def __init__(self, ts, ticker, corpus, filter):
self.ticker = ticker
self.corpus = corpus
self.dts = ts.select('price_{}'.format(ticker))[0]
self.price = ts.select('price_{}'.format(ticker))[1]
self.crsp = ts.select('CRSP')[1][1:]
self.returns = np.diff(np.log(self.price))
self.adj_returns = zscore(self.returns - self.crsp)
self.nt = ts.select('{}_{}_{}'.format(corpus, ticker, filter))[1][1:]
self.sent = zscore(self.nt)
self.friday = np.array([ts.select('friday')[1][1:]])
self.jan = np.array([ts.select('january')[1][1:]])
self.NWD = np.array([ts.select('NWD')[1][1:]])
def fit_model(self, bin=False):
mt = sio.loadmat(self.data_path +
self.mouse_filename) # neurons by timepoints
self.X = mt['Fsp']
self.motionSVD = np.array(mt['beh'][0]['face'][0]['motionSVD'][0][0]).T
self.parea = np.array(mt['beh'][0]['pupil'][0]['area'][0][0])
if bin == True:
self.X, self.motionSVD, self.parea = bin_data(
self.X, self.motionSVD, self.parea)
else:
self.nt = self.motionSVD.shape[1]
tbin = 1
self.motionSVD = np.reshape(
self.motionSVD[:, :self.nt * tbin],
(self.motionSVD.shape[0], self.nt, tbin)).mean(axis=-1)
self.parea = np.reshape(self.parea[:self.nt * tbin],
(self.nt, tbin)).mean(axis=-1)
if self.model == 'EnsemblePursuit_numpy':
options_dict = {'seed_neuron_av_nr': 100, 'min_assembly_size': 8}
ep_np = EnsemblePursuitNumpy(n_ensembles=self.nr_of_components,
lambd=0.01,
options_dict=options_dict)
U, V = ep_np.fit_transform(self.X)
if self.save == True:
bundle = {'U': U, 'V': V}
np.save(
self.save_path + self.mouse_filename +
'_spont_ep_numpy.npy', bundle)
return U, V
if self.model == 'EnsemblePursuit_pytorch':
options_dict = {'seed_neuron_av_nr': 100, 'min_assembly_size': 8}
ep_np = EnsemblePursuitPyTorch(n_ensembles=self.nr_of_components,
lambd=0.01,
options_dict=options_dict)
U, V = ep_np.fit_transform(self.X)
if self.save == True:
bundle = {'U': U, 'V': V}
np.save(
self.save_path + self.mouse_filename +
'_spont_ep_numpy.npy', bundle)
return U, V
if self.model == 'NMF':
print(self.X[self.X < 0])
#self.X-=self.X.min(axis=0)
self.X[self.X < 0] = 0
self.X = self.X.T
model = NMF(n_components=self.nr_of_components,
init='nndsvd',
random_state=7)
V = model.fit_transform(self.X)
U = model.components_
return U.T, V
if self.model == 'ICA':
self.X = zscore(self.X)
self.X = self.X.T
ICA = FastICA(n_components=self.nr_of_components, random_state=7)
V = ICA.fit_transform(self.X)
U = ICA.components_
return U.T, V
def variance_explained_across_neurons(self, U, V):
'''
The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares
((y_true - y_pred) ** 2).sum() and v is the total sum of squares
((y_true - y_true.mean()) ** 2).sum().
'''
#Fetch the original data and convert it into the same form as what goes into the
#matrix factorization model
mt = sio.loadmat(self.data_path +
self.mouse_filename) # neurons by timepoints
X = mt['Fsp']
if self.model == 'EnsemblePursuit_pytorch' or self.model == 'EnsemblePursuit_numpy':
X = zscore(X.T).T
u = []
v = []
approx = U @ V.T
for j in range(X.shape[0]):
u_j = ((X[j, :] - approx[j, :])**2).sum()
v_j = ((X[j, :] - np.mean(X[j, :]))**2).sum()
u.append(u_j)
v.append(v_j)
u = np.array(u)
v = np.array(v)
plt.plot(-np.divide(u, v) + 1)
plt.title('Variance explained across neurons')
plt.show()
print('Total variance explained, averaged over neurons is:',
(1 - np.mean(u) / np.mean(v)))
def plot_dat(self, anomaly, data, standardise = 1):
""" For a given detected anomaly, plots the data around that time point """
# Need to add input checks
sample_half = int(round( self.p['zt_sample_size']/2.))
