About get_coef in older version about skforecast HOT 3 CLOSED

# Libraries
# ==============================================================================
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from skforecast.ForecasterAutoregMultiOutput import ForecasterAutoregMultiOutput
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Create and fit forecaster
# ==============================================================================
y = pd.Series(list(range(50)))

forecaster = ForecasterAutoregMultiOutput(
                 regressor = LinearRegression(),
                 steps     = 5,
                 lags      = 2
             )

forecaster.fit(y=y)

# Get coef for step 1
# ==============================================================================
display(forecaster.get_coef(step=1))

# Get list of df
# ==============================================================================
coef_ = []

for i in list(range(forecaster.steps)):
    df = forecaster.get_coef(step=i+1) # First step is 1 not 0
    coef_.append(df)
    
# print Get coef for step 1
display(coef_[0])

# Libraries
# ==============================================================================
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from skforecast.ForecasterAutoregDirect import ForecasterAutoregDirect
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Create and fit forecaster
# ==============================================================================
y = pd.Series(list(range(50)))

forecaster = ForecasterAutoregDirect(
                 regressor = LinearRegression(),
                 steps     = 5,
                 lags      = 2
             )

forecaster.fit(y=y)

# Get coef for step 1
# ==============================================================================
display(forecaster.get_feature_importance(step=1))

# Get list of df
# ==============================================================================
coef_ = []

for i in list(range(forecaster.steps)):
    df = forecaster.get_feature_importance(step=i+1) # First step is 1 not 0
    coef_.append(df)
    
# print Get coef for step 1
display(coef_[0])