good first issueimprovement
描述
Describe the bug
While calling gridsearch for NeuralNets using multiple timeseries, we get an error: ValueError: The two TimeSeries sequences must have the same length. Note that in the example, the 2 timeseries are of the same length.
To Reproduce
import pandas as pd
from darts import TimeSeries
from darts.models import (
NHiTSModel
)
from darts.metrics import rmse
import numpy as np
data = [['item1', '01-01-2023', 10],
['item1', '01-02-2023', 20],
['item1', '01-03-2023', 30],
['item1', '01-04-2023', 40],
['item1', '01-05-2023', 50],
['item1', '01-06-2023', 60],
['item1', '01-07-2023', 70],
['item1', '01-08-2023', 80],
['item1', '01-09-2023', 90],
['item2', '01-01-2023', 100],
['item2', '01-02-2023', 200],
['item2', '01-03-2023', 300],
['item2', '01-04-2023', 400],
['item2', '01-05-2023', 500],
['item2', '01-06-2023', 600],
['item2', '01-07-2023', 700],
['item2', '01-08-2023', 800],
['item2', '01-09-2023', 900]
]
df = pd.DataFrame(data, columns=['item' , 'sale_date', 'units'])
df['sale_date'] = pd.to_datetime(df['sale_date'])
item_list = TimeSeries.from_group_dataframe(df, group_cols = 'item', value_cols = 'units', time_col = 'sale_date')
params = {
"input_chunk_length" : [6],
"output_chunk_length" : [1],
"num_layers": [1,2,3]
}
res = NHiTSModel.gridsearch(parameters=params,
series=item_list,
metric=rmse,
reduction=np.mean,
n_jobs=-1,
n_random_samples=0.99,
verbose=True,
forecast_horizon = 1
)
ValueError: The two TimeSeries sequences must have the same length.
Expected behavior The code should do a gridsearch successfully and be able to give us the best model.
System (please complete the following information):
- Python version: 3.9
- darts version: 0.23.1
Additional context Gridsearch works with a single timeseries data.