├── Project Report .pdf ├── README.md └── venv ├── Analysis.py ├── UtilityFunctions.py ├── X.csv ├── XMulti.csv ├── XToClassifyBin.csv ├── XToClassifyMulti.csv ├── keyBin.txt ├── keyMulti.txt ├── y.csv └── yMulti.csv /Project Report .pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Andrew878/ML-project-Radar-Object-Classification/c788e6d9830347d01f67ee3e32f3ab67fe86b410/Project Report .pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # RadarCATClassification 2 | 3 | EXECUTIVE SUMMARY: 4 | 5 | A master's machine learning project that classifies objects/materials based on how they interact with radar signals. 6 | 7 | Within the provided python scripts there are: 8 | 9 | ◦ The training and calibration of four classification models: 10 | 11 | ▪ Logistic Regression; 12 | 13 | ▪ K-Nearest Neighbours; 14 | 15 | ▪ Single layer neural network. 16 | 17 | ▪ Gradient Boosted Trees; 18 | 19 | ◦ Aggregation of above models into an ensemble voting classifier that achieves 100% accuracy on unseen test data. 20 | 21 | 22 | Full credit of original idea and data source: 23 | 24 | https://sachi.cs.st-andrews.ac.uk/research/interaction/radarcat-exploits-googles-soli-radar-sensor-for-object-and-material-recognition/ 25 | 26 | 27 | OBJECTIVE: 28 | 29 | Here, the ultimate desired outcome is not a better understanding of radar’s reflection and refraction properties to further physics, but rather it is the fast, accurate classification of objects. As such, when selecting a model, I did not restrict myself to choosing interpretable models only, and will focussed on accuracy and speed to classify. 30 | 31 | An ensemble classifier was considered given: 32 | 33 | • With the small amount of data per category suggests it could be difficult to train a single accurate model. The aggregated classifications of several different models with ‘OK’ accuracy might allow us overcome this by diversification of errors; 34 | 35 | • Evidence in both academia (Kittler, Hatef, Duin, & Matas, 1998)⁠ and actual data-science competitions (Géron, 2017; Murray, 2018)⁠ suggest that ensemble methods can work well for classification problems; 36 | 37 | 38 | GENERAL APPROACH: 39 | 40 | • Step 1: Trained a regularised logistic regression model. Measure performance on training and validation sets to obtain an initial accuracy benchmark. 41 | a) Adjust regularisation parameter (L2) as appropriate if under or over-fitting occurs. 42 | 43 | • Step 2: Trained the following models we get satisfiable accuracy. Use grid-search with cross-validation to tune hyper-parameters. 44 | a) A gradient boosted tree (‘GBT’) classifier; 45 | b) K-Nearest Neighbour (‘KNN’) classifier (note: KNN doesn’t require training, only hyper-parameter tuning); 46 | c) A feed-forward neural network classifier; 47 | 48 | • Step 3: Combined all four models into a voting ensemble classifier and test performance on training and validation sets. 49 | 50 | • Step 4: Combined training and validation data and retrain model parameters and predict against test set. This step will provide guidance if our ensemble approach is appropriate. 51 | 52 | • Step 5: Use all data points (training, validation and test), retrain the model parameters and then use this to test the unlabelled ‘XToClassifyData’. This last step is to ensure we make our final predictions with the most amount of data as possible. 53 | 54 | 55 | JUSTIFICATION FOR MODEL SELECTION: 56 | 57 | Per guidance in Kittler et al, combinations of classifiers are “particularly useful if they are different” (Kittler et al., 1998)⁠. As such, each of the proposed models was chosen because it offers different qualities (strengths/weaknesses) to the ensemble voter: 58 | 59 | Logistic regression: 60 | 61 | • Provides linear decision boundaries and so for linearly separable data, this approach should work well. 62 | 63 | • Takes the entire dataset into consideration when creating decision boundaries. 64 | 65 | • Because “logistic regression wants to create a large margin between boundary and (all of) the data”, it can result in decision boundaries tilting in odd angles to accommodate outliers near the decision boundary (Minka, 2003)⁠. 66 | 67 | Gradient Boosted Trees: 68 | 69 | • The GBT model is itself an ensemble learner that gradually adds and trains multiple decision-tree models where each incremental tree is trained on the residuals of the prior tree. It is somewhat similar to forward stage-wise liner regression (Hastie et al, Chapter 4) in the sense that the models next tree is determined by the residuals of the model’s prior iteration. 