def create_icd10_lookup():
"""Precomputes the mental and nervous system disorder columns of the ICD10 dataset for faster subject comparison.
See Chapters V and VI http://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=41270
:return dataframe row-indexed by subject IDs and and boolean columns indexed by disease.
"""
icd10 = pd.read_csv(data_icd10, sep=',')
icd10.index = ['UKB' + str(eid) for eid in icd10['eid']]
subject_ids = np.load(SUBJECT_IDS, allow_pickle=True)
biobank_uids = Phenotype.get_biobank_codes(Phenotype.ICD10)
icd10 = icd10.loc[subject_ids, biobank_uids]
icd10_lookup = pd.DataFrame(index=icd10.index)
# Determine if the the patient has the occurrence of a particular disease.
si = icd10.index.to_series()
ci = np.concatenate((Phenotype.get_icd10_mental_disorder_codes(),
Phenotype.get_icd10_nervous_system_disorder_codes()))
for c in ci:
icd10_lookup[c] = pd.Series(
si.apply(lambda s: np.any([
k.startswith(c)
for k in icd10.loc[s, :].to_numpy().astype('str')
])))
icd10_lookup = icd10_lookup.sort_index()
icd10_lookup.to_pickle(ICD10_LOOKUP)
return icd10_lookup
class Genotype:
#Add the map as a genotype and phenotype
def __init__(self):
self.chromosomes = np.zeros(0, dtype=int)
def randomize(self, chromosomeSize, trajectory):
self.level = Level(trajectory.level_width,trajectory.level_height)
self.level.generate_from_trajectory(trajectory, random.uniform(0,1))
self.phenotype = Phenotype(self.level)
self.chromosomes = self.level.cells.flatten()
self.trajectory = trajectory
# np.set_printoptions(threshold=np.nan)
# print(self.chromosomes)
# print("another genotype")
"""
for i in range(chromosomeSize):
self.chromosomes.append(random.randint(0,1))
"""
def getPhenotype(self):
self.phenotype.levelFromChromosomes(self.chromosomes, self.trajectory,
self.trajectory.level_width, self.trajectory.level_height)
return self.phenotype
def randomize(self, chromosomeSize, trajectory):
self.level = Level(trajectory.level_width,trajectory.level_height)
self.level.generate_from_trajectory(trajectory, random.uniform(0,1))
self.phenotype = Phenotype(self.level)
self.chromosomes = self.level.cells.flatten()
self.trajectory = trajectory
# np.set_printoptions(threshold=np.nan)
# print(self.chromosomes)
# print("another genotype")
"""
def __init__(self, nb_entrees, nb_sorties, idInd):
self.nb_e = nb_entrees
self.nb_s = nb_sorties
self.id = idInd
self.espece = None
self.genome = Genome(self.nb_e, self.nb_s)
self.phenotype = Phenotype(self.nb_e, self.nb_s)
self.idToPos = {
} #Ce tableau fera l'interface entre le genome et l'individu
self.fitness = None
self.sharedFitness = None
def evaluate_test_set_performance(model_dir):
"""Measures the test set performance of the model under the specified model directory.
:param model_dir: directory containing the model state dictionaries for each fold and the model
configuration (including the population graph parameterisation)
:return: the test set performance for each fold.
"""
with open(os.path.join(model_dir, 'config.yaml')) as file:
cfg = yaml.full_load(file)
graph_name = cfg['graph_name']['value']
conv_type = cfg['model']['value']
n_conv_layers = cfg['n_conv_layers']['value']
layer_sizes = ast.literal_eval(cfg['layer_sizes']['value'])
dropout_p = cfg['dropout']['value']
similarity_feature_set = [Phenotype(i) for i in ast.literal_eval(cfg['similarity']['value'])[0]]
similarity_threshold = ast.literal_eval(cfg['similarity']['value'])[1]
if graph_name not in GRAPH_NAMES:
graph_construct.construct_population_graph(similarity_feature_set=similarity_feature_set,
similarity_threshold=similarity_threshold,
functional=False,
structural=True,
euler=True)
graph = graph_construct.load_population_graph(graph_root, graph_name)
folds = brain_gnn_train.get_cv_subject_split(graph, n_folds=5)
results = {}
for i, fold in enumerate(folds):
brain_gnn_train.set_training_masks(graph, *fold)
graph_transform.graph_feature_transform(graph)
if ConvTypes(conv_type) == ConvTypes.GCN:
model = BrainGCN(graph.num_node_features, n_conv_layers, layer_sizes, dropout_p)
else:
model = BrainGAT(graph.num_node_features, n_conv_layers, layer_sizes, dropout_p)
