def output_rankings(args):
workdir = args["workdir"]
n_trees = args["trees"]
if n_trees is None:
print "Number of trees must be specified for outputting ranks"
sys.exit(1)
ranks_flname = args["ranks_file"]
if ranks_flname is None:
print "Output filename must be specified"
sys.exit(1)
all_models = read_snps(workdir)
if n_trees not in all_models:
print "No model with %s trees. The available models have %s trees" \
% (n_trees, sorted(all_models.keys()))
sys.exit(1)
snps1, snps2 = all_models[n_trees]
fl = open(ranks_flname, "w")
for i in xrange(len(snps1)):
chrom, pos = snps1.labels[i]
importance = snps1.importances[i]
fd = snps1.fixed_differences[i]
missing = snps1.missing_data[i]
fl.write("%s\t%s\t%s\t%s\t%s\n" % (chrom, pos, importance, fd, missing))
fl.close()
def analyze_rankings(args):
workdir = args["workdir"]
figures_dir = os.path.join(workdir, "figures")
if not os.path.exists(figures_dir):
os.makedirs(figures_dir)
all_snps = read_snps(workdir)
ordered_trees = sorted(all_snps.keys())
thresholds = [0.05, 0.1, 0.25, 0.5]
common_feature_counts = []
snp1_feature_counts = []
snp2_feature_counts = []
common_feature_threshold_percentages = defaultdict(list)
for n_trees in ordered_trees:
snps1, snps2 = all_snps[n_trees]
common_feature_counts.append(snps1.count_intersection(snps2))
snp1_feature_counts.append(len(snps1))
snp2_feature_counts.append(len(snps2))
for threshold in thresholds:
n = max(1, int(threshold * min(len(snps1), len(snps2))))
percentage = 100.0 * float(snps1.take(n).count_intersection(snps2.take(n))) \
/ float(n)
common_feature_threshold_percentages[threshold].append(percentage)
plt.clf()
plt.hold(True)
plt.grid(True)
plt.semilogx(ordered_trees, common_feature_counts, "k.-", label="Common")
plt.semilogx(ordered_trees, snp1_feature_counts, "c.-", label="Model 1")
plt.semilogx(ordered_trees, snp2_feature_counts, "m.-", label="Model 2")
plt.xlabel("Number of Trees", fontsize=16)
plt.ylabel("SNPs (Count)", fontsize=16)
plt.legend(loc="lower right")
plt.ylim([0, max(max(common_feature_counts), max(snp1_feature_counts), max(snp2_feature_counts)) + 10])
plt.xlim([min(ordered_trees), max(ordered_trees)])
plt.savefig(os.path.join(figures_dir, "snp_counts.png"), DPI=200)
plt.savefig(os.path.join(figures_dir, "snp_counts.pdf"), DPI=200)
plt.clf()
plt.hold(True)
plt.grid(True)
colors = ["r.-", "g.-", "b.-", "m.-", "c.-"]
for i, threshold in enumerate(thresholds):
c = colors[i]
label = str(int(100.0 * threshold))
plt.semilogx(ordered_trees, common_feature_threshold_percentages[threshold],
c, label="Top %s%%" % label)
plt.xlabel("Number of Trees", fontsize=16)
plt.ylabel("Common SNPs (%)", fontsize=16)
plt.legend(loc="lower right")
plt.ylim([0, 100])
plt.xlim([min(ordered_trees), max(ordered_trees)])
plt.savefig(os.path.join(figures_dir, "common_snps.png"), DPI=200)
plt.savefig(os.path.join(figures_dir, "common_snps.pdf"), DPI=200)
