def run_lda_2(X_train, X_test, y_train, y_test, dataset):
# model = LinearDiscriminantAnalysis(solver='eigen',n_components=y_train.groupby(y_train.columns[0]).count().shape[0])
model = LinearDiscriminantAnalysis(
n_components=y_train.groupby(y_train.columns[0]).count().shape[0] - 1)
score_df = pd.DataFrame()
# k_max = X_train.shape[1]-1
# if k_max > 120: k_max = 120
k_max = y_train.groupby(y_train.columns[0]).count().shape[0]
for i in range(1, k_max):
LOGGER.info('lda: k={}'.format(i))
model.set_params(n_components=i)
# model = LinearDiscriminantAnalysis(n_components=i)
# lda_X = model.fit_transform(X_train,y_train[y_train.columns[0]])
model.fit(X_train, y_train[y_train.columns[0]])
# lda_test_X = model.transform(X_test)
# y_pred = model.predict(lda_test_X)
# print(y_pred)
score_df.loc[i, 'test_score'] = model.score(X_test,
y_test[y_test.columns[0]])
score_df.loc[i,
'train_score'] = model.score(X_train,
y_train[y_train.columns[0]])
print(score_df)
model = LinearDiscriminantAnalysis(
n_components=y_train.groupby(y_train.columns[0]).count().shape[0] - 1)
model.fit(X_train, y_train[y_train.columns[0]])
result_df = pd.DataFrame(data=model.explained_variance_ratio_,
columns=['ex_variance'],
index=range(len(model.explained_variance_ratio_)))
title = 'lda_explained_variance'
x = 'components'
y = 'variance contributed'
LOGGER.info('plotting {}'.format(title))
plt.clf()
plt.title(title)
plt.xlabel(x)
plt.ylabel(y)
plt.grid()
# sig_vec = [np.abs(i)/np.sum(result_df['ex_variance']) for i in result_df['ex_variance']]
plt.step(range(0, result_df.shape[0]),
np.cumsum(result_df['ex_variance']),
label='cumulative explained variance')
plt.bar(range(0, result_df.shape[0]),
result_df['ex_variance'],
align='center',
label='explained variance')
def from_config(cls, config: Dict[str, Any]) -> "SkLDA":
"""Deserialize from an object."""
if not HAS_SKLEARN:
raise DataProcessorError(
f"SKlearn is needed to initialize an {cls.__name__}.")
lda = LinearDiscriminantAnalysis()
lda.set_params(**config["params"])
for name, value in config["attributes"].items():
if value is not None:
setattr(lda, name, value)
return SkLDA(lda)
def dimensionality_reduction(TrainFeatures, TestFeatures, Method, params):
""" It performs dimensionality reduction of a training and a test features matrix
stored in a .h5 file each.
It's possible to use 5 different methods for dimensionality reduction.
_____________________________________________________________________________________
Parameters:
- TrainFeatures: string
It is the path of an .h5 file of the training features.
It contains at least the following datasets:
- 'feats': array-like, shape (n_samples, n_features)
- 'labels': array-like, shape (n_samples, )
- 'img_ids': array-like, shape (n_samples, )
- TestFeatures: string
It is the path of an .h5 file of the test features.
It contains at least the same datasets.
- Method: string
Possible value are:
-'PCA': Principal component analysis
-'t-SNE': t-distributed Stochastic Neighbor Embedding
-'TruncatedSVD': Truncated SVD
-'NMF': Non-Negative Matrix Factorization
-'LDA': Linear Discriminant Analysis
- params: dict
It is a dictionary containig parameters for the selected estimator.
Keys and possible values are listed on the following websites:
http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
http://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html
http://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html
http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html
For t-SNE, an additional key is needed: params['reduce'] with possible values 'TruncatedSVD','PCA','None'.
It is highly recommended to use another dimensionality reduction method (e.g. PCA for dense data or TruncatedSVD
for sparse data) to reduce the number of dimensions to a reasonable amount (e.g. 50) if the number of features is
very high. This will suppress some noise and speed up the computation of pairwise distances between samples.
