Python read_score Beispiele

Beispiel #1

f_sem_sim = sys.argv[2]
f_dataset = sys.argv[3]
f_out = sys.argv[4]
bin_size = sys.argv[5]
bin_size = int(bin_size)
median_y_n = sys.argv[6]

# Redundant ens annotation
print "1) Read ens redundant annotations"
gene_convert = data.read_ens_rd(f_ens_rd)
print "Number of gene", len(gene_convert.keys())

# Read semantic similarity score (benchmark, y)
print "2) Read MGI similarity..."
sem_sim = data.read_sem_sim(f_sem_sim)

# Read genomic dataset (x) with gene pair benchmark value (y)
print "3) Read Score for pair with MGI Score..."
score_pair = data.read_score(f_dataset, gene_convert, sem_sim)
print "Number of gene pairs", len(score_pair.keys())

# sort pair according to score
print "4) Sort pair according Score..."
list_pair = functions.sort_by_val(score_pair)
print "Number of Pair", len(list_pair)

# Report dataset by bin versus semantic similarity
print "5) read and look at distribution mgi/hpo..."
data.report_data_ben_bin(f_out, score_pair, list_pair, sem_sim, bin_size,
                         median_y_n)

Beispiel #2

Datei: eval_with_scale_cum.py Projekt: csandorfr/AP-PLN

f_ens_rd=sys.argv[1]
f_sem_sim=sys.argv[2]
f_dataset=sys.argv[3] 
f_out=sys.argv[4] 
bin_size=sys.argv[5]
bin_size=int(bin_size)
median_y_n=sys.argv[6]


# Redundant ens annotation
print "1) Read ens redundant annotations"
gene_convert=data.read_ens_rd(f_ens_rd)
print "Number of gene",len(gene_convert.keys())

# Read semantic similarity score (benchmark, y)
print "2) Read MGI similarity..."
sem_sim=data.read_sem_sim(f_sem_sim)

# Read genomic dataset (x) with gene pair benchmark value (y)
print "3) Read Score for pair with MGI Score..."
score_pair,list_pair=data.read_score(f_dataset,gene_convert)
print "Number of gene pairs",len(score_pair.keys())


# Report dataset by bin versus semantic similarity
print "4) read and look at distribution mgi/hpo..."
data.report_data_ben_bin(f_out,score_pair,list_pair,sem_sim,bin_size,median_y_n)

Beispiel #3

Datei: eval_with_scale.py Projekt: csandorfr/AP-PLN

f_dataset=sys.argv[3] 
f_out=sys.argv[4] 
bin_size=sys.argv[5]
bin_size=int(bin_size)
median_y_n=sys.argv[6]


# Redundant ens annotation
print "1) Read ens redundant annotations"
gene_convert=data.read_ens_rd(f_ens_rd)
print "Number of gene",len(gene_convert.keys())

# Read semantic similarity score (benchmark, y)
print "2) Read MGI similarity..."
sem_sim=data.read_sem_sim(f_sem_sim)

# Read genomic dataset (x) with gene pair benchmark value (y)
print "3) Read Score for pair with MGI Score..."
score_pair=data.read_score(f_dataset,gene_convert,sem_sim)
print "Number of gene pairs",len(score_pair.keys())

# sort pair according to score
print "4) Sort pair according Score..."
list_pair=functions.sort_by_val(score_pair)
print "Number of Pair",len(list_pair)

# Report dataset by bin versus semantic similarity
print "5) read and look at distribution mgi/hpo..."
data.report_data_ben_bin(f_out,score_pair,list_pair,sem_sim,bin_size,median_y_n)

Beispiel #4

Datei: eval_with_scale_cum.py Projekt: violavol/AP-PLN

import sys
import data, functions

# parameters
f_ens_rd = sys.argv[1]
f_sem_sim = sys.argv[2]
f_dataset = sys.argv[3]
f_out = sys.argv[4]
bin_size = sys.argv[5]
bin_size = int(bin_size)
median_y_n = sys.argv[6]

# Redundant ens annotation
print "1) Read ens redundant annotations"
gene_convert = data.read_ens_rd(f_ens_rd)
print "Number of gene", len(gene_convert.keys())

# Read semantic similarity score (benchmark, y)
print "2) Read MGI similarity..."
sem_sim = data.read_sem_sim(f_sem_sim)

# Read genomic dataset (x) with gene pair benchmark value (y)
print "3) Read Score for pair with MGI Score..."
score_pair, list_pair = data.read_score(f_dataset, gene_convert)
print "Number of gene pairs", len(score_pair.keys())

# Report dataset by bin versus semantic similarity
print "4) read and look at distribution mgi/hpo..."
data.report_data_ben_bin(f_out, score_pair, list_pair, sem_sim, bin_size,
                         median_y_n)