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* version update post release v1.2.2 * automl option * import pandas * remove automl.utils * default * test * type hint and version update * dependency update * link to open in colab * use packging.version to close #725 --------- Co-authored-by: Li Jiang <lijiang1@microsoft.com> Co-authored-by: Li Jiang <bnujli@gmail.com>
1556 lines
90 KiB
Markdown
1556 lines
90 KiB
Markdown
# AutoML - Time Series Forecast
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### Prerequisites
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Install the [automl,ts_forecast] option.
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```bash
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pip install "flaml[automl,ts_forecast]"
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```
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### Simple NumPy Example
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```python
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import numpy as np
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from flaml import AutoML
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X_train = np.arange('2014-01', '2022-01', dtype='datetime64[M]')
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y_train = np.random.random(size=84)
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automl = AutoML()
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automl.fit(X_train=X_train[:84], # a single column of timestamp
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y_train=y_train, # value for each timestamp
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period=12, # time horizon to forecast, e.g., 12 months
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task='ts_forecast', time_budget=15, # time budget in seconds
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log_file_name="ts_forecast.log",
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eval_method="holdout",
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)
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print(automl.predict(X_train[84:]))
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```
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#### Sample output
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```
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[flaml.automl: 01-21 08:01:20] {2018} INFO - task = ts_forecast
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[flaml.automl: 01-21 08:01:20] {2020} INFO - Data split method: time
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[flaml.automl: 01-21 08:01:20] {2024} INFO - Evaluation method: holdout
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[flaml.automl: 01-21 08:01:20] {2124} INFO - Minimizing error metric: mape
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[flaml.automl: 01-21 08:01:21] {2181} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax']
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[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 0, current learner lgbm
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[flaml.automl: 01-21 08:01:21] {2547} INFO - Estimated sufficient time budget=1429s. Estimated necessary time budget=1s.
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[flaml.automl: 01-21 08:01:21] {2594} INFO - at 0.9s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811
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[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 1, current learner lgbm
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[flaml.automl: 01-21 08:01:21] {2594} INFO - at 0.9s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811
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[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 2, current learner lgbm
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[flaml.automl: 01-21 08:01:21] {2594} INFO - at 0.9s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811
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[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 3, current learner lgbm
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[flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811
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[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 4, current learner lgbm
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[flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811
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[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 5, current learner lgbm
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[flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9811, best estimator lgbm's best error=0.9811
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[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 6, current learner lgbm
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[flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9652, best estimator lgbm's best error=0.9652
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[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 7, current learner lgbm
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[flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 8, current learner lgbm
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[flaml.automl: 01-21 08:01:21] {2594} INFO - at 1.0s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:21] {2434} INFO - iteration 9, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.1s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 10, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.1s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 11, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.1s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 12, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.1s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 13, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.1s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 14, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.1s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 15, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.2s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 16, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.2s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 17, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.2s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 18, current learner rf
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.2s, estimator rf's best error=1.0994, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 19, current learner rf
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.2s, estimator rf's best error=1.0848, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 20, current learner xgboost
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.3s, estimator xgboost's best error=1.0271, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 21, current learner rf
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.3s, estimator rf's best error=1.0848, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 22, current learner xgboost
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.3s, estimator xgboost's best error=1.0015, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 23, current learner xgboost
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.3s, estimator xgboost's best error=1.0015, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 24, current learner xgboost
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.3s, estimator xgboost's best error=1.0015, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 25, current learner extra_tree
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.3s, estimator extra_tree's best error=1.0130, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 26, current learner extra_tree
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.4s, estimator extra_tree's best error=1.0130, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 27, current learner extra_tree
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.4s, estimator extra_tree's best error=1.0130, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 28, current learner extra_tree
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.4s, estimator extra_tree's best error=1.0130, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 29, current learner extra_tree
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.4s, estimator extra_tree's best error=0.9499, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 30, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.5s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 31, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.5s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 32, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.5s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 33, current learner extra_tree
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.5s, estimator extra_tree's best error=0.9499, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 34, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.5s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 35, current learner xgboost
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.5s, estimator xgboost's best error=1.0015, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 36, current learner extra_tree
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.6s, estimator extra_tree's best error=0.9499, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.6s, estimator extra_tree's best error=0.9499, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.6s, estimator extra_tree's best error=0.9499, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 39, current learner xgboost
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.6s, estimator xgboost's best error=1.0015, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 40, current learner extra_tree
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.6s, estimator extra_tree's best error=0.9499, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.7s, estimator extra_tree's best error=0.9499, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 42, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.7s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 43, current learner extra_tree
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.7s, estimator extra_tree's best error=0.9499, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 44, current learner xgb_limitdepth
