使用 VS 代碼從 python 函數打印結果

Question

我正在嘗試重現此網頁的結果：

“ https://machinelearningmastery.com/how-to-grid-search-triple-exponential-smoothing-for-time-series-forecasting-in-python/ ”

但是在 VS 代碼上運行后，我得到的結果是“完成”，而不是應該打印的結果。 完整的代碼是：

# grid search ets models for monthly car sales
from math import sqrt
from multiprocessing import cpu_count
from joblib import Parallel
from joblib import delayed
from warnings import catch_warnings
from warnings import filterwarnings
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from sklearn.metrics import mean_squared_error
from pandas import read_csv
from numpy import array
 
# one-step Holt Winter’s Exponential Smoothing forecast
def exp_smoothing_forecast(history, config):
    t,d,s,p,b,r = config
    # define model
    history = array(history)
    model = ExponentialSmoothing(history, trend=t, damped=d, seasonal=s, seasonal_periods=p)
    # fit model
    model_fit = model.fit(optimized=True, use_boxcox=b, remove_bias=r)
    # make one step forecast
    yhat = model_fit.predict(len(history), len(history))
    return yhat[0]
 
# root mean squared error or rmse
def measure_rmse(actual, predicted):
    return sqrt(mean_squared_error(actual, predicted))
 
# split a univariate dataset into train/test sets
def train_test_split(data, n_test):
    return data[:-n_test], data[-n_test:]
 
# walk-forward validation for univariate data
def walk_forward_validation(data, n_test, cfg):
    predictions = list()
    # split dataset
    train, test = train_test_split(data, n_test)
    # seed history with training dataset
    history = [x for x in train]
    # step over each time-step in the test set
    for i in range(len(test)):
        # fit model and make forecast for history
        yhat = exp_smoothing_forecast(history, cfg)
        # store forecast in list of predictions
        predictions.append(yhat)
        # add actual observation to history for the next loop
        history.append(test[i])
    # estimate prediction error
    error = measure_rmse(test, predictions)
    return error
 
# score a model, return None on failure
def score_model(data, n_test, cfg, debug=False):
    result = None
    # convert config to a key
    key = str(cfg)
    # show all warnings and fail on exception if debugging
    if debug:
        result = walk_forward_validation(data, n_test, cfg)
    else:
        # one failure during model validation suggests an unstable config
        try:
            # never show warnings when grid searching, too noisy
            with catch_warnings():
                filterwarnings("ignore")
                result = walk_forward_validation(data, n_test, cfg)
        except:
            error = None
    # check for an interesting result
    if result is not None:
        print(' > Model[%s] %.3f' % (key, result))
    return (key, result)
 
# grid search configs
def grid_search(data, cfg_list, n_test, parallel=True):
    scores = None
    if parallel:
        # execute configs in parallel
        executor = Parallel(n_jobs=cpu_count(), backend='multiprocessing')
        tasks = (delayed(score_model)(data, n_test, cfg) for cfg in cfg_list)
        scores = executor(tasks)
    else:
        scores = [score_model(data, n_test, cfg) for cfg in cfg_list]
    # remove empty results
    scores = [r for r in scores if r[1] != None]
    # sort configs by error, asc
    scores.sort(key=lambda tup: tup[1])
    return scores
 
# create a set of exponential smoothing configs to try
def exp_smoothing_configs(seasonal=[None]):
    models = list()
    # define config lists
    t_params = ['add', 'mul', None]
    d_params = [True, False]
    s_params = ['add', 'mul', None]
    p_params = seasonal
    b_params = [True, False]
    r_params = [True, False]
    # create config instances
    for t in t_params:
        for d in d_params:
            for s in s_params:
                for p in p_params:
                    for b in b_params:
                        for r in r_params:
                            cfg = [t,d,s,p,b,r]
                            models.append(cfg)
    return models
 
if __name__ == '__main__':
    # load dataset
    series = read_csv('monthly-car-sales.csv', header=0, index_col=0)
    data = series.values
    # data split
    n_test = 12
    # model configs
    cfg_list = exp_smoothing_configs(seasonal=[0,6,12])
    # grid search
    scores = grid_search(data[:,0], cfg_list, n_test)
    print('done')
    # list top 3 configs
    for cfg, error in scores[:3]:
        print(cfg, error)

我曾嘗試執行下面的 function，但效果不佳。

exp_smoothing_configs(seasonal=[None])

這可能非常簡單，因為我是編碼新手。 謝謝！

Answer 1

首先，這不是 vs 代碼問題，而是舊代碼問題。 自編寫此代碼以來，ExponentialSmoothing 已更新。 不推薦使用damped，現在應該使用damped_trend。 它還接受 use_boxcox 作為參數（而不是在 model.fit 中）

所以更換

model = ExponentialSmoothing(history, trend=t, damped=d, seasonal=s, seasonal_periods=p)
# fit model
model_fit = model.fit(optimized=True, use_boxcox=b, remove_bias=r)

和

model = ExponentialSmoothing(history, trend=t, damped_trend=d, seasonal=s, seasonal_periods=p, use_boxcox=b)
# fit model
model_fit = model.fit(optimized=True, remove_bias=r)

此外，seasonal_periods 不接受小於或等於 1 的值。因此，當您調用 exp_smoothing_configs 時，請考慮替換列表中的 0 值。