_join_addr": null, -"select_type_workflow_threshold": -1, -"myexperiment_target_url": "www.myexperiment.org:80", -"tool_recommendation_model_path": "https://github.com/galaxyproject/galaxy-test-data/raw/master/tool_recommendation_model.hdf5", -"simplified_workflow_run_ui_target_history": "current", -"interactivetools_enable": true, -"is_admin_user": false, -"show_welcome_with_login": true, -"welcome_url": "/static/welcome.html", -"allow_user_impersonation": true, -"overwrite_model_recommendations": false, -"topk_recommendations": 10, -"user_library_import_dir_available": false, -"ga_code": null, -"enable_beta_markdown_export": true, -"visualizations_visible": true, -"enable_tool_recommendations": true, -"enable_unique_workflow_defaults": false, -"registration_warning_message": "Please register only one account. 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Feel free to browse around and see what's available, but you won't be able to upload data or run jobs until you have verified your email address.", -"nginx_upload_path": "/_upload" -} -, - user: - -{ -"total_disk_usage": 0, -"nice_total_disk_usage": "0 bytes", -"quota_percent": null -} -, - session_csrf_token: 'c3ae71f65be7de55dd5bd5f97f316000' - }; - - config.set({ - options: options, - bootstrapped: bootstrapped - }); - - - </script> - - - - - - - - - - <script type="text/javascript"> - config.addInitialization(function() { - if (parent.handle_minwidth_hint) { - parent.handle_minwidth_hint(-1); - } - }); - </script> - - </head> - <body class="inbound"> - - - - <div class="message mt-2 alert alert-danger">You are not allowed to access this dataset</div> - - - </body> -</html> - - - - - - - - - - - - |