Mercurial > repos > goeckslab > pycaret_compare
changeset 4:4aa511539199 draft default tip
planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit cf47efb521b91a9cb44ae5c5ade860627f9b9030
| author | goeckslab |
|---|---|
| date | Tue, 03 Jun 2025 19:31:16 +0000 (3 hours ago) |
| parents | 02f7746e7772 |
| children | |
| files | Dockerfile LICENSE README.md base_model_trainer.py feature_importance.py pycaret_classification.py pycaret_macros.xml pycaret_predict.py pycaret_regression.py pycaret_train.py pycaret_train.xml utils.py |
| diffstat | 12 files changed, 263 insertions(+), 85 deletions(-) [+] |
line wrap: on
line diff
--- /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/*
--- /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.
--- /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
--- 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):
--- 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
--- 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
--- 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">
--- 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)
--- 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__)
--- 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)
--- 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>