original_dataset = pd.read_csv(dataset_path + 'chapter2_result.csv',
index_col=0)
original_dataset.index = original_dataset.index.to_datetime()
KalFilter = KalmanFilters()
kalman_dataset = KalFilter.apply_kalman_filter(original_dataset, 'acc_phone_x')
DataViz.plot_imputed_values(kalman_dataset, ['original', 'kalman'],
'acc_phone_x',
kalman_dataset['acc_phone_x_kalman'])
DataViz.plot_dataset(kalman_dataset, ['acc_phone_x', 'acc_phone_x_kalman'],
['exact', 'exact'], ['line', 'line'])
# We ignore the Kalman filter output for now...
# Let us apply a lowpass filter and reduce the importance of the data above 1.5 Hz
LowPass = LowPassFilter()
# Determine the sampling frequency.
fs = float(1000) / milliseconds_per_instance
cutoff = 1.5
# Let us study acc_phone_x:
new_dataset = LowPass.low_pass_filter(copy.deepcopy(dataset),
'acc_phone_x',
fs,
cutoff,
order=10)
DataViz.plot_dataset(
new_dataset.ix[int(0.4 *
len(new_dataset.index)):int(0.43 *
len(new_dataset.index)), :],
dataset = MisVal.impute_interpolate(dataset, col)
# Using the result from Chapter 2, let us try the Kalman filter on the light_phone_lux attribute and study the result.
original_dataset = pd.read_csv(DATA_PATH / ORIG_DATASET_FNAME, index_col=0)
original_dataset.index = pd.to_datetime(original_dataset.index)
KalFilter = KalmanFilters()
kalman_dataset = KalFilter.apply_kalman_filter(original_dataset, 'userAcceleration.x')
# DataViz.plot_imputed_values(kalman_dataset, ['original', 'kalman'], 'userAcceleration.x', kalman_dataset['userAcceleration.x_kalman'])
# DataViz.plot_dataset(kalman_dataset, ['userAcceleration.x', 'userAcceleration.x_kalman'], ['exact','exact'], ['line', 'line'])
# We ignore the Kalman filter output for now...
# Let us apply a lowpass filter and reduce the importance of the data above 1.5 Hz
LowPass = LowPassFilter()
# Determine the sampling frequency.
fs = float(1000)/milliseconds_per_instance
cutoff = 1.5
# Let us study acc_phone_x:
new_dataset = LowPass.low_pass_filter(copy.deepcopy(dataset), 'userAcceleration.x', fs, cutoff, order=10)
# DataViz.plot_dataset(new_dataset.iloc[int(0.3*len(new_dataset.index)):int(0.5*len(new_dataset.index)), :],
# ['userAcceleration.x', 'userAcceleration.x_lowpass'], ['exact','exact'], ['line', 'line'])
# And not let us include all measurements that have a form of periodicity (and filter them):
periodic_measurements = ['attitude.roll','attitude.pitch','attitude.yaw','gravity.x','gravity.y','gravity.z','rotationRate.x','rotationRate.y','rotationRate.z','userAcceleration.x','userAcceleration.y','userAcceleration.z']
for col in periodic_measurements:
dataset = LowPass.low_pass_filter(dataset, col, fs, cutoff, order=10)
plot.show()
# Figure 3.4
# Sample frequency (Hz)
fs = 100
# Create time points....
t = pd.DataFrame(np.arange(0, 16, float(1) / fs), columns=list('X'))
c1 = 3 * np.sin(2 * math.pi * 0.1 * t)
c2 = 2 * np.sin(2 * math.pi * t)
plot.hold(True)
plot.plot(t, c1, 'b--')
plot.plot(t, c2, 'b:')
plot.plot(t, c1 + c2, 'b-')
LowPass = LowPassFilter()
new_dataset = LowPass.low_pass_filter(c1 + c2,
'X',
fs,
0.5,
order=3,
phase_shift=True)
plot.plot(t, new_dataset['X_lowpass'], 'r-')
plot.legend([
'$3 \cdot sin(2 \cdot \pi \cdot 0.1 \cdot t))$',
'$2 \cdot sin(2 \cdot \pi \cdot t))$', '$combined$',
'$combined$ $after$ $filter (f_{c}=0.5Hz, n=3)$'
],
loc=4,
fontsize='small')
plot.xlabel('time')
dataset = MisVal.impute_interpolate(dataset, col) # Let us try the Kalman filter on the light_phone_lux attribute and study the result. # original_dataset = pd.read_csv(dataset_path + 'mydata_chapter2_result.csv', index_col=0) # original_dataset.index = original_dataset.index.to_datetime() # KalFilter = KalmanFilters() # kalman_dataset = KalFilter.apply_kalman_filter(original_dataset, 'acc_phone_x') # DataViz.plot_imputed_values(kalman_dataset, ['original', 'kalman'], 'acc_phone_x', kalman_dataset['acc_phone_x_kalman']) # DataViz.plot_dataset(kalman_dataset, ['acc_phone_x', 'acc_phone_x_kalman'], ['exact','exact'], ['line', 'line']) # We ignore