70 | 71 | • Tree methods such as GBT are robust to outliers (Hastie, Tibshirani, & Friedman, 2008)⁠. 72 | 73 | • The combination of these trees will produce linear decision boundaries that are orthogonal to the PCA axes. This will be different to the decision boundaries of the other models. 74 | 75 | • Lastly, it is difficult to ignore that GBT approaches tend to feature in many winning data-science entries such as Kaggle and the Netflix Prize (Koren, 2009)⁠. 76 | 77 | K-Nearest Neighbour: 78 | 79 | • KNN is a probabilistic classifier that formulates classifications based on the ‘K’ closest data points and nothing more (in this sense, the ‘K’ majority voting system shares similarities to an ensemble classifier). 80 | 81 | • Per Hastie et al, KNN’s produces piece-wise linear decision boundaries which can be used to represent “irregular” and non-linear class boundaries (Hastie et al., 2008)⁠. 82 | 83 | • Hastie et al also details how KNN are successful in many real world applications including electrocardiography signals. From my naive perspective, pictures of electrocardiography waves don’t look dissimilar to the waveforms in the RadarCat article. This leads me to believe KNN could be worth attempting. 84 | 85 | • Russell and Norvig also note that “in low-dimensional spaces with plenty of data, nearest neighbours works very well”(Russell & Norvig, 2016)⁠. Due to the curse of dimensionality, KNN might have been infeasible in the original, larger feature set, but with the smaller PCA features, the data-to-feature ratio is much higher, and so there is a possibility it will perform well. 86 | 87 | • Unfortunately, as the size of the data set gets very large, KNN will take longer and longer to classify (as it calculates the distance from every data point) but given the small data set, it is sufficient. 88 | 89 | Neural network: 90 | 91 | • NN’s can represent complex non-linear decision boundaries between classes and work well in instances where there is high-signal-to-noise ratios (Hastie et al., 2008)⁠. 92 | 93 | • NN’s are well suited to classification tasks, particularly when “modelling real world complex relationships” (G. P. Zhang, 2000)⁠ (which this problem is considered as). 94 | 95 | 96 | -------------------------------------------------------------------------------- /venv/Analysis.py: -------------------------------------------------------------------------------- 1 | import UtilityFunctions 2 | import numpy as np 3 | import sklearn as sk 4 | import sklearn.model_selection 5 | import sklearn.linear_model 6 | from sklearn.decomposition import PCA 7 | from sklearn.neighbors import KNeighborsClassifier as k_neigh 8 | from sklearn.ensemble import VotingClassifier as voter 9 | from sklearn.neural_network import MLPClassifier as nn 10 | import pandas as pd 11 | import time 12 | import sys 13 | import warnings 14 | 15 | # to remove annoying 'future' and 'soon to be deprecated' warnings 16 | #from sklearn.neural_network.tests.test_mlp import test_n_iter_no_change 17 | 18 | if not sys.warnoptions: 19 | warnings.simplefilter("ignore") 20 | 21 | # make it easier to read output 22 | pd.set_option('display.max_columns', 30) 23 | 24 | # amount of variation for PCA 25 | global PCA_THRESHOLD 26 | PCA_THRESHOLD = 0.999 27 | 28 | # dictionaries for classification 29 | key_binary = {0: 'book', 1: 'plastic case'} 30 | key_multi = {0: 'air', 1: 'book', 2: 'hand', 3: 'knife', 4: 'plastic case'} 31 | 32 | 33 | def initial_training_of_models(Xfile, Yfile): 34 | """Performs the initial training on all models. Each model calls upon its own Grid search function""" 35 | 36 | # first obtain and process the training sets 37 | x_train_val, x_test, y_train_val, y_test, x_train, x_val, y_train, y_val, x_all, y_all = UtilityFunctions.read_CSV_data( 38 | Xfile, Yfile) 39 | 40 | # this will help us record the results for ensemble voting 41 | df_val_predictions = y_val.copy() 42 | df_val_predictions.reset_index(inplace=True) 43 | 44 | # extract the PCAs from X and plot the correlation heat maps 45 | x_train_processed, pca = process_data(x_train) 46 | UtilityFunctions.plot_correlation_heatmap(pd.DataFrame(x_train_processed), 47 | "Correlation heatmap of PCA Components \n(individual labels not shown)", 48 | 0.1) 49 | x_val_processed = process_data_existingPCA(x_val, pca) 50 | 51 | # print the number of PCA features 52 | num_pca_features = len(x_train_processed[0]) 53 | print("Number of PCA features", num_pca_features) 54 | 55 | # train logisitc regression model 56 | logReg = logisitic_regression_initial_benchmark(df_val_predictions, x_train_processed, x_val_processed, y_train, 57 | y_val) 58 | 59 | # perform grid search on Grad Boost Trees 60 | param_grid_GradBoost = initialise_param_grid_GradientBoosted(num_pca_features) 61 | grid_search_gradBoost = GradientBoostedTreesGridSearch(x_train_processed, y_train, param_grid_GradBoost) 62 | UtilityFunctions.model_results(grid_search_gradBoost, x_val_processed, y_val, df_val_predictions, 'GrBoost') 63 | 64 | # perform grid search on KNN 65 | param_grid_kNearest = [{'n_neighbors': [i for i in range(1, 10)]}] 66 | grid_search_kNearest = KNearestNeigboursGridSearch(x_train_processed, y_train, param_grid_kNearest) 67 | UtilityFunctions.model_results(grid_search_kNearest, x_val_processed, y_val, df_val_predictions, 'K-Nearest') 68 | 69 | # perform grid search on NN 70 | param_grid_nn = initialise_param_grid_NeuralNet() 71 | grid_search_NN = NeuralNetworkGridSearch(x_train_processed, y_train, param_grid_nn) 72 | UtilityFunctions.model_results(grid_search_NN, x_val_processed, y_val, df_val_predictions, 'NN') 73 | 74 | # print predictions 75 | print("\n\n***** Predictions from initial models on validation set") 76 | print(df_val_predictions) 77 | 78 | return df_val_predictions, x_train_processed, pca, x_train_val, x_test, y_train_val, y_test, x_train, x_val, y_train, y_val, x_all, y_all, grid_search_gradBoost, grid_search_kNearest, grid_search_NN, logReg; 79 | 80 | 81 | def process_data(x_values): 82 | """Scale data using standard scaler and apply PCA transform""" 83 | 84 | x_values_stand = sklearn.preprocessing.StandardScaler().fit_transform(x_values) 85 | pca = PCA(n_components=PCA_THRESHOLD) 86 | x_values_stand_pca = pca.fit_transform(x_values_stand) 87 | 88 | # print distribution of PCA variation 89 | cumsum = np.cumsum(pca.explained_variance_ratio_) 90 | print("\n% of variation each PCA captures:") 91 | print(cumsum) 92 | return x_values_stand_pca, pca; 93 | 94 | 95 | def process_data_existingPCA(x_values, pca): 96 | """Scale data using standard scaler and apply existing PCA transform""" 97 | 98 | x_values_stand = sklearn.preprocessing.StandardScaler().fit_transform(x_values) 99 | x_values_stand_pca = pca.transform(x_values_stand) 100 | return x_values_stand_pca 101 | 102 | 103 | def logisitic_regression_initial_benchmark(df_val_predictions, x_train_processed, x_val_processed, y_train, y_val): 104 | """Performs regularised logistic regression""" 105 | 106 | logReg = sklearn.linear_model.LogisticRegression() 107 | logReg.fit(x_train_processed, y_train) 108 | print("\n***** Outcomes for logistic regression") 109 | print("\nLog Regression training set accuracy", logReg.score(x_train_processed, y_train)) 110 | print("\nLog Regression validation set accuracy", logReg.score(x_train_processed, y_train)) 111 | UtilityFunctions.model_results(logReg, x_val_processed, y_val, df_val_predictions, 'Log-Reg') 112 | return logReg 113 | 114 | 115 | def GradientBoostedTreesGridSearch(x_train_processed, y_train, param_grid): 116 | """A function that performs gridsearch and returns the results for gradient boosted trees""" 117 | 118 | grad_booster = sklearn.ensemble.GradientBoostingClassifier(random_state=30) 119 | grid_search_gradBoost = sk.model_selection.GridSearchCV(grad_booster, param_grid, cv=3, scoring='accuracy')#, 120 | #n_jobs=-1) 121 | grid_search_gradBoost.fit(x_train_processed, y_train.values.ravel()) 122 | 123 | # print output of best model 124 | print("\n***** Grid search outcomes for gradient boosting") 125 | print("Training score: ", grid_search_gradBoost.best_score_) 126 | print("Best hyper-parameters: ", grid_search_gradBoost.best_params_) 127 | return grid_search_gradBoost 128 | 129 | 130 | def KNearestNeigboursGridSearch(x_train_processed, y_train, param_grid): 131 | """A function that performs gridsearch and returns the results for K-Nearest Neighbours""" 132 | 133 | k_nearest = k_neigh(n_jobs=-1) 134 | grid_search_k_near = sk.model_selection.GridSearchCV(k_nearest, param_grid, cv=3, scoring='accuracy') 135 | grid_search_k_near.fit(x_train_processed, y_train.values.ravel()) 136 | 137 | # print output of best model 138 | print("\n***** Grid search outcomes for K-nearest") 139 | print("Training score: ", grid_search_k_near.best_score_) 140 | print("Best hyper-parameters: ", grid_search_k_near.best_params_) 141 | return grid_search_k_near 142 | 143 | 144 | def NeuralNetworkGridSearch(x_train_processed, y_train, param_grid): 145 | """A function that performs gridsearch and returns the results for neural networks""" 146 | 147 | neural_network = nn(activation='logistic', random_state=40, max_iter=1500, momentum=0, solver='sgd', 148 | early_stopping=True, validation_fraction=0.2) 149 | grid_search_NN = sk.model_selection.GridSearchCV(neural_network, param_grid, cv=3, scoring='accuracy') 150 | grid_search_NN.fit(x_train_processed, y_train.values.ravel()) 151 | 152 | # print output of best model 153 | print("\n***** Grid search outcomes for neural network") 154 | print("Training score: ", grid_search_NN.best_score_) 155 | print("Best hyper-parameters: ", grid_search_NN.best_params_) 156 | return grid_search_NN 157 | 158 | 159 | def initialise_param_grid_GradientBoosted(num_pca_features): 160 | """Returns the initial hyper-parameters for grid search for GBTs""" 161 | 162 | learning_rate_grad_boost = [0.1, 0.3, 0.5, 0.7, 0.9] 163 | max_depth = [2 ** i for i in range(1, 6)] 164 | 165 | # don't want features exceeding PCA factors 166 | max_features = [(2 * i) for i in range(1, 15, 2) if (2 * i) <= num_pca_features] 167 | n_estimators = [10 * i for i in range(1, 11)] 168 | 169 | param_grid = [{'max_depth': max_depth, 'learning_rate': learning_rate_grad_boost, 'max_features': max_features, 170 | 'n_estimators': n_estimators}] 171 | 172 | return param_grid 173 | 174 | 175 | def initialise_param_grid_NeuralNet(): 176 | """Returns the initial hyper-parameters for grid search for Neural Nets""" 177 | 178 | units_per_layer = [(i) for i in range(10, 111, 20)] 179 | units_per_layer += [(i, j) for i in range(10, 111, 20) for j in range(10, 111, 20)] 180 | learning_rate_init = [0.01, 0.1, 1] 181 | alpha = [0.001, 0.01, 0.1, 1] 182 | 183 | # print topologies to check 184 | print("\nNeural net topologies to test") 185 | print(units_per_layer) 186 | 187 | param_grid = [{'hidden_layer_sizes': units_per_layer, 'alpha': alpha, 'learning_rate_init': learning_rate_init}] 188 | 189 | return param_grid 190 | 191 | 192 | def fine_tune_binary_models(x_train, y_train, x_val, y_val, df_val_predictions, pca, params_gradBoost_intial, 193 | params_NN_initial): 194 | 195 | """Calls upon various grid-search functions to fine-tune the previous model iterations of Gradient boosted trees 196 | and the neural network""" 197 | 198 | x_train_processed = process_data_existingPCA(x_train, pca) 199 | x_val_processed = process_data_existingPCA(x_val, pca) 200 | 201 | # obtain the fine-tuned parameters for GB Trees and NN's 202 | param_grid_GradBoost, param_grid_nn = calulate_fine_tuned_grid_parameters(params_NN_initial, 203 | params_gradBoost_intial) 204 | # perform fine-tuned grid search for GB Trees 205 | grid_search_gradBoost = GradientBoostedTreesGridSearch(x_train_processed, y_train, param_grid_GradBoost) 206 | UtilityFunctions.model_results(grid_search_gradBoost, x_val_processed, y_val, df_val_predictions, 207 | 'GrBoost - finetuned') 208 | 209 | # perform fine-tuned grid search for Neural Nets 210 | grid_search_NN = NeuralNetworkGridSearch(x_train_processed, y_train, param_grid_nn) 211 | UtilityFunctions.model_results(grid_search_NN, x_val_processed, y_val, df_val_predictions, 'NN - finetuned') 212 | 213 | # print validation set results 214 | print("\n\n***** Predictions from fine-tuned models on validation set") 215 | print(df_val_predictions) 216 | 217 | return grid_search_gradBoost, grid_search_NN; 218 | 219 | 220 | def calulate_fine_tuned_grid_parameters(params_NN_initial, params_gradBoost_intial): 221 | 222 | """Provides fine-tuned parameter values for GBTs and NN's for grid search""" 223 | 224 | # fine tune Gradboosted trees hyper-parameters 225 | learning_rate_grad_boost = [params_gradBoost_intial['learning_rate'] + i / 100 for i in range(-5, 6, 1) if 226 | 0 < (params_gradBoost_intial['learning_rate'] + i / 100)] 227 | 228 | max_depth = [params_gradBoost_intial['max_depth'] + i for i in range(-2, 3, 1) if 229 | 0 < params_gradBoost_intial['max_depth'] + i] 230 | 231 | max_features = [params_gradBoost_intial['max_features'] + i for i in range(-2, 3, 1) if 232 | 0 < params_gradBoost_intial['max_features'] + i] 233 | 234 | n_estimators = [params_gradBoost_intial['n_estimators'] + i for i in range(-2, 3, 1) if 235 | 0 < params_gradBoost_intial['n_estimators'] + i] 236 | 237 | param_grid_GradBoost = [ 238 | {'max_depth': max_depth, 'learning_rate': learning_rate_grad_boost, 'max_features': max_features, 239 | 'n_estimators': n_estimators}] 240 | 241 | # fine tune Neural Network 242 | 243 | # fine tune topology 244 | hidden_layer = params_NN_initial['hidden_layer_sizes'] 245 | if (isinstance(hidden_layer, int) or len(hidden_layer) == 1): 246 | units_per_layer = [hidden_layer + i for i in range(-5, 6, 1)] 247 | else: 248 | units_L1 = hidden_layer[0] 249 | units_L2 = hidden_layer[1] 250 | units_per_layer = [(units_L1 + i, units_L2 + j) for i in range(-10, 11, 2) for j in range(-10, 11, 2)] 251 | 252 | # fine tune learning rate 253 | learn_rate_initial = params_NN_initial['learning_rate_init'] 254 | learning_rate_init = [learn_rate_initial + i / 10 for i in range(-2, 3, 1) if 255 | 0 < learn_rate_initial + i / 10] 256 | 257 | # fine tune regularisation 258 | alpha_initial = params_NN_initial['alpha'] 259 | alpha = [alpha_initial + i * alpha_initial / 10 for i in range(-3, 3, 1) if 260 | 0 < alpha_initial + i * alpha_initial / 10] 261 | 262 | 263 | param_grid_nn = [{'hidden_layer_sizes': units_per_layer, 'alpha': alpha, 'learning_rate_init': learning_rate_init}] 264 | 265 | return param_grid_GradBoost, param_grid_nn 266 | 267 | 268 | 269 | ###### Main Program Flow Begins Here ########### 270 | 271 | print("\n**** USER FYI: This program takes circa 15-20 minutes to completely run **** \n") 272 | 273 | print("\n\n----------------- Binary --------------------------------------------------------------------------------\n") 274 | Xfile = 'X.csv' 275 | Yfile = 'y.csv' 276 | 277 | # Train initial model 278 | df_val_predictions_binary, x_train_processed, pca, x_train_val, x_test, y_train_val, y_test, x_train, x_val, y_train, y_val, x_all, y_all, grid_search_gradBoost, grid_search_kNearest, grid_search_NN, log_regress = initial_training_of_models( 279 | Xfile, Yfile) 280 | 281 | # Fine tune the initial Gradient Boosted Tree and Neural Network models 282 | params_gradBoost = grid_search_gradBoost.best_params_ 283 | params_NN = grid_search_NN.best_params_ 284 | binary_GradBoost_final, binary_NN_final = fine_tune_binary_models(x_train, y_train, x_val, y_val, 285 | df_val_predictions_binary, pca, 286 | params_gradBoost, params_NN) 287 | 288 | # Add models into a voting ensemble 289 | voting_clf_binary = voter(estimators=[('Logistic Regression', log_regress), ('gradBoost', binary_GradBoost_final), 290 | ('K_nearest', grid_search_kNearest), 291 | ('Neural network', binary_NN_final)], voting='hard') 292 | voting_clf_binary.fit(x_train_processed, y_train) 293 | model_name = "Ensemble - Hard Voting" 294 | 295 | # print ensemble results on training set 296 | print("\n***** Binary, ensemble (training set)") 297 | UtilityFunctions.model_results(voting_clf_binary, x_train_processed, y_train, df_val_predictions_binary, 298 | model_name) 299 | 300 | # Test ensemble on validation set 301 | x_val_processed = process_data_existingPCA(x_val, pca) 302 | print("\n***** Binary, ensemble (validation set)") 303 | UtilityFunctions.model_results(voting_clf_binary, x_val_processed, y_val, df_val_predictions_binary, 304 | model_name) 305 | 306 | print("\n\n***** Predictions from binary ensemble model on validation set") 307 | print(df_val_predictions_binary) 308 | 309 | # Test ensemble on test set 310 | print("\n***** Binary, ensemble (test set)") 311 | x_train_val_processed = process_data_existingPCA(x_train_val, pca) 312 | voting_clf_binary.fit(x_train_val_processed, y_train_val) 313 | x_test_processed = process_data_existingPCA(x_test, pca) 314 | UtilityFunctions.model_results(voting_clf_binary, x_test_processed, y_test, df_val_predictions_binary, 315 | model_name) 316 | 317 | # retrain on all data and make predictions on unseen data 318 | x_all_processed = process_data_existingPCA(x_all, pca) 319 | voting_clf_binary.fit(x_all_processed, y_all) 320 | x_unlabelled = UtilityFunctions.read_unlabelled_CSV_data('XToClassifyBin.csv') 321 | x_unlabelled_processed = process_data_existingPCA(x_unlabelled, pca) 322 | y_pred = voting_clf_binary.predict(x_unlabelled_processed) 323 | 324 | print("\n\n----------------- Binary Training Finished --------------------------------------------------------------\n") 325 | 326 | # write predictions to file 327 | file1 = open("PredictedClassesBin.csv", "w") 328 | 329 | print("\nPredictions for unlabelled binary data\n") 330 | print("Row,Prediction,Category") 331 | file1.write("Row,Prediction,Category\n") 