model.load_state_dict(torch.load(os.path.join(model_dir, 'fold-{}_state_dict.pt'.format(i))))
model = model.to(device)
model.eval()
data = graph.to(device)
model = model(data)
predicted = model[data.test_mask].cpu()
actual = graph.y[data.test_mask].cpu()
r2 = r2_score(actual.detach().numpy(), predicted.detach().numpy())
r = pearsonr(actual.detach().numpy().flatten(), predicted.detach().numpy().flatten())
results['fold_{}'.format(i)] = {'r': [x.item() for x in r], 'r2': r2.item()}
mse = mean_squared_error(actual.detach().numpy(), predicted.detach().numpy())
results=mse
break
return results
def retrieveData():
'''
Retrieves data from API and puts them into a dictionary. A phenotype value is paired with a list of its name description and score key
'''
token = 'GENOMELINKTEST'
headers = {'Authorization': 'Bearer {}'.format(token)}
phenotypes = [
'carbohydrate-intake', 'protein-intake', 'vitamin-a', 'vitamin-b12',
'vitamin-d', 'vitamin-e', 'calcium', 'magnesium', 'iron',
'endurance-performance'
]
population = 'european'
for phenotype in phenotypes:
report_url = 'https://genomicexplorer.io/v1/reports/{}?population={}'.format(
phenotype, population)
response = requests.get(report_url, headers=headers)
data = response.json()
data_str = json.dumps(data)
data_dict = json.loads(data_str)
p = Phenotype(data_dict["phenotype"]["display_name"],
data_dict["summary"]["text"],
data_dict["summary"]["score"])
phenotypeDict[p._phenotype] = p._score
def __init__(self):
#-----location part
if parameters.location_mode:
self.mesh_size = 1./parameters.LD0range/2
f = lambda size: [[{} for x in xrange(int(size)+1)] for y in xrange(int(size)+1)]
self.mesh = f(self.mesh_size)
map_path = parameters.map_phenotype_image(parameters.maps)
self.load_terrain(map_path+".info.tmp", map_path+".tmp")
#------------------
from plant import Plant
from phenotype import Phenotype
self.plants = {}
self.allplantslist = []
self.generation = 0
self.__class__.default = self
self.__class__.environments += 1
debug.g("niche %d" % parameters.niche_size)
for i in xrange(parameters.niche_size):
if parameters.location_mode:
Plant.new(parameters.get_start_point(parameters.maps))
else:
Plant.new((0,0))
debug.g("*** %d" % len(self.plants))
self.optimal_global_phenotype = Phenotype()
self.base_phenotype = Phenotype()
self.survivors = parameters.niche_size
self.randomkiller = selectors.KillerRandom()
(self.killer, self.reproducer) = selectors.getSelectors()
self.phenotype_link = Phenotype
self.history = History(self)
self.history.update()
def average_distance(self, mc_samples = None):
if mc_samples == None:
ret = 0.0
for plant1 in self.plants.values():
for plant2 in self.plants.values():
ret += Phenotype.distance(plant1.phenotype, plant2.phenotype)
numpl = len(self.plants)
if numpl == 0: return 0.
return ret / (numpl**2 - numpl)
else:
ret = 0.0
tries_done = 0
tries_valid = 0
spk = self.plants.keys()
if spk == []: return 0.
while tries_done < mc_samples:
p1 = random.choice(spk)
p2 = random.choice(spk)
tries_done += 1
if p1 != p2:
tries_valid += 1
ret += self.phenotype_link.distance(self.plants[p1].phenotype, self.plants[p2].phenotype)
if tries_valid == 0:
tries_valid += 1
return ret / tries_valid
def __crossover__(self, population, ori):
aux = []
for f, m in population:
index = np.arange(int(len(f.__dict__)))
np.random.shuffle(index)
son1 = dict(
np.concatenate(
(np.array(list(f.__dict__.items()))[
index[:int(len(f.__dict__) * self.crossover)]],
np.array(list(m.__dict__.items()))[
index[int(len(f.__dict__) * self.crossover):]])))
son1 = Phenotype(son1)
son2 = dict(
np.concatenate(
(np.array(list(f.__dict__.items()))[
index[int(len(f.__dict__) * self.crossover):]],
np.array(list(m.__dict__.items()))[
index[:int(len(f.__dict__) * self.crossover)]])))
son2 = Phenotype(son2)
aux += [son1, son2]
ori.addPhenotype(aux)
return ori
def distance_to_opt(self):
#magic trick by @MKitlas
optimal_ph = 1410
plant_ph = self.phenotype
if p.location_mode:
optimal_ph = Environment.default.optimal_phenotype_on_map(self.location)
else:
optimal_ph = Environment.default.optimal_phenotype_without_map()
