def test(k_fold=False):
if k_fold:
print("performing kfold validation")
else:
print("test with split set")
#variables for the LSTM
if k_fold:
data_pandas = dp.remove_confidence_intervals(
pd.read_csv('dataframe_data.csv').values)
#stack the values
data = np.zeros((data_pandas.shape[0], input_step, input_features))
for i in range(data_pandas.shape[0]):
for j in range(input_step - 1, -1, -1):
data[i, j] = data_pandas[i, input_features *
j:input_features * j + input_features]
#load labels
labels = pd.read_csv('dataframe_labels.csv').values
labels = dp.remove_confidence_intervals(labels)
#generate folds
indicies = np.arange(data_pandas.shape[0])
np.random.seed(42)
np.random.shuffle(indicies)
fold_indicies = np.array_split(indicies, 10)
else:
#prepare data
x_train = dp.remove_confidence_intervals(
pd.read_csv('split_train_data.csv').values)
x_test = dp.remove_confidence_intervals(
pd.read_csv('split_test_data.csv').values)
# data = dp.calcAnglesBody(data)
# x_test = dp.relativeToFirstIndex(x_test, nrOfFeatures=input_features)
# x_train = dp.relativeToFirstIndex(x_train, nrOfFeatures=input_features)
#get labels
y_train = dp.remove_confidence_intervals(
pd.read_csv('split_train_labels.csv').values)
y_test = dp.remove_confidence_intervals(
pd.read_csv('split_test_labels.csv').values)
# labels = dp.calcAnglesBody(labels, number_of_frames=1 )
traindata = np.zeros((x_train.shape[0], input_step, input_features))
for i in range(x_train.shape[0]):
for j in range(input_step - 1, -1, -1):
traindata[i,
j] = x_train[i, input_features *
j:input_features * j + input_features]
testdata = np.zeros((x_test.shape[0], input_step, input_features))
for i in range(x_test.shape[0]):
for j in range(input_step - 1, -1, -1):
testdata[i, j] = x_test[i, input_features *
j:input_features * j + input_features]
#create the model
if k_fold:
k_fold_scores = np.arange(10)
for fold in range(10):
model = createModel()
trainmask = np.ones(data.shape[0], dtype=bool)
trainmask[fold_indicies[fold]] = False
testmask = np.zeros(data.shape[0], dtype=bool)
testmask[fold_indicies[fold]] = True
model.fit(data[trainmask],
labels[trainmask],
validation_data=(data[testmask], labels[testmask]),
epochs=20)
scores = model.evaluate(data[testmask], labels[testmask])
k_fold_scores[fold] = scores
print(k_fold_scores)
return np.average(k_fold_scores)
else:
model = createModel()
model.fit(traindata,
y_train,
validation_data=(testdata, y_test),
epochs=10)
scores = model.evaluate(testdata, y_test)
print(scores)
# pass
return scores
def test(k_fold=False):
if k_fold:
print("performing kfold validation")
else:
print("test with split set")
#variables for the LSTM
if k_fold:
data_pandas = dp.remove_confidence_intervals(
pd.read_csv('dataframe_data.csv').values)
#stack the values
data = np.zeros((data_pandas.shape[0], input_step, input_features))
for i in range(data_pandas.shape[0]):
for j in range(input_step - 1, -1, -1):
data[i, j] = data_pandas[i, input_features *
j:input_features * j + input_features]
#load labels
labels = pd.read_csv('dataframe_labels.csv').values
labels = dp.remove_confidence_intervals(labels)
#generate folds
indicies = np.arange(data_pandas.shape[0])
np.random.seed(42)
np.random.shuffle(indicies)
fold_indicies = np.array_split(indicies, 10)
else:
#prepare data