- params['reduce']='TruncatedSVD' : Truncated SVD --> t-SNE
- params['reduce']='PCA' : PCA --> t-SNE
- params['reduce']='None' : t-SNE directly
Returns:
- X_train: array-like, shape (n_samples, n_components)
- X_test: array-like, shape (n_samples, n_components)
- ax: matplotlib.axes._subplots.AxesSubplot object (if n_components<=3) or None (if n_components>3)
Furthermore, automatically 2 new .h5 files containing 3 datasets each (one for reduced features, one for labels and one for img_ids)
are generated in the folder Results/ReducedFeatures and also if n_components is <= 3 a scatter plot is saved in the folder
Results/Plots
Example usage:
import FeaturesReduction as fr
import matplotlib.pyplot as plt
params={'n_components':3}
X_train,X_test,ax=fr.dimensionality_reduction('TrainingFeatures.h5','TestFeatures.h5','PCA',params)
plt.show()
"""
s = os.sep
# Load training features file
train = h5py.File(TrainFeatures, 'r')
train_features = train['feats']
train_labels = train['labels']
train_labels = np.squeeze(train_labels)
train_img_ids = train['img_id']
# Get categories of the training set from features ids
categories = mf.get_categories(train_img_ids)
# Load test features file
test = h5py.File(TestFeatures, 'r')
test_features = test['feats']
test_labels = test['labels']
test_labels = np.squeeze(test_labels)
test_img_ids = test['img_id']
n_comp = params['n_components']
if Method != 'NMF':
# Standardize features by removing the mean and scaling to unit variance
scaler = StandardScaler().fit(train_features)
train_features = scaler.transform(train_features)
test_features = scaler.transform(test_features)
if Method == 'PCA':
# Get PCA model
pca = PCA()
# Set parameters
pca.set_params(**params)
# Fit the model with the training features and
# apply dimensional reduction to training features
X_train = pca.fit_transform(train_features)
# Apply dimensional reduction to test features
X_test = pca.transform(test_features)
elif Method == 'NMF':
params['verbose'] = True
# Get NMF model
nmf = NMF()
# Set parameters
nmf.set_params(**params)
# Fit the model with the training features and
# apply dimensional reduction to training features
X_train = nmf.fit_transform(train_features)
# Apply dimensional reduction to test features
X_test = nmf.transform(test_features)
elif Method == 'LDA':
# Get LDA model
lda = LDA()
# Set parameters
lda.set_params(**params)
# Fit the model with the training features
#lda.fit(train_features,train_labels)
# apply dimensional reduction to training features
#X_train = lda.transform(train_features)
X_train = lda.fit_transform(train_features, train_labels)
# apply dimensional reduction to training features
#X_train = lda.transform(train_features)
# Apply dimensional reduction to test features
X_test = lda.transform(test_features)
elif Method == 't-SNE':
red = params['reduce']
del params['reduce']
print(red)
