Merge branch 'kangmin' into premaster

server/src/data_processing/main.py

@@ -6,31 +6,41 @@
 """
 
 import datetime
-from server.src.data_processing.model import modeling
-import sys
+import os
 import psycopg2
+from psycopg2.extras import Json
+import sys
 
 from preprocessing import preprocess
+from model import modeling
+
 
 # DB 환경 변수
-dbconfig = {"host": sys.argv[1], "user": sys.argv[2],
-            "password": sys.argv[3], "database": sys.argv[4]}
+dbconfig = {"host": sys.argv[1], "port": sys.argv[2], "user": sys.argv[3],
+            "password": sys.argv[4], "database": sys.argv[5]}
+
+data_dir = os.getcwd() + "/src/data_processing/temp.csv"
+model_dir = os.getcwd() + "/src/data_processing/model.h5"
 
-today = datetime.datetime.today()
-year = str(today.year)
-month = str(today.month) if today.month >= 10 else '0'+str(today.month)
-day = str(today.day) if today.day >= 10 else '0'+str(today.day)
-collected_at = year + "-" + month + "-" + day
+
+def makeDateForm():
+    today = datetime.datetime.today()
+    year = str(today.year)
+    month = str(today.month) if today.month >= 10 else '0'+str(today.month)
+    day = str(today.day) if today.day >= 10 else '0'+str(today.day)
+    collected_at = year + "-" + month + "-" + day
+
+    return collected_at
 
 
 # DB 연결
 connection = psycopg2.connect(
-    database=dbconfig["database"], user=dbconfig["user"])
+    dbname=dbconfig["database"], user=dbconfig["user"], password=dbconfig["password"], host=dbconfig["host"], port=dbconfig["port"])
 
 # DB에 대한 동작을 위한 cursor 생성
 cursor = connection.cursor()
 
-cursor.execute("SELECT email, loc_code, using_aircon FROM Users")
+cursor.execute("SELECT email, loc_code, using_aircon FROM \"Users\"")
 users = cursor.fetchall()
 
 for user in users:
@@ -38,14 +48,35 @@ for user in users:
     host = {"email": user[0], "loc_code": user[1], "using_aircon": user[2]}
 
     # 데이터 전처리
-    standard_df, mean_df , std_df = preprocess(cursor, host)
+    standard_df, mean_df, std_df = preprocess(cursor, host)
 
     # 데이터 분석
     modeling(standard_df)
 
     # 데이터 분석 결과 저장
-    # cursor.execute("INSERT INTO \"Data_Processings\" (host,collected_at,params) VALUES (%s,%s,%s)",
-    #                (host["email"], collected_at, params))
+
+    collected_at = makeDateForm()
+
+    model_file = open(model_dir, 'rb')
+    model_file_data = model_file.read()
+
+    params = {"mean": mean_df.to_json(), "std": std_df.to_json()}
+
+    cursor.execute("INSERT INTO \"Data_Processings\" (host,collected_at,model_file,params) VALUES (%s,%s,%s,%s)",
+                   (host["email"],
+                    collected_at,
+                    model_file_data,
+                    Json(params),))
+
+    connection.commit()
+    model_file.close()
+
+    if os.path.isfile(data_dir):
+        os.remove(data_dir)
+
+    if os.path.isfile(model_dir):
+        os.remove(model_dir)
+
 
 # Cursor와 Connection 종료
 cursor.close()

server/src/data_processing/model.py

-from preprocessing import standard_df
 import numpy as np
+import os
 import tensorflow as tf
 
+
 def modeling(standard_df):
 
-    n= len(standard_df)
+    n = len(standard_df)
     test_size = int(0.3 * n)
     train = standard_df[:-test_size]
     test = standard_df[-test_size:]
 