x_train = dp.remove_confidence_intervals(
(pd.read_csv('split_train_data.csv').append(
pd.read_csv('split_test_data.csv'))).values)
x_test = dp.remove_confidence_intervals(
pd.read_csv('split_test_data.csv').values)
x_train
# data = dp.calcAnglesBody(data)
# x_test = dp.relativeToFirstIndex(x_test, nrOfFeatures=input_features)
# x_train = dp.relativeToFirstIndex(x_train, nrOfFeatures=input_features)
#get labels
y_train = dp.remove_confidence_intervals(
(pd.read_csv('split_train_labels.csv').append(
pd.read_csv('split_test_labels.csv'))).values)
y_test = dp.remove_confidence_intervals(
pd.read_csv('split_test_labels.csv').values)
# labels = dp.calcAnglesBody(labels, number_of_frames=1 )
mse = ((y_test - x_test[:, 0:50])**2).mean()
print("mse ===== ", mse)
traindata = np.zeros((x_train.shape[0], input_step, input_features))
for i in range(x_train.shape[0]):
for j in range(input_step - 1, -1, -1):
traindata[i,
j] = x_train[i, input_features *
j:input_features * j + input_features]
testdata = np.zeros((x_test.shape[0], input_step, input_features))
for i in range(x_test.shape[0]):
for j in range(input_step - 1, -1, -1):
testdata[i, j] = x_test[i, input_features *
j:input_features * j + input_features]
#create the model
if k_fold:
k_fold_scores = np.arange(10, dtype=np.float32)
zero_pred_scores = np.arange(10, dtype=np.float32)
print(k_fold_scores[4])
for fold in range(10):
model = createModel()
trainmask = np.ones(data.shape[0], dtype=bool)
trainmask[fold_indicies[fold]] = False
testmask = np.zeros(data.shape[0], dtype=bool)
testmask[fold_indicies[fold]] = True
mseZero = ((labels[testmask] - data[testmask, 1, :])**2).mean()
history = model.fit(data[trainmask],
labels[trainmask],
validation_data=(data[testmask],
labels[testmask]),
epochs=10)
scores = model.evaluate(data[testmask], labels[testmask])
y_predicted = model.predict(data[testmask])
print("test time")
mse = ((labels[testmask] - y_predicted)**2).mean()
zero_pred_scores[fold] = mseZero
print("mse ===== ", mse, " ", mseZero)
k_fold_scores[fold] = mse
print(k_fold_scores)
print(k_fold_scores)
pd.DataFrame(k_fold_scores).to_csv("k_fold_scores.csv")
pd.DataFrame(zero_pred_scores).to_csv("0_pred_scores.csv")
return np.mean(k_fold_scores)
else:
model = createModel()
history = model.fit(traindata,
y_train,
validation_split=0.33,
epochs=10)
y_predicted = model.predict(testdata)
print(y_predicted.shape)
pd.DataFrame(history.history).to_csv('nosplit_loss.csv')
print("test time")
mse = ((y_test - y_predicted)**2).mean()
print("mse ===== ", mse)
print(history.history.keys())
# pass
return scores
def test(k_fold=False):
if k_fold:
print("performing kfold validation")
else:
print("test with split set")
#variables for the LSTM
if k_fold:
data_pandas = dp.remove_confidence_intervals(
pd.read_csv('dataframe_data.csv').values)
#stack the values
data = np.zeros((data_pandas.shape[0], input_step, input_features))
for i in range(data_pandas.shape[0]):
for j in range(input_step - 1, -1, -1):
data[i, j] = data_pandas[i, input_features *
j:input_features * j + input_features]
#load labels
labels = pd.read_csv('dataframe_labels.csv').values
labels = dp.remove_confidence_intervals(labels)
#generate folds
indicies = np.arange(data_pandas.shape[0])
np.random.seed(42)
np.random.shuffle(indicies)
