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evaluate.py
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evaluate.py
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from functools import partial
from sklearn.model_selection import train_test_split, GridSearchCV, ShuffleSplit
from sklearn.neural_network import MLPRegressor, MLPClassifier
from sklearn.svm import SVR, SVC
from sklearn.metrics import confusion_matrix
from skorch import NeuralNetRegressor, NeuralNetClassifier
import skorch
import torch
import torch.nn as nn
import numpy as np
from pathlib import Path
torch.manual_seed(100)
np.random.seed(100)
def evaluate(experiment_name, frequency, device='cuda:2'):
experiments = {
'tool20_biotac_baseline_svmlinear' : partial(_evaluate_tool_svm, 20, 'biotac', 'default', 'linear'),
'tool20_biotac_baseline_svmrbf' : partial(_evaluate_tool_svm, 20, 'biotac', 'default', 'rbf'),
'tool20_biotac_baseline_mlp' : partial(_evaluate_tool_mlp, 20, 'biotac', 'default'),
'tool20_biotac_baseline_rnn' : partial(_evaluate_tool_rnn, 20, 'biotac', device),
'tool20_biotac_fft_svmlinear' : partial(_evaluate_tool_svm, 20, 'biotac', 'fft', 'linear'),
'tool20_biotac_fft_svmrbf' : partial(_evaluate_tool_svm, 20, 'biotac', 'fft', 'rbf'),
'tool20_biotac_fft_mlp' : partial(_evaluate_tool_mlp, 20, 'biotac', 'fft'),
'tool20_nuskin_baseline_svmlinear' : partial(_evaluate_tool_svm, 20, 'nuskin', 'default', 'linear'),
'tool20_nuskin_baseline_svmrbf' : partial(_evaluate_tool_svm, 20, 'nuskin', 'default', 'rbf'),
'tool20_nuskin_baseline_mlp' : partial(_evaluate_tool_mlp, 20, 'nuskin', 'default'),
'tool20_nuskin_baseline_rnn' : partial(_evaluate_tool_rnn, 20, 'nuskin', device),
'tool20_nuskin_fft_svmlinear' : partial(_evaluate_tool_svm, 20, 'nuskin', 'fft', 'linear'),
'tool20_nuskin_fft_svmrbf' : partial(_evaluate_tool_svm, 20, 'nuskin', 'fft', 'rbf'),
'tool20_nuskin_fft_mlp' : partial(_evaluate_tool_mlp, 20, 'nuskin', 'fft'),
'tool20_neuhalf_baseline_svmlinear' : partial(_evaluate_tool_svm, 20, 'neuhalf', 'default', 'linear'),
'tool20_neuhalf_baseline_svmrbf' : partial(_evaluate_tool_svm, 20, 'neuhalf', 'default', 'rbf'),
'tool20_neuhalf_baseline_mlp' : partial(_evaluate_tool_mlp, 20, 'neuhalf', 'default'),
'tool20_neuhalf_baseline_rnn' : partial(_evaluate_tool_rnn, 20, 'neuhalf', device),
'tool20_neuhalf_fft_svmlinear' : partial(_evaluate_tool_svm, 20, 'neuhalf', 'fft', 'linear'),
'tool20_neuhalf_fft_svmrbf' : partial(_evaluate_tool_svm, 20, 'neuhalf', 'fft', 'rbf'),
'tool20_neuhalf_fft_mlp' : partial(_evaluate_tool_mlp, 20, 'neuhalf', 'fft'),
'tool30_biotac_baseline_svmlinear' : partial(_evaluate_tool_svm, 30, 'biotac', 'default', 'linear'),
'tool30_biotac_baseline_svmrbf' : partial(_evaluate_tool_svm, 30, 'biotac', 'default', 'rbf'),
'tool30_biotac_baseline_mlp' : partial(_evaluate_tool_mlp, 30, 'biotac', 'default'),
'tool30_biotac_baseline_rnn' : partial(_evaluate_tool_rnn, 30, 'biotac', device),
'tool30_biotac_fft_svmlinear' : partial(_evaluate_tool_svm, 30, 'biotac', 'fft', 'linear'),
'tool30_biotac_fft_svmrbf' : partial(_evaluate_tool_svm, 30, 'biotac', 'fft', 'rbf'),
'tool30_biotac_fft_mlp' : partial(_evaluate_tool_mlp, 30, 'biotac', 'fft'),
'tool30_nuskin_baseline_svmlinear' : partial(_evaluate_tool_svm, 30, 'nuskin', 'default', 'linear'),
'tool30_nuskin_baseline_svmrbf' : partial(_evaluate_tool_svm, 30, 'nuskin', 'default', 'rbf'),
'tool30_nuskin_baseline_mlp' : partial(_evaluate_tool_mlp, 30, 'nuskin', 'default'),
'tool30_nuskin_baseline_rnn' : partial(_evaluate_tool_rnn, 30, 'nuskin', device),
'tool30_nuskin_fft_svmlinear' : partial(_evaluate_tool_svm, 30, 'nuskin', 'fft', 'linear'),
'tool30_nuskin_fft_svmrbf' : partial(_evaluate_tool_svm, 30, 'nuskin', 'fft', 'rbf'),
'tool30_nuskin_fft_mlp' : partial(_evaluate_tool_mlp, 30, 'nuskin', 'fft'),
