def process_data_set(data_set, filter_threshold=0.1):
# %%Preprocess andrews
print('#' * 10)
print(data_set)
print('#' * 10)
file_names = misc.folder_names(data_set, 'none')
pca_file = os.path.join(file_names['pca_file'])
data_file = os.path.join(file_names['npz_file'])
# Reading in all data
print('---> READING AND PREPROCESSING DATA')
files = os.listdir(data_set)
files = natsort.natsorted(files)
n_locations = len(files)
test_data, az, el = preprocess(os.path.join(data_set, files[0]),
verbose=False)
n_samples = test_data.shape[2]
all_mns = numpy.zeros([n_locations, 7, 31, n_samples])
all_vrs = numpy.zeros([n_locations, 7, 31, n_samples])
for i in range(len(files)):
data, _, _ = preprocess(os.path.join(data_set, files[i]))
mns = numpy.mean(data, axis=3)
vrs = numpy.var(data, axis=3)
print(data.shape)
all_mns[i, :, :, :] = mns
all_vrs[i, :, :, :] = vrs
# get ID vars
print('---> DATA2LONG')
n = numpy.arange(n_locations)
az_line = az[0, :]
el_line = el[:, 0]
locs, azs, els = numpy.meshgrid(n, az_line, el_line)
locs = numpy.transpose(locs, axes=(1, 2, 0))
azs = numpy.transpose(azs, axes=(1, 2, 0))
els = numpy.transpose(els, axes=(1, 2, 0))
# Reshape data to long format
long_data = numpy.reshape(all_mns, (n_locations * 7 * 31, n_samples))
long_lcs = numpy.reshape(locs, (n_locations * 7 * 31))
long_azs = numpy.reshape(azs, (n_locations * 7 * 31))
long_els = numpy.reshape(els, (n_locations * 7 * 31))
id_array = numpy.column_stack((long_lcs, long_azs, long_els))
# Get the variation across all measurements for each sample
sample_variance = numpy.mean(all_vrs, axis=(0, 1, 2))
# Select only those templates above threshold
summed = numpy.sum(long_data, axis=1)
summed = numpy.array(summed)
threshold = numpy.min(summed) + filter_threshold
include = summed > threshold
long_data = long_data[include, :]
id_array = id_array[include, :]
long_lcs = long_lcs[include]
long_azs = long_azs[include]
long_els = long_els[include]
# Save data
print('---> SAVING LONG FORMAT')
numpy.savez(data_file,
long_data=long_data,
long_lcs=long_lcs,
long_azs=long_azs,
long_els=long_els,
ids=id_array,
sample_variance=sample_variance,
include=include,
files=files)
# %%
print('---> RUN AND SAVE PCA MODEL')
pca_model = PCA()
pca_model.fit(long_data)
pickle_save(pca_file, pca_model)
print('#' * 10)
import misc
import numpy
import settings
import os
from matplotlib import pyplot
pca_plot_file = os.path.join(settings.figure_folder, 'pca.pdf')
pyplot.style.use(settings.style)
pyplot.figure(figsize=(4, 4))
for data_set in ['israel', 'royal']:
files = misc.folder_names(data_set, 'lcs')
pca = misc.pickle_load(files['pca_file'])
cvar = numpy.cumsum(pca.explained_variance_ratio_)
if data_set == 'royal':
pyplot.plot(cvar, color='k', linestyle=settings.royal_linestyle)
if data_set == 'israel':
pyplot.plot(cvar, color='k', linestyle=settings.israel_linestyle)
pyplot.legend(['Israel', 'Royal'])
pyplot.xlabel('Nr of PCs')
pyplot.ylabel('Proportion of explained variance')
pyplot.tight_layout()
pyplot.savefig(pca_plot_file)
pyplot.show()
from tensorflow import keras
import misc
import settings
import os
files = misc.folder_names('israel', 'azs')
model = keras.models.load_model(files['model_file'])
output = os.path.join(settings.figure_folder, 'model.pdf')
keras.utils.plot_model(model,
to_file=output,
show_shapes=True,
