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ANN_large.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jan 19 12:22:20 2017
+
+@author: juno
+"""
+
+# data preprocessing
+
+# from sklearn.preprocessing import OneHotEncoder #gebruik om 1,2,3,4 in [1000, 0100, 0010, 0001] om te zetten
+# onehotencoder = OneHotEncoder(categorical_featurs = [0]) #[0] = column to encode
+# X = onehotencoder.fit_transform(X).toarray()
+# Dummy variable trap: linear regression part. Always omit 1 dummy variable
+# Remove 1 of the dummy variables
+# Feature scaling om alle gegevens in dezelfde range te krijgen -> door gebruik Euclidean Distance . Niet per se voor 
+# Apart voor x en y?
+# from sklearn.preprocess import StandardScaler
+# sc = StandardScaler()
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import os
+import time
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.preprocessing import StandardScaler
+from sklearn.cross_validation import train_test_split
+from Ship import Ship
+import keras
+from keras.models import Sequential 
+from keras.layers import Dense 
+from sklearn.metrics import accuracy_score
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+from sklearn.decomposition import PCA
+from sklearn.grid_search import GridSearchCV
+from Ship import Ship
+from ContainerTerminal import ContainerTerminal
+import pickle
+
+#==============================================================================
+# PARAMETERS
+#==============================================================================
+
+number_of_ships = 6
+num_hidden_layers = 1
+num_nodes = 50
+scenario = 'equal'
+training_data = '/Users/juno/Desktop/Scriptie/Python/Training data/Groningen-{0}.csv'.format(scenario)
+save = True
+test_size = 0.2
+QCs = 7
+sample = False
+time_horizon = 1
+optimizer = 'SGD'
+ANNpath = '/Users/Juno/Desktop/Scriptie/Python/ANN/th1-{0}'.format(scenario)
+ANN = keras.models.load_model(ANNpath)
+
+#importing the dataset
+def makeList(number):
+    listname = list()
+    for i in range(number):
+        listname.append(i)
+    return listname
+
+def fitOneHotEncoder(QCs=QCs):
+    QCListOHE = [[QC]for QC in range(1,QCs)]
+    onehotencoder = OneHotEncoder()
+    onehotencoder.fit_transform(QCListOHE)    
+    return onehotencoder
+
+def preprocessData(test_size = test_size, sample = False):
+    dataset = pd.read_csv(training_data)
+    if time_horizon:
+        index = 3*time_horizon
+        dataset = dataset.loc[dataset['Ship {0} arrival time'.format(time_horizon+1)]==0]
+    if sample:
+        dataset = dataset.sample(n = sample)
+    allVandU = [i for i in dataset.columns if 'V' in i and 'Current' not in i or 'U' in i]
+    allShipsandX = [i for i in dataset.columns if 'Ship' in i or 'y' in i or 'Current' in i]
+    X = dataset.drop(allVandU, axis = 1)
+    X = X.drop('Current V 1', axis = 1)
+    X = X.values
+    y = dataset.drop(allShipsandX, axis = 1)
+    y = y.drop('V 1', axis = 1).values #deze regel verwijderen en .values bij regel hierboven toevoegen
+    onehotencoder = OneHotEncoder()
+    y = onehotencoder.fit_transform(y).toarray() #Transforms y to binary array
+    standardscaler = StandardScaler()
+    X = standardscaler.fit_transform(X) #Scales values of X 
+    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = test_size, random_state = 0)
+    return X_train, X_test, y_train, y_test, standardscaler
+
+
+def buildANN(X_train, X_test, y_train, y_test,num_hidden_layers =num_hidden_layers, num_nodes = num_nodes):
+    network = Sequential()
+    input_layer = Dense(output_dim = num_nodes, init = 'uniform', activation = 'relu', input_dim = X_train.shape[-1])
+    network.add(input_layer)
+    for i in range(num_hidden_layers):
+        layer = Dense(output_dim = num_nodes, init = 'uniform', activation = 'relu')
+        network.add(layer)
+    output_layer = Dense(output_dim = y_train.shape[-1], init = 'uniform', activation = 'softmax')
+    network.add(output_layer)
+    network.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy'])
+    network.fit(X_train,
+            y_train,
+            batch_size = 10,
+            nb_epoch = 100)
+    return network
+
+def findAccuracy(y_pred, y_test, number_of_ships = number_of_ships):
+    ship_pred = y_pred[:,:number_of_ships]
+    if time_horizon:
+        ship_pred = ship_pred[:,:time_horizon]
+    ship_pred = [i.argmax() for i in ship_pred]
+    ship_test = y_test[:,:number_of_ships]
+    ship_test = [i.argmax() for i in ship_test]
+    ship_accuracy = accuracy_score(ship_test, ship_pred)
+
+    QC_pred = y_pred[:,number_of_ships:]
+    QC_pred = [i.argmax() for i in QC_pred]
+    QC_test = y_test[:, number_of_ships:]
+    QC_test = [i.argmax() for i in QC_test]
+    QC_accuracy = accuracy_score(QC_test, QC_pred)
+
+    return ship_accuracy, QC_accuracy
+
+def main():
+    X_train, X_test, y_train, y_test, standardscaler = preprocessData(sample = sample)
+    starttime = time.time()
+    ANN = buildANN(X_train, X_test, y_train, y_test)
+    y_pred = ANN.predict(X_test)
+    ship_accuracy, QC_accuracy = findAccuracy(y_pred, y_test)
+    print(time.time()-starttime)
+    return ANN, standardscaler,ship_accuracy, QC_accuracy
+
+ANN, standardscaler, ship_accuracy, QC_accuracy = main()
+print('Ship accuracy: ', ship_accuracy)
+print('QC accuracy: ', QC_accuracy)
+
+if save:
+    ANN.save('th1-{0}'.format(scenario))
+    pickle.dump(standardscaler, open("standard_scaler_th1_{0}.p".format(scenario), "wb"))
+