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Dissertation_Code/Model.py
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| import numpy as np | |
| import tensorflow as tf | |
| # for reproducibility set a seed | |
| np.random.seed(1234) | |
| import pandas as pd | |
| from tensorflow.keras.layers import Activation, Dense | |
| from tensorflow.keras.optimizers import Adam | |
| from tensorflow.keras.metrics import categorical_crossentropy | |
| from keras.models import Sequential | |
| from keras.layers import * | |
| from sklearn.preprocessing import MinMaxScaler, RobustScaler | |
| from keras.models import load_model | |
| import csv | |
| import sys | |
| import os | |
| import math | |
| import joblib | |
| from datetime import datetime | |
| #Creates the models based on the Training Data, and then saves the X/Y/Z models and the Scaler used to scale things for later use | |
| def createTheModels(TrainingDataCSVFile): | |
| training_data_df = pd.read_csv(TrainingDataCSVFile) | |
| #Define the Scaler that will be used for all data | |
| TrainingScaler = MinMaxScaler(feature_range =(0,1)) | |
| TrainingScaler.fit(training_data_df) | |
| ScaledTraining = TrainingScaler.transform(training_data_df) | |
| #Create the csv file just so we can debug things later | |
| scaled_training_df = pd.DataFrame(ScaledTraining, columns=training_data_df.columns.values) | |
| scaled_training_df.to_csv("D:\OneDrive\Desktop\Scaled_Training_Data.csv", index = False) | |
| X = scaled_training_df.drop(['NewXPOS', 'NewYPOS', 'NewZPOS'], axis=1).values #<---- Drop them all | |
| inputLength = len(X[0]) | |
| XPOSY = scaled_training_df[['NewXPOS']].values | |
| YPOSY = scaled_training_df[['NewYPOS']].values | |
| ZPOSY = scaled_training_df[['NewZPOS']].values | |
| modelActivation = 'relu' | |
| modelLevels = [50,100,50,1] | |
| modelOptimizer = "adam" | |
| modelLoss = "mean_squared_error" | |
| model = Sequential() | |
| model.add(Dense(modelLevels[0], input_dim=inputLength, activation=modelActivation)) | |
| model.add(Dense(modelLevels[1], activation=modelActivation)) | |
| model.add(Dense(modelLevels[2], activation=modelActivation)) | |
| model.add(Dense(modelLevels[3], activation=modelActivation)) | |
| model.compile(loss=modelLoss, optimizer=modelOptimizer) | |
| es = tf.keras.callbacks.EarlyStopping(monitor='loss', mode='min', verbose=1, patience=10) | |
| print("Fitting X ...") | |
| model.fit(X,XPOSY,epochs=50,shuffle=True,verbose=2,callbacks=es) | |
| model.save("Xtrained_model.h5") | |
| print("Fitting Y ...") | |
| model.fit(X,YPOSY,epochs=50,shuffle=True,verbose=2,callbacks=es) | |
| model.save("Ytrained_model.h5") | |
| print("Fitting Z ...") | |
| model.fit(X,ZPOSY,epochs=50,shuffle=True,verbose=2,callbacks=es) | |
| model.save("Ztrained_model.h5") | |
| #Save the Scaler for later use | |
| joblib.dump(TrainingScaler, "scaler.save") | |
| #end def createTheModels | |
| #Test how good the models are on a set of testing data | |
| def testTheModels(TestDataCSVFile, simplePrediction): | |
| # Bring back all three models | |
| Xmodel = load_model("Xtrained_model.h5") | |
| Ymodel = load_model("Ytrained_model.h5") | |
| Zmodel = load_model("Ztrained_model.h5") | |
| # Load the scaler | |
| Scaler = joblib.load("scaler.save") | |
| testing_data_df = pd.read_csv(TestDataCSVFile) | |
| ScaledTesting = Scaler.transform(testing_data_df) | |
| scaled_testing_df = pd.DataFrame(ScaledTesting, columns=testing_data_df.columns.values) | |
| #Create the csv file just so we can debug things later | |
| scaled_testing_df.to_csv("D:\OneDrive\Desktop\Scaled_Testing_Data.csv", index = False) | |
| X_test = scaled_testing_df.drop(['NewXPOS', 'NewYPOS', 'NewZPOS'], axis=1).values #<---- Drop them all | |
| Y_testX = scaled_testing_df[['NewXPOS']].values | |
| Y_testY = scaled_testing_df[['NewYPOS']].values | |
| Y_testZ = scaled_testing_df[['NewZPOS']].values | |
| test_error_rate = Xmodel.evaluate(X_test, Y_testX, verbose=0) | |
| print("X Mean squared error is: {}".format(test_error_rate)) | |
| test_error_rate = Ymodel.evaluate(X_test, Y_testY, verbose=0) | |
| print("Y Mean squared error is: {}".format(test_error_rate)) | |
| test_error_rate = Zmodel.evaluate(X_test, Y_testZ, verbose=0) | |
| print("Z Mean squared error is: {}".format(test_error_rate)) | |
| if simplePrediction: | |
| #Load in the simple scaled prediction data | |
