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| a | b/utils.py | ||
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| 1 | import pandas as pd |
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| 2 | import numpy as np |
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| 3 | from sklearn import metrics |
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| 4 | from sklearn.metrics import confusion_matrix, classification_report, average_precision_score, precision_recall_curve, accuracy_score, confusion_matrix, average_precision_score |
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| 5 | |||
| 6 | |||
| 7 | |||
| 8 | def episode_metrics(model, x, parameters, rhythm, out_message=False): |
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| 9 | """ arg |
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| 10 | |||
| 11 | """ |
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| 12 | # Rhythmn predictions |
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| 13 | predictions_qa, predictions_r = model.predict(x) |
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| 14 | # If the estimated prob of AF(column 1) is higher than the estimated prob of non-AF(column 0) |
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| 15 | # than consider the window as an AF window. |
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| 16 | y_predictions = np.argmax(predictions_r, axis=1) |
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| 17 | y_truth = np.argmax(rhythm, axis=1) |
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| 18 | # Confusion_matrix |
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| 19 | cf = confusion_matrix(y_truth, y_predictions, labels=[0, 1]) |
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| 20 | TN, FP, FN, TP = cf.ravel() |
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| 21 | support = TN+FP+FN+TP |
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| 22 | # Sensitivity, recall, or true positive rate |
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| 23 | TPR = TP/(TP+FN) |
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| 24 | # Specificity or true negative rate |
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| 25 | TNR = TN/(TN+FP) |
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| 26 | # Precision or positive predictive value |
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| 27 | PPV = TP/(TP+FP) |
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| 28 | # Negative predictive value |
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| 29 | NPV = TN/(TN+FN) |
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| 30 | # Fall out or false positive rate |
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| 31 | FPR = FP/(FP+TN) |
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| 32 | # False negative rate |
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| 33 | FNR = FN/(TP+FN) |
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| 34 | # F1 score |
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| 35 | f1 = metrics.f1_score(y_truth, y_predictions, average=None) |
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| 36 | # selecting f1score for positive case if present |
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| 37 | f1_pos = f1[1] if len(f1) > 1 else f1[0] |
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| 38 | # Area under precision recall curve |
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| 39 | #auprc = metrics.average_precision_score(y_truth, rhythm[:,1]) |
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| 40 | if out_message: |
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| 41 | print(pd.DataFrame(cf).rename(columns={0: "Predicted Non-AF", 1:" Predicted AF"}, index={0: "True Non-AF", 1:"True AF"})) |
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| 42 | print("Sensitivity/Recall: %0.4f" % TPR) |
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| 43 | print("Specificity: %0.4f" % TNR) |
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| 44 | print("Precision/PPV: %0.4f" % PPV) |
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| 45 | print("Negative predictive value/NPV: %0.2f" % NPV) |
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| 46 | print("False positive rate: %0.4f" % FPR) |
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| 47 | print("False negative rate: %0.4f" % FNR) |
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| 48 | print("F1 score: %0.4f" % f1_pos) |
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| 49 | print('support: ', support) |
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| 50 | print('\n\n\n') |
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| 51 | episode_metrics = [TPR, TNR, PPV, NPV, FPR, FNR, f1_pos, support] |
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| 52 | return episode_metrics |
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| 53 | |||
| 54 | |||
| 55 | def episode_metrics_singletask(model, x, parameters, rhythm, out_message=False): |
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| 56 | """ arg |
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| 57 | |||
| 58 | """ |
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| 59 | # Rhythmn predictions |
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| 60 | predictions_r = model.predict(x) |
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| 61 | # If the estimated prob of AF(column 1) is higher than the estimated prob of non-AF(column 0) |
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| 62 | # than consider the window as an AF window. |
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| 63 | y_predictions = np.argmax(predictions_r, axis=1) |
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| 64 | y_truth = np.argmax(rhythm, axis=1) |
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| 65 | # Confusion_matrix |
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| 66 | cf = confusion_matrix(y_truth, y_predictions, labels=[0, 1]) |
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| 67 | TN, FP, FN, TP = cf.ravel() |
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| 68 | support = TN+FP+FN+TP |
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| 69 | # Sensitivity, recall, or true positive rate |
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| 70 | TPR = TP/(TP+FN) |