# TODO sum up TEs effect.
for t in self.aut_transposons_list:
if p.multidim_changes:
plant_ph = Phenotype.add(plant_ph, t.mutation_rate)
else:
plant_ph[t.trait_no] += t.mutation_rate
for t in self.nonaut_transposons_list:
if p.multidim_changes:
plant_ph = Phenotype.add(self.phenotype, t.mutation_rate)
else:
plant_ph[t.trait_no] += t.mutation_rate
return Phenotype.distance(plant_ph, optimal_ph)
def __init__(self, no_transp = None, transp_activity = None):
#debug.g("tworzenie nowej rosliny c.d. __init__()" )#, plant.fitness()))
self.aut_transposons = no_transp
if no_transp == None:
self.aut_transposons = p.starting_transposons()
self.nonaut_transposons = 0
##KG--begin
##sexual_mode reproduction
##initializing a list of transposons in a new plant
if p.sexual_mode :
self.sex = 1 if random.random() > 0.5 else 0
self.nonaut_transposons_list = []
self.aut_transposons_list = []
for i in range(self.aut_transposons):
te = Transposone(True)
self.aut_transposons_list.append(te)
else :
# asexual all plants have sex=0
self.sex = 0
##KG--end
self.phenotype = Phenotype.new()
self.id = self.__class__.counter
#Environment.default.register_new_plant(self.id, self) replaced to new()
self.environment = Environment.default
self.dead = False
self.__class__.counter += 1
self.transposase_activity = transp_activity
if transp_activity == None:
self.transposase_activity = p.starting_transposase_activity(self.aut_transposons)
self.inactive_transposons = 0
self.transpositions = 0
self.total_mutations = 0
self.random_mutations = 0
self.ord_counter = self.__class__.order_cnt
self.__class__.order_cnt += 1
def create_similarity_lookup():
"""Precomputes the columns of the phenotype dataset for faster subject comparison.
:return dataframe containing the values used for similarity comparison, row-indexed by subject ID and
column-indexed by phenotype code name (e.g. 'AGE', 'FTE' etc.)
"""
phenotypes = pd.read_csv(data_phenotype, sep=',')
phenotypes.index = ['UKB' + str(eid) for eid in phenotypes['eid']]
biobank_feature_list = []
for feature in Phenotype:
biobank_feature_list.extend(Phenotype.get_biobank_codes(feature))
phenotype_processed = phenotypes[biobank_feature_list]
for feature in Phenotype:
biobank_feature = Phenotype.get_biobank_codes(feature)
if feature == Phenotype.MENTAL_HEALTH:
mental_to_code = Phenotype.get_mental_to_code()
# column names for summary (total number of conditions) + 18 possible condidions: MEN0, MEN1, ..., MEN18.
mental_feature_codes = [
Phenotype.MENTAL_HEALTH.value + str(i) for i in range(19)
]
# Replace string descriptions with their codes for consistency.
phenotype_processed.loc[:,
biobank_feature[0]] = phenotype_processed[
biobank_feature[0]].apply(
lambda x: mental_to_code[x] if x in
mental_to_code.keys() else None)
# Determine if the the patient has the occurrence of a particular disease.
si = phenotype_processed.index.to_series()
for i in range(1, len(mental_feature_codes)):
phenotype_processed.loc[:, Phenotype.MENTAL_HEALTH.value +
str(i)] = si.apply(lambda s: int(
i in phenotype_processed
.loc[s, biobank_feature].to_numpy(
).astype(bool)))
phenotype_processed.loc[:, mental_feature_codes[0]] = si.apply(
lambda s: int(
np.sum(phenotype_processed.loc[s, mental_feature_codes[1:]]
)))
elif len(biobank_feature) > 1:
# handle the more/less recent values
si = phenotype_processed.index.to_series().copy()
phenotype_processed.loc[:, feature.value] = si.apply(
lambda s: get_most_recent(biobank_feature, s,
phenotype_processed))
else:
phenotype_processed.loc[:, feature.value] = phenotype_processed[
biobank_feature[0]].copy()
# Filter only the subjects used in the final dataset.
phenotype_processed = phenotype_processed.loc[precompute_subject_ids()]
# Return only the final feature columns (indexed by code names).
phenotype_processed.drop(biobank_feature_list, axis=1, inplace=True)
phenotype_processed = phenotype_processed.sort_index()
phenotype_processed.to_pickle(SIMILARITY_LOOKUP)
return phenotype_processed
# Population graph parameters
parser.add_argument('--functional', default=0, type=bool)
parser.add_argument('--structural', default=1, type=bool)
parser.add_argument('--euler', default=1, type=bool)
parser.add_argument('--similarity',
default="(['SEX', 'ICD10', 'FTE', 'NEU'], 0.8)",
type=str)
args = parser.parse_args()
