<tool id="cleanlab_issue_handler" name="Cleanlab Issue Handler" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="23.0">
    <description>Detect and optionally clean data issues using Cleanlab</description>
    <macros>
        <token name="@TOOL_VERSION@">2.7.1</token>
        <token name="@VERSION_SUFFIX@">1.0</token>
    </macros>

    <requirements>
        <requirement type="package" version="2.7.1">cleanlab</requirement>
        <requirement type="package" version="3.6.0">datasets</requirement>
        <requirement type="package" version="3.0.0">xgboost</requirement>
    </requirements>

    <command detect_errors="exit_code"><![CDATA[
        python '${__tool_directory__}/cleanlab_issue_handler.py'
        --input_file '$input_file' '$input_file.ext'
        --target_column '$target_column'
        --task "$task_block.task"
        --method "$task_block.method"
        #if $summary_only:
            --summary
        #end if
        #if str($task_block.task) == "classification":
            $task_block.label_issues
            $task_block.outliers
            $task_block.near_duplicates
            $task_block.non_iid
        #elif str($task_block.task) == "regression":
            --quality-threshold "$task_block.quality_threshold"
        #end if
    ]]></command>

    <inputs>
        <param name="input_file" type="data" format="csv,tsv,tabular" label="Input data file"/>
        <param name="target_column" type="text" optional="false" label="Target column name" value="target" help="Name of the target column in the input data file. Default is 'target'."/>
        <param name="summary_only" type="boolean" label="Only generate summary report?" checked="false"/>
        
        <conditional name="task_block">
            <param name="task" type="select" label="Task type">
                <option value="classification">Classification</option>
                <option value="regression">Regression</option>
            </param>
            
            <when value="classification">
                <param name="method" type="select" label="Cleaning method">
                    <option value="remove">Remove problematic rows</option>
                    <option value="replace">Replace problematic labels (classification only)</option>
                </param>
                <param name="label_issues" type="boolean" truevalue="" falsevalue="--no-label-issues" label="Remove/Replace label issues" checked="true"/>
                <param name="outliers" type="boolean" truevalue="" falsevalue="--no-outliers" label="Remove/Replace outlier issues" checked="true"/>
                <param name="near_duplicates" type="boolean" truevalue="" falsevalue="--no-near-duplicates" label="Remove/Replace near-duplicate issues" checked="true"/>
                <param name="non_iid" type="boolean" truevalue="" falsevalue="--no-non-iid" label="Remove/Replace non-IID issues" checked="true"/>
            </when>

            <when value="regression">
                <param name="method" type="select" label="Cleaning method">
                    <option value="remove">Remove problematic rows</option>
                    <!-- No "replace" option for regression -->
                </param>
                <param name="quality_threshold" type="float" label="Quality threshold" value="0.2" min="0.0" max="1.0" help="Threshold for low-quality labels. Default is 0.2."/>
                <!-- No issue type parameters shown -->
            </when>
        </conditional>
    </inputs>

    <outputs>
        <data name="report_file" from_work_dir="summary.txt" format="txt" label="Issue Report"/>

        <data name="output_file" from_work_dir="cleaned_data" format_source="input_file" label="cleaned_${input_file.name}">
            <filter>not summary_only</filter>
        </data>
    </outputs>

    <tests>
    <!-- Test1: only summary -->
    <test expect_num_outputs="1">
        <param name="input_file" value="breast_cancer.csv" />
        <param name="target_column" value="target" />
        <param name="summary_only" value="true" />

        <conditional name="task_block">
            <param name="task" value="classification" />
            <param name="method" value="remove" />
            <param name="label_issues" value="true" />
            <param name="outliers" value="true" />
            <param name="near_duplicates" value="true" />
            <param name="non_iid" value="true" />
        </conditional>

