From f718d18b5ea88c7fcbd8ecb49700963300f997f4 Mon Sep 17 00:00:00 2001 From: Kevin Chen <74878789+int-chaos@users.noreply.github.com> Date: Fri, 12 Aug 2022 11:39:22 -0400 Subject: [PATCH] time series forecasting with panel datasets (#541) * time series forecasting with panel datasets - integrate Temporal Fusion Transformer as a learner based on pytorchforecasting Signed-off-by: Kevin Chen * update setup.py Signed-off-by: Kevin Chen * update test_forecast.py Signed-off-by: Kevin Chen * update setup.py Signed-off-by: Kevin Chen * update setup.py Signed-off-by: Kevin Chen * update model.py and test_forecast.py - remove blank lines Signed-off-by: Kevin Chen * update model.py to prevent errors Signed-off-by: Kevin Chen * update automl.py and data.py - change forecast task name - update documentation for fit() method Signed-off-by: Kevin Chen * update test_forecast.py Signed-off-by: Kevin Chen * update test_forecast.py - add performance test - use 'fit_kwargs_by_estimator' Signed-off-by: Kevin Chen * add time index function Signed-off-by: Kevin Chen * update test_forecast.py performance test Signed-off-by: Kevin Chen * update data.py Signed-off-by: Kevin Chen * update automl.py Signed-off-by: Kevin Chen * update data.py to prevent type error Signed-off-by: Kevin Chen * update setup.py Signed-off-by: Kevin Chen * update for pytorch forecasting tft on panel datasets Signed-off-by: Kevin Chen * update automl.py documentations Signed-off-by: Kevin Chen * - rename estimator - add 'gpu_per_trial' for tft estimator Signed-off-by: Kevin Chen * update test_forecast.py Signed-off-by: Kevin Chen * include ts panel forecasting as an example Signed-off-by: Kevin Chen * update model.py Signed-off-by: Kevin Chen * update documentations Signed-off-by: Kevin Chen * update automl_time_series_forecast.ipynb Signed-off-by: Kevin Chen * update documentations Signed-off-by: Kevin Chen * "weights_summary" argument deprecated and removed for pl.Trainer() Signed-off-by: Kevin Chen * update model.py tft estimator prediction method Signed-off-by: Kevin Chen * update model.py Signed-off-by: Kevin Chen * update `fit_kwargs` documentation Signed-off-by: Kevin Chen * update automl.py Signed-off-by: Kevin Chen Signed-off-by: Kevin Chen Co-authored-by: Chi Wang --- flaml/automl.py | 141 +- flaml/data.py | 25 + flaml/ml.py | 3 + flaml/model.py | 188 + notebook/automl_time_series_forecast.ipynb | 5914 ++++++++++------- setup.py | 2 + test/automl/test_forecast.py | 162 +- .../Examples/AutoML-Time series forecast.md | 889 ++- .../docs/Use-Cases/Task-Oriented-AutoML.md | 2 + 9 files changed, 4841 insertions(+), 2485 deletions(-) diff --git a/flaml/automl.py b/flaml/automl.py index da0c59458..b959437f0 100644 --- a/flaml/automl.py +++ b/flaml/automl.py @@ -44,6 +44,8 @@ from .data import ( TOKENCLASSIFICATION, TS_FORECAST, TS_FORECASTREGRESSION, + TS_FORECASTPANEL, + TS_TIMESTAMP_COL, REGRESSION, _is_nlp_task, NLG_TASKS, @@ -582,7 +584,7 @@ class AutoML(BaseEstimator): ["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified. For regression tasks, valid choices are ["auto", 'uniform', 'time']. "auto" -> uniform. - For ts_forecast tasks, must be "auto" or 'time'. + For time series forecast tasks, must be "auto" or 'time'. For ranking task, must be "auto" or 'group'. hpo_method: str, default="auto" | The hyperparameter optimization method. By default, CFO is used for sequential @@ -897,7 +899,7 @@ class AutoML(BaseEstimator): Args: X: A numpy array of featurized instances, shape n * m, - or for ts_forecast tasks: + or for time series forcast tasks: a pandas dataframe with the first column containing timestamp values (datetime type) or an integer n for the predict steps (only valid when the estimator is @@ -1275,18 +1277,38 @@ class AutoML(BaseEstimator): # if eval_method = holdout, make holdout data if self._split_type == "time": if self._state.task in TS_FORECAST: - num_samples = X_train_all.shape[0] period = self._state.fit_kwargs[ "period" ] # NOTE: _prepare_data is before kwargs is updated to fit_kwargs_by_estimator - assert ( - period < num_samples - ), f"period={period}>#examples={num_samples}" - split_idx = num_samples - period - X_train = X_train_all[:split_idx] - y_train = y_train_all[:split_idx] - X_val = X_train_all[split_idx:] - y_val = y_train_all[split_idx:] + if self._state.task == TS_FORECASTPANEL: + X_train_all["time_idx"] -= X_train_all["time_idx"].min() + X_train_all["time_idx"] = X_train_all["time_idx"].astype("int") + ids = self._state.fit_kwargs["group_ids"].copy() + ids.append(TS_TIMESTAMP_COL) + ids.append("time_idx") + y_train_all = pd.DataFrame(y_train_all) + y_train_all[ids] = X_train_all[ids] + X_train_all = X_train_all.sort_values(ids) + y_train_all = y_train_all.sort_values(ids) + training_cutoff = X_train_all["time_idx"].max() - period + X_train = X_train_all[lambda x: x.time_idx <= training_cutoff] + y_train = y_train_all[ + lambda x: x.time_idx <= training_cutoff + ].drop(columns=ids) + X_val = X_train_all[lambda x: x.time_idx > training_cutoff] + y_val = y_train_all[ + lambda x: x.time_idx > training_cutoff + ].drop(columns=ids) + else: + num_samples = X_train_all.shape[0] + assert ( + period < num_samples + ), f"period={period}>#examples={num_samples}" + split_idx = num_samples - period + X_train = X_train_all[:split_idx] + y_train = y_train_all[:split_idx] + X_val = X_train_all[split_idx:] + y_val = y_train_all[split_idx:] else: if ( "sample_weight" in self._state.fit_kwargs @@ -1456,7 +1478,10 @@ class AutoML(BaseEstimator): ) elif self._split_type == "time": # logger.info("Using TimeSeriesSplit") - if self._state.task in TS_FORECAST: + if ( + self._state.task in TS_FORECAST + and self._state.task is not TS_FORECASTPANEL + ): period = self._state.fit_kwargs[ "period" ] # NOTE: _prepare_data is before kwargs is updated to fit_kwargs_by_estimator @@ -1468,6 +1493,14 @@ class AutoML(BaseEstimator): ) logger.info(f"Using nsplits={n_splits} due to data size limit.") self._state.kf = TimeSeriesSplit(n_splits=n_splits, test_size=period) + elif self._state.task is TS_FORECASTPANEL: + n_groups = X_train.groupby( + self._state.fit_kwargs.get("group_ids") + ).ngroups + period = self._state.fit_kwargs.get("period") + self._state.kf = TimeSeriesSplit( + n_splits=n_splits, test_size=period * n_groups + ) else: self._state.kf = TimeSeriesSplit(n_splits=n_splits) elif isinstance(self._split_type, str): @@ -1555,13 +1588,13 @@ class AutoML(BaseEstimator): Args: log_file_name: A string of the log file name. X_train: A numpy array or dataframe of training data in shape n*m. - For ts_forecast tasks, the first column of X_train + For time series forecast tasks, the first column of X_train must be the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). y_train: A numpy array or series of labels in shape n*1. dataframe: A dataframe of training data including label column. - For ts_forecast tasks, dataframe must be specified and should + For time series forecast tasks, dataframe must be specified and should have at least two columns: timestamp and label, where the first column is the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables @@ -1588,7 +1621,7 @@ class AutoML(BaseEstimator): ["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified. For regression tasks, valid choices are ["auto", 'uniform', 'time']. "auto" -> uniform. - For ts_forecast tasks, must be "auto" or 'time'. + For time series forecast tasks, must be "auto" or 'time'. For ranking task, must be "auto" or 'group'. groups: None or array-like | Group labels (with matching length to y_train) or groups counts (with sum equal to length of y_train) @@ -1634,10 +1667,29 @@ class AutoML(BaseEstimator): ``` **fit_kwargs: Other key word arguments to pass to fit() function of - the searched learners, such as sample_weight. Include: - period: int | forecast horizon for ts_forecast tasks. + the searched learners, such as sample_weight. Below are a few examples of + estimator-specific parameters: + period: int | forecast horizon for all time series forecast tasks. gpu_per_trial: float, default = 0 | A float of the number of gpus per trial, - only used by TransformersEstimator and XGBoostSklearnEstimator. + only used by TransformersEstimator, XGBoostSklearnEstimator, and + TemporalFusionTransformerEstimator. + group_ids: list of strings of column names identifying a time series, only + used by TemporalFusionTransformerEstimator, required for + 'ts_forecast_panel' task. `group_ids` is a parameter for TimeSeriesDataSet object + from PyTorchForecasting. + For other parameters to describe your dataset, refer to + [TimeSeriesDataSet PyTorchForecasting](https://pytorch-forecasting.readthedocs.io/en/stable/api/pytorch_forecasting.data.timeseries.TimeSeriesDataSet.html). + To specify your variables, use `static_categoricals`, `static_reals`, + `time_varying_known_categoricals`, `time_varying_known_reals`, + `time_varying_unknown_categoricals`, `time_varying_unknown_reals`, + `variable_groups`. To provide more information on your data, use + `max_encoder_length`, `min_encoder_length`, `lags`. + log_dir: str, default = "lightning_logs" | Folder into which to log results + for tensorboard, only used by TemporalFusionTransformerEstimator. + max_epochs: int, default = 20 | Maximum number of epochs to run training, + only used by TemporalFusionTransformerEstimator. + batch_size: int, default = 64 | Batch size for training model, only + used by TemporalFusionTransformerEstimator. """ task = task or self._settings.get("task") eval_method = eval_method or self._settings.get("eval_method") @@ -1771,11 +1823,15 @@ class AutoML(BaseEstimator): elif self._state.task in TS_FORECAST: assert split_type in ["auto", "time"] self._split_type = "time" - assert isinstance( self._state.fit_kwargs.get("period"), int, # NOTE: _decide_split_type is before kwargs is updated to fit_kwargs_by_estimator ), f"missing a required integer 'period' for '{TS_FORECAST}' task." + if self._state.fit_kwargs.get("group_ids"): + self._state.task == TS_FORECASTPANEL + assert isinstance( + self._state.fit_kwargs.get("group_ids"), list + ), f"missing a required List[str] 'group_ids' for '{TS_FORECASTPANEL}' task." elif self._state.task == "rank": assert ( self._state.groups is not None @@ -2082,13 +2138,13 @@ class AutoML(BaseEstimator): Args: X_train: A numpy array or a pandas dataframe of training data in - shape (n, m). For ts_forecast tasks, the first column of X_train + shape (n, m). For time series forecsat tasks, the first column of X_train must be the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). When using ray, X_train can be a ray.ObjectRef. y_train: A numpy array or a pandas series of labels in shape (n, ). dataframe: A dataframe of training data including label column. - For ts_forecast tasks, dataframe must be specified and must have + For time series forecast tasks, dataframe must be specified and must have at least two columns, timestamp and label, where the first column is the timestamp column (datetime type). Other columns in the dataframe are assumed to be exogenous variables (categorical or numeric). @@ -2139,7 +2195,7 @@ class AutoML(BaseEstimator): ``` task: A string of the task type, e.g., 'classification', 'regression', 'ts_forecast_regression', - 'ts_forecast_classification', 'rank', 'seq-classification', + 'ts_forecast_classification', 'ts_forecast_panel', 'rank', 'seq-classification', 'seq-regression', 'summarization'. n_jobs: An integer of the number of threads for training | default=-1. Use all available resources when n_jobs == -1. @@ -2204,7 +2260,7 @@ class AutoML(BaseEstimator): ["auto", 'stratified', 'uniform', 'time', 'group']. "auto" -> stratified. For regression tasks, valid choices are ["auto", 'uniform', 'time']. "auto" -> uniform. - For ts_forecast tasks, must be "auto" or 'time'. + For time series forecast tasks, must be "auto" or 'time'. For ranking task, must be "auto" or 'group'. hpo_method: str, default="auto" | The hyperparameter optimization method. By default, CFO is used for sequential @@ -2305,15 +2361,46 @@ class AutoML(BaseEstimator): "transformer": { "output_dir": "test/data/output/", "fp16": False, + }, + "tft": { + "max_encoder_length": 1, + "min_encoder_length": 1, + "static_categoricals": [], + "static_reals": [], + "time_varying_known_categoricals": [], + "time_varying_known_reals": [], + "time_varying_unknown_categoricals": [], + "time_varying_unknown_reals": [], + "variable_groups": {}, + "lags": {}, } } ``` **fit_kwargs: Other key word arguments to pass to fit() function of - the searched learners, such as sample_weight. Include: - period: int | forecast horizon for ts_forecast tasks. + the searched learners, such as sample_weight. Below are a few examples of + estimator-specific parameters: + period: int | forecast horizon for all time series forecast tasks. gpu_per_trial: float, default = 0 | A float of the number of gpus per trial, - only used by TransformersEstimator and XGBoostSklearnEstimator. + only used by TransformersEstimator, XGBoostSklearnEstimator, and + TemporalFusionTransformerEstimator. + group_ids: list of strings of column names identifying a time series, only + used by TemporalFusionTransformerEstimator, required for + 'ts_forecast_panel' task. `group_ids` is a parameter for TimeSeriesDataSet object + from PyTorchForecasting. + For other parameters to describe your dataset, refer to + [TimeSeriesDataSet PyTorchForecasting](https://pytorch-forecasting.readthedocs.io/en/stable/api/pytorch_forecasting.data.timeseries.TimeSeriesDataSet.html). + To specify your variables, use `static_categoricals`, `static_reals`, + `time_varying_known_categoricals`, `time_varying_known_reals`, + `time_varying_unknown_categoricals`, `time_varying_unknown_reals`, + `variable_groups`. To provide more information on your data, use + `max_encoder_length`, `min_encoder_length`, `lags`. + log_dir: str, default = "lightning_logs" | Folder into which to log results + for tensorboard, only used by TemporalFusionTransformerEstimator. + max_epochs: int, default = 20 | Maximum number of epochs to run training, + only used by TemporalFusionTransformerEstimator. + batch_size: int, default = 64 | Batch size for training model, only + used by TemporalFusionTransformerEstimator. """ self._state._start_time_flag = self._start_time_flag = time.time() @@ -2581,6 +2668,8 @@ class AutoML(BaseEstimator): estimator_list = ["lgbm", "xgboost", "xgb_limitdepth"] elif _is_nlp_task(self._state.task): estimator_list = ["transformer"] + elif self._state.task == TS_FORECASTPANEL: + estimator_list = ["tft"] else: try: import catboost diff --git a/flaml/data.py b/flaml/data.py index 28960a0e2..9deab1b79 100644 --- a/flaml/data.py +++ b/flaml/data.py @@ -32,9 +32,11 @@ TS_FORECASTREGRESSION = ( "ts_forecast_regression", ) TS_FORECASTCLASSIFICATION = "ts_forecast_classification" +TS_FORECASTPANEL = "ts_forecast_panel" TS_FORECAST = ( *TS_FORECASTREGRESSION, TS_FORECASTCLASSIFICATION, + TS_FORECASTPANEL, ) TS_TIMESTAMP_COL = "ds" TS_VALUE_COL = "y" @@ -248,6 +250,26 @@ def concat(X1, X2): return np.concatenate([X1, X2]) +def add_time_idx_col(X): + unique_dates = X[TS_TIMESTAMP_COL].drop_duplicates().sort_values(ascending=True) + # assume no missing timestamps + freq = pd.infer_freq(unique_dates) + if freq == "MS": + X["time_idx"] = X[TS_TIMESTAMP_COL].dt.year * 12 + X[TS_TIMESTAMP_COL].dt.month + elif freq == "Y": + X["time_idx"] = X[TS_TIMESTAMP_COL].dt.year + else: + # using time frequency to generate all time stamps and then indexing for time_idx + # full_range = pd.date_range(X[TS_TIMESTAMP_COL].min(), X[TS_TIMESTAMP_COL].max(), freq=freq).to_list() + # X["time_idx"] = [full_range.index(time) for time in X[TS_TIMESTAMP_COL]] + # taking minimum difference in timestamp + timestamps = unique_dates.view("int64") + freq = int(timestamps.diff().mode()) + X["time_idx"] = timestamps - timestamps.min() / freq + X["time_idx"] = X["time_idx"].astype("int") + return X + + class DataTransformer: """Transform input training data.""" @@ -281,6 +303,9 @@ class DataTransformer: drop = False if task in TS_FORECAST: X = X.rename(columns={X.columns[0]: TS_TIMESTAMP_COL}) + if task is TS_FORECASTPANEL: + if "time_idx" not in X: + X = add_time_idx_col(X) ds_col = X.pop(TS_TIMESTAMP_COL) if isinstance(y, Series): y = y.rename(TS_VALUE_COL) diff --git a/flaml/ml.py b/flaml/ml.py index cc7a0e4b8..e0872db1a 100644 --- a/flaml/ml.py +++ b/flaml/ml.py @@ -37,6 +37,7 @@ from .model import ( ARIMA, SARIMAX, TransformersEstimator, + TemporalFusionTransformerEstimator, TransformersEstimatorModelSelection, ) from .data import CLASSIFICATION, group_counts, TS_FORECAST @@ -122,6 +123,8 @@ def get_estimator_class(task, estimator_name): estimator_class = SARIMAX elif estimator_name == "transformer": estimator_class = TransformersEstimator + elif estimator_name == "tft": + estimator_class = TemporalFusionTransformerEstimator elif estimator_name == "transformer_ms": estimator_class = TransformersEstimatorModelSelection else: diff --git a/flaml/model.py b/flaml/model.py index 0eb6e1b61..8e31e4178 100644 --- a/flaml/model.py +++ b/flaml/model.py @@ -23,6 +23,7 @@ from . import tune from .data import ( group_counts, CLASSIFICATION, + add_time_idx_col, TS_FORECASTREGRESSION, TS_TIMESTAMP_COL, TS_VALUE_COL, @@ -2152,6 +2153,193 @@ class XGBoostLimitDepth_TS(TS_SKLearn): base_class = XGBoostLimitDepthEstimator +class TemporalFusionTransformerEstimator(SKLearnEstimator): + """The class for tuning Temporal Fusion Transformer""" + + @classmethod + def search_space(cls, data_size, pred_horizon, **params): + space = { + "gradient_clip_val": { + "domain": tune.loguniform(lower=0.01, upper=100.0), + "init_value": 0.01, + }, + "hidden_size": { + "domain": tune.lograndint(lower=8, upper=512), + "init_value": 16, + }, + "hidden_continuous_size": { + "domain": tune.randint(lower=1, upper=65), + "init_value": 8, + }, + "attention_head_size": { + "domain": tune.randint(lower=1, upper=5), + "init_value": 4, + }, + "dropout": { + "domain": tune.uniform(lower=0.1, upper=0.3), + "init_value": 0.1, + }, + "learning_rate": { + "domain": tune.loguniform(lower=0.00001, upper=1.0), + "init_value": 0.001, + }, + } + return space + + def transform_ds(self, X_train, y_train, **kwargs): + y_train = DataFrame(y_train, columns=[TS_VALUE_COL]) + self.data = X_train.join(y_train) + + max_prediction_length = kwargs["period"] + self.max_encoder_length = kwargs["max_encoder_length"] + training_cutoff = self.data["time_idx"].max() - max_prediction_length + + from pytorch_forecasting import TimeSeriesDataSet + from pytorch_forecasting.data import GroupNormalizer + + self.group_ids = kwargs["group_ids"].copy() + training = TimeSeriesDataSet( + self.data[lambda x: x.time_idx <= training_cutoff], + time_idx="time_idx", + target=TS_VALUE_COL, + group_ids=self.group_ids, + min_encoder_length=kwargs.get( + "min_encoder_length", self.max_encoder_length // 2 + ), # keep encoder length long (as it is in the validation set) + max_encoder_length=self.max_encoder_length, + min_prediction_length=1, + max_prediction_length=max_prediction_length, + static_categoricals=kwargs.get("static_categoricals", []), + static_reals=kwargs.get("static_reals", []), + time_varying_known_categoricals=kwargs.get( + "time_varying_known_categoricals", [] + ), + time_varying_known_reals=kwargs.get("time_varying_known_reals", []), + time_varying_unknown_categoricals=kwargs.get( + "time_varying_unknown_categoricals", [] + ), + time_varying_unknown_reals=kwargs.get("time_varying_unknown_reals", []), + variable_groups=kwargs.get( + "variable_groups", {} + ), # group of categorical variables can be treated as one variable + lags=kwargs.get("lags", {}), + target_normalizer=GroupNormalizer( + groups=kwargs["group_ids"], transformation="softplus" + ), # use softplus and normalize by group + add_relative_time_idx=True, + add_target_scales=True, + add_encoder_length=True, + ) + + # create validation set (predict=True) which means to predict the last max_prediction_length points in time + # for each series + validation = TimeSeriesDataSet.from_dataset( + training, self.data, predict=True, stop_randomization=True + ) + + # create dataloaders for model + batch_size = kwargs.get("batch_size", 64) + train_dataloader = training.to_dataloader( + train=True, batch_size=batch_size, num_workers=0 + ) + val_dataloader = validation.to_dataloader( + train=False, batch_size=batch_size * 10, num_workers=0 + ) + + return training, train_dataloader, val_dataloader + + def fit(self, X_train, y_train, budget=None, **kwargs): + import copy + from pathlib import Path + import warnings + import numpy as np + import pandas as pd + import pytorch_lightning as pl + from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor + from pytorch_lightning.loggers import TensorBoardLogger + import torch + from pytorch_forecasting import TemporalFusionTransformer + from pytorch_forecasting.metrics import QuantileLoss + import tensorboard as tb + + warnings.filterwarnings("ignore") + current_time = time.time() + training, train_dataloader, val_dataloader = self.transform_ds( + X_train, y_train, **kwargs + ) + params = self.params.copy() + gradient_clip_val = params.pop("gradient_clip_val") + params.pop("n_jobs") + max_epochs = kwargs.get("max_epochs", 20) + early_stop_callback = EarlyStopping( + monitor="val_loss", min_delta=1e-4, patience=10, verbose=False, mode="min" + ) + lr_logger = LearningRateMonitor() # log the learning rate + logger = TensorBoardLogger( + kwargs.get("log_dir", "lightning_logs") + ) # logging results to a tensorboard + default_trainer_kwargs = dict( + gpus=self._kwargs.get("gpu_per_trial", [0]) + if torch.cuda.is_available() + else None, + max_epochs=max_epochs, + gradient_clip_val=gradient_clip_val, + callbacks=[lr_logger, early_stop_callback], + logger=logger, + ) + trainer = pl.Trainer( + **default_trainer_kwargs, + ) + tft = TemporalFusionTransformer.from_dataset( + training, + **params, + lstm_layers=2, # 2 is mostly optimal according to documentation + output_size=7, # 7 quantiles by default + loss=QuantileLoss(), + log_interval=10, # uncomment for learning rate finder and otherwise, e.g. to 10 for logging every 10 batches + reduce_on_plateau_patience=4, + ) + # fit network + trainer.fit( + tft, + train_dataloaders=train_dataloader, + val_dataloaders=val_dataloader, + ) + best_model_path = trainer.checkpoint_callback.best_model_path + best_tft = TemporalFusionTransformer.load_from_checkpoint(best_model_path) + train_time = time.time() - current_time + self._model = best_tft + return train_time + + def predict(self, X): + import pandas as pd + + ids = self.group_ids.copy() + ids.append(TS_TIMESTAMP_COL) + encoder_data = self.data[ + lambda x: x.time_idx > x.time_idx.max() - self.max_encoder_length + ] + # following pytorchforecasting example, make all target values equal to the last data + last_data_cols = self.group_ids.copy() + last_data_cols.append(TS_VALUE_COL) + last_data = self.data[lambda x: x.time_idx == x.time_idx.max()][last_data_cols] + decoder_data = X + if "time_idx" not in decoder_data: + decoder_data = add_time_idx_col(decoder_data) + decoder_data["time_idx"] += ( + encoder_data["time_idx"].max() + 1 - decoder_data["time_idx"].min() + ) + # decoder_data[TS_VALUE_COL] = 0 + decoder_data = decoder_data.merge(last_data, how="inner", on=self.group_ids) + decoder_data = decoder_data.sort_values(ids) + new_prediction_data = pd.concat([encoder_data, decoder_data], ignore_index=True) + new_prediction_data["time_idx"] = new_prediction_data["time_idx"].astype("int") + new_raw_predictions = self._model.predict(new_prediction_data) + index = [decoder_data[idx].to_numpy() for idx in ids] + predictions = pd.Series(new_raw_predictions.numpy().ravel(), index=index) + return predictions + + class suppress_stdout_stderr(object): def __init__(self): # Open a pair of null files diff --git a/notebook/automl_time_series_forecast.ipynb b/notebook/automl_time_series_forecast.ipynb index 81ba9e536..719fdb6ee 100644 --- a/notebook/automl_time_series_forecast.ipynb +++ b/notebook/automl_time_series_forecast.ipynb @@ -26,110 +26,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: flaml[notebook,ts_forecast] in c:\\users\\pythonprojects\\flaml (0.9.2)\n", - "Requirement already satisfied: NumPy>=1.16.2 in c:\\users\\kevin chen\\anaconda3\\envs\\python38\\lib\\site-packages (from flaml[notebook,ts_forecast]) (1.18.5)\n", - "Requirement already satisfied: lightgbm>=2.3.1 in c:\\users\\kevin chen\\anaconda3\\envs\\python38\\lib\\site-packages (from flaml[notebook,ts_forecast]) (3.2.1)\n", - "Requirement already satisfied: xgboost<=1.3.3,>=0.90 in c:\\users\\kevin chen\\anaconda3\\envs\\python38\\lib\\site-packages (from flaml[notebook,ts_forecast]) (1.2.1)\n", - "Requirement already satisfied: scipy>=1.4.1 in c:\\users\\kevin chen\\anaconda3\\envs\\python38\\lib\\site-packages (from flaml[notebook,ts_forecast]) (1.5.2)\n", - "Requirement already satisfied: pandas>=1.1.4 in c:\\users\\kevin 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chen\\anaconda3\\envs\\python38\\lib\\site-packages (from requests->openml==0.10.2->flaml[notebook,ts_forecast]) (2021.5.30)\n" - ] - } - ], + "outputs": [], "source": [ "%pip install flaml[notebook,ts_forecast]\n", "# avoid version 1.0.2 to 1.0.5 for this notebook due to a bug for arima and sarimax's init config" @@ -176,6 +75,35 @@ "y_test = data[split_idx:]['co2'] # y_test is a series of the values corresponding to the dates for prediction" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "train_df\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(train_df['index'], train_df['co2'])\n", + "plt.xlabel('Date')\n", + "plt.ylabel('CO2 Levels')\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -187,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -198,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -214,1138 +142,938 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[flaml.automl: 02-28 21:28:18] {2060} INFO - task = ts_forecast\n", - "[flaml.automl: 02-28 21:28:18] {2062} INFO - Data split method: time\n", - "[flaml.automl: 02-28 21:28:18] {2066} INFO - Evaluation method: holdout\n", - "[flaml.automl: 02-28 21:28:18] {2147} INFO - Minimizing error metric: mape\n", - "[flaml.automl: 02-28 21:28:18] {2205} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax']\n", - "[flaml.automl: 02-28 21:28:18] {2458} INFO - iteration 0, current learner lgbm\n", - "[flaml.automl: 02-28 21:28:19] {2573} INFO - Estimated sufficient time budget=2854s. 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Consider increasing the time budget.\n" ] } ], @@ -1366,7 +1094,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1374,9 +1102,9 @@ "output_type": "stream", "text": [ "Best ML leaner: sarimax\n", - "Best hyperparmeter config: {'p': 8.0, 'd': 0.0, 'q': 8.0, 'P': 6.0, 'D': 3.0, 'Q': 1.0, 's': 6}\n", + "Best hyperparmeter config: {'p': 8, 'd': 0, 'q': 8, 'P': 6, 'D': 3, 'Q': 1, 's': 6}\n", "Best mape on validation data: 0.00043466573064228554\n", - "Training duration of best run: 0.6672513484954834s\n" + "Training duration of best run: 0.7340686321258545s\n" ] } ], @@ -1390,16 +1118,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1410,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -1422,7 +1150,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1469,7 +1197,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1495,7 +1223,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1509,12 +1237,10 @@ "{'Current Learner': 'lgbm', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 4, 'num_leaves': 8, 'min_child_samples': 11, 'learning_rate': 0.8116893577982964, 'log_max_bin': 8, 'colsample_bytree': 0.97502360023323, 'reg_alpha': 0.0012398377555843262, 'reg_lambda': 0.02776044509327881, 'optimize_for_horizon': False, 'lags': 4}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 4, 'num_leaves': 8, 'min_child_samples': 11, 'learning_rate': 0.8116893577982964, 'log_max_bin': 8, 'colsample_bytree': 0.97502360023323, 'reg_alpha': 0.0012398377555843262, 'reg_lambda': 0.02776044509327881, 'optimize_for_horizon': False, 'lags': 4}}\n", "{'Current Learner': 'lgbm', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 5, 'num_leaves': 16, 'min_child_samples': 7, 'learning_rate': 1.0, 'log_max_bin': 9, 'colsample_bytree': 0.9289697965752838, 'reg_alpha': 0.01291354098023607, 'reg_lambda': 0.012402833825431305, 'optimize_for_horizon': False, 'lags': 5}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 5, 'num_leaves': 16, 'min_child_samples': 7, 'learning_rate': 1.0, 'log_max_bin': 9, 'colsample_bytree': 0.9289697965752838, 'reg_alpha': 0.01291354098023607, 'reg_lambda': 0.012402833825431305, 'optimize_for_horizon': False, 'lags': 5}}\n", "{'Current Learner': 'lgbm', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 10, 'num_leaves': 13, 'min_child_samples': 8, 'learning_rate': 1.0, 'log_max_bin': 9, 'colsample_bytree': 0.915047969012756, 'reg_alpha': 0.1456985407754094, 'reg_lambda': 0.010186415963233664, 'optimize_for_horizon': False, 'lags': 9}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 10, 'num_leaves': 13, 'min_child_samples': 8, 'learning_rate': 1.0, 'log_max_bin': 9, 'colsample_bytree': 0.915047969012756, 'reg_alpha': 0.1456985407754094, 'reg_lambda': 0.010186415963233664, 'optimize_for_horizon': False, 'lags': 9}}\n", - "{'Current Learner': 'rf', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 4, 'max_features': 0.7336821866058406, 'max_leaves': 37, 'optimize_for_horizon': False, 'lags': 10}, 'Best Learner': 'rf', 'Best Hyper-parameters': {'n_estimators': 4, 'max_features': 0.7336821866058406, 'max_leaves': 37, 'optimize_for_horizon': False, 'lags': 10}}\n", - "{'Current Learner': 'rf', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 4, 'max_features': 0.776140805521135, 'max_leaves': 71, 'optimize_for_horizon': False, 'lags': 10}, 'Best Learner': 'rf', 'Best Hyper-parameters': {'n_estimators': 4, 'max_features': 0.776140805521135, 'max_leaves': 71, 'optimize_for_horizon': False, 'lags': 10}}\n", + "{'Current Learner': 'xgb', 'Current Sample': 502, 'Current Hyper-parameters': {'n_estimators': 17, 'max_depth': 6, 'min_child_weight': 1.1257301179325647, 'learning_rate': 0.3420575416463879, 'subsample': 1.0, 'colsample_bylevel': 0.8634518942394397, 'colsample_bytree': 0.8183410599521093, 'reg_alpha': 0.0031517221935712125, 'reg_lambda': 0.36563645650488746, 'optimize_for_horizon': False, 'lags': 1}, 'Best Learner': 'xgb', 'Best Hyper-parameters': {'n_estimators': 17, 'max_depth': 6, 'min_child_weight': 1.1257301179325647, 'learning_rate': 0.3420575416463879, 'subsample': 1.0, 'colsample_bylevel': 0.8634518942394397, 'colsample_bytree': 0.8183410599521093, 'reg_alpha': 0.0031517221935712125, 'reg_lambda': 0.36563645650488746, 'optimize_for_horizon': False, 'lags': 1}}\n", "{'Current Learner': 'prophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.05, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 10.0, 'seasonality_mode': 'multiplicative'}, 'Best Learner': 'prophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.05, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 10.0, 'seasonality_mode': 'multiplicative'}}\n", "{'Current Learner': 'prophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.02574943279263944, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 10.0, 'seasonality_mode': 'additive'}, 'Best Learner': 'prophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.02574943279263944, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 10.0, 'seasonality_mode': 'additive'}}\n", - "{'Current Learner': 'prophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.029044518309983725, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 8.831739687246309, 'seasonality_mode': 'additive'}, 'Best Learner': 'prophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.029044518309983725, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 8.831739687246309, 'seasonality_mode': 'additive'}}\n", - "{'Current Learner': 'prophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.02907295015483903, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 10.0, 'seasonality_mode': 'additive'}, 'Best Learner': 'prophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.02907295015483903, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 10.0, 'seasonality_mode': 'additive'}}\n" + "{'Current Learner': 'prophet', 'Current Sample': 502, 'Current Hyper-parameters': {'changepoint_prior_scale': 0.029044518309983725, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 8.831739687246309, 'seasonality_mode': 'additive'}, 'Best Learner': 'prophet', 'Best Hyper-parameters': {'changepoint_prior_scale': 0.029044518309983725, 'seasonality_prior_scale': 10.0, 'holidays_prior_scale': 8.831739687246309, 'seasonality_mode': 'additive'}}\n" ] } ], @@ -1529,12 +1255,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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timeStampdemandpreciptemp_above_monthly_avg
02012-01-014954.8333330.0024871
12012-01-025302.9541670.0000001
22012-01-036095.5125000.0000000
32012-01-046336.2666670.0000000
42012-01-056130.2458330.0000001
...............
18642017-02-075861.3198330.0119381
18652017-02-085667.6447080.0012581
18662017-02-095947.6619580.0270290
18672017-02-106195.1225000.0001790
18682017-02-115461.0260000.0004921
\n", + "

1869 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " timeStamp demand precip temp_above_monthly_avg\n", + "0 2012-01-01 4954.833333 0.002487 1\n", + "1 2012-01-02 5302.954167 0.000000 1\n", + "2 2012-01-03 6095.512500 0.000000 0\n", + "3 2012-01-04 6336.266667 0.000000 0\n", + "4 2012-01-05 6130.245833 0.000000 1\n", + "... ... ... ... ...\n", + "1864 2017-02-07 5861.319833 0.011938 1\n", + "1865 2017-02-08 5667.644708 0.001258 1\n", + "1866 2017-02-09 5947.661958 0.027029 0\n", + "1867 2017-02-10 6195.122500 0.000179 0\n", + "1868 2017-02-11 5461.026000 0.000492 1\n", + "\n", + "[1869 rows x 4 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# split data into train and test\n", "num_samples = multi_df.shape[0]\n", @@ -1687,7 +1545,9 @@ "multi_X_test = multi_test_df[\n", " [\"timeStamp\", \"precip\", \"temp_above_monthly_avg\"]\n", "] # test dataframe must contain values for the regressors / multivariate variables\n", - "multi_y_test = multi_test_df[\"demand\"]" + "multi_y_test = multi_test_df[\"demand\"]\n", + "\n", + "multi_train_df" ] }, { @@ -1699,141 +1559,111 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[flaml.automl: 02-28 21:32:20] {2060} INFO - task = ts_forecast\n", - "[flaml.automl: 02-28 21:32:20] {2062} INFO - Data split method: time\n", - "[flaml.automl: 02-28 21:32:20] {2066} INFO - Evaluation method: holdout\n", - "[flaml.automl: 02-28 21:32:20] {2147} INFO - Minimizing error metric: mape\n", - "[flaml.automl: 02-28 21:32:20] {2205} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth', 'prophet', 'arima', 'sarimax']\n", - "[flaml.automl: 02-28 21:32:20] {2458} INFO - iteration 0, current learner lgbm\n", - "[flaml.automl: 02-28 21:32:20] {2573} INFO - Estimated sufficient time budget=269s. 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DateSalesOpenPromoPromo2above_mean_sales
02015-02-0224894TrueTrueFalse1
12015-02-0322139TrueTrueFalse1
22015-02-0420452TrueTrueFalse1
32015-02-0520977TrueTrueFalse1
42015-02-0619151TrueTrueFalse1
.....................
