From 1da77c59b9669a44d41cc517af9268d60a238715 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Wed, 20 Nov 2024 17:11:38 -0600 Subject: [PATCH 01/16] 0.6.17 a1 --- autots/evaluator/validation.py | 8 ++++---- autots/tools/seasonal.py | 1 + 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/autots/evaluator/validation.py b/autots/evaluator/validation.py index 4b80a26a..a029cef3 100644 --- a/autots/evaluator/validation.py +++ b/autots/evaluator/validation.py @@ -21,10 +21,10 @@ def extract_seasonal_val_periods(validation_method): def validate_num_validations( - validation_method, - num_validations, - df_wide_numeric, - forecast_length, + validation_method="backwards", + num_validations=2, + df_wide_numeric=None, + forecast_length=None, min_allowed_train_percent=0.5, verbose=0, ): diff --git a/autots/tools/seasonal.py b/autots/tools/seasonal.py index 48696166..817fdbf7 100644 --- a/autots/tools/seasonal.py +++ b/autots/tools/seasonal.py @@ -751,6 +751,7 @@ def create_seasonality_feature(DTindex, t, seasonality, history_days=None): ["dayofweek", (365.25, 4)], ["dayofweek", (365.25, 14)], ["dayofweek", (365.25, 24)], + ["dayofweek", (365.25, 14), (354.37, 10)], # 354.37 should be islamic calendar avg length "other", ] From d20db474fc138f919453d461f8ecc0cdddd74fe3 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Fri, 22 Nov 2024 17:45:45 -0600 Subject: [PATCH 02/16] 0.6.17 a2 --- autots/evaluator/auto_model.py | 1 + 1 file changed, 1 insertion(+) diff --git a/autots/evaluator/auto_model.py b/autots/evaluator/auto_model.py index 4e158568..e6ac804f 100644 --- a/autots/evaluator/auto_model.py +++ b/autots/evaluator/auto_model.py @@ -1669,6 +1669,7 @@ def _eval_prediction_for_template( ).reset_index(drop=True) ps_metric = model_error.per_series_metrics + ps_metric["ValidationRound"] = validation_round ps_metric.index.name = "autots_eval_metric" ps_metric = ps_metric.reset_index(drop=False) ps_metric.index = [model_id] * ps_metric.shape[0] From 8f365d2c7ea44a0e209f7fa135955add8aa23124 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Sat, 23 Nov 2024 22:50:23 -0600 Subject: [PATCH 03/16] 0.6.17 a3 --- autots/tools/seasonal.py | 1 + 1 file changed, 1 insertion(+) diff --git a/autots/tools/seasonal.py b/autots/tools/seasonal.py index 817fdbf7..02a4e63f 100644 --- a/autots/tools/seasonal.py +++ b/autots/tools/seasonal.py @@ -782,6 +782,7 @@ def random_datepart(method='random'): 0.05, 0.05, 0.02, + 0.02, 0.3, ], )[0] From a13f3e4aed4b027f3bb67b64a5df25d83a746ffb Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Sun, 1 Dec 2024 09:54:25 -0600 Subject: [PATCH 04/16] 0.6.17 a4 --- autots/evaluator/auto_model.py | 36 +++++++++++++++++----------------- autots/evaluator/auto_ts.py | 4 ++-- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/autots/evaluator/auto_model.py b/autots/evaluator/auto_model.py index e6ac804f..c3864ad1 100644 --- a/autots/evaluator/auto_model.py +++ b/autots/evaluator/auto_model.py @@ -2361,58 +2361,58 @@ def virtual_memory(): ) ps = template_result.per_series_metrics template_result.per_series_mae = ps[ps['autots_eval_metric'] == 'mae'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_made = ps[ps['autots_eval_metric'] == 'made'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_contour = ps[ ps['autots_eval_metric'] == 'contour' - ].drop(columns='autots_eval_metric') + ].drop(columns=['autots_eval_metric', "ValidationRound"]) template_result.per_series_rmse = ps[ps['autots_eval_metric'] == 'rmse'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_spl = ps[ps['autots_eval_metric'] == 'spl'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_mle = ps[ps['autots_eval_metric'] == 'mle'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_imle = ps[ps['autots_eval_metric'] == 'imle'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_maxe = ps[ps['autots_eval_metric'] == 'maxe'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_oda = ps[ps['autots_eval_metric'] == 'oda'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_mqae = ps[ps['autots_eval_metric'] == 'mqae'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_dwae = ps[ps['autots_eval_metric'] == 'dwae'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_ewmae = ps[ps['autots_eval_metric'] == 'ewmae'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_uwmse = ps[ps['autots_eval_metric'] == 'uwmse'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_smoothness = ps[ ps['autots_eval_metric'] == 'smoothness' - ].drop(columns='autots_eval_metric') + ].drop(columns=['autots_eval_metric', "ValidationRound"]) template_result.per_series_mate = ps[ps['autots_eval_metric'] == 'mate'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_matse = ps[ps['autots_eval_metric'] == 'matse'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) template_result.per_series_wasserstein = ps[ ps['autots_eval_metric'] == 'wasserstein' - ].drop(columns='autots_eval_metric') + ].drop(columns=['autots_eval_metric', "ValidationRound"]) template_result.per_series_dwd = ps[ps['autots_eval_metric'] == 'dwd'].drop( - columns='autots_eval_metric' + columns=['autots_eval_metric', "ValidationRound"] ) else: template_result.per_series_metrics = pd.DataFrame() diff --git a/autots/evaluator/auto_ts.py b/autots/evaluator/auto_ts.py index 19401791..bbd9ea2b 100644 --- a/autots/evaluator/auto_ts.py +++ b/autots/evaluator/auto_ts.py @@ -3557,9 +3557,9 @@ def plot_validations( if subset is None: series = random.choice(df_wide.columns) else: - scores = self.best_model_per_series_mape().index.tolist() + scores = self.best_model_per_series_score().index.tolist() scores = [x for x in scores if "_lltmicro" not in str(x)] - mapes = self.best_model_per_series_score().index.tolist() + mapes = self.best_model_per_series_mape().index.tolist() mapes = [x for x in mapes if "_lltmicro" not in str(x)] if str(subset).lower() == "best": series = mapes[-1] From 1bdb4c89bc553d0621136a94937ac5a60f993fea Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Mon, 2 Dec 2024 15:09:09 -0600 Subject: [PATCH 05/16] 0.6.17 a5 --- autots/tools/transform.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/autots/tools/transform.py b/autots/tools/transform.py index 9d8ee566..523302aa 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -2847,8 +2847,8 @@ def __init__( @staticmethod def get_new_params(method: str = "random"): return { - "rows": random.choices([1, 2, 4, 7], [0.83, 0.02, 0.05, 0.1])[0], - "lag": random.choices([1, 2, 7, 28], [0.8, 0.05, 0.1, 0.05])[0], + "rows": random.choices([1, 2, 4, 7, 24, 84, 168], [0.83, 0.02, 0.05, 0.1, 0.01, 0.05, 0.05])[0], + "lag": random.choices([1, 2, 7, 28, 84, 168], [0.8, 0.05, 0.1, 0.05, 0.05, 0.01])[0], "method": random.choices(["additive", "multiplicative"], [0.9, 0.1])[0], "strength": random.choices( [1.0, 0.9, 0.7, 0.5, 0.2], [0.8, 0.05, 0.05, 0.05, 0.05] From dc0510cad3f8b0fdb2f3b097ec82b9eab637e660 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Wed, 4 Dec 2024 09:53:42 -0600 Subject: [PATCH 06/16] 0.6.17 a6 --- autots/models/basics.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/autots/models/basics.py b/autots/models/basics.py index 2546763e..4c4c6227 100644 --- a/autots/models/basics.py +++ b/autots/models/basics.py @@ -1141,6 +1141,9 @@ def looped_motif( if point_method == "closest": idx = np.argsort(A, axis=0)[:k].flatten() else: + if k > A.shape[0]: + print("k too large for size of data in motif") + k = A.shape[0] idx = np.argpartition(A, k, axis=0)[:k].flatten() # distances for weighted mean results = y[idx] From a30b50ab2c7375e54949d846f579a5b0ccd13e97 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Wed, 4 Dec 2024 11:30:59 -0600 Subject: [PATCH 07/16] 0.6.17 a7 --- TODO.md | 30 ++---------------------------- autots/tools/transform.py | 9 ++++++--- extended_tutorial.md | 2 ++ 3 files changed, 10 insertions(+), 31 deletions(-) diff --git a/TODO.md b/TODO.md index 4949a4d2..a2d0aa50 100644 --- a/TODO.md +++ b/TODO.md @@ -13,34 +13,8 @@ * Forecasts are desired for the future immediately following the most recent data. * trimmed_mean to