# HG changeset patch
# User goeckslab
# Date 1748979076 0
# Node ID 4aa51153919969deaffe5c3d5d595c763b736c4e
# Parent  02f7746e77728b3b461968e03b1bd5ff76a30072
planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit cf47efb521b91a9cb44ae5c5ade860627f9b9030

diff -r 02f7746e7772 -r 4aa511539199 Dockerfile
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Dockerfile	Tue Jun 03 19:31:16 2025 +0000
@@ -0,0 +1,19 @@
+FROM python:3.11-slim
+
+ARG VERSION=3.3.2
+
+# Install necessary dependencies, including libgomp1
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends git unzip libgomp1 && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install Python packages
+RUN pip install -U pip && \
+    pip install --no-cache-dir --no-compile joblib && \
+    pip install --no-cache-dir --no-compile h5py && \
+    pip install --no-cache-dir --no-compile pycaret[analysis,models]==${VERSION} && \
+    pip install --no-cache-dir --no-compile explainerdashboard
+
+# Clean up unnecessary packages
+RUN apt-get -y autoremove && apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
diff -r 02f7746e7772 -r 4aa511539199 LICENSE
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/LICENSE	Tue Jun 03 19:31:16 2025 +0000
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 JunhaoQiu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff -r 02f7746e7772 -r 4aa511539199 README.md
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/README.md	Tue Jun 03 19:31:16 2025 +0000
@@ -0,0 +1,106 @@
+# Galaxy-Pycaret
+A library of Galaxy machine learning tools based on PyCaret — part of the Galaxy ML2 tools, aiming to provide simple, powerful, and robust machine learning capabilities for Galaxy users.
+
+# Install Galaxy-Pycaret into Galaxy
+
+* Update `tool_conf.xml` to include Galaxy-Pycaret tools. See [documentation](https://docs.galaxyproject.org/en/master/admin/tool_panel.html) for more details. This is an example:
+```
+<section id="pycaret" name="Pycaret Applications">
+  <tool file="galaxy-pycaret/tools/pycaret_train.xml" />
+</section>
+```
+
+* Configure the `job_conf.yml` under `lib/galaxy/config/sample` to enable the docker for the environment you want the Ludwig related job running in. This is an example:
+```
+execution:
+ default: local
+ environments:
+   local:
+     runner: local
+     docker_enabled: true
+```
+If you are using an older version of Galaxy, then `job_conf.xml` would be something you want to configure instead of `job_conf.yml`. Then you would want to configure destination instead of execution and environment. 
+See [documentation](https://docs.galaxyproject.org/en/master/admin/jobs.html#running-jobs-in-containers) for job_conf configuration. 
+* If you haven’t set `sanitize_all_html: false` in `galaxy.yml`, please set it to False to enable our HTML report functionality.
+* Should be good to go. 
+
+# Make contributions
+
+## Getting Started
+
+To get started, you’ll need to fork the repository, clone it locally, and create a new branch for your contributions.
+
+1. **Fork the Repository**: Click the "Fork" button at the top right of this page.
+2. **Clone the Fork**:
+  ```bash
+    git clone https://github.com/<your-username>/Galaxy-Pycaret.git
+    cd <your-repo>
+  ```
+3. **Create a Feature/hotfix/bugfix Branch**:
+  ```bash
+    git checkout -b feature/<feature-branch-name>
+  ```
+  or
+  ```bash
+    git checkout -b hotfix/<hoxfix-branch-name>
+  ```
+  or
+  ```bash
+    git checkout -b bugfix/<bugfix-branch-name>
+  ```
+
+## How We Manage the Repo
+
+We follow a structured branching and merging strategy to ensure code quality and stability.
+
+1. **Main Branches**:
+   - **`main`**: Contains production-ready code.
+   - **`dev`**: Contains code that is ready for the next release.
+
+2. **Supporting Branches**:
+   - **Feature Branches**: Created from `dev` for new features.
+   - **Bugfix Branches**: Created from `dev` for bug fixes.
+   - **Release Branches**: Created from `dev` when preparing a new release.
+   - **Hotfix Branches**: Created from `main` for critical fixes in production. 
+
+### Workflow
+
+- **Feature Development**: 
+  - Branch from `dev`.
+  - Work on your feature.
+  - Submit a Pull Request (PR) to `dev`.
+- **Hotfixes**: 
+  - Branch from `main`.
+  - Fix the issue.
+  - Merge back into both `main` and `dev`.
+
+## Contribution Guidelines
+
+We welcome contributions of all kinds. To make contributions easy and effective, please follow these guidelines:
+
+1. **Create an Issue**: Before starting work on a major change, create an issue to discuss it.
+2. **Fork and Branch**: Fork the repo and create a feature branch.
+3. **Write Tests**: Ensure your changes are well-tested if applicable.
+4. **Code Style**: Follow the project’s coding conventions.
+5. **Commit Messages**: Write clear and concise commit messages.
+6. **Pull Request**: Submit a PR to the `dev` branch. Ensure your PR description is clear and includes the issue number.
+
+### Submitting a Pull Request
+
+1. **Push your Branch**:
+    ```bash
+    git push origin feature/<feature-branch-name>
+    ```
+2. **Open a Pull Request**:
+   - Navigate to the original repository where you created your fork.
+   - Click on the "New Pull Request" button.
+   - Select `dev` as the base branch and your feature branch as the compare branch. 
+   - Fill in the PR template with details about your changes.
+
+3. **Rebase or Merge `dev` into Your Feature Branch**:
+    - Before submitting your PR or when `dev` has been updated, rebase or merge `dev` into your feature branch to ensure your branch is up to date:
+    
+4. **Resolve Conflicts**:
+    - If there are any conflicts during the rebase or merge, Git will pause and allow you to resolve the conflicts.
+
+5. **Review Process**: Your PR will be reviewed by a team member. Please address any feedback and update your PR as needed.
\ No newline at end of file
diff -r 02f7746e7772 -r 4aa511539199 base_model_trainer.py
--- a/base_model_trainer.py	Wed Jan 01 03:19:40 2025 +0000
+++ b/base_model_trainer.py	Tue Jun 03 19:31:16 2025 +0000
@@ -3,18 +3,12 @@
 import os
 import tempfile
 