params['verbose'] = True
# Use another dimensionality reduction method (PCA for dense
# data or TruncatedSVD for sparse data) to reduce the number of
# dimensions to a reasonable amount (e.g. 50) if the number of
# features is very high. This will suppress some noise and speed
# up the computation of pairwise distances between samples.
if n_comp < 50:
K = 50
else:
K = n_comp * 2
if red == 'TruncatedSVD':
# Get TruncatedSVD model
svd = TruncatedSVD(n_components=K)
# Fit the model with the training features and
# apply dimensional reduction to training features
train_features = svd.fit_transform(train_features)
# Apply dimensional reduction to test features
test_features = svd.transform(test_features)
elif red == 'PCA':
# Get PCA model
pca = PCA(n_components=K)
# Fit the model with the training features and
# apply dimensional reduction to training features
train_features = pca.fit_transform(train_features)
# Apply dimensional reduction to test features
test_features = pca.transform(test_features)
else:
pass
# Get t-SNE model
tsne = TSNE()
# Set parameters
tsne.set_params(**params)
# Concatenate training and test set
n_train = train_features.shape[0]
features = np.concatenate((train_features, test_features), axis=0)
# Fit the model with the data and apply dimensional reduction
X = tsne.fit_transform(features)
# Separate training and test set
X_train = X[:n_train, :]
X_test = X[n_train:, :]
elif Method == 'TruncatedSVD':
# Get TruncatedSVD model
svd = TruncatedSVD()
# Set parameters
svd.set_params(**params)
# Fit the model with the training features and
# apply dimensional reduction to training features
X_train = svd.fit_transform(train_features)
# Apply dimensional reduction to test features
X_test = svd.transform(test_features)
else:
raise TypeError(
"Invalid method: possible methods are 'PCA', 't-SNE', 'TruncatedSVD', 'NMF' and 'LDA'"
)
# Create folder in which save reduced features
mf.folders_creator('Results', ['ReducedFeatures'])
# Create an .h5 file and store in it reduced training set
name = 'Results' + s + 'ReducedFeatures' + s + Method + str(
n_comp) + '_' + TrainFeatures.split(s)[-1].split('.')[0] + '.h5'
f = h5py.File(name, "w")
f.create_dataset('img_id', data=train_img_ids[:], dtype="S40")
f.create_dataset('labels', data=train_labels.T, compression="gzip")
if Method == 'PCA':
f.create_dataset('pca', data=X_train.T, compression="gzip")
elif Method == 't-SNE':
f.create_dataset('tsne', data=X_train.T, compression="gzip")
elif Method == 'TruncatedSVD':
f.create_dataset('tsvd', data=X_train.T, compression="gzip")
elif Method == 'LDA':
f.create_dataset('lda', data=X_train.T, compression="gzip")
elif Method == 'NMF':
f.create_dataset('nmf', data=X_train.T, compression="gzip")
f.close()
# Create an .h5 file and store in it reduced test set
name = 'Results' + s + 'ReducedFeatures' + s + Method + str(
n_comp) + '_' + TestFeatures.split(s)[-1].split('.')[0] + '.h5'
f = h5py.File(name, "w")
f.create_dataset('img_id', data=test_img_ids[:], dtype="S40")
f.create_dataset('labels', data=test_labels.T, compression="gzip")
if Method == 'PCA':
f.create_dataset('pca', data=X_test.T, compression="gzip")
elif Method == 't-SNE':
f.create_dataset('tsne', data=X_test.T, compression="gzip")
elif Method == 'TruncatedSVD':
f.create_dataset('tsvd', data=X_test.T, compression="gzip")
elif Method == 'LDA':
f.create_dataset('lda', data=X_test.T, compression="gzip")
elif Method == 'NMF':
f.create_dataset('nmf', data=X_test.T, compression="gzip")
f.close()
if n_comp < 4:
# Get folders list of the test set from features ids
test_folders = mf.get_categories(test_img_ids)
# Get number of folders
n_folders_test = len(test_folders)
# Make some names for the plot legend
tf = []
for i in range(n_folders_test):
tf.append('Test' + str(i))
# Define a list of colors in exadecimal format
if len(categories) + n_folders_test < 9:
colors = [
'#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF',
'#808080', '#FF00FF', '#000000'
]
else:
n = 250
max_value = 255**3
interval = int(max_value / n)
colors = [
'#' + hex(i)[2:].zfill(6)
for i in range(0, max_value, interval)
]
colors = colors[:int((n + 1) / 10 * 9)]
random.shuffle(colors)