AverageValueNaive -# 0.6.16 🇺🇦 🇺🇦 🇺🇦 -* export_template added focus_models option -* added OneClassSVM and GaussianMixture anomaly model options -* added plot_unpredictability_score -* added a few more NeuralForecast search options -* bounds_only to Constraint transformer -* updates for deprecated upstream args -* FIRFilter transformer added -* mle and imle downscaled to reduce score imbalance issues with these two in generate score -* SectionalMotif now more robust to forecast lengths longer than history -* new transformer and metric options for SectionalMotif -* NaN robustness to matse -* 'round' option to Constraint -* minor change to mosaic min style ensembles to remove edge case errors -* 'mosaic-profile', 'filtered', 'unpredictability_adjusted' and 'median' style mosaics added -* updated profiler, and improved feature generation for horizontal generalization -* changepoint style trend as an option to GLM and GLS -* added ShiftFirstValue which is only a minor nuance on PositiveShift transformer -* added BasicLinearModel model -* datepart_method, scale, and fourier encodig to WindowRegression -* trimmed_mean and more date part options to SeasonalityMotif -* some additional options to MultivariateRegression -* added ThetaTransformer -* added TVVAR model (time varying VAR) -* added ChangepointDetrend transformer -* added MeanPercentSplitter transformer -* updated load_daily with more recent history -* added support for passing a custom metric +# 0.6.17 🇺🇦 🇺🇦 🇺🇦 +* minor adjustments and bug fixes ### Unstable Upstream Pacakges (those that are frequently broken by maintainers) * Pytorch-Forecasting diff --git a/autots/tools/transform.py b/autots/tools/transform.py index 523302aa..e5906e69 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -6644,17 +6644,20 @@ def transformer_list_to_dict(transformer_list): transformer_list = "superfast" if not transformer_list or transformer_list == "all": transformer_list = transformer_dict - elif transformer_list in ["fast", "default", "Fast", "auto"]: + elif transformer_list in ["fast", "default", "Fast", "auto", "fast_no_slice"]: transformer_list = fast_transformer_dict - elif transformer_list == "superfast": + elif transformer_list in ["superfast", "superfast_no_slice"]: transformer_list = superfast_transformer_dict - elif transformer_list == "scalable": + elif transformer_list in ["scalable", "scalable_no_slice"]: # "scalable" meant to be even smaller than "fast" subset of transformers transformer_list = fast_transformer_dict.copy() del transformer_list["SinTrend"] # no observed issues, but for efficiency # del transformer_list["HolidayTransformer"] # improved, should be good enough del transformer_list["ReplaceConstant"] del transformer_list["ThetaTransformer"] # just haven't tested it enough yet + elif "no_slice" in transformer_list: + # slice can be a problem child in some cases, so can remove by adding this + del transformer_list["Slice"] if isinstance(transformer_list, dict): transformer_prob = list(transformer_list.values()) diff --git a/extended_tutorial.md b/extended_tutorial.md index 118554fe..44c0c660 100644 --- a/extended_tutorial.md +++ b/extended_tutorial.md @@ -76,6 +76,8 @@ There are some basic things to beware of that can commonly lead to poor results: What you don't need to do before the automated forecasting is any typical preprocessing. It is best to leave it up to the model selection process to choose, as different models do better with different types of preprocessing. +One of the most common causes of failures for loading a template on a new dataset is models failing on series that are too short (or essentially all missing). Filter out series that are too new or have been discontinued, before proceeding. + ### Validation and Cross Validation Cross validation helps assure that the optimal model is stable over the dynamics of a time series. Cross validation can be tricky in time series data due to the necessity of preventing data leakage from future data points. From bc215f4215e7abebf780191b7a15cf2c700308a0 