def main():
parser = argparse.ArgumentParser(
description="Plot training sets statistics")
parser.add_argument("path", metavar="PATH",
help="The statistics json file")
parser.add_argument("-o", dest="out_path", metavar="PATH",
help="The path to save the plot image.")
parser.add_argument("-W", "--width", dest="fig_width", metavar="WIDTH", type=int,
help="The image width.")
parser.add_argument("-H", "--height", dest="fig_height", metavar="HEIGHT", type=int,
help="The image height.")
parser.add_argument("--dpi", dest="fig_dpi", metavar="DPI", type=int, default=100,
help="The image dpi.")
parser.add_argument("-p", "--predictors", dest="predictor_names", metavar="NAMES",
help="The names of the predictors to represent (seppareted by commas).")
parser.add_argument("-i", "--interactive", dest="interactive", action="store_true", default=False,
help="Show the plot in interactive mode.")
bglogging.add_logging_arguments(parser)
args = parser.parse_args()
bglogging.initialize(args)
log = bglogging.get_logger("plot-stats")
log.info("Loading state from {} ...".format(os.path.basename(args.path)))
state = load_weights(args.path)
predictor_names, stats = [state[k] for k in ["predictor_names", "stats"]]
if args.predictor_names is not None:
valid_names = set(predictor_names)
args.predictor_names = [s.strip() for s in args.predictor_names.split(",")]
predictor_names = [name for name in args.predictor_names if name in valid_names]
if len(predictor_names) == 0:
log.error("No scores selected. Please choose between: {}".format(", ".join(valid_names)))
exit(-1)
#log.info("Plotting ...")
fig = plt.figure(figsize=(args.fig_width or 12, args.fig_height or 10.4), dpi=args.fig_dpi or 100)
alpha = 0.7
num_predictors = len(predictor_names)
for i in range(num_predictors):
predictor_name = predictor_names[i]
predictor_stats = stats[predictor_name]
(intervals, dp, dn, cump, cumn, cdp, cdn, tp, tn, fp, fn, mcc, accuracy, cutoff) = [
predictor_stats[k] for k in [
"values", "dp", "dn", "cump", "cumn", "cdp", "cdn", "tp", "tn", "fp", "fn", "mcc", "acc", "cutoff"]]
dax = fig.add_subplot(4, num_predictors, i + 1, title="{}".format(predictor_name))
cdax = fig.add_subplot(4, num_predictors, 1 * num_predictors + i + 1)
tfax = fig.add_subplot(4, num_predictors, 2 * num_predictors + i + 1)
aax = fig.add_subplot(4, num_predictors, 3 * num_predictors + i + 1)
# distribution
dax.grid()
dax.plot(intervals, arrdiv(dp, max(dp)), "r-", alpha=alpha)
dax.plot(intervals, arrdiv(dn, max(dn)), "b-", alpha=alpha)
dax.plot([cutoff, cutoff], [0.0, 1.0], "k--")
dax.legend(('POS', 'NEG'), 'upper center', ncol=2, frameon=False, prop={'size':10})
# cummulative distribution
cdax.grid()
cdax.plot(intervals, arrdiv(cdp, cump), "r-", alpha=alpha)
cdax.plot(intervals, arrdiv(cdn, cumn), "b-", alpha=alpha)
cdax.plot([cutoff, cutoff], [0.0, 1.0], "k--")
cdax.legend(('POS', 'NEG'), 'upper center', ncol=2, frameon=False, prop={'size':10})
# TP/FN/FP/TN
tfax.grid()
tfax.plot(intervals, arrdiv(tp, cump), "r-", alpha=alpha)
tfax.plot(intervals, arrdiv(fn, cump), "c--", alpha=alpha)
tfax.plot(intervals, arrdiv(fp, cumn), "b-", alpha=alpha)
tfax.plot(intervals, arrdiv(tn, cumn), "m--", alpha=alpha)
tfax.plot([cutoff, cutoff], [0.0, 1.0], "k--")
tfax.legend(('TP', 'FN', 'FP', 'TN'), 'upper center', ncol=4, frameon=False, prop={'size':8})
# MCC/Accuracy
aax.grid()
aax.plot(intervals, mcc, "g-", alpha=alpha)
aax.plot(intervals, accuracy, "y-", alpha=alpha)
aax.plot([cutoff, cutoff], [0.0, 1.0], "k--")
