端到端演练

import random
import tempfile
from pathlib import Path

import pandas as pd
from datasetsforecast.m4 import M4
from utilsforecast.plotting import plot_series

await M4.async_download('data', group='Hourly')
df, *_ = M4.load('data', 'Hourly')
uids = df['unique_id'].unique()
random.seed(0)
sample_uids = random.choices(uids, k=4)
df = df[df['unique_id'].isin(sample_uids)].reset_index(drop=True)
df['ds'] = df['ds'].astype('int64')
df

fig = plot_series(df, max_insample_length=24 * 14)

from mlforecast import MLForecast
from mlforecast.target_transforms import Differences

fcst = MLForecast(
    models=[],  # we're not interested in modeling yet
    freq=1,  # our series have integer timestamps, so we'll just add 1 in every timestep
    target_transforms=[Differences([24])],
)
prep = fcst.preprocess(df)
prep

fig = plot_series(prep)

fcst = MLForecast(
    models=[],
    freq=1,
    lags=[1, 24],
    target_transforms=[Differences([24])],    
)
prep = fcst.preprocess(df)
prep

prep.drop(columns=['unique_id', 'ds']).corr()['y']

y        1.000000
lag1     0.622531
lag24   -0.234268
Name: y, dtype: float64

from mlforecast.lag_transforms import ExpandingMean, RollingMean

from numba import njit
from window_ops.rolling import rolling_mean

@njit
def rolling_mean_48(x):
    return rolling_mean(x, window_size=48)


fcst = MLForecast(
    models=[],
    freq=1,
    target_transforms=[Differences([24])],    
    lag_transforms={
        1: [ExpandingMean()],
        24: [RollingMean(window_size=48), rolling_mean_48],
    },
)
prep = fcst.preprocess(df)
prep

def hour_index(times):
    return times % 24

fcst = MLForecast(
    models=[],
    freq=1,
    target_transforms=[Differences([24])],
    date_features=[hour_index],
)
fcst.preprocess(df)

from mlforecast.target_transforms import LocalStandardScaler

fcst = MLForecast(
    models=[],
    freq=1,
    lags=[1],
    target_transforms=[LocalStandardScaler()]
)
fcst.preprocess(df)

from sklearn.base import BaseEstimator

class Naive(BaseEstimator):
    def fit(self, X, y):
        return self

    def predict(self, X):
        return X['lag1']

fcst = MLForecast(
    models=[Naive()],
    freq=1,
    lags=[1],
    target_transforms=[LocalStandardScaler()]
)
fcst.fit(df)
preds = fcst.predict(1)
preds

last_vals = df.groupby('unique_id').tail(1)
last_vals

import numpy as np

np.testing.assert_allclose(preds['Naive'], last_vals['y'])

import lightgbm as lgb

lgb_params = {
    'verbosity': -1,
    'num_leaves': 512,
}

fcst = MLForecast(
    models={
        'avg': lgb.LGBMRegressor(**lgb_params),
        'q75': lgb.LGBMRegressor(**lgb_params, objective='quantile', alpha=0.75),
        'q25': lgb.LGBMRegressor(**lgb_params, objective='quantile', alpha=0.25),
    },
    freq=1,
    target_transforms=[Differences([24])],
    lags=[1, 24],
    lag_transforms={
        1: [ExpandingMean()],
        24: [RollingMean(window_size=48)],
    },
    date_features=[hour_index],
)
fcst.fit(df)

MLForecast(models=[avg, q75, q25], freq=1, lag_features=['lag1', 'lag24', 'expanding_mean_lag1', 'rolling_mean_lag24_window_size48'], date_features=[<function hour_index>], num_threads=1)

preds = fcst.predict(48)
preds

fig = plot_series(df, preds, max_insample_length=24 * 7)

with tempfile.TemporaryDirectory() as tmpdir:
    save_dir = Path(tmpdir) / 'mlforecast'
    fcst.save(save_dir)
    fcst2 = MLForecast.load(save_dir)
    preds2 = fcst2.predict(48)
    pd.testing.assert_frame_equal(preds, preds2)

fcst = MLForecast(
    models=[Naive()],
    freq=1,
    lags=[1, 2, 3],
)
fcst.fit(df)
fcst.predict(1)

new_values = pd.DataFrame({
    'unique_id': ['H196', 'H256'],
    'ds': [1009, 1009],
    'y': [17.0, 14.0],
})
fcst.update(new_values)
preds = fcst.predict(1)
preds

fcst = MLForecast(
    models=lgb.LGBMRegressor(**lgb_params),
    freq=1,
    target_transforms=[Differences([24])],
    lags=[1, 24],
    lag_transforms={
        1: [ExpandingMean()],
        24: [RollingMean(window_size=48)],
    },
    date_features=[hour_index],
)
cv_result = fcst.cross_validation(
    df,
    n_windows=4,  # number of models to train/splits to perform
    h=48,  # length of the validation set in each window
)
cv_result

fig = plot_series(forecasts_df=cv_result.drop(columns='cutoff'))

from utilsforecast.losses import rmse

def evaluate_cv(df):
    return rmse(df, models=['LGBMRegressor'], id_col='cutoff').set_index('cutoff')

split_rmse = evaluate_cv(cv_result)
split_rmse

split_rmse.mean()

LGBMRegressor    27.971949
dtype: float64

from mlforecast.lag_transforms import ExponentiallyWeightedMean

fcst = MLForecast(
    models=lgb.LGBMRegressor(**lgb_params),
    freq=1,
    lags=[1, 24],
    lag_transforms={
        1: [ExponentiallyWeightedMean(alpha=0.5)],
        24: [RollingMean(window_size=48)],      
    },
    date_features=[hour_index],    
)
cv_result2 = fcst.cross_validation(
    df,
    n_windows=4,
    h=48,
)
evaluate_cv(cv_result2).mean()

LGBMRegressor    25.874439
dtype: float64

from mlforecast.lgb_cv import LightGBMCV

cv = LightGBMCV(
    freq=1,
    target_transforms=[Differences([24])],
    lags=[1, 24],
    lag_transforms={
        1: [ExpandingMean()],
        24: [RollingMean(window_size=48)],
    },
    date_features=[hour_index],
    num_threads=2,
)
cv_hist = cv.fit(
    df,
    n_windows=4,
    h=48,
    params=lgb_params,
    eval_every=5,
    early_stopping_evals=5,    
    compute_cv_preds=True,
)

[5] mape: 0.158639
[10] mape: 0.163739
[15] mape: 0.161535
[20] mape: 0.169491
[25] mape: 0.163690
[30] mape: 0.164198
Early stopping at round 30
Using best iteration: 5

cv.cv_preds_

fig = plot_series(forecasts_df=cv.cv_preds_.drop(columns='window'))

final_fcst = MLForecast.from_cv(cv)
final_fcst.fit(df)
preds = final_fcst.predict(48)
fig = plot_series(df, preds, max_insample_length=24 * 14)