from qtt import QuickOptimizer, QuickTuner

Expand to copy examples/step_by_step.py (top right)

Description

from qtt.predictors import PerfPredictor, CostPredictor from qtt.finetune.cv.classification import finetune_script, extract_task_info_metafeat import pandas as pd from ConfigSpace import ConfigurationSpace

config = pd.read_csv("config.csv", index_col=0) # pipeline configurations meta = pd.read_csv("meta.csv", index_col=0) # if meta-features are available curve = pd.read_csv("curve.csv", index_col=0) # learning curves cost = pd.read_csv("cost.csv", index_col=0) # runtime costs

X = pd.concat([config, meta], axis=1) curve = curve.values # predictors expect curves as numpy arrays cost = cost.values # predictors expect costs as numpy arrays

perf_predictor = PerfPredictor().fit(X, curve) cost_predictor = CostPredictor().fit(X, cost)

Define/Load the search space

cs = ConfigurationSpace() # ConfigurationSpace.from_json("cs.json")

Define the optimizer

optimizer = QuickOptimizer( cs=cs, max_fidelity=50, perf_predictor=perf_predictor, cost_predictor=cost_predictor, )

task_info, metafeat = extract_task_info_metafeat("path/to/dataset")

optimizer.setup( 512, metafeat=metafeat, )

Define the tuner

tuner = QuickTuner( optimizer=optimizer, f=finetune_script, ) tuner.run(task_info=task_info, fevals=100, time_budget=3600)