| pred = pd.read_csv("D:\OneDrive\Desktop\Scaled_Prediction_Data.csv").values | |
| else: | |
| pred = scaled_testing_df.drop(['NewXPOS', 'NewYPOS', 'NewZPOS'], axis=1).values #<---- Drop them all | |
| predictions = [] | |
| CompleteXPredictions = Xmodel.predict(pred) | |
| CompleteYPredictions = Ymodel.predict(pred) | |
| CompleteZPredictions = Zmodel.predict(pred) | |
| for x in range(0,len(pred)): | |
| Xprediction = CompleteXPredictions[x][0] | |
| Yprediction = CompleteYPredictions[x][0] | |
| Zprediction = CompleteZPredictions[x][0] | |
| Xpred = (Xprediction * (Scaler.data_max_[23] - Scaler.data_min_[23])) + Scaler.data_min_[23] | |
| Ypred = (Yprediction * (Scaler.data_max_[24] - Scaler.data_min_[24])) + Scaler.data_min_[24] | |
| Zpred = (Zprediction * (Scaler.data_max_[25] - Scaler.data_min_[25])) + Scaler.data_min_[25] | |
| if simplePrediction: | |
| print("Prediction of X_POS is {}".format(Xprediction)) | |
| print("Prediction of Y_POS is {}".format(Yprediction)) | |
| print("Prediction of Z_POS is {}".format(Zprediction)) | |
| print("Scaled prediction of X_POS is {}".format(Xpred)) | |
| print("Scaled prediction of Y_POS is {}".format(Ypred)) | |
| print("Scaled prediction of Z_POS is {}".format(Zpred)) | |
| predictions.append([Xpred,Ypred,Zpred]) | |
| return(predictions) | |
| #end def testTheModels | |
| #Write a CSV file which compares what the actual new X/Y/Z positions are vs. the predicted ones, just so we can see how good or bad things are | |
| def compareActualVsPrediction(TestDataCSVFile, logFile): | |
| # Load the scaler | |
| Scaler = joblib.load("scaler.save") | |
| comparisonCSVFile = "Comparison.csv" | |
| #Open the csvFile. Note that the newline='' stops the writer from inserting extra lines in the csv file | |
| try: | |
| csvFile = open(comparisonCSVFile, "w+", newline='') | |
| except IOError: | |
| print("Could not write file:", comparisonCSVFile) | |
| sys.exit() | |
| testing_data_df = pd.read_csv(TestDataCSVFile).values | |
| #Create a list with all of the column headers for the CSV file | |
| csv_columns = ["OldJD","OldJDFR","NewXPOS","NewYPOS","NewZPOS","PredXPOS","PredYPOS","PredZPOS","XErrorKM","YErrorKM","ZErrorKM"] | |
| csv_writer = csv.writer(csvFile) | |
| csv_writer.writerow(csv_columns) # write header | |
| predictions = testTheModels(TestDataCSVFile, False) | |
| count = 0 | |
| LargestXError = 0 | |
| LargestYError = 0 | |
| LargestZError = 0 | |
| for item in testing_data_df: | |
| OldJD = item[0] | |
| OldJDFR = item[1] | |
| NewXPOS = item[23] | |
| NewYPOS = item[24] | |
| NewZPOS = item[25] | |
| PredXPOS = predictions[count][0] | |
| PredYPOS = predictions[count][1] | |
| PredZPOS = predictions[count][2] | |
| XError = math.sqrt((NewXPOS - PredXPOS)**2) | |
| YError = math.sqrt((NewYPOS - PredYPOS)**2) | |
| ZError = math.sqrt((NewZPOS - PredZPOS)**2) | |
| if XError > LargestXError: | |
| LargestXError = XError | |
| if YError > LargestYError: | |
| LargestYError = YError | |
| if ZError > LargestZError: | |
| LargestZError = ZError | |
| row = [OldJD,OldJDFR,NewXPOS,NewYPOS,NewZPOS,PredXPOS,PredYPOS,PredZPOS,XError,YError,ZError] | |
| csv_writer.writerow(row) | |
| count = count + 1 | |
| csvFile.close() | |
| print("Written the CSV File:", comparisonCSVFile) | |
| largestError = max(LargestXError, LargestYError, LargestZError) | |
| print("Largest Absolute Error:", round(largestError,2), "XError:YError:ZError", round(LargestXError,2), round(LargestYError,2), round(LargestZError,2), file=logFile, flush=True) | |
| #end def testTheModel | |
| #Main Processing loop which runs all the experiments | |
| #Define what to run, and how long to run it for | |
| #Normally run it twice including training just to get round any restarting inconsistencies | |
| #Or simply set the range to zero to stop running them | |
| Run24946onTest = 0 | |
| Run24907onTest = 0 | |
| RunMergedSatellite = 0 | |
| Run34367Debris = 0 | |
| Run35479Debris = 0 | |
| Run37558Debris = 0 | |
| RunMergedDebris = 0 | |
| logFile = open("D:\OneDrive\Desktop\Results.txt", 'w') | |
| print("Running the experiments ...", file=logFile, flush=True) | |
| dataToTrainOn = ["D:/OneDrive/Desktop/Training/Ext_Labelled24946_20200101_20201230.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled24946_20190101_20201230.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled24946_20180101_20201230.csv"] | |