aax.legend(('MCC', 'Accuracy'), 'upper center', ncol=2, frameon=False, prop={'size':10})
if args.out_path is not None:
from matplotlib import pylab
log.info("Saving image into {} ...".format(os.path.basename(args.out_path)))
pylab.savefig(args.out_path, bbox_inches=0)
if args.interactive:
plt.show()
def main():
parser = argparse.ArgumentParser(
description="Calculate TransFIC labels")
cmd = Command.withtraits(DbTrait, PredictorsInDbTrait)(parser)
cmd.add_db_args()
parser.add_argument("cutoffs_path", metavar="CUTOFFS",
help="File containing the cutoffs")
cmd.add_selected_predictors_args()
parser.add_argument("-u", "--updated-predictors", dest="updated_predictors", metavar="NAMES",
help="Updated predictor names")
args, logger = cmd.parse_args("calc-label")
db = cmd.open_db()
try:
logger.info("Loading state ...")
state = load_weights(args.cutoffs_path)
avail_predictors, stats = [state[k] for k in ["predictors", "stats"]]
predictors = cmd.get_selected_predictors(default_all=True)
missing_predictors = [p for p in predictors if p not in set(avail_predictors)]
if len(missing_predictors) > 0:
raise Exception("Missing cutoff stats for predictors: {}".format(", ".join(missing_predictors)))
if args.updated_predictors is not None:
if len(predictors) != len(args.updated_predictors):
raise Exception("The number of selected predictors does not match the number of predictor names to update")
updated_predictors = dict([(p, u) for p, u in zip(predictors, args.updated_predictors)])
else:
updated_predictors = dict([(p, "{}_LABEL".format(p)) for p in predictors])
# create predictors in the database if required
db_predictors = set([p["id"] for p in db.predictors()])
for predictor, updated_predictor in updated_predictors.items():
if updated_predictor not in db_predictors:
logger.info("Creating predictor {} ...".format(updated_predictor))
db.add_predictor(updated_predictor, FannsDb.CALCULATED_PREDICTOR_TYPE, source=[predictor])
cutoffs = {}
for predictor in predictors:
cutoff_low_mid, cutoff_mid_high = [stats[predictor][v] for v in ["cutoff_low_mid", "cutoff_mid_high"]]
logger.info("{}: cutoffs: low_mid={}, mid_high={}".format(predictor, cutoff_low_mid, cutoff_mid_high))
cutoffs[predictor] = (cutoff_low_mid, cutoff_mid_high)
logger.info("Calculating ...")
progress = RatedProgress(logger, name="SNVs")
for num_rows, row in enumerate(db.query_scores(predictors=predictors), start=1):
scores = row["scores"]
uscores = {}
for predictor in predictors:
score = scores[predictor]
if score is None:
continue
cutoff_low_mid, cutoff_mid_high = cutoffs[predictor]
updated_predictor = updated_predictors[predictor]
uscores[updated_predictor] = 0.0 if score < cutoff_low_mid else 1.0 if score < cutoff_mid_high else 2.0
if len(uscores) > 0:
db.update_scores(row["id"], uscores)
progress.update()
db.commit()
except:
cmd.handle_error()
return 0
def main():
parser = argparse.ArgumentParser(
description="Calculate Condel label")
parser.add_argument("db_path", metavar="DB_PATH",
help="Functional scores database")
parser.add_argument("weights_path", metavar="WEIGHTS",
help="File containing the scores weights and cutoffs")
parser.add_argument("-p", "--predictors", dest="predictors", metavar="PREDICTORS",
help="Comma separated list of predictors")
parser.add_argument("-u", "--updated-predictors", dest="updated_predictors", metavar="NAMES",
help="Updated predictor names")
bglogging.add_logging_arguments(parser)
args = parser.parse_args()
bglogging.initialize(args)
log = bglogging.get_logger("calculate-label")
log.info("Opening functional scores database ...")