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.7s, estimator xgb_limitdepth's best error=1.5815, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 45, current learner xgb_limitdepth
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.8s, estimator xgb_limitdepth's best error=0.9683, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 46, current learner xgb_limitdepth
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.8s, estimator xgb_limitdepth's best error=0.9683, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 47, current learner xgb_limitdepth
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.8s, estimator xgb_limitdepth's best error=0.9683, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 48, current learner xgb_limitdepth
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.9s, estimator xgb_limitdepth's best error=0.9683, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 49, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.9s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 50, current learner extra_tree
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.9s, estimator extra_tree's best error=0.9499, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 51, current learner xgb_limitdepth
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 1.9s, estimator xgb_limitdepth's best error=0.9683, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 52, current learner xgboost
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 2.0s, estimator xgboost's best error=1.0015, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 53, current learner xgboost
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 2.0s, estimator xgboost's best error=1.0015, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 54, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 2.0s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 55, current learner lgbm
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 2.0s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 56, current learner xgb_limitdepth
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 2.0s, estimator xgb_limitdepth's best error=0.9683, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 57, current learner rf
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[flaml.automl: 01-21 08:01:22] {2594} INFO - at 2.0s, estimator rf's best error=1.0848, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:22] {2434} INFO - iteration 58, current learner xgboost
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[flaml.automl: 01-21 08:01:23] {2594} INFO - at 2.1s, estimator xgboost's best error=1.0015, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:23] {2434} INFO - iteration 59, current learner extra_tree
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[flaml.automl: 01-21 08:01:23] {2594} INFO - at 2.1s, estimator extra_tree's best error=0.9499, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:23] {2434} INFO - iteration 60, current learner lgbm
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[flaml.automl: 01-21 08:01:23] {2594} INFO - at 2.1s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:23] {2434} INFO - iteration 61, current learner extra_tree
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[flaml.automl: 01-21 08:01:23] {2594} INFO - at 2.1s, estimator extra_tree's best error=0.9499, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:23] {2434} INFO - iteration 62, current learner lgbm
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[flaml.automl: 01-21 08:01:23] {2594} INFO - at 2.1s, estimator lgbm's best error=0.9466, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:23] {2434} INFO - iteration 63, current learner xgb_limitdepth
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[flaml.automl: 01-21 08:01:23] {2594} INFO - at 2.2s, estimator xgb_limitdepth's best error=0.9683, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:23] {2434} INFO - iteration 64, current learner prophet
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[flaml.automl: 01-21 08:01:25] {2594} INFO - at 4.2s, estimator prophet's best error=1.5706, best estimator lgbm's best error=0.9466
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[flaml.automl: 01-21 08:01:25] {2434} INFO - iteration 65, current learner arima
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[flaml.automl: 01-21 08:01:25] {2594} INFO - at 4.2s, estimator arima's best error=0.5693, best estimator arima's best error=0.5693
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[flaml.automl: 01-21 08:01:25] {2434} INFO - iteration 66, current learner arima
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[flaml.automl: 01-21 08:01:25] {2594} INFO - at 4.4s, estimator arima's best error=0.5693, best estimator arima's best error=0.5693
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[flaml.automl: 01-21 08:01:25] {2434} INFO - iteration 67, current learner sarimax
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[flaml.automl: 01-21 08:01:25] {2594} INFO - at 4.4s, estimator sarimax's best error=0.5693, best estimator arima's best error=0.5693
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[flaml.automl: 01-21 08:01:25] {2434} INFO - iteration 68, current learner xgb_limitdepth
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[flaml.automl: 01-21 08:01:25] {2594} INFO - at 4.5s, estimator xgb_limitdepth's best error=0.9683, best estimator arima's best error=0.5693
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[flaml.automl: 01-21 08:01:25] {2434} INFO - iteration 69, current learner sarimax
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[flaml.automl: 01-21 08:01:25] {2594} INFO - at 4.6s, estimator sarimax's best error=0.5693, best estimator arima's best error=0.5693
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[flaml.automl: 01-21 08:01:25] {2434} INFO - iteration 70, current learner sarimax
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[flaml.automl: 01-21 08:01:25] {2594} INFO - at 4.6s, estimator sarimax's best error=0.5693, best estimator arima's best error=0.5693
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[flaml.automl: 01-21 08:01:25] {2434} INFO - iteration 71, current learner arima
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[flaml.automl: 01-21 08:01:25] {2594} INFO - at 4.6s, estimator arima's best error=0.5693, best estimator arima's best error=0.5693
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```
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### Univariate time series
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|
|
|
```python
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import statsmodels.api as sm
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data = sm.datasets.co2.load_pandas().data
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# data is given in weeks, but the task is to predict monthly, so use monthly averages instead
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data = data['co2'].resample('MS').mean()
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data = data.bfill().ffill() # makes sure there are no missing values
|
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data = data.to_frame().reset_index()
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num_samples = data.shape[0]
|
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time_horizon = 12
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split_idx = num_samples - time_horizon
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train_df = data[:split_idx] # train_df is a dataframe with two columns: timestamp and label
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X_test = data[split_idx:]['index'].to_frame() # X_test is a dataframe with dates for prediction
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y_test = data[split_idx:]['co2'] # y_test is a series of the values corresponding to the dates for prediction
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from flaml import AutoML
|
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automl = AutoML()
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settings = {
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"time_budget": 10, # total running time in seconds
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"metric": 'mape', # primary metric for validation: 'mape' is generally used for forecast tasks
|
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"task": 'ts_forecast', # task type
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"log_file_name": 'CO2_forecast.log', # flaml log file
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"eval_method": "holdout", # validation method can be chosen from ['auto', 'holdout', 'cv']
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"seed": 7654321, # random seed
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}
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automl.fit(dataframe=train_df, # training data
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label='co2', # label column
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period=time_horizon, # key word argument 'period' must be included for forecast task)
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**settings)
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```
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#### Sample output
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|
```
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[flaml.automl: 01-21 07:54:04] {2018} INFO - task = ts_forecast
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[flaml.automl: 01-21 07:54:04] {2020} INFO - Data split method: time
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[flaml.automl: 01-21 07:54:04] {2024} INFO - Evaluation method: holdout
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[flaml.automl: 01-21 07:54:04] {2124} INFO - Minimizing error metric: mape
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Importing plotly failed. Interactive plots will not work.
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[flaml.automl: 01-21 07:54:04] {2181} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax']
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[flaml.automl: 01-21 07:54:04] {2434} INFO - iteration 0, current learner lgbm
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[flaml.automl: 01-21 07:54:05] {2547} INFO - Estimated sufficient time budget=2145s. Estimated necessary time budget=2s.
