Talos --> TypeError: __init__() got an unexpected keyword argument 'grid_downsample' - talos

Question

I am trying to run a hyperparameters optimization with Talos. As I have a lot of parameters to test, I want to use a 'grid_downsample' argument that will select 30% of all possible hyperparameters combinations. However when I run my code I get: TypeError: __init__() got an unexpected keyword argument 'grid_downsample'
I tested the code below without the 'grid_downsample' option and with less hyperparameters.
#load data
data = pd.read_csv('data.txt', sep="\t", encoding = "latin1")
# split into input (X) and output (y) variables
Y = np.array(data['Y'])
data_bis = data.drop(['Y'], axis = 1)
X = np.array(data_bis)
p = {'activation':['relu'],
'optimizer': ['Nadam'],
'first_hidden_layer': [12],
'second_hidden_layer': [12],
'batch_size': [20],
'epochs': [10,20],
'dropout_rate':[0.0, 0.2]}
def dnn_model(x_train, y_train, x_val, y_val, params):
model = Sequential()
#input layer
model.add(Dense(params['first_hidden_layer'], input_shape=(1024,)))
model.add(Dropout(params['dropout_rate']))
model.add(Activation(params['activation']))
#hidden layer 2
model.add(Dense(params['second_hidden_layer']))
model.add(Dropout(params['dropout_rate']))
model.add(Activation(params['activation']))
# output layer with one node
model.add(Dense(1))
model.add(Activation(params['activation']))
# Compile model
model.compile(loss='binary_crossentropy', optimizer=params['optimizer'], metrics=['accuracy'])
out = model.fit(x_train, y_train,
batch_size=params['batch_size'],
epochs=params['epochs'],
validation_data=[x_val, y_val],
verbose=0)
return out, model
scan_object = ta.Scan(X, Y, model=dnn_model, params=p, experiment_name="test")
reporting = ta.Reporting(scan_object)
report = reporting.data
report.to_csv('./Random_search/dnn/report_talos.txt', sep = '\t')
This code works well. If I change the scan_object as the end to: scan_object = ta.Scan(X, Y, model=dnn_model, grid_downsample=0.3, params=p, experiment_name="test"), it gives me the error: TypeError: __init__() got an unexpected keyword argument 'grid_downsample' while I was expecting to have the same results format as a normal grid search but with less combinations. What am I missing? Did the name of the argument change? I'm using Talos 0.6.3 in a conda environment.
Thank you!

Answer 1

might be too late for you now but they've switched it to fraction_limit. It would give this for you
scan_object = ta.Scan(X, Y, model=dnn_model, params=p, experiment_name="test", fraction_limit = 0.1)
Sadly, the doc isn't well updated
Check out their examples on GitHub:
https://github.com/autonomio/talos/blob/master/examples/Hyperparameter%20Optimization%20with%20Keras%20for%20the%20Iris%20Prediction.ipynb