db = FannsSQLiteDb(args.db_path)
db.open()
log.info("Loading state ...")
state = load_weights(args.weights_path)
avail_predictors, precision, step, stats = [state[k] for k in ["predictor_names", "precision", "step", "stats"]]
if args.predictors is not None:
predictors = [p for p in [p.strip() for p in args.predictors.split(",")] if p in avail_predictors]
if len(predictors) == 0:
log.error("Unknown predictors: {}".format(args.predictors))
log.error("Available predictor names are: {}".format(", ".join(avail_predictors)))
exit(-1)
else:
predictors = avail_predictors
if args.updated_predictors is not None:
updated_predictors = [p.strip() for p in args.updated_predictors.split(",")]
if len(predictors) != len(updated_predictors):
log.error("Number of updated predictors does not match with the list of number of predictors")
exit(-1)
else:
updated_predictors = ["{}_CLASS".format(p.upper()) for p in predictors]
log.info("Available predictors: {}".format(", ".join(avail_predictors)))
log.info("Selected predictors: {}".format(", ".join(predictors)))
for predictor, updated_predictor in zip(predictors, updated_predictors):
log.info("Creating predictor {} ...".format(updated_predictor))
db.add_predictor(updated_predictor, FannsDb.CALCULATED_PREDICTOR_TYPE, source=[predictor])
cutoffs = []
for predictor in predictors:
cutoff, mcc, acc = [stats[predictor][v] for v in ["cutoff", "cutoff_mcc", "cutoff_acc"]]
log.info("{}: cutoff={}, MCC={}, accuracy={}".format(predictor, cutoff, mcc, acc))
cutoffs += [cutoff]
log.info("Calculating ...")
start_time = partial_start_time = time.time()
try:
for num_rows, row in enumerate(db.query_scores(predictors=predictors), start=1):
scores = row["scores"]
d = {}
for i, predictor in enumerate(predictors):
score = scores[predictor]
if score is None:
continue
cutoff = cutoffs[i]
updated_predictor = updated_predictors[i]
d[updated_predictor] = 0.0 if score < cutoff else 1.0
db.update_scores(row["id"], d)
partial_time = time.time() - partial_start_time
if partial_time > 5.0:
partial_start_time = time.time()
elapsed_time = time.time() - start_time
log.debug(" {} rows, {:.1f} rows/second".format(hsize(num_rows), num_rows / elapsed_time))
db.commit()
except KeyboardInterrupt:
log.warn("Interrupted by Ctrl-C")
db.rollback()
except:
db.rollback()
raise
finally:
db.close()
def main():
parser = argparse.ArgumentParser(
description="Plot training sets statistics")
parser.add_argument("path", metavar="PATH",
help="The statistics json file")
parser.add_argument("-o", dest="out_path", metavar="PATH",
help="The path to save the plot image.")
parser.add_argument("-p", "--predictors", dest="predictor_names", metavar="NAMES",
help="The names of the predictors to represent (seppareted by commas).")
parser.add_argument("-i", "--interactive", dest="interactive", action="store_true", default=False,
help="Show the plot in interactive mode.")
bglogging.add_logging_arguments(parser)
args = parser.parse_args()
bglogging.initialize(args)
log = bglogging.get_logger("plot-stats")
log.info("Loading state ...")
state = load_weights(args.path)
predictor_names, stats = [state[k] for k in ["predictor_names", "stats"]]
if args.predictor_names is not None:
valid_names = set(predictor_names)
args.predictor_names = [s.strip() for s in args.predictor_names.split(",")]
predictor_names = [name for name in args.predictor_names if name in valid_names]
if len(predictor_names) == 0:
log.error("No scores selected. Please choose between: {}".format(", ".join(valid_names)))
exit(-1)
log.info("Plotting ...")