dat = data[anomaly-sample_half:anomaly+sample_half,:]
if standardise:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(zscore(dat))
bpList = bp_lookup(self.p['SAX_alphabet_size'])
for bp in bpList:
ax.axhline(y=bp, xmin=0, xmax=dat.shape[0], ls = '--', color = 'k')
adjust_spines(ax, ['bottom'])
ax.set_yticklabels([])
ax.yaxis.set_ticks([])
ax.set_xticklabels(range(anomaly-sample_half,anomaly+sample_half+1))
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(18)
else:
plt.figure()
plt.plot(dat)
bpList = bp_lookup(self.p['SAX_alphabet_size'])
plt.hlines(bpList, xmin=0, xmax=dat.shape[0]-1, linestyles = 'dashed', color = 'k')
def variance_explained_across_neurons(self, U, V):
'''
From sklearn:
The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares
((y_true - y_pred) ** 2).sum() and v is the total sum of squares
((y_true - y_true.mean()) ** 2).sum().
'''
#Fetch the original data and convert it into the same form as what goes into the
#matrix factorization model
data = io.loadmat(self.data_path + self.mouse_filename)
resp = data['stim'][0]['resp'][0]
spont = data['stim'][0]['spont'][0]
X = subtract_spont(spont, resp).T
X = zscore(X.T).T
u = []
v = []
approx = U @ V.T
for j in range(X.shape[0]):
u_j = ((X[j, :] - approx[j, :])**2).sum()
v_j = ((X[j, :] - np.mean(X[j, :]))**2).sum()
u.append(u_j)
v.append(v_j)
u = np.array(u)
v = np.array(v)
plt.plot(-np.divide(u, v) + 1)
plt.title('Variance explained across neurons')
plt.show()
print('Total variance explained, averaged over neurons is:',
(1 - np.mean(u) / np.mean(v)))
def __init__(*args):
repeat = int(args[0]) if len(args) >0 else 0
if len(args) >1:
if type(args[1]) == str:
date1 = datetime.strptime(args[1], '%Y-%m-%d').date()
else:
date1 = args[1]
# date1 = datetime.strptime(args[1], '%Y-%m-%d').date() if len(args) >1 else None
offset = int(args[2]) if len(args) >2 else None
exclude_index = 1
results = []
need_create = {}
for i in range(repeat):
delta = i * REPORT_OFFSET2
start_date = date1 - timedelta(offset*(delta+1))
end_date = date1 - timedelta(offset*delta)
# print(date1, start_date, end_date)
path = os.path.join('data', 'zscore', f'{start_date}_{end_date}.json')
try:
results.append(load_json(path))
except Exception as e:
results.append({})
need_create[i] = (start_date, end_date)
if len(need_create):
data_all = load_stocklist_json()
print('len: ', len(data_all))
for i, d in enumerate(data_all):
if exclude_index and is_index_stock(d['codeName']):
continue
full_code = d['full_code']
output = load_stock_json(full_code, start_date=datetime.now().date())['output']
for ii, dates in need_create.items():
z_score = zscore(output, dates[0], dates[1])
if z_score:
print(i, full_code, dates[0], dates[1], z_score)
results[ii][full_code] = z_score
for ii, dates in need_create.items():
path = os.path.join('data', 'zscore', f'{dates[0]}_{dates[1]}.json')
save_json(results[ii], path)
return results
def SAX(data, alphabet_size, word_size, minstd = 1.0, pre_normed = False):
""" Returns one word for each data stream
word_size == Number of segments data is split into for PAA
alphabet_size == Number of symbols used
Also now compatable with a single data stream.