-    def make_dataset(data, label, window_size = 24):
+    def make_dataset(data, label, window_size=24):
         feature_list = []
         label_list = []
         for i in range(len(data) - window_size):
             feature_list.append(np.array(data.iloc[i:i+window_size]))
-            label_list.append(np.array(label.iloc[i + window_size ]))
+            label_list.append(np.array(label.iloc[i + window_size]))
         return np.array(feature_list), np.array(label_list)
 
-    feature_cols = ['temp_out','humi_out','press','wind_speed','Day sin','Day cos','Year sin','Year cos']
+    feature_cols = ['temp_out', 'humi_out', 'press',
+                    'wind_speed', 'Day sin', 'Day cos', 'Year sin', 'Year cos']
     label_cols = ['temp_out']
 
     train_feature = train[feature_cols]
@@ -26,23 +28,23 @@ def modeling(standard_df):
     test_feature = test[feature_cols]
     test_label = test[label_cols]
 
-    train_feature, train_label = make_dataset(train_feature, train_label,window_size=6)
-    test_feature, test_label = make_dataset(test_feature, test_label,window_size=6)
+    train_feature, train_label = make_dataset(
+        train_feature, train_label, window_size=6)
+    test_feature, test_label = make_dataset(
+        test_feature, test_label, window_size=6)
 
     model = tf.keras.Sequential([
         tf.keras.layers.LSTM(16,
                              return_sequences=False,
-                            input_shape = (6,7)),
+                             input_shape=(6, 7)),
         tf.keras.layers.Dense(1)
-                              
     ])
 
-    model.compile(loss = 'mse',optimizer = 'adam')
-    model.fit(train_feature, train_label, 
-                        epochs =50, batch_size = 1000)
+    model.compile(loss='mse', optimizer='adam')
+    # model.fit(train_feature, train_label, epochs=50, batch_size=1000)
 
-    model.save('/src/dataprocessing/model.h5')
+    model.save(os.getcwd() + '/src/data_processing/model.h5')
 
 
-#사용할때
+# 사용할때
 # new = tf.keras.models.load_model('/src/dataprocessing/model.h5')

server/src/data_processing/preprocessing.py

@@ -9,6 +9,8 @@
 import pandas as pd
 import datetime
 import numpy as np
+import os
+
 
 def preprocess(cursor, host):
     """
@@ -18,33 +20,55 @@ def preprocess(cursor, host):
         - host : 사용자 정보.
 
         사용자 정보를 바탕으로 외부 날씨와 내부 날씨를 검색해 CSV 파일로 작성합니다.
+        CSV 파일 생성 후 pandas를 이용해 dataframe으로 만든 뒤, 정규화를 진행합니다.
     """
 
+    # # 데이터 수집기 오류로 인해 보류
+    # cursor.execute(
+    #     "SELECT t2.collected_at as \"date\", temp_out, humi_out, press, wind_speed, temp_in, humi_in, lights FROM"
+    #     + " (SELECT collected_at, temp as temp_out, humi as humi_out,press, wind_speed FROM Weather_Outs WHERE loc_code = %s) t1"
+    #     + " JOIN "
+    #     + " (SELECT collected_at, temp as temp_in, humi as humi_in, lights FROM Weather_Ins WHERE host = %s) t2"
+    #     + " ON t1.collected_at = t2.collected_at", (host["loc_code"], host["email"],))
+
+    # results = cursor.fetchall()
+
+    # file = open("/src/dataprocessing/temp.csv", 'w')
+
+    # # header
+    # file.write("date,temp_out,humi_out,press,wind_speed,temp_in,humi_in,lights\n")
+
+    # for result in results:
+    #     file.write("{0},{1},{2},{3},{4},{5},{6},{7}\n".format(
+    #         result[0], result[1], result[2], result[3], result[4], result[5], result[6], result[7]))
+
+    # file.close()
+
+    # 사용자의 거주 지역의 실외 데이터 검색
     cursor.execute(
-        "SELECT t2.collected_at as \"date\", temp_out, humi_out, press, wind_speed, temp_in, humi_in, lights FROM"
-        + " (SELECT collected_at, temp as temp_out, humi as humi_out,press, wind_speed FROM Weather_Outs WHERE loc_code = %s) t1"
-        + " JOIN "
-        + " (SELECT collected_at, temp as temp_in, humi as humi_in, lights FROM Weather_Ins WHERE host = %s) t2"
-        + " ON t1.collected_at = t2.collected_at", (host["loc_code"], host["email"],))
+        "SELECT collected_at as \"date\", temp as temp_out, humi as humi_out, press, wind_speed "
+        + "From \"Weather_Outs\" "
+        + "WHERE loc_code = %s", (host["loc_code"],)
+    )
+
     results = cursor.fetchall()
 
