print_section("TOOLS: Measure precision and recall of model")
print("-data: path to dataset | -store: job_gui id to store curve in GUI "\
"| -store_path: store results locally | -model: stored model to use")
#====== Arguments ===============================================
is_dataset_path, dataset_path = get_command(
'-data', default='/home/olav/Pictures/Mass_roads_alpha')
store_gui, job_id = get_command('-store_gui', default='-1')
is_store_path, store_path = get_command('-store_path',
default='./pr_data.json')
is_model, model_path = get_command('-model', default='./results/params.pkl')
#==============================================================
store = ParamStorage()
data = store.load_params(path=model_path)
batch_size = data['optimization'].batch_size
measurer = PrecisionRecallCurve(dataset_path, data['params'], data['model'],
data['dataset'])
datapoints = measurer.get_curves_datapoints(batch_size)
if store_gui:
send_precision_recall_data(datapoints, None, job_id=job_id)
else:
with open(store_path, 'w') as outfile:
json.dump([{"curve": datapoints, "events": []}], outfile)
plt.suptitle('Precision and recall')
plt.xlabel('Recall')
plt.ylabel('Precision')
verify, stage = get_command('-verify', default="0")
stage = "stage" + stage
is_tradeoff, tradeoff = get_command('-tradeoff', default="0.5")
tradeoff = float(tradeoff)
#Dataset path. Config used if not supplied
is_alt_dataset, alt_dataset = get_command('-dataset')
if is_alt_dataset:
dataset_path = alt_dataset
#==============================================================
store = ParamStorage()
teacher = store.load_params(path=teacher_location)
evaluate = util.create_simple_predictor(teacher['model'], teacher['params'])
if not verify:
creator = Creator(
pr_path,
dim=(dataset_params.input_dim, dataset_params.output_dim),
preproccessing=dataset_params.use_preprocessing,
std=dataset_params.dataset_std,
reduce_training=dataset_params.reduce_training,
reduce_testing=dataset_params.reduce_testing,
reduce_validation=dataset_params.reduce_validation
)
creator.load_dataset()
data, labels = creator.sample_data(
'-teacher', default=filename_params.curriculum_teacher)
verify, stage = get_command('-verify', default="0")
stage = "stage" + stage
is_tradeoff, tradeoff = get_command('-tradeoff', default="0.5")
tradeoff = float(tradeoff)
#Dataset path. Config used if not supplied
is_alt_dataset, alt_dataset = get_command('-dataset')
if is_alt_dataset:
dataset_path = alt_dataset
#==============================================================
store = ParamStorage()
teacher = store.load_params(path=teacher_location)
evaluate = util.create_simple_predictor(teacher['model'], teacher['params'])
if not verify:
creator = Creator(pr_path,
dim=(dataset_params.input_dim,
dataset_params.output_dim),
preproccessing=dataset_params.use_preprocessing,
std=dataset_params.dataset_std,
reduce_training=dataset_params.reduce_training,
reduce_testing=dataset_params.reduce_testing,
reduce_validation=dataset_params.reduce_validation)
creator.load_dataset()
data, labels = creator.sample_data(creator.train,
samples,
print_section("TOOLS: Measure precision and recall of model")
print(
"-data: path to dataset | -store: job_gui id to store curve in GUI "
"| -store_path: store results locally | -model: stored model to use"
)
# ====== Arguments ===============================================
is_dataset_path, dataset_path = get_command("-data", default="/home/olav/Pictures/Mass_roads_alpha")
store_gui, job_id = get_command("-store_gui", default="-1")
is_store_path, store_path = get_command("-store_path", default="./pr_data.json")
is_model, model_path = get_command("-model", default="./results/params.pkl")
# ==============================================================
store = ParamStorage()
data = store.load_params(path=model_path)
batch_size = data["optimization"].batch_size
measurer = PrecisionRecallCurve(dataset_path, data["params"], data["model"], data["dataset"])
datapoints = measurer.get_curves_datapoints(batch_size)
if store_gui:
send_precision_recall_data(datapoints, None, job_id=job_id)
else:
with open(store_path, "w") as outfile:
json.dump([{"curve": datapoints, "events": []}], outfile)
plt.suptitle("Precision and recall")
plt.xlabel("Recall")
plt.ylabel("Precision")