the Kalman filter output for now... # Let us apply a lowpass filter and reduce the importance of the data above 1.5 Hz LowPass = LowPassFilter() # Determine the sampling frequency. fs = float(1000) / milliseconds_per_instance cutoff = 1.5 # # Let us study acc_phone_x: # new_dataset = LowPass.low_pass_filter(copy.deepcopy(dataset), 'acc_phone_x', fs, cutoff, order=10) # DataViz.plot_dataset(new_dataset.ix[int(0.4*len(new_dataset.index)):int(0.43*len(new_dataset.index)), :], ['acc_phone_x', 'acc_phone_x_lowpass'], ['exact','exact'], ['line', 'line']) # # # Let us study acc_phone_y: # new_dataset = LowPass.low_pass_filter(copy.deepcopy(dataset), 'acc_phone_y', fs, cutoff, order=10) # DataViz.plot_dataset(new_dataset.ix[int(0.4*len(new_dataset.index)):int(0.43*len(new_dataset.index)), :], ['acc_phone_y', 'acc_phone_y_lowpass'], ['exact','exact'], ['line', 'line']) # # # Let us study acc_phone_z: # new_dataset = LowPass.low_pass_filter(copy.deepcopy(dataset), 'acc_phone_z', fs, cutoff, order=10)
def main():
# Import the data from the specified location and parse the date index
try:
dataset = pd.read_csv(Path(DATA_PATH / DATASET_FNAME), index_col=0)
dataset.index = pd.to_datetime(dataset.index)
except IOError as e:
print(
'File not found, try to run previous crowdsignals scripts first!')
raise e
# Create an instance of our visualization class to plot the results
DataViz = VisualizeDataset(__file__)
# Compute the number of milliseconds covered by an instance based on the first two rows
milliseconds_per_instance = (dataset.index[1] -
dataset.index[0]).microseconds / 1000
# Create objects for value imputation, low pass filter and PCA
MisVal = ImputationMissingValues()
LowPass = LowPassFilter()
PCA = PrincipalComponentAnalysis()
if FLAGS.mode == 'imputation':
# Impute the missing values and plot an example
imputed_mean_dataset = MisVal.impute_mean(
dataset=copy.deepcopy(dataset), col='hr_watch_rate')
imputed_median_dataset = MisVal.impute_median(
dataset=copy.deepcopy(dataset), col='hr_watch_rate')
imputed_interpolation_dataset = MisVal.impute_interpolate(
dataset=copy.deepcopy(dataset), col='hr_watch_rate')
DataViz.plot_imputed_values(
dataset, ['original', 'mean', 'median', 'interpolation'],
'hr_watch_rate', imputed_mean_dataset['hr_watch_rate'],
imputed_median_dataset['hr_watch_rate'],
imputed_interpolation_dataset['hr_watch_rate'])
elif FLAGS.mode == 'kalman':
# Using the result from Chapter 2, try the Kalman filter on the light_phone_lux attribute and study the result
try:
original_dataset = pd.read_csv(DATA_PATH / ORIG_DATASET_FNAME,
index_col=0)
original_dataset.index = pd.to_datetime(original_dataset.index)
except IOError as e:
print(
'File not found, try to run previous crowdsignals scripts first!'
)
raise e
KalFilter = KalmanFilters()
kalman_dataset = KalFilter.apply_kalman_filter(
data_table=original_dataset, col='acc_phone_x')
DataViz.plot_imputed_values(kalman_dataset, ['original', 'kalman'],
'acc_phone_x',
kalman_dataset['acc_phone_x_kalman'])
DataViz.plot_dataset(data_table=kalman_dataset,
columns=['acc_phone_x', 'acc_phone_x_kalman'],
match=['exact', 'exact'],
display=['line', 'line'])
elif FLAGS.mode == 'lowpass':
# Apply a lowpass filter and reduce the importance of the data above 1.5 Hz
# Determine the sampling frequency
fs = float(1000) / milliseconds_per_instance
# Study acc_phone_x
new_dataset = LowPass.low_pass_filter(
data_table=copy.deepcopy(dataset),
col='acc_phone_x',
sampling_frequency=fs,
cutoff_frequency=FLAGS.cutoff,
order=10)
DataViz.plot_dataset(
new_dataset.iloc[int(0.4 * len(new_dataset.index)
):int(0.43 * len(new_dataset.index)), :],
['acc_phone_x', 'acc_phone_x_lowpass'], ['exact', 'exact'],
['line', 'line'])
elif FLAGS.mode == 'PCA':
# First impute again, as PCA can not deal with missing values
for col in [c for c in dataset.columns if 'label' not in c]:
dataset = MisVal.impute_interpolate(dataset, col)
# Determine the PC's for all but the target columns (the labels and the heart rate)
selected_predictor_cols = [