# Create a folder to save images
mf.folders_creator('Results', ['Plots'])
# Create a name to save image
name = Method + str(n_comp) + '_' + TrainFeatures.split(s)[-1].split(
'.')[0]
name = name.split('_')
name = '_'.join(name[:-1])
print(X_train.shape)
print(X_test.shape)
if n_comp == 1:
# Plot 1D Data with different colors
fig, ax = plt.subplots()
for i in range(len(categories)):
ax.scatter(X_train[train_labels == i, 0],
np.ones(X_train[train_labels == i, 0].shape),
c=colors[i],
label=categories[i])
k = len(categories)
for i in range(n_folders_test):
ax.scatter(X_test[test_labels == i, 0],
np.ones(X_test[test_labels == i, 0].shape),
c=colors[k],
label=tf[i])
k += 1
ax.legend()
# Save image in .png format
plt.savefig('Results' + s + 'Plots' + s + name + '.png')
if n_comp == 2:
# Plot 2D Data with different colors
fig, ax = plt.subplots()
for i in range(len(categories)):
ax.scatter(X_train[train_labels == i, 0],
X_train[train_labels == i, 1],
c=colors[i],
label=categories[i])
k = len(categories)
for i in range(n_folders_test):
ax.scatter(X_test[test_labels == i, 0],
X_test[test_labels == i, 1],
c=colors[k],
label=tf[i])
k += 1
ax.legend()
# Save image in .png format
plt.savefig('Results' + s + 'Plots' + s + name + '.png')
# Remove outliers
out_train = mf.is_outlier(X_train, thresh=3.5)
out_test = mf.is_outlier(X_test, thresh=3.5)
out_train = np.logical_not(out_train)
out_test = np.logical_not(out_test)
X_train2 = X_train[out_train, :]
X_test2 = X_test[out_test, :]
if X_train2.shape[0] != X_train.shape[0] or X_test2.shape[
0] != X_test.shape[0]:
train_labels2 = train_labels[out_train]
test_labels2 = test_labels[out_test]
# Plot 2D Data without outliers with different colors
fig, ax = plt.subplots()
for i in range(len(categories)):
ax.scatter(X_train2[train_labels2 == i, 0],
X_train2[train_labels2 == i, 1],
c=colors[i],
label=categories[i])
k = len(categories)
for i in range(n_folders_test):
ax.scatter(X_test2[test_labels2 == i, 0],
X_test2[test_labels2 == i, 1],
c=colors[k],
label=tf[i])
k += 1
ax.legend()
# Save image in .png format
plt.savefig('Results' + s + 'Plots' + s + name +
'_noOutliers.png')
if n_comp == 3:
mf.folders_creator('Results' + s + 'Plots', ['tmp'])
# Plot 3-D Data with different colors
ax = plt.subplot(111, projection='3d')
for i in range(len(categories)):
ax.scatter(X_train[train_labels == i, 0],
X_train[train_labels == i, 1],
X_train[train_labels == i, 2],
c=colors[i],
label=categories[i])
k = len(categories)
for i in range(n_folders_test):
ax.scatter(X_test[test_labels == i, 0],
X_test[test_labels == i, 1],
X_test[test_labels == i, 2],
c=colors[k],
label=tf[i])
k += 1
ax.legend(loc='upper left',
numpoints=1,
ncol=3,
fontsize=8,
bbox_to_anchor=(0, 0))
# Rotate for 360° and save every 10°
for angle in range(0, 360, 10):
ax.view_init(30, angle)
plt.savefig('Results' + s + 'Plots' + s + 'tmp' + s + name +
str(angle) + '.png')
# Save as a .gif image
mf.imagesfolder_to_gif('Results' + s + 'Plots' + s + name + '.gif',
'Results' + s + 'Plots' + s + 'tmp', 0.2)
shutil.rmtree('Results' + s + 'Plots' + s + 'tmp')
else:
ax = None
return X_train, X_test, ax
def run_classifier(args):
"""
Main function runs training and testing of Heuristic based machine
learning models (SVM, LDA)
Input: argument passes through argparse. Each argument is described
in the --help of each arguments.
Output: No return, but generates a .txt file results of testing
including accuracy of the models.
"""
########## PRAMETER SETTINGS ##############
MODE = args.laterality
CLASSIFIER = args.classifiers
SENSOR = args.sensors
############################################
sensor_str = '_'.join(SENSOR)
RESULT_NAME = './results/' + CLASSIFIER + '/' + CLASSIFIER + '_' + MODE + '_' + sensor_str + '_subjects_accuracy.txt'
SAVE_NAME = './checkpoints/' + CLASSIFIER + '/' + CLASSIFIER + '_' + MODE + '_' + sensor_str + '_subjects.pkl'
if not os.path.exists('./results/' + CLASSIFIER):
os.makedirs('./results/' + CLASSIFIER)
subjects = [
'156', '185', '186', '188', '189', '190', '191', '192', '193', '194'
]
subject_data = []
# Loading/saving the ENABL3S dataset
if args.data_saving:
print("Loading datasets...")
