1 赛题
全球人工智能技术创新大赛【赛道一】-官方赛道地址
2021年赛道一：医学影像报告异常检测
2 数据分析
2.1 读取数据
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif字体）
plt.rcParams['axes.unicode_minus'] = False # 步骤二（解决坐标轴负数的负号显示问题）
train_data_file = './raw_data/track1_round1_train_20210222.csv'
test_data_file = './raw_data/track1_round1_testA_20210222.csv'
picture_file = './documentation/picture/'
2.2 统计词数
with open(train_data_file, 'r') as f:
lines = f.readlines()
train_texts, train_labels = [], []
for id, line in enumerate(lines):
line = line.strip().replace('|', '').split(',')
text = line[1].strip().split(' ')
text = [int(word) for word in text]
train_texts.append(text)
train_labels.append(line[2])
with open(test_data_file, 'r') as f:
lines = f.readlines()
test_texts = []
for id, line in enumerate(lines):
line = line.strip().replace('|', '').split(',')
text = line[1].strip().split(' ')
text = [int(word) for word in text]
test_texts.append(text)
（1）训练集的词数
vocab_size = max(max(text) for text in train_texts) - min(min(text) for text in train_texts) + 1
print("训练集的vpcab_size: {}".format(vocab_size))
训练集的vpcab_size: 858
（2）测试集的词数
vocab_size = max(max(text) for text in train_texts) - min(min(text) for text in test_texts) + 1
print("测试集的vocab_size: {}".format(vocab_size))
测试集的vpcab_size: 858
测试集中所用字和训练集相同
2.3 统计词频
word_count = np.zeros(vocab_size, dtype='int32')
for text in train_texts:
for word in text:
word_count[word] += 1
sorted_index = word_count.argsort()
（1）统计词频最低的top20
# 最小的词频
for i in range(20):
word = sorted_index[i]
print(str(word) + "|" + str(word_count[word]))
236|18
466|18
246|19
82|19
29|19
263|20
451|21
849|22
804|22
384|22
684|23
360|23
714|23
210|23
275|24
157|24
678|24
186|24
696|24
467|24
（2）统计词频最高的top20
# 最大的词频
for i in range(-1,-21,-1):
word = sorted_index[i]
print(str(word) + "|" + str(word_count[word]))
693|5477
328|4009
698|3592
380|3198
177|2844
415|2785
381|2699
809|2111
266|1501
623|1467
14|1434
852|1387
256|1344
842|1267
832|1245
172|1243
399|1155
204|1142
382|1135
582|1130
2.4 统计句子长度
df_train = pd.DataFrame()
df_train['train_text'] = train_texts
df_train['train_label'] = train_label
df_train['text_length'] = df.train_text.apply(len)
df_train['disease_num'] = df.train_label.apply(lambda x:len(x.strip().split()))
df_test = pd.DataFrame()
df_test['test_text'] = test_texts
df_test['text_length'] = df_test.test_text.apply(len)
text_length = [len(text) for text in train_texts]
_ = plt.hist(text_length, bins=50, label='句长分布')
_ = plt.legend()
（1）训练集句子长度统计
df_train.text_length.describe()
count 10000.000000
mean 41.564800
std 18.349127
min 4.000000
25% 29.000000
50% 38.000000
75% 52.000000
max 104.000000
Name: text_length, dtype: float64
（2）测试集句子长度统计
df_test.text_length.describe()
count 3000.000000
mean 40.409667
std 17.695561
min 4.000000
25% 28.000000
50% 37.000000
75% 50.000000
max 102.000000
Name: text_length, dtype: float64
2.5 label类别分布
label_counts = np.zeros(17, dtype='int32')
for labels in train_labels:
labels = labels.strip().split(' ')
for label in labels:
if label != '':
label_counts[int(label)]+=1
_ = plt.bar(range(17), label_counts, label='各标签数量统计')
for x, count in enumerate(label_counts):
plt.text(x-0.5, count+20, "{}".format(count))
_ = plt.xlabel("label")
_ = plt.xticks(range(0,17))
_ = plt.ylabel("频数")
_ = plt.title("各个标签出现频数")
plt.savefig(picture_file+'label_freq.jpg')
2.6 正负样本分布
total_num = df_train.shape[0]
for id, count in enumerate(label_counts):
print("{}|{}|{}|{:.2f}%".format(id, count, total_num-count, 100*count/total_num))
0|1107|8893|11.07%
1|1150|8850|11.50%
2|930|9070|9.30%
3|445|9555|4.45%
4|1231|8769|12.31%
5|386|9614|3.86%
6|209|9791|2.09%
7|1080|8920|10.80%
8|1077|8923|10.77%
9|845|9155|8.45%
10|377|9623|3.77%
11|1033|8967|10.33%
12|417|9583|4.17%
13|269|9731|2.69%
14|549|9451|5.49%
15|1846|8154|18.46%
16|777|9223|7.77%
2.7 缺失值
（１）text
无
（2）label
有，暂未统计
2.8 统计句子最后一个字符词频
