Scikit-learn transformers and pipelines¶

Skforecast has two arguments in all the forecasters that allow more detailed control over input data transformations. This feature is particularly useful as many machine learning models require specific data pre-processing transformations. For example, linear models may benefit from features being scaled, or categorical features being transformed into numerical values.

• transformer_y: an instance of a transformer (preprocessor) compatible with the scikit-learn preprocessing API with the methods: fit, transform, fit_transform and, inverse_transform. Scikit-learn ColumnTransformer is not allowed since they do not have the inverse_transform method.

• transformer_exog: an instance of a transformer (preprocessor) compatible with the scikit-learn preprocessing API. Scikit-learn ColumnTransformer can be used if the preprocessing transformations only apply to some specific columns or if different transformations are needed for different columns. For example, scale numeric features and one hot encode categorical ones.

Transformations are learned and applied before training the forecaster and are automatically used when calling predict. The output of predict is always on the same scale as the original series y.

Although skforecast has allowed using scikit-learn pipelines as regressors since version 0.4.0, it is recommended to use transformer_y and transformer_exog instead.

  Note

When using ForecasterAutoregMultiSeries or ForecasterAutoregMultiVariate the transformer_series argument replaces transformer_y. If it is a transformer, the same transformation will be applied to all series. If it is a dict a different transformation can be set for each series.

Libraries¶

In [11]:

# Libraries
# ==============================================================================
import numpy as np
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import FunctionTransformer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import make_pipeline
from skforecast.ForecasterAutoreg import ForecasterAutoreg
from skforecast.model_selection import grid_search_forecaster
from skforecast.datasets import fetch_dataset

# Libraries # ============================================================================== import numpy as np from sklearn.linear_model import Ridge from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import OneHotEncoder from sklearn.preprocessing import FunctionTransformer from sklearn.compose import ColumnTransformer from sklearn.pipeline import make_pipeline from skforecast.ForecasterAutoreg import ForecasterAutoreg from skforecast.model_selection import grid_search_forecaster from skforecast.datasets import fetch_dataset

Data¶

In [12]:

# Download data
# ==============================================================================
data = fetch_dataset("h2o_exog")

# Download data # ============================================================================== data = fetch_dataset("h2o_exog")

h2o_exog
--------
Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health
system had between 1991 and 2008. Two additional variables (exog_1, exog_2) are
simulated.
Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice (3rd
Edition). http://pkg.robjhyndman.com/fpp3package/,
https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3.
Shape of the dataset: (195, 3)

In [13]:

# Data preprocessing
# ==============================================================================
data.index.name = 'date'
# Add an extra categorical variable
data['exog_3'] = (["A"] * int(len(data)/2)) + (["B"] * (int(len(data)/2) +1))
data.head()

# Data preprocessing # ============================================================================== data.index.name = 'date' # Add an extra categorical variable data['exog_3'] = (["A"] * int(len(data)/2)) + (["B"] * (int(len(data)/2) +1)) data.head()

Out[13]:

	y	exog_1	exog_2	exog_3
date
1992-04-01	0.379808	0.958792	1.166029	A
1992-05-01	0.361801	0.951993	1.117859	A
1992-06-01	0.410534	0.952955	1.067942	A
1992-07-01	0.483389	0.958078	1.097376	A
1992-08-01	0.475463	0.956370	1.122199	A

Transforming input series¶

The following example shows how include a transformer that scales the input series y.