332 | for i in range(0, len(y_pred)): 333 | string_line = str(i) + "," + str(y_pred[i]) + "," + str(key_binary[y_pred[i]]) 334 | print(string_line) 335 | file1.write(string_line + "\n") 336 | file1.close() 337 | 338 | 339 | 340 | 341 | 342 | # begin multi-class training 343 | print("\n\n----------------- Starting Multi-class training ---------------------------------------------------------\n") 344 | 345 | XfileMulti = 'XMulti.csv' 346 | YfileMulti = 'yMulti.csv' 347 | 348 | # Train initial model 349 | df_val_predictions_multi, x_train_processed, pca, x_train_val, x_test, y_train_val, y_test, x_train, x_val, y_train, y_val, x_all, y_all, grid_search_gradBoost, grid_search_kNearest, grid_search_NN, log_regress = initial_training_of_models( 350 | XfileMulti, YfileMulti) 351 | 352 | # Fine tune the initial Gradient Boosted Tree and Neural Network models 353 | params_gradBoost = grid_search_gradBoost.best_params_ 354 | params_NN = grid_search_NN.best_params_ 355 | multi_GradBoost_final, multi_NN_final = fine_tune_binary_models(x_train, y_train, x_val, y_val, 356 | df_val_predictions_multi, pca, 357 | params_gradBoost, params_NN) 358 | # Add models into a voting ensemble 359 | voting_clf_multi = voter(estimators=[('Logistic Regression', log_regress), ('gradBoost', multi_GradBoost_final), 360 | ('K_nearest', grid_search_kNearest), 361 | ('Neural network', multi_NN_final)], voting='hard') 362 | voting_clf_multi.fit(x_train_processed, y_train) 363 | 364 | # print ensemble results on training set 365 | print("\n***** Multi, ensemble (training set)") 366 | UtilityFunctions.model_results(voting_clf_multi, x_train_processed, y_train, df_val_predictions_multi, 367 | model_name) 368 | 369 | # Test ensemble on validation set 370 | x_val_processed = process_data_existingPCA(x_val, pca) 371 | print("\n***** Multi, ensemble (validation set)") 372 | UtilityFunctions.model_results(voting_clf_multi, x_val_processed, y_val, df_val_predictions_multi, model_name) 373 | 374 | print("\n\n***** Predictions from multi ensemble model on validation set") 375 | print(df_val_predictions_multi) 376 | 377 | # recombine training and validation and retrain 378 | x_train_val_processed = process_data_existingPCA(x_train_val, pca) 379 | voting_clf_multi.fit(x_train_val_processed, y_train_val) 380 | print("\n***** Multi, ensemble training and val combined") 381 | UtilityFunctions.model_results(voting_clf_multi, x_train_val_processed, y_train_val, df_val_predictions_multi, 382 | model_name) 383 | # Test ensemble on test set 384 | print("\n***** Multi, ensemble Test set") 385 | x_test_processed = process_data_existingPCA(x_test, pca) 386 | UtilityFunctions.model_results(voting_clf_multi, x_test_processed, y_test, df_val_predictions_multi, 387 | model_name) 388 | 389 | # retrain on all data and make predictions on unseen data 390 | x_all_processed = process_data_existingPCA(x_all, pca) 391 | voting_clf_multi.fit(x_all_processed, y_all) 392 | x_unlabelled = UtilityFunctions.read_unlabelled_CSV_data('XToClassifyMulti.csv') 393 | x_unlabelled_processed = process_data_existingPCA(x_unlabelled, pca) 394 | y_pred = voting_clf_multi.predict(x_unlabelled_processed) 395 | 396 | print("\n\n----------------- Multi-class training finished ---------------------------------------------------------\n") 397 | 398 | # write predictions to file 399 | print("\nPredictions for unlabelled multi-class data\n") 400 | file1 = open("PredictedClassesMulti.csv", "w") 401 | 402 | print("Row,Prediction,Category") 403 | file1.write("Row,Prediction,Category\n") 404 | for i in range(0, len(y_pred)): 405 | string_line = str(i) + "," + str(y_pred[i]) + "," + str(key_multi[y_pred[i]]) 406 | print(string_line) 407 | file1.write(string_line + "\n") 408 | file1.close() 409 | 410 | 411 | # measure time taken per observation 412 | time_start = time.time() 413 | 414 | # do PCA transform three times to ensure which represent real-world conidtions 415 | x_single_example_processed = process_data_existingPCA(x_train_val.iloc[[0, 1, 2], :], pca) 416 | x_single_example_processed = process_data_existingPCA(x_train_val.iloc[[0, 1, 2], :], pca) 417 | x_single_example_processed = process_data_existingPCA(x_train_val.iloc[[0, 1, 2], :], pca) 418 | voting_clf_multi.predict(x_single_example_processed) 419 | time_end = time.time() 420 | 421 | # then average over three obervations 422 | print("\nAverage time taken for single observation (secs) ", (time_end - time_start) / 3) 423 | -------------------------------------------------------------------------------- /venv/UtilityFunctions.