functional = args.functional
structural = args.structural
euler = args.euler
similarity_feature_set = [
Phenotype(i) for i in ast.literal_eval(args.similarity)[0]
]
similarity_threshold = ast.literal_eval(args.similarity)[1]
graph_name = graph_construct.get_graph_name(
functional=functional,
structural=structural,
euler=euler,
similarity_feature_set=similarity_feature_set,
similarity_threshold=similarity_threshold)
if graph_name not in GRAPH_NAMES:
graph_construct.construct_population_graph(
similarity_feature_set=similarity_feature_set,
similarity_threshold=similarity_threshold,
functional=functional,
class Individu():
def __init__(self, nb_entrees, nb_sorties, idInd):
self.nb_e = nb_entrees
self.nb_s = nb_sorties
self.id = idInd
self.espece = None
self.genome = Genome(self.nb_e, self.nb_s)
self.phenotype = Phenotype(self.nb_e, self.nb_s)
self.idToPos = {
} #Ce tableau fera l'interface entre le genome et l'individu
self.fitness = None
self.sharedFitness = None
def __repr__(self):
s = "Ind " + str(self.id) + ":"
s += "\n Fitness: " + str(self.fitness)
s += "\n Shared Fitness: " + str(self.sharedFitness)
s += "Espece: " + str(self.espece)
return s
def generer(self):
#On ajoute au début les entrées et les sorties
self.idToPos = {i: (0, i) for i in range(self.nb_e)}
self.idToPos.update({self.nb_e + j: (1, j) for j in range(self.nb_s)})
#On met les valeurs de poids de genome dans le phenotype
self.genome.generer()
self.phenotype.generer()
for innov in self.genome.connexions:
c = self.genome.connexions[innov]
k, l = self.idToPos[c.sortie][1], self.idToPos[c.entree][1]
self.phenotype.liens[0][1][k, l] = c.poids
def calculateFitness(self):
pass
def output(self):
return self.phenotype.couches[-1]
def rawFitness(self):
if self.fitness == None:
self.fitness = 0
return self.fitness
def add_key(self, nouvid, couche, num):
"""Met à jour la table idToPos en ajoutant un noeud qui
sera en la couche et dont le numéro est num"""
assert nouvid not in self.idToPos, "Le nouvel identifiant ne doit pas être existent"
self.idToPos[nouvid] = (couche, num)
def insertLayer(self, couche):
"""Insère une couche aprés la couche indiqué en paramètre"""
#Décale tous les position d'une couche
for i in self.idToPos:
if self.idToPos[i][0] > couche:
n, h = self.idToPos[i]
self.idToPos[i] = (n + 1, h)
#Ajoute une nouvelle couche en inserant de nouvelles matrices liens
self.phenotype.insertLayer(couche)
def posToId(self, pos):
for i in self.idToPos:
if self.idToPos[i] == pos:
return i
def estRecursive(self, con):
ce, ne = self.idToPos[con.entree]
cs, ns = self.idToPos[con.sortie]
return ce >= cs
def insertNoeudCouche(self, couche, idNouvNoeud):
assert idNouvNoeud not in self.idToPos, "Noeud déja existant"
self.phenotype.insertNode(couche)
self.idToPos[idNouvNoeud] = (couche,
len(self.phenotype.couches[couche]) - 1)
def insertNoeud(self, con, p1, p2, innov, idNouvNoeud):
"""Cette fonction prend une connexion déja existante et la remplace par deux
nouvelle connexions et un noeud intermédiaire qui occupera la couche milieu si elle existe
et créera une nouvelle couche si la connexion relie deux couches succéssives ou la même couche"""
#On désactive la connexion précèdente
idN1 = con.entree
idN2 = con.sortie
con.desactiver()
#On récupère la position dans le phénotype des deux noeuds précèdemment reliés
c1, n1 = self.idToPos[idN1]
c2, n2 = self.idToPos[idN2]
assert c2 - c1 > 0, "Un lien recursif ne peut pas etre coupé"
#On a une disjonction de cas selon que les deux noeuds était dans deux couches successifs ou pas
if c2 - c1 >= 2:
#Si les deux noeuds ne sont pas dans de ux couches succéssifs alors on met le nouveau noeud
#dans une couche au milieu des deux couches
m = (c1 + c2) // 2
p = len(self.phenotype.couches[m])
#On met à jour la table idToPos
self.add_key(idNouvNoeud, m, p)
#On insère le noeud dans la couche m
self.phenotype.insertNode(m)
self.phenotype.modifierConnexion(idN1, idNouvNoeud, self.idToPos,
p1)
self.phenotype.modifierConnexion(idNouvNoeud, idN2, self.idToPos,
p2)
self.phenotype.modifierConnexion(idN1, idN2, self.idToPos, 0)
self.genome.ajouterConnexion(idN1, idNouvNoeud, p1, innov)
self.genome.ajouterConnexion(idNouvNoeud, idN2, p2, innov + 1)
elif c2 - c1 == 1:
#On ajoute la nouvelle couche en dessus de la couche en dessous (ie le min)