| print("Running the first part of the experiments ...", file=logFile, flush=True) | |
| for x in range (Run24946onTest): | |
| for trainingData in dataToTrainOn: | |
| now = datetime.now() | |
| current_time = now.strftime("%H:%M:%S") | |
| print("Training for ID 24946 (1997-051C) on: ", trainingData, "at:", current_time, file=logFile, flush=True) | |
| createTheModels(trainingData) | |
| print("Testing the model trained on ID 24946 (1997-051C) data on ID 24946 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled24946_20210101_20210101.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24946_20210101_20210103.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24946_20210101_20210106.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24946_20210101_20210113.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| #See if the 24946 data can be used for the other Satellite 1997-043E – Id: 24907 | |
| print("Testing the model trained on ID 24946 (1997-051C) data on ID 24907 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled24907_20210101_20210101.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24907_20210101_20210103.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24907_20210101_20210106.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24907_20210101_20210113.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| print("Running the second part of the experiments ...", file=logFile, flush=True) | |
| #Repeat the test for the 24907 data | |
| dataToTrainOn = ["D:/OneDrive/Desktop/Training/Ext_Labelled24907_20200101_20201230.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled24907_20190101_20201230.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled24907_20180101_20201230.csv"] | |
| for x in range (Run24907onTest): | |
| for trainingData in dataToTrainOn: | |
| now = datetime.now() | |
| current_time = now.strftime("%H:%M:%S") | |
| print("Training for ID 24907 (1997-043E) on: ", trainingData, "at:", current_time, file=logFile, flush=True) | |
| createTheModels(trainingData) | |
| print("Testing the model trained on ID 24907 (1997-043E) data on ID 24946 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled24946_20210101_20210101.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24946_20210101_20210103.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24946_20210101_20210106.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24946_20210101_20210113.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| #See if the 24946 data can be used for the other Satellite 1997-043E – Id: 24907 | |
| print("Testing the model trained on ID 24907 (1997-043E) data on ID 24907 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled24907_20210101_20210101.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24907_20210101_20210103.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24907_20210101_20210106.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24907_20210101_20210113.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| print("Running the third part of the experiments ...", file=logFile, flush=True) | |
| dataToTrainOn = ["D:/OneDrive/Desktop/Training/Ext_LabelledM24946_24907_20200101_20201230.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_LabelledM24946_24907_20190101_20201230.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_LabelledM24946_24907_20180101_20201230.csv"] | |
| for x in range (RunMergedSatellite): | |
| for trainingData in dataToTrainOn: | |
| now = datetime.now() | |
| current_time = now.strftime("%H:%M:%S") | |
| print("Training for Merged Dataset of 24946 and 24907 on: ", trainingData, "at:", current_time, file=logFile, flush=True) | |
| createTheModels(trainingData) | |
| print("Testing the model trained on Merged Dataset of 24946 and 24907 on ID 24946 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled24946_20210101_20210101.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24946_20210101_20210103.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24946_20210101_20210106.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24946_20210101_20210113.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| #See if the 24946 data can be used for the other Satellite 1997-043E – Id: 24907 | |