'tool30_neuhalf_baseline_svmlinear' : partial(_evaluate_tool_svm, 30, 'neuhalf', 'default', 'linear'),
'tool30_neuhalf_baseline_svmrbf' : partial(_evaluate_tool_svm, 30, 'neuhalf', 'default', 'rbf'),
'tool30_neuhalf_baseline_mlp' : partial(_evaluate_tool_mlp, 30, 'neuhalf', 'default'),
'tool30_neuhalf_baseline_rnn' : partial(_evaluate_tool_rnn, 30, 'neuhalf', device),
'tool30_neuhalf_fft_svmlinear' : partial(_evaluate_tool_svm, 30, 'neuhalf', 'fft', 'linear'),
'tool30_neuhalf_fft_svmrbf' : partial(_evaluate_tool_svm, 30, 'neuhalf', 'fft', 'rbf'),
'tool30_neuhalf_fft_mlp' : partial(_evaluate_tool_mlp, 30, 'neuhalf', 'fft'),
'tool50_biotac_baseline_svmlinear' : partial(_evaluate_tool_svm, 50, 'biotac', 'default', 'linear'),
'tool50_biotac_baseline_svmrbf' : partial(_evaluate_tool_svm, 50, 'biotac', 'default', 'rbf'),
'tool50_biotac_baseline_mlp' : partial(_evaluate_tool_mlp, 50, 'biotac', 'default'),
'tool50_biotac_baseline_rnn' : partial(_evaluate_tool_rnn, 50, 'biotac', device),
'tool50_biotac_fft_svmlinear' : partial(_evaluate_tool_svm, 50, 'biotac', 'fft', 'linear'),
'tool50_biotac_fft_svmrbf' : partial(_evaluate_tool_svm, 50, 'biotac', 'fft', 'rbf'),
'tool50_biotac_fft_mlp' : partial(_evaluate_tool_mlp, 50, 'biotac', 'fft'),
'tool50_nuskin_baseline_svmlinear' : partial(_evaluate_tool_svm, 50, 'nuskin', 'default', 'linear'),
'tool50_nuskin_baseline_svmrbf' : partial(_evaluate_tool_svm, 50, 'nuskin', 'default', 'rbf'),
'tool50_nuskin_baseline_mlp' : partial(_evaluate_tool_mlp, 50, 'nuskin', 'default'),
'tool50_nuskin_baseline_rnn' : partial(_evaluate_tool_rnn, 50, 'nuskin', device),
'tool50_nuskin_fft_svmlinear' : partial(_evaluate_tool_svm, 50, 'nuskin', 'fft', 'linear'),
'tool50_nuskin_fft_svmrbf' : partial(_evaluate_tool_svm, 50, 'nuskin', 'fft', 'rbf'),
'tool50_nuskin_fft_mlp' : partial(_evaluate_tool_mlp, 50, 'nuskin', 'fft'),
'tool50_neuhalf_baseline_svmlinear' : partial(_evaluate_tool_svm, 50, 'neuhalf', 'default', 'linear'),
'tool50_neuhalf_baseline_svmrbf' : partial(_evaluate_tool_svm, 50, 'neuhalf', 'default', 'rbf'),
'tool50_neuhalf_baseline_mlp' : partial(_evaluate_tool_mlp, 50, 'neuhalf', 'default'),
'tool50_neuhalf_baseline_rnn' : partial(_evaluate_tool_rnn, 50, 'neuhalf', device),
'tool50_neuhalf_fft_svmlinear' : partial(_evaluate_tool_svm, 50, 'neuhalf', 'fft', 'linear'),
'tool50_neuhalf_fft_svmrbf' : partial(_evaluate_tool_svm, 50, 'neuhalf', 'fft', 'rbf'),
'tool50_neuhalf_fft_mlp' : partial(_evaluate_tool_mlp, 50, 'neuhalf', 'fft'),
'tool20_nuskin_autoencoder_svmlinear' : partial(_evaluate_tool_svm, 20, 'nuskin', 'autoencoder', 'linear'),
'tool20_nuskin_autoencoder_svmrbf' : partial(_evaluate_tool_svm, 20, 'nuskin', 'autoencoder', 'rbf'),
'tool20_nuskin_autoencoder_mlp' : partial(_evaluate_tool_mlp, 20, 'nuskin', 'autoencoder'),
'tool20_biotac_autoencoder_svmlinear' : partial(_evaluate_tool_svm, 20, 'biotac', 'autoencoder', 'linear'),
'tool20_biotac_autoencoder_svmrbf' : partial(_evaluate_tool_svm, 20, 'biotac', 'autoencoder', 'rbf'),
'tool20_biotac_autoencoder_mlp' : partial(_evaluate_tool_mlp, 20, 'biotac', 'autoencoder'),
'tool30_nuskin_autoencoder_svmlinear' : partial(_evaluate_tool_svm, 30, 'nuskin', 'autoencoder', 'linear'),
'tool30_nuskin_autoencoder_svmrbf' : partial(_evaluate_tool_svm, 30, 'nuskin', 'autoencoder', 'rbf'),
'tool30_nuskin_autoencoder_mlp' : partial(_evaluate_tool_mlp, 30, 'nuskin', 'autoencoder'),
'tool30_biotac_autoencoder_svmlinear' : partial(_evaluate_tool_svm, 30, 'biotac', 'autoencoder', 'linear'),
'tool30_biotac_autoencoder_svmrbf' : partial(_evaluate_tool_svm, 30, 'biotac', 'autoencoder', 'rbf'),
'tool30_biotac_autoencoder_mlp' : partial(_evaluate_tool_mlp, 30, 'biotac', 'autoencoder'),
'tool50_nuskin_autoencoder_svmlinear' : partial(_evaluate_tool_svm, 50, 'nuskin', 'autoencoder', 'linear'),
