Forecasting with scikit-learn pipelines
Since version 0.4.0, skforecast allows using scikit-learn pipelines as regressors. This is useful since, many machine learning models, need specific data preprocessing transformations. For example, linear models with Ridge or Lasso regularization benefits from features been scaled.
⚠ WARNING: here .
Libraries
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from skforecast.ForecasterAutoreg import ForecasterAutoreg
from skforecast.model_selection import grid_search_forecaster
Data
url = ( 'https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o_exog.csv' )
data = pd . read_csv ( url , sep = ',' , header = 0 , names = [ 'date' , 'y' , 'exog_1' , 'exog_2' ])
data [ 'date' ] = pd . to_datetime ( data [ 'date' ], format = '%Y/%m/ %d ' )
data = data . set_index ( 'date' )
data = data . asfreq ( 'MS' )
Create pipeline
pipe = make_pipeline ( StandardScaler (), Ridge ())
pipe
Pipeline(steps=[('standardscaler', StandardScaler()), ('ridge', Ridge())])
Create and train forecaster
pipe = make_pipeline ( StandardScaler (), Ridge ())
forecaster = ForecasterAutoreg (
regressor = pipe ,
lags = 10
)
forecaster . fit ( y = data [ 'y' ], exog = data [[ 'exog_1' , 'exog_2' ]])
forecaster
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16 =================
ForecasterAutoreg
=================
Regressor: Pipeline(steps=[('standardscaler', StandardScaler()), ('ridge', Ridge())])
Lags: [ 1 2 3 4 5 6 7 8 9 10]
Window size: 10
Included exogenous: True
Type of exogenous variable: <class 'pandas.core.frame.DataFrame'>
Exogenous variables names: ['exog_1', 'exog_2']
Training range: [Timestamp('1992-04-01 00:00:00'), Timestamp('2008-06-01 00:00:00')]
Training index type: DatetimeIndex
Training index frequency: MS
Regressor parameters: {'standardscaler__copy': True, 'standardscaler__with_mean': True, 'standardscaler__with_std': True, 'ridge__alpha': 1.0, 'ridge__copy_X': True, 'ridge__fit_intercept': True, 'ridge__max_iter': None, 'ridge__normalize': 'deprecated', 'ridge__positive': False, 'ridge__random_state': None, 'ridge__solver': 'auto', 'ridge__tol': 0.001}
Creation date: 2022-01-02 16:47:46
Last fit date: 2022-01-02 16:47:46
Skforecast version: 0.4.2
Grid Search
When performing grid search over a sklearn pipeline, the name of the parameters is preceded by the name of the model.
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25 pipe = make_pipeline ( StandardScaler (), Ridge ())
forecaster = ForecasterAutoreg (
regressor = pipe ,
lags = 10 # This value will be replaced in the grid search
)
# Regressor's hyperparameters
param_grid = { 'ridge__alpha' : np . logspace ( - 3 , 5 , 10 )}
# Lags used as predictors
lags_grid = [ 5 , 24 , [ 1 , 2 , 3 , 23 , 24 ]]
results_grid = grid_search_forecaster (
forecaster = forecaster ,
y = data [ 'y' ],
exog = data [[ 'exog_1' , 'exog_2' ]],
param_grid = param_grid ,
lags_grid = lags_grid ,
steps = 5 ,
metric = 'mean_absolute_error' ,
refit = False ,
initial_train_size = len ( data . loc [: '2000-04-01' ]),
return_best = True ,
verbose = False
)
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27 Number of models compared: 30
loop lags_grid: 0%| | 0/3 [00:00<?, ?it/s]
loop param_grid: 0%| | 0/10 [00:00<?, ?it/s]
loop param_grid: 10%|███▋ | 1/10 [00:00<00:01, 7.71it/s]
loop param_grid: 30%|███████████ | 3/10 [00:00<00:00, 10.74it/s]
loop param_grid: 50%|██████████████████▌ | 5/10 [00:00<00:00, 12.45it/s]
loop param_grid: 70%|█████████████████████████▉ | 7/10 [00:00<00:00, 13.30it/s]
loop param_grid: 90%|█████████████████████████████████▎ | 9/10 [00:00<00:00, 12.91it/s]
loop lags_grid: 33%|█████████████ | 1/3 [00:00<00:01, 1.17it/s]
loop param_grid: 0%| | 0/10 [00:00<?, ?it/s]