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid()
ax.set_xlabel("False Positive Rate (1 - especificity)")
ax.set_ylabel("True Positive Rate (sensitivity)")
num_predictors = len(predictor_names)
for predictor_name in predictor_names:
predictor_stats = stats[predictor_name]
(size, tp, tn, fp, fn) = [predictor_stats[k] for k in ["size", "tp", "tn", "fp", "fn"]]
tpr = [1.0] * (size + 1)
fpr = [1.0] * (size + 1)
for i in range(size):
tpr[i + 1] = (float(tp[i]) / (tp[i] + fn[i]))
fpr[i + 1] = (float(fp[i]) / (fp[i] + tn[i]))
ax.plot(fpr, tpr, "-")
ax.legend(tuple(predictor_names), "lower right", shadow=False)
ax.plot([0.0, 1.0], [0.0, 1.0], "--", color="0.75")
if args.out_path is not None:
from matplotlib import pylab
pylab.savefig(args.out_path, bbox_inches=0)
if args.interactive:
plt.show()
def main():
parser = argparse.ArgumentParser(
description="Calculate Condel score")
parser.add_argument("db_path", metavar="DB_PATH",
help="Functional scores database")
parser.add_argument("weights_path", metavar="WEIGHTS",
help="File containing the scores weights and cutoffs")
parser.add_argument("-p", "--predictors", dest="predictors", metavar="PREDICTORS",
help="Comma separated list of predictors")
parser.add_argument("-u", "--updated-predictor", dest="updated_predictor", metavar="NAME",
help="Updated predictor name")
bglogging.add_logging_arguments(parser)
args = parser.parse_args()
bglogging.initialize(args)
log = bglogging.get_logger("calculate")
log.info("Opening functional scores database ...")
db = FannsSQLiteDb(args.db_path)
db.open()
updated_predictor = args.updated_predictor or "CONDEL"
predictors = set([p["id"] for p in db.predictors()])
if updated_predictor not in predictors:
log.info(" Creating predictor {} ...".format(updated_predictor))
db.add_predictor(updated_predictor, FannsDb.CALCULATED_PREDICTOR_TYPE, source=predictors)
log.info("Loading state ...")
state = load_weights(args.weights_path)
avail_predictors, precision, step, stats = [state[k] for k in ["predictor_names", "precision", "step", "stats"]]
if args.predictors is not None:
predictors = [p for p in [p.strip() for p in args.predictors.split(",")] if p in avail_predictors]
if len(predictors) == 0:
log.error("Unknown predictors: {}".format(args.predictors))
log.error("Available predictor names are: {}".format(", ".join(avail_predictors)))
exit(-1)
else:
predictors = avail_predictors
log.info("Available predictors: {}".format(", ".join(avail_predictors)))
log.info("Selected predictors: {}".format(", ".join(predictors)))
log.info("Calculating ...")
start_time = partial_start_time = time.time()
try:
for num_rows, row in enumerate(db.query_scores(predictors=predictors), start=1):
scores = row["scores"]
condel = wsum = 0
for predictor, score in scores.items():
if score is None:
continue
predictor_stats = stats[predictor]
rmin, rmax, dim, size, cdp, cdn, cutoff = [predictor_stats[k] for k in [
"rmin", "rmax", "dim", "size", "cdp", "cdn", "cutoff"]]
if predictor in PREDICTOR_TRANSFORM:
score = PREDICTOR_TRANSFORM[predictor](score)
r = (score - rmin) / dim
index = int(r * size) if score < rmax else size - 1
if score < cutoff:
w = 1 - cdn[index]
else:
w = 1 - cdp[index]
wsum += w
condel += w * score
#log.info("{}={}, w={} -> {}".format(predictor_name, score, w, score * w))
if wsum != 0:
condel /= wsum
d = {updated_predictor : condel}
db.update_scores(row["id"], d)
#log.info(">>> CONDEL={}".format(condel))
else:
log.warn("wsum = 0, condel={}, scores={}".format(condel, repr(scores)))
partial_time = time.time() - partial_start_time
if partial_time > 5.0:
partial_start_time = time.time()
elapsed_time = time.time() - start_time
log.debug(" {} rows, {:.1f} rows/second".format(hsize(num_rows), num_rows / elapsed_time))
log.info("Commit ...")