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[flaml.automl: 01-21 07:54:05] {2594} INFO - at 0.9s, estimator lgbm's best error=0.0621, best estimator lgbm's best error=0.0621
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[flaml.automl: 01-21 07:54:06] {2594} INFO - at 2.1s, estimator rf's best error=0.0228, best estimator lgbm's best error=0.0017
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[flaml.automl: 01-21 07:54:06] {2594} INFO - at 2.4s, estimator extra_tree's best error=0.0245, best estimator lgbm's best error=0.0017
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[flaml.automl: 01-21 07:54:07] {2594} INFO - at 3.3s, estimator xgb_limitdepth's best error=0.0018, best estimator lgbm's best error=0.0017
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[flaml.automl: 01-21 07:54:07] {2434} INFO - iteration 43, current learner prophet
|
|
[flaml.automl: 01-21 07:54:09] {2594} INFO - at 5.5s, estimator prophet's best error=0.0008, best estimator prophet's best error=0.0008
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|
[flaml.automl: 01-21 07:54:09] {2434} INFO - iteration 44, current learner arima
|
|
[flaml.automl: 01-21 07:54:10] {2594} INFO - at 6.1s, estimator arima's best error=0.0047, best estimator prophet's best error=0.0008
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[flaml.automl: 01-21 07:54:10] {2434} INFO - iteration 45, current learner sarimax
|
|
[flaml.automl: 01-21 07:54:10] {2594} INFO - at 6.4s, estimator sarimax's best error=0.0047, best estimator prophet's best error=0.0008
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|
[flaml.automl: 01-21 07:54:10] {2434} INFO - iteration 46, current learner lgbm
|
|
[flaml.automl: 01-21 07:54:10] {2594} INFO - at 6.5s, estimator lgbm's best error=0.0017, best estimator prophet's best error=0.0008
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[flaml.automl: 01-21 07:54:10] {2434} INFO - iteration 47, current learner sarimax
|
|
[flaml.automl: 01-21 07:54:10] {2594} INFO - at 6.6s, estimator sarimax's best error=0.0047, best estimator prophet's best error=0.0008
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[flaml.automl: 01-21 07:54:10] {2434} INFO - iteration 48, current learner sarimax
|
|
[flaml.automl: 01-21 07:54:11] {2594} INFO - at 6.9s, estimator sarimax's best error=0.0047, best estimator prophet's best error=0.0008
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[flaml.automl: 01-21 07:54:11] {2434} INFO - iteration 49, current learner arima
|
|
[flaml.automl: 01-21 07:54:11] {2594} INFO - at 6.9s, estimator arima's best error=0.0047, best estimator prophet's best error=0.0008
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[flaml.automl: 01-21 07:54:11] {2434} INFO - iteration 50, current learner xgb_limitdepth
|
|
[flaml.automl: 01-21 07:54:11] {2594} INFO - at 7.0s, estimator xgb_limitdepth's best error=0.0018, best estimator prophet's best error=0.0008
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[flaml.automl: 01-21 07:54:11] {2434} INFO - iteration 51, current learner sarimax
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[flaml.automl: 01-21 07:54:11] {2594} INFO - at 7.5s, estimator sarimax's best error=0.0047, best estimator prophet's best error=0.0008
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[flaml.automl: 01-21 07:54:11] {2434} INFO - iteration 52, current learner xgboost
|
|
[flaml.automl: 01-21 07:54:11] {2594} INFO - at 7.6s, estimator xgboost's best error=0.0242, best estimator prophet's best error=0.0008
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[flaml.automl: 01-21 07:54:11] {2434} INFO - iteration 53, current learner prophet
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|
[flaml.automl: 01-21 07:54:13] {2594} INFO - at 9.3s, estimator prophet's best error=0.0005, best estimator prophet's best error=0.0005
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[flaml.automl: 01-21 07:54:13] {2434} INFO - iteration 54, current learner sarimax
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[flaml.automl: 01-21 07:54:13] {2594} INFO - at 9.4s, estimator sarimax's best error=0.0047, best estimator prophet's best error=0.0005
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[flaml.automl: 01-21 07:54:13] {2434} INFO - iteration 55, current learner xgb_limitdepth
|
|
[flaml.automl: 01-21 07:54:13] {2594} INFO - at 9.8s, estimator xgb_limitdepth's best error=0.0018, best estimator prophet's best error=0.0005
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[flaml.automl: 01-21 07:54:13] {2434} INFO - iteration 56, current learner xgboost
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[flaml.automl: 01-21 07:54:13] {2594} INFO - at 9.8s, estimator xgboost's best error=0.0242, best estimator prophet's best error=0.0005
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[flaml.automl: 01-21 07:54:13] {2434} INFO - iteration 57, current learner lgbm
|
|
[flaml.automl: 01-21 07:54:14] {2594} INFO - at 9.9s, estimator lgbm's best error=0.0017, best estimator prophet's best error=0.0005
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[flaml.automl: 01-21 07:54:14] {2434} INFO - iteration 58, current learner rf
|
|
[flaml.automl: 01-21 07:54:14] {2594} INFO - at 10.0s, estimator rf's best error=0.0146, best estimator prophet's best error=0.0005
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[flaml.automl: 01-21 07:54:14] {2824} INFO - retrain prophet for 0.6s
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[flaml.automl: 01-21 07:54:14] {2831} INFO - retrained model: <prophet.forecaster.Prophet object at 0x7fb68ea65d60>
|
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[flaml.automl: 01-21 07:54:14] {2210} INFO - fit succeeded
|
|
[flaml.automl: 01-21 07:54:14] {2211} INFO - Time taken to find the best model: 9.339771270751953
|
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[flaml.automl: 01-21 07:54:14] {2222} WARNING - Time taken to find the best model is 93% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.
|
|
```
|
|
|
|
#### Compute and plot predictions
|
|
|
|
The example plotting code requires matplotlib.
|
|
|
|
```python
|
|
flaml_y_pred = automl.predict(X_test)
|
|
import matplotlib.pyplot as plt
|
|
|
|
plt.plot(X_test, y_test, label='Actual level')
|
|
plt.plot(X_test, flaml_y_pred, label='FLAML forecast')
|
|
plt.xlabel('Date')
|
|
plt.ylabel('CO2 Levels')
|
|
plt.legend()
|
|
```
|
|
|
|
|
|
|
|
### Multivariate Time Series (Forecasting with Exogenous Variables)
|
|
```python
|
|
import pandas as pd
|
|
|
|
# pd.set_option("display.max_rows", None, "display.max_columns", None)
|
|
multi_df = pd.read_csv(
|
|
"https://raw.githubusercontent.com/srivatsan88/YouTubeLI/master/dataset/nyc_energy_consumption.csv"
|
|
)
|
|
|
|
# preprocessing data
|
|
multi_df["timeStamp"] = pd.to_datetime(multi_df["timeStamp"])
|
|
multi_df = multi_df.set_index("timeStamp")
|
|
multi_df = multi_df.resample("D").mean()
|
|
multi_df["temp"] = multi_df["temp"].fillna(method="ffill")
|
|
multi_df["precip"] = multi_df["precip"].fillna(method="ffill")
|
|
multi_df = multi_df[:-2] # last two rows are NaN for 'demand' column so remove them
|
|
multi_df = multi_df.reset_index()