for subject in subjects:
subject_data.append(
EnableDataset(subject_list=[subject],
model_type=CLASSIFIER,
sensors=SENSOR,
mode=MODE))
save_object(subject_data, SAVE_NAME)
else:
with open(SAVE_NAME, 'rb') as input:
subject_data = pickle.load(input)
correct = 0
steady_state_correct = 0
tot_steady_state = 0
transitional_correct = 0
tot_transitional = 0
# Define cross-validation parameters
skf = KFold(n_splits=len(subject_data), shuffle=True)
# Define PCA parameters
scale = preprocessing.StandardScaler()
pca = PCA()
scale_PCA = Pipeline([('norm', scale), ('dimred', pca)])
if CLASSIFIER == 'LDA':
model = LinearDiscriminantAnalysis()
elif CLASSIFIER == 'SVM':
model = SVC(kernel='linear', C=10)
accuracies = []
ss_accuracies = []
tr_accuracies = []
subject_numb = []
i = 0
# main training/testing loop
for train_index, test_index in skf.split(subject_data):
print("**************FOLD {}*********".format(i + 1))
print(train_index, test_index)
train_set = [subject_data[i] for i in train_index]
test_set = [subject_data[i] for i in test_index]
BIO_train = torch.utils.data.ConcatDataset(train_set)
wholeloader = DataLoader(BIO_train, batch_size=len(BIO_train))
for batch, label, dtype in tqdm(wholeloader):
X_train = batch
y_train = label
types_train = dtype
BIO_test = torch.utils.data.ConcatDataset(test_set)
wholeloader = DataLoader(BIO_test, batch_size=len(BIO_train))
for batch, label, dtype in tqdm(wholeloader):
X_test = batch
y_test = label
types_test = dtype
if CLASSIFIER == 'LDA':
scale_PCA.fit(X_train)
feats_train_PCA = scale_PCA.transform(X_train)
feats_test_PCA = scale_PCA.transform(X_test)
pcaexplainedvar = np.cumsum(
scale_PCA.named_steps['dimred'].explained_variance_ratio_)
pcanumcomps = min(min(np.where(pcaexplainedvar > 0.95))) + 1
unique_modes = np.unique(y_train)
model.set_params(priors=np.ones(len(unique_modes)) /
len(unique_modes))
model.fit(feats_train_PCA, y_train)
y_pred = model.predict(feats_test_PCA)
elif CLASSIFIER == 'SVM':
scale.fit(X_train)
feats_train_norm = scale.transform(X_train)
feats_test_norm = scale.transform(X_test)
model.fit(feats_train_norm, y_train)
y_pred = model.predict(feats_test_norm)
# append model performance metrics
correct = (y_pred == np.array(y_test)).sum().item()
tot = len(y_test)
steady_state_correct = (np.logical_and(y_pred == np.array(y_test),
types_test == 1)).sum().item()
tot_steady_state = (types_test == 1).sum().item()
transitional_correct = (np.logical_and(y_pred == np.array(y_test),
types_test == 0)).sum().item()
tot_transitional = (types_test == 0).sum().item()
accuracies.append(accuracy_score(y_test, y_pred))
tot_acc = correct / tot
ss_acc = steady_state_correct / tot_steady_state if tot_steady_state != 0 else "No steady state samples used"
tr_acc = transitional_correct / tot_transitional if tot_transitional != 0 else "No transitional samples used"
ss_accuracies.append(
ss_acc
) if tot_steady_state != 0 else "No steady state samples used"
tr_accuracies.append(
tr_acc
) if tot_transitional != 0 else "No transitional samples used"
subject_numb.append(test_index[0])
print("Total accuracy: {}".format(accuracy_score(y_test, y_pred)))
print("Total correct: {}, number: {}, accuracy: {}".format(
correct, tot, tot_acc))
print("Steady-state correct: {}, number: {}, accuracy: {}".format(
steady_state_correct, tot_steady_state, ss_acc))
print("Transistional correct: {}, number: {}, accuracy: {}".format(
transitional_correct, tot_transitional, tr_acc))
i += 1
print('********************SUMMARY*****************************')
print('Accuracy_,mean:', np.mean(accuracies), 'Accuracy_std: ',
np.std(accuracies))
print('SR Accuracy_,mean:', np.mean(ss_accuracies), 'Accuracy_std: ',
np.std(ss_accuracies))
print('TR Accuracy_,mean:', np.mean(tr_accuracies), 'Accuracy_std: ',
np.std(tr_accuracies))
print('writing...')
