| Next changeset 1:389227fa1864 (2019-01-09) |
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Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_regression_performance_plots commit c17efec384ad7438f54675fae1ab0c3a57c22869 |
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added:
plot_regression_performance.py plotly_regression_performance_plots.xml test-data/output_actual_vs_pred_gtbr.html test-data/output_residual_plot_gtbr.html test-data/output_scatter_plot_gtbr.html test-data/predictions_gtbr test-data/test_targets |
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| diff -r 000000000000 -r 0800a1b66bbd plot_regression_performance.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/plot_regression_performance.py Thu Nov 08 13:28:04 2018 -0500 |
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| @@ -0,0 +1,98 @@ +import argparse +import pandas as pd +import plotly +import plotly.graph_objs as go + + +def main(infile_input, infile_output): + """ + Produce an interactive actual vs predicted curves and residual plots + Args: + infile_input: str, input tabular file with true values + infile_output: str, input tabular file with predicted values + """ + + df_input = pd.read_csv(infile_input, sep='\t', parse_dates=True) + df_output = pd.read_csv(infile_output, sep='\t', parse_dates=True) + true_values = df_input.iloc[:, -1].copy() + predicted_values = df_output.iloc[:, -1].copy() + axis_labels = list(range(1, len(true_values)+1)) + + # true vs predicted curves + trace_true = go.Scatter( + x=axis_labels, + y=true_values, + mode='lines+markers', + name='True values' + ) + + trace_predicted = go.Scatter( + x=axis_labels, + y=predicted_values, + mode='lines+markers', + name='Predicted values' + ) + + layout_tp = go.Layout( + title='True vs predicted values', + xaxis=dict(title='Number of data points'), + yaxis=dict(title='Values') + ) + + data_tp = [trace_true, trace_predicted] + fig_tp = go.Figure(data=data_tp, layout=layout_tp) + plotly.offline.plot(fig_tp, filename="output_actual_vs_pred.html", auto_open=False) + + # scatter plot + max_tv = int(max(true_values)) + x_y_values = list(range(0, max_tv)) + + trace_x_eq_y = go.Scatter( + x=x_y_values, + y=x_y_values, + mode='lines', + name='X = Y curve' + ) + + trace_true_pred = go.Scatter( + x=true_values, + y=predicted_values, + mode='markers', + name='True and predicted values' + ) + + layout_true_pred = go.Layout( + title='True vs predicted values', + xaxis=dict(title='True values'), + yaxis=dict(title='Predicted values') + ) + + data_true_pred = [trace_true_pred, trace_x_eq_y] + fig_true_pred = go.Figure(data=data_true_pred, layout=layout_true_pred) + plotly.offline.plot(fig_true_pred, filename="output_scatter_plot.html", auto_open=False) + + # residual plot + residual = predicted_values - true_values + trace_residual = go.Scatter( + x=predicted_values, + y=residual, + mode='markers' + ) + + layout_residual = go.Layout( + title='Residual vs predicted values', + xaxis=dict(title='Predicted values'), + yaxis=dict(title='Residual (Predicted - True)') + ) + + data_residual = [trace_residual] + fig_residual = go.Figure(data=data_residual, layout=layout_residual) + plotly.offline.plot(fig_residual, filename="output_residual_plot.html", auto_open=False) + + +if __name__ == "__main__": + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--input", dest="infile_input", required=True) + aparser.add_argument("-j", "--output", dest="infile_output", required=True) + args = aparser.parse_args() + main(args.infile_input, args.infile_output) |
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| diff -r 000000000000 -r 0800a1b66bbd plotly_regression_performance_plots.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/plotly_regression_performance_plots.xml Thu Nov 08 13:28:04 2018 -0500 |
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| @@ -0,0 +1,43 @@ +<tool id="plotly_regression_performance_plots" name="Plot actual vs predicted curves and residual plots" version="0.1"> + <description>of tabular data</description> + <requirements> + <requirement type="package" version="3.6">python</requirement> + <requirement type="package" version="0.22.0">pandas</requirement> + <requirement type="package" version="3.1.1">plotly</requirement> + </requirements> + <version_command>echo $version</version_command> + <command detect_errors="aggressive"><![CDATA[ + python '$__tool_directory__/plot_regression_performance.py' + -i '$infile_input' + -j '$infile_output' +]]> + </command> + <inputs> + <param name="infile_input" type="data" format="tabular" label="Select input data file :" help="Input data is a tabular file in which the last column (including the column if there is only one) contains the target values."/> + <param name="infile_output" type="data" format="tabular" label="Select predicted data file :" help="Predicted data is a tabular file in which the last column (including the column if there is only one) contains the predicted values."/> + </inputs> + + <outputs> + <data name="output_actual_vs_pred" format="html" from_work_dir="output_actual_vs_pred.html" label="Actual vs predicted curves of tabular data on ${on_string}"/> + <data name="output_scatter_plot" format="html" from_work_dir="output_scatter_plot.html" label="Scatter plot of actual and predicted values of tabular data on ${on_string}"/> + <data name="output_residual_plot" format="html" from_work_dir="output_residual_plot.html" label="Residual plot of tabular data on ${on_string}"/> + </outputs> + + <tests> + <test> + <param name="infile_input" value="test_targets" ftype="tabular"/> + <param name="infile_output" value="predictions_gtbr" ftype="tabular"/> + <output name="output_actual_vs_pred" file="output_actual_vs_pred_gtbr.html" compare="sim_size"/> + <output name="output_scatter_plot" file="output_scatter_plot_gtbr.html" compare="sim_size"/> + <output name="output_residual_plot" file="output_residual_plot_gtbr.html" compare="sim_size"/> + </test> + </tests> + <help><![CDATA[ +**What it does** + + +Produce a `line and scatter curves <https://plot.ly/python/line-and-scatter/>`_ from tabular files. The input data contains the true values (last column) and the predicted data contains the predicted values (last column). The true and predicted values are plotted against each other. The plot is buried in a html file which provides rich interactive features. Image can be saved in various format, such as 'png', 'svg', 'jpeg' and so on. + + ]]> + </help> +</tool> |
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| diff -r 000000000000 -r 0800a1b66bbd test-data/output_actual_vs_pred_gtbr.html --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output_actual_vs_pred_gtbr.html Thu Nov 08 13:28:04 2018 -0500 |
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| diff -r 000000000000 -r 0800a1b66bbd test-data/output_residual_plot_gtbr.html --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/output_residual_plot_gtbr.html Thu Nov 08 13:28:04 2018 -0500 |
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| b'@@ -0,0 +1,14 @@\n+<html><head><meta charset="utf-8" /></head><body><script type="text/javascript">/**\n+* plotly.js v1.39.4\n+* Copyright 2012-2018, Plotly, Inc.\n+* All rights reserved.\n+* Licensed under the MIT license\n+*/\n+!function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof define&&define.amd)define([],t);else{("undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this).Plotly=t()}}(function(){return function(){return function t(e,r,n){function i(o,s){if(!r[o]){if(!e[o]){var l="function"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var c=new Error("Cannot find module \'"+o+"\'");throw c.code="MODULE_NOT_FOUND",c}var u=r[o]={exports:{}};e[o][0].call(u.exports,function(t){var r=e[o][1][t];return i(r||t)},u,u.exports,t,e,r,n)}return r[o].exports}for(var a="function"==typeof require&&require,o=0;o<n.length;o++)i(n[o]);return 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|
| b |
| diff -r 000000000000 -r 0800a1b66bbd test-data/test_targets --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/test_targets Thu Nov 08 13:28:04 2018 -0500 |
| b |
| b'@@ -0,0 +1,301 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