Mercurial > repos > bgruening > plotly_regression_performance_plots
comparison plot_regression_performance.py @ 0:157659f94256 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_regression_performance_plots commit c17efec384ad7438f54675fae1ab0c3a57c22869
author | bgruening |
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date | Thu, 08 Nov 2018 13:27:01 -0500 |
parents | |
children | ca5ffd01f136 |
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-1:000000000000 | 0:157659f94256 |
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1 import argparse | |
2 import pandas as pd | |
3 import plotly | |
4 import plotly.graph_objs as go | |
5 | |
6 | |
7 def main(infile_input, infile_output): | |
8 """ | |
9 Produce an interactive actual vs predicted curves and residual plots | |
10 Args: | |
11 infile_input: str, input tabular file with true values | |
12 infile_output: str, input tabular file with predicted values | |
13 """ | |
14 | |
15 df_input = pd.read_csv(infile_input, sep='\t', parse_dates=True) | |
16 df_output = pd.read_csv(infile_output, sep='\t', parse_dates=True) | |
17 true_values = df_input.iloc[:, -1].copy() | |
18 predicted_values = df_output.iloc[:, -1].copy() | |
19 axis_labels = list(range(1, len(true_values)+1)) | |
20 | |
21 # true vs predicted curves | |
22 trace_true = go.Scatter( | |
23 x=axis_labels, | |
24 y=true_values, | |
25 mode='lines+markers', | |
26 name='True values' | |
27 ) | |
28 | |
29 trace_predicted = go.Scatter( | |
30 x=axis_labels, | |
31 y=predicted_values, | |
32 mode='lines+markers', | |
33 name='Predicted values' | |
34 ) | |
35 | |
36 layout_tp = go.Layout( | |
37 title='True vs predicted values', | |
38 xaxis=dict(title='Number of data points'), | |
39 yaxis=dict(title='Values') | |
40 ) | |
41 | |
42 data_tp = [trace_true, trace_predicted] | |
43 fig_tp = go.Figure(data=data_tp, layout=layout_tp) | |
44 plotly.offline.plot(fig_tp, filename="output_actual_vs_pred.html", auto_open=False) | |
45 | |
46 # scatter plot | |
47 max_tv = int(max(true_values)) | |
48 x_y_values = list(range(0, max_tv)) | |
49 | |
50 trace_x_eq_y = go.Scatter( | |
51 x=x_y_values, | |
52 y=x_y_values, | |
53 mode='lines', | |
54 name='X = Y curve' | |
55 ) | |
56 | |
57 trace_true_pred = go.Scatter( | |
58 x=true_values, | |
59 y=predicted_values, | |
60 mode='markers', | |
61 name='True and predicted values' | |
62 ) | |
63 | |
64 layout_true_pred = go.Layout( | |
65 title='True vs predicted values', | |
66 xaxis=dict(title='True values'), | |
67 yaxis=dict(title='Predicted values') | |
68 ) | |
69 | |
70 data_true_pred = [trace_true_pred, trace_x_eq_y] | |
71 fig_true_pred = go.Figure(data=data_true_pred, layout=layout_true_pred) | |
72 plotly.offline.plot(fig_true_pred, filename="output_scatter_plot.html", auto_open=False) | |
73 | |
74 # residual plot | |
75 residual = predicted_values - true_values | |
76 trace_residual = go.Scatter( | |
77 x=predicted_values, | |
78 y=residual, | |
79 mode='markers' | |
80 ) | |
81 | |
82 layout_residual = go.Layout( | |
83 title='Residual vs predicted values', | |
84 xaxis=dict(title='Predicted values'), | |
85 yaxis=dict(title='Residual (Predicted - True)') | |
86 ) | |
87 | |
88 data_residual = [trace_residual] | |
89 fig_residual = go.Figure(data=data_residual, layout=layout_residual) | |
90 plotly.offline.plot(fig_residual, filename="output_residual_plot.html", auto_open=False) | |
91 | |
92 | |
93 if __name__ == "__main__": | |
94 aparser = argparse.ArgumentParser() | |
95 aparser.add_argument("-i", "--input", dest="infile_input", required=True) | |
96 aparser.add_argument("-j", "--output", dest="infile_output", required=True) | |
97 args = aparser.parse_args() | |
98 main(args.infile_input, args.infile_output) |