Upload New File

Imputer/impute_mean.py

+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar  1 10:06:55 2021
+
+@author: Christoph
+"""
+
+import pandas as pd
+import numpy as np
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from sklearn.model_selection import train_test_split
+np.random.seed(42)
+
+data = pd.read_csv('data_unfilled.csv')
+data= data.iloc[:,1:]
+
+#komplette Daten fürs Training:
+data_cmpl = data.loc[data['Streckenvorhersage.Dauer']!= 0]
+X_cmpl = data_cmpl[['Streckenvorhersage.ZielortID','Streckenvorhersage.StartortID','time']]
+Y_cmpl = data_cmpl['Streckenvorhersage.Dauer']
+X_cmpl_train, X_cmpl_test, y_cmpl_train, y_cmpl_test = train_test_split(X_cmpl, Y_cmpl, test_size=0.2)
+
+
+# fehlende Daten für Test:
+data_incmpl = data.loc[data['Streckenvorhersage.Dauer']== 0]
+X_incmpl = data_incmpl[['Streckenvorhersage.ZielortID','Streckenvorhersage.StartortID','time']]
+Y_incmpl = data_incmpl['Streckenvorhersage.Dauer']
+
+
+#Prediction anhand vorhandener Daten
+
+
+myANN = Sequential()
+myANN.add(Dense(80, activation='relu', input_dim=X_cmpl.shape[1]))
+myANN.add(Dense(50,activation='relu'))
+myANN.add(Dense(30,activation='relu'))
+myANN.add(Dense(1,activation='linear'))
+myANN.compile(loss='mean_squared_error', optimizer='adam')
+
+myANN.fit(X_cmpl_train,y_cmpl_train, epochs=100,shuffle=True,verbose=False)
+yp = myANN.predict(X_cmpl_test)
+yp=np.squeeze(yp)
+
+yDiff = yp - y_cmpl_test
+print('Mittlere Abweichung auf fehlende Daten: %e ' % (np.mean(np.abs(yDiff))))
+
+
+
+#impute Dauer auf vorhandenen Daten
+for index,row in data_incmpl.iterrows():
+    relevant_data= data_cmpl[data_cmpl['time']==row['time']]
+    data_incmpl.loc[index,'Streckenvorhersage.Dauer']=pd.DataFrame.mean(relevant_data['Streckenvorhersage.Dauer'])
+    
+data_incmpl = data_incmpl[~np.isnan(data_incmpl['Streckenvorhersage.Dauer'])] # testing here
+
+X_incmpl = data_incmpl[['Streckenvorhersage.ZielortID','Streckenvorhersage.StartortID','time']]
+Y_incmpl = data_incmpl['Streckenvorhersage.Dauer']
+
+
+X_train, X_test, y_train, y_test = train_test_split(X_cmpl, Y_cmpl, test_size=0.35)
+y_train = pd.DataFrame(data=y_train,columns=['Streckenvorhersage.Dauer'])
+
+X_train=X_train.append(X_incmpl)
+Y_incmpl = pd.DataFrame(data=Y_incmpl,columns=['Streckenvorhersage.Dauer'])
+y_train=pd.concat([y_train,Y_incmpl])
+
+
+myANN = Sequential()
+myANN.add(Dense(80, activation='relu', input_dim=X_cmpl.shape[1]))
+myANN.add(Dense(50,activation='relu'))
+myANN.add(Dense(30,activation='relu'))
+myANN.add(Dense(1,activation='linear'))
+myANN.compile(loss='mean_squared_error', optimizer='adam')
+
+myANN.fit(X_train,y_train, epochs=100,shuffle=True,verbose=False)
+yp = myANN.predict(X_test)
+yp=np.squeeze(yp)
+
+yDiff = yp - y_test
+print('Mittlere Abweichung mit aufgefüllten Daten(mean): %e ' % (np.mean(np.abs(yDiff))))
+
+
+y_test = pd.DataFrame(data=y_test,columns=['Streckenvorhersage.Dauer'])
+
+X_all=X_train.append(X_test)
+y_all=pd.concat([y_train,y_test])
+data= X_all
+y_all= np.asarray(y_all)
+data['Streckenvorhersage.Dauer']=y_all
+data.to_csv('data_filled(mean).csv')
+    
\ No newline at end of file