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Wed, 4 Dec 2024 14:02:08 -0600 Subject: [PATCH 08/16] 0.6.17 a8 --- autots/evaluator/auto_model.py | 8 +++++ autots/models/basics.py | 54 ++++++++++++++++++++++++++++++++-- 2 files changed, 59 insertions(+), 3 deletions(-) diff --git a/autots/evaluator/auto_model.py b/autots/evaluator/auto_model.py index c3864ad1..20277073 100644 --- a/autots/evaluator/auto_model.py +++ b/autots/evaluator/auto_model.py @@ -2017,6 +2017,14 @@ def _eval_prediction_for_template( }, }, }, + { # best on VPV, 19.7 smape + "fillna": "quadratic", + "transformations": {"0": "AlignLastValue", "1": "ChangepointDetrend"}, + "transformation_params": { + "0": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": False, "threshold": None, "threshold_method": "mean"}, + "1": {"model": "Linear", "changepoint_spacing": 180, "changepoint_distance_end": 360, "datepart_method": None} + } + }, ] diff --git a/autots/models/basics.py b/autots/models/basics.py index 4c4c6227..f586b871 100644 --- a/autots/models/basics.py +++ b/autots/models/basics.py @@ -3158,6 +3158,8 @@ def __init__( distance_metric: str = "canberra", k: int = 10, sample_fraction=None, + comparison_transformation: dict = None, + combination_transformation: dict = None, **kwargs, ): ModelObject.__init__( @@ -3175,6 +3177,8 @@ def __init__( self.distance_metric = distance_metric self.k = k self.sample_fraction = sample_fraction + self.comparison_transformation = comparison_transformation + self.combination_transformation = combination_transformation def fit(self, df, future_regressor=None): """Train algorithm given data supplied. @@ -3203,10 +3207,27 @@ def predict( """ predictStartTime = datetime.datetime.now() phrase_n = self.window + forecast_length + # fit transform only, no need for inverse as this is only for finding windows + if self.comparison_transformation is not None: + self.comparison_transformer = GeneralTransformer( + **self.comparison_transformation + ) + compare_df = self.comparison_transformer.fit_transform(self.df) + else: + compare_df = self.df + # applied once, then inversed after windows combined as forecast + if self.combination_transformation is not None: + self.combination_transformer = GeneralTransformer( + **self.combination_transformation + ) + wind_arr = self.combination_transformer.fit_transform(self.df) + else: + wind_arr = self.df + if False: # OLD WAY x = sliding_window_view( - self.df.to_numpy(dtype=np.float32), phrase_n, axis=0 + compare_df.to_numpy(dtype=np.float32), phrase_n, axis=0 ) Xa = x.reshape(-1, x.shape[-1]) if self.sample_fraction is not None: @@ -3222,7 +3243,7 @@ def predict( else: # shared with WindowRegression Xa = chunk_reshape( - self.df.to_numpy(dtype=np.float32), + compare_df.to_numpy(dtype=np.float32), phrase_n, sample_fraction=self.sample_fraction, random_seed=self.random_seed, @@ -3239,7 +3260,7 @@ def predict( tree = BallTree(Xa[:, : self.window], metric=self.distance_metric) # Query the KDTree to find k nearest neighbors for each point in Xa - Xb = self.df.iloc[-self.window :].to_numpy().T + Xb = wind_arr.iloc[-self.window :].to_numpy().T A, self.windows = tree.query(Xb, k=self.k) # (k, forecast_length, n_series) self.result_windows = Xa[self.windows][:, :, self.window :].transpose(1, 2, 0) @@ -3273,6 +3294,14 @@ def predict( upper_forecast = pd.DataFrame( upper_forecast, index=test_index, columns=self.column_names ) + if self.combination_transformation is not None: + forecast = self.combination_transformer.inverse_transform(forecast) + lower_forecast = self.combination_transformer.inverse_transform( + lower_forecast + ) + upper_forecast = self.combination_transformer.inverse_transform( + upper_forecast + ) if just_point_forecast: return forecast else: @@ -3331,6 +3360,21 @@ def get_new_params(self, method: str = 'random'): k_choice = random.choices( [1, 3, 5, 10, 15, 20, 100], [0.02, 0.2, 0.2, 0.5, 0.1, 0.1, 0.1] )[0] + transformers_none = random.choices([True, False], [0.7, 0.3])[0] + if transformers_none: + comparison_transformation = None + combination_transformation = None + else: + comparison_transformation = RandomTransform( + transformer_list=superfast_transformer_dict, + transformer_max_depth=1, + allow_none=True, + ) + combination_transformation = RandomTransform( + transformer_list=superfast_transformer_dict, + transformer_max_depth=1, + allow_none=True, + ) return { "window": random.choices( [2, 3, 5, 7, 10, 14, 28, 60], @@ -3343,6 +3387,8 @@ def get_new_params(self, method: str = 'random'): "distance_metric": random.choices(metric_list, metric_probabilities)[0], "k": k_choice, "sample_fraction": sample_fraction, + "comparison_transformation": comparison_transformation, + "combination_transformation": combination_transformation, } def get_params(self): @@ -3353,6 +3399,8 @@ def get_params(self): "distance_metric": self.distance_metric, "k": self.k, "sample_fraction": self.sample_fraction, + "comparison_transformation": self.comparison_transformation, + "combination_transformation": self.combination_transformation, } From 38c1267f8f0ab434df0ff360f3a78448996caffb Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Wed, 4 Dec 2024 21:19:53 -0600 Subject: [PATCH 09/16] 0.6.17 a9 --- tests/test_autots.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/tests/test_autots.py b/tests/test_autots.py index b3e24b51..2aaeac16 100644 --- a/tests/test_autots.py +++ b/tests/test_autots.py @@ -495,10 +495,17 @@ def test_subset_expansion(self): id_col="series_id" if long else None, ) model.expand_horizontal() + orig_param = json.loads(model.best_model_original.iloc[0]['ModelParameters']) + new_param = json.loads(model.best_model.iloc[0]['ModelParameters']) + diff_mods = [x for x in orig_param['models'].keys() if x not in new_param['models'].keys()] + if diff_mods: + details = orig_param['models'][diff_mods] + else: + details = "" self.assertCountEqual( - json.loads(model.best_model_original.iloc[0]['ModelParameters'])['models'].keys(), - json.loads(model.best_model.iloc[0]['ModelParameters'])['models'].keys(), - msg="model expansion failed to use the same models" + orig_param['models'].keys(), + new_param['models'].keys(), + msg=f"model expansion failed to use the same models {details}" ) num_series = len(df['series_id'].unique().tolist()) if long else df.shape[1] self.assertEqual( From 96a519dca4c87cf6e466b3ae3585200548193962 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Wed, 4 Dec 2024 22:36:00 -0600 Subject: [PATCH 10/16] 0.6.17 a10 --- autots/models/statsmodels.py | 7 ++++--- autots/tools/transform.py | 6 ++++-- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/autots/models/statsmodels.py b/autots/models/statsmodels.py index 12ec7911..fd240cc1 100644 --- a/autots/models/statsmodels.py +++ b/autots/models/statsmodels.py @@ -550,7 +550,6 @@ def predict( if just_point_forecast == True, a dataframe of point forecasts """ predictStartTime = datetime.datetime.now() - from statsmodels.tsa.holtwinters import ExponentialSmoothing test_index = self.create_forecast_index(forecast_length=forecast_length) parallel = True @@ -566,6 +565,8 @@ def predict( def ets_forecast_by_column(current_series, args): """Run one series of ETS and return prediction.""" + from statsmodels.tsa.holtwinters import ExponentialSmoothing + series_name = current_series.name with warnings.catch_warnings(): if args['verbose'] < 2: @@ -616,7 +617,7 @@ def ets_forecast_by_column(current_series, args): # joblib multiprocessing to loop through series if parallel: df_list = Parallel(n_jobs=self.n_jobs)( - delayed(ets_forecast_by_column)(self.df_train[col], args) + delayed(ets_forecast_by_column)(self.df_train[col].astype(float), args) for (col) in cols ) forecast = pd.concat(df_list, axis=1) @@ -665,7 +666,7 @@ def get_new_params(self, method: str = 'random'): seasonal_probability = [0.2, 0.2, 0.6] seasonal_choice = random.choices(seasonal_list, seasonal_probability)[0] if seasonal_choice in ["additive", "multiplicative"]: - seasonal_period_choice = seasonal_int() + seasonal_period_choice = seasonal_int(small=True if method != "deep" else