|
|
|
|
# Using temperature values create categorical values
|
|
# where 1 denotes daily tempurature is above monthly average and 0 is below.
|
|
def get_monthly_avg(data):
|
|
data["month"] = data["timeStamp"].dt.month
|
|
data = data[["month", "temp"]].groupby("month")
|
|
data = data.agg({"temp": "mean"})
|
|
return data
|
|
|
|
monthly_avg = get_monthly_avg(multi_df).to_dict().get("temp")
|
|
|
|
def above_monthly_avg(date, temp):
|
|
month = date.month
|
|
if temp > monthly_avg.get(month):
|
|
return 1
|
|
else:
|
|
return 0
|
|
|
|
multi_df["temp_above_monthly_avg"] = multi_df.apply(
|
|
lambda x: above_monthly_avg(x["timeStamp"], x["temp"]), axis=1
|
|
)
|
|
|
|
del multi_df["month"] # remove temperature column to reduce redundancy
|
|
|
|
# split data into train and test
|
|
num_samples = multi_df.shape[0]
|
|
multi_time_horizon = 180
|
|
split_idx = num_samples - multi_time_horizon
|
|
multi_train_df = multi_df[:split_idx]
|
|
multi_test_df = multi_df[split_idx:]
|
|
|
|
multi_X_test = multi_test_df[
|
|
["timeStamp", "precip", "temp", "temp_above_monthly_avg"]
|
|
] # test dataframe must contain values for the regressors / multivariate variables
|
|
multi_y_test = multi_test_df["demand"]
|
|
|
|
# initialize AutoML instance
|
|
automl = AutoML()
|
|
|
|
# configure AutoML settings
|
|
settings = {
|
|
"time_budget": 10, # total running time in seconds
|
|
"metric": "mape", # primary metric
|
|
"task": "ts_forecast", # task type
|
|
"log_file_name": "energy_forecast_categorical.log", # flaml log file
|
|
"eval_method": "holdout",
|
|
"log_type": "all",
|
|
"label": "demand",
|
|
}
|
|
|
|
# train the model
|
|
automl.fit(dataframe=df, **settings, period=time_horizon)
|
|
|
|
# predictions
|
|
print(automl.predict(multi_X_test))
|
|
```
|
|
|
|
#### Sample Output
|
|
|
|
```
|
|
[flaml.automl: 08-13 01:03:11] {2540} INFO - task = ts_forecast
|
|
[flaml.automl: 08-13 01:03:11] {2542} INFO - Data split method: time
|
|
[flaml.automl: 08-13 01:03:11] {2545} INFO - Evaluation method: holdout
|
|
[flaml.automl: 08-13 01:03:11] {2664} INFO - Minimizing error metric: mape
|
|
[flaml.automl: 08-13 01:03:12] {2806} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax']
|
|
[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 0, current learner lgbm
|
|
[flaml.automl: 08-13 01:03:12] {3241} INFO - Estimated sufficient time budget=7681s. Estimated necessary time budget=8s.
|
|
[flaml.automl: 08-13 01:03:12] {3288} INFO - at 0.8s, estimator lgbm's best error=0.0854, best estimator lgbm's best error=0.0854
|
|
[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 1, current learner lgbm
|
|
[flaml.automl: 08-13 01:03:12] {3288} INFO - at 0.9s, estimator lgbm's best error=0.0854, best estimator lgbm's best error=0.0854
|
|
[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 2, current learner lgbm
|
|
[flaml.automl: 08-13 01:03:12] {3288} INFO - at 0.9s, estimator lgbm's best error=0.0525, best estimator lgbm's best error=0.0525
|
|
[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 3, current learner lgbm
|
|
[flaml.automl: 08-13 01:03:12] {3288} INFO - at 0.9s, estimator lgbm's best error=0.0525, best estimator lgbm's best error=0.0525
|
|
[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 4, current learner lgbm
|
|
[flaml.automl: 08-13 01:03:12] {3288} INFO - at 1.0s, estimator lgbm's best error=0.0406, best estimator lgbm's best error=0.0406
|
|
[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 5, current learner lgbm
|
|
[flaml.automl: 08-13 01:03:12] {3288} INFO - at 1.0s, estimator lgbm's best error=0.0406, best estimator lgbm's best error=0.0406
|
|
[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 6, current learner lgbm
|
|
[flaml.automl: 08-13 01:03:12] {3288} INFO - at 1.0s, estimator lgbm's best error=0.0406, best estimator lgbm's best error=0.0406
|
|
[flaml.automl: 08-13 01:03:12] {3108} INFO - iteration 7, current learner lgbm
|
|
[flaml.automl: 08-13 01:03:13] {3288} INFO - at 1.1s, estimator lgbm's best error=0.0393, best estimator lgbm's best error=0.0393
|
|
[flaml.automl: 08-13 01:03:13] {3108} INFO - iteration 8, current learner lgbm
|
|
[flaml.automl: 08-13 01:03:13] {3288} INFO - at 1.1s, estimator lgbm's best error=0.0393, best estimator lgbm's best error=0.0393
|
|
[flaml.automl: 08-13 01:03:13] {3108} INFO - iteration 9, current learner lgbm
|
|
...
|
|
silent=True, subsample=1.0, subsample_for_bin=200000,
|
|
subsample_freq=0, verbose=-1)
|
|
[flaml.automl: 08-13 01:03:22] {2837} INFO - fit succeeded
|
|
[flaml.automl: 08-13 01:03:22] {2838} INFO - Time taken to find the best model: 3.4941744804382324
|
|
```
|
|
|
|
### Forecasting Discrete Variables
|
|
```python
|
|
from hcrystalball.utils import get_sales_data
|
|
import numpy as np
|
|
from flaml import AutoML
|
|
|
|
time_horizon = 30
|
|
df = get_sales_data(n_dates=180, n_assortments=1, n_states=1, n_stores=1)
|
|
df = df[["Sales", "Open", "Promo", "Promo2"]]
|
|
|
|
# feature engineering - create a discrete value column
|
|
# 1 denotes above mean and 0 denotes below mean
|
|
df["above_mean_sales"] = np.where(df["Sales"] > df["Sales"].mean(), 1, 0)
|
|
df.reset_index(inplace=True)
|
|
|
|
# train-test split
|
|
discrete_train_df = df[:-time_horizon]
|
|
discrete_test_df = df[-time_horizon:]
|
|
discrete_X_train, discrete_X_test = (
|
|
discrete_train_df[["Date", "Open", "Promo", "Promo2"]],
|
|
discrete_test_df[["Date", "Open", "Promo", "Promo2"]],
|
|
)
|
|
discrete_y_train, discrete_y_test = discrete_train_df["above_mean_sales"], discrete_test_df["above_mean_sales"]
|
|
|
|
# initialize AutoML instance
|
|
automl = AutoML()
|
|
|
|
# configure the settings
|
|
settings = {
|
|
"time_budget": 15, # total running time in seconds
|
|
"metric": "accuracy", # primary metric
|
|
"task": "ts_forecast_classification", # task type
|
|
"log_file_name": "sales_classification_forecast.log", # flaml log file
|
|
"eval_method": "holdout",
|
|
}
|
|
|
|
# train the model
|
|
automl.fit(X_train=discrete_X_train,
|
|
y_train=discrete_y_train,
|
|
**settings,
|
|
period=time_horizon)
|
|
|
|
# make predictions
|
|
discrete_y_pred = automl.predict(discrete_X_test)
|
|
print("Predicted label", discrete_y_pred)
|
|
print("True label", discrete_y_test)
|
|
```
|
|
|
|
#### Sample Output
|
|
|
|
```
|
|
[flaml.automl: 02-28 21:53:03] {2060} INFO - task = ts_forecast_classification
|
|
[flaml.automl: 02-28 21:53:03] {2062} INFO - Data split method: time
|
|
[flaml.automl: 02-28 21:53:03] {2066} INFO - Evaluation method: holdout
|
|
[flaml.automl: 02-28 21:53:03] {2147} INFO - Minimizing error metric: 1-accuracy
|
|
[flaml.automl: 02-28 21:53:03] {2205} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth']
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 0, current learner lgbm
|
|
[flaml.automl: 02-28 21:53:03] {2573} INFO - Estimated sufficient time budget=269s. Estimated necessary time budget=0s.