| print("Testing the model trained on Merged Dataset of 24946 and 24907 on ID 24907 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled24907_20210101_20210101.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24907_20210101_20210103.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24907_20210101_20210106.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled24907_20210101_20210113.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| print("Running the fourth part of the experiments ...", file=logFile, flush=True) | |
| #Now repeat for one of the debris models | |
| dataToTrainOn = ["D:/OneDrive/Desktop/Training/Ext_Labelled34367_20201201_20201230.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled34367_20201001_20201230.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled34367_20200701_20201230.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled34367_20200401_20201230.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled34367_20200101_20201230.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled34367_20190101_20201230.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled34367_20180101_20201230.csv"] | |
| for x in range (Run34367Debris): | |
| for trainingData in dataToTrainOn: | |
| now = datetime.now() | |
| current_time = now.strftime("%H:%M:%S") | |
| print("Training for ID 34367 (1997-051FK) on: ", trainingData, "at:", current_time, file=logFile, flush=True) | |
| createTheModels(trainingData) | |
| print("Testing the model trained on ID 34367 (1997-051FK) data on ID 34367 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210103.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210106.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210113.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210120.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210127.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| print("Running the fifth part of the experiments ...", file=logFile, flush=True) | |
| #Now repeat for another of the debris models | |
| dataToTrainOn = ["D:/OneDrive/Desktop/Training/Ext_Labelled35479_20201201_20201225.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled35479_20201001_20201225.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled35479_20200702_20201225.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled35479_20200401_20201225.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled35479_20200101_20201225.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled35479_20190101_20201225.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled35479_20180101_20201225.csv"] | |
| for x in range (Run35479Debris): | |
| for trainingData in dataToTrainOn: | |
| now = datetime.now() | |
| current_time = now.strftime("%H:%M:%S") | |
| print("Training for ID 35479 (1997-051PZ) on: ", trainingData, "at:", current_time, file=logFile, flush=True) | |
| createTheModels(trainingData) | |
| print("Testing the model trained on ID 35479 (1997-051PZ) data on ID 35479 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled35479_20210102_20210110.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled35479_20210102_20210117.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled35479_20210102_20210124.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| print("Running the sixth part of the experiments ...", file=logFile, flush=True) | |
| #Now repeat for another of the debris models | |
| dataToTrainOn = ["D:/OneDrive/Desktop/Training/Ext_Labelled37558_20201201_20201228.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled37558_20201002_20201228.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled37558_20200702_20201228.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled37558_20200401_20201228.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled37558_20200101_20201228.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled37558_20190101_20201228.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_Labelled37558_20180101_20201228.csv"] | |
| for x in range (Run37558Debris): | |
| for trainingData in dataToTrainOn: | |
| now = datetime.now() | |
| current_time = now.strftime("%H:%M:%S") | |
| print("Training for ID 37558 (1997-051XT) on: ", trainingData, "at:", current_time, file=logFile, flush=True) | |
| createTheModels(trainingData) | |
| print("Testing the model trained on ID 37558 (1997-051XT) data on ID 37558 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled37558_20210103_20210105.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled37558_20210103_20210112.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled37558_20210103_20210119.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled37558_20210103_20210126.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| print("Running the seventh and final part of the experiments with merged debris data ...", file=logFile, flush=True) | |