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| 71 | # Specificity or true negative rate |
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| 72 | TNR = TN/(TN+FP) |
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| 73 | # Precision or positive predictive value |
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| 74 | PPV = TP/(TP+FP) |
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| 75 | # Negative predictive value |
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| 76 | NPV = TN/(TN+FN) |
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| 77 | # Fall out or false positive rate |
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| 78 | FPR = FP/(FP+TN) |
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| 79 | # False negative rate |
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| 80 | FNR = FN/(TP+FN) |
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| 81 | # F1 score |
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| 82 | f1 = metrics.f1_score(y_truth, y_predictions, average=None) |
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| 83 | # selecting f1score for positive case if present |
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| 84 | f1_pos = f1[1] if len(f1) > 1 else f1[0] |
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| 85 | # Area under precision recall curve |
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| 86 | #auprc = metrics.average_precision_score(y_truth, rhythm[:,1]) |
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| 87 | if out_message: |
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| 88 | print(pd.DataFrame(cf).rename(columns={0: "Predicted Non-AF", 1:" Predicted AF"}, index={0: "True Non-AF", 1:"True AF"})) |
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| 89 | print("Sensitivity/Recall: %0.4f" % TPR) |
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| 90 | print("Specificity: %0.4f" % TNR) |
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| 91 | print("Precision/PPV: %0.4f" % PPV) |
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| 92 | print("Negative predictive value/NPV: %0.2f" % NPV) |
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| 93 | print("False positive rate: %0.4f" % FPR) |
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| 94 | print("False negative rate: %0.4f" % FNR) |
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| 95 | print("F1 score: %0.4f" % f1_pos) |
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| 96 | print('support: ', support) |
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| 97 | print('\n\n\n') |
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| 98 | episode_metrics = [TPR, TNR, PPV, NPV, FPR, FNR, f1_pos, support] |
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| 99 | return episode_metrics |
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| 100 | |||
| 101 | |||
| 102 | def collecting_individual_metrics(model, x, parameters, rhythm, out_message=False): |
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| 103 | """ |
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| 104 | """ |
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| 105 | individual_metrics = {} |
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| 106 | for i in np.unique(parameters['ID']): |
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| 107 | # Sub-selecting individuals |
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| 108 | p_indx = np.where(parameters['ID'] == i)[0] |
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| 109 | x_pID = x[p_indx] |
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| 110 | parameters_pID = parameters.iloc[p_indx] |
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| 111 | rhythm_pID = rhythm[p_indx] |
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| 112 | # Rhythmn predictions |
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| 113 | predictions_qa, predictions_r = model.predict(x_pID) |
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| 114 | # If the estimated prob of AF(column 1) is higher than the estimated prob of non-AF(column 0) |
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| 115 | # than consider the window as an AF window. |
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| 116 | y_predictions = np.argmax(predictions_r, axis=1) |
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| 117 | y_truth = np.argmax(rhythm_pID, axis=1) |
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| 118 | # Confusion_matrix |
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| 119 | cf = confusion_matrix(y_truth, y_predictions, labels=[0, 1]) |
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| 120 | TN, FP, FN, TP = cf.ravel() |
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| 121 | support = TN+FP+FN+TP |
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| 122 | # Sensitivity, recall, or true positive rate |
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| 123 | TPR = TP/(TP+FN) |
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| 124 | # Specificity or true negative rate |
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| 125 | TNR = TN/(TN+FP) |
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| 126 | # Fall out or false positive rate |
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| 127 | FPR = FP/(FP+TN) |
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| 128 | # False negative rate |
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| 129 | FNR = FN/(TP+FN) |
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| 130 | # F1 score |
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| 131 | f1 = metrics.f1_score(y_truth, y_predictions, average=None) |
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| 132 | # selecting f1score for positive case if present |
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| 133 | f1_pos = f1[1] if len(f1) > 1 else f1[0] |
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| 134 | # Area under precision recall curve |
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| 135 | #auprc = metrics.average_precision_score(y_test_truth, rhythm_pID[:,1]) |
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| 136 | if (TP + FN) > 0: |
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| 137 | individual_metrics[i] = [TPR, np.NaN, np.NaN, FNR, f1_pos, support] |
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| 138 | if (FP + TN) > 0: |