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.1s, estimator lgbm's best error=0.2667, best estimator lgbm's best error=0.2667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 1, current learner lgbm
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.1s, estimator lgbm's best error=0.2667, best estimator lgbm's best error=0.2667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 2, current learner lgbm
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.1s, estimator lgbm's best error=0.1333, best estimator lgbm's best error=0.1333
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 3, current learner rf
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.2s, estimator rf's best error=0.1333, best estimator lgbm's best error=0.1333
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 4, current learner xgboost
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.2s, estimator xgboost's best error=0.1333, best estimator lgbm's best error=0.1333
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 5, current learner lgbm
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.2s, estimator lgbm's best error=0.1333, best estimator lgbm's best error=0.1333
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 6, current learner rf
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.3s, estimator rf's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 7, current learner lgbm
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.3s, estimator lgbm's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 8, current learner lgbm
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.3s, estimator lgbm's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 9, current learner lgbm
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.4s, estimator lgbm's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 10, current learner rf
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.4s, estimator rf's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 11, current learner rf
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.4s, estimator rf's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 12, current learner xgboost
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.5s, estimator xgboost's best error=0.1333, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 13, current learner extra_tree
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.5s, estimator extra_tree's best error=0.1333, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 14, current learner xgb_limitdepth
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.5s, estimator xgb_limitdepth's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 15, current learner xgboost
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.6s, estimator xgboost's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 16, current learner xgb_limitdepth
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.6s, estimator xgb_limitdepth's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 17, current learner rf
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.6s, estimator rf's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 18, current learner xgb_limitdepth
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.7s, estimator xgb_limitdepth's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 19, current learner lgbm
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.7s, estimator lgbm's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 20, current learner extra_tree
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.7s, estimator extra_tree's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 21, current learner xgboost
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.7s, estimator xgboost's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 22, current learner extra_tree
|
|
[flaml.automl: 02-28 21:53:03] {2620} INFO - at 0.8s, estimator extra_tree's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:03] {2458} INFO - iteration 23, current learner rf
|
|
[flaml.automl: 02-28 21:53:04] {2620} INFO - at 0.8s, estimator rf's best error=0.0667, best estimator rf's best error=0.0667
|
|
[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 24, current learner xgboost
|
|
[flaml.automl: 02-28 21:53:04] {2620} INFO - at 0.9s, estimator xgboost's best error=0.0333, best estimator xgboost's best error=0.0333
|
|
[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 25, current learner xgb_limitdepth
|
|
[flaml.automl: 02-28 21:53:04] {2620} INFO - at 0.9s, estimator xgb_limitdepth's best error=0.0667, best estimator xgboost's best error=0.0333
|
|
[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 26, current learner xgb_limitdepth
|
|
[flaml.automl: 02-28 21:53:04] {2620} INFO - at 0.9s, estimator xgb_limitdepth's best error=0.0667, best estimator xgboost's best error=0.0333
|
|
[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 27, current learner xgboost
|
|
[flaml.automl: 02-28 21:53:04] {2620} INFO - at 0.9s, estimator xgboost's best error=0.0333, best estimator xgboost's best error=0.0333
|
|
[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 28, current learner extra_tree
|
|
[flaml.automl: 02-28 21:53:04] {2620} INFO - at 1.0s, estimator extra_tree's best error=0.0667, best estimator xgboost's best error=0.0333
|
|
[flaml.automl: 02-28 21:53:04] {2458} INFO - iteration 29, current learner xgb_limitdepth
|
|
[flaml.automl: 02-28 21:53:04] {2620} INFO - at 1.0s, estimator xgb_limitdepth's best error=0.0667, best estimator xgboost's best error=0.0333
|
|
[flaml.automl: 02-28 21:53:04] {2850} INFO - retrain xgboost for 0.0s
|
|
[flaml.automl: 02-28 21:53:04] {2857} INFO - retrained model: XGBClassifier(base_score=0.5, booster='gbtree',
|
|
colsample_bylevel=0.9826753651836615, colsample_bynode=1,
|
|
colsample_bytree=0.9725493834064914, gamma=0, gpu_id=-1,
|
|
grow_policy='lossguide', importance_type='gain',
|
|
interaction_constraints='', learning_rate=0.1665803484560213,
|
|
max_delta_step=0, max_depth=0, max_leaves=4,
|
|
min_child_weight=0.5649012460525115, missing=nan,
|
|
monotone_constraints='()', n_estimators=4, n_jobs=-1,
|
|
num_parallel_tree=1, objective='binary:logistic', random_state=0,
|
|
reg_alpha=0.009638363373006869, reg_lambda=0.143703802530408,
|
|
scale_pos_weight=1, subsample=0.9643606787051899,
|
|
tree_method='hist', use_label_encoder=False,
|
|
validate_parameters=1, verbosity=0)
|
|
[flaml.automl: 02-28 21:53:04] {2234} INFO - fit succeeded
|
|
[flaml.automl: 02-28 21:53:04] {2235} INFO - Time taken to find the best model: 0.8547139167785645
|
|
```
|
|
|
|
### Forecasting with Panel Datasets
|
|
|
|
Panel time series datasets involves multiple individual time series. For example, see Stallion demand dataset from PyTorch Forecasting, orginally from Kaggle.