show_layer_names=False,
rankdir='TB',
expand_nested=True,
dpi=96)
import scipy.stats as stats
from matplotlib import pyplot
import os
import misc
import numpy
import settings
pyplot.style.use(settings.style)
data_set = 'royal'
loss_function = 'Categorical Cross Entropy'
files_lcs = misc.folder_names(data_set, 'lcs')
files_azs = misc.folder_names(data_set, 'azs')
files_els = misc.folder_names(data_set, 'els')
results_azs = misc.pickle_load(files_azs['results_file'])
results_els = misc.pickle_load(files_els['results_file'])
results_lcs = misc.pickle_load(files_lcs['results_file'])
perfect_memory = misc.pickle_load(files_lcs['perfect_memory_file'])
output_file = os.path.join(settings.figure_folder,
data_set + '_performance.pdf')
# %% Plot errors
fig, axes = pyplot.subplots(nrows=3, ncols=3)
fig.set_figheight(10)
fig.set_figwidth(10)
pyplot.sca(axes[0, 0])
import numpy
import pandas
import scipy.spatial.distance as distance
import misc
import process_functions
import settings
data_set = 'israel'
generate_data = False
iterations = 10
print('Running perfect memory for', data_set)
file_names = misc.folder_names(data_set, None)
output_file = file_names['perfect_memory_file']
if generate_data: process_functions.process_data_set(data_set)
# Read prepared data
data = numpy.load(file_names['npz_file'])
corridor_distances = misc.map_lcs_to_distances(data)
pca = misc.pickle_load(file_names['pca_file'])
correct_ids = data['ids']
correct_ids[:, 0] = corridor_distances
templates = data['long_data']
pca_templates = pca.transform(templates)
n_components = settings.n_components
inputs = pca_templates[:, :n_components]
import os
from matplotlib import pyplot
from tensorflow import keras
import pandas
import misc
import process_functions
import settings
ns = []
ws = []
ws_nb = []
rs = []
for data_set in ['israel', 'royal']:
selected_dimension = 'lcs'
file_names = misc.folder_names(data_set, selected_dimension)
data = numpy.load(file_names['npz_file'])
templates = data['long_data']
total_n_numbers = templates.shape[0] * settings.n_components
new_model = keras.models.load_model(file_names['model_file'])
weights = new_model.get_weights()
total_n_weights = 0
total_n_weights_no_bias = 0
for w in weights:
m = numpy.matrix(w)
total_n_weights = total_n_weights + (m.shape[0] * m.shape[1])
if m.shape[0] > 1:
total_n_weights_no_bias = total_n_weights_no_bias + (m.shape[0] *
m.shape[1])
import settings
import os
from matplotlib import pyplot
import smoothn
training_history_plot_file = os.path.join(settings.figure_folder, 'history.pdf')
pyplot.style.use(settings.style)
pyplot.figure(figsize=(10,5))
for data_set in ['israel', 'royal']:
if data_set == 'israel': pyplot.subplot(1, 2, 1)
if data_set == 'royal': pyplot.subplot(1, 2, 2)
for dimension in ['azs', 'els', 'lcs']:
files = misc.folder_names(data_set, dimension)
history_file = files['history_file']
history = misc.pickle_load(history_file)
if data_set == 'israel': linestyle = settings.israel_linestyle
if data_set == 'royal': linestyle = settings.royal_linestyle
if dimension == 'azs': color = settings.azs_color
if dimension == 'els': color = settings.els_color
if dimension == 'lcs': color = settings.lcs_color
trace = numpy.array(history['loss'])
trace = smoothn.smoothn(trace, s0=1)[0]
pyplot.plot(trace, color=color)
pyplot.ylim([0, 4])
pyplot.xlabel('Epoch')