c for c in dataset.columns
if (not ('label' in c)) and (not (c == 'hr_watch_rate'))
]
pc_values = PCA.determine_pc_explained_variance(
data_table=dataset, cols=selected_predictor_cols)
cumulated_variance = np.cumsum(pc_values)
# Plot the explained variance and cumulated variance
comp_numbers = np.arange(1, len(pc_values) + 1)
DataViz.plot_xy(x=[comp_numbers, comp_numbers],
y=[pc_values, cumulated_variance],
xlabel='principal component number',
ylabel='explained variance',
ylim=[0, 1],
line_styles=['b-', 'r-'],
names=['Variance', 'Cumulated variance'])
# Select 7 as the best number of PC's as this explains most of the variance
n_pcs = 7
dataset = PCA.apply_pca(data_table=copy.deepcopy(dataset),
cols=selected_predictor_cols,
number_comp=n_pcs)
# Visualize the result of the PC's and the overall final dataset
DataViz.plot_dataset(dataset, ['pca_', 'label'], ['like', 'like'],
['line', 'points'])
DataViz.plot_dataset(dataset, [
'acc_', 'gyr_', 'hr_watch_rate', 'light_phone_lux', 'mag_',
'press_phone_', 'pca_', 'label'
], [
'like', 'like', 'like', 'like', 'like', 'like', 'like', 'like',
'like'
], [
'line', 'line', 'line', 'line', 'line', 'line', 'line', 'points',
'points'
])
elif FLAGS.mode == 'final':
# Carry out that operation over all columns except for the label
print('Imputing missing values.')
for col in tqdm([c for c in dataset.columns if 'label' not in c]):
dataset = MisVal.impute_interpolate(dataset=dataset, col=col)
# Include all measurements that have a form of periodicity and filter them
periodic_measurements = [
'acc_phone_x', 'acc_phone_y', 'acc_phone_z', 'acc_watch_x',
'acc_watch_y', 'acc_watch_z', 'gyr_phone_x', 'gyr_phone_y',
'gyr_phone_z', 'gyr_watch_x', 'gyr_watch_y', 'gyr_watch_z',
'mag_phone_x', 'mag_phone_y', 'mag_phone_z', 'mag_watch_x',
'mag_watch_y', 'mag_watch_z'
]
print('Applying low pass filter on peridic measurements.')
# Determine the sampling frequency.
fs = float(1000) / milliseconds_per_instance
for col in tqdm(periodic_measurements):
dataset = LowPass.low_pass_filter(data_table=dataset,
col=col,
sampling_frequency=fs,
cutoff_frequency=FLAGS.cutoff,
order=10)
dataset[col] = dataset[col + '_lowpass']
del dataset[col + '_lowpass']
# Use the optimal found parameter n_pcs = 7 to apply PCA to the final dataset
selected_predictor_cols = [
c for c in dataset.columns
if (not ('label' in c)) and (not (c == 'hr_watch_rate'))
]
n_pcs = 7
dataset = PCA.apply_pca(data_table=copy.deepcopy(dataset),
cols=selected_predictor_cols,
number_comp=n_pcs)
# Visualize the final overall dataset
DataViz.plot_dataset(dataset, [
'acc_', 'gyr_', 'hr_watch_rate', 'light_phone_lux', 'mag_',
'press_phone_', 'pca_', 'label'
], [
'like', 'like', 'like', 'like', 'like', 'like', 'like', 'like',
'like'
], [
'line', 'line', 'line', 'line', 'line', 'line', 'line', 'points',
'points'
])
# Store the final outcome
dataset.to_csv(DATA_PATH / RESULT_FNAME)
dataSet.data_table = dataSet.data_table[(length - 53): (length - 1)] # same length for every sample
FreqAbs = FourierTransformation()
transformations = []
number_frequencies = 50
for column in list(dataSet.data_table.columns):
transformation = np.abs(np.fft.fft(dataSet.data_table[column], number_frequencies))
transformations.append((column,transformation))
cutoff_frequency = 20
sampling_frequency = 50
order = 3
LowPass = LowPassFilter()
if len(dataSet.data_table[ 'AccelerometerX (m/s^2)']) < 50:
print path + "\\" + label + "\\" + sample
new_dataset = LowPass.low_pass_filter(dataSet.data_table, 'AccelerometerX (m/s^2)', sampling_frequency,
cutoff_frequency,
order=order, phase_shift=True)
new_dataset = LowPass.low_pass_filter(new_dataset, 'AccelerometerY (m/s^2)', sampling_frequency,
cutoff_frequency, order=order,
phase_shift=True)
new_dataset = LowPass.low_pass_filter(new_dataset, 'AccelerometerZ (m/s^2)', sampling_frequency,
cutoff_frequency, order=order,
phase_shift=True)
new_dataset = LowPass.low_pass_filter(new_dataset, 'GyroscopeX (rad/s)', sampling_frequency, cutoff_frequency,
order=order,