with open(RESULT_NAME, 'w') as f:
f.write('total ')
for item in accuracies:
f.write("%s " % item)
f.write('\n')
f.write('steadystate ')
for item in ss_accuracies:
f.write("%s " % item)
f.write('\n')
f.write('transitional ')
for item in tr_accuracies:
f.write("%s " % item)
f.write('\n')
f.write('subject_numb ')
for item in subject_numb:
f.write("%s " % item)
f.close()
def run_classifier(mode='bilateral',
classifier='LDA',
sensor=["imu", "emg", "goin"]):
MODE = mode
CLASSIFIER = classifier
SENSOR = sensor
sensor_str = '_'.join(SENSOR)
RESULT_NAME = './results/' + CLASSIFIER + '/' + CLASSIFIER + '_' + MODE + '_' + sensor_str + '_subjects_accuracy.txt'
if not os.path.exists('./results/' + CLASSIFIER):
os.makedirs('./results/' + CLASSIFIER)
subjects = [
'156', '185', '186', '188', '189', '190', '191', '192', '193', '194'
]
subject_data = []
for subject in subjects:
subject_data.append(
EnableDataset(subject_list=[subject],
model_type=CLASSIFIER,
sensors=SENSOR,
mode=MODE))
correct = 0
steady_state_correct = 0
tot_steady_state = 0
transitional_correct = 0
tot_transitional = 0
# Define cross-validation parameters
skf = KFold(n_splits=len(subject_data), shuffle=True)
scale = preprocessing.StandardScaler()
pca = PCA()
scale_PCA = Pipeline([('norm', scale), ('dimred', pca)])
if CLASSIFIER == 'LDA':
model = LinearDiscriminantAnalysis()
elif CLASSIFIER == 'SVM':
model = SVC(kernel='linear', C=10)
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
accuracies = []
ss_accuracies = []
tr_accuracies = []
subject_numb = []
i = 0
for train_index, test_index in skf.split(subject_data):
print("**************FOLD {}*********".format(i + 1))
print(train_index, test_index)
train_set = [subject_data[i] for i in train_index]
test_set = [subject_data[i] for i in test_index]
BIO_train = torch.utils.data.ConcatDataset(train_set)
wholeloader = DataLoader(BIO_train, batch_size=len(BIO_train))
for batch, label, dtype in tqdm(wholeloader):
X_train = batch
y_train = label
types_train = dtype
BIO_test = torch.utils.data.ConcatDataset(test_set)
wholeloader = DataLoader(BIO_test, batch_size=len(BIO_train))
for batch, label, dtype in tqdm(wholeloader):
X_test = batch
y_test = label
types_test = dtype
if CLASSIFIER == 'LDA':
scale_PCA.fit(X_train)
feats_train_PCA = scale_PCA.transform(X_train)
feats_test_PCA = scale_PCA.transform(X_test)
pcaexplainedvar = np.cumsum(
scale_PCA.named_steps['dimred'].explained_variance_ratio_)
pcanumcomps = min(min(np.where(pcaexplainedvar > 0.95))) + 1
unique_modes = np.unique(y_train)
model.set_params(priors=np.ones(len(unique_modes)) /
len(unique_modes))
model.fit(feats_train_PCA, y_train)
y_pred = model.predict(feats_test_PCA)
elif CLASSIFIER == 'SVM':
scale.fit(X_train)
feats_train_norm = scale.transform(X_train)
feats_test_norm = scale.transform(X_test)
model.fit(feats_train_norm, y_train)
y_pred = model.predict(feats_test_norm)
correct = (y_pred == np.array(y_test)).sum().item()
tot = len(y_test)
steady_state_correct = (np.logical_and(y_pred == np.array(y_test),
types_test == 1)).sum().item()
tot_steady_state = (types_test == 1).sum().item()
transitional_correct = (np.logical_and(y_pred == np.array(y_test),
types_test == 0)).sum().item()
tot_transitional = (types_test == 0).sum().item()
accuracies.append(accuracy_score(y_test, y_pred))
tot_acc = correct / tot
ss_acc = steady_state_correct / tot_steady_state if tot_steady_state != 0 else "No steady state samples used"
tr_acc = transitional_correct / tot_transitional if tot_transitional != 0 else "No transitional samples used"
