%load_ext autoreload
%autoreload 2
Convert your dataframes to arrays to use less memory and train faster
Most of the machine learning libraries use numpy arrays, even when you provide a dataframe it ends up being converted into a numpy array. By providing an array to those models we can make the process faster, since the conversion will only happen once.

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Data setup

from mlforecast.utils import generate_daily_series

series = generate_daily_series(5)

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fit and cross_validation methods

import numpy as np
from lightgbm import LGBMRegressor
from sklearn.linear_model import LinearRegression

from mlforecast import MLForecast

fcst = MLForecast(
    models={'lr': LinearRegression(), 'lgbm': LGBMRegressor(verbosity=-1)},
    freq='D',
    lags=[7, 14],
    date_features=['dayofweek'],
)
If you’re using the fit/cross_validation methods from MLForecast all you have to do to train with numpy arrays is provide the as_numpy argument, which will cast the features to an array before passing them to the models. 
fcst.fit(series, as_numpy=True)

MLForecast(models=[lr, lgbm], freq=<Day>, lag_features=['lag7', 'lag14'], date_features=['dayofweek'], num_threads=1)
When predicting, the new features will also be cast to arrays, so it can also be faster. 
fcst.predict(1)
unique_iddslrlgbm
0id_02000-08-105.2687876.322262
1id_12000-04-074.4373165.213255
2id_22000-06-163.2465184.373904
3id_32000-08-300.1448601.285219
4id_42001-01-082.2113183.236700
For cross_validation we also just need to specify as_numpy=True. 
cv_res = fcst.cross_validation(series, n_windows=2, h=2, as_numpy=True)

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preprocess method

Having the features as a numpy array can also be helpful in cases where you have categorical columns and the library doesn’t support them, for example LightGBM with polars. In order to use categorical features with LightGBM and polars we have to convert them to their integer representation and tell LightGBM to treat those features as categorical, which we can achieve in the following way: 
series_pl = generate_daily_series(5, n_static_features=1, engine='polars')
series_pl.head(2)
unique_iddsystatic_0
catdatetime[ns]f64cat
”id_0”2000-01-01 00:00:0036.462689”84"
"id_0”2000-01-02 00:00:00121.008199”84”
fcst = MLForecast(
    models=[],
    freq='1d',
    lags=[7, 14],
    date_features=['weekday'],
)
In order to get the features as an array with the preprocess method we also have to ask for the X, y tuple. 
X, y = fcst.preprocess(series_pl, return_X_y=True, as_numpy=True)
X[:2]

array([[  0.        ,  20.30076749,  36.46268875,   6.        ],
       [  0.        , 119.51717097, 121.0081989 ,   7.        ]])
The feature names are available in fcst.ts.features_order_ 
fcst.ts.features_order_

['static_0', 'lag7', 'lag14', 'weekday']
Now we can just train a LightGBM model specifying the feature names and which features should be treated as categorical. 
model = LGBMRegressor(verbosity=-1)
model.fit(
    X=X,
    y=y,
    feature_name=fcst.ts.features_order_,
    categorical_feature=['static_0', 'weekday'],
);
We can now add this model to our models dict, as described in the custom training guide. 
fcst.models_ = {'lgbm': model}
And use it to predict. 
fcst.predict(1)
unique_iddslgbm
catdatetime[ns]f64
”id_0”2000-08-10 00:00:00448.796188
”id_1”2000-04-07 00:00:0081.058211
”id_2”2000-06-16 00:00:004.450549
”id_3”2000-08-30 00:00:0014.219603
”id_4”2001-01-08 00:00:0087.361881