def run():
## Load example data frame
dataframe = pd.read_csv("../data/insults/train.tsv", sep="\t")
train_sequences = dataframe['text_a'].values.tolist()
train_targets = dataframe['label'].values
autoBOTLibObj = autoBOTLib.GAlearner(
train_sequences, # input sequences
train_targets, # target space
time_constraint=3, # time in hoursc
num_cpu="all", # number of CPUs to use
latent_dim=768, ## latent dim for neural representations
sparsity=0.1, ## latent_dim/sparsity dim for sparse representations
task_name="example test", # task identifier
scoring_metric="f1", # sklearn-compatible scoring metric as the fitness.
hof_size=3, # size of the hall of fame
top_k_importances=25, # how many top features to output as final ranking
memory_storage="./memory", # tripled base for concept features
representation_type="neurosymbolic") # or symbolic or neural
autoBOTLibObj.evolve(
nind=8, ## population size
strategy="evolution", ## optimization strategy
crossover_proba=0.6, ## crossover rate
mutpb=0.4) ## mutation rate
## Persistence demonstration (how to store models for further use?)
autoBOTLib.store_autobot_model(
autoBOTLibObj, "../stored_models/example_insults_model.pickle")
autoBOTLibObj = autoBOTLib.load_autobot_model(
"../stored_models/example_insults_model.pickle")
dataframe2 = pd.read_csv("../data/insults/test.tsv", sep="\t")
test_sequences = dataframe2['text_a'].values.tolist()
test_targets = dataframe2['label'].values
predictions = autoBOTLibObj.predict(test_sequences)
print(predictions)
performance = autoBOTLib.compute_metrics(
"first_run_task_name", predictions,
test_targets) ## compute F1, acc and F1_acc (as in GLUE)
## visualize performance
print(performance)
## Visualize importances (global -> type, local -> individual features)
importances_local, importances_global = autoBOTLibObj.feature_type_importances(
)
print(importances_global)
print(importances_local)
final_learners = autoBOTLibObj.summarise_final_learners()
print(final_learners)
## Visualize the fitness trace
fitness_summary = autoBOTLibObj.visualize_fitness(
image_path="./fitness_new.png")
print(fitness_summary)
def run():
## Load example data frame
dataframe = pd.read_csv("../data/insults/train.tsv", sep="\t").iloc[:]
train_sequences = dataframe['text_a']
train_targets = dataframe['label']
reptype = "neurosymbolic"
autoBOTLibObj = autoBOTLib.GAlearner(
train_sequences,
train_targets,
representation_type=
reptype, ## See the documentation for all possible representation types.
n_fold_cv=3,
framework="torch",
memory_storage="memory",
learner_preset="default",
verbose=1,
sparsity=0.1,
visualize_progress=
True, ## Stores progress as PROGRESS_{generation}.pdf file
upsample=
False, ## Suitable for imbalanced data - randomized upsampling tends to help.
time_constraint=1).evolve(
strategy="evolution",
nind=3) ## strategy = "direct-learning" trains a single learner.
# Store
autoBOTLib.store_autobot_model(autoBOTLibObj, f"model.pickle")
# Load
autoBOTObj = autoBOTLib.load_autobot_model(f"model.pickle")
# Predict
dataframe2 = pd.read_csv("../data/insults/test.tsv", sep="\t")
test_sequences = dataframe2['text_a']
predictions = autoBOTLibObj.predict(test_sequences)
autoBOTLibObj.predict_proba(test_sequences)
# autoBOTLibObj.generate_report(output_folder="./report/",
# job_id="REPORTNEW")
test_classes = dataframe2['label'].values.tolist()
output_classification_results(predictions,
test_classes,
f"./predictions/TORCH.json",
model_spec={})
def run():
jid = secrets.token_hex(nbytes=16)
df_path = None
## Load example data frame
dataframe = pd.read_csv(df_path, sep="\t")
train_sequences = None
train_sequences = None
train_targets = None
print(len(train_sequences))
print(len(train_targets))
classx = "genericTargetName"
autoBOTObj = autoBOTLib.GAlearner(
train_sequences, # input sequences
train_targets, # target space
time_constraint=1, # time in hoursc
num_cpu=32, # number of CPUs to use
sparsity=0.1,
task_name="example test", # task identifier
scoring_metric="f1", # sklearn-compatible scoring metric as the fitness.
hof_size=3, # size of the hall of fame
top_k_importances=25, # how many top features to output as final ranking
memory_storage="./memory", # tripled base for concept features
representation_type="neurosymbolic") # or symbolic or neural
autoBOTObj.evolve(
nind=8, ## population size
strategy="evolution", ## optimization strategy
crossover_proba=0.6, ## crossover rate
mutpb=0.4) ## mutation rate
autoBOTLib.store_autobot_model(autoBOTObj,
f"./models/{jid}_{classx}_model.pickle")
test_sequences = None
autoBOTObj = autoBOTLib.load_autobot_model(
f"./models/{jid}_{classx}_model.pickle")
autoBOTObj.predict(test_sequences)
scoring_metric="f1", # sklearn-compatible scoring metric as the fitness.
hof_size=3, # size of the hall of fame
top_k_importances=25, # how many top features to output as final ranking
memory_storage="./memory", # tripled base for concept features
representation_type="neurosymbolic") # or symbolic or neural
autoBOTLibObj.evolve(
nind=8, ## population size
strategy="evolution", ## optimization strategy
crossover_proba=0.6, ## crossover rate
mutpb=0.4) ## mutation rate
## Persistence demonstration (how to store models for further use?)
autoBOTLib.store_autobot_model(
autoBOTLibObj, "../stored_models/example_insults_model.pickle")
autoBOTLibObj = autoBOTLib.load_autobot_model(
"../stored_models/example_insults_model.pickle")
dataframe2 = pd.read_csv("../data/insults/test.tsv", sep="\t")
test_sequences = dataframe2['text_a'].values.tolist()
test_targets = dataframe2['label'].values
predictions = autoBOTLibObj.predict(test_sequences)
performance = autoBOTLib.compute_metrics(
"first_run_task_name", predictions,
test_targets) ## compute F1, acc and F1_acc (as in GLUE)
## visualize performance
print(performance)
## Visualize importances (global -> type, local -> individual features)
importances_local, importances_global = autoBOTLibObj.feature_type_importances(
)