1452015-06-2713108TrueFalseFalse0
1462015-06-280FalseFalseFalse0
1472015-06-2928456TrueTrueFalse1
1482015-06-3027140TrueTrueFalse1
1492015-07-0124957TrueTrueFalse1
\n", + "

150 rows × 6 columns

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" + ], + "text/plain": [ + " Date Sales Open Promo Promo2 above_mean_sales\n", + "0 2015-02-02 24894 True True False 1\n", + "1 2015-02-03 22139 True True False 1\n", + "2 2015-02-04 20452 True True False 1\n", + "3 2015-02-05 20977 True True False 1\n", + "4 2015-02-06 19151 True True False 1\n", + ".. ... ... ... ... ... ...\n", + "145 2015-06-27 13108 True False False 0\n", + "146 2015-06-28 0 False False False 0\n", + "147 2015-06-29 28456 True True False 1\n", + "148 2015-06-30 27140 True True False 1\n", + "149 2015-07-01 24957 True True False 1\n", + "\n", + "[150 rows x 6 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "discrete_train_df" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2018,7 +2013,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -2028,7 +2023,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -2043,890 +2038,486 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[flaml.automl: 02-28 21:54:50] {2060} INFO - task = ts_forecast_classification\n", - "[flaml.automl: 02-28 21:54:50] {2062} INFO - Data split method: time\n", - "[flaml.automl: 02-28 21:54:50] {2066} INFO - Evaluation method: holdout\n", - "[flaml.automl: 02-28 21:54:50] {2147} INFO - Minimizing error metric: 1-accuracy\n", - "[flaml.automl: 02-28 21:54:50] {2205} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth']\n", - "[flaml.automl: 02-28 21:54:50] {2458} INFO - iteration 0, current learner lgbm\n", - "[flaml.automl: 02-28 21:54:50] {2573} INFO - Estimated sufficient time budget=249s. 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Consider increasing the time budget.\n" + "[flaml.automl: 08-03 20:33:26] {2520} INFO - task = ts_forecast_classification\n", + "[flaml.automl: 08-03 20:33:26] {2522} INFO - Data split method: time\n", + "[flaml.automl: 08-03 20:33:26] {2525} INFO - Evaluation method: holdout\n", + "[flaml.automl: 08-03 20:33:26] {2644} INFO - Minimizing error metric: 1-accuracy\n", + "[flaml.automl: 08-03 20:33:27] {2786} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'xgboost', 'extra_tree', 'xgb_limitdepth']\n", + "[flaml.automl: 08-03 20:33:27] {3088} INFO - iteration 0, current learner lgbm\n", + "[flaml.automl: 08-03 20:33:29] {3221} INFO - Estimated sufficient time budget=11912s. 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fit succeeded\n", + "[flaml.automl: 08-03 20:33:41] {2818} INFO - Time taken to find the best model: 2.6732513904571533\n" ] } ], @@ -2947,24 +2538,25 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Best ML leaner: extra_tree\n", - "Best hyperparmeter config: {'n_estimators': 6, 'max_leaves': 8, 'optimize_for_horizon': False, 'max_features': 0.1, 'lags': 8}\n", - "Best mape on validation data: 0.0\n", - "Training duration of best run: 0.022936344146728516s\n", - "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n", - " criterion='gini', max_depth=None, max_features=0.1,\n", - " max_leaf_nodes=8, max_samples=None,\n", - " min_impurity_decrease=0.0, min_samples_leaf=1,\n", - " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", - " n_estimators=6, n_jobs=-1, oob_score=False,\n", - " random_state=None, verbose=0, warm_start=False)\n" + "Best ML leaner: lgbm\n", + "Best hyperparmeter config: {'n_estimators': 4, 'num_leaves': 5, 'min_child_samples': 8, 'learning_rate': 0.7333523408279569, 'log_max_bin': 5, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 7.593190995489472, 'optimize_for_horizon': False, 'lags': 5}\n", + "Best mape on validation data: 0.033333333333333326\n", + "Training duration of best run: 0.017951011657714844s\n", + "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n", + " importance_type='split', learning_rate=0.7333523408279569,\n", + " max_bin=31, max_depth=-1, min_child_samples=8,\n", + " min_child_weight=0.001, min_split_gain=0.0, n_estimators=4,\n", + " n_jobs=-1, num_leaves=5, objective=None, random_state=None,\n", + " reg_alpha=0.0009765625, reg_lambda=7.593190995489472,\n", + " silent=True, subsample=1.0, subsample_for_bin=200000,\n", + " subsample_freq=0, verbose=-1)\n" ] } ], @@ -2979,7 +2571,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -3030,7 +2622,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -3050,7 +2642,1424 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 5. Comparison with Alternatives (CO2 Dataset)" + "## 5. Forecast Problems with Panel Datasets (Multiple Time Series)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data and preprocess\n", + "\n", + "Import Stallion & Co.'s beverage sales data from pytorch-forecasting, orginally from Kaggle. The dataset contains about 21,000 monthly historic sales record as well as additional information about the sales price, the location of the agency, special days such as holidays, and volume sold in the entire industry. There are thousands of unique wholesaler-SKU/products combinations, each representing an individual time series. The task is to provide a six month forecast of demand at SKU level for each wholesaler." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def get_stalliion_data():\n", + " from pytorch_forecasting.data.examples import get_stallion_data\n", + "\n", + " data = get_stallion_data()\n", + " # add time index\n", + " data[\"time_idx\"] = data[\"date\"].dt.year * 12 + data[\"date\"].dt.month\n", + " data[\"time_idx\"] -= data[\"time_idx\"].min()\n", + " # add additional features\n", + " data[\"month\"] = data.date.dt.month.astype(str).astype(\n", + " \"category\"\n", + " ) # categories have be strings\n", + " data[\"log_volume\"] = np.log(data.volume + 1e-8)\n", + " data[\"avg_volume_by_sku\"] = data.groupby(\n", + " [\"time_idx\", \"sku\"], observed=True\n", + " ).volume.transform(\"mean\")\n", + " data[\"avg_volume_by_agency\"] = data.groupby(\n", + " [\"time_idx\", \"agency\"], observed=True\n", + " ).volume.transform(\"mean\")\n", + " # we want to encode special days as one variable and thus need to first reverse one-hot encoding\n", + " special_days = [\n", + " \"easter_day\",\n", + " \"good_friday\",\n", + " \"new_year\",\n", + " \"christmas\",\n", + " \"labor_day\",\n", + " \"independence_day\",\n", + " \"revolution_day_memorial\",\n", + " \"regional_games\",\n", + " \"beer_capital\",\n", + " \"music_fest\",\n", + " ]\n", + " data[special_days] = (\n", + " data[special_days]\n", + " .apply(lambda x: x.map({0: \"-\", 1: x.name}))\n", + " .astype(\"category\")\n", + " )\n", + " return data, special_days" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data, special_days = get_stalliion_data()\n", + "time_horizon = 6 # predict six months\n", + "# make time steps first column\n", + "data[\"time_idx\"] = data[\"date\"].dt.year * 12 + data[\"date\"].dt.month\n", + "data[\"time_idx\"] -= data[\"time_idx\"].min()\n", + "training_cutoff = data[\"time_idx\"].max() - time_horizon\n", + "ts_col = data.pop(\"date\")\n", + "data.insert(0, \"date\", ts_col)\n", + "# FLAML assumes input is not sorted, but we sort here for comparison purposes with y_test\n", + "data = data.sort_values([\"agency\", \"sku\", \"date\"])\n", + "X_train = data[lambda x: x.time_idx <= training_cutoff]\n", + "X_test = data[lambda x: x.time_idx > training_cutoff]\n", + "y_train = X_train.pop(\"volume\")\n", + "y_test = X_test.pop(\"volume\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dateagencyskuindustry_volumesoda_volumeavg_max_tempprice_regularprice_actualdiscountavg_population_2017...football_gold_cupbeer_capitalmusic_festdiscount_in_percenttimeseriestime_idxmonthlog_volumeavg_volume_by_skuavg_volume_by_agency
252013-01-01Agency_01SKU_0149261270371839421917.0720001141.5000001033.432731108.067269153733...0--9.467128249014.3904412613.37750174.829600
71832013-02-01Agency_01SKU_0143193734675393844419.9840001141.5000001065.41719576.082805153733...0--6.665160249124.5856202916.97808790.036700
89282013-03-01Agency_01SKU_0150928153189219209224.6000001179.3458201101.13363378.212187153733...0-music_fest6.631828249234.8956283215.061952130.487150
105882013-04-01Agency_01SKU_0153239038983809950127.5320001226.6875001138.28335788.404143153733...0--7.206737249344.9925533515.822697130.246150
122602013-05-01Agency_01SKU_0155175525486442000329.3960001230.3311041148.96963481.361470153733...0--6.612974249455.1682543688.107793159.051550
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84032017-02-01Agency_60SKU_2353025201085091304825.2426574261.2945654087.082609174.2119562180611...0--4.0882401904920.9242592.4187502664.670179
103592017-03-01Agency_60SKU_2361314399088612911125.3748164259.7690004126.776000132.9930002180611...0-music_fest3.1220711905030.5364934.3537502965.472829
121142017-04-01Agency_60SKU_2358996939694091294127.1092044261.8964284115.753572146.1428562180611...0--3.4290571905140.2311122.3962502861.802300
138842017-05-01Agency_60SKU_2362875946191741248228.4792720.0000000.0000000.0000002180611...0--0.000000190525-18.4206812.1825003489.190286
156692017-06-01Agency_60SKU_2363684697392836625629.6092594256.6750004246.01875010.6562502180611...0--0.2503421905360.9242592.3625003423.810793
\n", + "

18900 rows × 30 columns

\n", + "
