forked from mila-iqia/gene-graph-conv
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clinical-task-hpsearch.py
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clinical-task-hpsearch.py
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#!/usr/bin/env python
import sys
sys.path.append('../')
import meta_dataloader.TCGA
import models.mlp, models.gcn
import numpy as np
import data.gene_graphs
import collections
import sklearn.metrics
import sklearn.model_selection
import pandas as pd
import sys
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-seed', type=int, default=0)
parser.add_argument('-ntrain', type=int, default=50)
parser.add_argument('-task', type=str, default="histological_type")
parser.add_argument('-study', type=str, default="LGG")
parser.add_argument('-graph', type=str, default="stringdb")
args = parser.parse_args()
print(args)
tasks = meta_dataloader.TCGA.TCGAMeta(download=True,
min_samples_per_class=10,
gene_symbol_map_file="data/genenames_code_map_Feb2019.txt")
# for taskid in tasks.task_ids:
# if "BRCA" in taskid:
# print(taskid)
# clinical_M PAM50Call_RNAseq
task = meta_dataloader.TCGA.TCGATask((args.task, args.study), gene_symbol_map_file="data/genenames_code_map_Feb2019.txt")
print(task.id)
print(task._samples.shape)
print(np.asarray(task._labels).shape)
print(collections.Counter(task._labels))
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(task._samples,
task._labels,
stratify=task._labels,
train_size=args.ntrain,
test_size=100,
shuffle=True,
random_state=args.seed
)
X_test, X_valid, y_test, y_valid = sklearn.model_selection.train_test_split(X_test,
y_test,
stratify=y_test,
train_size=50,
test_size=50,
shuffle=True,
random_state=args.seed
)
import skopt, collections
from skopt.space import Real, Integer, Categorical
def doMLP():
skopt_args = collections.OrderedDict()
skopt_args["lr"]=Integer(2, 6)
skopt_args["channels"]=Integer(6, 12)
skopt_args["layers"]=Integer(1, 3)
optimizer = skopt.Optimizer(dimensions=skopt_args.values(),
base_estimator="GP",
n_initial_points=3,
random_state=args.seed)
print(skopt_args)
best_valid_metric = 0
test_for_best_valid_metric = 0
best_config = None
already_done = set()
for i in range(30):
suggestion = optimizer.ask()
if str(suggestion) in already_done:
continue
already_done.add(str(suggestion))
sdict = dict(zip(skopt_args.keys(),suggestion))
sdict["lr"] = 10**float((-sdict["lr"]))
sdict["channels"] = 2**sdict["channels"]
print(sdict)
model = models.mlp.MLP(name="MLP",
num_layer=sdict["layers"],
channels=sdict["channels"],
lr=sdict["lr"],
num_epochs=100,
patience=50,
cuda=True,
metric=sklearn.metrics.accuracy_score,
verbose=False,
seed=args.seed)
model.fit(X_train, y_train)
y_valid_pred = model.predict(X_valid)
valid_metric = sklearn.metrics.accuracy_score(y_valid, np.argmax(y_valid_pred,axis=1))
opt_results = optimizer.tell(suggestion, - valid_metric)
#record metrics to write and plot
if best_valid_metric < valid_metric:
best_valid_metric = valid_metric
best_config = sdict
y_test_pred = model.predict(X_test)
test_metric = sklearn.metrics.accuracy_score(y_test, np.argmax(y_test_pred,axis=1))
test_for_best_valid_metric = test_metric
print(i,"This result:",valid_metric, sdict)
print("#Final Results", test_for_best_valid_metric, best_config)
return test_metric, best_config
results_mlp = doMLP()
def doGGC():
if args.graph == "stringdb":
graph = data.gene_graphs.StringDBGraph(datastore="./data")
elif args.graph == "genemania":
graph = data.gene_graphs.GeneManiaGraph()
else:
print("unknown graph")
sys.exit(1)
adj = graph.adj()
import gc
gc.collect()
skopt_args = collections.OrderedDict()
skopt_args["lr"]=Integer(3, 5)
#skopt_args["channels"]=Integer(4, 5)
#skopt_args["embedding"]=Integer(4, 5)
skopt_args["num_layer"]=Integer(2, 3)
skopt_args["prepool_extralayers"]=Integer(1, 2)
optimizer = skopt.Optimizer(dimensions=skopt_args.values(),
base_estimator="GP",
n_initial_points=4,
random_state=args.seed)
print(skopt_args)
best_valid_metric = 0
test_for_best_valid_metric = 0
best_config = None
already_done = set()
for i in range(10):
import gc
gc.collect()
suggestion = optimizer.ask()
if str(suggestion) in already_done:
continue
already_done.add(str(suggestion))
sdict = dict(zip(skopt_args.keys(),suggestion))
sdict["lr"] = 10**float((-sdict["lr"]))
sdict["channels"] = 32#2**sdict["channels"]
sdict["embedding"] = 32#2**sdict["embedding"]
print(sdict)
model = models.gcn.GCN(name="GCN_lay3_chan64_emb32_dropout_agg_hierarchy",
dropout=False,
cuda=True,
num_layer=sdict["num_layer"],
prepool_extralayers=sdict["prepool_extralayers"],
channels=sdict["channels"],
embedding=sdict["embedding"],
aggregation="hierarchy",
lr=sdict["lr"],
num_epochs=100,
patience=20,
verbose=True,
seed=args.seed
)
model.fit(X_train, y_train, adj)
y_valid_pred = model.predict(X_valid)
valid_metric = sklearn.metrics.accuracy_score(y_valid, np.argmax(y_valid_pred,axis=1))
opt_results = optimizer.tell(suggestion, - valid_metric)
#record metrics to write and plot
if best_valid_metric < valid_metric:
best_valid_metric = valid_metric
print("best_valid_metric", best_valid_metric, sdict)
best_config = sdict
y_test_pred = model.predict(X_test)
test_metric = sklearn.metrics.accuracy_score(y_test, np.argmax(y_test_pred,axis=1))
test_for_best_valid_metric = test_metric
print(i,"This result:",valid_metric, sdict)
print("#Final Results", test_for_best_valid_metric, best_config)
return test_for_best_valid_metric, best_config
results_ggc = doGGC()
print("####GGC", args,results_ggc)
print("####MLP", args,results_mlp)