        <output name="report_file">
            <assert_contents>
                <has_text_matching expression="issue_type\s+score\s+num_issues"/>
                <has_text_matching expression="label\s+(0(\.\d+)?|1(\.0+)?)\s+\d+"/>
                <has_text_matching expression="outlier\s+(0(\.\d+)?|1(\.0+)?)\s+\d+"/>
                <has_text_matching expression="non_iid\s+(0(\.\d+)?|1(\.0+)?)\s+\d+"/>
            </assert_contents>
        </output>
    </test>

    <!-- Test2: summary and cleaned all -->
    <test expect_num_outputs="2">
        <param name="input_file" value="breast_cancer.csv" />
        <param name="target_column" value="target" />
        <param name="summary_only" value="false" />

        <conditional name="task_block">
            <param name="task" value="classification" />
            <param name="method" value="remove" />
            <param name="label_issues" value="true" />
            <param name="outliers" value="true" />
            <param name="near_duplicates" value="true" />
            <param name="non_iid" value="true" />
        </conditional>

        <output name="report_file">
            <assert_contents>
                <has_text_matching expression="issue_type\s+score\s+num_issues"/>
                <has_text_matching expression="label\s+(0(\.\d+)?|1(\.0+)?)\s+\d+"/>
                <has_text_matching expression="outlier\s+(0(\.\d+)?|1(\.0+)?)\s+\d+"/>
                <has_text_matching expression="non_iid\s+(0(\.\d+)?|1(\.0+)?)\s+\d+"/>
            </assert_contents>
        </output>

        <output name="output_file">
            <assert_contents>
                <has_text_matching expression=".*target.*"/>
                <has_text_matching expression="^.*,.+,.+"/>
            </assert_contents>
        </output>
    </test>

    <!-- Test3: summary and cleaned label issues only -->
    <test expect_num_outputs="2">
        <param name="input_file" value="breast_cancer.csv" />
        <param name="target_column" value="target" />
        <param name="summary_only" value="false" />

        <conditional name="task_block">
            <param name="task" value="classification" />
            <param name="method" value="remove" />
            <param name="label_issues" value="true" />
            <param name="outliers" value="false" />
            <param name="near_duplicates" value="false" />
            <param name="non_iid" value="false" />
        </conditional>

        <output name="report_file">
            <assert_contents>
                <has_text_matching expression="label\s+(0(\.\d+)?|1(\.0+)?)\s+\d+"/>
            </assert_contents>
        </output>

        <output name="output_file">
            <assert_contents>
                <has_text_matching expression=".*target.*"/>
                <has_text_matching expression="^.*,.+,.+"/>
            </assert_contents>
        </output>
    </test>

    <!-- Test4: summary and cleaned outliers only -->
    <test expect_num_outputs="2">
        <param name="input_file" value="breast_cancer.csv" />
        <param name="target_column" value="target" />
        <param name="summary_only" value="false" />

        <conditional name="task_block">
            <param name="task" value="classification" />
            <param name="method" value="remove" />
            <param name="label_issues" value="false" />
            <param name="outliers" value="true" />
            <param name="near_duplicates" value="false" />
            <param name="non_iid" value="false" />
        </conditional>

        <output name="report_file">
            <assert_contents>
                <has_text_matching expression="outlier\s+(0(\.\d+)?|1(\.0+)?)\s+\d+"/>
            </assert_contents>
        </output>

        <output name="output_file">
            <assert_contents>
                <has_text_matching expression=".*target.*"/>
                <has_text_matching expression="^.*,.+,.+"/>
            </assert_contents>
        </output>
    </test>

    <!-- Test5: summary and clean all for tsv -->
    <test expect_num_outputs="2">
        <param name="input_file" value="breast_cancer.tsv" />
        <param name="target_column" value="target" />
        <param name="summary_only" value="false" />

        <conditional name="task_block">
            <param name="task" value="classification" />
            <param name="method" value="remove" />
            <param name="label_issues" value="true" />
            <param name="outliers" value="true" />
            <param name="near_duplicates" value="true" />
            <param name="non_iid" value="true" />
        </conditional>

