def plot_confidence_matrix(
bird,
block,
segment,
hemi,
e1=5,
e2=4,
pfile="/auto/tdrive/mschachter/data/aggregate/decoders_pairwise_coherence_single.h5",
):
# read the confusion matrix for this decoder
pcf = AggregatePairwiseDecoder.load(pfile)
i = (
(pcf.df["bird"] == bird)
& (pcf.df["block"] == block)
& (pcf.df["segment"] == segment)
& (pcf.df["hemi"] == hemi)
& (pcf.df["order"] == "self+cross")
& (pcf.df["e1"] == e1)
& (pcf.df["e2"] == e2)
& (pcf.df["decomp"] == "locked")
)
assert i.sum() == 1, "More than one model (i.sum()=%d)" % i.sum()
index = pcf.df["index"][i].values[0]
print "confidence_matrices.keys()=", pcf.confidence_matrices.keys()
# shuffle the entries in the confidence matrix around
key = ("locked", "self+cross", "pair")
cnames = list(pcf.class_names[key][index])
print "cnames=", cnames
cmats = pcf.confidence_matrices[key]
print "cmats.shape=", cmats.shape
C1 = cmats[index]
C = np.zeros_like(C1)
for i, cname1 in enumerate(cnames):
for j, cname2 in enumerate(cnames):
ii = DECODER_CALL_TYPES.index(cname1)
jj = DECODER_CALL_TYPES.index(cname2)
C[ii, jj] = C1[i, j]
tick_labels = [CALL_TYPE_SHORT_NAMES[ct] for ct in DECODER_CALL_TYPES]
# plot the confidence matrix
figsize = (15.5, 11)
fig = plt.figure(figsize=figsize)
plt.imshow(C, origin="lower", interpolation="nearest", aspect="auto", vmin=0, vmax=1, cmap=plt.cm.afmhot)
plt.xticks(range(len(CALL_TYPES)), tick_labels)
plt.yticks(range(len(CALL_TYPES)), tick_labels)
plt.axis("tight")
plt.colorbar()
plt.title("PCC=%0.2f" % (np.diag(C).mean()))
fname = os.path.join(get_this_dir(), "confidence_matrix.svg")
plt.savefig(fname, facecolor="w", edgecolor="none")
def reorder_confidence_matrix(C, class_names):
Cro = np.zeros_like(C)
cnames = list(class_names)
for i,cname1 in enumerate(cnames):
for j,cname2 in enumerate(cnames):
ii = DECODER_CALL_TYPES.index(cname1)
jj = DECODER_CALL_TYPES.index(cname2)
Cro[ii, jj] = C[i, j]
return Cro
def draw_category_perf_and_confusion(agg, df_me):
df0 = df_me[df_me.band == 0]
lfp_perfs = df0['perf_category_lfp'].values
spike_perfs = df0['perf_category_spike'].values
spike_rate_perfs = df0['perf_category_spike_rate'].values
bp_data = {'Vocalization Type':[lfp_perfs, spike_perfs, spike_rate_perfs]}
cmats = dict()
for decomp in ['locked', 'spike_psd', 'spike_rate']:
# compute average confusion matrix for spikes and LFP
i = (agg.df.e1 == -1) & (agg.df.e2 == -1) & (agg.df.decomp == decomp) & (agg.df.band == 0) & \
(agg.df.aprop == 'category') & (agg.df.exfreq == False) & (agg.df.exel == False)
print '%s, i.sum()=%d' % (decomp, i.sum())
df = agg.df[i]
ci = df.cmat_index.values
C = agg.confusion_matrices[ci, :, :]
Cmean = C.mean(axis=0)
cnames = agg.stim_class_names[ci][0]
# reorder confusion matrix
Cro = np.zeros_like(Cmean)
for k,cname1 in enumerate(cnames):
for j,cname2 in enumerate(cnames):
i1 = DECODER_CALL_TYPES.index(cname1)
i2 = DECODER_CALL_TYPES.index(cname2)
Cro[i1, i2] = Cmean[k, j]
cmats[decomp] = Cro
figsize = (16, 12)
fig = plt.figure(figsize=figsize)
plt.subplots_adjust(top=0.95, bottom=0.05, left=0.05, right=0.99, hspace=0.40, wspace=0.20)
# gs = plt.GridSpec(1, 100)
gs = plt.GridSpec(2, 2)
# make a boxplot
ax = plt.subplot(gs[0, 0])
grouped_boxplot(bp_data, subgroup_names=['LFP', 'Spike PSD', 'Spike Rate'],
subgroup_colors=[COLOR_BLUE_LFP, COLOR_YELLOW_SPIKE, COLOR_RED_SPIKE_RATE], box_spacing=1.5, ax=ax)
plt.xticks([])
plt.ylabel('PCC')
plt.title('Vocalization Type Decoder Performance')
# plot the mean LFP confusion matrix
ax = plt.subplot(gs[0, 1])
plt.imshow(cmats['locked'], origin='lower', interpolation='nearest', aspect='auto', vmin=0, vmax=1, cmap=plt.cm.afmhot)
xtks = [CALL_TYPE_SHORT_NAMES[ct] for ct in DECODER_CALL_TYPES]
plt.xticks(range(len(DECODER_CALL_TYPES)), xtks)
plt.yticks(range(len(DECODER_CALL_TYPES)), xtks)
plt.colorbar(label='PCC')
plt.title('Mean LFP Decoder Confusion Matrix')
# plot the mean spike confusion matrix
ax = plt.subplot(gs[1, 0])
plt.imshow(cmats['spike_psd'], origin='lower', interpolation='nearest', aspect='auto', vmin=0, vmax=1, cmap=plt.cm.afmhot)
xtks = [CALL_TYPE_SHORT_NAMES[ct] for ct in DECODER_CALL_TYPES]
plt.xticks(range(len(DECODER_CALL_TYPES)), xtks)
plt.yticks(range(len(DECODER_CALL_TYPES)), xtks)
plt.colorbar(label='PCC')
plt.title('Mean Spike PSD Decoder Confusion Matrix')
fname = os.path.join(get_this_dir(), 'perf_boxplots_category.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
# plot the mean spike rate confusion matrix
ax = plt.subplot(gs[1, 1])
plt.imshow(cmats['spike_rate'], origin='lower', interpolation='nearest', aspect='auto', vmin=0, vmax=1, cmap=plt.cm.afmhot)
xtks = [CALL_TYPE_SHORT_NAMES[ct] for ct in DECODER_CALL_TYPES]
plt.xticks(range(len(DECODER_CALL_TYPES)), xtks)
plt.yticks(range(len(DECODER_CALL_TYPES)), xtks)
plt.colorbar(label='PCC')
plt.title('Mean Spike Rate Decoder Confusion Matrix')
fname = os.path.join(get_this_dir(), 'perf_boxplots_category.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
