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import os | ||
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from collections import OrderedDict | ||
import itertools | ||
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import pandas as pd | ||
import yaml | ||
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from batchglm.api.models.nb_glm import Simulator, Estimator | ||
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def init_benchmark( | ||
root_dir: str, | ||
sim: Simulator, | ||
config_file="config.yml", | ||
**kwargs | ||
): | ||
os.makedirs(root_dir, exist_ok=True) | ||
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config = { | ||
"sim_data": "sim_data.h5", | ||
"plot_dir": "plot_dir", | ||
} | ||
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os.makedirs(os.path.join(root_dir, config["plot_dir"]), exist_ok=True) | ||
sim.save(os.path.join(root_dir, config["sim_data"])) | ||
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benchmark_samples = dict() | ||
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kvlist = [ | ||
(key, val) if isinstance(val, tuple) or isinstance(val, list) else (key, [val]) for key, val in kwargs.items() | ||
] | ||
od = OrderedDict(sorted(kvlist)) | ||
cart = list(itertools.product(*od.values())) | ||
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df = pd.DataFrame(cart, columns=od.keys()) | ||
df.reset_index(inplace=True) | ||
df["working_dir"] = ["idx_%i" % i for i in df["index"]] | ||
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for idx, row in df.iterrows(): | ||
name = row["working_dir"] | ||
benchmark_samples[name] = prepare_benchmark_sample( | ||
root_dir=root_dir, | ||
**row.to_dict() | ||
) | ||
config["benchmark_samples"] = benchmark_samples | ||
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config_file = os.path.join(root_dir, config_file) | ||
with open(config_file, mode="w") as f: | ||
yaml.dump(config, f, default_flow_style=False) | ||
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def prepare_benchmark_sample( | ||
root_dir: str, | ||
working_dir: str, | ||
batch_size: int, | ||
save_checkpoint_steps=25, | ||
save_summaries_steps=25, | ||
export_steps=25, | ||
learning_rate: float = 0.05, | ||
convergence_criteria="step", | ||
stopping_criteria=5000, | ||
train_mu: bool = None, | ||
train_r: bool = None, | ||
use_batching=True, | ||
optim_algo="gradient_descent", | ||
**kwargs | ||
): | ||
os.makedirs(os.path.join(root_dir, working_dir), exist_ok=True) | ||
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sample_config = { | ||
"working_dir": working_dir, | ||
} | ||
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setup_args = { | ||
"batch_size": batch_size, | ||
"extended_summary": True, | ||
"init_a": "standard", | ||
"init_b": "standard", | ||
} | ||
sample_config["setup_args"] = setup_args | ||
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init_args = { | ||
# "working_dir": working_dir, | ||
"save_checkpoint_steps": save_checkpoint_steps, | ||
"save_summaries_steps": save_summaries_steps, | ||
"export_steps": export_steps, | ||
"export": ["a", "b", "loss", "gradient", "full_loss", "full_gradient", "batch_log_probs"], | ||
} | ||
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sample_config["init_args"] = init_args | ||
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training_args = { | ||
"learning_rate": learning_rate, | ||
"convergence_criteria": convergence_criteria, | ||
"stopping_criteria": stopping_criteria, | ||
"train_mu": train_mu, | ||
"train_r": train_r, | ||
"use_batching": use_batching, | ||
"optim_algo": optim_algo, | ||
} | ||
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sample_config["training_args"] = training_args | ||
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return sample_config | ||
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def get_benchmark_samples(root_dir: str, config_file="config.yml"): | ||
config_file = os.path.join(root_dir, config_file) | ||
with open(config_file, mode="r") as f: | ||
config = yaml.load(f) | ||
return list(config["benchmark_samples"].keys()) | ||
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def touch(path): | ||
with open(path, 'a'): | ||
os.utime(path, None) | ||
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def run_benchmark(root_dir: str, sample: str, config_file="config.yml"): | ||
config_file = os.path.join(root_dir, config_file) | ||
with open(config_file, mode="r") as f: | ||
config = yaml.load(f) | ||
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sim_data_file = os.path.join(root_dir, config["sim_data"]) | ||
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sample_config = config["benchmark_samples"][sample] | ||
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working_dir = os.path.join(root_dir, sample_config["working_dir"]) | ||
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# working space locking | ||
if os.path.exists(os.path.join(working_dir, "ready")): | ||
print("benchmark sample '%s' was already estimated" % sample) | ||
return | ||
if os.path.exists(os.path.join(working_dir, "lock")): | ||
print(( | ||
"benchmark sample '%s' is locked. " + | ||
"Maybe there is already another instance estimating this sample?" | ||
) % sample) | ||
return | ||
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print("locking dir...", end="", flush=True) | ||
touch(os.path.join(working_dir, "lock")) | ||
print("\t[OK]") | ||
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print("loading data...", end="", flush=True) | ||
sim = Simulator() | ||
sim.load(sim_data_file) | ||
print("\t[OK]") | ||
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setup_args = sample_config["setup_args"] | ||
setup_args["input_data"] = sim.input_data | ||
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init_args = sample_config["init_args"] | ||
init_args["working_dir"] = working_dir | ||
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training_args = sample_config["training_args"] | ||
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print("starting estimation of benchmark sample '%s'..." % sample) | ||
estimator = Estimator(**setup_args) | ||
estimator.initialize(**init_args) | ||
estimator.train(**training_args) | ||
print("estimation of benchmark sample '%s' ready" % sample) | ||
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print("unlocking dir and finalizing...", end="", flush=True) | ||
os.remove(os.path.join(working_dir, "lock")) | ||
touch(os.path.join(working_dir, "ready")) | ||
print("\t[OK]") |
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