c = min(c1, c2)
self.insertLayer(c)
self.insertNoeudCouche(c + 1, idNouvNoeud)
c1, n1 = self.idToPos[idN1]
c2, n2 = self.idToPos[idN2]
self.phenotype.modifierConnexion(idN1, idNouvNoeud, self.idToPos,
p1)
self.phenotype.modifierConnexion(idNouvNoeud, idN2, self.idToPos,
p2)
self.phenotype.modifierConnexion(idN1, idN2, self.idToPos, 0)
self.genome.ajouterConnexion(idN1, idNouvNoeud, p1, innov)
self.genome.ajouterConnexion(idNouvNoeud, idN2, p2, innov + 1)
self.phenotype.reinit()
def connexionPossible(self):
if not (self.phenotype.estComplet()):
tries = 0
noeuds = self.idToPos.keys()
noeudsSansEntree = [
i for i in noeuds if (i not in range(self.nb_e))
]
e = ut.randomPick(noeuds)
s = ut.randomPick(noeudsSansEntree)
c = self.genome.entreeSortieToCon(e, s)
while tries < 10 and c != None and c.activation:
e = ut.randomPick(noeuds)
s = ut.randomPick(noeudsSansEntree)
c = self.genome.entreeSortieToCon(e, s)
tries += 1
if tries < 10:
if c != None:
return c
else:
return Connexion(e, s, 1)
def mutationPoids(self):
for i in self.genome.connexions:
c = self.genome.connexions[i]
if rand.random() < prob.mutation.poids:
if rand.random() < prob.mutation.poids_radical:
c.poids = 30 * rand.random() - 15
else:
c.poids += 0.5 * rand.random()
self.phenotype.modifierConnexion(c.entree, c.sortie,
self.idToPos, c.poids)
def insertLien(self, c, innov):
self.phenotype.modifierConnexion(c.entree, c.sortie, self.idToPos,
c.poids)
if not (c.activation):
c.activer()
else:
self.genome.ajouter(c, innov)
def draw(self, pos):
self.phenotype.draw(pos, self.posToId)
def advance_generation(self):
from phenotype import Phenotype
from plant import Plant
from math import log, sqrt
#self.avg_transpositions_in_this_generation = 0
#if self.generation%10==0:
# debug.g("====")
# debug.g(self.optimal_global_phenotype.properties)
# debug.g(self.base_phenotype.properties)
if parameters.random_pressure > 0.0:
# for plant in self.plants.values():
#if happened(random_pressure):
# plant.die()
self.randomkiller.eliminate(self.plants.values())
self.killer.eliminate(self.plants.values())
#=========LOCATION
if parameters.location_mode:
r = parameters.LD0range
r2 = r*r
def important_fields_in_mesh(location):
f = lambda (x,y): [(x,y),(x-r,y-r),(x,y-r),(x+r,y-r),(x-r,y),(x+r,y),(x-r,y+r),(x,y+r),(x+r,y+r)]
g = lambda x: -1<x and x<self.mesh_size
h = lambda (x,y): (Plant.scale(x),Plant.scale(y))
i = lambda (x,y): g(x) and g(y)
#debug.g(location)
#debug.g(r)
fields = set()
for x in filter(i, map(h, f(location))): fields.add(x)
return fields
def shuffle(l):
random.shuffle(l)
return l
def maybe_neighbour_kill(p1, p2):
fitness_val = p1.fitness()
d2_fun = lambda ((x1,y1), (x2,y2)): (x2-x1)**2+(y2-y1)**2
d2 = d2_fun((p1.location, p2.location))
##range_val = -2*log(d/r)
#try: range_val = 2*logr-log(d2)
#except: range_val = 1
import math
f = lambda (y,ymax,ymin): (y-ymin)/(ymax-ymin)
range_val = f((math.e**(d2), math.e**(r2), 1.))
if range_val <= 1.:
return not distributions.happened(range_val * fitness_val)
else: return False
#debug.g("%f, %f" %(range_val, math.sqrt(d2)))
for plant in shuffle(self.plants.values()):
if not plant.dead:
for (x,y) in important_fields_in_mesh(plant.location):
if plant.dead: break
for killer_plant in (self.mesh[x][y]).values():
if maybe_neighbour_kill(plant, killer_plant) and plant.id != killer_plant.id:
plant.die()
break
#=================
#transpositions_itg = 0
#for plant in self.plants.values():
# transpositions_itg += plant.transpositions
#self.avg_transpositions_in_this_generation = float(transpositions_itg) / float(len(self.plants.values()))
self.history.update()
v = self.plants.values()
self.survivors = len(v)
self.reproducer.reproduce(self.plants)
##KG
# for plant in self.plants.values():
# print " >> after reproduction part" + str(plant.aut_transposons) + "==" + str(len(plant.aut_transposons_list))
# for x in plant.aut_transposons_list :
# print "TE #" + str(x.id) + ", parent: " + str(x.parent) + ", aut= " + str(x.is_aut)
for plant in self.plants.values():
plant.evolve()
self.allplantslist = self.plants.values()
if parameters.expected_horiz_transfers > 0.0:
for plant in self.plants.values():
plant.perform_horizontal_transfers()
if self.generation >= parameters.stability_period:
if parameters.is_drift_directed:
