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generate_predictions.gen2.py
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"""
This script generates a prediction file given a results file as input.
Usage: generate_predictions.py <results_file> <predictions_file>
WORKS WITH GEN2 FILES
"""
from matplotlib.mlab import specgram
import numpy as np
import scipy.signal as sig
from numpy.lib import stride_tricks
import time
import cPickle as pickle
from scipy import ndimage
import kmeans
from sklearn import cross_validation, ensemble, metrics, svm
import sys
DATA_PATH = "X_train.npy"
LABEL_PATH = "Y_train.npy"
TEST_DATA_PATH = "X_test.npy"
if len(sys.argv) != 3:
sys.exit("Usage: generate_predictions.py <results_file> <predictions_file>")
results_path = sys.argv[1]
predictions_path = sys.argv[2]
with open(results_path, 'r') as f:
results = pickle.load(f)
settings = results['settings']
params = results['params']
P = params['P']
means = params['means']
centroids = params['centroids']
fmins = params['fmins']
fmaxs = params['fmaxs']
print "GENERATING PREDICTIONS FOR: %s" % results_path
print
print settings
print
start_time = time.time()
def tock():
elapsed = time.time() - start_time
print " running for %.2f s" % elapsed
# load data
print "Load training data"
X = np.load(DATA_PATH)
Y = np.load(LABEL_PATH)
tock()
# downsample
print "Downsample"
X_downsampled = sig.decimate(X, 2, axis=1).astype(X.dtype)
tock()
# normalise
if settings['normalise_volume']:
print "Normalise volume"
X_downsampled -= X_downsampled.mean(1).reshape(-1, 1)
X_downsampled /= X_downsampled.std(1).reshape(-1, 1)
tock()
# compute spectrograms
print "Compute spectrograms"
nfft = settings['specgram_num_components']
noverlap = nfft * (1 - 1. / settings['specgram_redundancy'])
log_scale = settings['log_scale']
dummy = specgram(X_downsampled[0], NFFT=nfft, noverlap=noverlap)[0] # to get the dimensions
X_specgram = np.zeros((X.shape[0], dummy.shape[0], dummy.shape[1]), dtype=X.dtype)
for k in xrange(X.shape[0]):
X_specgram[k] = specgram(X_downsampled[k], NFFT=nfft, noverlap=noverlap)[0]
X_specgram = np.log(1 + log_scale * X_specgram)
tock()
if 'lnorm' in settings and settings['lnorm']: # check presence first for backwards compatibility
print "Locally normalise spectrograms"
lnorm_sigma_mean = settings['lnorm_sigma_mean']
lnorm_sigma_std = settings['lnorm_sigma_std']
def local_normalise(im, sigma_mean, sigma_std):
"""
based on matlab code by Guanglei Xiong, see http://www.mathworks.com/matlabcentral/fileexchange/8303-local-normalization
"""
means = ndimage.gaussian_filter(im, sigma_mean)
im_centered = im - means
stds = np.sqrt(ndimage.gaussian_filter(im_centered**2, sigma_std))
return im_centered / stds
for k in xrange(X_specgram.shape[0]):
X_specgram[k] = local_normalise(X_specgram[k], lnorm_sigma_mean, lnorm_sigma_std)
tock()
# patch extraction
print "Patch extraction"
w, h = settings['patch_width'], settings['patch_height']
shape = X_specgram.shape
strides = X_specgram.strides
new_shape = (shape[0], shape[1] - h + 1, h, shape[2] - w + 1, w)
new_strides = (strides[0], strides[1], strides[1], strides[2], strides[2])
patches = stride_tricks.as_strided(X_specgram, shape=new_shape, strides=new_strides)
tock()
# feature extraction and summarisation
print "Feature extraction and summarisation"
threshold = settings['threshold']
PC = np.dot(P, centroids.T)
# def summarise_features(features):
# features = features.reshape(features.shape[0], -1, features.shape[3]) # merge time and frequency axes
# return np.hstack([features.mean(1), features.std(1), features.min(1), features.max(1)]) # summarize over time axis
# # return features.mean(1)
# # return features.max(1)
def summarise_features(features):
features_freq_pooled = features.max(1) # max pool over frequency
n_timesteps = features_freq_pooled.shape[1]
# quadrant pooling over time
parts = [features_freq_pooled.max(1)] # max pooling over the whole timelength
n_timeslices = 4
slice_size = n_timesteps // n_timeslices # floor
for k in xrange(n_timeslices):
features_slice = features_freq_pooled[:, k*slice_size:(k+1)*slice_size].max(1)
parts.append(features_slice)
return np.hstack(parts)
# return features_freq_pooled.max(1) # time pooling
def extract_features(batch, means, PC, threshold=None):