|
|
|
|
```python
|
|
def get_stalliion_data():
|
|
from pytorch_forecasting.data.examples import get_stallion_data
|
|
|
|
data = get_stallion_data()
|
|
# add time index - For datasets with no missing values, FLAML will automate this process
|
|
data["time_idx"] = data["date"].dt.year * 12 + data["date"].dt.month
|
|
data["time_idx"] -= data["time_idx"].min()
|
|
# add additional features
|
|
data["month"] = data.date.dt.month.astype(str).astype(
|
|
"category"
|
|
) # categories have be strings
|
|
data["log_volume"] = np.log(data.volume + 1e-8)
|
|
data["avg_volume_by_sku"] = data.groupby(
|
|
["time_idx", "sku"], observed=True
|
|
).volume.transform("mean")
|
|
data["avg_volume_by_agency"] = data.groupby(
|
|
["time_idx", "agency"], observed=True
|
|
).volume.transform("mean")
|
|
# we want to encode special days as one variable and thus need to first reverse one-hot encoding
|
|
special_days = [
|
|
"easter_day",
|
|
"good_friday",
|
|
"new_year",
|
|
"christmas",
|
|
"labor_day",
|
|
"independence_day",
|
|
"revolution_day_memorial",
|
|
"regional_games",
|
|
"beer_capital",
|
|
"music_fest",
|
|
]
|
|
data[special_days] = (
|
|
data[special_days]
|
|
.apply(lambda x: x.map({0: "-", 1: x.name}))
|
|
.astype("category")
|
|
)
|
|
return data, special_days
|
|
|
|
data, special_days = get_stalliion_data()
|
|
time_horizon = 6 # predict six months
|
|
training_cutoff = data["time_idx"].max() - time_horizon
|
|
data["time_idx"] = data["time_idx"].astype("int")
|
|
ts_col = data.pop("date")
|
|
data.insert(0, "date", ts_col)
|
|
# FLAML assumes input is not sorted, but we sort here for comparison purposes with y_test
|
|
data = data.sort_values(["agency", "sku", "date"])
|
|
X_train = data[lambda x: x.time_idx <= training_cutoff]
|
|
X_test = data[lambda x: x.time_idx > training_cutoff]
|
|
y_train = X_train.pop("volume")
|
|
y_test = X_test.pop("volume")
|
|
automl = AutoML()
|
|
# Configure settings for FLAML model
|
|
settings = {
|
|
"time_budget": budget, # total running time in seconds
|
|
"metric": "mape", # primary metric
|
|
"task": "ts_forecast_panel", # task type
|
|
"log_file_name": "test/stallion_forecast.log", # flaml log file
|
|
"eval_method": "holdout",
|
|
}
|
|
# Specify kwargs for TimeSeriesDataSet used by TemporalFusionTransformerEstimator
|
|
fit_kwargs_by_estimator = {
|
|
"tft": {
|
|
"max_encoder_length": 24,
|
|
"static_categoricals": ["agency", "sku"],
|
|
"static_reals": ["avg_population_2017", "avg_yearly_household_income_2017"],
|
|
"time_varying_known_categoricals": ["special_days", "month"],
|
|
"variable_groups": {
|
|
"special_days": special_days
|
|
}, # group of categorical variables can be treated as one variable
|
|
"time_varying_known_reals": [
|
|
"time_idx",
|
|
"price_regular",
|
|
"discount_in_percent",
|
|
],
|
|
"time_varying_unknown_categoricals": [],
|
|
"time_varying_unknown_reals": [
|
|
"y", # always need a 'y' column for the target column
|
|
"log_volume",
|
|
"industry_volume",
|
|
"soda_volume",
|
|
"avg_max_temp",
|
|
"avg_volume_by_agency",
|
|
"avg_volume_by_sku",
|
|
],
|
|
"batch_size": 256,
|
|
"max_epochs": 1,
|
|
"gpu_per_trial": -1,
|
|
}
|
|
}
|
|
# Train the model
|
|
automl.fit(
|
|
X_train=X_train,
|
|
y_train=y_train,
|
|
**settings,
|
|
period=time_horizon,
|
|
group_ids=["agency", "sku"],
|
|
fit_kwargs_by_estimator=fit_kwargs_by_estimator,
|
|
)
|
|
# Compute predictions of testing dataset
|
|
y_pred = automl.predict(X_test)
|
|
print(y_test)
|
|
print(y_pred)
|
|
# best model
|
|
print(automl.model.estimator)
|
|
```
|
|
|
|
#### Sample Output
|
|
|
|
```
|
|
[flaml.automl: 07-28 21:26:03] {2478} INFO - task = ts_forecast_panel
|
|
[flaml.automl: 07-28 21:26:03] {2480} INFO - Data split method: time
|
|
[flaml.automl: 07-28 21:26:03] {2483} INFO - Evaluation method: holdout
|
|
[flaml.automl: 07-28 21:26:03] {2552} INFO - Minimizing error metric: mape
|
|
[flaml.automl: 07-28 21:26:03] {2694} INFO - List of ML learners in AutoML Run: ['tft']
|
|
[flaml.automl: 07-28 21:26:03] {2986} INFO - iteration 0, current learner tft
|
|
GPU available: False, used: False
|
|
TPU available: False, using: 0 TPU cores
|
|
IPU available: False, using: 0 IPUs
|
|
|
|
| Name | Type | Params
|
|
----------------------------------------------------------------------------------------
|
|
0 | loss | QuantileLoss | 0
|
|
1 | logging_metrics | ModuleList | 0
|
|
2 | input_embeddings | MultiEmbedding | 1.3 K
|
|
3 | prescalers | ModuleDict | 256
|
|
4 | static_variable_selection | VariableSelectionNetwork | 3.4 K
|
|
5 | encoder_variable_selection | VariableSelectionNetwork | 8.0 K
|
|
6 | decoder_variable_selection | VariableSelectionNetwork | 2.7 K
|
|
7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K
|
|
8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K
|
|
9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K
|
|
10 | static_context_enrichment | GatedResidualNetwork | 1.1 K
|
|
11 | lstm_encoder | LSTM | 4.4 K
|
|
12 | lstm_decoder | LSTM | 4.4 K
|
|
13 | post_lstm_gate_encoder | GatedLinearUnit | 544
|
|
14 | post_lstm_add_norm_encoder | AddNorm | 32
|
|
15 | static_enrichment | GatedResidualNetwork | 1.4 K
|
|
16 | multihead_attn | InterpretableMultiHeadAttention | 676
|
|
17 | post_attn_gate_norm | GateAddNorm | 576
|
|
18 | pos_wise_ff | GatedResidualNetwork | 1.1 K
|
|
19 | pre_output_gate_norm | GateAddNorm | 576
|
|
20 | output_layer | Linear | 119
|
|
----------------------------------------------------------------------------------------
|
|
33.6 K Trainable params
|
|
0 Non-trainable params
|
|
33.6 K Total params
|
|
0.135 Total estimated model params size (MB)
|
|
|
|
Epoch 19: 100%|██████████| 129/129 [00:56<00:00, 2.27it/s, loss=45.9, v_num=2, train_loss_step=43.00, val_loss=65.20, train_loss_epoch=46.50]
|
|
|
|
[flaml.automl: 07-28 21:46:46] {3114} INFO - Estimated sufficient time budget=12424212s. Estimated necessary time budget=12424s.