| dataToTrainOn = ["D:/OneDrive/Desktop/Training/Ext_LabelledMergedDec2020.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_LabelledMergedOctDec2020.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_LabelledMergedJulyDec2020.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_LabelledMergedAprDec2020.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_LabelledMerged2020.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_LabelledMerged2019_2020.csv", | |
| "D:/OneDrive/Desktop/Training/Ext_LabelledMerged2018_2020.csv"] | |
| for x in range (RunMergedDebris): | |
| for trainingData in dataToTrainOn: | |
| now = datetime.now() | |
| current_time = now.strftime("%H:%M:%S") | |
| print("Training for Merged Debris Dataset on: ", trainingData, "at:", current_time, file=logFile, flush=True) | |
| createTheModels(trainingData) | |
| print("Testing the model trained on the merged Debris dataset on ID 34367 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210103.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210106.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210113.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210120.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210127.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| print("Testing the model trained on the merged Debris dataset on ID 35479 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled35479_20210102_20210110.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled35479_20210102_20210117.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled35479_20210102_20210124.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| print("Testing the model trained on the merged Debris dataset on ID 37558 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled37558_20210103_20210105.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled37558_20210103_20210112.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled37558_20210103_20210119.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled37558_20210103_20210126.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| #Top up tests | |
| #Now repeat for another of the debris models | |
| print("Running the seventh and final part of the experiments with merged debris data ...", file=logFile, flush=True) | |
| dataToTrainOn = ["D:/OneDrive/Desktop/Training/Ext_LabelledMergedOctDec2020.csv"] | |
| for x in range (5): | |
| for trainingData in dataToTrainOn: | |
| now = datetime.now() | |
| current_time = now.strftime("%H:%M:%S") | |
| print("Training for Merged Debris Dataset on: ", trainingData, "at:", current_time, file=logFile, flush=True) | |
| createTheModels(trainingData) | |
| print("Testing the model trained on the merged Debris dataset on ID 34367 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210103.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210106.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210113.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210120.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled34367_20210101_20210127.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| print("Testing the model trained on the merged Debris dataset on ID 35479 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled35479_20210102_20210110.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled35479_20210102_20210117.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled35479_20210102_20210124.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| print("Testing the model trained on the merged Debris dataset on ID 37558 test data.", file=logFile, flush=True) | |
| dataToTestOn = ["D:/OneDrive/Desktop/Test/Labelled37558_20210103_20210105.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled37558_20210103_20210112.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled37558_20210103_20210119.csv", | |
| "D:/OneDrive/Desktop/Test/Labelled37558_20210103_20210126.csv"] | |
| for testData in dataToTestOn: | |
| print("Accuracy for : ", testData, file=logFile, flush=True) | |
| testTheModels(testData,True) | |
| compareActualVsPrediction(testData,logFile) | |
| logFile.close() |