batch_shape = batch.shape
batch = batch.transpose(0,1,3,2,4).reshape(-1, h * w)
features = np.dot(batch - means.reshape(1, -1), PC)
if threshold is not None: # if no threshold specified, use linear features
features = np.maximum(features - threshold, 0) # thresholding
# features = features.reshape(batch_shape[0], -1, PC.shape[1]) # split examples and other axes
features = features.reshape(batch_shape[0], batch_shape[1], batch_shape[3], PC.shape[1]) # (examples, frequency bins, time, features)
# import pdb; pdb.set_trace()
features = summarise_features(features)
return features
batch_size = 100
num_examples = patches.shape[0]
num_batches = int(np.ceil(num_examples / float(batch_size)))
features = np.zeros((num_examples, 5 * PC.shape[1]), dtype=X.dtype)
for b in xrange(num_batches):
print " batch %d of %d" % (b+1, num_batches)
batch = patches[b*batch_size:(b+1)*batch_size]
current_batch_size = batch.shape[0]
batch_features = extract_features(batch, means, PC, threshold)
features[b*batch_size:b*batch_size + current_batch_size] = batch_features
tock()
# interval normalisation
print "Normalisation of features"
features -= fmins.reshape(1, -1)
features /= (fmaxs - fmins).reshape(1, -1)
tock()
print "Do some memory cleanup"
del X
del X_downsampled
del X_specgram
del patches
# classifier training
print "Classifier training"
clf = svm.LinearSVC(C=10e-3)
clf.fit(features, Y)
tock()
print "Further cleanup: no longer need training data"
del features
del Y
# load test data
print "Load test data"
X_test = np.load(TEST_DATA_PATH).astype('float32')
tock()
# downsample
print "Downsample (test)"
X_downsampled = sig.decimate(X_test, 2, axis=1).astype(X_test.dtype)
tock()
# normalise
if settings['normalise_volume']:
print "Normalise volume"
X_downsampled -= X_downsampled.mean(1).reshape(-1, 1)
X_downsampled /= X_downsampled.std(1).reshape(-1, 1)
tock()
# compute spectrograms
print "Compute spectrograms (test)"
nfft = settings['specgram_num_components']
noverlap = nfft * (1 - 1. / settings['specgram_redundancy'])
log_scale = settings['log_scale']
dummy = specgram(X_downsampled[0], NFFT=nfft, noverlap=noverlap)[0] # to get the dimensions
X_specgram = np.zeros((X_test.shape[0], dummy.shape[0], dummy.shape[1]), dtype=X_test.dtype)
for k in xrange(X_test.shape[0]):
X_specgram[k] = specgram(X_downsampled[k], NFFT=nfft, noverlap=noverlap)[0]
X_specgram = np.log(1 + log_scale * X_specgram)
del X_test
del X_downsampled
tock()
if 'lnorm' in settings and settings['lnorm']:
print "Locally normalise spectrograms"
lnorm_sigma_mean = settings['lnorm_sigma_mean']
lnorm_sigma_std = settings['lnorm_sigma_std']
for k in xrange(X_specgram.shape[0]):
X_specgram[k] = local_normalise(X_specgram[k], lnorm_sigma_mean, lnorm_sigma_std)
tock()
# patch extraction
print "Patch extraction (test)"
w, h = settings['patch_width'], settings['patch_height']
shape = X_specgram.shape
strides = X_specgram.strides
new_shape = (shape[0], shape[1] - h + 1, h, shape[2] - w + 1, w)
new_strides = (strides[0], strides[1], strides[1], strides[2], strides[2])
patches = stride_tricks.as_strided(X_specgram, shape=new_shape, strides=new_strides)
tock()
# feature extraction and summarisation
print "Feature extraction and summarisation (test)"
threshold = settings['threshold']
PC = np.dot(P, centroids.T)
batch_size = 100
num_examples = patches.shape[0]
num_batches = int(np.ceil(num_examples / float(batch_size)))
features = np.zeros((num_examples, 5 * PC.shape[1]), dtype=X_specgram.dtype)
for b in xrange(num_batches):
print " batch %d of %d" % (b+1, num_batches)
batch = patches[b*batch_size:(b+1)*batch_size]
current_batch_size = batch.shape[0]
batch_features = extract_features(batch, means, PC, threshold)
features[b*batch_size:b*batch_size + current_batch_size] = batch_features
tock()
# interval normalisation
print "Normalisation of features (test)"
features -= fmins.reshape(1, -1)
features /= (fmaxs - fmins).reshape(1, -1)
tock()
print "Do some memory cleanup (again)"
del X_specgram
del patches
print "Compute predictions"
scores = clf.decision_function(features)
print "Store predictions"
with open(predictions_path, 'w') as f:
for score in scores:
f.write(str(score) + '\n')
print " stored in %s" % predictions_path