'tool50_nuskin_autoencoder_svmrbf' : partial(_evaluate_tool_svm, 50, 'nuskin', 'autoencoder', 'rbf'),
'tool50_nuskin_autoencoder_mlp' : partial(_evaluate_tool_mlp, 50, 'nuskin', 'autoencoder'),
'tool50_biotac_autoencoder_svmlinear' : partial(_evaluate_tool_svm, 50, 'biotac', 'autoencoder', 'linear'),
'tool50_biotac_autoencoder_svmrbf' : partial(_evaluate_tool_svm, 50, 'biotac', 'autoencoder', 'rbf'),
'tool50_biotac_autoencoder_mlp' : partial(_evaluate_tool_mlp, 50, 'biotac', 'autoencoder'),
'tool20_neusingle_baseline_svmlinear' : partial(_evaluate_tool_neusingle_svm, 20, 'linear', False),
'tool20_neusingle_baseline_svmrbf' : partial(_evaluate_tool_neusingle_svm, 20, 'rbf', False),
'tool20_neusingle_fft_svmlinear' : partial(_evaluate_tool_neusingle_svm, 20, 'linear', True),
'tool20_neusingle_fft_svmrbf' : partial(_evaluate_tool_neusingle_svm, 20, 'rbf', True),
'tool30_neusingle_baseline_svmlinear' : partial(_evaluate_tool_neusingle_svm, 30, 'linear', False),
'tool30_neusingle_baseline_svmrbf' : partial(_evaluate_tool_neusingle_svm, 30, 'rbf', False),
'tool30_neusingle_fft_svmlinear' : partial(_evaluate_tool_neusingle_svm, 30, 'linear', True),
'tool30_neusingle_fft_svmrbf' : partial(_evaluate_tool_neusingle_svm, 30, 'rbf', True),
'tool50_neusingle_baseline_svmlinear' : partial(_evaluate_tool_neusingle_svm, 50, 'linear', False),
'tool50_neusingle_baseline_svmrbf' : partial(_evaluate_tool_neusingle_svm, 50, 'rbf', False),
'tool50_neusingle_fft_svmlinear' : partial(_evaluate_tool_neusingle_svm, 50, 'linear', True),
'tool50_neusingle_fft_svmrbf' : partial(_evaluate_tool_neusingle_svm, 50, 'rbf', True),
'handoverrod_biotac_baseline_svmlinear' : partial(_evaluate_handover_svm, 'rod', 'biotac', False, 'linear'),
'handoverrod_biotac_baseline_svmrbf' : partial(_evaluate_handover_svm, 'rod', 'biotac', False, 'rbf'),
'handoverrod_biotac_baseline_mlp' : partial(_evaluate_handover_mlp, 'rod', 'biotac', False),
'handoverrod_biotac_baseline_rnn' : partial(_evaluate_handover_rnn, 'rod', 'biotac', device),
'handoverrod_biotac_fft_svmlinear' : partial(_evaluate_handover_svm, 'rod', 'biotac', True, 'linear'),
'handoverrod_biotac_fft_svmrbf' : partial(_evaluate_handover_svm, 'rod', 'biotac', True, 'rbf'),
'handoverrod_biotac_fft_mlp' : partial(_evaluate_handover_mlp, 'rod', 'biotac', True),
'handoverrod_nuskin_baseline_svmlinear' : partial(_evaluate_handover_svm, 'rod', 'nuskin', False, 'linear'),
'handoverrod_nuskin_baseline_svmrbf' : partial(_evaluate_handover_svm, 'rod', 'nuskin', False, 'rbf'),
'handoverrod_nuskin_baseline_mlp' : partial(_evaluate_handover_mlp, 'rod', 'nuskin', False),
'handoverrod_nuskin_baseline_rnn' : partial(_evaluate_handover_rnn, 'rod', 'nuskin', device),
'handoverrod_nuskin_fft_svmlinear' : partial(_evaluate_handover_svm, 'rod', 'nuskin', True, 'linear'),
'handoverrod_nuskin_fft_svmrbf' : partial(_evaluate_handover_svm, 'rod', 'nuskin', True, 'rbf'),
'handoverrod_nuskin_fft_mlp' : partial(_evaluate_handover_mlp, 'rod', 'nuskin', True),
'handoverrod_neuhalf_baseline_svmlinear' : partial(_evaluate_handover_svm, 'rod', 'neuhalf', False, 'linear'),
'handoverrod_neuhalf_baseline_svmrbf' : partial(_evaluate_handover_svm, 'rod', 'neuhalf', False, 'rbf'),
'handoverrod_neuhalf_baseline_mlp' : partial(_evaluate_handover_mlp, 'rod', 'neuhalf', False),
'handoverrod_neuhalf_baseline_rnn' : partial(_evaluate_handover_rnn, 'rod', 'neuhalf', device),
'handoverrod_neuhalf_fft_svmlinear' : partial(_evaluate_handover_svm, 'rod', 'neuhalf', True, 'linear'),
'handoverrod_neuhalf_fft_svmrbf' : partial(_evaluate_handover_svm, 'rod', 'neuhalf', True, 'rbf'),
'handoverrod_neuhalf_fft_mlp' : partial(_evaluate_handover_mlp, 'rod', 'neuhalf', True),
'handoverbox_biotac_baseline_svmlinear' : partial(_evaluate_handover_svm, 'box', 'biotac', False, 'linear'),
