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feat: add func to store predictions from test sets in csv

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Skudalen 2021-07-14 17:36:00 +02:00
parent 968759d205
commit 439238e070
9 changed files with 84 additions and 59 deletions
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Exp20201205_2myo_hard**
Exp20201205_2myo_soft**
Documents
python_speech_features**
data
docs
logs
psf_lib

4
Handle_emg_data.py
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@ -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

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Neural_Network_Analysis.py
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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')
'''
#'''

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python_speech_features.egg-info/PKG-INFO
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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

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python_speech_features.egg-info/SOURCES.txt
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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

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python_speech_features.egg-info/dependency_links.txt
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python_speech_features.egg-info/requires.txt
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numpy
scipy

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python_speech_features.egg-info/top_level.txt
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python_speech_features