In [14]:

# Create and fit forecaster that scales the input series
# ==============================================================================
forecaster = ForecasterAutoreg(
                 regressor        = Ridge(random_state=123),
                 lags             = 3,
                 transformer_y    = StandardScaler(),
                 transformer_exog = None
             )

forecaster.fit(y=data['y'], exog=data[['exog_1', 'exog_2']])
forecaster

# Create and fit forecaster that scales the input series # ============================================================================== forecaster = ForecasterAutoreg( regressor = Ridge(random_state=123), lags = 3, transformer_y = StandardScaler(), transformer_exog = None ) forecaster.fit(y=data['y'], exog=data[['exog_1', 'exog_2']]) forecaster

Out[14]:

================= 
ForecasterAutoreg 
================= 
Regressor: Ridge(random_state=123) 
Lags: [1 2 3] 
Transformer for y: StandardScaler() 
Transformer for exog: None 
Window size: 3 
Weight function included: False 
Differentiation order: None 
Exogenous included: 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: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'positive': False, 'random_state': 123, 'solver': 'auto', 'tol': 0.0001} 
fit_kwargs: {} 
Creation date: 2024-05-15 13:27:58 
Last fit date: 2024-05-15 13:27:58 
Skforecast version: 0.12.0 
Python version: 3.11.8 
Forecaster id: None 

Transforming exogenous variables¶

The following example shows how to apply the same transformation (scaling) to all exogenous variables.

In [15]:

# Create and fit forecaster with same transformer for all exogenous variables
# ==============================================================================
forecaster = ForecasterAutoreg(
                 regressor        = Ridge(random_state=123),
                 lags             = 3,
                 transformer_y    = None,
                 transformer_exog = StandardScaler()
             )

forecaster.fit(y=data['y'], exog=data[['exog_1', 'exog_2']])
forecaster

# Create and fit forecaster with same transformer for all exogenous variables # ============================================================================== forecaster = ForecasterAutoreg( regressor = Ridge(random_state=123), lags = 3, transformer_y = None, transformer_exog = StandardScaler() ) forecaster.fit(y=data['y'], exog=data[['exog_1', 'exog_2']]) forecaster

Out[15]:

================= 
ForecasterAutoreg 
================= 
Regressor: Ridge(random_state=123) 
Lags: [1 2 3] 
Transformer for y: None 
Transformer for exog: StandardScaler() 
Window size: 3 
Weight function included: False 
Differentiation order: None 
Exogenous included: 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: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'positive': False, 'random_state': 123, 'solver': 'auto', 'tol': 0.0001} 
fit_kwargs: {} 
Creation date: 2024-05-15 13:27:58 
Last fit date: 2024-05-15 13:27:58 
Skforecast version: 0.12.0 
Python version: 3.11.8 
Forecaster id: None 

It is also possible to apply a different transformation to each exogenous variable making use of ColumnTransformer.

In [16]:

# Create and fit forecaster with different transformer for each exog variable
# ==============================================================================
transformer_exog = ColumnTransformer(
                       [('scale_1', StandardScaler(), ['exog_1']),
                        ('scale_2', StandardScaler(), ['exog_2']),
                        ('onehot', OneHotEncoder(), ['exog_3']),
                       ],
                       remainder = 'passthrough',
                       verbose_feature_names_out = False
                   )

forecaster = ForecasterAutoreg(
                 regressor        = Ridge(random_state=123),
                 lags             = 3,
                 transformer_y    = None,
                 transformer_exog = transformer_exog
             )

forecaster.fit(y=data['y'], exog=data[['exog_1', 'exog_2', 'exog_3']])
forecaster

# Create and fit forecaster with different transformer for each exog variable # ============================================================================== transformer_exog = ColumnTransformer( [('scale_1', StandardScaler(), ['exog_1']), ('scale_2', StandardScaler(), ['exog_2']), ('onehot', OneHotEncoder(), ['exog_3']), ], remainder = 'passthrough', verbose_feature_names_out = False ) forecaster = ForecasterAutoreg( regressor = Ridge(random_state=123), lags = 3, transformer_y = None, transformer_exog = transformer_exog ) forecaster.fit(y=data['y'], exog=data[['exog_1', 'exog_2', 'exog_3']]) forecaster

Out[16]:

