Save and load forecaster¶

Skforecast models can be stored and loaded from disk using pickle or joblib library. To simplify the process, two functions are available: save_forecaster and load_forecaster, a simple example is shown below.

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 model¶

In [3]:

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

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

# Create and train forecaster forecaster = ForecasterAutoreg( regressor = RandomForestRegressor(random_state=123), lags = 5 ) 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.py', verbose=False)

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

In [5]:

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

# Load model forecaster_loaded = load_forecaster('forecaster.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 
Included exogenous: 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} 
Creation date: 2022-09-24 09:14:11 
Last fit date: 2022-09-24 09:14:11 
Skforecast version: 0.5.0 
Python version: 3.9.13 

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]:

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