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[spade] fixed the calculation of the duration of a pattern in the out…
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…put (NeuralEnsemble#254)

* fixed the calculation of the duration of a pattern, in the output of spade. Previously, the calculation of the pattern duration was not always correct, depending on the order of lags and neurons. We fixed the bug by accounting the duration independently from the order of neurons;
* added raise ValueErrors in code when the output_format is not one of the two parameters allowed
* fixed bug of undefined spectrum type when running spade without surrogates (only FIM)
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@stellalessandra @dizcza
stellalessandra authored and dizcza committed Oct 9, 2019
1 parent c78af7e commit a833a96
Showing 1 changed file with 32 additions and 25 deletions.
57 changes: 32 additions & 25 deletions elephant/spade.py
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Original file line number Diff line number Diff line change
Expand Up @@ -56,12 +56,11 @@
:copyright: Copyright 2017 by the Elephant team, see `doc/authors.rst`.
:license: BSD, see LICENSE.txt for details.
'''
import numpy
import numpy as np
import neo
import elephant.spike_train_surrogates as surr
import elephant.conversion as conv
from itertools import chain, combinations
import numpy as np
import time
import quantities as pq
import warnings
Expand Down Expand Up @@ -163,7 +162,7 @@ def spade(data, binsize, winlen, min_spikes=2, min_occ=2, max_spikes=None,
filtering the pattern spectrum. A spike at time t is placed randomly
within ]t-dither, t+dither[ (see also
elephant.spike_train_surrogates.dither_spikes).
Default: 15*pq.s
Default: 15*pq.ms
spectrum: str
Define the signature of the patterns, it can assume values:
'#': pattern spectrum using the as signature the pair:
Expand Down Expand Up @@ -244,7 +243,7 @@ def spade(data, binsize, winlen, min_spikes=2, min_occ=2, max_spikes=None,
pattern.
lags: array containing the lags (integers corresponding to the
number of bins) between the spikes of the patterns. The
first lag is always assumed to be 0 and correspond to the
first lag is always assumed to be 0 and corresponds to the
first spike ['times'] array containing the times.
(integers corresponding to the bin idx) of the occurrences of the
patterns
Expand Down Expand Up @@ -379,8 +378,12 @@ def spade(data, binsize, winlen, min_spikes=2, min_occ=2, max_spikes=None,
winlen, binsize,
pv_spec,
data[0].t_start)
else:
elif output_format == 'concepts':
output['patterns'] = concepts
else:
raise ValueError(
"The output_format value has to be one between"
"'patterns' and 'concepts'")
return output
# rank!=0 returning None
else:
Expand Down Expand Up @@ -459,7 +462,7 @@ def concepts_mining(data, binsize, winlen, min_spikes=2, min_occ=2,
rel_matrix : numpy.array
A binary matrix with shape (number of windows, winlen*len(data)). Each
row corresponds to a window (order according to their position in
time). Each column correspond to one bin and one neuron and it is 0 if
time). Each column corresponds to one bin and one neuron and it is 0 if
no spikes or 1 if one or more spikes occurred in that bin for that
particular neuron. For example, the entry [0,0] of this matrix
corresponds to the first bin of the first window position for the first
Expand Down Expand Up @@ -555,11 +558,12 @@ def _build_context(binary_matrix, winlen, only_windows_with_first_spike=True):
attributes of the corresponding object.
rel_matrix : numpy.array
A binary matrix with shape (number of windows, winlen*len(data)). Each
row correspond to a window (order according to their position in time).
Each column correspond to one bin and one neuron and it is 0 if no
row corresponds to a window (order according to
their position in time).
Each column corresponds to one bin and one neuron and it is 0 if no
spikes or 1 if one or more spikes occurred in that bin for that
particular neuron.
E.g. the entry [0,0] of this matrix correspond to the first bin of the
E.g. the entry [0,0] of this matrix corresponds to the first bin of the
first window position for the first neuron, the entry [0,winlen] to the
first bin of the first window position for the second neuron.
"""
Expand Down Expand Up @@ -646,11 +650,12 @@ def _fpgrowth(transactions, min_c=2, min_z=2, max_z=None,
Default: 'a'
rel_matrix : None or numpy.array
A binary matrix with shape (number of windows, winlen*len(data)). Each
row correspond to a window (order according to their position in time).
Each column correspond to one bin and one neuron and it is 0 if no
row corresponds to a window (order according to
their position in time).
Each column corresponds to one bin and one neuron and it is 0 if no
spikes or 1 if one or more spikes occurred in that bin for that
particular neuron.
E.g. the entry [0,0] of this matrix correspond to the first bin of the
E.g. the entry [0,0] of this matrix corresponds to the first bin of the
first window position for the first neuron, the entry [0,winlen] to the
first bin of the first window position for the second neuron.
If == None only the closed frequent itemsets (intent) are returned and
Expand Down Expand Up @@ -1130,8 +1135,8 @@ def _fdr(pvalues, alpha):
"""