-from feature_importance import FeatureImportanceAnalyzer
-
 import h5py
-
 import joblib
-
 import numpy as np
-
 import pandas as pd
-
+from feature_importance import FeatureImportanceAnalyzer
 from sklearn.metrics import average_precision_score
-
 from utils import get_html_closing, get_html_template
 
 logging.basicConfig(level=logging.DEBUG)
@@ -31,8 +25,7 @@
             task_type,
             random_seed,
             test_file=None,
-            **kwargs
-            ):
+            **kwargs):
         self.exp = None  # This will be set in the subclass
         self.input_file = input_file
         self.target_col = target_col
@@ -71,7 +64,7 @@
             LOG.info(f"Non-numeric columns found: {non_numeric_cols.tolist()}")
 
         names = self.data.columns.to_list()
-        target_index = int(self.target_col)-1
+        target_index = int(self.target_col) - 1
         self.target = names[target_index]
         self.features_name = [name
                               for i, name in enumerate(names)
@@ -97,7 +90,7 @@
                 pd.to_numeric, errors='coerce')
             self.test_data.columns = self.test_data.columns.str.replace(
                 '.', '_'
-                )
+            )
 
     def setup_pycaret(self):
         LOG.info("Initializing PyCaret")
@@ -206,19 +199,22 @@
             for k, v in self.setup_params.items() if k not in excluded_params
         }
         setup_params_table = pd.DataFrame(
-            list(filtered_setup_params.items()),
-            columns=['Parameter', 'Value'])
+            list(filtered_setup_params.items()), columns=['Parameter', 'Value']
+        )
 