for _unused in range(parameters.number_of_mutations):
self.base_phenotype[distributions.runifint(0, parameters.no_phenotype_properties-1)] += parameters.expected_mutation_shift
else:
for _unused in range(parameters.number_of_mutations):
self.base_phenotype.mutate_once(stdev = parameters.expected_mutation_shift)
self.optimal_global_phenotype = None
if parameters.fluctuations_magnitude > 0.0:
self.optimal_global_phenotype = self.base_phenotype.add(Phenotype.new_random(parameters.fluctuations_magnitude))
else:
self.optimal_global_phenotype = self.base_phenotype
allpl = sorted(self.plants.values(), key = lambda p: p.ord_counter)
i = 0
for p in allpl:
p.ord_counter = i
i += 1
#self.history.update()
self.generation += 1
class Environment:
environments = 0
def __init__(self):
#-----location part
if parameters.location_mode:
self.mesh_size = 1./parameters.LD0range/2
f = lambda size: [[{} for x in xrange(int(size)+1)] for y in xrange(int(size)+1)]
self.mesh = f(self.mesh_size)
map_path = parameters.map_phenotype_image(parameters.maps)
self.load_terrain(map_path+".info.tmp", map_path+".tmp")
#------------------
from plant import Plant
from phenotype import Phenotype
self.plants = {}
self.allplantslist = []
self.generation = 0
self.__class__.default = self
self.__class__.environments += 1
debug.g("niche %d" % parameters.niche_size)
for i in xrange(parameters.niche_size):
if parameters.location_mode:
Plant.new(parameters.get_start_point(parameters.maps))
else:
Plant.new((0,0))
debug.g("*** %d" % len(self.plants))
self.optimal_global_phenotype = Phenotype()
self.base_phenotype = Phenotype()
self.survivors = parameters.niche_size
self.randomkiller = selectors.KillerRandom()
(self.killer, self.reproducer) = selectors.getSelectors()
self.phenotype_link = Phenotype
self.history = History(self)
self.history.update()
def load_terrain(self, info_filename, data_filename):
size = (0,0)
with open(info_filename) as f:
arrinfo = array.array('L')
arrinfo.fromfile(f, 2)
size = (arrinfo[0], arrinfo[1])
self.map_size = size
self.phenotype_map = [[(1,1,1) for y in xrange(size[1])] for x in xrange(size[0])]
with open(data_filename) as f:
scale = lambda x: (float(x-127)/128)*parameters.map_phenotype_amplitude/2
arr = array.array('B')
arr.fromfile(f, size[0]*size[1]*3)
for y in xrange(size[1]):
for x in xrange(size[0]):
self.phenotype_map[x][y] = map(scale, (lambda nr: arr[nr:nr+3])(3*x+3*size[0]*y))
#debug.g(size)
#for x in xrange(63):
# debug.g(self.phenotype_map[x*10][0])
def register_new_plant(self, number, plant):
self.plants[number] = plant
##TODO
if parameters.location_mode:
try:
self.mesh[plant.scalex()][plant.scaley()][number] = plant
except:
debug.g(plant.scalex())
debug.g(plant.scaley())
debug.g(number)
0/0
def unregister_plant(self, number):
##TODO
if parameters.location_mode:
plant = self.plants[number]
del self.mesh[plant.scalex()][plant.scaley()][number]
del self.plants[number]
def advance_generation(self):
from phenotype import Phenotype
from plant import Plant
from math import log, sqrt
#self.avg_transpositions_in_this_generation = 0
#if self.generation%10==0:
# debug.g("====")
# debug.g(self.optimal_global_phenotype.properties)
# debug.g(self.base_phenotype.properties)
if parameters.random_pressure > 0.0:
# for plant in self.plants.values():
#if happened(random_pressure):
# plant.die()
self.randomkiller.eliminate(self.plants.values())
self.killer.eliminate(self.plants.values())
#=========LOCATION
if parameters.location_mode:
r = parameters.LD0range
r2 = r*r
def important_fields_in_mesh(location):
f = lambda (x,y): [(x,y),(x-r,y-r),(x,y-r),(x+r,y-r),(x-r,y),(x+r,y),(x-r,y+r),(x,y+r),(x+r,y+r)]
g = lambda x: -1<x and x<self.mesh_size
h = lambda (x,y): (Plant.scale(x),Plant.scale(y))
i = lambda (x,y): g(x) and g(y)
#debug.g(location)
#debug.g(r)
fields = set()
for x in filter(i, map(h, f(location))): fields.add(x)
return fields
def shuffle(l):
random.shuffle(l)
return l
def maybe_neighbour_kill(p1, p2):
fitness_val = p1.fitness()
d2_fun = lambda ((x1,y1), (x2,y2)): (x2-x1)**2+(y2-y1)**2
d2 = d2_fun((p1.location, p2.location))
##range_val = -2*log(d/r)
#try: range_val = 2*logr-log(d2)
#except: range_val = 1
import math
f = lambda (y,ymax,ymin): (y-ymin)/(ymax-ymin)
range_val = f((math.e**(d2), math.e**(r2), 1.))