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import sklearn as sk 3 | from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score 4 | import pandas as pd 5 | import seaborn as sns 6 | import matplotlib.pyplot as plt 7 | from sklearn.metrics import confusion_matrix 8 | from sklearn.utils.multiclass import unique_labels 9 | 10 | 11 | # global variable to print graph 12 | global PLOT_GRAPHS 13 | PLOT_GRAPHS = True 14 | 15 | # dictionaries for classification 16 | global key_binary 17 | key_binary = {0: 'book', 1: 'plastic case'} 18 | global key_multi 19 | key_multi = {0: 'air', 1: 'book', 2: 'hand', 3: 'knife', 4: 'plastic case'} 20 | 21 | 22 | def get_column_names(): 23 | """Computes the column name of each feature""" 24 | 25 | global column_names, column_name 26 | CHANNEL_SIZE = 64 27 | NUM_MEASURE = 3 28 | column_names = [] 29 | for i in range(1, 769): 30 | feature = i 31 | component = (i - 1) // NUM_MEASURE + 1 32 | channel = (i - 1) // (CHANNEL_SIZE * NUM_MEASURE) + 1 33 | 34 | if (i % 3 == 0): 35 | description = 'max' 36 | 37 | elif (i % 2 == 0): 38 | description = 'min' 39 | else: 40 | description = 'mean' 41 | 42 | column_name = str(feature) + "," + str(description) + "," + str(component) + "," + str(channel) 43 | column_names.append(column_name) 44 | 45 | return column_names 46 | 47 | 48 | def read_CSV_data(x_filename, y_filename): 49 | """Reads the data from the input filenames, stores in a panda, and then performs test/train/validation split""" 50 | 51 | # get column names 52 | column_names = get_column_names() 53 | 54 | # read files 55 | x_all = pd.read_csv(x_filename, header=None, names=column_names) 56 | y_all = pd.read_csv(y_filename, header=None, names=['Y']) 57 | 58 | # check for null values 59 | is_null_X = x_all.isna() 60 | is_null_Y = y_all.isna() 61 | for column_name in column_names: 62 | if (True in is_null_X[column_name].values): 63 | print("Null value in ", column_name) 64 | if (True in is_null_Y.values): 65 | print("Null value in Y") 66 | 67 | # split into test, val and training sets. Constant random seed to ensure reproducibility 68 | x_train_val, x_test, y_train_val, y_test = sk.model_selection.train_test_split(x_all, y_all, test_size=0.10, 69 | random_state=20, stratify=y_all) 70 | x_train, x_val, y_train, y_val = sk.model_selection.train_test_split(x_train_val, y_train_val, test_size=0.10, 71 | random_state=20) 72 | 73 | # plot correlation heat-map of features 74 | plot_correlation_heatmap(x_train, 'Correlation heat-map of 768 original features \n(individual labels not shown)') 75 | 76 | return x_train_val, x_test, y_train_val, y_test, x_train, x_val, y_train, y_val, x_all, y_all; 77 | 78 | 79 | def read_unlabelled_CSV_data(x_filename): 80 | """Read the unlabelled CSV files. Converts into pandas """ 81 | 82 | x_all = pd.read_csv(x_filename, header=None, names=column_names) 83 | is_null_X = x_all.isna() 84 | 85 | # check no missing values 86 | for column_name in column_names: 87 | if (True in is_null_X[column_name].values): 88 | print("Null value in ", column_name) 89 | 90 | return x_all; 91 | 92 | 93 | def plot_correlation_heatmap(d, name, linewidths=0.0001): 94 | """Plots a correlation heat-map graph. Code adapted from Seaborn website. Accessed 4/04/2019. 95 | https://seaborn.pydata.org/examples/many_pairwise_correlations.html""" 96 | 97 | sns.set(style="white") 98 | 99 | # Compute the correlation matrix 100 | corr = d.corr() 101 | 102 | # Generate a mask for the upper triangle 103 | mask = np.zeros_like(corr, dtype=np.bool) 104 | mask[np.triu_indices_from(mask)] = True 105 | 106 | # Set up the matplotlib figure 107 | f, ax = plt.subplots(figsize=(11, 9)) 108 | 109 | # Generate a custom diverging colormap 110 | cmap = sns.diverging_palette(220, 10, as_cmap=True) 111 | 112 | # Draw the heatmap with the mask and correct aspect ratio 113 | 114 | sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0, 115 | square=True, linewidths=linewidths, cbar_kws={"shrink": .5}, xticklabels=False, yticklabels=False) 116 | f.suptitle(name, fontsize=16) 117 | if PLOT_GRAPHS: 118 | plt.show() 119 | 120 | 121 | def plot_confusion_matrix(y_true, y_pred, classes, 122 | normalize=False, 123 | title=None, 124 | cmap=plt.cm.Blues): 125 | """ 126 | This function prints and plots the confusion matrix. 127 | Normalization can be applied by setting `normalize=True` 128 | 129 | Code taken from sklearn website. Accessed 4/04/2019. 