Mercurial > repos > bgruening > plotly_ml_performance_plots

annotate plot_ml_performance.py @ 3:1c5dcef5ce0f draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
author bgruening
date Tue, 07 May 2024 14:11:16 +0000
parents 62e3a4e8c54c
children f234e2e59d76
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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
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1c5dcef5ce0f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
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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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3 import pandas as pd
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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4 import plotly
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
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1c5dcef5ce0f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
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6 from galaxy_ml.model_persist import load_model_from_h5
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7 from galaxy_ml.utils import clean_params
1c5dcef5ce0f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
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8 from sklearn.metrics import (auc, confusion_matrix,
1c5dcef5ce0f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
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9 precision_recall_fscore_support, roc_curve)
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10 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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13 def main(infile_input, infile_output, infile_trained_model):
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14 """
4fac53da862f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 8496ba724e35ba551172ea975b0fed091d4bbe88
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15 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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16 Args:
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17 infile_input: str, input tabular file with true labels
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18 infile_output: str, input tabular file with predicted labels
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19 infile_trained_model: str, input trained model file (zip)
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20 """
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21
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1c5dcef5ce0f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
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22 df_input = pd.read_csv(infile_input, sep="\t", parse_dates=True)
1c5dcef5ce0f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
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23 df_output = pd.read_csv(infile_output, sep="\t", parse_dates=True)
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24 true_labels = df_input.iloc[:, -1].copy()
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25 predicted_labels = df_output.iloc[:, -1].copy()
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26 axis_labels = list(set(true_labels))
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27 c_matrix = confusion_matrix(true_labels, predicted_labels)
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28 data = [
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29 go.Heatmap(
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30 z=c_matrix,
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31 x=axis_labels,
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32 y=axis_labels,
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33 colorscale="Portland",
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34 )
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35 ]
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37 layout = go.Layout(
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1c5dcef5ce0f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
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38 title="Confusion Matrix between true and predicted class labels",
1c5dcef5ce0f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
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39 xaxis=dict(title="Predicted class labels"),
1c5dcef5ce0f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
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40 yaxis=dict(title="True class labels"),
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41 )
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43 fig = go.Figure(data=data, layout=layout)
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44 plotly.offline.plot(fig, filename="output_confusion.html", auto_open=False)
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46 # plot precision, recall and f_score for each class label
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47 precision, recall, f_score, _ = precision_recall_fscore_support(
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48 true_labels, predicted_labels
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49 )
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51 trace_precision = go.Scatter(
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52 x=axis_labels, y=precision, mode="lines+markers", name="Precision"
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53 )
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55 trace_recall = go.Scatter(
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56 x=axis_labels, y=recall, mode="lines+markers", name="Recall"
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57 )
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59 trace_fscore = go.Scatter(
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60 x=axis_labels, y=f_score, mode="lines+markers", name="F-score"
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61 )
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63 layout_prf = go.Layout(
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64 title="Precision, recall and f-score of true and predicted class labels",
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65 xaxis=dict(title="Class labels"),
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66 yaxis=dict(title="Precision, recall and f-score"),
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67 )
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69 data_prf = [trace_precision, trace_recall, trace_fscore]
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70 fig_prf = go.Figure(data=data_prf, layout=layout_prf)
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71 plotly.offline.plot(fig_prf, filename="output_prf.html", auto_open=False)
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73 # plot roc and auc curves for different classes
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74 classifier_object = load_model_from_h5(infile_trained_model)
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75 model = clean_params(classifier_object)
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77 # remove the last column (label column)
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78 test_data = df_input.iloc[:, :-1]
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79 model_items = dir(model)
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81 try:
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82 # find the probability estimating method
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83 if "predict_proba" in model_items:
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84 y_score = model.predict_proba(test_data)
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85 elif "decision_function" in model_items:
0
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86 y_score = model.decision_function(test_data)
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87
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88 true_labels_list = true_labels.tolist()
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89 one_hot_labels = label_binarize(true_labels_list, classes=axis_labels)
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90 data_roc = list()
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91
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92 if len(axis_labels) > 2:
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93 fpr = dict()
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94 tpr = dict()
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95 roc_auc = dict()
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96 for i in axis_labels:
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97 fpr[i], tpr[i], _ = roc_curve(one_hot_labels[:, i], y_score[:, i])
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98 roc_auc[i] = auc(fpr[i], tpr[i])
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99 for i in range(len(axis_labels)):
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100 trace = go.Scatter(
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101 x=fpr[i],
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102 y=tpr[i],
3
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103 mode="lines+markers",
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104 name="ROC curve of class {0} (AUC = {1:0.2f})".format(
1c5dcef5ce0f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
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105 i, roc_auc[i]
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106 ),
0
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107 )
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108 data_roc.append(trace)
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109 else:
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110 try:
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111 y_score_binary = y_score[:, 1]
3
1c5dcef5ce0f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
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112 except Exception:
0
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113 y_score_binary = y_score
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114 fpr, tpr, _ = roc_curve(one_hot_labels, y_score_binary, pos_label=1)
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115 roc_auc = auc(fpr, tpr)
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116 trace = go.Scatter(
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117 x=fpr,
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118 y=tpr,
3
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119 mode="lines+markers",
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120 name="ROC curve (AUC = {0:0.2f})".format(roc_auc),
0
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121 )
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122 data_roc.append(trace)
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123
3
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124 trace_diag = go.Scatter(x=[0, 1], y=[0, 1], mode="lines", name="Chance")
0
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125 data_roc.append(trace_diag)
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126 layout_roc = go.Layout(
3
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127 title="Receiver operating characteristics (ROC) and area under curve (AUC)",
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128 xaxis=dict(title="False positive rate"),
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129 yaxis=dict(title="True positive rate"),
0
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130 )
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131
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132 fig_roc = go.Figure(data=data_roc, layout=layout_roc)
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133 plotly.offline.plot(fig_roc, filename="output_roc.html", auto_open=False)
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134
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135 except Exception as exp:
3
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136 print(
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137 "Plotting the ROC-AUC graph failed. This exception was raised: {}".format(
1c5dcef5ce0f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_ml_performance_plots commit 271a4454eea5902e29da4b8dfa7b9124fefac6bc
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138 exp
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139 )
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140 )
0
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141
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142
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143 if __name__ == "__main__":
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144 aparser = argparse.ArgumentParser()
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145 aparser.add_argument("-i", "--input", dest="infile_input", required=True)
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146 aparser.add_argument("-j", "--output", dest="infile_output", required=True)
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147 aparser.add_argument("-k", "--model", dest="infile_trained_model", required=True)
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148 args = aparser.parse_args()
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149 main(args.infile_input, args.infile_output, args.infile_trained_model)