if range_val <= 1.:
return not distributions.happened(range_val * fitness_val)
else: return False
#debug.g("%f, %f" %(range_val, math.sqrt(d2)))
for plant in shuffle(self.plants.values()):
if not plant.dead:
for (x,y) in important_fields_in_mesh(plant.location):
if plant.dead: break
for killer_plant in (self.mesh[x][y]).values():
if maybe_neighbour_kill(plant, killer_plant) and plant.id != killer_plant.id:
plant.die()
break
#=================
#transpositions_itg = 0
#for plant in self.plants.values():
# transpositions_itg += plant.transpositions
#self.avg_transpositions_in_this_generation = float(transpositions_itg) / float(len(self.plants.values()))
self.history.update()
v = self.plants.values()
self.survivors = len(v)
self.reproducer.reproduce(self.plants)
##KG
# for plant in self.plants.values():
# print " >> after reproduction part" + str(plant.aut_transposons) + "==" + str(len(plant.aut_transposons_list))
# for x in plant.aut_transposons_list :
# print "TE #" + str(x.id) + ", parent: " + str(x.parent) + ", aut= " + str(x.is_aut)
for plant in self.plants.values():
plant.evolve()
self.allplantslist = self.plants.values()
if parameters.expected_horiz_transfers > 0.0:
for plant in self.plants.values():
plant.perform_horizontal_transfers()
if self.generation >= parameters.stability_period:
if parameters.is_drift_directed:
for _unused in range(parameters.number_of_mutations):
self.base_phenotype[distributions.runifint(0, parameters.no_phenotype_properties-1)] += parameters.expected_mutation_shift
else:
for _unused in range(parameters.number_of_mutations):
self.base_phenotype.mutate_once(stdev = parameters.expected_mutation_shift)
self.optimal_global_phenotype = None
if parameters.fluctuations_magnitude > 0.0:
self.optimal_global_phenotype = self.base_phenotype.add(Phenotype.new_random(parameters.fluctuations_magnitude))
else:
self.optimal_global_phenotype = self.base_phenotype
allpl = sorted(self.plants.values(), key = lambda p: p.ord_counter)
i = 0
for p in allpl:
p.ord_counter = i
i += 1
#self.history.update()
self.generation += 1
def optimal_phenotype_on_map(self, (x,y)):
scale = lambda (v, length): int(round((length-1)*(v+1)/2))
xp = scale((x, self.map_size[0]))
yp = scale((y, self.map_size[1]))
return self.optimal_global_phenotype.get_map_phenotype(self.phenotype_map[xp][yp])
def __init__(self, data_dict, nphenom):
self.__dict__ = dict(
map(lambda x: (x, Chromosome(data_dict[x])), data_dict))
self.__phenotype__ = list(
map(lambda x: Phenotype(data_dict["J"]), list(range(nphenom))))
def label_permutation_test(model_dir):
"""Permutation test measuring the performance of the model when the labels are shuffled.
:param model_dir: directory containing the model state dictionaries for each fold and the model
configuration (including the population graph parameterisation)
:return: the test set performance for each permutation.
"""
with open(os.path.join(model_dir, 'config.yaml')) as file:
cfg = yaml.full_load(file)
graph_name = cfg['graph_name']['value']
conv_type = cfg['model']['value']
n_conv_layers = cfg['n_conv_layers']['value']
layer_sizes = ast.literal_eval(cfg['layer_sizes']['value'])
dropout_p = cfg['dropout']['value']
similarity_feature_set = [Phenotype(i) for i in ast.literal_eval(cfg['similarity']['value'])[0]]
similarity_threshold = ast.literal_eval(cfg['similarity']['value'])[1]
if graph_name not in GRAPH_NAMES:
graph_construct.construct_population_graph(similarity_feature_set=similarity_feature_set,
similarity_threshold=similarity_threshold,
functional=False,
structural=True,
euler=True)
graph = graph_construct.load_population_graph(graph_root, graph_name)
folds = brain_gnn_train.get_cv_subject_split(graph, n_folds=5)
fold = folds[0]
brain_gnn_train.set_training_masks(graph, *fold)
graph_transform.graph_feature_transform(graph)
rs = []
r2s = []
mses = []
for i in range(1000):
graph.to('cpu')
permute_population_graph_labels(graph, i)
if ConvTypes(conv_type) == ConvTypes.GCN:
model = BrainGCN(graph.num_node_features, n_conv_layers, layer_sizes, dropout_p)
else:
model = BrainGAT(graph.num_node_features, n_conv_layers, layer_sizes, dropout_p)
model.load_state_dict(torch.load(os.path.join(model_dir, 'fold-{}_state_dict.pt'.format(0))))
model = model.to(device)
data = graph.to(device)
model.eval()
model = model(data)
predicted = model[data.test_mask].cpu()
actual = graph.y[data.test_mask].cpu()
r2 = r2_score(actual.detach().numpy(), predicted.detach().numpy())
r = pearsonr(actual.detach().numpy().flatten(), predicted.detach().numpy().flatten())
mse = mean_squared_error(actual.detach().numpy(), predicted.detach().numpy())
rs.append(r[0])
r2s.append(r2)
mses.append(mse)
print(r[0], r2, mse)
np.save(os.path.join('notebooks', 'permutations_{}_{}'.format(conv_type, 'r')), rs)
np.save(os.path.join('notebooks', 'permutations_{}_{}'.format(conv_type, 'r2')), r2s)
np.save(os.path.join('notebooks', 'permutations_{}_{}'.format(conv_type, 'mse')), mses)
return [rs, r2s]
from json import JSONEncoder GFF = '/home/ethan/Documents/github/CoRNonCOB/corncob/killers/Lc20.fasta/prokka_results/PROKKA_03222020.gff' GENOMES = '/home/ethan/Documents/ecoli_genome/putonti_seqs/nice' RUN_DIR = '/home/ethan/Documents/phenotype_test' PROKA = '/home/ethan/prokka/bin/./prokka' from phenotype import Phenotype p = Phenotype(GENOMES, RUN_DIR, phenotype='n') p.pull_peptides(prokka_exec=PROKA) p.get_conserved_sequences()
def translate_to_phenotype(self):
return Phenotype(self)
data_timeseries = 'data/raw_ts'
data_phenotype = 'data/phenotype.csv'
data_similarity = 'data/similarity'
data_ct = 'data/CT.csv'
data_sa = 'data/SA.csv'
data_vol = 'data/Vol.csv'
data_euler = 'data/Euler.csv'
data_computed_fcms = 'data/processed_ts'
SUBJECT_IDS = 'data/subject_ids.npy'