130 | https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html""" 131 | 132 | if not title: 133 | if normalize: 134 | title = 'Normalized confusion matrix' 135 | else: 136 | title = 'Confusion matrix, without normalization' 137 | 138 | # Compute confusion matrix 139 | cm = confusion_matrix(y_true, y_pred) 140 | # Only use the labels that appear in the data 141 | #classes =classes[unique_labels(y_true, y_pred)] 142 | if normalize: 143 | cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] 144 | print("Normalized confusion matrix") 145 | else: 146 | print('Confusion matrix, without normalization') 147 | 148 | print(cm) 149 | 150 | fig, ax = plt.subplots() 151 | im = ax.imshow(cm, interpolation='nearest', cmap=cmap) 152 | ax.figure.colorbar(im, ax=ax) 153 | # We want to show all ticks... 154 | ax.set(xticks=np.arange(cm.shape[1]), 155 | yticks=np.arange(cm.shape[0]), 156 | # ... and label them with the respective list entries 157 | xticklabels=classes, yticklabels=classes, 158 | title=title, 159 | ylabel='True label', 160 | xlabel='Predicted label') 161 | 162 | # Rotate the tick labels and set their alignment. 163 | plt.setp(ax.get_xticklabels(), rotation=45, ha="right", 164 | rotation_mode="anchor") 165 | 166 | # Loop over data dimensions and create text annotations. 167 | fmt = '.2f' if normalize else 'd' 168 | thresh = cm.max() / 2. 169 | for i in range(cm.shape[0]): 170 | for j in range(cm.shape[1]): 171 | ax.text(j, i, format(cm[i, j], fmt), 172 | ha="center", va="center", 173 | color="white" if cm[i, j] > thresh else "black") 174 | fig.tight_layout() 175 | 176 | if PLOT_GRAPHS: 177 | plt.show() 178 | 179 | return ax 180 | 181 | 182 | 183 | 184 | 185 | def model_results(model, x_val_set, y_val_set, val_predictions, name_of_model): 186 | """This function presents the results of a model and calculates some performance measures.""" 187 | 188 | # # Make predictions using the testing set 189 | y_pred_val = model.predict(x_val_set) 190 | 191 | # print key model data 192 | print("Model: " + name_of_model) 193 | 194 | # print performance data 195 | print('Accuracy: %.3f' 196 | % accuracy_score(y_val_set, y_pred_val)) 197 | 198 | print('Precision (macro): %.3f' 199 | % precision_score(y_val_set, y_pred_val, average='macro')) 200 | 201 | print('Recall (macro): %.3f' % recall_score(y_val_set, y_pred_val, average='macro')) 202 | 203 | print('F1 score (macro): %.3f' % f1_score(y_val_set, y_pred_val, average='macro')) 204 | 205 | val_predictions[name_of_model] = pd.DataFrame(y_pred_val) 206 | 207 | 208 | list_class = [int(i) for i in y_val_set.values] 209 | set_class = set(list_class) 210 | if(len(set_class)==2): 211 | list_class = [int(i) for i in range(0,2)] 212 | list_class = [key_binary[int(i)] for i in range(0,2)] 213 | elif (len(set_class)==5): 214 | list_class = [int(i) for i in range(0,5)] 215 | list_class = [key_multi[int(i)] for i in range(0,5)] 216 | 217 | 218 | 219 | np.set_printoptions(precision=4) 220 | plot_confusion_matrix(y_val_set, y_pred_val, classes=list_class, normalize=True, 221 | title="Normalized confusion matrix for " + name_of_model) 222 | -------------------------------------------------------------------------------- /venv/XToClassifyBin.csv: -------------------------------------------------------------------------------- 1 | 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21 | -------------------------------------------------------------------------------- /venv/keyBin.txt: -------------------------------------------------------------------------------- 1 | {0: 'book', 1: 'plastic case'} 2 | -------------------------------------------------------------------------------- /venv/keyMulti.txt: -------------------------------------------------------------------------------- 1 | {0: 'air', 1: 'book', 2: 'hand', 3: 'knife', 4: 'plastic case'} 2 | -------------------------------------------------------------------------------- /venv/y.csv: -------------------------------------------------------------------------------- 1 | 0.00E+00 2 | 0.00E+00 3 | 0.00E+00 4 | 0.00E+00 5 | 0.00E+00 6 | 0.00E+00 7 | 0.00E+00 8 | 0.00E+00 9 | 0.00E+00 10 | 0.00E+00 11 | 0.00E+00 12 | 0.00E+00 13 | 0.00E+00 14 | 0.00E+00 15 | 0.00E+00 16 | 0.00E+00 17 | 0.00E+00 18 | 0.00E+00 19 | 0.00E+00 20 | 0.00E+00 21 | 0.00E+00 22 | 0.00E+00 23 | 0.00E+00 24 | 0.00E+00 25 | 0.00E+00 26 | 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