| Previous changeset 3:02f7746e7772 (2025-01-01) |
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Commit message:
planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit cf47efb521b91a9cb44ae5c5ade860627f9b9030 |
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modified:
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 |
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added:
Dockerfile LICENSE README.md |
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| 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 |
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| @@ -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/* |
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| 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 |
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| @@ -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. |
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| 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 |
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| @@ -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 |
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| 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 |
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| b'@@ -3,18 +3,12 @@\n import os\n import tempfile\n \n-from feature_importance import FeatureImportanceAnalyzer\n-\n import h5py\n-\n import joblib\n-\n import numpy as np\n-\n import pandas as pd\n-\n+from feature_importance import FeatureImportanceAnalyzer\n from sklearn.metrics import average_precision_score\n-\n from utils import get_html_closing, get_html_template\n \n logging.basicConfig(level=logging.DEBUG)\n@@ -31,8 +25,7 @@\n task_type,\n random_seed,\n test_file=None,\n- **kwargs\n- ):\n+ **kwargs):\n self.exp = None # This will be set in the subclass\n self.input_file = input_file\n self.target_col = target_col\n@@ -71,7 +64,7 @@\n LOG.info(f"Non-numeric columns found: {non_numeric_cols.tolist()}")\n \n names = self.data.columns.to_list()\n- target_index = int(self.target_col)-1\n+ target_index = int(self.target_col) - 1\n self.target = names[target_index]\n self.features_name = [name\n for i, name in enumerate(names)\n@@ -97,7 +90,7 @@\n pd.to_numeric, errors=\'coerce\')\n self.test_data.columns = self.test_data.columns.str.replace(\n \'.\', \'_\'\n- )\n+ )\n \n def setup_pycaret(self):\n LOG.info("Initializing PyCaret")\n@@ -206,19 +199,22 @@\n for k, v in self.setup_params.items() if k not in excluded_params\n }\n setup_params_table = pd.DataFrame(\n- list(filtered_setup_params.items()),\n- columns=[\'Parameter\', \'Value\'])\n+ list(filtered_setup_params.items()), columns=[\'Parameter\', \'Value\']\n+ )\n \n best_model_params = pd.DataFrame(\n self.best_model.get_params().items(),\n- columns=[\'Parameter\', \'Value\'])\n+ columns=[\'Parameter\', \'Value\']\n+ )\n best_model_params.to_csv(\n- os.path.join(self.output_dir, \'best_model.csv\'),\n- index=False)\n- self.results.to_csv(os.path.join(\n- self.output_dir, "comparison_results.csv"))\n- self.test_result_df.to_csv(os.path.join(\n- self.output_dir, "test_results.csv"))\n+ os.path.join(self.output_dir, "best_model.csv"), index=False\n+ )\n+ self.results.to_csv(\n+ os.path.join(self.output_dir, "comparison_results.csv")\n+ )\n+ self.test_result_df.to_csv(\n+ os.path.join(self.output_dir, "test_results.csv")\n+ )\n \n plots_html = ""\n length = len(self.plots)\n@@ -250,7 +246,8 @@\n data=self.data,\n target_col=self.target_col,\n task_type=self.task_type,\n- output_dir=self.output_dir)\n+ output_dir=self.output_dir,\n+ )\n feature_importance_html = analyzer.run()\n \n html_content = f"""\n@@ -263,38 +260,37 @@\n Best Model Plots</div>\n <div class="tab" onclick="openTab(event, \'feature\')">\n Feature Importance</div>\n- """\n+ """\n if self.plots_explainer_html:\n html_content += """\n- "<div class="tab" onclick="openTab(event, \'explainer\')">"\n+ <div class="tab" onclick="openTab(event, \'explainer\')">\n Explainer Plots</div>\n """\n html_content += f"""\n </div>\n <div id="summary" class="tab-content">\n <h2>Setup Parameters</h2>\n- <table>\n- <tr><th>Parameter</th><th>Value</th></tr>\n- {setup_params_table.to_html(\n- index=False, header=False, classes=\'table\')}\n- </table>\n+ {setup_params_table.to_html(\n+ index=False,\n+ header=True,\n+ classes=\'table sortable\'\n+ )}\n <h5>If you want to know all the experiment setup paramete'..b'ses=\'table sortable\'\n+ )}\n <h2>Comparison Results on the Cross-Validation Set</h2>\n- <table>\n- {self.results.to_html(index=False, classes=\'table\')}\n- </table>\n+ {self.results.to_html(index=False, classes=\'table sortable\')}\n <h2>Results on the Test Set for the best model</h2>\n- <table>\n- {self.test_result_df.to_html(index=False, classes=\'table\')}\n- </table>\n+ {self.test_result_df.to_html(\n+ index=False,\n+ classes=\'table sortable\'\n+ )}\n </div>\n <div id="plots" class="tab-content">\n <h2>Best Model Plots on the testing set</h2>\n@@ -310,14 +306,66 @@\n {self.plots_explainer_html}\n {tree_plots}\n </div>\n- {get_html_closing()}\n """\n- else:\n- html_content += f"""\n- {get_html_closing()}\n- """\n- with open(os.path.join(\n- self.output_dir, "comparison_result.html"), "w") as file:\n+ html_content += """\n+ <script>\n+ document.addEventListener("DOMContentLoaded", function() {\n+ var tables = document.querySelectorAll("table.sortable");\n+ tables.forEach(function(table) {\n+ var headers = table.querySelectorAll("th");\n+ headers.forEach(function(header, index) {\n+ header.style.cursor = "pointer";\n+ // Add initial arrow (up) to indicate sortability\n+ header.innerHTML += \'<span class="sort-arrow"> \xe2\x86\x91</span>\';\n+ header.addEventListener("click", function() {\n+ var direction = this.getAttribute(\n+ "data-sort-direction"\n+ ) || "asc";\n+ // Reset arrows in all headers of this table\n+ headers.forEach(function(h) {\n+ var arrow = h.querySelector(".sort-arrow");\n+ if (arrow) arrow.textContent = " \xe2\x86\x91";\n+ });\n+ // Set arrow for clicked header\n+ var arrow = this.querySelector(".sort-arrow");\n+ arrow.textContent = direction === "asc" ? " \xe2\x86\x93" : " \xe2\x86\x91";\n+ sortTable(table, index, direction);\n+ this.setAttribute("data-sort-direction",\n+ direction === "asc" ? "desc" : "asc");\n+ });\n+ });\n+ });\n+ });\n+\n+ function sortTable(table, colNum, direction) {\n+ var tb = table.tBodies[0];\n+ var tr = Array.prototype.slice.call(tb.rows, 0);\n+ var multiplier = direction === "asc" ? 1 : -1;\n+ tr = tr.sort(function(a, b) {\n+ var aText = a.cells[colNum].textContent.trim();\n+ var bText = b.cells[colNum].textContent.trim();\n+ // Remove arrow from text comparison\n+ aText = aText.replace(/[\xe2\x86\x91\xe2\x86\x93]/g, \'\').trim();\n+ bText = bText.replace(/[\xe2\x86\x91\xe2\x86\x93]/g, \'\').trim();\n+ if (!isNaN(aText) && !isNaN(bText)) {\n+ return multiplier * (\n+ parseFloat(aText) - parseFloat(bText)\n+ );\n+ } else {\n+ return multiplier * aText.localeCompare(bText);\n+ }\n+ });\n+ for (var i = 0; i < tr.length; ++i) tb.appendChild(tr[i]);\n+ }\n+ </script>\n+ """\n+ html_content += f"""\n+ {get_html_closing()}\n+ """\n+ with open(\n+ os.path.join(self.output_dir, "comparison_result.html"),\n+ "w"\n+ ) as file:\n file.write(html_content)\n \n def save_dashboard(self):\n' |
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| 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 |
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| @@ -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 |
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| 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 |
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| @@ -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 |
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| 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 |
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| @@ -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"> |
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| 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 |
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| @@ -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) |
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| 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 |
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| @@ -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__) |
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| 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 |
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| @@ -2,7 +2,6 @@ import logging from pycaret_classification import ClassificationModelTrainer - from pycaret_regression import RegressionModelTrainer logging.basicConfig(level=logging.DEBUG) |
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| 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 |
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| @@ -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> |
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| 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 |
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| @@ -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 |