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import operator |
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import pickle |
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import numpy as np |
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import pandas as pd |
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s2_label_dict = { |
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'0': 0, |
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'1': 1, |
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'2': 2, |
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'3': 3, |
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'4': 4, |
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'5': 5, |
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'6': 6, |
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'7': 7, |
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'8': 8, |
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'9': 9, |
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'a': 10, |
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'b': 11, |
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'c': 12, |
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'd': 13, |
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'e': 14, |
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'f': 15 |
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} |
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s2_label_decode_dict = {v: k for k, v in s2_label_dict.items()} |
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s2_weights = [0.025, 0.025, 0.025, |
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0.025, 0.025, 0.025, |
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0.025, 0.025, 0.025, |
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0.0325, 0.0325, 0.0325, |
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0.035, 0.035, 0.035, |
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0.0375, 0.0375, 0.0375, |
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0.04, 0.04, 0.04, |
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0.0425, 0.0425, 0.0425, |
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0.045, 0.045, 0.0475, |
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0.025, 0.025, 0.025, |
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0.0, 0.0, 0.0] |
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def generate_s2_index(s2_label): |
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result = [0 for _ in range(33)] |
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for i, char_ in enumerate(s2_label): |
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result[i] = s2_label_dict[char_] |
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return result |
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def decode_s2(x): |
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result = [] |
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for i in x: |
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result.append(s2_label_decode_dict[i]) |
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return ''.join(result) |
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def sample_csv2pkl(csv_path, pkl_path): |
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df = pd.read_csv(csv_path, sep='^', encoding="utf_8_sig") |
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data = [] |
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for index, row in df.iterrows(): |
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node_s = [] |
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label = [] |
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node1 = [row['node_t1'], row['poi_address_mask1'], row['node1'], generate_s2_index(row['node1'])] |
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node2 = [row['node_t2'], row['poi_address_mask2'], row['node2'], generate_s2_index(row['node2'])] |
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node3 = [row['node_t3'], row['poi_address_mask3'], row['node3'], generate_s2_index(row['node3'])] |
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node4 = [row['node_t4'], row['poi_address_mask4'], row['node4'], generate_s2_index(row['node4'])] |
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node5 = [row['node_t5'], row['poi_address_mask5'], row['node5'], generate_s2_index(row['node5'])] |
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node6 = [row['node_t6'], row['poi_address_mask6'], row['node6'], generate_s2_index(row['node6'])] |
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label.extend(node1[3]) |
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label.extend(node2[3]) |
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label.extend(node3[3]) |
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label.extend(node4[3]) |
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label.extend(node5[3]) |
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label.extend(node6[3]) |
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node1.append(label) |
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node2.append(label) |
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node3.append(label) |
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node4.append(label) |
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node5.append(label) |
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node6.append(label) |
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node_s.append(node1) |
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node_s.append(node2) |
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node_s.append(node3) |
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node_s.append(node4) |
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node_s.append(node5) |
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node_s.append(node6) |
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data.append(node_s) |
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with open(pkl_path,'wb') as f: |
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pickle.dump(data,f) |
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def calculate_multi_s2_acc(predicted_s2, y): |
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acc_cnt = np.array([0, 0, 0, 0, 0, 0, 0]) |
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y = y.view(-1, 33).tolist() |
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predicted = predicted_s2.view(-1, 33).tolist() |
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for index, s2 in enumerate(y): |
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for c, i in enumerate(range(12, 33, 3)): |
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y_l10 = y[index][12:i+3] |
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p_l10 = predicted[index][12:i+3] |
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if operator.eq(y_l10, p_l10): |
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acc_cnt[c] += 1 |
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return acc_cnt |
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def calculate_multi_s2_acc_batch(predicted_s2, y, sequence_len = 6): |
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acc_cnt = np.array([0, 0, 0, 0, 0, 0, 0]) |
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y = y.view(-1, sequence_len, 33).tolist() |
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predicted = predicted_s2.view(-1, sequence_len, 33).tolist() |
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batch_size = len(y) |
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for batch_i in range(batch_size): |
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for index, s2 in enumerate(y[batch_i]): |
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for c, i in enumerate(range(12, 33, 3)): |
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y_l10 = y[batch_i][index][12:i+3] |
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p_l10 = predicted[batch_i][index][12:i+3] |
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if operator.eq(y_l10, p_l10): |
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acc_cnt[c] += 1 |
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return acc_cnt |
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def calculate_alias_acc(predicted, y): |
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tp, fp, fn, tn = 0, 0, 0, 0 |
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acc = 0 |
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for index, label in enumerate(y): |
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if int(label) == int(predicted[index]): |
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acc += 1 |
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if int(label) == 1: |
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fn += 1 |
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if int(predicted[index]) == 1: |
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tp += 1 |
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if fn == 0: |
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precision = 0 |
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else: |
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precision = tp / fn * 100 |
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return tp, fn, acc |
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def calculate_aoi_acc(predicted, y): |
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tp, fp, fn, tn = 0, 0, 0, 0 |
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acc = 0 |
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for index, label in enumerate(y): |
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if int(label) == int(predicted[index]): |
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acc += 1 |
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if int(label) == 0: |
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fn += 1 |
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if int(predicted[index]) == 0: |
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tp += 1 |
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if fn == 0: |
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precision = 0 |
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else: |
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precision = tp / fn * 100 |
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return tp, fn, acc |
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