|
|
[flaml.automl: 07-28 21:46:46] {3161} INFO - at 1242.6s,\testimator tft's best error=1324290483134574.7500,\tbest estimator tft's best error=1324290483134574.7500
|
|
GPU available: False, used: False
|
|
TPU available: False, using: 0 TPU cores
|
|
IPU available: False, using: 0 IPUs
|
|
|
|
| Name | Type | Params
|
|
----------------------------------------------------------------------------------------
|
|
0 | loss | QuantileLoss | 0
|
|
1 | logging_metrics | ModuleList | 0
|
|
2 | input_embeddings | MultiEmbedding | 1.3 K
|
|
3 | prescalers | ModuleDict | 256
|
|
4 | static_variable_selection | VariableSelectionNetwork | 3.4 K
|
|
5 | encoder_variable_selection | VariableSelectionNetwork | 8.0 K
|
|
6 | decoder_variable_selection | VariableSelectionNetwork | 2.7 K
|
|
7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K
|
|
8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K
|
|
9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K
|
|
10 | static_context_enrichment | GatedResidualNetwork | 1.1 K
|
|
11 | lstm_encoder | LSTM | 4.4 K
|
|
12 | lstm_decoder | LSTM | 4.4 K
|
|
13 | post_lstm_gate_encoder | GatedLinearUnit | 544
|
|
14 | post_lstm_add_norm_encoder | AddNorm | 32
|
|
15 | static_enrichment | GatedResidualNetwork | 1.4 K
|
|
16 | multihead_attn | InterpretableMultiHeadAttention | 676
|
|
17 | post_attn_gate_norm | GateAddNorm | 576
|
|
18 | pos_wise_ff | GatedResidualNetwork | 1.1 K
|
|
19 | pre_output_gate_norm | GateAddNorm | 576
|
|
20 | output_layer | Linear | 119
|
|
----------------------------------------------------------------------------------------
|
|
33.6 K Trainable params
|
|
0 Non-trainable params
|
|
33.6 K Total params
|
|
0.135 Total estimated model params size (MB)
|
|
Epoch 19: 100%|██████████| 145/145 [01:03<00:00, 2.28it/s, loss=45.2, v_num=3, train_loss_step=46.30, val_loss=67.60, train_loss_epoch=48.10]
|
|
[flaml.automl: 07-28 22:08:05] {3425} INFO - retrain tft for 1279.6s
|
|
[flaml.automl: 07-28 22:08:05] {3432} INFO - retrained model: TemporalFusionTransformer(
|
|
(loss): QuantileLoss()
|
|
(logging_metrics): ModuleList(
|
|
(0): SMAPE()
|
|
(1): MAE()
|
|
(2): RMSE()
|
|
(3): MAPE()
|
|
)
|
|
(input_embeddings): MultiEmbedding(
|
|
(embeddings): ModuleDict(
|
|
(agency): Embedding(58, 16)
|
|
(sku): Embedding(25, 10)
|
|
(special_days): TimeDistributedEmbeddingBag(11, 6, mode=sum)
|
|
(month): Embedding(12, 6)
|
|
)
|
|
)
|
|
(prescalers): ModuleDict(
|
|
(avg_population_2017): Linear(in_features=1, out_features=8, bias=True)
|
|
(avg_yearly_household_income_2017): Linear(in_features=1, out_features=8, bias=True)
|
|
(encoder_length): Linear(in_features=1, out_features=8, bias=True)
|
|
(y_center): Linear(in_features=1, out_features=8, bias=True)
|
|
(y_scale): Linear(in_features=1, out_features=8, bias=True)
|
|
(time_idx): Linear(in_features=1, out_features=8, bias=True)
|
|
(price_regular): Linear(in_features=1, out_features=8, bias=True)
|
|
(discount_in_percent): Linear(in_features=1, out_features=8, bias=True)
|
|
(relative_time_idx): Linear(in_features=1, out_features=8, bias=True)
|
|
(y): Linear(in_features=1, out_features=8, bias=True)
|
|
(log_volume): Linear(in_features=1, out_features=8, bias=True)
|
|
(industry_volume): Linear(in_features=1, out_features=8, bias=True)
|
|
(soda_volume): Linear(in_features=1, out_features=8, bias=True)
|
|
(avg_max_temp): Linear(in_features=1, out_features=8, bias=True)
|
|
(avg_volume_by_agency): Linear(in_features=1, out_features=8, bias=True)
|
|
(avg_volume_by_sku): Linear(in_features=1, out_features=8, bias=True)
|
|
)
|
|
(static_variable_selection): VariableSelectionNetwork(
|
|
(flattened_grn): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((7,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=66, out_features=7, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=7, out_features=7, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=7, out_features=14, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((7,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(single_variable_grns): ModuleDict(
|
|
(agency): ResampleNorm(
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(sku): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(avg_population_2017): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(avg_yearly_household_income_2017): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(encoder_length): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(y_center): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(y_scale): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
(prescalers): ModuleDict(
|
|
(avg_population_2017): Linear(in_features=1, out_features=8, bias=True)
|
|
(avg_yearly_household_income_2017): Linear(in_features=1, out_features=8, bias=True)
|
|
(encoder_length): Linear(in_features=1, out_features=8, bias=True)
|
|
(y_center): Linear(in_features=1, out_features=8, bias=True)
|
|
(y_scale): Linear(in_features=1, out_features=8, bias=True)
|
|
)
|
|
(softmax): Softmax(dim=-1)
|
|
)
|
|
(encoder_variable_selection): VariableSelectionNetwork(
|
|
(flattened_grn): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((13,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=100, out_features=13, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(context): Linear(in_features=16, out_features=13, bias=False)
|
|
(fc2): Linear(in_features=13, out_features=13, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=13, out_features=26, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((13,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(single_variable_grns): ModuleDict(
|
|
(special_days): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(month): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(time_idx): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(price_regular): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(discount_in_percent): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(relative_time_idx): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(y): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(log_volume): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(industry_volume): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(soda_volume): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(avg_max_temp): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(avg_volume_by_agency): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(avg_volume_by_sku): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
(prescalers): ModuleDict(
|
|
(time_idx): Linear(in_features=1, out_features=8, bias=True)
|
|
(price_regular): Linear(in_features=1, out_features=8, bias=True)
