from mmm_functions import *

# データセット読み込み
dataset = 'MMM_ts_manga5.csv'
df = pd.read_csv(
    dataset,
    parse_dates=['week'],
    index_col='week',
)

# アドストックを考慮する特徴量のリストを作成
apply_effects_features = ['traditional', 'internet', 'promotional']

# 三角関数特徴量を追加したデータフレームを表示して確認
df = add_fourier_terms(df, num=5, seasonal=52.25)

# 対象データ期間、ここでは直近5年間（52週間×5）を指定
data_term = int(52.25 * 5)

# 説明変数Xと目的変数yに分解
X = df.drop(columns=['sales'])[-data_term:]
y = df['sales'][-data_term:]

# テストデータ期間、ここでは直近1年間（52週間）を指定
term = 52

# 学習データとテストデータに分割
X_train, X_test = X[:-term], X[-term:]
y_train, y_test = y[:-term], y[-term:]

study = run_optimization(
    regarima_objective, 
    X_train, y_train, 
    apply_effects_features, 
    n_trials=1000
)

Best trial: 465. Best value: 1.39196e+08: 100%|██████████| 1000/1000 [2:22:07<00:00,  8.53s/it]Best trial:
Value: 139195577.01378402
Params: 
carryover_length_traditional: 2
carryover_peak_traditional: 1
carryover_rate1_traditional: 0.6486743800140834
carryover_rate2_traditional: 0.5714128641590107
carryover_c1_traditional: 1.1023551799789082
carryover_c2_traditional: 1.2471345305654304
saturation_function_traditional: exponential
curve_param_d_traditional: 4.754232174124576
carryover_length_internet: 1
carryover_peak_internet: 1
carryover_rate1_internet: 0.7627560266710607
carryover_rate2_internet: 0.38881893338218193
carryover_c1_internet: 1.560448887792296
carryover_c2_internet: 1.1816466447208518
saturation_function_internet: exponential
curve_param_d_internet: 5.865583617579571
carryover_length_promotional: 1
carryover_peak_promotional: 1
carryover_rate1_promotional: 0.6736610202821939
carryover_rate2_promotional: 0.5597957240432434
carryover_c1_promotional: 0.8672204210246754
carryover_c2_promotional: 1.9616376745909556
saturation_function_promotional: exponential
curve_param_d_promotional: 5.075326716961253
alpha: 0.039424834140418086

# optunaのstudyの保存

joblib.dump(study, 'train_ridgeMMM_study.joblib')

['train_ridgeMMM_study.joblib']

trained_model,model_params,pred = create_model_from_trial_regarima(
    study.best_trial, 
    X_train, y_train, 
    apply_effects_features
)

RMSE: 58429219.42892168
MAE: 43509340.96651112
MAPE: 0.038400402224866625
R2: 0.9939853574754446

pred = test_evaluate_regarima(trained_model, X_test, y_test, term)

RMSE: 63439231.308501214
MAE: 45591859.71753966
MAPE: 0.03413458585900201
R2: 0.9920426478968533

MMMが将来予測で利用できるかの検討¶

準備¶

モジュールの読み込み¶

データセットの読み込み¶

アドストックを考慮する特徴量のリスト¶

季節性を表現する三角関数特徴量を追加¶

説明変数と目的変数の設定¶

学習データとテストデータに分割¶

学習データでMMM構築¶

ハイパーパラメータ自動調整¶

MMM構築¶

テストデータで検証¶