def main(tseries_fpath, predict_fpath, bestby_fpath):
X = np.genfromtxt(tseries_fpath)[:, 1:] + 0.0001
cls_pred = np.loadtxt(predict_fpath, dtype='i')
rgr_true = X.sum(axis=1)
bestby = np.genfromtxt(bestby_fpath)
cls_labels = set(cls_pred[cls_pred != -1])
tt = X.shape[1]
models = {}
models_per_clust = {}
ref_time = np.arange(1, tt + 1)
#tr = 7
#ref_time = np.array([tr])
#bestby = np.zeros(bestby.shape[0]) + tr
for tr in ref_time:
models[tr] = fit(X, tr, tt)
for k in sorted(cls_labels):
Xk = X[cls_pred == k]
models_per_clust[tr, k] = fit(Xk, tr, tt)
errors_all = []
errors_cls = []
errors_per_cls = defaultdict(list)
for tr in ref_time:
idx = bestby == tr
ols = models[tr]
errors_all.extend(ols.gcv_sqerrors[idx])
classes = cls_pred[idx]
for cls in set(classes):
bestby_for_cls = bestby[cls_pred == cls]
idx_cls = bestby_for_cls == tr
ols = models_per_clust[tr, cls]
errors_cls.extend(ols.gcv_sqerrors[idx_cls])
errors_per_cls[cls].extend(ols.gcv_sqerrors[idx_cls])
print('Glob model:', np.mean(errors_all), '+-', hci(errors_all, .95))
print('Spec model:', np.mean(errors_cls), '+-', hci(errors_cls, .95))
print()
print('Per class')
for cls in cls_labels:
err = errors_per_cls[cls]
print('Cls = ', cls, np.mean(err), '+-', hci(err, .95))
def main(tseries_fpath, predict_fpath, bestby_fpath):
X = np.genfromtxt(tseries_fpath)[:,1:] + 0.0001
cls_pred = np.loadtxt(predict_fpath, dtype='i')
rgr_true = X.sum(axis=1)
bestby = np.genfromtxt(bestby_fpath)
cls_labels = set(cls_pred[cls_pred != -1])
tt = X.shape[1]
models = {}
models_per_clust = {}
ref_time = np.arange(1, tt + 1)
#tr = 7
#ref_time = np.array([tr])
#bestby = np.zeros(bestby.shape[0]) + tr
for tr in ref_time:
models[tr] = fit(X, tr, tt)
for k in sorted(cls_labels):
Xk = X[cls_pred == k]
models_per_clust[tr, k] = fit(Xk, tr, tt)
errors_all = []
errors_cls = []
errors_per_cls = defaultdict(list)
for tr in ref_time:
idx = bestby == tr
ols = models[tr]
errors_all.extend(ols.gcv_sqerrors[idx])
classes = cls_pred[idx]
for cls in set(classes):
bestby_for_cls = bestby[cls_pred == cls]
idx_cls = bestby_for_cls == tr
ols = models_per_clust[tr, cls]
errors_cls.extend(ols.gcv_sqerrors[idx_cls])
errors_per_cls[cls].extend(ols.gcv_sqerrors[idx_cls])
print('Glob model:', np.mean(errors_all), '+-', hci(errors_all, .95))
print('Spec model:', np.mean(errors_cls), '+-', hci(errors_cls, .95))
print()
print('Per class')
for cls in cls_labels:
err = errors_per_cls[cls]
print('Cls = ', cls, np.mean(err), '+-', hci(err, .95))
def main(features_fpath, tseries_fpath, tags_fpath, classes_fpath, clf_name):
X, params = create_input_table(features_fpath, tseries_fpath, tags_fpath)
y = np.loadtxt(classes_fpath)
clf = create_grid_search(clf_name)
class_matrices, conf_matrices = run_classifier(clf, X, y)
metric_means = np.mean(class_matrices, axis=0)
metric_ci = hci(class_matrices, .95, axis=0)
print(clf_summary(metric_means, metric_ci))
print()
conf_means = np.mean(conf_matrices, axis=0)
conf_ci = hci(conf_matrices, .95, axis=0)
print("Average confusion matrix with .95 confidence interval")
print(" \ttrue ")
print("predic")
for i in range(conf_means.shape[0]):
print(i, end="\t \t")
for j in range(conf_means.shape[1]):
print('%.3f +- %.3f' % (conf_means[i, j], conf_ci[i, j]), end="\t")
print()
def main(features_fpath, tseries_fpath, tags_fpath, classes_fpath, clf_name):
X, params = create_input_table(features_fpath, tseries_fpath, tags_fpath)
y = np.loadtxt(classes_fpath)
clf = create_grid_search(clf_name)
class_matrices, conf_matrices = run_classifier(clf, X, y)