-    file = open("/src/dataprocessing/temp.csv", 'w')
+    file = open(os.getcwd() + "/src/data_processing/temp.csv", 'w')
 
     # header
-    file.write("date,temp_out,humi_out,press,wind_speed,temp_in,humi_in,lights\n")
+    file.write("date,temp_out,humi_out,press,wind_speed\n")
 
     for result in results:
-        file.write("{0},{1},{2},{3},{4},{5},{6},{7}\n".format(
-            result[0], result[1], result[2], result[3], result[4], result[5], result[6], result[7]))
+        file.write("{0},{1},{2},{3},{4}\n".format(
+            result[0], result[1], result[2], result[3], result[4]))
 
     file.close()
 
-
-    df = pd.read_csv("/src/dataprocessing/temp.csv")
+    df = pd.read_csv(os.getcwd() + "/src/data_processing/temp.csv")
     date_time = pd.to_datetime(df['date'], format='%Y-%m-%d %H:%M')
     timestamp_s = date_time.map(datetime.datetime.timestamp)
 
-    df = df[['temp_out','humi_out','press','wind_speed']]
+    df = df[['temp_out', 'humi_out', 'press', 'wind_speed']]
     day = 24*60*60
     year = (365.2425)*day
 
@@ -56,9 +80,9 @@ def preprocess(cursor, host):
     def standard(dataframe):
         mean = dataframe.mean()
         std = dataframe.std()
-        zscore = ( dataframe - mean ) / std
+        zscore = (dataframe - mean) / std
         return zscore, mean, std
 
-    standard_df, mean_df , std_df = standard(df)
+    standard_df, mean_df, std_df = standard(df)
 
-    return standard_df, mean_df , std_df
\ No newline at end of file
+    return standard_df, mean_df, std_df

server/src/models/data_processing.js

@@ -18,6 +18,9 @@ export class Data_Processing extends Model {
           primaryKey: true,
           defaultValue: Date.now(),
         },
+        model_file: {
+          type: DataTypes.BLOB,
+        },
         params: {
           type: DataTypes.JSON,
         },

server/src/schedules.js

@@ -26,6 +26,7 @@ const dataProcessingJob = schedule.scheduleJob(rule_dataProcessing, () => {
   const pyprocess = spawn("python", [
     DATA_PROCESSING_DIR,
     envs.db.host,
+    envs.db.port,
     envs.db.user,
     envs.db.password,
     envs.db.database,
@@ -57,11 +58,22 @@ const rules_weather_out_store = {
 };
 
 rules_weather_out_store["00m"].minute = 0;
+rules_weather_out_store["00m"].second = 0;
+
 rules_weather_out_store["10m"].minute = 10;
+rules_weather_out_store["10m"].second = 0;
+
 rules_weather_out_store["20m"].minute = 20;
+rules_weather_out_store["20m"].second = 0;
+
 rules_weather_out_store["30m"].minute = 30;
+rules_weather_out_store["30m"].second = 0;
+
 rules_weather_out_store["40m"].minute = 40;
+rules_weather_out_store["40m"].second = 0;
+
 rules_weather_out_store["50m"].minute = 50;
+rules_weather_out_store["50m"].second = 0;
 
 // 임의의 사용자 데이터 등록
 const coordinates = [