================= 
ForecasterAutoreg 
================= 
Regressor: Ridge(random_state=123) 
Lags: [1 2 3] 
Transformer for y: None 
Transformer for exog: ColumnTransformer(remainder='passthrough',
                  transformers=[('scale_1', StandardScaler(), ['exog_1']),
                                ('scale_2', StandardScaler(), ['exog_2']),
                                ('onehot', OneHotEncoder(), ['exog_3'])],
                  verbose_feature_names_out=False) 
Window size: 3 
Weight function included: False 
Differentiation order: None 
Exogenous included: True 
Type of exogenous variable: <class 'pandas.core.frame.DataFrame'> 
Exogenous variables names: ['exog_1', 'exog_2', 'exog_3'] 
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: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'positive': False, 'random_state': 123, 'solver': 'auto', 'tol': 0.0001} 
fit_kwargs: {} 
Creation date: 2024-05-15 13:27:58 
Last fit date: 2024-05-15 13:27:58 
Skforecast version: 0.12.0 
Python version: 3.11.8 
Forecaster id: None 

It is possible to verify if the data transformation has been applied correctly by examining the training matrices. The training matrices should reflect the data transformation that was specified using the transformer_y or transformer_exog arguments.

In [17]:

X_train, y_train = forecaster.create_train_X_y(
                       y    = data['y'],
                       exog = data[['exog_1', 'exog_2', 'exog_3']]
                   )

X_train, y_train = forecaster.create_train_X_y( y = data['y'], exog = data[['exog_1', 'exog_2', 'exog_3']] )

In [18]:

X_train.head(4)

X_train.head(4)

Out[18]:

	lag_1	lag_2	lag_3	exog_1	exog_2	exog_3_A	exog_3_B
date
1992-07-01	0.410534	0.361801	0.379808	-2.119529	-2.135088	1.0	0.0
1992-08-01	0.483389	0.410534	0.361801	-2.131024	-1.996017	1.0	0.0
1992-09-01	0.475463	0.483389	0.410534	-2.109222	-1.822392	1.0	0.0
1992-10-01	0.534761	0.475463	0.483389	-2.132137	-1.590667	1.0	0.0

In [19]:

y_train.head(4)

y_train.head(4)

Out[19]:

date
1992-07-01    0.483389
1992-08-01    0.475463
1992-09-01    0.534761
1992-10-01    0.568606
Freq: MS, Name: y, dtype: float64

Custom transformers¶

Using scikit-learn FunctionTransformer it is possible to include custom transformers in the forecaster object, for example, a logarithmic transformation.

Scikit-learn's FunctionTransformer can be used to incorporate custom transformers, such as a logarithmic transformation, in the forecaster object. To implement this, a user-defined transformation function can be created and then passed to the FunctionTransformer. Detailed information on how to use FunctionTransformer can be found in the scikit-learn documentation.

  Warning

For versions 1.1.0 >= scikit-learn <= 1.2.0 sklearn.preprocessing.FunctionTransformer.inverse_transform does not support DataFrames that are all numerical when check_inverse=True. It will raise an Exception which is fixed in scikit-learn 1.2.1.

More info: https://scikit-learn.org/stable/whats_new/v1.2.html#version-1-2-1

In [20]:

# Create custom transformer
# =============================================================================
def log_transform(x):
    """ 
    Calculate log adding 1 to avoid calculation errors if x is very close to 0.
    """
    return np.log(x+1)

def exp_transform(x):
    """
    Inverse of log_transform.
    """
    return np.exp(x) - 1

transformer_y = FunctionTransformer(func=log_transform, inverse_func=exp_transform)

# Create forecaster and train
# ==============================================================================
forecaster = ForecasterAutoreg(
                 regressor     = Ridge(random_state=123),
                 lags          = 3,
                 transformer_y = transformer_y
             )

forecaster.fit(y=data['y'])

# Create custom transformer # ============================================================================= def log_transform(x): """ Calculate log adding 1 to avoid calculation errors if x is very close to 0. """ return np.log(x+1) def exp_transform(x): """ Inverse of log_transform. """ return np.exp(x) - 1 transformer_y = FunctionTransformer(func=log_transform, inverse_func=exp_transform) # Create forecaster and train # ============================================================================== forecaster = ForecasterAutoreg( regressor = Ridge(random_state=123), lags = 3, transformer_y = transformer_y ) forecaster.fit(y=data['y'])

If the FunctionTransformer has an inverse function, the output of the predict method is automatically transformed back to the original scale.