" + ], + "text/plain": [ + " date agency sku industry_volume soda_volume \\\n", + "25 2013-01-01 Agency_01 SKU_01 492612703 718394219 \n", + "7183 2013-02-01 Agency_01 SKU_01 431937346 753938444 \n", + "8928 2013-03-01 Agency_01 SKU_01 509281531 892192092 \n", + "10588 2013-04-01 Agency_01 SKU_01 532390389 838099501 \n", + "12260 2013-05-01 Agency_01 SKU_01 551755254 864420003 \n", + "... ... ... ... ... ... \n", + "8403 2017-02-01 Agency_60 SKU_23 530252010 850913048 \n", + "10359 2017-03-01 Agency_60 SKU_23 613143990 886129111 \n", + "12114 2017-04-01 Agency_60 SKU_23 589969396 940912941 \n", + "13884 2017-05-01 Agency_60 SKU_23 628759461 917412482 \n", + "15669 2017-06-01 Agency_60 SKU_23 636846973 928366256 \n", + "\n", + " avg_max_temp price_regular price_actual discount \\\n", + "25 17.072000 1141.500000 1033.432731 108.067269 \n", + "7183 19.984000 1141.500000 1065.417195 76.082805 \n", + "8928 24.600000 1179.345820 1101.133633 78.212187 \n", + "10588 27.532000 1226.687500 1138.283357 88.404143 \n", + "12260 29.396000 1230.331104 1148.969634 81.361470 \n", + "... ... ... ... ... \n", + "8403 25.242657 4261.294565 4087.082609 174.211956 \n", + "10359 25.374816 4259.769000 4126.776000 132.993000 \n", + "12114 27.109204 4261.896428 4115.753572 146.142856 \n", + "13884 28.479272 0.000000 0.000000 0.000000 \n", + "15669 29.609259 4256.675000 4246.018750 10.656250 \n", + "\n", + " avg_population_2017 ... football_gold_cup beer_capital music_fest \\\n", + "25 153733 ... 0 - - \n", + "7183 153733 ... 0 - - \n", + "8928 153733 ... 0 - music_fest \n", + "10588 153733 ... 0 - - \n", + "12260 153733 ... 0 - - \n", + "... ... ... ... ... ... \n", + "8403 2180611 ... 0 - - \n", + "10359 2180611 ... 0 - music_fest \n", + "12114 2180611 ... 0 - - \n", + "13884 2180611 ... 0 - - \n", + "15669 2180611 ... 0 - - \n", + "\n", + " discount_in_percent timeseries time_idx month log_volume \\\n", + "25 9.467128 249 0 1 4.390441 \n", + "7183 6.665160 249 1 2 4.585620 \n", + "8928 6.631828 249 2 3 4.895628 \n", + "10588 7.206737 249 3 4 4.992553 \n", + "12260 6.612974 249 4 5 5.168254 \n", + "... ... ... ... ... ... \n", + "8403 4.088240 190 49 2 0.924259 \n", + "10359 3.122071 190 50 3 0.536493 \n", + "12114 3.429057 190 51 4 0.231112 \n", + "13884 0.000000 190 52 5 -18.420681 \n", + "15669 0.250342 190 53 6 0.924259 \n", + "\n", + " avg_volume_by_sku avg_volume_by_agency \n", + "25 2613.377501 74.829600 \n", + "7183 2916.978087 90.036700 \n", + "8928 3215.061952 130.487150 \n", + "10588 3515.822697 130.246150 \n", + "12260 3688.107793 159.051550 \n", + "... ... ... \n", + "8403 2.418750 2664.670179 \n", + "10359 4.353750 2965.472829 \n", + "12114 2.396250 2861.802300 \n", + "13884 2.182500 3489.190286 \n", + "15669 2.362500 3423.810793 \n", + "\n", + "[18900 rows x 30 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run FLAML" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Missing timestamps detected. To avoid error with estimators, set estimator list to ['prophet']. \n", + "[flaml.automl: 07-28 21:26:03] {2478} INFO - task = ts_forecast_panel\n", + "[flaml.automl: 07-28 21:26:03] {2480} INFO - Data split method: time\n", + "[flaml.automl: 07-28 21:26:03] {2483} INFO - Evaluation method: holdout\n", + "[flaml.automl: 07-28 21:26:03] {2552} INFO - Minimizing error metric: mape\n", + "[flaml.automl: 07-28 21:26:03] {2694} INFO - List of ML learners in AutoML Run: ['tft']\n", + "[flaml.automl: 07-28 21:26:03] {2986} INFO - iteration 0, current learner tft\n", + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "\n", + " | Name | Type | Params\n", + "----------------------------------------------------------------------------------------\n", + "0 | loss | QuantileLoss | 0 \n", + "1 | logging_metrics | ModuleList | 0 \n", + "2 | input_embeddings | MultiEmbedding | 1.3 K \n", + "3 | prescalers | ModuleDict | 256 \n", + "4 | static_variable_selection | VariableSelectionNetwork | 3.4 K \n", + "5 | encoder_variable_selection | VariableSelectionNetwork | 8.0 K \n", + "6 | decoder_variable_selection | VariableSelectionNetwork | 2.7 K \n", + "7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K \n", + "8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K \n", + "9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K \n", + "10 | static_context_enrichment | GatedResidualNetwork | 1.1 K \n", + "11 | lstm_encoder | LSTM | 4.4 K \n", + "12 | lstm_decoder | LSTM | 4.4 K \n", + "13 | post_lstm_gate_encoder | GatedLinearUnit | 544 \n", + "14 | post_lstm_add_norm_encoder | AddNorm | 32 \n", + "15 | static_enrichment | GatedResidualNetwork | 1.4 K \n", + "16 | multihead_attn | InterpretableMultiHeadAttention | 676 \n", + "17 | post_attn_gate_norm | GateAddNorm | 576 \n", + "18 | pos_wise_ff | GatedResidualNetwork | 1.1 K \n", + "19 | pre_output_gate_norm | GateAddNorm | 576 \n", + "20 | output_layer | Linear | 119 \n", + "----------------------------------------------------------------------------------------\n", + "33.6 K Trainable params\n", + "0 Non-trainable params\n", + "33.6 K Total params\n", + "0.135 Total estimated model params size (MB)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[flaml.automl: 07-28 21:46:46] {3114} INFO - Estimated sufficient time budget=12424212s. Estimated necessary time budget=12424s.\n", + "[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\n", + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "\n", + " | Name | Type | Params\n", + "----------------------------------------------------------------------------------------\n", + "0 | loss | QuantileLoss | 0 \n", + "1 | logging_metrics | ModuleList | 0 \n", + "2 | input_embeddings | MultiEmbedding | 1.3 K \n", + "3 | prescalers | ModuleDict | 256 \n", + "4 | static_variable_selection | VariableSelectionNetwork | 3.4 K \n", + "5 | encoder_variable_selection | VariableSelectionNetwork | 8.0 K \n", + "6 | decoder_variable_selection | VariableSelectionNetwork | 2.7 K \n", + "7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K \n", + "8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K \n", + "9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K \n", + "10 | static_context_enrichment | GatedResidualNetwork | 1.1 K \n", + "11 | lstm_encoder | LSTM | 4.4 K \n", + "12 | lstm_decoder | LSTM | 4.4 K \n", + "13 | post_lstm_gate_encoder | GatedLinearUnit | 544 \n", + "14 | post_lstm_add_norm_encoder | AddNorm | 32 \n", + "15 | static_enrichment | GatedResidualNetwork | 1.4 K \n", + "16 | multihead_attn | InterpretableMultiHeadAttention | 676 \n", + "17 | post_attn_gate_norm | GateAddNorm | 576 \n", + "18 | pos_wise_ff | GatedResidualNetwork | 1.1 K \n", + "19 | pre_output_gate_norm | GateAddNorm | 576 \n", + "20 | output_layer | Linear | 119 \n", + "----------------------------------------------------------------------------------------\n", + "33.6 K Trainable params\n", + "0 Non-trainable params\n", + "33.6 K Total params\n", + "0.135 Total estimated model params size (MB)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[flaml.automl: 07-28 22:08:05] {3425} INFO - retrain tft for 1279.6s\n", + "[flaml.automl: 07-28 22:08:05] {3432} INFO - retrained model: TemporalFusionTransformer(\n", + " (loss): QuantileLoss()\n", + " (logging_metrics): ModuleList(\n", + " (0): SMAPE()\n", + " (1): MAE()\n", + " (2): RMSE()\n", + " (3): MAPE()\n", + " )\n", + " (input_embeddings): MultiEmbedding(\n", + " (embeddings): ModuleDict(\n", + " (agency): Embedding(58, 16)\n", + " (sku): Embedding(25, 10)\n", + " (special_days): TimeDistributedEmbeddingBag(11, 6, mode=sum)\n", + " (month): Embedding(12, 6)\n", + " )\n", + " )\n", + " (prescalers): ModuleDict(\n", + " (avg_population_2017): Linear(in_features=1, out_features=8, bias=True)\n", + " (avg_yearly_household_income_2017): 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ELU(alpha=1.0)\n", + " (fc2): Linear(in_features=16, out_features=16, bias=True)\n", + " (gate_norm): GateAddNorm(\n", + " (glu): GatedLinearUnit(\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (fc): Linear(in_features=16, out_features=32, bias=True)\n", + " )\n", + " (add_norm): AddNorm(\n", + " (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " )\n", + " )\n", + " (pre_output_gate_norm): GateAddNorm(\n", + " (glu): GatedLinearUnit(\n", + " (fc): Linear(in_features=16, out_features=32, bias=True)\n", + " )\n", + " (add_norm): AddNorm(\n", + " (norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " )\n", + " (output_layer): Linear(in_features=16, out_features=7, bias=True)\n", + ")\n", + "[flaml.automl: 07-28 22:08:05] {2725} INFO - fit succeeded\n", + "[flaml.automl: 07-28 22:08:05] {2726} INFO - Time taken to find the best model: 1242.6435902118683\n", + "[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" + ] + } + ], + "source": [ + "from flaml import AutoML\n", + "automl = AutoML()\n", + "settings = {\n", + " \"time_budget\": 300, # total running time in seconds\n", + " \"metric\": \"mape\", # primary metric\n", + " \"task\": \"ts_forecast_panel\", # task type\n", + " \"log_file_name\": \"stallion_forecast.log\", # flaml log file\n", + " \"eval_method\": \"holdout\",\n", + "}\n", + "fit_kwargs_by_estimator = {\n", + " \"tft\": {\n", + " \"max_encoder_length\": 24,\n", + " \"static_categoricals\": [\"agency\", \"sku\"],\n", + " \"static_reals\": [\"avg_population_2017\", \"avg_yearly_household_income_2017\"],\n", + " \"time_varying_known_categoricals\": [\"special_days\", \"month\"],\n", + " \"variable_groups\": {\n", + " \"special_days\": special_days\n", + " }, # group of categorical variables can be treated as one variable\n", + " \"time_varying_known_reals\": [\n", + " \"time_idx\",\n", + " \"price_regular\",\n", + " \"discount_in_percent\",\n", + " ],\n", + " \"time_varying_unknown_categoricals\": [],\n", + " \"time_varying_unknown_reals\": [\n", + " \"y\", # always need a 'y' column for the target column\n", + " \"log_volume\",\n", + " \"industry_volume\",\n", + " \"soda_volume\",\n", + " \"avg_max_temp\",\n", + " \"avg_volume_by_agency\",\n", + " \"avg_volume_by_sku\",\n", + " ],\n", + " \"batch_size\": 128,\n", + " \"gpu_per_trial\": -1,\n", + " }\n", + "}\n", + "\"\"\"The main flaml automl API\"\"\"\n", + "automl.fit(\n", + " X_train=X_train,\n", + " y_train=y_train,\n", + " **settings,\n", + " period=time_horizon,\n", + " group_ids=[\"agency\", \"sku\"],\n", + " fit_kwargs_by_estimator=fit_kwargs_by_estimator,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prediction and Metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "17156 59.292\n", + "18946 66.420\n", + "20680 95.904\n", + "3189 52.812\n", + "4954 37.908\n", + " ... \n", + "19207 1.980\n", + "20996 1.260\n", + "3499 0.990\n", + "5248 0.090\n", + "6793 2.250\n", + "Name: volume, Length: 2100, dtype: float64\n", + "Agency_01 SKU_01 2017-07-01 2017-07-01 77.331932\n", + " 2017-08-01 2017-08-01 71.502121\n", + " 2017-09-01 2017-09-01 88.353912\n", + " 2017-10-01 2017-10-01 60.969868\n", + " 2017-11-01 2017-11-01 60.205246\n", + " ... \n", + "Agency_60 SKU_23 2017-08-01 