loop param_grid: 20%|███████▍ | 2/10 [00:00<00:00, 11.57it/s]
loop param_grid: 40%|██████████████▊ | 4/10 [00:00<00:00, 10.51it/s]
loop param_grid: 60%|██████████████████████▏ | 6/10 [00:00<00:00, 11.47it/s]
loop param_grid: 80%|█████████████████████████████▌ | 8/10 [00:00<00:00, 12.24it/s]
loop param_grid: 100%|████████████████████████████████████| 10/10 [00:00<00:00, 11.99it/s]
loop lags_grid: 67%|██████████████████████████ | 2/3 [00:01<00:00, 1.17it/s]
loop param_grid: 0%| | 0/10 [00:00<?, ?it/s]
loop param_grid: 20%|███████▍ | 2/10 [00:00<00:00, 13.18it/s]
loop param_grid: 40%|██████████████▊ | 4/10 [00:00<00:00, 11.01it/s]
loop param_grid: 60%|██████████████████████▏ | 6/10 [00:00<00:00, 11.97it/s]
loop param_grid: 80%|█████████████████████████████▌ | 8/10 [00:00<00:00, 12.65it/s]
loop param_grid: 100%|████████████████████████████████████| 10/10 [00:00<00:00, 11.66it/s]
loop lags_grid: 100%|███████████████████████████████████████| 3/3 [00:02<00:00, 1.17it/s]
Refitting `forecaster` using the best found lags and parameters and the whole data set:
Lags: [1 2 3 4 5]
Parameters: {'ridge__alpha': 0.001}
Backtesting metric: 6.845311709573567e-05
lags
params
metric
ridge__alpha
0
[1 2 3 4 5]
{'ridge__alpha': 0.001}
6.84531e-05
0.001
10
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]
{'ridge__alpha': 0.001}
0.000187797
0.001
1
[1 2 3 4 5]
{'ridge__alpha': 0.007742636826811269}
0.000526168
0.00774264
11
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]
{'ridge__alpha': 0.007742636826811269}
0.00141293
0.00774264
2
[1 2 3 4 5]
{'ridge__alpha': 0.05994842503189409}
0.00385988
0.0599484
12
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]
{'ridge__alpha': 0.05994842503189409}
0.00896885
0.0599484
3
[1 2 3 4 5]
{'ridge__alpha': 0.46415888336127775}
0.0217507
0.464159
13
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]
{'ridge__alpha': 0.46415888336127775}
0.0295054
0.464159
14
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]
{'ridge__alpha': 3.593813663804626}
0.046323
3.59381
23
[ 1 2 3 23 24]
{'ridge__alpha': 0.46415888336127775}
0.0606231
0.464159
22
[ 1 2 3 23 24]
{'ridge__alpha': 0.05994842503189409}
0.0615665
0.0599484
21
[ 1 2 3 23 24]
{'ridge__alpha': 0.007742636826811269}
0.0617473
0.00774264
20
[ 1 2 3 23 24]
{'ridge__alpha': 0.001}
0.0617715
0.001
24
[ 1 2 3 23 24]
{'ridge__alpha': 3.593813663804626}
0.0635121
3.59381
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[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]
{'ridge__alpha': 27.825594022071257}
0.0645505
27.8256
4
[1 2 3 4 5]
{'ridge__alpha': 3.593813663804626}
0.0692201
3.59381
25
[ 1 2 3 23 24]
{'ridge__alpha': 27.825594022071257}
0.077934
27.8256
16
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]
{'ridge__alpha': 215.44346900318823}
0.130016
215.443
5
[1 2 3 4 5]
{'ridge__alpha': 27.825594022071257}
0.143189
27.8256
26
[ 1 2 3 23 24]
{'ridge__alpha': 215.44346900318823}
0.146446
215.443
17
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]
{'ridge__alpha': 1668.1005372000557}
0.204469
1668.1
6
[1 2 3 4 5]
{'ridge__alpha': 215.44346900318823}
0.205496
215.443
27
[ 1 2 3 23 24]
{'ridge__alpha': 1668.1005372000557}
0.212896
1668.1
18
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]
{'ridge__alpha': 12915.496650148827}
0.227536
12915.5
28
[ 1 2 3 23 24]
{'ridge__alpha': 12915.496650148827}
0.228974
12915.5
19
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]
{'ridge__alpha': 100000.0}
0.231157
100000
29
[ 1 2 3 23 24]
{'ridge__alpha': 100000.0}
0.231356
100000
7
[1 2 3 4 5]
{'ridge__alpha': 1668.1005372000557}
0.236227
1668.1
8
[1 2 3 4 5]
{'ridge__alpha': 12915.496650148827}
0.244788
12915.5
9
[1 2 3 4 5]
{'ridge__alpha': 100000.0}
0.246091
100000