# Sort the p-values from largest to smallest
pvs_array = numpy.array(pvalues) # Convert PVs to an array
pvs_sorted = numpy.sort(pvs_array)[::-1] # Sort PVs in decreasing order
pvs_array = np.array(pvalues) # Convert PVs to an array
pvs_sorted = np.sort(pvs_array)[::-1] # Sort PVs in decreasing order

# Perform FDR on the sorted p-values
m = len(pvalues)
Expand Down Expand Up @@ -1226,7 +1231,7 @@ def test_signature_significance(pvalue_spectrum, alpha, corr='',
# If alpha == 1 all signatures are significant
if alpha == 1:
return []
x_array = numpy.array(pvalue_spectrum)
x_array = np.array(pvalue_spectrum)
# Compute significance...
if corr == '' or corr == 'no': # ...without statistical correction
tests = x_array[:, -1] <= alpha
Expand Down Expand Up @@ -1299,7 +1304,7 @@ def approximate_stability(concepts, rel_matrix, n_subsets, delta=0, epsilon=0):
rel_matrix: numpy.array
A binary matrix with shape (number of windows, winlen*len(data)). Each
row corresponds to a window (order according to their position in
time). Each column correspond to one bin and one neuron and it is 0 if
time). Each column corresponds to one bin and one neuron and it is 0 if
no spikes or 1 if one or more spikes occurred in that bin for that
particular neuron. For example, the entry [0,0] of this matrix
corresponds to the first bin of the first window position for the first
Expand Down Expand Up @@ -1841,7 +1846,7 @@ def concept_output_to_patterns(concepts, winlen, binsize, pvalue_spectrum=None,
Parameters
----------
concepts: tuple
Each element of the tuple correspond to a pattern and it is itself a
Each element of the tuple corresponds to a pattern and it is itself a
tuple consisting of:
((spikes in the pattern), (occurrences of the patterns))
winlen: int
Expand All @@ -1850,14 +1855,14 @@ def concept_output_to_patterns(concepts, winlen, binsize, pvalue_spectrum=None,
The time precision used to discretize the data (binning).
pvalue_spectrum: None or tuple
Contains a tuple of signatures and the corresponding p-value. If equal
to None all the pvalues are set to -1
to None all pvalues are set to -1
t_start: Quantity
t_start of the analyzed spike trains
Returns
--------
output: list
List of dictionaries. Each dictionary correspond to a patterns and
List of dictionaries. Each dictionary corresponds to a pattern and
has the following entries:
['itemset'] list of the spikes in the pattern
expressed in the form of itemset, each spike is encoded by:
Expand All @@ -1867,17 +1872,19 @@ def concept_output_to_patterns(concepts, winlen, binsize, pvalue_spectrum=None,
['neurons'] array containing the idx of the neurons of the pattern
['lags'] array containing the lags (integers corresponding to the
number of bins) between the spikes of the patterns. The first
lag is always assumed to be 0 and correspond to the first
lag is always assumed to be 0 and corresponds to the first
spike.
['times'] array contianing the times (integers corresponding to the
['times'] array containing the times (integers corresponding to the
bin idx) of the occurrences of the patterns.
['signature'] tuple containing two integers
(number of spikes of the patterns,
number of occurrences of the pattern)
['pvalue'] the pvalue corresponding to the pattern. If n_surr==0
the pvalues are set to -1.
then all pvalues are set to -1.
"""
if pvalue_spectrum is not None:
if pvalue_spectrum is None:
spectrum = '#'
else:
if len(pvalue_spectrum) == 0:
spectrum = '#'
pass
Expand Down Expand Up @@ -1921,8 +1928,8 @@ def concept_output_to_patterns(concepts, winlen, binsize, pvalue_spectrum=None,
output_dict['pvalue'] = -1
# Signature (size, n occ) of the pattern
elif spectrum == '3d#':
sgnt = (len(conc[0]), len(conc[1]), max(
np.abs(np.diff(np.array(conc[0]) % winlen))))
duration = (max(conc[0]) - min(conc[0])) % winlen
sgnt = (len(conc[0]), len(conc[1]), duration)
output_dict['signature'] = sgnt
# p-value assigned to the pattern from the pvalue spectrum
try:
Expand Down

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