2017-08-01 1.713270\n", + " 2017-09-01 2017-09-01 1.513947\n", + " 2017-10-01 2017-10-01 0.993663\n", + " 2017-11-01 2017-11-01 1.144696\n", + " 2017-12-01 2017-12-01 1.989883\n", + "Length: 2100, dtype: float32\n" + ] + } + ], + "source": [ + "\"\"\" compute predictions of testing dataset \"\"\"\n", + "y_pred = automl.predict(X_test)\n", + "print(y_test)\n", + "print(y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mape = 2743417592614313.0\n", + "smape = 52.37\n" + ] + } + ], + "source": [ + "\"\"\" compute different metric values on testing dataset\"\"\"\n", + "from flaml.ml import sklearn_metric_loss_score\n", + "print(\"mape\", \"=\", sklearn_metric_loss_score(\"mape\", y_pred, y_test))\n", + "\n", + "def smape(y_pred, y_test):\n", + " import numpy as np\n", + "\n", + " y_test, y_pred = np.array(y_test), np.array(y_pred)\n", + " return round(\n", + " np.mean(\n", + " np.abs(y_pred - y_test) /\n", + " ((np.abs(y_pred) + np.abs(y_test)) / 2)\n", + " ) * 100, 2\n", + " )\n", + "\n", + "print(\"smape\", \"=\", smape(y_pred, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Comparison with Alternatives (CO2 Dataset)" ] }, { @@ -3062,7 +4071,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -3087,7 +4096,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -3097,16 +4106,16 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 31, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -3119,7 +4128,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -3172,7 +4181,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -3197,7 +4206,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -3213,37 +4222,37 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=1638.009, Time=0.04 sec\n", - " ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=1344.207, Time=0.11 sec\n", + " ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=1638.009, Time=0.02 sec\n", + " ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=1344.207, Time=0.09 sec\n", " ARIMA(0,1,2)(0,0,0)[0] intercept : AIC=1222.286, Time=0.14 sec\n", - " ARIMA(0,1,3)(0,0,0)[0] intercept : AIC=1174.928, Time=0.18 sec\n", - " ARIMA(0,1,4)(0,0,0)[0] intercept : AIC=1188.947, Time=0.38 sec\n", - " ARIMA(0,1,5)(0,0,0)[0] intercept : AIC=1091.452, Time=0.52 sec\n", - " ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=1298.693, Time=0.06 sec\n", - " ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=1240.963, Time=0.10 sec\n", - " ARIMA(1,1,2)(0,0,0)[0] intercept : AIC=1196.535, Time=0.15 sec\n", - " ARIMA(1,1,3)(0,0,0)[0] intercept : AIC=1176.484, Time=0.28 sec\n", - " ARIMA(1,1,4)(0,0,0)[0] intercept : AIC=inf, Time=1.19 sec\n", - " ARIMA(2,1,0)(0,0,0)[0] intercept : AIC=1180.404, Time=0.10 sec\n", - " ARIMA(2,1,1)(0,0,0)[0] intercept : AIC=990.719, Time=0.28 sec\n", - " ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=988.094, Time=0.55 sec\n", - " ARIMA(2,1,3)(0,0,0)[0] intercept : AIC=1140.469, Time=0.57 sec\n", - " ARIMA(3,1,0)(0,0,0)[0] intercept : AIC=1126.139, Time=0.27 sec\n", - " ARIMA(3,1,1)(0,0,0)[0] intercept : AIC=989.496, Time=0.57 sec\n", - " ARIMA(3,1,2)(0,0,0)[0] intercept : AIC=991.555, Time=1.02 sec\n", - " ARIMA(4,1,0)(0,0,0)[0] intercept : AIC=1125.025, Time=0.17 sec\n", - " ARIMA(4,1,1)(0,0,0)[0] intercept : AIC=988.660, Time=1.12 sec\n", + " ARIMA(0,1,3)(0,0,0)[0] intercept : AIC=1174.928, Time=0.20 sec\n", + " ARIMA(0,1,4)(0,0,0)[0] intercept : AIC=1188.947, Time=0.43 sec\n", + " ARIMA(0,1,5)(0,0,0)[0] intercept : AIC=1091.452, Time=0.55 sec\n", + " ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=1298.693, Time=0.08 sec\n", + " ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=1240.963, Time=0.12 sec\n", + " ARIMA(1,1,2)(0,0,0)[0] intercept : AIC=1196.535, Time=0.19 sec\n", + " ARIMA(1,1,3)(0,0,0)[0] intercept : AIC=1176.484, Time=0.34 sec\n", + " ARIMA(1,1,4)(0,0,0)[0] intercept : AIC=inf, Time=1.18 sec\n", + " ARIMA(2,1,0)(0,0,0)[0] intercept : AIC=1180.404, Time=0.08 sec\n", + " ARIMA(2,1,1)(0,0,0)[0] intercept : AIC=990.719, Time=0.26 sec\n", + " ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=988.094, Time=0.53 sec\n", + " ARIMA(2,1,3)(0,0,0)[0] intercept : AIC=1140.469, Time=0.53 sec\n", + " ARIMA(3,1,0)(0,0,0)[0] intercept : AIC=1126.139, Time=0.21 sec\n", + " ARIMA(3,1,1)(0,0,0)[0] intercept : AIC=989.496, Time=0.51 sec\n", + " ARIMA(3,1,2)(0,0,0)[0] intercept : AIC=991.558, Time=1.17 sec\n", + " ARIMA(4,1,0)(0,0,0)[0] intercept : AIC=1125.025, Time=0.19 sec\n", + " ARIMA(4,1,1)(0,0,0)[0] intercept : AIC=988.660, Time=0.98 sec\n", " ARIMA(5,1,0)(0,0,0)[0] intercept : AIC=1113.673, Time=0.22 sec\n", "\n", "Best model: ARIMA(2,1,2)(0,0,0)[0] intercept\n", - "Total fit time: 8.065 seconds\n" + "Total fit time: 8.039 seconds\n" ] } ], @@ -3260,142 +4269,142 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " ARIMA(0,1,0)(0,0,0)[12] intercept : AIC=1638.009, Time=0.01 sec\n", - " ARIMA(0,1,0)(0,0,1)[12] intercept : AIC=1238.943, Time=0.21 sec\n", - " ARIMA(0,1,0)(0,0,2)[12] intercept : AIC=1040.890, Time=0.57 sec\n", - " ARIMA(0,1,0)(0,0,3)[12] intercept : AIC=911.545, Time=1.81 sec\n", - " ARIMA(0,1,0)(0,0,4)[12] intercept : AIC=823.103, Time=3.23 sec\n", - " ARIMA(0,1,0)(0,0,5)[12] intercept : AIC=792.850, Time=6.07 sec\n", - " ARIMA(0,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.24 sec\n", - " ARIMA(0,1,0)(1,0,1)[12] intercept : AIC=inf, Time=1.14 sec\n", - " ARIMA(0,1,0)(1,0,2)[12] intercept : AIC=inf, Time=2.78 sec\n", - " ARIMA(0,1,0)(1,0,3)[12] intercept : AIC=447.738, Time=6.32 sec\n", - " ARIMA(0,1,0)(1,0,4)[12] intercept : AIC=inf, Time=11.02 sec\n", - " ARIMA(0,1,0)(2,0,0)[12] intercept : AIC=inf, Time=1.11 sec\n", - " ARIMA(0,1,0)(2,0,1)[12] intercept : AIC=inf, Time=3.27 sec\n", - " ARIMA(0,1,0)(2,0,2)[12] intercept : AIC=inf, Time=3.04 sec\n", - " ARIMA(0,1,0)(2,0,3)[12] intercept : AIC=427.344, Time=8.22 sec\n", - " ARIMA(0,1,0)(3,0,0)[12] intercept : AIC=inf, Time=3.70 sec\n", - " ARIMA(0,1,0)(3,0,1)[12] intercept : AIC=425.322, Time=6.95 sec\n", - " ARIMA(0,1,0)(3,0,2)[12] intercept : AIC=431.465, Time=7.77 sec\n", - " ARIMA(0,1,0)(4,0,0)[12] intercept : AIC=inf, Time=10.95 sec\n", - " ARIMA(0,1,0)(4,0,1)[12] intercept : AIC=430.340, Time=11.56 sec\n", - " ARIMA(0,1,0)(5,0,0)[12] intercept : AIC=inf, Time=18.31 sec\n", - " ARIMA(0,1,1)(0,0,0)[12] intercept : AIC=1344.207, Time=0.07 sec\n", - " ARIMA(0,1,1)(0,0,1)[12] intercept : AIC=1112.274, Time=0.38 sec\n", - " ARIMA(0,1,1)(0,0,2)[12] intercept : AIC=993.565, Time=0.87 sec\n", - " ARIMA(0,1,1)(0,0,3)[12] intercept : AIC=891.683, Time=3.02 sec\n", - " ARIMA(0,1,1)(0,0,4)[12] intercept : AIC=820.025, Time=5.93 sec\n", - " ARIMA(0,1,1)(1,0,0)[12] intercept : AIC=612.811, Time=0.55 sec\n", - " ARIMA(0,1,1)(1,0,1)[12] intercept : AIC=392.446, Time=1.55 sec\n", - " ARIMA(0,1,1)(1,0,2)[12] intercept : AIC=398.980, Time=4.08 sec\n", - " ARIMA(0,1,1)(1,0,3)[12] intercept : AIC=424.632, Time=8.78 sec\n", - " ARIMA(0,1,1)(2,0,0)[12] intercept : AIC=510.637, Time=1.92 sec\n", - " ARIMA(0,1,1)(2,0,1)[12] intercept : AIC=396.708, Time=3.45 sec\n", - " ARIMA(0,1,1)(2,0,2)[12] intercept : AIC=396.399, Time=4.38 sec\n", - " ARIMA(0,1,1)(3,0,0)[12] intercept : AIC=467.985, Time=5.55 sec\n", - " ARIMA(0,1,1)(3,0,1)[12] intercept : AIC=412.398, Time=8.44 sec\n", - " ARIMA(0,1,1)(4,0,0)[12] intercept : AIC=448.948, Time=7.91 sec\n", - " ARIMA(0,1,2)(0,0,0)[12] intercept : AIC=1222.286, Time=0.13 sec\n", - " ARIMA(0,1,2)(0,0,1)[12] intercept : AIC=1046.922, Time=0.33 sec\n", - " ARIMA(0,1,2)(0,0,2)[12] intercept : AIC=947.532, Time=1.05 sec\n", - " ARIMA(0,1,2)(0,0,3)[12] intercept : AIC=867.310, Time=2.79 sec\n", - " ARIMA(0,1,2)(1,0,0)[12] intercept : AIC=608.450, Time=0.70 sec\n", - " ARIMA(0,1,2)(1,0,1)[12] intercept : AIC=386.324, Time=1.79 sec\n", - " ARIMA(0,1,2)(1,0,2)[12] intercept : AIC=421.305, Time=4.21 sec\n", - " ARIMA(0,1,2)(2,0,0)[12] intercept : AIC=507.685, Time=2.19 sec\n", - " ARIMA(0,1,2)(2,0,1)[12] intercept : AIC=408.351, Time=3.86 sec\n", - " ARIMA(0,1,2)(3,0,0)[12] intercept : AIC=460.596, Time=7.99 sec\n", - " ARIMA(0,1,3)(0,0,0)[12] intercept : AIC=1174.928, Time=0.17 sec\n", - " ARIMA(0,1,3)(0,0,1)[12] intercept : AIC=1037.324, Time=0.50 sec\n", - " ARIMA(0,1,3)(0,0,2)[12] intercept : AIC=947.471, Time=1.55 sec\n", + " ARIMA(0,1,0)(0,0,0)[12] intercept : AIC=1638.009, Time=0.02 sec\n", + " ARIMA(0,1,0)(0,0,1)[12] intercept : AIC=1238.943, Time=0.23 sec\n", + " ARIMA(0,1,0)(0,0,2)[12] intercept : AIC=1040.890, Time=0.53 sec\n", + " ARIMA(0,1,0)(0,0,3)[12] intercept : AIC=911.545, Time=1.76 sec\n", + " ARIMA(0,1,0)(0,0,4)[12] intercept : AIC=823.103, Time=3.18 sec\n", + " ARIMA(0,1,0)(0,0,5)[12] intercept : AIC=792.850, Time=5.99 sec\n", + " ARIMA(0,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.26 sec\n", + " ARIMA(0,1,0)(1,0,1)[12] intercept : AIC=inf, Time=1.37 sec\n", + " ARIMA(0,1,0)(1,0,2)[12] intercept : AIC=inf, Time=2.60 sec\n", + " ARIMA(0,1,0)(1,0,3)[12] intercept : AIC=447.302, Time=5.94 sec\n", + " ARIMA(0,1,0)(1,0,4)[12] intercept : AIC=inf, Time=11.23 sec\n", + " ARIMA(0,1,0)(2,0,0)[12] intercept : AIC=inf, Time=1.10 sec\n", + " ARIMA(0,1,0)(2,0,1)[12] intercept : AIC=inf, Time=2.37 sec\n", + " ARIMA(0,1,0)(2,0,2)[12] intercept : AIC=inf, Time=2.75 sec\n", + " ARIMA(0,1,0)(2,0,3)[12] intercept : AIC=427.135, Time=7.49 sec\n", + " ARIMA(0,1,0)(3,0,0)[12] intercept : AIC=inf, Time=3.56 sec\n", + " ARIMA(0,1,0)(3,0,1)[12] intercept : AIC=424.286, Time=6.44 sec\n", + " ARIMA(0,1,0)(3,0,2)[12] intercept : AIC=431.435, Time=6.86 sec\n", + " ARIMA(0,1,0)(4,0,0)[12] intercept : AIC=inf, Time=8.12 sec\n", + " ARIMA(0,1,0)(4,0,1)[12] intercept : AIC=430.321, Time=11.65 sec\n", + " ARIMA(0,1,0)(5,0,0)[12] intercept : AIC=inf, Time=17.56 sec\n", + " ARIMA(0,1,1)(0,0,0)[12] intercept : AIC=1344.207, Time=0.08 sec\n", + " ARIMA(0,1,1)(0,0,1)[12] intercept : AIC=1112.274, Time=0.37 sec\n", + " ARIMA(0,1,1)(0,0,2)[12] intercept : AIC=993.565, Time=0.76 sec\n", + " ARIMA(0,1,1)(0,0,3)[12] intercept : AIC=891.683, Time=3.11 sec\n", + " ARIMA(0,1,1)(0,0,4)[12] intercept : AIC=820.025, Time=5.52 sec\n", + " ARIMA(0,1,1)(1,0,0)[12] intercept : AIC=612.811, Time=0.60 sec\n", + " ARIMA(0,1,1)(1,0,1)[12] intercept : AIC=393.876, Time=1.61 sec\n", + " ARIMA(0,1,1)(1,0,2)[12] intercept : AIC=416.358, Time=3.64 sec\n", + " ARIMA(0,1,1)(1,0,3)[12] intercept : AIC=424.837, Time=8.45 sec\n", + " ARIMA(0,1,1)(2,0,0)[12] intercept : AIC=510.637, Time=1.63 sec\n", + " ARIMA(0,1,1)(2,0,1)[12] intercept : AIC=398.093, Time=3.18 sec\n", + " ARIMA(0,1,1)(2,0,2)[12] intercept : AIC=401.837, Time=4.14 sec\n", + " ARIMA(0,1,1)(3,0,0)[12] intercept : AIC=467.985, Time=8.25 sec\n", + " ARIMA(0,1,1)(3,0,1)[12] intercept : AIC=412.757, Time=10.34 sec\n", + " ARIMA(0,1,1)(4,0,0)[12] intercept : AIC=448.948, Time=7.42 sec\n", + " ARIMA(0,1,2)(0,0,0)[12] intercept : AIC=1222.286, Time=0.14 sec\n", + " ARIMA(0,1,2)(0,0,1)[12] intercept : AIC=1046.922, Time=0.32 sec\n", + " ARIMA(0,1,2)(0,0,2)[12] intercept : AIC=947.532, Time=0.92 sec\n", + " ARIMA(0,1,2)(0,0,3)[12] intercept : AIC=867.310, Time=2.67 sec\n", + " ARIMA(0,1,2)(1,0,0)[12] intercept : AIC=608.450, Time=0.65 sec\n", + " ARIMA(0,1,2)(1,0,1)[12] intercept : AIC=389.029, Time=1.72 sec\n", + " ARIMA(0,1,2)(1,0,2)[12] intercept : AIC=421.446, Time=3.85 sec\n", + " ARIMA(0,1,2)(2,0,0)[12] intercept : AIC=507.685, Time=2.02 sec\n", + " ARIMA(0,1,2)(2,0,1)[12] intercept : AIC=408.463, Time=3.61 sec\n", + " ARIMA(0,1,2)(3,0,0)[12] intercept : AIC=460.596, Time=5.28 sec\n", + " ARIMA(0,1,3)(0,0,0)[12] intercept : AIC=1174.928, Time=0.18 sec\n", + " ARIMA(0,1,3)(0,0,1)[12] intercept : AIC=1037.324, Time=0.56 sec\n", + " ARIMA(0,1,3)(0,0,2)[12] intercept : AIC=947.471, Time=1.46 sec\n", " ARIMA(0,1,3)(1,0,0)[12] intercept : AIC=602.141, Time=0.82 sec\n", - " ARIMA(0,1,3)(1,0,1)[12] intercept : AIC=397.131, Time=2.42 sec\n", - " ARIMA(0,1,3)(2,0,0)[12] intercept : AIC=500.296, Time=2.70 sec\n", - " ARIMA(0,1,4)(0,0,0)[12] intercept : AIC=1188.947, Time=0.37 sec\n", - " ARIMA(0,1,4)(0,0,1)[12] intercept : AIC=999.240, Time=0.86 sec\n", - " ARIMA(0,1,4)(1,0,0)[12] intercept : AIC=604.133, Time=1.00 sec\n", - " ARIMA(0,1,5)(0,0,0)[12] intercept : AIC=1091.452, Time=0.51 sec\n", - " ARIMA(1,1,0)(0,0,0)[12] intercept : AIC=1298.693, Time=0.06 sec\n", + " ARIMA(0,1,3)(1,0,1)[12] intercept : AIC=399.084, Time=2.40 sec\n", + " ARIMA(0,1,3)(2,0,0)[12] intercept : AIC=500.296, Time=2.60 sec\n", + " ARIMA(0,1,4)(0,0,0)[12] intercept : AIC=1188.947, Time=0.42 sec\n", + " ARIMA(0,1,4)(0,0,1)[12] intercept : AIC=999.240, Time=0.87 sec\n", + " ARIMA(0,1,4)(1,0,0)[12] intercept : AIC=604.133, Time=0.99 sec\n", + " ARIMA(0,1,5)(0,0,0)[12] intercept : AIC=1091.452, Time=0.53 sec\n", + " ARIMA(1,1,0)(0,0,0)[12] intercept : AIC=1298.693, Time=0.05 sec\n", " ARIMA(1,1,0)(0,0,1)[12] intercept : AIC=1075.553, Time=0.25 sec\n", - " ARIMA(1,1,0)(0,0,2)[12] intercept : AIC=971.074, Time=0.73 sec\n", - " ARIMA(1,1,0)(0,0,3)[12] intercept : AIC=882.846, Time=2.86 sec\n", - " ARIMA(1,1,0)(0,0,4)[12] intercept : AIC=818.711, Time=5.36 sec\n", - " ARIMA(1,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.64 sec\n", - " ARIMA(1,1,0)(1,0,1)[12] intercept : AIC=401.107, Time=1.22 sec\n", - " ARIMA(1,1,0)(1,0,2)[12] intercept : AIC=408.857, Time=3.70 sec\n", - " ARIMA(1,1,0)(1,0,3)[12] intercept : AIC=429.002, Time=7.05 sec\n", - " ARIMA(1,1,0)(2,0,0)[12] intercept : AIC=inf, Time=1.83 sec\n", - " ARIMA(1,1,0)(2,0,1)[12] intercept : AIC=419.393, Time=2.12 sec\n", - " ARIMA(1,1,0)(2,0,2)[12] intercept : AIC=409.260, Time=4.23 sec\n", - " ARIMA(1,1,0)(3,0,0)[12] intercept : AIC=inf, Time=5.46 sec\n", - " ARIMA(1,1,0)(3,0,1)[12] intercept : AIC=419.508, Time=7.69 sec\n", - " ARIMA(1,1,0)(4,0,0)[12] intercept : AIC=inf, Time=10.61 sec\n", - " ARIMA(1,1,1)(0,0,0)[12] intercept : AIC=1240.963, Time=0.09 sec\n", - " ARIMA(1,1,1)(0,0,1)[12] intercept : AIC=1069.162, Time=0.41 sec\n", - " ARIMA(1,1,1)(0,0,2)[12] intercept : AIC=973.065, Time=1.28 sec\n", - " ARIMA(1,1,1)(0,0,3)[12] intercept : AIC=884.323, Time=4.08 sec\n", - " ARIMA(1,1,1)(1,0,0)[12] intercept : AIC=588.156, Time=1.35 sec\n", - " ARIMA(1,1,1)(1,0,1)[12] intercept : AIC=399.034, Time=1.60 sec\n", - " ARIMA(1,1,1)(1,0,2)[12] intercept : AIC=409.556, Time=4.85 sec\n", - " ARIMA(1,1,1)(2,0,0)[12] intercept : AIC=503.551, Time=2.00 sec\n", - " ARIMA(1,1,1)(2,0,1)[12] intercept : AIC=399.923, Time=3.45 sec\n", - " ARIMA(1,1,1)(3,0,0)[12] intercept : AIC=457.277, Time=7.95 sec\n", - " ARIMA(1,1,2)(0,0,0)[12] intercept : AIC=1196.535, Time=0.16 sec\n", - " ARIMA(1,1,2)(0,0,1)[12] intercept : AIC=1042.432, Time=0.45 sec\n", - " ARIMA(1,1,2)(0,0,2)[12] intercept : AIC=948.444, Time=1.39 sec\n", - " ARIMA(1,1,2)(1,0,0)[12] intercept : AIC=589.937, Time=1.47 sec\n", - " ARIMA(1,1,2)(1,0,1)[12] intercept : AIC=399.533, Time=1.78 sec\n", - " ARIMA(1,1,2)(2,0,0)[12] intercept : AIC=502.534, Time=4.66 sec\n", - " ARIMA(1,1,3)(0,0,0)[12] intercept : AIC=1176.484, Time=0.31 sec\n", - " ARIMA(1,1,3)(0,0,1)[12] intercept : AIC=1039.309, Time=0.97 sec\n", - " ARIMA(1,1,3)(1,0,0)[12] intercept : AIC=604.131, Time=1.65 sec\n", - " ARIMA(1,1,4)(0,0,0)[12] intercept : AIC=inf, Time=1.16 sec\n", - " ARIMA(2,1,0)(0,0,0)[12] intercept : AIC=1180.404, Time=0.10 sec\n", - " ARIMA(2,1,0)(0,0,1)[12] intercept : AIC=1058.115, Time=0.34 sec\n", - " ARIMA(2,1,0)(0,0,2)[12] intercept : AIC=973.051, Time=0.95 sec\n", - " ARIMA(2,1,0)(0,0,3)[12] intercept : AIC=883.377, Time=2.91 sec\n", - " ARIMA(2,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.59 sec\n", - " ARIMA(2,1,0)(1,0,1)[12] intercept : AIC=400.994, Time=1.63 sec\n", - " ARIMA(2,1,0)(1,0,2)[12] intercept : AIC=407.847, Time=3.51 sec\n", - " ARIMA(2,1,0)(2,0,0)[12] intercept : AIC=inf, Time=2.49 sec\n", - " ARIMA(2,1,0)(2,0,1)[12] intercept : AIC=403.427, Time=4.40 sec\n", - " ARIMA(2,1,0)(3,0,0)[12] intercept : AIC=inf, Time=6.75 sec\n", - " ARIMA(2,1,1)(0,0,0)[12] intercept : AIC=990.719, Time=0.24 sec\n", - " ARIMA(2,1,1)(0,0,1)[12] intercept : AIC=881.526, Time=1.03 sec\n", - " ARIMA(2,1,1)(0,0,2)[12] intercept : AIC=837.402, Time=3.12 sec\n", - " ARIMA(2,1,1)(1,0,0)[12] intercept : AIC=584.703, Time=1.86 sec\n", - " ARIMA(2,1,1)(1,0,1)[12] intercept : AIC=438.400, Time=1.78 sec\n", - " ARIMA(2,1,1)(2,0,0)[12] intercept : AIC=494.774, Time=4.37 sec\n", - " ARIMA(2,1,2)(0,0,0)[12] intercept : AIC=988.094, Time=0.51 sec\n", - " ARIMA(2,1,2)(0,0,1)[12] intercept : AIC=inf, Time=1.98 sec\n", - " ARIMA(2,1,2)(1,0,0)[12] intercept : AIC=590.680, Time=2.26 sec\n", - " ARIMA(2,1,3)(0,0,0)[12] intercept : AIC=1140.469, Time=0.54 sec\n", - " ARIMA(3,1,0)(0,0,0)[12] intercept : AIC=1126.139, Time=0.23 sec\n", - " ARIMA(3,1,0)(0,0,1)[12] intercept : AIC=996.923, Time=0.41 sec\n", - " ARIMA(3,1,0)(0,0,2)[12] intercept : AIC=918.438, Time=1.17 sec\n", - " ARIMA(3,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.78 sec\n", - " ARIMA(3,1,0)(1,0,1)[12] intercept : AIC=407.208, Time=1.74 sec\n", - " ARIMA(3,1,0)(2,0,0)[12] intercept : AIC=inf, Time=3.23 sec\n", - " ARIMA(3,1,1)(0,0,0)[12] intercept : AIC=989.496, Time=0.54 sec\n", - " ARIMA(3,1,1)(0,0,1)[12] intercept : AIC=856.486, Time=1.86 sec\n", - " ARIMA(3,1,1)(1,0,0)[12] intercept : AIC=604.951, Time=0.84 sec\n", - " ARIMA(3,1,2)(0,0,0)[12] intercept : AIC=991.555, Time=0.93 sec\n", - " ARIMA(4,1,0)(0,0,0)[12] intercept : AIC=1125.025, Time=0.16 sec\n", - " ARIMA(4,1,0)(0,0,1)[12] intercept : AIC=987.621, Time=0.44 sec\n", - " ARIMA(4,1,0)(1,0,0)[12] intercept : AIC=inf, Time=1.06 sec\n", - " ARIMA(4,1,1)(0,0,0)[12] intercept : AIC=988.660, Time=0.98 sec\n", - " ARIMA(5,1,0)(0,0,0)[12] intercept : AIC=1113.673, Time=0.20 sec\n", + " ARIMA(1,1,0)(0,0,2)[12] intercept : AIC=971.074, Time=0.69 sec\n", + " ARIMA(1,1,0)(0,0,3)[12] intercept : AIC=882.846, Time=2.63 sec\n", + " ARIMA(1,1,0)(0,0,4)[12] intercept : AIC=818.711, Time=4.91 sec\n", + " ARIMA(1,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.59 sec\n", + " ARIMA(1,1,0)(1,0,1)[12] intercept : AIC=414.969, Time=1.19 sec\n", + " ARIMA(1,1,0)(1,0,2)[12] intercept : AIC=402.836, Time=3.25 sec\n", + " ARIMA(1,1,0)(1,0,3)[12] intercept : AIC=429.921, Time=6.47 sec\n", + " ARIMA(1,1,0)(2,0,0)[12] intercept : AIC=inf, Time=1.76 sec\n", + " ARIMA(1,1,0)(2,0,1)[12] intercept : AIC=419.397, Time=2.89 sec\n", + " ARIMA(1,1,0)(2,0,2)[12] intercept : AIC=409.246, Time=4.10 sec\n", + " ARIMA(1,1,0)(3,0,0)[12] intercept : AIC=inf, Time=4.96 sec\n", + " ARIMA(1,1,0)(3,0,1)[12] intercept : AIC=419.507, Time=7.41 sec\n", + " ARIMA(1,1,0)(4,0,0)[12] intercept : AIC=inf, Time=11.83 sec\n", + " ARIMA(1,1,1)(0,0,0)[12] intercept : AIC=1240.963, Time=0.11 sec\n", + " ARIMA(1,1,1)(0,0,1)[12] intercept : AIC=1069.162, Time=0.45 sec\n", + " ARIMA(1,1,1)(0,0,2)[12] intercept : AIC=973.065, Time=1.21 sec\n", + " ARIMA(1,1,1)(0,0,3)[12] intercept : AIC=884.323, Time=4.46 sec\n", + " ARIMA(1,1,1)(1,0,0)[12] intercept : AIC=588.156, Time=1.52 sec\n", + " ARIMA(1,1,1)(1,0,1)[12] intercept : AIC=399.035, Time=1.88 sec\n", + " ARIMA(1,1,1)(1,0,2)[12] intercept : AIC=409.509, Time=4.49 sec\n", + " ARIMA(1,1,1)(2,0,0)[12] intercept : AIC=503.551, Time=1.88 sec\n", + " ARIMA(1,1,1)(2,0,1)[12] intercept : AIC=399.929, Time=3.30 sec\n", + " ARIMA(1,1,1)(3,0,0)[12] intercept : AIC=457.277, Time=7.70 sec\n", + " ARIMA(1,1,2)(0,0,0)[12] intercept : AIC=1196.535, Time=0.18 sec\n", + " ARIMA(1,1,2)(0,0,1)[12] intercept : AIC=1042.432, Time=0.50 sec\n", + " ARIMA(1,1,2)(0,0,2)[12] intercept : AIC=948.444, Time=1.55 sec\n", + " ARIMA(1,1,2)(1,0,0)[12] intercept : AIC=587.318, Time=1.60 sec\n", + " ARIMA(1,1,2)(1,0,1)[12] intercept : AIC=403.282, Time=1.93 sec\n", + " ARIMA(1,1,2)(2,0,0)[12] intercept : AIC=498.922, Time=3.90 sec\n", + " ARIMA(1,1,3)(0,0,0)[12] intercept : AIC=1176.484, Time=0.29 sec\n", + " ARIMA(1,1,3)(0,0,1)[12] intercept : AIC=1039.309, Time=0.94 sec\n", + " ARIMA(1,1,3)(1,0,0)[12] intercept : AIC=604.131, Time=1.21 sec\n", + " ARIMA(1,1,4)(0,0,0)[12] intercept : AIC=inf, Time=1.19 sec\n", + " ARIMA(2,1,0)(0,0,0)[12] intercept : AIC=1180.404, Time=0.09 sec\n", + " ARIMA(2,1,0)(0,0,1)[12] intercept : AIC=1058.115, Time=0.33 sec\n", + " ARIMA(2,1,0)(0,0,2)[12] intercept : AIC=973.051, Time=0.92 sec\n", + " ARIMA(2,1,0)(0,0,3)[12] intercept : AIC=883.377, Time=2.84 sec\n", + " ARIMA(2,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.60 