"""
if data.ndim == 1:
num_streams = 1
data = np.atleast_2d(data)
data = data.T
else:
num_streams = data.shape[1]
# Need to insert check here for stationary segemnts
mask = data.std(axis=0) < minstd
passed = np.invert(mask)
if np.any(mask):
# Scale data to have a mean of 0 and a standard deviation of 1.
if pre_normed == False:
data[:,passed] = zscore(data[:, passed])
symbol4skips = string.ascii_letters[int(np.ceil(alphabet_size/2.))]
else:
# Scale data to have a mean of 0 and a standard deviation of 1.
if pre_normed == False:
data = zscore(data)
# Calculate our breakpoint locations.
breakpoints = bp_lookup(alphabet_size)
breakpoints = np.concatenate((breakpoints, np.array([np.Inf])))
# Split the data into a list of word_size pieces.
dataWords = np.array_split(data, word_size, axis=0)
# Predifine Matrices
segment_means = np.zeros((word_size,num_streams))
#segment_symbol = np.zeros((word_size,num_streams), dtype = np.str)
p_array = np.zeros((num_streams,),dtype = ('a1,' * word_size + 'i2'))
p_dict = {}
# Calculate the mean for each section.
for i in range(word_size):
segment_means[i,passed] = dataWords[i][:,passed].mean(axis = 0)
# Figure out which break each section is in based on the section_means and
# calculated breakpoints.
for i in range(num_streams):
for j in range(word_size):
if passed[i]:
idx = int(np.where(breakpoints > segment_means[j,i])[0][0])
# Store in phrase_array
p_array[i][j] = string.ascii_letters[idx]
else:
p_array[i][j] = symbol4skips
# Store in phrase_dict
phrase = ''.join(tuple(p_array[i])[:word_size])
if p_dict.has_key(phrase):
p_dict[phrase].append(i)
else:
p_dict[phrase] = [i]
# Put frequency of pattern in p_array
for vals in p_dict.itervalues():
count = len(vals)
for i in range(count):
p_array[vals[i]][-1] = count
return p_array, p_dict, segment_means
def fit(self, X):
nK = self.n_components
lam = self.lam
n_kmeans = self.n_kmeans
NT, NN = X.shape
# z-score along time dimension
X = utils.zscore(X, axis=0)
# convert to float64 for numerical precision
X = np.float64(X)
# initialize k-means clusters and compute their variance in vm
V, vm = initialize_kmeans(X, n_kmeans, lam)
# initialize vectors in ensemble pursuit (Vs)
vs = np.zeros((NT, nK))
# initialize U
U = np.zeros((NN, nK))
# precompute covariance matrix of neurons
C = X.T @ X
# keep track of number of neurons per ensemble
ns = np.zeros(nK, )
# time the ensemble pursuit
t0 = time.time()
# keep track of neuron order in ensembles
self.order = []
self.seed = np.zeros((NT, nK))
self.cost_deltas = []
# outer loop
for j in range(nK):
# initialize with "biggest" k-means ensemble (by variance)
imax = np.argmax(vm)
# zscore the seed trace
seed = zscore(V[:, imax])
# fit one ensemble starting from this seed
iorder, current_v, cost_delta_lst = new_ensemble(X, C, seed, lam)
self.order.append(iorder)
self.seed[:, j] = seed
# keep track of number of neurons
ns[j] = len(iorder)
# normalize current_v to unit norm
current_v /= np.sum(current_v**2)**.5
# update column of Vs
vs[:, j] = current_v
# projection of each neuron onto this ensemble trace
w = current_v @ X
# update weights for neurons in this ensemble
U[iorder, j] = w[iorder]
# update activity trace
X[:, iorder] -= np.outer(current_v, w[iorder])
# rank one update to C using wtw
wtw = np.outer(w[iorder], w)
# update the columns
C[:, iorder] -= wtw.T
# update the rows
C[iorder, :] -= wtw
# add back term for the submatrix of neurons in this ensemble
C[iorder[:, np.newaxis], iorder] += wtw[:, iorder]
# run one round of k-means because we changed X
V, vm = one_round_of_kmeans(V, X, lam)
self.cost_deltas.append(cost_delta_lst)
if j % 25 == 0 or j == nK - 1:
print('ensemble %d, time %2.2f, nr neurons %d, EV %2.4f' %
