Documentation Index
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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.
Data setup
from mlforecast.utils import generate_daily_series
series = generate_daily_series(5)
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'],
)
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)
| unique_id | ds | lr | lgbm |
|---|
| 0 | id_0 | 2000-08-10 | 5.268787 | 6.322262 |
| 1 | id_1 | 2000-04-07 | 4.437316 | 5.213255 |
| 2 | id_2 | 2000-06-16 | 3.246518 | 4.373904 |
| 3 | id_3 | 2000-08-30 | 0.144860 | 1.285219 |
| 4 | id_4 | 2001-01-08 | 2.211318 | 3.236700 |
as_numpy=True.
cv_res = fcst.cross_validation(series, n_windows=2, h=2, as_numpy=True)
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_id | ds | y | static_0 |
|---|
| cat | datetime[ns] | f64 | cat |
| ”id_0” | 2000-01-01 00:00:00 | 36.462689 | ”84" |
| "id_0” | 2000-01-02 00:00:00 | 121.008199 | ”84” |
fcst = MLForecast(
models=[],
freq='1d',
lags=[7, 14],
date_features=['weekday'],
)
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. ]])
fcst.ts.features_order_
['static_0', 'lag7', 'lag14', 'weekday']
model = LGBMRegressor(verbosity=-1)
model.fit(
X=X,
y=y,
feature_name=fcst.ts.features_order_,
categorical_feature=['static_0', 'weekday'],
);
fcst.models_ = {'lgbm': model}
| unique_id | ds | lgbm |
|---|
| cat | datetime[ns] | f64 |
| ”id_0” | 2000-08-10 00:00:00 | 448.796188 |
| ”id_1” | 2000-04-07 00:00:00 | 81.058211 |
| ”id_2” | 2000-06-16 00:00:00 | 4.450549 |
| ”id_3” | 2000-08-30 00:00:00 | 14.219603 |
| ”id_4” | 2001-01-08 00:00:00 | 87.361881 |
