Open karamata opened 5 months ago
Hi @karamata! Are you able to share the code you're using to generate predictions? It seems like product_name_probabilities_Ống_thoát_nước__xifong__dùng_cho_chậu_rửa_trong_nhà_bếp___nắp_ống_bằng_inox__thân_ống_và_đường_ống_bằng_nhựa__lõi_ống_dẫn_bằng_thép__bộ_gồm_nắp_đậy__ống_dẫn__khớp_nối__hàng_mới_100.csv
is too big of a file name and the error is not coming from Ludwig, but the host machine since it's an Errno 36.
Hi @karamata! Are you able to share the code you're using to generate predictions? It seems like
product_name_probabilities_Ống_thoát_nước__xifong__dùng_cho_chậu_rửa_trong_nhà_bếp___nắp_ống_bằng_inox__thân_ống_và_đường_ống_bằng_nhựa__lõi_ống_dẫn_bằng_thép__bộ_gồm_nắp_đậy__ống_dẫn__khớp_nối__hàng_mới_100.csv
is too big of a file name and the error is not coming from Ludwig, but the host machine since it's an Errno 36.
Hi @arnavgarg1, pls see my code base below, and this code I running on colab
config = {
"input_features": [
{
"name": "col1",
"type": "category",
"preprocessing": {
"missing_value_strategy": "drop_row"
},
},
{
"name": "col2",
"type": "category",
"preprocessing": {
"missing_value_strategy": "drop_row"
},
},
{
"name": "col3",
"type": "category",
"preprocessing": {
"missing_value_strategy": "drop_row"
},
},
{
"name": "col4",
"type": "category",
"preprocessing": {
"missing_value_strategy": "drop_row"
},
},
{
"name": "col5",
"type": "category",
"preprocessing": {
"missing_value_strategy": "drop_row"
},
},
{
"name": "col6",
"type": "category",
"preprocessing": {
"missing_value_strategy": "fill_with_const",
"fill_value": "",
},
},
{
"name": "col7",
"type": "category",
"preprocessing": {
"missing_value_strategy": "fill_with_const",
"fill_value": "",
},
},
{
"name": "col8",
"type": "category",
"preprocessing": {
"missing_value_strategy": "fill_with_const",
"fill_value": "",
},
},
{
"name": "col9",
"type": "sequence",
"preprocessing": {
"encoder": {
"type": "embed",
"reduce_output": None,
},
},
},
],
"output_features": [
{
"name": "product_name",
"type": "category",
"preprocessing": {
"missing_value_strategy": "drop_row"
},
},
{
"name": "product_type",
"type": "category",
"preprocessing": {
"missing_value_strategy": "fill_with_const",
"fill_value": "",
},
},
{
"name": "output1",
"type": "category",
"preprocessing": {
"missing_value_strategy": "fill_with_const",
"fill_value": "",
},
},
{
"name": "output2",
"type": "category",
"preprocessing": {
"missing_value_strategy": "fill_with_const",
"fill_value": "",
},
},
],
"trainer": {
"epochs": 2,
"batch_size": 512,
"max_batch_size": 512,
"learning_rate": 0.01,
"use_mixed_precision": True,
}
}
dataset = dataset.sample(frac=1).reset_index()
train_df, test_df = train_test_split(dataset, train_size=0.9)
model = LudwigModel(config=config, logging_level=logging.INFO)
train_stats, preprocessed_data, output_directory = model.train(training_set=train_df, test_set=test_df)
np.random.seed(13)
eval_df = test_df.sample(n=1000)
test_stats, predictions, output_directory = model.evaluate(
eval_df,
collect_predictions=True,
collect_overall_stats=True,
# skip_save_eval_stats=True,
# skip_save_predictions=True,
# output_directory="test_results",
# return_type="dict"
)
predictions, prediction_results = model.predict(dataset=eval_df)
predictions.head()
I face this problem, but don't know how to by pass, anyone help me please