'handoverbox_biotac_baseline_svmrbf' : partial(_evaluate_handover_svm, 'box', 'biotac', False, 'rbf'),
'handoverbox_biotac_baseline_mlp' : partial(_evaluate_handover_mlp, 'box', 'biotac', False),
'handoverbox_biotac_baseline_rnn' : partial(_evaluate_handover_rnn, 'box', 'biotac', device),
'handoverbox_biotac_fft_svmlinear' : partial(_evaluate_handover_svm, 'box', 'biotac', True, 'linear'),
'handoverbox_biotac_fft_svmrbf' : partial(_evaluate_handover_svm, 'box', 'biotac', True, 'rbf'),
'handoverbox_biotac_fft_mlp' : partial(_evaluate_handover_mlp, 'box', 'biotac', True),
'handoverbox_nuskin_baseline_svmlinear' : partial(_evaluate_handover_svm, 'box', 'nuskin', False, 'linear'),
'handoverbox_nuskin_baseline_svmrbf' : partial(_evaluate_handover_svm, 'box', 'nuskin', False, 'rbf'),
'handoverbox_nuskin_baseline_mlp' : partial(_evaluate_handover_mlp, 'box', 'nuskin', False),
'handoverbox_nuskin_baseline_rnn' : partial(_evaluate_handover_rnn, 'box', 'nuskin', device),
'handoverbox_nuskin_fft_svmlinear' : partial(_evaluate_handover_svm, 'box', 'nuskin', True, 'linear'),
'handoverbox_nuskin_fft_svmrbf' : partial(_evaluate_handover_svm, 'box', 'nuskin', True, 'rbf'),
'handoverbox_nuskin_fft_mlp' : partial(_evaluate_handover_mlp, 'box', 'nuskin', True),
'handoverbox_neuhalf_baseline_svmlinear' : partial(_evaluate_handover_svm, 'box', 'neuhalf', False, 'linear'),
'handoverbox_neuhalf_baseline_svmrbf' : partial(_evaluate_handover_svm, 'box', 'neuhalf', False, 'rbf'),
'handoverbox_neuhalf_baseline_mlp' : partial(_evaluate_handover_mlp, 'box', 'neuhalf', False),
'handoverbox_neuhalf_baseline_rnn' : partial(_evaluate_handover_rnn, 'box', 'neuhalf', device),
'handoverbox_neuhalf_fft_svmlinear' : partial(_evaluate_handover_svm, 'box', 'neuhalf', True, 'linear'),
'handoverbox_neuhalf_fft_svmrbf' : partial(_evaluate_handover_svm, 'box', 'neuhalf', True, 'rbf'),
'handoverbox_neuhalf_fft_mlp' : partial(_evaluate_handover_mlp, 'box', 'neuhalf', True),
'handoverplate_biotac_baseline_svmlinear' : partial(_evaluate_handover_svm, 'plate', 'biotac', False, 'linear'),
'handoverplate_biotac_baseline_svmrbf' : partial(_evaluate_handover_svm, 'plate', 'biotac', False, 'rbf'),
'handoverplate_biotac_baseline_mlp' : partial(_evaluate_handover_mlp, 'plate', 'biotac', False),
'handoverplate_biotac_baseline_rnn' : partial(_evaluate_handover_rnn, 'plate', 'biotac', device),
'handoverplate_biotac_fft_svmlinear' : partial(_evaluate_handover_svm, 'plate', 'biotac', True, 'linear'),
'handoverplate_biotac_fft_svmrbf' : partial(_evaluate_handover_svm, 'plate', 'biotac', True, 'rbf'),
'handoverplate_biotac_fft_mlp' : partial(_evaluate_handover_mlp, 'plate', 'biotac', True),
'handoverplate_nuskin_baseline_svmlinear' : partial(_evaluate_handover_svm, 'plate', 'nuskin', False, 'linear'),
'handoverplate_nuskin_baseline_svmrbf' : partial(_evaluate_handover_svm, 'plate', 'nuskin', False, 'rbf'),
'handoverplate_nuskin_baseline_mlp' : partial(_evaluate_handover_mlp, 'plate', 'nuskin', False),
'handoverplate_nuskin_baseline_rnn' : partial(_evaluate_handover_rnn, 'plate', 'nuskin', device),
'handoverplate_nuskin_fft_svmlinear' : partial(_evaluate_handover_svm, 'plate', 'nuskin', True, 'linear'),
'handoverplate_nuskin_fft_svmrbf' : partial(_evaluate_handover_svm, 'plate', 'nuskin', True, 'rbf'),
'handoverplate_nuskin_fft_mlp' : partial(_evaluate_handover_mlp, 'plate', 'nuskin', True),
'handoverplate_neuhalf_baseline_svmlinear' : partial(_evaluate_handover_svm, 'plate', 'neuhalf', False, 'linear'),
'handoverplate_neuhalf_baseline_svmrbf' : partial(_evaluate_handover_svm, 'plate', 'neuhalf', False, 'rbf'),
'handoverplate_neuhalf_baseline_mlp' : partial(_evaluate_handover_mlp, 'plate', 'neuhalf', False),
'handoverplate_neuhalf_baseline_rnn' : partial(_evaluate_handover_rnn, 'plate', 'neuhalf', device),
