From 439238e0708c1bd1c068424b12c01bc5e18fc5a9 Mon Sep 17 00:00:00 2001 From: Skudalen Date: Wed, 14 Jul 2021 17:36:00 +0200 Subject: [PATCH] feat: add func to store predictions from test sets in csv --- .DS_Store | Bin 6148 -> 6148 bytes .gitignore | 8 +- Handle_emg_data.py | 4 +- Neural_Network_Analysis.py | 110 +++++++++++++----- python_speech_features.egg-info/PKG-INFO | 10 -- python_speech_features.egg-info/SOURCES.txt | 7 -- .../dependency_links.txt | 1 - python_speech_features.egg-info/requires.txt | 2 - python_speech_features.egg-info/top_level.txt | 1 - 9 files changed, 84 insertions(+), 59 deletions(-) delete mode 100644 python_speech_features.egg-info/PKG-INFO delete mode 100644 python_speech_features.egg-info/SOURCES.txt delete mode 100644 python_speech_features.egg-info/dependency_links.txt delete mode 100644 python_speech_features.egg-info/requires.txt delete mode 100644 python_speech_features.egg-info/top_level.txt diff --git a/.DS_Store b/.DS_Store index e2b43ea86f4fa9a68d9f383997766188ebc7d164..4e49e88ae394daab91daec2dfc0c03d1c570c3cf 100644 GIT binary patch delta 203 zcmZoMXfc=|#>B)qu~2NHo+2aX#(>?7jGU8sSPa=&7*ZJW8ImXau_($w*$l-g#mPBI z`T02vKmb!PpG8z1t{^wx#U&{xKM5$$vCCws5r^YpM*yb~CY)E0=%+A5j0d(qSL5}atlles)IY4?Cf$rEGA+m-U0KTFw AGXMYp literal 6148 zcmeHKO;6iE5PjQ5B7ll=s*1Sy7oY}}yAwGFZb;!Ch?9~^PHe@bYHt2BoO|z;U)582 zGrL00M+H!xb5u-BAhIy{L#Fh@T>vliQr-df;W{cM}Pm*D#X`8>k+@3T$cQV+mo}(vv7L$$4 zhv?x43Y_4xYFq6o2E-|rBx8UHt}y53cfscZBTIjbdmpD@de^DSkT{H3fJFz=Z2zu}UYvsBiHet zTXO#Q{(k>o4YDU=z!-Q_47f(pPr95^I9oR+CueO$y`+kWU*%ClXu@%<1v!d8QB~L$ XNr4y#tUR)YVt)jb1~bOMi!yKzX>Of3 diff --git a/.gitignore b/.gitignore index 049dc59..a0d6e19 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,4 @@ -Exp20201205_2myo_hard** -Exp20201205_2myo_soft** -Documents -python_speech_features** \ No newline at end of file +data +docs +logs +psf_lib diff --git a/Handle_emg_data.py b/Handle_emg_data.py index 4df52b8..465e2cd 100644 --- a/Handle_emg_data.py +++ b/Handle_emg_data.py @@ -6,8 +6,8 @@ from pathlib import Path import numpy as np from pandas.core.frame import DataFrame import sys -sys.path.insert(0, '/Users/Markus/Prosjekter git/Slovakia 2021/python_speech_features/python_speech_features') -from python_speech_features.python_speech_features import mfcc +sys.path.insert(0, '/Users/Markus/Prosjekter git/Slovakia 2021/psf_lib/python_speech_features/python_speech_features') +from psf_lib.python_speech_features.python_speech_features import mfcc import json diff --git a/Neural_Network_Analysis.py b/Neural_Network_Analysis.py index 9f1ec33..7445880 100644 --- a/Neural_Network_Analysis.py +++ b/Neural_Network_Analysis.py @@ -1,15 +1,19 @@ import json -from python_speech_features.python_speech_features.base import mfcc + +from keras import callbacks +from psf_lib.python_speech_features.python_speech_features.base import mfcc import numpy as np from sklearn.model_selection import train_test_split import tensorflow as tf import tensorflow.keras as keras from keras import backend as K from keras.regularizers import l2 +from keras.callbacks import Callback, CSVLogger, ModelCheckpoint from pathlib import Path import pandas as pd import matplotlib.pyplot as plt import statistics +import csv # Path to json file that stores MFCCs and subject labels for each processed sample DATA_PATH_MFCC = str(Path.cwd()) + "/mfcc_data.json" @@ -158,12 +162,16 @@ def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_su # Trains the model # Input: Keras.model, batch_size, nr epochs, training, and validation data # Ouput: History -def train(model, X_train, y_train, verbose, batch_size=64, epochs=30, X_validation=None, y_validation=None): +def train( model, X_train, y_train, verbose, batch_size=64, epochs=30, + X_validation=None, y_validation=None): optimiser = keras.optimizers.Adam(learning_rate=0.0001) model.compile(optimizer=optimiser, loss='categorical_crossentropy', metrics=['accuracy']) + + #csv_path = str(Path.cwd()) + '/logs/{}/{}_train_log.csv'.format(MODEL_NAME, MODEL_NAME) + #csv_logger = CSVLogger(csv_path, append=False) if X_validation != None: history = model.fit(X_train, @@ -233,11 +241,11 @@ def batch_formatting(X_train, X_test, y_train, y_test, batch_size=64, nr_classes return X_train_batch, X_test_batch, y_train_batch, y_test_batch -# Retrieves data sets for each session as test set and evalutes +# Retrieves data sets for each session as test set and evalutes. DOES USE prediction_csv_logger