metric_means = np.mean(class_matrices, axis=0)
metric_ci = hci(class_matrices, .95, axis=0)
print(clf_summary(metric_means, metric_ci))
print()
conf_means = np.mean(conf_matrices, axis=0)
conf_ci = hci(conf_matrices, .95, axis=0)
print("Average confusion matrix with .95 confidence interval")
print(" \ttrue ")
print("predic")
for i in xrange(conf_means.shape[0]):
print(i, end="\t \t")
for j in xrange(conf_means.shape[1]):
print('%.3f +- %.3f' % (conf_means[i, j], conf_ci[i, j]), end="\t")
print()
def main(result_fpath):
parameters = OrderedDict()
errors = OrderedDict()
with open(result_fpath) as results_file:
obj_id = None
for i, line in enumerate(results_file):
if i % 3 == 0:
obj_id = line.strip()
if i % 3 == 1:
line = line.replace('array(', '')
line = line.replace(', dtype=int32)', '')
line = line.replace('\'', '"')
params = json.loads(line.strip())
parameters[obj_id] = params
if i % 3 == 2:
err = np.asarray([float(x) for x in line.split()])
errors[obj_id] = err
obj_id = None
bics_phx = []
bics_kir = []
wins = []
diff = []
for key in parameters:
params = parameters[key]
err = errors[key]
bic_phoenix = err[0]
bic_kir = err[[2, 4, 6, 8]].min()
#bic_phoenix = err[1]
#bic_kir = min(err[[3, 5, 7, 9]])
bics_phx.append(bic_phoenix)
bics_kir.append(bic_kir)
diff.append((bic_kir - bic_phoenix) / bic_phoenix)
wins.append(bic_kir - bic_phoenix > 0)
bics_phx = np.asarray(bics_phx)
bics_kir = np.asarray(bics_kir)
#ks = robjects.r['ks.test']
#res = ks(bics_phx, bics_kir)#, alternative='less')
#val = res.rx2('statistic')[0]
#p_val = res.rx2('p.value')[0]
from vod.stats.ci import half_confidence_interval_size as hci
print(sum(wins) / bics_phx.shape[0], '&', np.mean(bics_phx), hci(bics_phx, .95), np.mean(bics_kir), hci(bics_kir, .95),
(np.mean(bics_kir) - np.mean(bics_phx)) / np.mean(bics_phx))
def print_results(clf_scores, micro, macro, r2_all, mse_all, mrse_all):
metric_means = np.mean(clf_scores, axis=0)
metric_ci = hci(clf_scores, .95, axis=0)
print(clf_summary(metric_means, metric_ci))
print('Micro F1 - mean: %f +- %f' % (np.mean(micro), hci(micro, .95)))
print('Macro F1 - mean: %f +- %f' % (np.mean(macro), hci(macro, .95)))
print('R2 all - mean: %f +- %f' % (np.mean(r2_all), hci(r2_all, .95)))
print('MSE all - mean: %f +- %f' % (np.mean(mse_all), hci(mse_all, .95)))
print('MRSE all - mean: %f +- %f' %
(np.mean(mrse_all), hci(mrse_all, .95)))
def print_results(clf_scores, micro, macro, r2_all, mse_all, mrse_all):
metric_means = np.mean(clf_scores, axis=0)
metric_ci = hci(clf_scores, .95, axis=0)
print(clf_summary(metric_means, metric_ci))
print('Micro F1 - mean: %f +- %f' % (np.mean(micro), hci(micro, .95)))
print('Macro F1 - mean: %f +- %f' % (np.mean(macro), hci(macro, .95)))
print('R2 all - mean: %f +- %f' % (np.mean(r2_all), hci(r2_all, .95)))
print('MSE all - mean: %f +- %f' % (np.mean(mse_all), hci(mse_all, .95)))
print('MRSE all - mean: %f +- %f' % (np.mean(mrse_all),
hci(mrse_all, .95)))
def main(tseries_fpath, plot_foldpath):
assert os.path.isdir(plot_foldpath)
initialize_matplotlib()
X = np.genfromtxt(tseries_fpath)[:, 1:].copy()
n_samples = X.shape[0]
sample_rows = np.arange(n_samples)
clust_range = range(2, 16)
n_clustering_vals = len(clust_range)
intra_array = np.zeros(shape=(25, n_clustering_vals))
inter_array = np.zeros(shape=(25, n_clustering_vals))
bcvs_array = np.zeros(shape=(25, n_clustering_vals))
costs_array = np.zeros(shape=(25, n_clustering_vals))
r = 0
for i in xrange(5):
np.random.shuffle(sample_rows)
rand_sample = sample_rows[:200]
X_new = X[rand_sample]
