Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/node_classification/utils.py @ 0:4f3585e2f14b draft default tip
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author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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1 def _propagate(P, F, B): |
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2 """Propagate labels by one step |
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3 |
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4 Parameters |
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5 ---------- |
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6 P : scipy sparse matrix, shape = [n_samples, n_samples] |
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7 Propagation matrix |
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8 F : numpy array, shape = [n_samples, n_classes] |
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9 Label matrix |
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10 B : numpy array, shape = [n_samples, n_classes] |
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11 Base matrix |
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12 |
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13 Returns |
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14 ---------- |
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15 F_new : array, shape = [n_samples, n_classes] |
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16 Label matrix |
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17 """ |
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18 F_new = P.dot(F) + B |
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19 return F_new |
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20 |
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21 |
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22 def _get_label_info(G, label_name): |
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23 """Get and return information of labels from the input graph |
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24 |
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25 Parameters |
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26 ---------- |
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27 G : Network X graph |
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28 label_name : string |
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29 Name of the target label |
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30 |
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31 Returns |
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32 ---------- |
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33 labels : numpy array, shape = [n_labeled_samples, 2] |
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34 Array of pairs of labeled node ID and label ID |
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35 label_dict : numpy array, shape = [n_classes] |
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36 Array of labels |
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37 i-th element contains the label corresponding label ID `i` |
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38 """ |
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39 import numpy as np |
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40 |
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41 labels = [] |
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42 label_to_id = {} |
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43 lid = 0 |
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44 for i, n in enumerate(G.nodes(data=True)): |
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45 if label_name in n[1]: |
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46 label = n[1][label_name] |
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47 if label not in label_to_id: |
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48 label_to_id[label] = lid |
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49 lid += 1 |
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50 labels.append([i, label_to_id[label]]) |
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51 labels = np.array(labels) |
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52 label_dict = np.array( |
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53 [label for label, _ in sorted(label_to_id.items(), key=lambda x: x[1])] |
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54 ) |
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55 return (labels, label_dict) |
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56 |
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57 |
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58 def _init_label_matrix(n_samples, n_classes): |
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59 """Create and return zero matrix |
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60 |
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61 Parameters |
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62 ---------- |
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63 n_samples : integer |
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64 The number of nodes (samples) on the input graph |
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65 n_classes : integer |
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66 The number of classes (distinct labels) on the input graph |
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67 |
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68 Returns |
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69 ---------- |
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70 F : numpy array, shape = [n_samples, n_classes] |
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71 Label matrix |
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72 """ |
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73 import numpy as np |
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74 |
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75 F = np.zeros((n_samples, n_classes)) |
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76 return F |
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77 |
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78 |
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79 def _predict(F, label_dict): |
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80 """Predict labels by learnt label matrix |
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81 |
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82 Parameters |
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83 ---------- |
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84 F : numpy array, shape = [n_samples, n_classes] |
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85 Learnt (resulting) label matrix |
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86 label_dict : numpy array, shape = [n_classes] |
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87 Array of labels |
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88 i-th element contains the label corresponding label ID `i` |
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89 |
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90 Returns |
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91 ---------- |
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92 predicted : numpy array, shape = [n_samples] |
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93 Array of predicted labels |
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94 """ |
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95 import numpy as np |
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96 |
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97 predicted_label_ids = np.argmax(F, axis=1) |
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98 predicted = label_dict[predicted_label_ids].tolist() |
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99 return predicted |