comparison plot_regression_performance.py @ 1:389227fa1864 draft default tip

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_regression_performance_plots commit 2473a53fde6d8e646e90d2a5201999c8c6a48695

0:0800a1b66bbd

import argparse
import pandas as pd
import plotly
import plotly.graph_objs as go


def main(infile_input, infile_output):

    # 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'

        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]

1:389227fa1864

import argparse
import pandas as pd
import numpy as np
import plotly
import plotly.graph_objs as go


def main(infile_input, infile_output):

    # scatter plot
    max_tv = int(max(true_values))
    x_y_values = list(range(0, max_tv))

    true_mean = np.mean(true_values)
    res_true_predicted = np.sum((true_values - predicted_values) ** 2)
    res_total = np.sum((true_values - true_mean) ** 2)
    r2 = 1 - (res_true_predicted / float(res_total))
    rmse = np.sqrt(np.mean([(x - y) ** 2 for x, y in zip(true_values, predicted_values)]))

    trace_x_eq_y = go.Scatter(
        x=x_y_values,
        y=x_y_values,
        mode='lines',
        name='X = Y curve'

        mode='markers',
        name='True and predicted values'
    )

    layout_true_pred = go.Layout(
        title='True vs predicted values (RMSE: %s, R2: %s)' % (str(np.round(rmse, 2)), str(np.round(r2, 2))),
        xaxis=dict(title='True values'),
        yaxis=dict(title='Predicted values')
    )

    data_true_pred = [trace_true_pred, trace_x_eq_y]