119 lines
4.2 KiB
Python
119 lines
4.2 KiB
Python
import json
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import numpy as np
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import matplotlib.pyplot as plt
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import numpy as np
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import random
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def showHist(same, cross):
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Same = np.array(same)
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Cross = np.array(cross)
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fig, axs = plt.subplots(2, 1)
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axs[0].hist(Same, bins=50, edgecolor='black')
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axs[0].set_xlim([-0.1, 1])
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axs[0].set_title('Same Barcode')
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axs[1].hist(Cross, bins=50, edgecolor='black')
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axs[1].set_xlim([-0.1, 1])
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axs[1].set_title('Cross Barcode')
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# plt.savefig('plot.png')
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plt.show()
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def showgrid(recall, recall_TN, PrecisePos, PreciseNeg, Correct):
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x = np.linspace(start=0, stop=1.0, num=50, endpoint=True).tolist()
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plt.figure(figsize=(10, 6))
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plt.plot(x, recall, color='red', label='recall:TP/TPFN')
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plt.plot(x, recall_TN, color='black', label='recall_TN:TN/TNFP')
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plt.plot(x, PrecisePos, color='blue', label='PrecisePos:TP/TPFN')
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plt.plot(x, PreciseNeg, color='green', label='PreciseNeg:TN/TNFP')
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plt.plot(x, Correct, color='m', label='Correct：(TN+TP)/(TPFN+TNFP)')
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plt.legend()
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plt.xlabel('threshold')
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# plt.ylabel('Similarity')
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plt.grid(True, linestyle='--', alpha=0.5)
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plt.savefig('grid.png')
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plt.show()
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plt.close()
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def compute_accuracy_recall(score, labels):
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th = 0.1
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squence = np.linspace(-1, 1, num=50)
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recall, PrecisePos, PreciseNeg, recall_TN, Correct = [], [], [], [], []
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Same = score[:len(score) // 2]
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Cross = score[len(score) // 2:]
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for th in squence:
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t_score = (score > th)
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t_labels = (labels == 1)
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TP = np.sum(np.logical_and(t_score, t_labels))
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FN = np.sum(np.logical_and(np.logical_not(t_score), t_labels))
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f_score = (score < th)
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f_labels = (labels == 0)
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TN = np.sum(np.logical_and(f_score, f_labels))
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FP = np.sum(np.logical_and(np.logical_not(f_score), f_labels))
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print("Threshold:{} TP:{},FP:{},TN:{},FN:{}".format(th, TP, FP, TN, FN))
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PrecisePos.append(0 if TP / (TP + FP) == 'nan' else TP / (TP + FP))
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PreciseNeg.append(0 if TN == 0 else TN / (TN + FN))
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recall.append(0 if TP == 0 else TP / (TP + FN))
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recall_TN.append(0 if TN == 0 else TN / (TN + FP))
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Correct.append(0 if TP == 0 else (TP + TN) / (TP + FP + TN + FN))
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showHist(Same, Cross)
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showgrid(recall, recall_TN, PrecisePos, PreciseNeg, Correct)
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def get_similarity(features1, features2, n, m):
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features1 = np.array(features1)
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features2 = np.array(features2)
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all_similarity = []
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for feature1 in features1:
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for feature2 in features2:
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similarity = np.dot(feature1, feature2) / (np.linalg.norm(feature1) * np.linalg.norm(feature2))
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all_similarity.append(similarity)
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test_similarity = np.array(all_similarity)
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np_all_array = np.array(all_similarity).reshape(len(features1), len(features2))
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if n == 5 and m == 5:
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print(all_similarity)
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return np.mean(np_all_array), all_similarity
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# return sum(all_similarity)/len(all_similarity), all_similarity
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# return max(all_similarity), all_similarity
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def deal_similarity(dicts):
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all_similarity = []
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similarity = []
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same_barcode, diff_barcode = [], []
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for n, (key1, value1) in enumerate(dicts.items()):
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print('key1 >> {}'.format(key1))
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for m, (key2, value2) in enumerate(dicts.items()):
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print('key1 >> {} key2 >> {} peidui {}{}'.format(key1, key2, n, m))
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max_similarity, some_similarity = get_similarity(value1, value2, n, m)
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similarity.append(max_similarity)
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if key1 == key2:
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same_barcode += some_similarity
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else:
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diff_barcode += some_similarity
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all_similarity.append(similarity)
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similarity = []
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all_similarity = np.array(all_similarity)
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random.shuffle(diff_barcode)
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same_list = [1] * len(same_barcode)
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diff_list = [0] * len(same_barcode)
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all_list = same_list + diff_list
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all_score = same_barcode + diff_barcode[:len(same_barcode)]
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compute_accuracy_recall(np.array(all_score), np.array(all_list))
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print(all_similarity.shape)
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with open('../search_library/data_zhanting.json', 'r') as file:
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data = json.load(file)
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dicts = {}
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for dict in data['total']:
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key = dict['key']
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value = dict['value']
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dicts[key] = value
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deal_similarity(dicts)
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