         best_model_params = pd.DataFrame(
             self.best_model.get_params().items(),
-            columns=['Parameter', 'Value'])
+            columns=['Parameter', 'Value']
+        )
         best_model_params.to_csv(
-            os.path.join(self.output_dir, 'best_model.csv'),
-            index=False)
-        self.results.to_csv(os.path.join(
-            self.output_dir, "comparison_results.csv"))
-        self.test_result_df.to_csv(os.path.join(
-            self.output_dir, "test_results.csv"))
+            os.path.join(self.output_dir, "best_model.csv"), index=False
+        )
+        self.results.to_csv(
+            os.path.join(self.output_dir, "comparison_results.csv")
+        )
+        self.test_result_df.to_csv(
+            os.path.join(self.output_dir, "test_results.csv")
+        )
 
         plots_html = ""
         length = len(self.plots)
@@ -250,7 +246,8 @@
             data=self.data,
             target_col=self.target_col,
             task_type=self.task_type,
-            output_dir=self.output_dir)
+            output_dir=self.output_dir,
+        )
         feature_importance_html = analyzer.run()
 
         html_content = f"""
@@ -263,38 +260,37 @@
                 Best Model Plots</div>
                 <div class="tab" onclick="openTab(event, 'feature')">
                 Feature Importance</div>
-            """
+        """
         if self.plots_explainer_html:
             html_content += """
-                "<div class="tab" onclick="openTab(event, 'explainer')">"
+                <div class="tab" onclick="openTab(event, 'explainer')">
                 Explainer Plots</div>
             """
         html_content += f"""
             </div>
             <div id="summary" class="tab-content">
                 <h2>Setup Parameters</h2>
-                <table>
-                    <tr><th>Parameter</th><th>Value</th></tr>
-                    {setup_params_table.to_html(
-                        index=False, header=False, classes='table')}
-                </table>
+                {setup_params_table.to_html(
+                    index=False,
+                    header=True,
+                    classes='table sortable'
+                )}
                 <h5>If you want to know all the experiment setup parameters,
                   please check the PyCaret documentation for
                   the classification/regression <code>exp</code> function.</h5>
                 <h2>Best Model: {model_name}</h2>
-                <table>
-                    <tr><th>Parameter</th><th>Value</th></tr>
-                    {best_model_params.to_html(
-                        index=False, header=False, classes='table')}
-                </table>
+                {best_model_params.to_html(
+                    index=False,
+                    header=True,
+                    classes='table sortable'
+                )}
                 <h2>Comparison Results on the Cross-Validation Set</h2>
-                <table>
-                    {self.results.to_html(index=False, classes='table')}
-                </table>
+                {self.results.to_html(index=False, classes='table sortable')}
                 <h2>Results on the Test Set for the best model</h2>
-                <table>
-                    {self.test_result_df.to_html(index=False, classes='table')}
-                </table>
+                {self.test_result_df.to_html(
+                    index=False,
+                    classes='table sortable'
+                )}
             </div>
             <div id="plots" class="tab-content">
                 <h2>Best Model Plots on the testing set</h2>
@@ -310,14 +306,66 @@
                 {self.plots_explainer_html}
                 {tree_plots}
             </div>
-            {get_html_closing()}
             """
-        else:
-            html_content += f"""
-            {get_html_closing()}
-            """
-        with open(os.path.join(
-                self.output_dir, "comparison_result.html"), "w") as file:
+        html_content += """
+        <script>
+        document.addEventListener("DOMContentLoaded", function() {
+            var tables = document.querySelectorAll("table.sortable");
+            tables.forEach(function(table) {
+                var headers = table.querySelectorAll("th");
+                headers.forEach(function(header, index) {
+                    header.style.cursor = "pointer";
+                    // Add initial arrow (up) to indicate sortability
+                    header.innerHTML += '<span class="sort-arrow"> ↑</span>';
+                    header.addEventListener("click", function() {
+                        var direction = this.getAttribute(
+                            "data-sort-direction"
+                        ) || "asc";
+                        // Reset arrows in all headers of this table
+                        headers.forEach(function(h) {
+                            var arrow = h.querySelector(".sort-arrow");
+                            if (arrow) arrow.textContent = " ↑";
+                        });
+                        // Set arrow for clicked header
+                        var arrow = this.querySelector(".sort-arrow");
+                        arrow.textContent = direction === "asc" ? " ↓" : " ↑";
+                        sortTable(table, index, direction);
+                        this.setAttribute("data-sort-direction",
+                        direction === "asc" ? "desc" : "asc");
+                    });
+                });
+            });
+        });
+
+        function sortTable(table, colNum, direction) {
+            var tb = table.tBodies[0];
+            var tr = Array.prototype.slice.call(tb.rows, 0);
+            var multiplier = direction === "asc" ? 1 : -1;
+            tr = tr.sort(function(a, b) {
+                var aText = a.cells[colNum].textContent.trim();
+                var bText = b.cells[colNum].textContent.trim();
+                // Remove arrow from text comparison
+                aText = aText.replace(/[↑↓]/g, '').trim();
+                bText = bText.replace(/[↑↓]/g, '').trim();
+                if (!isNaN(aText) && !isNaN(bText)) {
+                    return multiplier * (
+                        parseFloat(aText) - parseFloat(bText)
+                    );
+                } else {
+                    return multiplier * aText.localeCompare(bText);
+                }
+            });
+            for (var i = 0; i < tr.length; ++i) tb.appendChild(tr[i]);
+        }
+        </script>
+        """
+        html_content += f"""
+        {get_html_closing()}
+        """
+        with open(
+            os.path.join(self.output_dir, "comparison_result.html"),
+            "w"
+        ) as file:
             file.write(html_content)
 
