def build_correct_setup():
search_space = search_spaces.TextClassifierSearchSpace()
search_strategy = search_strategies.RandomSearch()
search_space.add_budget(param.Budget.GENERATIONS, 10)
search_space.add_evaluation_metric(param.EvaluationMetric.MICRO_F1_SCORE)
search_space.add_optimization_value(param.OptimizationValue.DEV_SCORE)
search_space.add_parameter(param.ModelTrainer.LEARNING_RATE,
options=[0.01, 0.05, 0.1])
search_space.add_parameter(param.DocumentRNNEmbeddings.HIDDEN_SIZE,
options=[128, 256, 512])
search_space.add_parameter(param.DocumentRNNEmbeddings.WORD_EMBEDDINGS,
options=[['glove'], ['en'], ['en', 'glove']])
return search_space, search_strategy
from FlairParamOptimizer import search_strategies, search_spaces, orchestrator
import FlairParamOptimizer.parameter_listings.parameters_for_user_input as param
from flair.datasets import SENTEVAL_SUBJ
from torch.optim import SGD, Adam
# 1.) Define your corpus
corpus = SENTEVAL_SUBJ()
# 2.) create an search space
search_space = search_spaces.TextClassifierSearchSpace()
search_strategy = search_strategies.RandomSearch()
# 3.) depending on your task add the respective parameters you want to optimize over
search_space.add_budget(param.Budget.TIME_IN_H, 24)
search_space.add_evaluation_metric(param.EvaluationMetric.MICRO_F1_SCORE)
search_space.add_optimization_value(param.OptimizationValue.DEV_SCORE)
search_space.add_max_epochs_per_training_run(15)
#Depending on your downstream task, add embeddings and specify these with the respective Parameters below
search_space.add_parameter(param.ModelTrainer.LEARNING_RATE,
options=[0.01, 0.05, 0.1])
search_space.add_parameter(param.ModelTrainer.MINI_BATCH_SIZE,
options=[16, 32, 64])
search_space.add_parameter(param.ModelTrainer.ANNEAL_FACTOR,
options=[0.25, 0.5])
search_space.add_parameter(param.ModelTrainer.OPTIMIZER, options=[SGD, Adam])
search_space.add_parameter(param.Optimizer.WEIGHT_DECAY, options=[1e-2, 0])
#Define parameters for document embeddings RNN
search_space.add_parameter(param.DocumentRNNEmbeddings.HIDDEN_SIZE,
options=[128, 256, 512])
from FlairParamOptimizer import search_strategies, search_spaces, orchestrator
import FlairParamOptimizer.parameter_listings.parameters_for_user_input as param
from flair.datasets import SENTEVAL_SST_GRANULAR
from torch.optim import SGD, Adam
# 1.) Define your corpus
corpus = SENTEVAL_SST_GRANULAR()
# 2.) create an search space
search_space = search_spaces.TextClassifierSearchSpace(multi_label=True)
search_strategy = search_strategies.RandomSearch()
# 3.) depending on your task add the respective parameters you want to optimize over
search_space.add_budget(param.Budget.TIME_IN_H, 24)
search_space.add_evaluation_metric(param.EvaluationMetric.MICRO_F1_SCORE)
search_space.add_optimization_value(param.OptimizationValue.DEV_SCORE)
search_space.add_max_epochs_per_training_run(15)
#Depending on your downstream task, add embeddings and specify these with the respective Parameters below
search_space.add_parameter(param.ModelTrainer.LEARNING_RATE,
options=[0.01, 0.05, 0.1])
search_space.add_parameter(param.ModelTrainer.MINI_BATCH_SIZE,
options=[16, 32, 64])
search_space.add_parameter(param.ModelTrainer.ANNEAL_FACTOR,
options=[0.25, 0.5])
search_space.add_parameter(param.ModelTrainer.OPTIMIZER, options=[SGD, Adam])
search_space.add_parameter(param.Optimizer.WEIGHT_DECAY, options=[1e-2, 0])
#Define parameters for document embeddings RNN
search_space.add_parameter(param.DocumentRNNEmbeddings.HIDDEN_SIZE,
options=[128, 256, 512])