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prediction.py
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import pandas as pd
import numpy as np
import tensorflow as tf
import sys
import progressbar
from neighbor_genes import read_genome_maps
from process_data import create_data_homology_ls
from read_get_gene_seq import read_gene_sequences
from access_data_rest import update_rest
from threads import Procerssrunner
from prepare_synteny_matrix import read_data_synteny
from tree_data import create_tree_data
from process_data import create_map_list
def read_database(fname):
df = pd.read_csv(fname, sep="\t", header=None)
label_dict = dict(ortholog_one2one=1,
other_paralog=0,
non_homolog=2,
ortholog_one2many=1,
ortholog_many2many=1,
within_species_paralog=0,
gene_split=4)
label = []
for _, row in df.iterrows():
label.append(label_dict[row[7]])
df = df.assign(label=label)
df = df.drop(7, axis=1)
df = df.drop(0, axis=1)
df.columns = [
"gene_stable_id",
"species",
"homology_gene_stable_id",
"homology_species",
"goc",
"wga",
"label"]
return df
def select_data_by_length(df, st, end):
try:
if end < len(df):
if st < end:
df = df.loc[df.index.values[st:end]]
else:
raise ValueError()
except BaseException:
print("Making Predictions for the complete dataframe:)")
print(len(df))
return df
def create_synteny_features(a_h, d_h, n, a, d, ld, ldg, cmap, cimap, name):
lsy = create_data_homology_ls(a_h, d_h, n, a, d, ld, ldg, cmap, cimap, 0)
gene_sequences = read_gene_sequences(
a_h, lsy, "geneseq", "prediction_" + name)
gene_sequences = update_rest(gene_sequences, "prediction_" + name)
print("Gene Sequences Loaded.")
return lsy, gene_sequences
def threadmaker(nop, df, lsy, gene_sequences, n, name):
part = len(df) // nop
pr = Procerssrunner()
pr.start_processes(nop, df, gene_sequences, lsy, part, n, name)
smg, sml, indexes = read_data_synteny(nop, name)
sml = np.array(sml)
smg = np.array(smg)
indexes = np.array(indexes)
return sml, smg, indexes
def get_prediction(smg, sml, indexes, bls, blhs, dis, dps, dphs, model_name):
preds = np.zeros((len(smg), 3))
w = [0.86, 0.8, 0.06]
for i in range(1, 4):
try:
model = tf.train.import_meta_graph(
model_name + '_v' + str(i) + '/model.ckpt.meta')
except BaseException:
print("Something wrong with the model.")
continue
with tf.Session() as sess:
try:
model.restore(sess, model_name + '_v' + str(i) + "/model.ckpt")
graph = tf.get_default_graph()
synmgt, synmlt, blst, \
blhst, dpst, dphst, \
dist, lrt, yt = graph.get_collection("input_nodes")
predictions = graph.get_tensor_by_name("Predictions/BiasAdd:0")
print("Model Loaded Successfully :)")
except BaseException:
print(":(")
sys.exit()
fd = {synmgt: smg,
synmlt: sml,
blst: bls,
blhst: blhs,
dpst: dps.reshape((len(blhs), 1)),
dist: dis.reshape((len(blhs), 1)),
dphst: dphs.reshape((len(blhs), 1))}
preds_t_1 = sess.run([predictions], feed_dict=fd)
preds_t_1 = np.array(preds_t_1)[0]
fd = {synmgt: smg.transpose((0, 2, 1, 3)),
synmlt: sml.transpose((0, 2, 1, 3)),
blst: blhs,
blhst: bls,
dpst: dphs.reshape((len(blhs), 1)),
dist: dis.reshape((len(blhs), 1)),
dphst: dps.reshape((len(blhs), 1))}
preds_t_2 = sess.run([predictions], feed_dict=fd)
preds_t_2 = np.array(preds_t_2)[0]
preds = preds + w[i - 1] * (preds_t_1 + preds_t_2) / 2
tf.reset_default_graph()
preds = np.argmax(preds, axis=1)
print(preds.shape)
return preds
def write_preds(fname, model_name, name, preds, index_dict, df):
print(
"Writing predcitions to:",
"prediction_" +
fname +
"_" +
model_name +
"_" +
name +
"_multiple.txt")
with open("prediction_" + fname + "_" + model_name + "_" + name + "_multiple.txt", "w") as file:
for index, row in progressbar.progressbar(df.iterrows()):
file.write(str(row[0]))
file.write("\t")
file.write(row[1])
file.write("\t")
file.write(row[3])
file.write("\t")
file.write(str(row["label"]))
file.write("\t")
if index in index_dict:
file.write(str(preds[index_dict[index]]))
file.write("\t")
if preds[index_dict[index]] == row["label"]:
file.write(str(1))
else:
file.write(str(0))
else:
file.write("Error")
file.write("\t")
file.write("NaN")
file.write("\n")
def main():
arg = sys.argv
fname = arg[-6]
model_name = arg[-5]
nop = int(arg[-4])
st = int(arg[-3])
end = int(arg[-2])
name = arg[-1]
df = read_database(fname)
df = select_data_by_length(df, st, end)
n = 3
a, d, ld, ldg, cmap, cimap = read_genome_maps() # read the genome mapd
print("Genome Maps Loaded.")
a_h = [df]
d_h = ["prediction"]
lsy, gene_sequences = create_synteny_features(
a_h, d_h, n, a, d, ld, ldg, cmap, cimap, name)
sml, smg, indexes = threadmaker(nop, df, lsy, gene_sequences, n, name)
df_temp = df.loc[indexes]
bls, blhs, dis, dps, dphs = create_tree_data("species_tree.tree", df_temp)
index_dict = create_map_list(indexes)
preds = get_prediction(
smg,
sml,
indexes,
bls,
blhs,
dis,
dps,
dphs,
model_name)
write_preds(fname, model_name, name, preds, index_dict, df)
if __name__ == "__main__":
main()