     def save_dashboard(self):
diff -r 02f7746e7772 -r 4aa511539199 feature_importance.py
--- a/feature_importance.py	Wed Jan 01 03:19:40 2025 +0000
+++ b/feature_importance.py	Tue Jun 03 19:31:16 2025 +0000
@@ -3,9 +3,7 @@
 import os
 
 import matplotlib.pyplot as plt
-
 import pandas as pd
-
 from pycaret.classification import ClassificationExperiment
 from pycaret.regression import RegressionExperiment
 
diff -r 02f7746e7772 -r 4aa511539199 pycaret_classification.py
--- a/pycaret_classification.py	Wed Jan 01 03:19:40 2025 +0000
+++ b/pycaret_classification.py	Tue Jun 03 19:31:16 2025 +0000
@@ -1,11 +1,8 @@
 import logging
 
 from base_model_trainer import BaseModelTrainer
-
 from dashboard import generate_classifier_explainer_dashboard
-
 from pycaret.classification import ClassificationExperiment
-
 from utils import add_hr_to_html, add_plot_to_html, predict_proba
 
 LOG = logging.getLogger(__name__)
@@ -64,8 +61,7 @@
                                                         'macro': False,
                                                         'per_class': False,
                                                         'binary': True
-                                                        }
-                                                    )
+                                                    })
                     self.plots[plot_name] = plot_path
                     continue
 
diff -r 02f7746e7772 -r 4aa511539199 pycaret_macros.xml
--- a/pycaret_macros.xml	Wed Jan 01 03:19:40 2025 +0000
+++ b/pycaret_macros.xml	Tue Jun 03 19:31:16 2025 +0000
@@ -1,6 +1,6 @@
 <macros>
     <token name="@PYCARET_VERSION@">3.3.2</token>
-    <token name="@SUFFIX@">0</token>
+    <token name="@SUFFIX@">1</token>
     <token name="@VERSION@">@PYCARET_VERSION@+@SUFFIX@</token>
     <token name="@PROFILE@">21.05</token>
     <xml name="python_requirements">
diff -r 02f7746e7772 -r 4aa511539199 pycaret_predict.py
--- a/pycaret_predict.py	Wed Jan 01 03:19:40 2025 +0000
+++ b/pycaret_predict.py	Tue Jun 03 19:31:16 2025 +0000
@@ -3,16 +3,11 @@
 import tempfile
 
 import h5py
-
 import joblib
-
 import pandas as pd
-
 from pycaret.classification import ClassificationExperiment
 from pycaret.regression import RegressionExperiment
-
 from sklearn.metrics import average_precision_score
-
 from utils import encode_image_to_base64, get_html_closing, get_html_template
 