        <output name="report_file">
            <assert_contents>
                <has_text_matching expression="issue_type\s+score\s+num_issues"/>
                <has_text_matching expression="label\s+(0(\.\d+)?|1(\.0+)?)\s+\d+"/>
                <has_text_matching expression="outlier\s+(0(\.\d+)?|1(\.0+)?)\s+\d+"/>
                <has_text_matching expression="non_iid\s+(0(\.\d+)?|1(\.0+)?)\s+\d+"/>
            </assert_contents>
        </output>

        <output name="output_file">
            <assert_contents>
                <has_text_matching expression=".*target.*"/>
                <has_text_matching expression="^.*\t.+\t.+"/>
            </assert_contents>
        </output>
    </test>

    <!-- Test6: regression with summary and cleaned output -->
    <test expect_num_outputs="2">
        <param name="input_file" value="reg_1027_ESL.csv" />
        <param name="target_column" value="target" />
        <param name="summary_only" value="false" />

        <conditional name="task_block">
            <param name="task" value="regression" />
            <param name="method" value="remove" />
        </conditional>

        <output name="report_file">
            <assert_contents>
                <has_text text="Regression Issue Summary:"/>
                <has_text_matching expression="Num low quality:"/>
                <has_text_matching expression="Mean label quality:"/>
            </assert_contents>
        </output>

        <output name="output_file">
            <assert_contents>
                <has_text_matching expression=".*target.*"/>
                <has_text_matching expression="^.*,.+,.+"/>
            </assert_contents>
        </output>
    </test>
</tests>

    <help><![CDATA[
**Cleanlab Issue Handler**

This Galaxy tool identifies and optionally removes or corrects data issues in supervised learning datasets using the [Cleanlab](https://cleanlab.io/) Python library. It supports **classification** and **regression** tasks and helps improve dataset quality by detecting label errors, outliers, near-duplicate entries, and non-IID samples.

The tool internally fits a cross-validated model (e.g., via XGBoost) to estimate label quality and identify problematic samples. These issues can be summarized in a report, and optionally addressed via removal or correction (depending on task and selected method).

--------------------

**Detected Issue Types (with technical examples)**

- **Label Issues**  
  These are samples whose label in the dataset is likely incorrect.
  **Example:** In a medical classification dataset, a patient's record is labeled as "benign," but its feature pattern is highly similar to correctly labeled "malignant" cases.


- **Outliers**  
  Points that are statistically distant from the rest of the dataset.  
  **Example**: An entry with unusually high or low feature values (e.g., several standard deviations away from the mean).

- **Near-Duplicates**  
  Highly similar or repeated samples.  
  **Example**: Two rows with nearly identical features and labels — possibly a duplication or copy artifact.

- **Non-IID Samples** *(classification only)*  
  Samples that violate the assumption of independent and identically distributed data.
  **Example**: A subset from a different population source (e.g., a different hospital or device) introducing distributional shift.

--------------------

**Parameters**

- **Input file**: Tabular file (CSV/TSV) with a `target` column.
- **Task type**: `classification` or `regression`.
- **Method**: `remove` (delete problematic rows) or `replace` (correct labels — classification only).
- **Only report issues**: If checked, input data is unchanged; only a summary report is produced.
- **Issue types**: Choose which issues to detect and handle.
- **Quality threshold** *(regression only)*: A float between 0.0 and 1.0 that determines how aggressively the tool flags low-quality labels in regression. Labels with quality scores below this threshold will be removed.

--------------------

**Outputs**

- **summary.txt**: Report listing each issue type, confidence score, and number of affected rows.
- **cleaned_data**: Cleaned dataset (CSV/TSV), only produced if "Only report issues" is unchecked.

    ]]></help>

    <citations>
        <citation type="bibtex">
@inproceedings{northcutt2021confident,
  title={Confident learning: Estimating uncertainty in dataset labels},
  author={Northcutt, Curtis G and Jiang, Lu and Chuang, Alex},
  booktitle={Journal of Artificial Intelligence Research},
  year={2021},
  volume={70},
  pages={1373--1411}
}
        </citation>
    </citations>
</tool>