'handoverplate_neuhalf_fft_svmlinear' : partial(_evaluate_handover_svm, 'plate', 'neuhalf', True, 'linear'),
'handoverplate_neuhalf_fft_svmrbf' : partial(_evaluate_handover_svm, 'plate', 'neuhalf', True, 'rbf'),
'handoverplate_neuhalf_fft_mlp' : partial(_evaluate_handover_mlp, 'plate', 'neuhalf', True),
'food_biotac_baseline_svmlinear' : partial(_evaluate_food_svm, 'biotac', False, 'linear'),
'food_biotac_baseline_svmrbf' : partial(_evaluate_food_svm, 'biotac', False, 'rbf'),
'food_biotac_baseline_mlp' : partial(_evaluate_food_mlp, 'biotac', False),
'food_biotac_baseline_rnn' : partial(_evaluate_food_rnn, 'biotac', device),
'food_biotac_fft_svmlinear' : partial(_evaluate_food_svm, 'biotac', True, 'linear'),
'food_biotac_fft_svmrbf' : partial(_evaluate_food_svm, 'biotac', True, 'rbf'),
'food_biotac_fft_mlp' : partial(_evaluate_food_mlp, 'biotac', True),
'food_nuskin_baseline_svmlinear' : partial(_evaluate_food_svm, 'nuskin', False, 'linear'),
'food_nuskin_baseline_svmrbf' : partial(_evaluate_food_svm, 'nuskin', False, 'rbf'),
'food_nuskin_baseline_mlp' : partial(_evaluate_food_mlp, 'nuskin', False),
'food_nuskin_baseline_rnn' : partial(_evaluate_food_rnn, 'nuskin', device),
'food_nuskin_fft_svmlinear' : partial(_evaluate_food_svm, 'nuskin', True, 'linear'),
'food_nuskin_fft_svmrbf' : partial(_evaluate_food_svm, 'nuskin', True, 'rbf'),
'food_nuskin_fft_mlp' : partial(_evaluate_food_mlp, 'nuskin', True),
'food_neuhalf_baseline_svmlinear' : partial(_evaluate_food_svm, 'neuhalf', False, 'linear'),
'food_neuhalf_baseline_svmrbf' : partial(_evaluate_food_svm, 'neuhalf', False, 'rbf'),
'food_neuhalf_baseline_mlp' : partial(_evaluate_food_mlp, 'neuhalf', False),
'food_neuhalf_baseline_rnn' : partial(_evaluate_food_rnn, 'neuhalf', device),
'food_neuhalf_fft_svmlinear' : partial(_evaluate_food_svm, 'neuhalf', True, 'linear'),
'food_neuhalf_fft_svmrbf' : partial(_evaluate_food_svm, 'neuhalf', True, 'rbf'),
'food_neuhalf_fft_mlp' : partial(_evaluate_food_mlp, 'neuhalf', True),
}
if experiment_name not in experiments: raise Exception('Experiment not found')
test_loss_mean, test_loss_std = experiments[experiment_name](frequency)
print('Result for {:s} ({:4d} Hz): {:0.4f} ± {:0.4f}'.format(experiment_name, frequency, test_loss_mean, test_loss_std))
print()
class RNNModule(nn.Module):
def __init__(self, input_dim, output_dim):
super(RNNModule, self).__init__()
self.rnn = nn.GRU(input_dim, 16, batch_first=True)
self.linear1 = nn.Linear(16, 8)
self.linear2 = nn.Linear(8, output_dim)
def forward(self, X):
X, _ = self.rnn(X)
X = torch.squeeze(X[:, -1, :])
X = torch.relu(self.linear1(X))
X = self.linear2(X)
return X
# ______ _ _
# | ____| | | | |
# | |____ ____ _| |_ _ __ _| |_ ___ _ __ ___
# | __\ \ / / _` | | | | |/ _` | __/ _ \| '__/ __|
# | |___\ V / (_| | | |_| | (_| | || (_) | | \__ \
# |______\_/ \__,_|_|\__,_|\__,_|\__\___/|_| |___/
#
def _create_evaluator(estimator, param_grid, scoring, cv=4, N=5, callback=None, cm_name=None):
gs_estimator = GridSearchCV(estimator=estimator, param_grid=param_grid, scoring=scoring, cv=cv, n_jobs=3, refit=True)
def evaluate(X, y, verbose=True):
test_losses = np.zeros(N)
cms = []
for n in range(N):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=n, stratify=y)
gs_estimator.fit(X_train, y_train)
test_loss = -gs_estimator.score(X_test, y_test)
test_losses[n] = test_loss
if callback is not None: callback(gs_estimator, X_test, y_test)
if verbose: print('Iteration {:02d} | Test Loss = {:0.4f}'.format(n, test_loss))
if cm_name is not None:
cms.append( confusion_matrix(y_test, gs_estimator.predict(X_test)) )
if cm_name is not None:
import pickle
pickle.dump(cms, open(f'{cm_name}.pkl' ,'wb'))
return np.mean(test_losses), np.std(test_losses)
return evaluate