fold_indicies = np.array_split(indicies, 10)
else:
#prepare data
x_train = dp.remove_confidence_intervals(
pd.read_csv('split_train_data.csv').values)
x_test = dp.remove_confidence_intervals(
pd.read_csv('split_test_data.csv').values)
# data = dp.calcAnglesBody(data)
# x_test = dp.relativeToFirstIndex(x_test, nrOfFeatures=50)
# x_train = dp.relativeToFirstIndex(x_train,nrOfFeatures= 50)
x_test_angle = dp.calcAnglesBody(x_test)
x_train_angle = dp.calcAnglesBody(x_train)
pd.DataFrame(x_test).to_csv("noci_data.csv")
#get labels
y_train = dp.remove_confidence_intervals(
pd.read_csv('split_train_labels.csv').values)
y_test = dp.remove_confidence_intervals(
pd.read_csv('split_test_labels.csv').values)
# labels = dp.calcAnglesBody(labels, number_of_frames=1 )
pd.DataFrame(y_test).to_csv("noci_labels.csv")
print(x_test)
#calculate mse
mse = ((x_test[:, 12] - x_test[:, 62])**2).mean()
print("mse ===== ", mse)
traindata = np.zeros((x_train.shape[0], input_step, 50))
for i in range(x_train.shape[0]):
for j in range(input_step - 1, -1, -1):
traindata[i, j] = x_train[i, 50 * j:50 * j + 50]
traindata_angles = np.zeros((x_train.shape[0], input_step, 13))
for i in range(x_train.shape[0]):
for j in range(input_step - 1, -1, -1):
traindata_angles[i, j] = x_train[i, 13 * j:13 * j + 13]
testdata = np.zeros((x_test.shape[0], input_step, 50))
for i in range(x_test.shape[0]):
for j in range(input_step - 1, -1, -1):
testdata[i, j] = x_test[i, 50 * j:50 * j + 50]
testdata_angles = np.zeros((x_test.shape[0], input_step, 13))
for i in range(x_test.shape[0]):
for j in range(input_step - 1, -1, -1):
testdata_angles[i, j] = x_test[i, 13 * j:13 * j + 13]
traindata = np.nan_to_num(np.append(traindata, traindata_angles, 2))
testdata = np.nan_to_num(np.append(testdata, testdata_angles, 2))
print(testdata.shape)
print(testdata_angles.shape)
#create the model
if k_fold:
k_fold_scores = np.arange(10)
for fold in range(10):
model = createModel()
trainmask = np.ones(data.shape[0], dtype=bool)
trainmask[fold_indicies[fold]] = False
testmask = np.zeros(data.shape[0], dtype=bool)
testmask[fold_indicies[fold]] = True
model.fit(data[trainmask],
labels[trainmask],
validation_data=(data[testmask], labels[testmask]),
epochs=1)
scores = model.evaluate(data[testmask], labels[testmask])
k_fold_scores[fold] = scores
print(k_fold_scores)
return np.average(k_fold_scores)
else:
model = createModel()
model.fit(traindata,
y_train,
validation_data=(testdata, y_test),
epochs=1)
scores = model.evaluate(testdata, y_test)
print(scores)
outs = model.predict(x_test)
print(outs.shape)
# pass
return scores
k_fold = False
if argv[1] == '1':
print("performing kfold validation")
k_fold = True
else:
print("test with split set")
#variables for the LSTM
input_step = 5
input_features = 50
#prepare data
data_pandas = pd.read_csv('dataframe_data.csv')
data = data_pandas.values
data = dp.remove_confidence_intervals(data)
# data = dp.calcAnglesBody(data)
# data = dp.relativeToFirstIndex(data, nrOfFeatures=50)
#get labels
labels = pd.read_csv('dataframe_labels.csv').values
labels = dp.remove_confidence_intervals(labels)
# labels = dp.calcAnglesBody(labels, number_of_frames=1 )
#create train and test set
x_train, x_test, y_train, y_test = train_test_split(data,
labels,
test_size=0.5,
random_state=4)
#now stack the data sets
