Merge branch 'kangmin' into premaster

server/src/controllers/dataController.js

@@ -135,8 +135,21 @@ export const getUserWeatherData = async (req, res) => {
       logging: false,
     });
 
-    const weather_out = result_weather.slice(-9);
-    const weather_predict = result_weather.slice(-3);
+    let temp_weather = result_weather.slice(-6);
+    let weather = [];
+
+    temp_weather.map((data) => {
+      weather.push({
+        loc_code: data["loc_code"],
+        collected_at: new Date(
+          new Date(data["collected_at"]).getTime() + 9 * 60 * 60 * 1000
+        ),
+        temp: data["temp"],
+        humi: data["humi"],
+        press: data["press"],
+        wind_speed: data["wind_speed"],
+      });
+    });
 
     const pyprocess = spawn("python", [
       envs.inner_dir.data_processing_prediction,
@@ -144,28 +157,37 @@ export const getUserWeatherData = async (req, res) => {
     ]);
 
     pyprocess.stdout.on("data", (data) => {
+      let weather_predict = [];
       const str_result = data.toString();
-      console.log(data.toString()); // Buffer to String.
 
-      const temp_predict = str_result.split(" ");
+      let temp_predict = str_result.trim();
+      temp_predict = temp_predict.replace("]]", "");
+      temp_predict = temp_predict.replace("[[", "");
+      temp_predict = temp_predict.split(" ");
 
-      res.json({
-        msg: resForm.msg.ok,
-        contents: { weather_in: weather_out, weather_predict: temp_predict },
+      let date_10m = new Date(weather[weather.length - 1]["collected_at"]);
+      date_10m.setMinutes(date_10m.getMinutes() + 10);
+
+      let date_20m = new Date(weather[weather.length - 1]["collected_at"]);
+      date_20m.setMinutes(date_20m.getMinutes() + 20);
+
+      let date_30m = new Date(weather[weather.length - 1]["collected_at"]);
+      date_30m.setMinutes(date_30m.getMinutes() + 30);
+
+      let dates = [date_10m, date_20m, date_30m];
+
+      temp_predict.map((temp, index) => {
+        weather_predict.push({
+          collected_at: dates[index],
+          temp: Number(temp),
+        });
       });
-    });
 
-    pyprocess.stderr.on("data", (error) => {
-      console.log("Error in the data predicting.");
-      console.log(error.toString());
       res.json({
-        msg: resForm.msg.err,
-        contents: {
-          weather_in: weather_out,
-          weather_predict: weather_predict,
-          error: error.toString(),
-        },
+        msg: resForm.msg.ok,
+        contents: { weather_in: weather, weather_predict: weather_predict },
       });
+      return;
     });
 
     pyprocess.on("close", () => {

server/src/data_processing/main.py

@@ -61,11 +61,18 @@ for user in users:
 
     params = {"mean": mean_df.to_json(), "std": std_df.to_json()}
 
-    cursor.execute("INSERT INTO \"Data_Processings\" (host,collected_at,model_file_path,params) VALUES (%s,%s,%s,%s)",
-                   (host["email"],
-                    collected_at,
-                    model_file_path,
-                    Json(params),))
+    cursor.execute(
+        "SELECT * FROM \"Data_Processings\" WHERE host=%s", (host["email"],))
+    user_checker = cursor.fetchall()
+
+    if len(user_checker) == 0:
+        cursor.execute("INSERT INTO \"Data_Processings\" (host,collected_at,model_file_path,params) VALUES (%s,%s,%s,%s)",
+                       (host["email"], collected_at, model_file_path, Json(params),))
+
+    else:
+        cursor.execute("UPDATE \"Data_Processings\" SET collected_at=%s, params=%s WHERE host=%s", (
+            collected_at, Json(params), host["email"],
+        ))
 
     connection.commit()
 

server/src/data_processing/model.py

@@ -13,9 +13,10 @@ def modeling(standard_df, host):
     def make_dataset(data, label, window_size=24):
         feature_list = []
         label_list = []
-        for i in range(len(data) - window_size):
+        for i in range(len(data) - (window_size+3)):
             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:i + window_size + 3]))
         return np.array(feature_list), np.array(label_list)
 
     feature_cols = ['temp_out', 'humi_out', 'press',
@@ -37,11 +38,11 @@ def modeling(standard_df, host):
         tf.keras.layers.LSTM(16,
                              return_sequences=False,
                              input_shape=(6, 8)),
-        tf.keras.layers.Dense(1)
+        tf.keras.layers.Dense(3)
     ])
 
     model.compile(loss='mse', optimizer='adam')
-    # model.fit(train_feature, train_label, epochs=50, batch_size=1000)
+    model.fit(train_feature, train_label, epochs=50, batch_size=10)
 
     model.save(os.getcwd() +
                '/src/data_processing/models/{0}/model.h5'.format(host))

server/src/data_processing/prediction.py

@@ -85,6 +85,10 @@ if __name__ == "__main__":
     for col in feature_cols:
         new_data[col] = (new_data[col] - mean[col]) / std[col]
 
+    df = pd.DataFrame(new_data, columns=feature_cols)
+    new_data = df.to_numpy()
+    new_data = new_data.reshape(1, 6, 8)
+
     model_pro = tf.keras.models.load_model(os.getcwd() + model_file_path)
     prediction = model_pro.predict(new_data)
     prediction = prediction * std['temp_out'] + mean['temp_out']