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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
author bgruening
date Thu, 11 Oct 2018 14:37:54 -0400
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children 85da91bbdbfb
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4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
bgruening
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diff changeset
1 import argparse
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
bgruening
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2 import pandas as pd
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
bgruening
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3 import plotly
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
bgruening
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4 import pickle
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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5 import plotly.graph_objs as go
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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6 from sklearn.metrics import confusion_matrix, precision_recall_fscore_support, roc_curve, auc
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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7 from sklearn.preprocessing import label_binarize
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4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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10 def main(infile_input, infile_output, infile_trained_model):
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11 """
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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12 Produce an interactive confusion matrix (heatmap), precision, recall, fscore and auc plots
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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13 Args:
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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14 infile_input: str, input tabular file with true labels
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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15 infile_output: str, input tabular file with predicted labels
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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16 infile_trained_model: str, input trained model file (zip)
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17 """
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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19 df_input = pd.read_csv(infile_input, sep='\t', parse_dates=True)
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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20 df_output = pd.read_csv(infile_output, sep='\t', parse_dates=True)
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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21 true_labels = df_input.iloc[:, -1].copy()
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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22 predicted_labels = df_output.iloc[:, -1].copy()
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23 axis_labels = list(set(true_labels))
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24 c_matrix = confusion_matrix(true_labels, predicted_labels)
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25 data = [
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26 go.Heatmap(
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27 z=c_matrix,
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28 x=axis_labels,
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29 y=axis_labels,
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30 colorscale='Portland',
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31 )
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32 ]
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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34 layout = go.Layout(
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35 title='Confusion Matrix between true and predicted class labels',
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36 xaxis=dict(title='True class labels'),
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37 yaxis=dict(title='Predicted class labels')
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38 )
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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40 fig = go.Figure(data=data, layout=layout)
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41 plotly.offline.plot(fig, filename="output_confusion.html", auto_open=False)
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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43 # plot precision, recall and f_score for each class label
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44 precision, recall, f_score, _ = precision_recall_fscore_support(true_labels, predicted_labels)
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46 trace_precision = go.Scatter(
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47 x=axis_labels,
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48 y=precision,
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49 mode='lines+markers',
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50 name='Precision'
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51 )
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53 trace_recall = go.Scatter(
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54 x=axis_labels,
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55 y=recall,
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56 mode='lines+markers',
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57 name='Recall'
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58 )
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60 trace_fscore = go.Scatter(
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61 x=axis_labels,
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62 y=f_score,
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63 mode='lines+markers',
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64 name='F-score'
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65 )
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67 layout_prf = go.Layout(
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68 title='Precision, recall and f-score of true and predicted class labels',
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69 xaxis=dict(title='Class labels'),
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70 yaxis=dict(title='Precision, recall and f-score')
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71 )
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73 data_prf = [trace_precision, trace_recall, trace_fscore]
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74 fig_prf = go.Figure(data=data_prf, layout=layout_prf)
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75 plotly.offline.plot(fig_prf, filename="output_prf.html", auto_open=False)
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76
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77 # plot roc and auc curves for different classes
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78 with open(infile_trained_model, 'rb') as model_file:
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79 model = pickle.load(model_file)
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80
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81 # remove the last column (label column)
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82 test_data = df_input.iloc[:, :-1]
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83 model_items = dir(model)
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84
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85 try:
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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86 # find the probability estimating method
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87 if 'predict_proba' in model_items:
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88 y_score = model.predict_proba(test_data)
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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89 elif 'decision_function' in model_items:
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90 y_score = model.decision_function(test_data)
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91
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92 true_labels_list = true_labels.tolist()
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93 one_hot_labels = label_binarize(true_labels_list, classes=axis_labels)
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94 data_roc = list()
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95
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96 if len(axis_labels) > 2:
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97 fpr = dict()
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98 tpr = dict()
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99 roc_auc = dict()
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100 for i in axis_labels:
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101 fpr[i], tpr[i], _ = roc_curve(one_hot_labels[:, i], y_score[:, i])
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102 roc_auc[i] = auc(fpr[i], tpr[i])
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103 for i in range(len(axis_labels)):
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104 trace = go.Scatter(
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105 x=fpr[i],
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106 y=tpr[i],
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107 mode='lines+markers',
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108 name='ROC curve of class {0} (AUC = {1:0.2f})'.format(i, roc_auc[i])
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109 )
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110 data_roc.append(trace)
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111 else:
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112 try:
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113 y_score_binary = y_score[:, 1]
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114 except:
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115 y_score_binary = y_score
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116 fpr, tpr, _ = roc_curve(one_hot_labels, y_score_binary, pos_label=1)
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117 roc_auc = auc(fpr, tpr)
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118 trace = go.Scatter(
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119 x=fpr,
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120 y=tpr,
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121 mode='lines+markers',
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122 name='ROC curve (AUC = {0:0.2f})'.format(roc_auc)
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123 )
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124 data_roc.append(trace)
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125
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126 trace_diag = go.Scatter(
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127 x=[0, 1],
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128 y=[0, 1],
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129 mode='lines',
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130 name='Chance'
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131 )
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132 data_roc.append(trace_diag)
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133 layout_roc = go.Layout(
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134 title='Receiver operating characteristics (ROC) and area under curve (AUC)',
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135 xaxis=dict(title='False positive rate'),
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136 yaxis=dict(title='True positive rate')
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137 )
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138
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139 fig_roc = go.Figure(data=data_roc, layout=layout_roc)
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140 plotly.offline.plot(fig_roc, filename="output_roc.html", auto_open=False)
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141
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142 except Exception as exp:
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143 pass
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144
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145
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146 if __name__ == "__main__":
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147 aparser = argparse.ArgumentParser()
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148 aparser.add_argument("-i", "--input", dest="infile_input", required=True)
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149 aparser.add_argument("-j", "--output", dest="infile_output", required=True)
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150 aparser.add_argument("-k", "--model", dest="infile_trained_model", required=True)
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151 args = aparser.parse_args()
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152 main(args.infile_input, args.infile_output, args.infile_trained_model)