comparison image_learner.xml @ 8:85e6f4b2ad18 draft

planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6

7:801a8b6973fb

<tool id="image_learner" name="Image Learner" version="0.1.1" profile="22.05">
    <description>trains and evaluates an image classification/regression model</description>
    <requirements>
        <container type="docker">quay.io/goeckslab/galaxy-ludwig-gpu:0.10.1</container>
    </requirements>
    <required_files>
        <include path="utils.py" />
        <include path="constants.py" />
        <include path="image_learner_cli.py" />

        </param>

        <conditional name="scratch_fine_tune">
            <param name="use_pretrained" type="select"
                label="Use pretrained weights?"
                help="If select no, the encoder, combiner, and decoder will all be initialized and trained from scratch. (e.g. when your images are very different from ImageNet or no suitable pretrained model exists.)">
                <option value="false">No</option>
                <option value="true" selected="true">Yes</option>
            </param>
            <when value="true">
                <param name="fine_tune" type="select" label="Fine tune the encoder?"
                    help="Whether to fine tune the encoder(combiner and decoder will be fine-tued anyway)" >
                    <option value="false" >No</option>
                    <option value="true" selected="true">Yes</option>
                </param>
            </when>
            <when value="false">

                        help="Fraction of data for validation (e.g., 0.1). train split + val split + test split should = 1"/>
                <param name="test_split"  type="float"
                        label="Test split proportion (only works if no split column in the metadata csv)"
                        value="0.2"
                        help="Fraction of data for testing (e.g., 0.2) train split + val split + test split should = 1."/>
            </when>
            <when value="false">
                <!-- No additional parameters to show if the user selects 'No' -->
            </when>
        </conditional>

                    <has_text text="Results Summary" />
                    <has_text text="Train/Validation Results" />
                    <has_text text="Test Results" />
                </assert_contents>
            </output>
            <output name="output_report" file="expected_regression.html" compare="sim_size"/>

            <output_collection name="output_pred_csv" type="list" >
                <element name="predictions.csv" >
                    <assert_contents>
                        <has_n_columns n="1" />
                    </assert_contents>
                </element>
            </output_collection>
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**
Image Learner for Classification/regression: trains and evaluates a image classification/regression model. 
It uses the metadata csv to find the image paths and labels. 
The metadata csv should contain a column with the name 'image_path' and a column with the name 'label'.
Optionally, you can also add a column with the name 'split' to specify which split each row belongs to (train, val, test). 
If you do not provide a split column, the tool will automatically split the data into train, val, and test sets based on the proportions you specify or [0.7, 0.1, 0.2] by default.

**If the selected label column has more than 10 unique values, the tool will automatically treat the task as a regression problem and apply appropriate metrics (e.g., MSE, RMSE, R²).**


**Outputs**
The tool will output a trained model in the form of a ludwig_model file,
a report in the form of an HTML file, and a collection of CSV/json/png files containing the predictions, experiment stats and visualizations.

8:85e6f4b2ad18

<tool id="image_learner" name="Image Learner for Classification" version="0.1.2" profile="22.05">
    <description>trains and evaluates a image classification model</description>
    <requirements>
        <container type="docker">quay.io/goeckslab/galaxy-ludwig-gpu:latest</container>
    </requirements>
    <required_files>
        <include path="utils.py" />
        <include path="constants.py" />
        <include path="image_learner_cli.py" />

        </param>

        <conditional name="scratch_fine_tune">
            <param name="use_pretrained" type="select"
                label="Use pretrained weights?"
                help="If select no, the encoder, combiner, and decoder will all be initialized and trained from scratch.  
               (e.g. when your images are very different from ImageNet or no suitable pretrained model exists.)">
                <option value="false">No</option>
                <option value="true" selected="true">Yes</option>
            </param>
            <when value="true">
                <param name="fine_tune" type="select" label="Fine tune the encoder?"
                    help="Whether to fine tune the encoder(combiner and decoder will be fine-tuned anyway)" >
                    <option value="false" >No</option>
                    <option value="true" selected="true">Yes</option>
                </param>
            </when>
            <when value="false">

                        help="Fraction of data for validation (e.g., 0.1). train split + val split + test split should = 1"/>
                <param name="test_split"  type="float"
                        label="Test split proportion (only works if no split column in the metadata csv)"
                        value="0.2"
                        help="Fraction of data for testing (e.g., 0.2) train split + val split + test split should = 1."/>
                <param name="threshold" type="float" value="0.5" min="0.0" max="1.0" optional="true" label="Decision Threshold (binary only)" help="Set the decision threshold for binary classification (0.0–1.0). Only applies when task is binary; default is 0.5." />
            </when>
            <when value="false">
                <!-- No additional parameters to show if the user selects 'No' -->
            </when>
        </conditional>

                    <has_text text="Results Summary" />
                    <has_text text="Train/Validation Results" />
                    <has_text text="Test Results" />
                </assert_contents>
            </output>
            <output_collection name="output_pred_csv" type="list" >
                <element name="predictions.csv" >
                    <assert_contents>
                        <has_n_columns n="1" />
                    </assert_contents>
                </element>
            </output_collection>
        </test>
        </tests>
    <help>
        <![CDATA[
**What it does**
Image Learner for Classification: trains and evaluates a image classification model. 
It uses the metadata csv to find the image paths and labels. 
The metadata csv should contain a column with the name 'image_path' and a column with the name 'label'.
Optionally, you can also add a column with the name 'split' to specify which split each row belongs to (train, val, test). 
If you do not provide a split column, the tool will automatically split the data into train, val, and test sets based on the proportions you specify or [0.7, 0.1, 0.2] by default.


**Outputs**
The tool will output a trained model in the form of a ludwig_model file,
a report in the form of an HTML file, and a collection of CSV/json/png files containing the predictions, experiment stats and visualizations.