# _______ _ ______ _ _
# |__ __| | | | ____| (_) | |
# | | ___ ___ | | | |__ __ ___ __ ___ _ __ _ _ __ ___ ___ _ __ | |_ ___
# | |/ _ \ / _ \| | | __| \ \/ / '_ \ / _ \ '__| | '_ ` _ \ / _ \ '_ \| __/ __|
# | | (_) | (_) | | | |____ > <| |_) | __/ | | | | | | | | __/ | | | |_\__ \
# |_|\___/ \___/|_| |______/_/\_\ .__/ \___|_| |_|_| |_| |_|\___|_| |_|\__|___/
# | |
def _load_tool(tool_length, signal_type, transformation, frequency):
data_dir = Path('data/')
if signal_type == 'biotac':
npzfile = np.load(data_dir / f'preprocessed/biotac_tool_{tool_length}_{frequency}hz.npz')
X = npzfile['signals'] / 1000
y = npzfile['labels'] * 100
if signal_type == 'nuskin':
npzfile = np.load(data_dir / f'preprocessed/nuskin_tool_{tool_length}_{frequency}hz.npz')
X = npzfile['signals'] / 1000
y = npzfile['labels'] * 100
if signal_type == 'neuhalf':
npzfile = np.load(data_dir / f'preprocessed/nuskin_tool_{tool_length}_4000hz.npz')
X = npzfile['signals'][:, 0:40, :] / 40
y = npzfile['labels'] * 100
if transformation == 'default':
X = np.reshape(X, (X.shape[0], -1))
if transformation == 'autoencoder':
X = np.load(f'autoencoder/{signal_type}_tool_{tool_length}.npy')
if transformation == 'fft':
X = np.abs(np.fft.fft(X)) / 10
X = np.reshape(X, (X.shape[0], -1))
if transformation == 'tensor' and signal_type == 'biotac':
X = torch.Tensor(np.expand_dims(X, 2))
y = torch.Tensor(np.reshape(y, (-1, 1)))
if transformation == 'tensor' and (signal_type == 'nuskin' or signal_type == 'neuhalf'):
X = torch.Tensor(np.swapaxes(X, 1, 2))
y = torch.Tensor(np.reshape(y, (-1, 1)))
return X, y
def _evaluate_tool_svm(tool_length, signal_type, transformation, kernel, frequency):
X, y = _load_tool(tool_length, signal_type, transformation, frequency)
param_grid = { 'C': [1, 3, 10, 30, 100] }
estimator = SVR(kernel=kernel, max_iter=5000)
evaluate = _create_evaluator(estimator, param_grid, 'neg_mean_absolute_error')
return evaluate(X, y)
def _evaluate_tool_mlp(tool_length, signal_type, transformation, frequency):
X, y = _load_tool(tool_length, signal_type, transformation, frequency)
param_grid = {
'learning_rate_init': [0.01, 0.03, 0.1, 0.3],
'alpha': [0.0001, 0.001]
}
estimator = MLPRegressor(hidden_layer_sizes=(16, 8), max_iter=2000, random_state=100)
evaluate = _create_evaluator(estimator, param_grid, 'neg_mean_absolute_error')
return evaluate(X, y)
def _evaluate_tool_rnn(tool_length, signal_type, device, frequency):
X, y = _load_tool(tool_length, signal_type, 'tensor', frequency)
param_grid = { 'lr': [0.001, 0.01, 0.01] }
estimator = NeuralNetRegressor(RNNModule,
module__input_dim=X.shape[2],
module__output_dim=1,
iterator_train__shuffle=True,
optimizer=torch.optim.Adam,
max_epochs=500,
batch_size=256,
train_split=False,
device=device,
verbose=0)
evaluate = _create_evaluator(estimator,
param_grid,
'neg_mean_absolute_error',
ShuffleSplit(n_splits=1, test_size=.2, random_state=0))
return evaluate(X, y)
def _evaluate_tool_neusingle_svm(tool_length, kernel, perform_fft, frequency):
X, y = _load_tool(tool_length, 'nuskin', 'fft' if perform_fft else 'default', frequency)
X = np.reshape(X, (X.shape[0], 80, -1))
best_taxel = 0
best_test_loss_mean = float('inf')
best_test_loss_std = float('inf')
with open(f'results/nuskin_singletool_{tool_length}.csv', 'w') as file:
for taxel in range(1, 81):
param_grid = { 'C': [1, 3, 10, 30, 100] }
estimator = SVR(kernel=kernel, max_iter=5000)
evaluate = _create_evaluator(estimator, param_grid, 'neg_mean_absolute_error')
test_loss_mean, test_loss_std = evaluate(X[:, taxel-1, :], y)
file.write(f'{taxel},{test_loss_mean},{test_loss_std}\n')
if test_loss_mean < best_test_loss_mean:
best_taxel = taxel
best_test_loss_mean = test_loss_mean
best_test_loss_std = test_loss_std
print('Result for taxel {:02d}: {:0.4f} ± {:0.4f}'.format(taxel, test_loss_mean, test_loss_std), flush=True)
print(f'Best performing taxel is {best_taxel}')