 LOG = logging.getLogger(__name__)
@@ -49,7 +44,7 @@
             exp = ClassificationExperiment()
             names = data.columns.to_list()
             LOG.error(f"Column names: {names}")
-            target_index = int(self.target)-1
+            target_index = int(self.target) - 1
             target_name = names[target_index]
             exp.setup(data, target=target_name, test_data=data, index=False)
             exp.add_metric(id='PR-AUC-Weighted',
@@ -73,8 +68,7 @@
                                                        'micro': False,
                                                        'macro': False,
                                                        'per_class': False,
-                                                       'binary': True
-                                                    })
+                                                       'binary': True})
                         plot_paths[plot_name] = plot_path
                         continue
 
@@ -101,7 +95,7 @@
         data = pd.read_csv(data_path, engine='python', sep=None)
         if self.target:
             names = data.columns.to_list()
-            target_index = int(self.target)-1
+            target_index = int(self.target) - 1
             target_name = names[target_index]
             exp = RegressionExperiment()
             exp.setup(data, target=target_name, test_data=data, index=False)
diff -r 02f7746e7772 -r 4aa511539199 pycaret_regression.py
--- a/pycaret_regression.py	Wed Jan 01 03:19:40 2025 +0000
+++ b/pycaret_regression.py	Tue Jun 03 19:31:16 2025 +0000
@@ -1,11 +1,8 @@
 import logging
 
 from base_model_trainer import BaseModelTrainer
-
 from dashboard import generate_regression_explainer_dashboard
-
 from pycaret.regression import RegressionExperiment
-
 from utils import add_hr_to_html, add_plot_to_html
 
 LOG = logging.getLogger(__name__)
diff -r 02f7746e7772 -r 4aa511539199 pycaret_train.py
--- a/pycaret_train.py	Wed Jan 01 03:19:40 2025 +0000
+++ b/pycaret_train.py	Tue Jun 03 19:31:16 2025 +0000
@@ -2,7 +2,6 @@
 import logging
 
 from pycaret_classification import ClassificationModelTrainer
-
 from pycaret_regression import RegressionModelTrainer
 