as default # the average of networks trained om them # Input: raw data, session_lengths list, total nr of sessions, batch_size, and nr of epochs # Ouput: tuple(cross validation average, list(result for each dataset(len=nr_sessions))) -def session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions, batch_size=64, epochs=30): +def session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions, log_to_csv=True, batch_size=64, epochs=30): session_training_results = [] for i in range(nr_sessions): @@ -246,25 +254,29 @@ def session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions, # Model: if model_name == 'LSTM': model = LSTM(input_shape=(1, 208)) + elif model_name == 'GRU': model = GRU(input_shape=(1, 208)) - elif model_name == 'CNN': - print(X_train_session.shape) - print(X_test_session.shape) + + elif model_name == 'CNN_1D': X_train_session = np.reshape(X_train_session, (X_train_session.shape[0], 208, 1)) X_test_session = np.reshape(X_test_session, (X_test_session.shape[0], 208, 1)) - model = CNN(input_shape=(208, 1)) - elif model_name == 'FNN': + model = CNN_1D(input_shape=(208, 1)) + + elif model_name == 'FFN': model = FFN(input_shape=(1, 208)) + else: raise Exception('Model not found') - model.summary() + #model.summary() train(model, X_train_session, y_train_session, verbose=1, batch_size=batch_size, epochs=epochs) test_loss, test_acc = model.evaluate(X_test_session, y_test_session, verbose=2) session_training_results.append(test_acc) + if log_to_csv: + prediction_csv_logger(X_test_session, y_test_session, model_name, model, i) del model K.clear_session() #print('Session', i, 'as test data gives accuracy:', test_acc) @@ -273,6 +285,22 @@ def session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions, return average_result, session_training_results +# Takes in test data and logs input data and the prediction from a model +# Input: raw data, session_lengths list, total nr of sessions, batch_size, and nr of epochs +# Ouput: tuple(cross validation average, list(result for each dataset(len=nr_sessions))) +def prediction_csv_logger(X, y, model_name, model, session_nr): + + csv_path = str(Path.cwd()) + '/logs/{}/{}_session{}_log.csv'.format(model_name, model_name, session_nr+1) + + layerOutput = model.predict(X, verbose=0) + + with open(csv_path, 'w') as csv_file: + writer = csv.writer(csv_file) + writer.writerow(['input', 'prediction', 'solution']) + data = zip(X, layerOutput, y) + writer.writerows(data) + csv_file.close() + # ----- MODELS ------ @@ -323,17 +351,13 @@ def FFN(input_shape, nr_classes=5): # Creates a keras.model with focus on Convulotion layers # Input: input shape, classes of classification # Ouput: model:Keras.model -def CNN(input_shape, nr_classes=5): +def CNN_1D(input_shape, nr_classes=5): model = keras.Sequential(name='CNN_model') - #model.add(keras.layers.Input(name='the_input', shape=input_shape, dtype='float32')) - model.add(keras.layers.Conv1D(32, kernel_size= 5, activation='relu', input_shape=input_shape)) # , input_shape=input_shape + model.add(keras.layers.Conv1D(32, kernel_size=5, activation='relu', input_shape=input_shape)) model.add(keras.layers.MaxPooling1D(pool_size=5)) - #model.add(keras.layers.BatchNormalization()) - model.add(keras.layers.Flatten()) - #model.add(keras.layers.GlobalAveragePooling1D()) - model.add(keras.layers.Dense(64, activation='relu')) # , input_shape=(...,1) + model.add(keras.layers.Dense(128, activation='relu')) model.add(keras.layers.Dropout(0.3)) # Ouput layer model.add(keras.layers.Dense(nr_classes, activation='softmax', name='Softmax')) @@ -353,10 +377,12 @@ if __name__ == "__main__": NR_SUBJECTS = 5 NR_SESSIONS = 4 BATCH_SIZE = 64 - EPOCHS = 30 + EPOCHS = 5 TEST_SESSION_NR = 4 - VERBOSE = 0 + VERBOSE = 1 + MODEL_NAME = 'CNN_1D' + LOG = True # ----- Get prepared data: train, validation, and test ------ # X_train.shape = (2806-X_test, 1, 208) @@ -367,7 +393,7 @@ if __name__ == "__main__": X_train, X_test, y_train, y_test = prepare_datasets_sessions(X, y, session_lengths, TEST_SESSION_NR) - #''' + ''' # ----- Make model ------ #model_GRU = GRU(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients) #model_LSTM = LSTM(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients) @@ -381,33 +407,53 @@ if __name__ == "__main__": # ----- Train network ------ #history_GRU = train(model_GRU, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS) #history_LSTM = train(model_LSTM, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS) - history_CNN_1D = train(model_CNN_1D, np.reshape(X_train, (X_train.shape[0], 208, 1)), y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS) + history_CNN_1D = train( model_CNN_1D, np.reshape(X_train, (X_train.shape[0], 208, 1)), + y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS) + # ----- Plot train accuracy/error ----- #plot_train_history(history) # ----- Evaluate model on test set ------ + #test_loss, test_acc = model_GRU.evaluate(X_test, y_test, verbose=VERBOSE) #print('\nTest accuracy GRU:', test_acc, '\n') #test_loss, test_acc = model_LSTM.evaluate(X_test, y_test, verbose=VERBOSE) #print('\nTest accuracy LSTM:', test_acc, '\n') - test_loss, test_acc = model_CNN_1D.evaluate(np.reshape(X_test, (X_test.shape[0], 208, 1)), y_test, verbose=VERBOSE) + test_loss, test_acc = model_CNN_1D.evaluate(np.reshape(X_test, (X_test.shape[0], 208, 1)), y_test, verbose=0) print('\nTest accuracy CNN_1D:', test_acc, '\n') - #''' + + # ----- Store test predictions in CSV ------ + prediction_csv_logger(np.reshape(X_test, (X_test.shape[0], 208, 1)), y_test, MODEL_NAME, model_CNN_1D, TEST_SESSION_NR) ''' + + + #''' # ----- Cross validation ------ - #average_GRU = session_cross_validation('GRU', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS) - #verage_LSTM = session_cross_validation('LSTM', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS) - #average_FFN = session_cross_validation('FNN', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS) - average_CNN = session_cross_validation('CNN', X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS) + average_GRU = session_cross_validation('GRU', X, y, session_lengths, nr_sessions=NR_SESSIONS, + log_to_csv=LOG, + batch_size=BATCH_SIZE, + epochs=EPOCHS) + average_LSTM = session_cross_validation('LSTM', X, y, session_lengths, nr_sessions=NR_SESSIONS, + log_to_csv=LOG, + batch_size=BATCH_SIZE, + epochs=EPOCHS) + average_FFN = session_cross_validation('FFN', X, y, session_lengths, nr_sessions=NR_SESSIONS, + log_to_csv=LOG, + batch_size=BATCH_SIZE, + epochs=EPOCHS) + average_CNN = session_cross_validation('CNN_1D', X, y, session_lengths, nr_sessions=NR_SESSIONS, + log_to_csv=LOG, + batch_size=BATCH_SIZE, + epochs=EPOCHS) print('\n') - #print('Crossvalidated GRU:', average_GRU) - #print('Crossvalidated LSTM:', average_LSTM) - #print('Crossvalidated FFN:', average_FFN) - print('Cross-validated CNN:', average_CNN) + print('Crossvalidated GRU:', average_GRU) + print('Crossvalidated LSTM:', average_LSTM) + print('Crossvalidated FFN:', average_FFN) + print('Cross-validated CNN_1D:', average_CNN) print('\n') - ''' + #''' diff --git a/python_speech_features.egg-info/PKG-INFO b/python_speech_features.egg-info/PKG-INFO deleted file mode 100644 index 7ae6330..0000000 --- a/python_speech_features.egg-info/PKG-INFO +++ /dev/null @@ -1,10 +0,0 @@ -Metadata-Version: 1.0 -Name: python-speech-features -Version: 0.6.1 -Summary: Python Speech Feature extraction -Home-page: https://github.com/jameslyons/python_speech_features -Author: James Lyons -Author-email: james.lyons0@gmail.com -License: MIT -Description: UNKNOWN -Platform: UNKNOWN diff --git a/python_speech_features.egg-info/SOURCES.txt b/python_speech_features.egg-info/SOURCES.txt deleted file mode 100644 index b33bcb8..0000000 --- a/python_speech_features.egg-info/SOURCES.txt +++ /dev/null @@ -1,7 +0,0 @@ -python_speech_features/example.py -python_speech_features/setup.py -python_speech_features.egg-info/PKG-INFO -python_speech_features.egg-info/SOURCES.txt -python_speech_features.egg-info/dependency_links.txt -python_speech_features.egg-info/requires.txt -python_speech_features.egg-info/top_level.txt \ No newline at end of file diff --git a/python_speech_features.egg-info/dependency_links.txt b/python_speech_features.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/python_speech_features.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/python_speech_features.egg-info/requires.txt b/python_speech_features.egg-info/requires.txt deleted file mode 100644 index 6bad103..0000000 --- a/python_speech_features.egg-info/requires.txt +++ /dev/null @@ -1,2 +0,0 @@ -numpy -scipy diff --git a/python_speech_features.egg-info/top_level.txt b/python_speech_features.egg-info/top_level.txt deleted file mode 100644 index 42c4020..0000000 --- a/python_speech_features.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -python_speech_features