comparison classification.xml @ 9:24c000517173 draft

"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit 6e8b69ee3aff3c93f745a5de11cc9169130f2e5e"

8:277dc652246e

<tool id="cardinal_classification" name="MSI classification" version="@VERSION@.1">
    <description>spatial classification of mass spectrometry imaging data</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements">
        <requirement type="package" version="2.3">r-gridextra</requirement>
        <requirement type="package" version="3.2.1">r-ggplot2</requirement>
    </expand>
    <command detect_errors="exit_code">
    <![CDATA[

        @INPUT_LINKING@

                       theme(legend.position="bottom",legend.direction="vertical")+
                       guides(fill=guide_legend(ncol=4,byrow=TRUE))
                coord_labels = aggregate(cbind(x,y)~predicted_classes, data=prediction_df, mean, na.rm=TRUE, na.action="na.pass")
                coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$predicted_classes)
                print(prediction_plot)


                ## optional output as .RData
                #if $output_rdata:
                    save(msidata.ssc, file="$classification_rdata")
                #end if

               theme(legend.position="bottom",legend.direction="vertical")+
               guides(fill=guide_legend(ncol=4,byrow=TRUE))
        coord_labels = aggregate(cbind(x,y)~predicted_classes, data=prediction_df, mean, na.rm=TRUE, na.action="na.pass")
        coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$predicted_classes)
        print(prediction_plot)

        ## Summary table prediction
        summary_table = summary(prediction)\$accuracy[[names(prediction@resultData)]]
        summary_table2 = round(as.numeric(summary_table), digits=2)
        summary_matrix = matrix(summary_table2, nrow=4, ncol=ncol(summary_table))

9:24c000517173

<tool id="cardinal_classification" name="MSI classification" version="@VERSION@.0">
    <description>spatial classification of mass spectrometry imaging data</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements">
        <requirement type="package" version="2.3">r-gridextra</requirement>
        <requirement type="package" version="3.3.2">r-ggplot2</requirement>
    </expand>
    <command detect_errors="exit_code">
    <![CDATA[

        @INPUT_LINKING@

                       theme(legend.position="bottom",legend.direction="vertical")+
                       guides(fill=guide_legend(ncol=4,byrow=TRUE))
                coord_labels = aggregate(cbind(x,y)~predicted_classes, data=prediction_df, mean, na.rm=TRUE, na.action="na.pass")
                coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$predicted_classes)
                print(prediction_plot)
                
                
               ## image with right and wrong classes: 
               comparison_df = cbind(prediction_df, y_vector)
               comparison_df\$correct<- ifelse(comparison_df\$predicted_classes==comparison_df\$y_vector, T, F)

               correctness_plot = ggplot(comparison_df, aes(x=x, y=y, fill=correct))+
                       geom_tile() +
                       coord_fixed()+
                       ggtitle("Correctness of classification")+
                       theme_bw()+
                       theme(text=element_text(family="ArialMT", face="bold", size=15))+
                       theme(legend.position="bottom",legend.direction="vertical")+
                       guides(fill=guide_legend(ncol=2,byrow=TRUE))
               ## coord_labels = aggregate(cbind(x,y)~correct, data=comparison_df, mean, na.rm=TRUE, na.action="na.pass")
                ##coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$predicted_classes)
                print(correctness_plot)

                ## optional output as .RData
                #if $output_rdata:
                    save(msidata.ssc, file="$classification_rdata")
                #end if

               theme(legend.position="bottom",legend.direction="vertical")+
               guides(fill=guide_legend(ncol=4,byrow=TRUE))
        coord_labels = aggregate(cbind(x,y)~predicted_classes, data=prediction_df, mean, na.rm=TRUE, na.action="na.pass")
        coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$predicted_classes)
        print(prediction_plot)
        
        ## image with right and wrong classes:     

	comparison_df = cbind(prediction_df, new_y_vector)
	comparison_df\$correct<- as.factor(ifelse(comparison_df\$predicted_classes==comparison_df\$new_y_vector, T, F))

	correctness_plot = ggplot(comparison_df, aes(x=x, y=y, fill=correct))+
                       geom_tile()+
                       scale_fill_manual(values = c("TRUE" = "orange","FALSE" = "darkblue"))+
                       coord_fixed()+
                       ggtitle("Correctness of classification")+
                       theme_bw()+
                       theme(text=element_text(family="ArialMT", face="bold", size=15))+
                       theme(legend.position="bottom",legend.direction="vertical")+
                       guides(fill=guide_legend(ncol=2,byrow=TRUE))
               ## coord_labels = aggregate(cbind(x,y)~correct, data=comparison_df, mean, na.rm=TRUE, na.action="na.pass")
                ##coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$predicted_classes)
        print(correctness_plot)
                

        ## Summary table prediction
        summary_table = summary(prediction)\$accuracy[[names(prediction@resultData)]]
        summary_table2 = round(as.numeric(summary_table), digits=2)
        summary_matrix = matrix(summary_table2, nrow=4, ncol=ncol(summary_table))