 logging.basicConfig(level=logging.DEBUG)
diff -r 02f7746e7772 -r 4aa511539199 pycaret_train.xml
--- a/pycaret_train.xml	Wed Jan 01 03:19:40 2025 +0000
+++ b/pycaret_train.xml	Tue Jun 03 19:31:16 2025 +0000
@@ -1,5 +1,5 @@
-<tool id="pycaret_compare" name="PyCaret Model Comparison" version="@VERSION@" profile="@PROFILE@">
-    <description>compares different machine learning models on a dataset using PyCaret. Do feature analyses using Random Forest and LightGBM. </description>
+<tool id="pycaret_compare" name="Tabular Learner" version="@VERSION@" profile="@PROFILE@">
+    <description>applies and evaluates multiple machine learning models on a tabular dataset</description>
     <macros>
         <import>pycaret_macros.xml</import>
     </macros>
@@ -53,12 +53,12 @@
         ]]>
     </command>
     <inputs>
-        <param name="input_file" type="data" format="csv,tabular" label="Train Dataset (CSV or TSV)" />
-        <param name="test_file" type="data" format="csv,tabular" optional="true" label="Test Dataset (CSV or TSV)"
-        help="If a test set is not provided, 
-        the selected training set will be split into training, validation, and test sets. 
-        If a test set is provided, the training set will only be split into training and validation sets. 
-        BTW, cross-validation is always applied by default." />
+        <param name="input_file" type="data" format="csv,tabular" label="Tabular Input Dataset" />
+        <param name="test_file" type="data" format="csv,tabular" optional="true" label="Tabular Test Dataset"
+        help="If a test dataset is not provided, 
+        the input dataset will be split into training, validation, and test sets. 
+        If a test set is provided, the input dataset will be split into training and validation sets. 
+        Cross-validation is applied by default during training." />
        <param name="target_feature" multiple="false" type="data_column" use_header_names="true" data_ref="input_file" label="Select the target column:" />
         <conditional name="model_selection">
             <param name="model_type" type="select" label="Task">
@@ -124,25 +124,25 @@
                 <option value="true">Yes</option>
             </param>
             <when value="true">
-                <param name="train_size" type="float" value="0.7" min="0.1" max="0.9" label="Train Size" help="Proportion of the dataset to include in the train split." />
+                <param name="train_size" type="float" value="0.7" min="0.1" max="0.9" label="Train Size" help="Proportion of the input dataset to include in the train split." />
                 <param name="normalize" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Normalize Data" help="Whether to normalize data before training." />
                 <param name="feature_selection" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Feature Selection" help="Whether to perform feature selection." />
                 <conditional name="cross_validation">
-                    <param name="enable_cross_validation" type="select" label="Enable Cross Validation?" help="Select whether to enable cross-validation. Default: Yes" >
+                    <param name="enable_cross_validation" type="select" label="Enable Cross Validation?" help="Select whether to enable cross-validation." >
                         <option value="false" >No</option>
                         <option value="true" selected="true">Yes</option>
                     </param>
                     <when value="true">
-                        <param name="cross_validation_folds" type="integer" value="10" min="2" max="20" label="Cross Validation Folds" help="Number of folds to use for cross-validation. Default: 10" />
+                        <param name="cross_validation_folds" type="integer" value="10" min="2" max="20" label="Cross Validation Folds" help="Number of folds to use for cross-validation." />
                     </when>
                     <when value="false">
                         <!-- No additional parameters to show if the user selects 'No' -->
                     </when>
                 </conditional>
-                <param name="remove_outliers" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Remove Outliers" help="Whether to remove outliers from the dataset before training. Default: False" />
-                <param name="remove_multicollinearity" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Remove Multicollinearity" help="Whether to remove multicollinear features before training. Default: False" />
-                <param name="polynomial_features" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Polynomial Features" help="Whether to create polynomial features before training. Default: False" />
-                <param name="fix_imbalance" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Fix Imbalance" help="ONLY for classfication! Whether to use SMOTE or similar methods to fix imbalance in the dataset. Default: False" />
+                <param name="remove_outliers" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Remove Outliers" help="Whether to remove outliers from the input dataset before training." />
+                <param name="remove_multicollinearity" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Remove Multicollinearity" help="Whether to remove multicollinear features before training." />
+                <param name="polynomial_features" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Polynomial Features" help="Whether to create polynomial features before training." />
+                <param name="fix_imbalance" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Fix Imbalance" help="ONLY for classfication! Whether to use SMOTE or similar methods to fix imbalance in the input dataset." />
             </when>
             <when value="false">
                 <!-- No additional parameters to show if the user selects 'No' -->
@@ -150,9 +150,9 @@
         </conditional>
     </inputs>
     <outputs>
+        <data name="comparison_result" format="html" from_work_dir="comparison_result.html" label="${tool.name} analysis report on ${on_string}"/>
         <data name="model" format="h5" from_work_dir="pycaret_model.h5" label="${tool.name} best model on ${on_string}" />
-        <data name="comparison_result" format="html" from_work_dir="comparison_result.html" label="${tool.name} Comparison result on ${on_string}"/>
-        <data name="best_model_csv" format="csv" from_work_dir="best_model.csv" label="${tool.name} The prams of the best model on ${on_string}" hidden="true" />
+        <data name="best_model_csv" format="csv" from_work_dir="best_model.csv" label="${tool.name} The parameters of the best model on ${on_string}" hidden="true" />
     </outputs>
     <tests>
         <test>
diff -r 02f7746e7772 -r 4aa511539199 utils.py
--- a/utils.py	Wed Jan 01 03:19:40 2025 +0000
+++ b/utils.py	Tue Jun 03 19:31:16 2025 +0000
@@ -161,4 +161,4 @@
 
 def predict_proba(self, X):
     pred = self.predict(X)
-    return np.array([1-pred, pred]).T
+    return np.array([1 - pred, pred]).T