D_new = dist.dist_all(X_new, X_new, rolling=True)[0]
for j in xrange(5):
for k in clust_range:
intra, inter, bcv, cost = run_clustering(X_new, k, D_new)
intra_array[r, k - 2] = intra
inter_array[r, k - 2] = inter
bcvs_array[r, k - 2] = bcv
costs_array[r, k - 2] = cost
r += 1
print(r)
intra_err = np.zeros(n_clustering_vals)
inter_err = np.zeros(n_clustering_vals)
bcvs_err = np.zeros(n_clustering_vals)
costs_err = np.zeros(n_clustering_vals)
for k in clust_range:
j = k - 2
intra_err[j] = hci(intra_array[:, j], 0.95)
inter_err[j] = hci(inter_array[:, j], 0.95)
bcvs_err[j] = hci(bcvs_array[:, j], 0.95)
costs_err[j] = hci(costs_array[:, j], 0.95)
plt.errorbar(clust_range, np.mean(inter_array, axis=0), fmt="gD", label="Inter Cluster", yerr=inter_err)
plt.errorbar(clust_range, np.mean(bcvs_array, axis=0), fmt="bo", label="BetaCV", yerr=bcvs_err)
plt.errorbar(clust_range, np.mean(intra_array, axis=0), fmt="rs", label="Intra Cluster", yerr=intra_err)
plt.ylabel("Average Distance")
plt.xlabel("Number of clusters")
plt.xlim((0.0, 16))
plt.ylim((0.0, 1.0))
plt.legend(frameon=False, loc="lower left")
plt.savefig(os.path.join(plot_foldpath, "bcv.pdf"))
plt.close()
plt.errorbar(clust_range, np.mean(costs_array, axis=0), fmt="bo", label="Cost", yerr=costs_err)
plt.ylabel("Cost (F)")
plt.xlabel("Number of clusters")
plt.xlim((0.0, 16))
plt.ylim((0.0, 1.0))
plt.legend(frameon=False, loc="lower left")
plt.savefig(os.path.join(plot_foldpath, "cost.pdf"))
plt.close()
def main(tseries_fpath, plot_foldpath):
assert os.path.isdir(plot_foldpath)
initialize_matplotlib()
X = np.genfromtxt(tseries_fpath)[:, 1:].copy()
n_samples = X.shape[0]
sample_rows = np.arange(n_samples)
clust_range = range(2, 16)
n_clustering_vals = len(clust_range)
intra_array = np.zeros(shape=(25, n_clustering_vals))
inter_array = np.zeros(shape=(25, n_clustering_vals))
bcvs_array = np.zeros(shape=(25, n_clustering_vals))
costs_array = np.zeros(shape=(25, n_clustering_vals))
r = 0
for i in xrange(5):
np.random.shuffle(sample_rows)
rand_sample = sample_rows[:200]
X_new = X[rand_sample]
D_new = dist.dist_all(X_new, X_new, rolling=True)[0]
for j in xrange(5):
for k in clust_range:
intra, inter, bcv, cost = run_clustering(X_new, k, D_new)
intra_array[r, k - 2] = intra
inter_array[r, k - 2] = inter
bcvs_array[r, k - 2] = bcv
costs_array[r, k - 2] = cost
r += 1
print(r)
intra_err = np.zeros(n_clustering_vals)
inter_err = np.zeros(n_clustering_vals)
bcvs_err = np.zeros(n_clustering_vals)
costs_err = np.zeros(n_clustering_vals)
for k in clust_range:
j = k - 2
intra_err[j] = hci(intra_array[:, j], .95)
inter_err[j] = hci(inter_array[:, j], .95)
bcvs_err[j] = hci(bcvs_array[:, j], .95)
costs_err[j] = hci(costs_array[:, j], .95)
plt.errorbar(clust_range,
np.mean(inter_array, axis=0),
fmt='gD',
label='Inter Cluster',
yerr=inter_err)
plt.errorbar(clust_range,
np.mean(bcvs_array, axis=0),
fmt='bo',
label='BetaCV',
yerr=bcvs_err)
plt.errorbar(clust_range,
np.mean(intra_array, axis=0),
fmt='rs',
label='Intra Cluster',
yerr=intra_err)
plt.ylabel('Average Distance')
plt.xlabel('Number of clusters')
plt.xlim((0., 16))
plt.ylim((0., 1.))
plt.legend(frameon=False, loc='lower left')
plt.savefig(os.path.join(plot_foldpath, 'bcv.pdf'))
plt.close()
plt.errorbar(clust_range,
np.mean(costs_array, axis=0),
fmt='bo',
label='Cost',
yerr=costs_err)
plt.ylabel('Cost (F)')
plt.xlabel('Number of clusters')
plt.xlim((0., 16))
plt.ylim((0., 1.))
plt.legend(frameon=False, loc='lower left')
plt.savefig(os.path.join(plot_foldpath, 'cost.pdf'))
plt.close()
