Save and load forecasters¶

Skforecast models can be easily saved and loaded from disk using the pickle or joblib library. To streamline this process, two convenient functions, save_forecaster and load_forecaster, are available. See below for a simple example.

A forecaster_id has been included when initializing the forecaster, this may help to identify the target of the forecaster.

  Note

Since version 0.7.0, when initializing the forecaster, a forecaster_id can be included to help identify the target of the forecaster.

Libraries¶

In [1]:

# Libraries
# ==============================================================================
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from skforecast.ForecasterAutoreg import ForecasterAutoreg
from skforecast.utils import save_forecaster
from skforecast.utils import load_forecaster

# Libraries # ============================================================================== import numpy as np import pandas as pd from sklearn.ensemble import RandomForestRegressor from skforecast.ForecasterAutoreg import ForecasterAutoreg from skforecast.utils import save_forecaster from skforecast.utils import load_forecaster

Data¶

In [2]:

# Download data
# ==============================================================================
url = (
    'https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/'
    'data/h2o.csv'
)
data = pd.read_csv(url, sep=',', header=0, names=['y', 'date'])
data['date'] = pd.to_datetime(data['date'], format='%Y-%m-%d')
data = data.set_index('date')
data = data.asfreq('MS')

# Download data # ============================================================================== url = ( 'https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/' 'data/h2o.csv' ) data = pd.read_csv(url, sep=',', header=0, names=['y', 'date']) data['date'] = pd.to_datetime(data['date'], format='%Y-%m-%d') data = data.set_index('date') data = data.asfreq('MS')

Save and load forecaster model¶

In [3]:

# Create and train forecaster
forecaster = ForecasterAutoreg(
                 regressor     = RandomForestRegressor(random_state=123),
                 lags          = 5,
                 forecaster_id = "forecaster_001"
             )

forecaster.fit(y=data['y'])
forecaster.predict(steps=3)

# Create and train forecaster forecaster = ForecasterAutoreg( regressor = RandomForestRegressor(random_state=123), lags = 5, forecaster_id = "forecaster_001" ) forecaster.fit(y=data['y']) forecaster.predict(steps=3)

Out[3]:

2008-07-01    0.714526
2008-08-01    0.789144
2008-09-01    0.818433
Freq: MS, Name: pred, dtype: float64

In [4]:

# Save model
save_forecaster(forecaster, file_name='forecaster_001.py', verbose=False)

# Save model save_forecaster(forecaster, file_name='forecaster_001.py', verbose=False)

In [5]:

# Load model
forecaster_loaded = load_forecaster('forecaster_001.py', verbose=True)

# Load model forecaster_loaded = load_forecaster('forecaster_001.py', verbose=True)

================= 
ForecasterAutoreg 
================= 
Regressor: RandomForestRegressor(random_state=123) 
Lags: [1 2 3 4 5] 
Transformer for y: None 
Transformer for exog: None 
Window size: 5 
Weight function included: False 
Exogenous included: False 
Type of exogenous variable: None 
Exogenous variables names: None 
Training range: [Timestamp('1991-07-01 00:00:00'), Timestamp('2008-06-01 00:00:00')] 
Training index type: DatetimeIndex 
Training index frequency: MS 
Regressor parameters: {'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 1.0, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 123, 'verbose': 0, 'warm_start': False} 
fit_kwargs: {} 
Creation date: 2023-05-29 13:19:47 
Last fit date: 2023-05-29 13:19:47 
Skforecast version: 0.8.1 
Python version: 3.10.11 
Forecaster id: forecaster_001 

In [6]:

# Predict
forecaster_loaded.predict(steps=3)

# Predict forecaster_loaded.predict(steps=3)

Out[6]:

2008-07-01    0.714526
2008-08-01    0.789144
2008-09-01    0.818433
Freq: MS, Name: pred, dtype: float64

In [7]:

# Forecaster identifier
# ==============================================================================
forecaster.forecaster_id

# Forecaster identifier # ============================================================================== forecaster.forecaster_id

Out[7]:

'forecaster_001'

In [8]:

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