In [21]:

forecaster.predict(steps=4)

forecaster.predict(steps=4)

Out[21]:

2008-07-01    0.776206
2008-08-01    0.775471
2008-09-01    0.777200
2008-10-01    0.777853
Freq: MS, Name: pred, dtype: float64

Scikit-learn pipelines¶

  Warning

Starting from version 0.4.0, skforecast permits the usage of scikit-learn pipelines as regressors. It is important to note that ColumnTransformer cannot be included in the pipeline; thus, the same transformation will be applied to both the modeled series and all exogenous variables. However, if the preprocessing transformations only apply to specific columns, then they need to be applied separately using transformer_y and transformer_exog.

In [22]:

pipe = make_pipeline(StandardScaler(), Ridge())
pipe

pipe = make_pipeline(StandardScaler(), Ridge()) pipe

Out[22]:

Pipeline(steps=[('standardscaler', StandardScaler()), ('ridge', Ridge())])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

In [23]:

# Create and fit forecaster
# ==============================================================================
forecaster = ForecasterAutoreg(
                 regressor = pipe,
                 lags      = 10
             )

forecaster.fit(y=data['y'], exog=data[['exog_1', 'exog_2']])
forecaster

# Create and fit forecaster # ============================================================================== forecaster = ForecasterAutoreg( regressor = pipe, lags = 10 ) forecaster.fit(y=data['y'], exog=data[['exog_1', 'exog_2']]) forecaster

Out[23]:

================= 
ForecasterAutoreg 
================= 
Regressor: Pipeline(steps=[('standardscaler', StandardScaler()), ('ridge', Ridge())]) 
Lags: [ 1  2  3  4  5  6  7  8  9 10] 
Transformer for y: None 
Transformer for exog: None 
Window size: 10 
Weight function included: False 
Differentiation order: None 
Exogenous included: 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__positive': False, 'ridge__random_state': None, 'ridge__solver': 'auto', 'ridge__tol': 0.0001} 
fit_kwargs: {} 
Creation date: 2024-05-15 13:27:59 
Last fit date: 2024-05-15 13:27:59 
Skforecast version: 0.12.0 
Python version: 3.11.8 
Forecaster id: None 

When performing a grid search over a scikit-learn pipeline, the model's name precedes the parameters' name.

In [24]:

# Hyperparameter grid search using a scikit-learn pipeline
# ==============================================================================
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,
                   show_progress      = True
               )

results_grid.head(4)

# Hyperparameter grid search using a scikit-learn pipeline # ============================================================================== 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, show_progress = True ) results_grid.head(4)

Number of models compared: 30.

lags grid:   0%|          | 0/3 [00:00<?, ?it/s]

params grid:   0%|          | 0/10 [00:00<?, ?it/s]

`Forecaster` refitted 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: 6.845311709559406e-05

Out[24]:

	lags	lags_label	params	mean_absolute_error	ridge__alpha
0	[1, 2, 3, 4, 5]	[1, 2, 3, 4, 5]	{'ridge__alpha': 0.001}	0.000068	0.001000
10	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...	{'ridge__alpha': 0.001}	0.000188	0.001000
1	[1, 2, 3, 4, 5]	[1, 2, 3, 4, 5]	{'ridge__alpha': 0.007742636826811269}	0.000526	0.007743
11	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...	{'ridge__alpha': 0.007742636826811269}	0.001413	0.007743