(j, time.time() - t0, len(iorder), 1 - np.mean(X**2)))
print('average sparsity is %2.4f' % (np.mean(U > 1e-5)))
self.components_ = vs
self.weights = U
self.residual_kmeans = V
# the fit function has to return the model
return self
def fit_model(self):
for filename in self.mat_file_lst:
print(filename)
data = io.loadmat(self.data_path + filename)
resp = data['stim'][0]['resp'][0]
spont = data['stim'][0]['spont'][0]
if self.model == 'EnsemblePursuit_numpy':
X = subtract_spont(spont, resp).T
options_dict = {
'seed_neuron_av_nr': 100,
'min_assembly_size': 8
}
ep_np = EnsemblePursuitNumpy(n_ensembles=self.nr_of_components,
lambd=self.lambd_,
options_dict=options_dict)
start = time.time()
U, V = ep_np.fit_transform(X)
end = time.time()
tm = end - start
print('Time', tm)
np.save(self.save_path + filename + '_V_ep_numpy.npy', V)
np.save(self.save_path + filename + '_U_ep_numpy.npy', U)
np.save(self.save_path + filename + '_timing_ep_numpy.npy', tm)
if self.model == 'EnsemblePursuit_pytorch':
X = subtract_spont(spont, resp).T
options_dict = {
'seed_neuron_av_nr': 100,
'min_assembly_size': 8
}
ep_pt = EnsemblePursuitPyTorch(
n_ensembles=self.nr_of_components,
lambd=self.lambd_,
options_dict=options_dict)
start = time.time()
U, V = ep_pt.fit_transform(X)
end = time.time()
tm = end - start
print('Time', tm)
np.save(self.save_path + filename + '_V_ep_pytorch.npy', V)
np.save(self.save_path + filename + '_U_ep_pytorch.npy', U)
np.save(self.save_path + filename + '_timing_ep_pytorch.npy',
tm)
if self.model == 'EnsemblePursuit_adaptive':
X = subtract_spont(spont, resp).T
options_dict = {
'seed_neuron_av_nr': 100,
'min_assembly_size': 8
}
ep_pt = EnsemblePursuitPyTorch(
n_ensembles=self.nr_of_components,
lambd=self.lambd_,
options_dict=options_dict)
start = time.time()
U, V = ep_pt.fit_transform(X)
end = time.time()
tm = end - start
print('Time', tm)
np.save(self.save_path + filename + '_V_ep_adaptive.npy', V)
np.save(self.save_path + filename + '_U_ep_adaptive.npy', U)
if self.model == 'SparsePCA':
X = subtract_spont(spont, resp)
X = zscore(X)
sPCA = SparsePCA(n_components=self.nr_of_components,
random_state=7,
max_iter=100,
n_jobs=-1,
verbose=1)
start = time.time()
model = sPCA.fit(X)
end = time.time()
elapsed_time = end - start
U = model.components_
V = sPCA.transform(X)
np.save(self.save_path + filename + '_U_sPCA.npy', U)
np.save(self.save_path + filename + '_V_sPCA.npy', V)
np.save(self.save_path + filename + '_time_sPCA.npy',
elapsed_time)
if self.model == 'ICA':
X = subtract_spont(spont, resp)
X = zscore(X)
ICA = FastICA(n_components=self.nr_of_components,
random_state=7)
start = time.time()
V = ICA.fit_transform(X)
end = time.time()
elapsed_time = end - start
U = ICA.components_
np.save(self.save_path + filename + '_U_ICA.npy', U)
np.save(self.save_path + filename + '_V_ICA.npy', V)
np.save(self.save_path + filename + '_time_ICA.npy',
elapsed_time)
def CharacterTrajectories():
symbols = [
'a', 'b', 'c', 'd', 'e', 'g', 'h', 'l', 'm', 'n', 'o', 'p', 'q', 'r',
's', 'u', 'v', 'w', 'y', 'z'
]
statecounts = [4, 3, 2, 4, 3, 4, 3, 2, 6, 4, 3, 3, 4, 3, 3, 4, 2, 4, 2, 3]
gaussiancounts = [
2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2
]
CT = np.load(
'/home/scw4750/songbinxu/datasets/CharacterTrajectories/CharacterTrajectories.npz'
)
x_train = CT['x_train'][:, :, :2]
x_train = [remove_padding(seq) for seq in x_train]
x_train = [filtering(seq, window=5) for seq in x_train]
x_train = [zscore(seq) for seq in x_train]
y_train = CT['y_train']
'''train'''
# for label in range(20):
# sub_data = get_oneclass_data(x_train, y_train, label)
# chmm = CHMM(sub_data, state_num=statecounts[label], gaussian_num=gaussiancounts[label],
# name='character_'+symbols[label], simplify=False)
# chmm.train(500)
# chmm.save_model('save/CharacterTrajectories/chmm_'+symbols[label]+'.npz')
'''test'''
