老人心脏病诊断

2 老年人的远程智能诊疗/老人心脏病诊断-张鹏涛/README.md

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+## 项目介绍
+
+通过aws的sagemaker建立模型，模型的应用场景是预测老人是否患有心脏病，同时使用aws进行模型的部署
+
+## 团队介绍
+
+张鹏涛，来自明略科技，从事nlp以及推荐领域，对机器学习以及深度学习的应用，有着丰富的经验，联系方式： [email protected]
+
+## 使用到的aws技术
+
+1， sagemaker    
+2， aws的存储功能
+

2 老年人的远程智能诊疗/老人心脏病诊断-张鹏涛/data/data_raw.csv

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+44,1,2,130,219,0,2,188,0,0,1,0,3,1
+44,1,4,112,290,0,2,153,0,0,1,1,3,2
+51,1,3,110,175,0,0,123,0,0.6,1,0,3,1
+59,1,3,150,212,1,0,157,0,1.6,1,0,3,1
+71,0,2,160,302,0,0,162,0,0.4,1,2,3,1
+61,1,3,150,243,1,0,137,1,1,2,0,3,1
+55,1,4,132,353,0,0,132,1,1.2,2,1,7,2
+64,1,3,140,335,0,0,158,0,0,1,0,3,2
+43,1,4,150,247,0,0,171,0,1.5,1,0,3,1
+58,0,3,120,340,0,0,172,0,0,1,0,3,1
+60,1,4,130,206,0,2,132,1,2.4,2,2,7,2
+58,1,2,120,284,0,2,160,0,1.8,2,0,3,2
+49,1,2,130,266,0,0,171,0,0.6,1,0,3,1
+48,1,2,110,229,0,0,168,0,1,3,0,7,2
+52,1,3,172,199,1,0,162,0,0.5,1,0,7,1
+44,1,2,120,263,0,0,173,0,0,1,0,7,1
+56,0,2,140,294,0,2,153,0,1.3,2,0,3,1
+57,1,4,140,192,0,0,148,0,0.4,2,0,6,1
+67,1,4,160,286,0,2,108,1,1.5,2,3,3,2

2 老年人的远程智能诊疗/老人心脏病诊断-张鹏涛/src/main.py

@@ -0,0 +1,74 @@
+from sklearn.model_selection import train_test_split
+import sagemaker
+from sagemaker import get_execution_role
+from sagemaker.amazon.amazon_estimator import get_image_uri
+from sagemaker.predictor import csv_serializer
+from utils import read_data
+from sklearn.metrics import accuracy_score
+import os
+import pandas as pd
+import sys
+sys.path.append("./")
+
+data_file = "../data/data_raw.csv"
+data_columns = ["ATTR" + str(i) for i in range(1, 14)]
+data_columns.append("Label")
+
+X, Y = read_data(data_file=data_file, data_columns=data_columns)
+
+X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.1, random_state=42)
+
+pd.concat([X_train, y_train], axis=1).to_csv("../data/train.csv", index=False)
+pd.concat([X_test, y_test], axis=1).to_csv("../data/test.csv", index=False)
+
+# 将原始的数据以及处理好的数据集存储到s3上面
+session = sagemaker.Session()
+data_dir = "../data/"
+prefix = 'sentiment-web-app'
+
+test_location = session.upload_data(os.path.join(data_dir, 'test.csv'), key_prefix=prefix)
+train_location = session.upload_data(os.path.join(data_dir, 'train.csv'), key_prefix=prefix)
+
+# 使用sagemaker来进行模型的训练
+role = get_execution_role()
+
+container = get_image_uri(session.boto_region_name, 'xgboost')
+
+xgb = sagemaker.estimator.Estimator(container,
+                                    role,
+                                    train_instance_count=1,
+                                    train_instance_type='ml.m4.xlarge',
+                                    output_path='s3://{}/{}/output'.format(session.default_bucket(), prefix),
+                                    sagemaker_session=session)
+
+# 指定xgb需要的参数
+xgb.set_hyperparameters(max_depth=5,
+                        eta=0.2,
+                        gamma=4,
+                        min_child_weight=6,
+                        subsample=0.8,
+                        silent=0,
+                        objective='binary:logistic',
+                        early_stopping_rounds=10,
+                        num_round=500)
+# 从s3中读取数据
+s3_input_train = sagemaker.s3_input(s3_data=train_location, content_type='csv')
+s3_input_test = sagemaker.s3_input(s3_data=test_location, content_type='csv')
+
+# 模型训练
+xgb.fit({'train': s3_input_train, 'test': s3_input_test})
+
+# 模型测试
+xgb_transformer = xgb.transformer(instance_count=1, instance_type='ml.m4.xlarge')
+
+xgb_transformer.transform(test_location, content_type='text/csv', split_type='Line')
+
+xgb_transformer.wait()
+
+predictions = pd.read_csv(os.path.join(data_dir, 'test.csv.out'), header=None)
+predictions = [round(num) for num in predictions.squeeze().values]
+
+accuracy_score(y_test, predictions)
+
+# 模型的部署
+xgb_predictor = xgb.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')

2 老年人的远程智能诊疗/老人心脏病诊断-张鹏涛/src/utils.py

@@ -0,0 +1,19 @@
+import pandas as pd
+import numpy as np
+
+
+def read_data(data_file, data_columns=None):
+    """
+    :param data_file:  要读出的数据
+    :param data_columns: 要读取的数据特征名称
+    :return:  经过处理后的特征值以及标签值
+    """
+    data = pd.read_csv(data_file)
+    data.columns = data_columns
+
+    label = data.iloc[:, -1]
+    feat = data.iloc[:, :-1]
+    # 对数据进行缺失值的填充
+    feat = feat.fillna(0)
+
+    return feat, label