db.commit()
except KeyboardInterrupt:
log.warn("Interrupted by Ctrl-C")
db.rollback()
except:
db.rollback()
raise
finally:
db.close()
def main():
parser = argparse.ArgumentParser(
description="Plot cutoffs")
cmd = DefaultCommandHelper(parser)
parser.add_argument("path", metavar="PATH",
help="The statistics json file")
parser.add_argument("-o", dest="out_path", metavar="PATH",
help="The path to save the plot image.")
cmd.add_selected_predictors_args()
parser.add_argument("-i", "--interactive", dest="interactive", action="store_true", default=False,
help="Show the plot in interactive mode.")
args, logger = cmd.parse_args("plot-cutoffs")
logger.info("Loading state ...")
state = load_weights(args.path)
available_predictors, stats = [state[k] for k in ["predictors", "stats"]]
predictors = cmd.get_selected_predictors(available_predictors)
logger.info("Plotting ...")
fig = plt.figure(figsize=(12.4, 10.5), dpi=100)
fig.subplots_adjust(left=0.06, bottom=0.03, right=0.99, top=0.96, wspace=0.22, hspace=0.15)
num_predictors = len(predictors)
for i, predictor in enumerate(predictors):
predictor_stats = stats[predictor]
(intervals, vmin, vmax, dp, dn, cump, cumn, cdp, cdn, cutoff_low_mid, cutoff_mid_high) = [predictor_stats[k] for k in [
"values", "vmin", "vmax", "dp", "dn", "cump", "cumn", "cdp", "cdn", "cutoff_low_mid", "cutoff_mid_high"]]
dax = fig.add_subplot(2, num_predictors, i + 1, title="{}".format(predictor))
cdax = fig.add_subplot(2, num_predictors, 1 * num_predictors + i + 1)
dax.grid()
dax.set_xlim(vmin, vmax)
dax.plot(intervals, dp, "r-", alpha=0.5)
dax.plot(intervals, dn, "b-", alpha=0.5)
dax.plot([cutoff_low_mid, cutoff_low_mid], [0.0, max(dp + dn)], "k--")
dax.plot([cutoff_mid_high, cutoff_mid_high], [0.0, max(dp + dn)], "k--")
dax.axvspan(vmin, cutoff_low_mid, facecolor='g', alpha=0.3)
dax.axvspan(cutoff_low_mid, cutoff_mid_high, facecolor='y', alpha=0.3)
dax.axvspan(cutoff_mid_high, vmax, facecolor='r', alpha=0.3)
dax.legend(('HIGH-REC', 'NON-REC'), 'upper center', ncol=2, frameon=False, prop={'size':10})
cdax.grid()
cdax.set_xlim(vmin, vmax)
cdax.plot(intervals, [v / float(cump) for v in cdp], "r-")
cdax.plot(intervals, [v / float(cumn) for v in cdn], "b-")
cdax.plot([cutoff_low_mid, cutoff_low_mid], [0.0, 1.0], "k--")
cdax.plot([cutoff_mid_high, cutoff_mid_high], [0.0, 1.0], "k--")
cdax.axvspan(vmin, cutoff_low_mid, facecolor='g', alpha=0.3)
cdax.axvspan(cutoff_low_mid, cutoff_mid_high, facecolor='y', alpha=0.3)
cdax.axvspan(cutoff_mid_high, vmax, facecolor='r', alpha=0.3)
cdax.legend(('HIGH-REC', 'NON-REC'), 'upper center', ncol=2, frameon=False, prop={'size':10})
if args.out_path is not None:
from matplotlib import pylab
pylab.savefig(args.out_path, bbox_inches=0)
if args.interactive:
plt.show()