# result = []
# for label in range(20):
# print symbols[label]
# chmm = CHMM(x_train, state_num=statecounts[label], gaussian_num=gaussiancounts[label],
# name='character_'+symbols[label], mode='test', simplify=False)
# chmm.load_model('save/CharacterTrajectories/chmm_'+symbols[label]+'.npz')
# chmm.Viterbi_decode()
# result.append(np.max(chmm.delta, 1)[:, None])
# pred = np.argmax(np.concatenate(result, 1), 1)
# correct, total = np.sum(np.equal(y_train, pred).astype(int)), len(y_train)
# print "train: correct=%d total=%d accuracy=%.4f" % (correct, total, correct/float(total))
x_test = CT['x_test'][:, :, :2]
x_test = [remove_padding(seq) for seq in x_test]
x_test = [filtering(seq, window=5) for seq in x_test]
x_test = [zscore(seq) for seq in x_test]
y_test = CT['y_test']
result = []
for label in range(20):
print symbols[label],
chmm = CHMM(x_test,
state_num=statecounts[label],
gaussian_num=gaussiancounts[label],
name='character_' + symbols[label],
mode='test',
simplify=False)
chmm.load_model('save/CharacterTrajectories/chmm_' + symbols[label] +
'.npz')
chmm.Viterbi_decode()
result.append(np.max(chmm.delta, 1)[:, None])
pred = np.argmax(np.concatenate(result, 1), 1)
correct, total = np.sum(np.equal(y_test, pred).astype(int)), len(y_test)
print "test: correct=%d total=%d accuracy=%.4f" % (
correct, total, correct / float(total)) # 88.44%
confuse_matrix = np.zeros((20, 20))
for i in range(len(y_test)):
confuse_matrix[y_test[i], pred[i]] += 1.0
confuse_matrix = 1.0 - (confuse_matrix - np.min(confuse_matrix)) / (
np.max(confuse_matrix) - np.min(confuse_matrix))
ax = plt.subplot(111)
plt.imshow(confuse_matrix,
origin='lower',
cmap='gray',
interpolation='nearest')
plt.xticks(range(20))
plt.yticks(range(20))
ax.set_xticklabels(symbols)
ax.set_yticklabels(symbols)
plt.savefig('save/CharacterTrajectories/confuse_matrix.png')
plt.clf()
trainy_all = all_data[:, -2:] # alpha, theta
X_temp, X_test, y_temp, y_test = train_test_split(trainx_all,
trainy_all,
test_size=test_fraction,
random_state=42)
X_train, X_valid, y_train, y_valid = train_test_split(X_temp,
y_temp,
test_size=valid_fraction,
random_state=42)
# array of length n_inputs, containing the mean of each feature
train_mean = np.mean(X_train, axis=0)
# array of length n_inputs, containing the standard dev of each feature
train_std = np.std(X_train, axis=0)
train_data = Data(torch.FloatTensor(zscore(X_train, train_mean, train_std)),
torch.FloatTensor(y_train))
valid_data = Data(torch.FloatTensor(zscore(X_valid, train_mean, train_std)),
torch.FloatTensor(y_valid))
test_data = Data(torch.FloatTensor(zscore(X_test, train_mean, train_std)),
torch.FloatTensor(y_test))
### ------
### Training
### ------
def train_model(model,
dset_loaders,
dset_sizes,
criterion,
optimizer,
data_name = 'isp_routers'
raw_data = load_ts_data(data_name, 'full')
data = raw_data.copy()
''' Sensor Motes data sets '''
#data_name = 'motes_l'
#raw_data = load_data(data_name)
#data = clean_zeros(raw_data, cpy=1)
''' Data Preprocessing '''
""" Data is loaded into memory, mean centered and standardised
then converted to an iterable to read by the CD-ST each iteration"""
#data = zscore_win(data, 100) # Sliding window implimentation
data = zscore(data) # Batch method implimentation
data = np.nan_to_num(data)
z_iter = iter(data)
numStreams = data.shape[1]
''' Initialise CDST Algorithm '''
CDST_alg = CDST('F-FHST.A-SREboth', p, numStreams)
''' Main Loop '''
for zt in z_iter:
zt = zt.reshape(zt.shape[0],1) # Convert to a column Vector
# Reset anomaly flag if last iteration flagged anomaly
if np.any(CDST_alg.st['anomaly']):
CDST_alg.st['anomaly'][:] = False