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| 139 | individual_metrics[i] = [np.NaN, TNR, FPR, np.NaN, np.NaN, support] |
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| 140 | if out_message: |
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| 141 | print("PATIENT :", i , '\n') |
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| 142 | print(pd.DataFrame(cf).rename(columns={0: "Predicted Non-AF", 1:" Predicted AF"}, index={0: "True Non-AF", 1:"True AF"})) |
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| 143 | if (TP + FN) > 0: |
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| 144 | print("Sensitivity/Recall: %0.4f" % TPR) |
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| 145 | print("False negative rate: %0.4f" % FNR) |
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| 146 | print("F1 score: %0.4f" % f1_pos) |
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| 147 | print('support: ' , support) |
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| 148 | if (FP + TN) > 0: |
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| 149 | print("Specificity: %0.4f" % TNR) |
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| 150 | print("False positive rate: %0.4f" % FPR) |
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| 151 | print('support: ' , support) |
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| 152 | return individual_metrics |
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| 153 | |||
| 154 | def collecting_individual_metrics_singletask(model, x, parameters, rhythm, out_message=False): |
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| 155 | """ |
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| 156 | """ |
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| 157 | individual_metrics = {} |
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| 158 | for i in np.unique(parameters['ID']): |
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| 159 | # Sub-selecting individuals |
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| 160 | p_indx = np.where(parameters['ID'] == i)[0] |
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| 161 | x_pID = x[p_indx] |
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| 162 | parameters_pID = parameters.iloc[p_indx] |
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| 163 | rhythm_pID = rhythm[p_indx] |
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| 164 | # Rhythmn predictions |
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| 165 | predictions_r = model.predict(x_pID) |
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| 166 | # If the estimated prob of AF(column 1) is higher than the estimated prob of non-AF(column 0) |
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| 167 | # than consider the window as an AF window. |
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| 168 | y_predictions = np.argmax(predictions_r, axis=1) |
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| 169 | y_truth = np.argmax(rhythm_pID, axis=1) |
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| 170 | # Confusion_matrix |
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| 171 | cf = confusion_matrix(y_truth, y_predictions, labels=[0, 1]) |
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| 172 | TN, FP, FN, TP = cf.ravel() |
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| 173 | support = TN+FP+FN+TP |
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| 174 | # Sensitivity, recall, or true positive rate |
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| 175 | TPR = TP/(TP+FN) |
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| 176 | # Specificity or true negative rate |
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| 177 | TNR = TN/(TN+FP) |
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| 178 | # Fall out or false positive rate |
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| 179 | FPR = FP/(FP+TN) |
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| 180 | # False negative rate |
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| 181 | FNR = FN/(TP+FN) |
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| 182 | # F1 score |
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| 183 | f1 = metrics.f1_score(y_truth, y_predictions, average=None) |
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| 184 | # selecting f1score for positive case if present |
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| 185 | f1_pos = f1[1] if len(f1) > 1 else f1[0] |
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| 186 | # Area under precision recall curve |
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| 187 | #auprc = metrics.average_precision_score(y_test_truth, rhythm_pID[:,1]) |
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| 188 | if (TP + FN) > 0: |
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| 189 | individual_metrics[i] = [TPR, np.NaN, np.NaN, FNR, f1_pos, support] |
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| 190 | if (FP + TN) > 0: |
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| 191 | individual_metrics[i] = [np.NaN, TNR, FPR, np.NaN, np.NaN, support] |
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| 192 | if out_message: |
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| 193 | print("PATIENT :", i , '\n') |
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| 194 | print(pd.DataFrame(cf).rename(columns={0: "Predicted Non-AF", 1:" Predicted AF"}, index={0: "True Non-AF", 1:"True AF"})) |
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| 195 | if (TP + FN) > 0: |
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| 196 | print("Sensitivity/Recall: %0.4f" % TPR) |
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| 197 | print("False negative rate: %0.4f" % FNR) |
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| 198 | print("F1 score: %0.4f" % f1_pos) |
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| 199 | print('support: ' , support) |
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| 200 | if (FP + TN) > 0: |
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| 201 | print("Specificity: %0.4f" % TNR) |
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| 202 | print("False positive rate: %0.4f" % FPR) |
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| 203 | print('support: ' , support) |
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| 204 | return individual_metrics |