# Exclude the following raw timeseries due to incorrect size.
EXCLUDED_UKB_IDS = ['UKB2203847', 'UKB2208238', 'UKB2697888']
# Graph construction phenotypic parameters.
AGE_UID = Phenotype.get_biobank_codes(Phenotype.AGE)[0]
def get_subject_ids(num_subjects=None, randomise=True, seed=0):
"""Gets the list of subject IDs for a spcecified number of subjects.
:param num_subjects: number of subjects. Use the entire dataset when set to None.
:param randomise: indicates whether to use a random seed for selection of subjects.
:param seed: random seed value.
:return: list of subject IDs.
"""
if not os.path.isfile(os.path.join(data_root, 'subject_ids.npy')):
ukb_preprocess.precompute_subject_ids()
subject_ids = np.load(os.path.join(data_root, 'subject_ids.npy'),
def init(self, cartSpace, cartPrice, limit):
for _ in range(self.populationSize):
self.population.append(Phenotype(cartSpace, cartPrice, limit))
self.bestSolution = self.population[0]
def evaluate_noise_performance(model_dir, noise_type='node'):
"""Measures the test set performance of the model under the specified model directory when noise is added.
:param model_dir: directory containing the model state dictionaries for each fold and the model
configuration (including the population graph parameterisation)
:param noise_type: 'node', 'node_feature_permutation' or 'edge'.
:return: the dictionary of results under five different random seeds and increasing probabilities of added noise.
"""
with open(os.path.join(model_dir, 'config.yaml')) as file:
cfg = yaml.full_load(file)
graph_name = cfg['graph_name']['value']
conv_type = cfg['model']['value']
n_conv_layers = cfg['n_conv_layers']['value']
layer_sizes = ast.literal_eval(cfg['layer_sizes']['value'])
dropout_p = cfg['dropout']['value']
lr = cfg['learning_rate']['value']
weight_decay = cfg['weight_decay']['value']
similarity_feature_set = [Phenotype(i) for i in ast.literal_eval(cfg['similarity']['value'])[0]]
similarity_threshold = ast.literal_eval(cfg['similarity']['value'])[1]
if graph_name not in GRAPH_NAMES:
graph_construct.construct_population_graph(similarity_feature_set=similarity_feature_set,
similarity_threshold=similarity_threshold,
functional=False,
structural=True,
euler=True)
graph = graph_construct.load_population_graph(graph_root, graph_name)
folds = brain_gnn_train.get_cv_subject_split(graph, n_folds=5)
fold = folds[0]
results = {}
for i in range(1, 5):
brain_gnn_train.set_training_masks(graph, *fold)
results_fold = {}
for p in [0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 0.8, 0.95]:
graph.to('cpu')
graph_transform.graph_feature_transform(graph)
if noise_type == 'node':
add_population_graph_noise(graph, p, random_state=i)
if noise_type == 'edge':
remove_population_graph_edges(graph, p, random_state=i)
if noise_type == 'node-feature-permutation':
permute_population_graph_features(graph, p, random_state=i)
data = graph.to(device)
epochs = 10000
model, _ = brain_gnn_train.train(conv_type, graph, device, n_conv_layers, layer_sizes, epochs, lr,
dropout_p, weight_decay, patience=100)
model.eval()
model = model(data)
predicted = model[data.test_mask].cpu()
actual = data.y[data.test_mask].cpu()
r2 = r2_score(actual.detach().numpy(), predicted.detach().numpy())
r = pearsonr(actual.detach().numpy().flatten(), predicted.detach().numpy().flatten())
results_fold['p={}_metric=r'.format(p)] = [x.item() for x in r][0]
wandb.run.summary['{}_{}_{}_p={}_metric=r'.format(conv_type, noise_type, i, p)] = [x.item() for x in r][0]
results_fold['p={}_metric=r2'.format(p)] = r2.item()
wandb.run.summary['{}_{}_{}_p={}_metric=r2'.format(conv_type, noise_type, i, p)] = r2.item()
gc.collect()
results['{}_{}_{}'.format(conv_type, noise_type, i)] = results_fold
return results