return best_test_loss_mean, best_test_loss_std
# _ _ _ ______ _ _
# | | | | | | | ____| (_) | |
# | |__| | __ _ _ __ __| | _____ _____ _ __ | |__ __ ___ __ ___ _ __ _ _ __ ___ ___ _ __ | |_ ___
# | __ |/ _` | '_ \ / _` |/ _ \ \ / / _ \ '__| | __| \ \/ / '_ \ / _ \ '__| | '_ ` _ \ / _ \ '_ \| __/ __|
# | | | | (_| | | | | (_| | (_) \ V / __/ | | |____ > <| |_) | __/ | | | | | | | | __/ | | | |_\__ \
# |_| |_|\__,_|_| |_|\__,_|\___/ \_/ \___|_| |______/_/\_\ .__/ \___|_| |_|_| |_| |_|\___|_| |_|\__|___/
# | |
def _load_handover(item, signal_type, transformation, frequency):
data_dir = Path('data/')
if signal_type == 'biotac':
npzfile = np.load(data_dir / f'preprocessed/biotac_handover_{item}_{frequency}hz.npz')
X = npzfile['signals'] / 1000
y = npzfile['labels'][:, 0].astype(int)
if signal_type == 'nuskin':
npzfile = np.load(data_dir / f'preprocessed/nuskin_handover_{item}_{frequency}hz.npz')
X = npzfile['signals'] / 40
y = npzfile['labels'][:, 0].astype(int)
if signal_type == 'neuhalf':
npzfile = np.load(data_dir / f'preprocessed/nuskin_handover_{item}_4000hz.npz')
X = npzfile['signals'][:, 0:40, :] / 40
y = npzfile['labels'][:, 0].astype(int)
if transformation == 'default':
X = np.reshape(X, (X.shape[0], -1))
if transformation == 'fft':
X = np.abs(np.fft.fft(X)) / 10
X = np.reshape(X, (X.shape[0], -1))
if transformation == 'tensor' and signal_type == 'biotac':
X = torch.Tensor(np.expand_dims(X, 2))
y = torch.Tensor(np.reshape(y, (-1))).type(torch.long)
if transformation == 'tensor' and (signal_type == 'nuskin' or signal_type == 'neuhalf'):
X = torch.Tensor(np.swapaxes(X, 1, 2))
y = torch.Tensor(np.reshape(y, (-1))).type(torch.long)
return X, y
def _evaluate_handover_svm(item, signal_type, perform_fft, kernel, frequency):
X, y = _load_handover(item, signal_type, 'fft' if perform_fft else 'default', frequency)
# param_grid = { 'C': [0.0001, 0.01, 0.1, 0.5, 0.6, 1, 3, 10, 30, 100, 200, 300, 500, 1000] }
param_grid = { 'C': [1, 3, 10, 30, 100] }
estimator = SVC(kernel=kernel, max_iter=5000)
evaluate = _create_evaluator(estimator, param_grid, 'accuracy', N=20)
return evaluate(X, y)
def _evaluate_handover_mlp(item, signal_type, perform_fft, frequency):
X, y = _load_handover(item, signal_type, 'fft' if perform_fft else 'default', frequency)
param_grid = {
'learning_rate_init': [0.01, 0.03, 0.1, 0.3],
'alpha': [0.0001, 0.001]
}
estimator = MLPClassifier(hidden_layer_sizes=(16, 8), max_iter=2000, random_state=100)
evaluate = _create_evaluator(estimator, param_grid, 'accuracy', N=20)
return evaluate(X, y)
def _evaluate_handover_rnn(item, signal_type, device, frequency):
X, y = _load_handover(item, signal_type, 'tensor', frequency)
param_grid = { 'lr': [0.001, 0.003, 0.01] }
estimator = NeuralNetClassifier(RNNModule,
module__input_dim=X.shape[2],
module__output_dim=2,
iterator_train__shuffle=True,
optimizer=torch.optim.Adam,
criterion=nn.CrossEntropyLoss,
max_epochs=500,
batch_size=256,
train_split=False,
device=device,
verbose=0)
evaluate = _create_evaluator(estimator,
param_grid,
'accuracy',
ShuffleSplit(n_splits=1, test_size=.2, random_state=0))
return evaluate(X, y)
# ______ _ ______ _ _
# | ____| | | | ____| (_) | |
# | |__ ___ ___ __| | | |__ __ ___ __ ___ _ __ _ _ __ ___ ___ _ __ | |_ ___
# | __/ _ \ / _ \ / _` | | __| \ \/ / '_ \ / _ \ '__| | '_ ` _ \ / _ \ '_ \| __/ __|
# | | | (_) | (_) | (_| | | |____ > <| |_) | __/ | | | | | | | | __/ | | | |_\__ \
# |_| \___/ \___/ \__,_| |______/_/\_\ .__/ \___|_| |_|_| |_| |_|\___|_| |_|\__|___/
# | |
def _load_food(signal_type, transformation, frequency):
classes = [ 'empty', 'water', 'tofu', 'watermelon', 'banana', 'apple', 'pepper' ]
base_signal_type = signal_type if signal_type != 'neuhalf' else 'nuskin'
data_dir = Path('data/')
npzfile0 = np.load(data_dir / f'preprocessed/{base_signal_type}_food_empty_{frequency}hz.npz')