sec\n", + " ARIMA(2,1,0)(1,0,1)[12] intercept : AIC=416.548, Time=1.59 sec\n", + " ARIMA(2,1,0)(1,0,2)[12] intercept : AIC=420.663, Time=3.27 sec\n", + " ARIMA(2,1,0)(2,0,0)[12] intercept : AIC=inf, Time=2.23 sec\n", + " ARIMA(2,1,0)(2,0,1)[12] intercept : AIC=402.478, Time=4.16 sec\n", + " ARIMA(2,1,0)(3,0,0)[12] intercept : AIC=inf, Time=6.51 sec\n", + " ARIMA(2,1,1)(0,0,0)[12] intercept : AIC=990.719, Time=0.26 sec\n", + " ARIMA(2,1,1)(0,0,1)[12] intercept : AIC=881.526, Time=1.10 sec\n", + " ARIMA(2,1,1)(0,0,2)[12] intercept : AIC=837.402, Time=3.23 sec\n", + " ARIMA(2,1,1)(1,0,0)[12] intercept : AIC=584.045, Time=2.20 sec\n", + " ARIMA(2,1,1)(1,0,1)[12] intercept : AIC=443.982, Time=2.03 sec\n", + " ARIMA(2,1,1)(2,0,0)[12] intercept : AIC=501.152, Time=2.59 sec\n", + " ARIMA(2,1,2)(0,0,0)[12] intercept : AIC=988.094, Time=0.50 sec\n", + " ARIMA(2,1,2)(0,0,1)[12] intercept : AIC=757.710, Time=2.77 sec\n", + " ARIMA(2,1,2)(1,0,0)[12] intercept : AIC=595.703, Time=3.85 sec\n", + " ARIMA(2,1,3)(0,0,0)[12] intercept : AIC=1140.469, Time=0.95 sec\n", + " ARIMA(3,1,0)(0,0,0)[12] intercept : AIC=1126.139, Time=0.39 sec\n", + " ARIMA(3,1,0)(0,0,1)[12] intercept : AIC=996.923, Time=0.66 sec\n", + " ARIMA(3,1,0)(0,0,2)[12] intercept : AIC=918.438, Time=1.53 sec\n", + " ARIMA(3,1,0)(1,0,0)[12] intercept : AIC=inf, Time=0.88 sec\n", + " ARIMA(3,1,0)(1,0,1)[12] intercept : AIC=406.495, Time=2.17 sec\n", + " ARIMA(3,1,0)(2,0,0)[12] intercept : AIC=inf, Time=3.32 sec\n", + " ARIMA(3,1,1)(0,0,0)[12] intercept : AIC=989.496, Time=0.51 sec\n", + " ARIMA(3,1,1)(0,0,1)[12] intercept : AIC=856.486, Time=1.64 sec\n", + " ARIMA(3,1,1)(1,0,0)[12] intercept : AIC=604.951, Time=0.94 sec\n", + " ARIMA(3,1,2)(0,0,0)[12] intercept : AIC=991.558, Time=1.11 sec\n", + " ARIMA(4,1,0)(0,0,0)[12] intercept : AIC=1125.025, Time=0.18 sec\n", + " ARIMA(4,1,0)(0,0,1)[12] intercept : AIC=987.621, Time=0.50 sec\n", + " ARIMA(4,1,0)(1,0,0)[12] intercept : AIC=inf, Time=1.05 sec\n", + " ARIMA(4,1,1)(0,0,0)[12] intercept : AIC=988.660, Time=1.00 sec\n", + " ARIMA(5,1,0)(0,0,0)[12] intercept : AIC=1113.673, Time=0.22 sec\n", "\n", "Best model: ARIMA(0,1,2)(1,0,1)[12] intercept\n", - "Total fit time: 352.159 seconds\n" + "Total fit time: 343.809 seconds\n" ] } ], @@ -3419,15 +4428,15 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "auto arima mape = 0.0032060283828607705\n", - "auto sarima mape = 0.0007319806481537022\n" + "auto arima mape = 0.0032060326207122916\n", + "auto sarima mape = 0.0007347495325972257\n" ] } ], @@ -3446,7 +4455,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -3455,8 +4464,8 @@ "text": [ "flaml mape = 0.0005706814258795216\n", "default prophet mape = 0.0011396920680673015\n", - "auto arima mape = 0.0032060283828607705\n", - "auto sarima mape = 0.0007319806481537022\n" + "auto arima mape = 0.0032060326207122916\n", + "auto sarima mape = 0.0007347495325972257\n" ] } ], @@ -3470,12 +4479,12 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -3502,11 +4511,9 @@ } ], "metadata": { - "interpreter": { - "hash": "8b6c8c3ba4bafbc4530f534c605c8412f25bf61ef13254e4f377ccd42b838aa4" - }, "kernelspec": { - "display_name": "Python 3.8.10 64-bit ('python38': conda)", + "display_name": "Python ('pytorch_forecasting')", + "language": "python", "name": "python3" }, "language_info": { @@ -3519,7 +4526,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.8.1" + }, + "vscode": { + "interpreter": { + "hash": "25a19fbe0a9132dfb9279d48d161753c6352f8f9478c2e74383d340069b907c3" + } } }, "nbformat": 4, diff --git a/setup.py b/setup.py index dc5edd77c..e8bfb3b07 100644 --- a/setup.py +++ b/setup.py @@ -65,6 +65,7 @@ setuptools.setup( "rouge_score", "hcrystalball==0.1.10", "seqeval", + "pytorch-forecasting>=0.9.0", ], "catboost": ["catboost>=0.26"], "blendsearch": ["optuna==2.8.0"], @@ -98,6 +99,7 @@ setuptools.setup( "prophet>=1.0.1", "statsmodels>=0.12.2", "hcrystalball==0.1.10", + "pytorch-forecasting>=0.9.0", ], "benchmark": ["catboost>=0.26", "psutil==5.8.0", "xgboost==1.3.3"], }, diff --git a/test/automl/test_forecast.py b/test/automl/test_forecast.py index 1fb009b24..34ffb2ec5 100644 --- a/test/automl/test_forecast.py +++ b/test/automl/test_forecast.py @@ -60,7 +60,9 @@ def test_forecast_automl(budget=5): """ compute different metric values on testing dataset""" from flaml.ml import sklearn_metric_loss_score - print("mape", "=", sklearn_metric_loss_score("mape", y_pred, y_test)) + mape = sklearn_metric_loss_score("mape", y_pred, y_test) + print("mape", "=", mape) + assert mape <= 0.005, "the mape of flaml should be less than 0.005" from flaml.data import get_output_from_log ( @@ -415,7 +417,7 @@ def test_forecast_classification(budget=5): print(y_test) print(y_pred) - print("accuracy", "=", 1 - sklearn_metric_loss_score("accuracy", y_test, y_pred)) + print("accuracy", "=", 1 - sklearn_metric_loss_score("accuracy", y_pred, y_test)) from flaml.data import get_output_from_log ( @@ -440,9 +442,159 @@ def test_forecast_classification(budget=5): # plt.show() +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 + + +def test_forecast_panel(budget=5): + 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() + 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", + } + 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, + } + } + """The main flaml automl API""" + 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, + ) + """ retrieve best config and best learner""" + print("Best ML leaner:", automl.best_estimator) + print("Best hyperparmeter config:", automl.best_config) + print(f"Best mape on validation data: {automl.best_loss}") + print(f"Training duration of best run: {automl.best_config_train_time}s") + print(automl.model.estimator) + """ pickle and save the automl object """ + import pickle + + with open("automl.pkl", "wb") as f: + pickle.dump(automl, f, pickle.HIGHEST_PROTOCOL) + """ compute predictions of testing dataset """ + y_pred = automl.predict(X_test) + """ compute different metric values on testing dataset""" + from flaml.ml import sklearn_metric_loss_score + + print(y_test) + print(y_pred) + print("mape", "=", sklearn_metric_loss_score("mape", y_pred, y_test)) + + def smape(y_pred, y_test): + import numpy as np + + y_test, y_pred = np.array(y_test), np.array(y_pred) + return round( + np.mean(np.abs(y_pred - y_test) / ((np.abs(y_pred) + np.abs(y_test)) / 2)) + * 100, + 2, + ) + + print("smape", "=", smape(y_pred, y_test)) + # TODO: compute prediction for a specific time series + # """compute prediction for a specific time series""" + # a01_sku01_preds = automl.predict(X_test[(X_test["agency"] == "Agency_01") & (X_test["sku"] == "SKU_01")]) + # print("Agency01 SKU_01 predictions: ", a01_sku01_preds) + from flaml.data import get_output_from_log + + ( + time_history, + best_valid_loss_history, + valid_loss_history, + config_history, + metric_history, + ) = get_output_from_log(filename=settings["log_file_name"], time_budget=budget) + for config in config_history: + print(config) + print(automl.resource_attr) + print(automl.max_resource) + print(automl.min_resource) + + if __name__ == "__main__": test_forecast_automl(60) - test_multivariate_forecast_num(60) - test_multivariate_forecast_cat(60) + test_multivariate_forecast_num(5) + test_multivariate_forecast_cat(5) test_numpy() - test_forecast_classification(60) + test_forecast_classification(5) + test_forecast_panel(5) diff --git a/website/docs/Examples/AutoML-Time series forecast.md b/website/docs/Examples/AutoML-Time series forecast.md index 72ff979e3..8f34efa9f 100644 --- a/website/docs/Examples/AutoML-Time series forecast.md +++ b/website/docs/Examples/AutoML-Time series forecast.md @@ -28,7 +28,7 @@ print(automl.predict(X_train[84:])) #### Sample output -```python +``` [flaml.automl: 01-21 08:01:20] {2018} INFO - task = ts_forecast [flaml.automl: 01-21 08:01:20] {2020} INFO - Data split method: time [flaml.automl: 01-21 08:01:20] {2024} INFO - Evaluation method: holdout @@ -502,7 +502,7 @@ print(automl.predict(multi_X_test)) #### Sample Output -```python +``` [flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 15, current learner xgboost [flaml.automl: 02-28 21:32:26] {2620} INFO - at 6.2s, estimator xgboost's best error=0.0959, best estimator prophet's best error=0.0592 [flaml.automl: 02-28 21:32:26] {2458} INFO - iteration 16, current learner extra_tree @@ -594,7 +594,8 @@ print("True label", discrete_y_test) ``` #### Sample Output -```python + +``` [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 @@ -679,4 +680,886 @@ print("True label", discrete_y_test) [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) \ No newline at end of file diff --git a/website/docs/Use-Cases/Task-Oriented-AutoML.md b/website/docs/Use-Cases/Task-Oriented-AutoML.md index 3b478aea7..fabd0de89 100644 --- a/website/docs/Use-Cases/Task-Oriented-AutoML.md +++ b/website/docs/Use-Cases/Task-Oriented-AutoML.md @@ -12,6 +12,7 @@ - 'regression': regression. - 'ts_forecast': time series forecasting. - 'ts_forecast_classification': time series forecasting for classification. + - 'ts_forecast_panel': time series forecasting for panel datasets (multiple time series). - 'rank': learning to rank. - 'seq-classification': sequence classification. - 'seq-regression': sequence regression. @@ -119,6 +120,7 @@ The estimator list can contain one or more estimator names, each corresponding t - 'arima': ARIMA for task "ts_forecast". Hyperparameters: p, d, q. - 'sarimax': SARIMAX for task "ts_forecast". Hyperparameters: p, d, q, P, D, Q, s. - 'transformer': Huggingface transformer models for task "seq-classification", "seq-regression", "multichoice-classification", "token-classification" and "summarization". Hyperparameters: learning_rate, num_train_epochs, per_device_train_batch_size, warmup_ratio, weight_decay, adam_epsilon, seed. + - 'temporal_fusion_transform': TemporalFusionTransformerEstimator for task "ts_forecast_panel". Hyperparameters: gradient_clip_val, hidden_size, hidden_continuous_size, attention_head_size, dropout, learning_rate. * Custom estimator. Use custom estimator for: - tuning an estimator that is not built-in; - customizing search space for a built-in estimator.