|
|
(discount_in_percent): Linear(in_features=1, out_features=8, bias=True)
|
|
(relative_time_idx): Linear(in_features=1, out_features=8, bias=True)
|
|
(y): Linear(in_features=1, out_features=8, bias=True)
|
|
(log_volume): Linear(in_features=1, out_features=8, bias=True)
|
|
(industry_volume): Linear(in_features=1, out_features=8, bias=True)
|
|
(soda_volume): Linear(in_features=1, out_features=8, bias=True)
|
|
(avg_max_temp): Linear(in_features=1, out_features=8, bias=True)
|
|
(avg_volume_by_agency): Linear(in_features=1, out_features=8, bias=True)
|
|
(avg_volume_by_sku): Linear(in_features=1, out_features=8, bias=True)
|
|
)
|
|
(softmax): Softmax(dim=-1)
|
|
)
|
|
(decoder_variable_selection): VariableSelectionNetwork(
|
|
(flattened_grn): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((6,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=44, out_features=6, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(context): Linear(in_features=16, out_features=6, bias=False)
|
|
(fc2): Linear(in_features=6, out_features=6, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=6, out_features=12, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((6,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(single_variable_grns): ModuleDict(
|
|
(special_days): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(month): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(time_idx): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(price_regular): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(discount_in_percent): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(relative_time_idx): GatedResidualNetwork(
|
|
(resample_norm): ResampleNorm(
|
|
(resample): TimeDistributedInterpolation()
|
|
(gate): Sigmoid()
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(fc1): Linear(in_features=8, out_features=8, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=8, out_features=8, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=8, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
(prescalers): ModuleDict(
|
|
(time_idx): Linear(in_features=1, out_features=8, bias=True)
|
|
(price_regular): Linear(in_features=1, out_features=8, bias=True)
|
|
(discount_in_percent): Linear(in_features=1, out_features=8, bias=True)
|
|
(relative_time_idx): Linear(in_features=1, out_features=8, bias=True)
|
|
)
|
|
(softmax): Softmax(dim=-1)
|
|
)
|
|
(static_context_variable_selection): GatedResidualNetwork(
|
|
(fc1): Linear(in_features=16, out_features=16, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=16, out_features=16, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=16, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(static_context_initial_hidden_lstm): GatedResidualNetwork(
|
|
(fc1): Linear(in_features=16, out_features=16, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=16, out_features=16, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=16, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(static_context_initial_cell_lstm): GatedResidualNetwork(
|
|
(fc1): Linear(in_features=16, out_features=16, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=16, out_features=16, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=16, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(static_context_enrichment): GatedResidualNetwork(
|
|
(fc1): Linear(in_features=16, out_features=16, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=16, out_features=16, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=16, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(lstm_encoder): LSTM(16, 16, num_layers=2, batch_first=True, dropout=0.1)
|
|
(lstm_decoder): LSTM(16, 16, num_layers=2, batch_first=True, dropout=0.1)
|
|
(post_lstm_gate_encoder): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=16, out_features=32, bias=True)
|
|
)
|
|
(post_lstm_gate_decoder): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=16, out_features=32, bias=True)
|
|
)
|
|
(post_lstm_add_norm_encoder): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(post_lstm_add_norm_decoder): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
(static_enrichment): GatedResidualNetwork(
|
|
(fc1): Linear(in_features=16, out_features=16, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(context): Linear(in_features=16, out_features=16, bias=False)
|
|
(fc2): Linear(in_features=16, out_features=16, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=16, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(multihead_attn): InterpretableMultiHeadAttention(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(v_layer): Linear(in_features=16, out_features=4, bias=True)
|
|
(q_layers): ModuleList(
|
|
(0): Linear(in_features=16, out_features=4, bias=True)
|
|
(1): Linear(in_features=16, out_features=4, bias=True)
|
|
(2): Linear(in_features=16, out_features=4, bias=True)
|
|
(3): Linear(in_features=16, out_features=4, bias=True)
|
|
)
|
|
(k_layers): ModuleList(
|
|
(0): Linear(in_features=16, out_features=4, bias=True)
|
|
(1): Linear(in_features=16, out_features=4, bias=True)
|
|
(2): Linear(in_features=16, out_features=4, bias=True)
|
|
(3): Linear(in_features=16, out_features=4, bias=True)
|
|
)
|
|
(attention): ScaledDotProductAttention(
|
|
(softmax): Softmax(dim=2)
|
|
)
|
|
(w_h): Linear(in_features=4, out_features=16, bias=False)
|
|
)
|
|
(post_attn_gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=16, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
(pos_wise_ff): GatedResidualNetwork(
|
|
(fc1): Linear(in_features=16, out_features=16, bias=True)
|
|
(elu): ELU(alpha=1.0)
|
|
(fc2): Linear(in_features=16, out_features=16, bias=True)
|
|
(gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(dropout): Dropout(p=0.1, inplace=False)
|
|
(fc): Linear(in_features=16, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
)
|
|
(pre_output_gate_norm): GateAddNorm(
|
|
(glu): GatedLinearUnit(
|
|
(fc): Linear(in_features=16, out_features=32, bias=True)
|
|
)
|
|
(add_norm): AddNorm(
|
|
(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
|
|
)
|
|
)
|
|
(output_layer): Linear(in_features=16, out_features=7, bias=True)
|
|
)
|
|
[flaml.automl: 07-28 22:08:05] {2725} INFO - fit succeeded
|
|
[flaml.automl: 07-28 22:08:05] {2726} INFO - Time taken to find the best model: 1242.6435902118683
|
|
[flaml.automl: 07-28 22:08:05] {2737} WARNING - Time taken to find the best model is 414% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n"
|
|
]
|
|
}
|
|
],
|
|
```
|
|
|
|
[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/automl_time_series_forecast.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_time_series_forecast.ipynb)
|