npzfile1 = np.load(data_dir / f'preprocessed/{base_signal_type}_food_water_{frequency}hz.npz')
npzfile2 = np.load(data_dir / f'preprocessed/{base_signal_type}_food_tofu_{frequency}hz.npz')
npzfile3 = np.load(data_dir / f'preprocessed/{base_signal_type}_food_watermelon_{frequency}hz.npz')
npzfile4 = np.load(data_dir / f'preprocessed/{base_signal_type}_food_banana_{frequency}hz.npz')
npzfile5 = np.load(data_dir / f'preprocessed/{base_signal_type}_food_apple_{frequency}hz.npz')
npzfile6 = np.load(data_dir / f'preprocessed/{base_signal_type}_food_pepper_{frequency}hz.npz')
X = np.vstack((npzfile0['signals'],
npzfile1['signals'],
npzfile2['signals'],
npzfile3['signals'],
npzfile4['signals'],
npzfile5['signals'],
npzfile6['signals']))
y = np.concatenate((npzfile0['labels'] * 0,
npzfile1['labels'] * 1,
npzfile2['labels'] * 2,
npzfile3['labels'] * 3,
npzfile4['labels'] * 4,
npzfile5['labels'] * 5,
npzfile6['labels'] * 6)).astype(int)
if signal_type == 'biotac':
X = X / 1000
if signal_type == 'nuskin':
X = X / 40
if signal_type == 'neuhalf':
X = X[:, 0:40, :] / 40
if transformation == 'default':
X = np.reshape(X, (X.shape[0], -1))
if transformation == 'fft':
X = np.abs(np.fft.fft(X)) / 10
X = np.reshape(X, (X.shape[0], -1))
if transformation == 'tensor' and signal_type == 'biotac':
X = torch.Tensor(np.expand_dims(X, 2))
y = torch.Tensor(np.reshape(y, (-1))).type(torch.long)
if transformation == 'tensor' and (signal_type == 'nuskin' or signal_type == 'neuhalf'):
X = torch.Tensor(np.swapaxes(X, 1, 2))
y = torch.Tensor(np.reshape(y, (-1))).type(torch.long)
return X, y
def _evaluate_food_svm(signal_type, perform_fft, kernel, frequency):
X, y = _load_food(signal_type, 'fft' if perform_fft else 'default', frequency)
param_grid = { 'C': [0.1, 1, 3, 10, 30, 100, 200, 300, 500] }
features = 'fft' if perform_fft else 'baseline'
kernel_name = 'svmlinear' if kernel == 'linear' else 'svmrbf'
cm_name = f'results/food_{signal_type}_{features}_{kernel_name}_{frequency}'
estimator = SVC(kernel=kernel, max_iter=5000)
evaluate = _create_evaluator(estimator, param_grid, 'accuracy', N=20, cm_name=cm_name)
return evaluate(X, y)
def _evaluate_food_mlp(signal_type, perform_fft, frequency):
X, y = _load_food(signal_type, 'fft' if perform_fft else 'default', frequency)
param_grid = {
'learning_rate_init': [0.01, 0.03, 0.1, 0.3],
'alpha': [0.0001, 0.001]
}
features = 'fft' if perform_fft else 'baseline'
cm_name = f'results/food_{signal_type}_{features}_mlp_{frequency}'
estimator = MLPClassifier(hidden_layer_sizes=(16, 8), max_iter=2000, random_state=100)
evaluate = _create_evaluator(estimator, param_grid, 'accuracy', N=20, cm_name=cm_name)
return evaluate(X, y)
def _evaluate_food_rnn(signal_type, device, frequency):
X, y = _load_food(signal_type, 'tensor', frequency)
param_grid = { 'lr': [0.001, 0.003, 0.01] }
estimator = NeuralNetClassifier(RNNModule,
module__input_dim=X.shape[2],
module__output_dim=7,
iterator_train__shuffle=True,
optimizer=torch.optim.Adam,
criterion=nn.CrossEntropyLoss,
max_epochs=500,
batch_size=256,
train_split=False,
device=device,
verbose=0)
cm_name = f'results/food_{signal_type}_baseline_rnn_{frequency}'
evaluate = _create_evaluator(estimator,
param_grid,
'accuracy',
ShuffleSplit(n_splits=1, test_size=.2, random_state=0),
cm_name=cm_name)
return evaluate(X, y)
# _____ _ _____ _____ _ __
# / ____| | |_ _| |_ _| | | / _|
# | | | | | | | | _ __ | |_ ___ _ __| |_ __ _ ___ ___
# | | | | | | | | | '_ \| __/ _ \ '__| _/ _` |/ __/ _ \
# | |____| |____ _| |_ _| |_| | | | || __/ | | || (_| | (_| __/
# \_____|______|_____| |_____|_| |_|\__\___|_| |_| \__,_|\___\___|
#
if __name__ == '__main__':
import sys
if len(sys.argv) == 3: evaluate(sys.argv[1], int(sys.argv[2]))
if len(sys.argv) == 4: evaluate(sys.argv[1], int(sys.argv[2]), sys.argv[3])