ss_accuracies.append(
ss_acc
) if tot_steady_state != 0 else "No steady state samples used"
tr_accuracies.append(
tr_acc
) if tot_transitional != 0 else "No transitional samples used"
subject_numb.append(test_index[0])
print("Total accuracy: {}".format(accuracy_score(y_test, y_pred)))
print("Total correct: {}, number: {}, accuracy: {}".format(
correct, tot, tot_acc))
print("Steady-state correct: {}, number: {}, accuracy: {}".format(
steady_state_correct, tot_steady_state, ss_acc))
print("Transistional correct: {}, number: {}, accuracy: {}".format(
transitional_correct, tot_transitional, tr_acc))
# print(accuracy_score(y_test, y_pred))
i += 1
print('********************SUMMARY*****************************')
# print('Accuracy_total:', correct/len(BIO_train))
print('Accuracy_,mean:', np.mean(accuracies), 'Accuracy_std: ',
np.std(accuracies))
print('SR Accuracy_,mean:', np.mean(ss_accuracies), 'Accuracy_std: ',
np.std(ss_accuracies))
print('TR Accuracy_,mean:', np.mean(tr_accuracies), 'Accuracy_std: ',
np.std(tr_accuracies))
# model.fit(X_train, y_train)
# total_accuracies = accuracies + ss_accuracies + tr_accuracies
print('writing...')
with open(RESULT_NAME, 'w') as f:
f.write('total ')
for item in accuracies:
f.write("%s " % item)
f.write('\n')
f.write('steadystate ')
for item in ss_accuracies:
f.write("%s " % item)
f.write('\n')
f.write('transitional ')
for item in tr_accuracies:
f.write("%s " % item)
f.write('\n')
f.write('subject_numb ')
for item in subject_numb:
f.write("%s " % item)
f.close()
class LDA(object):
def __init__(self,
solver="svd",
shrinkage=None,
priors=None,
n_components=None,
store_covariance=False,
tol=1e-4):
"""
:param solver: string， 可选项，"svd","lsqr", "eigen"。 默认使用svd, 不计算协方差矩阵，适用于大量特征
的数据， 最小二乘 lsqr, 结合shrinkage 使用。 eigen 特征值分解， 集合shrinkage 使用
:param shrinkage: str/float 可选项，概率值，默认为None, "auto", 自动收缩， 0到1内的float, 固定的收缩参数
:param priors: array, optional, shape (n_classes,) 分类优先
:param n_components: # 分量数， 默认None， int， 可选项
:param store_covariance: bool, 可选项， 只用于”svd“ 额外计算分类协方差矩阵
:param tol: 浮点型，默认1e-4, 在svd 中，用于排序评估的阈值
"""
self.model = LinearDiscriminantAnalysis(
solver=solver,
shrinkage=shrinkage,
priors=priors,
n_components=n_components,
store_covariance=store_covariance,
tol=tol)
def fit(self, x, y):
self.model.fit(X=x, y=y)
def transform(self, x):
return self.model.transform(X=x)
def fit_transform(self, x, y):
return self.model.fit_transform(X=x, y=y)
def get_params(self, deep=True):
return self.model.get_params(deep=deep)
def set_params(self, **params):
self.model.set_params(**params)
def decision_function(self, x):
self.model.decision_function(X=x)
def predict(self, x):
return self.model.predict(X=x)
def predict_log_proba(self, x):
return self.model.predict_log_proba(X=x)
def predict_proba(self, x):
return self.model.predict_proba(X=x)
def score(self, x, y, sample_weight):
return self.model.score(X=x, y=y, sample_weight=sample_weight)
def get_attributes(self): # 生成模型之后才能获取相关属性值
coef = self.model.coef_ # 权重向量，
intercept = self.model.intercept_ # 截距项
covariance = self.model.covariance_ # 协方差矩阵
explained_variance_ratio = self.model.explained_variance_ratio_
means = self.model.means_
priors = self.model.priors_ # 分类等级， 求和为1 shape (n_classes)
scalings = self.model.scalings_ # shape(rank,n_classes-1). 缩放
xbar = self.model.xbar_ # 所有的均值
classes = self.model.classes_ # 分类标签
return coef, intercept, covariance, explained_variance_ratio, means, priors, scalings, xbar, classes