False) else: seasonal_period_choice = None parameter_dict = { diff --git a/autots/tools/transform.py b/autots/tools/transform.py index e5906e69..95f63805 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -1207,7 +1207,7 @@ def get_new_params(method: str = "random", holiday_countries_used=None): model_dict={ "ElasticNet": 0.5, "DecisionTree": 0.25, - "KNN": 0.02, + # "KNN": 0.02, } ) else: @@ -2238,6 +2238,7 @@ def fit_transform(self, df): def get_new_params(method: str = "random"): if method == "fast": method = random.choice(['butter', 'savgol_filter']) + polyorder = random.choices([1, 2, 3, 4], [0.5, 0.5, 0.25, 0.2])[0] else: method = random.choices( [ @@ -2253,6 +2254,7 @@ def get_new_params(method: str = "random"): [0.1, 0.1, 0.9, 0.9], k=1, )[0] + polyorder = random.choice([1, 2, 3, 4]) # analog_choice = bool(random.randint(0, 1)) analog_choice = False xn = random.randint(1, 99) @@ -2262,7 +2264,7 @@ def get_new_params(method: str = "random"): elif method == "savgol_filter": method_args = { 'window_length': random.choices([7, 31, 91], [0.4, 0.3, 0.3])[0], - 'polyorder': random.choice([1, 2, 3, 4]), + 'polyorder': polyorder, 'deriv': random.choices([0, 1], [0.8, 0.2])[0], 'mode': random.choice(['mirror', 'nearest', 'interp']), } From 9ea7affeb468d57a3457ec1073d6118635298c30 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 5 Dec 2024 09:15:19 -0600 Subject: [PATCH 11/16] 0.6.17 a11 --- autots/models/basics.py | 16 +++++++++++++--- autots/models/sklearn.py | 10 +++++++--- autots/tools/transform.py | 4 ++-- 3 files changed, 22 insertions(+), 8 deletions(-) diff --git a/autots/models/basics.py b/autots/models/basics.py index f586b871..c733aba8 100644 --- a/autots/models/basics.py +++ b/autots/models/basics.py @@ -3260,10 +3260,20 @@ def predict( tree = BallTree(Xa[:, : self.window], metric=self.distance_metric) # Query the KDTree to find k nearest neighbors for each point in Xa - Xb = wind_arr.iloc[-self.window :].to_numpy().T - A, self.windows = tree.query(Xb, k=self.k) + Xb = compare_df.iloc[-self.window :].to_numpy().T + A, self.windows = tree.query(Xb, k=self.k) # dualtree=True + if self.combination_transformation is not None or self.comparison_transformation is not None: + del Xa + Xc = chunk_reshape( + wind_arr.to_numpy(dtype=np.float32), + phrase_n, + sample_fraction=self.sample_fraction, + random_seed=self.random_seed, + ) + else: + Xc = Xa # (k, forecast_length, n_series) - self.result_windows = Xa[self.windows][:, :, self.window :].transpose(1, 2, 0) + self.result_windows = Xc[self.windows][:, :, self.window :].transpose(1, 2, 0) # now aggregate results into point and bound forecasts if self.point_method == "weighted_mean": diff --git a/autots/models/sklearn.py b/autots/models/sklearn.py index 43ece341..ca110962 100644 --- a/autots/models/sklearn.py +++ b/autots/models/sklearn.py @@ -291,6 +291,7 @@ def rolling_x_regressor_regressor( cointegration_lag: int = 1, series_id=None, slice_index=None, + series_id_to_multiindex=None, ): """Adds in the future_regressor.""" X = rolling_x_regressor( @@ -327,6 +328,9 @@ def rolling_x_regressor_regressor( X['series_id'] = df.columns[0] X = X.merge(static_regressor, left_on="series_id", right_index=True, how='left') X = X.drop(columns=['series_id']) + if series_id_to_multiindex is not None: + X["series_id"] = str(series_id_to_multiindex) + X = X.set_index("series_id", append=True) if series_id is not None: hashed = ( int(hashlib.sha256(str(series_id).encode('utf-8')).hexdigest(), 16) @@ -1148,7 +1152,7 @@ def generate_regressor_params( "n_neighbors": random.choices([3, 5, 10, 14], [0.2, 0.7, 0.1, 0.1])[ 0 ], - "weights": random.choices(['uniform', 'distance'], [0.7, 0.3])[0], + "weights": random.choices(['uniform', 'distance'], [0.999, 0.001])[0], 'p': random.choices([2, 1, 1.5], [0.7, 0.1, 0.1])[0], 'leaf_size': random.choices([30, 10, 50], [0.8, 0.1, 0.1])[0], }, @@ -3442,7 +3446,7 @@ def fit( n_jobs=self.n_jobs, verbose=self.verbose, timeout=3600 )( delayed(rolling_x_regressor_regressor)( - base[x_col].to_frame(), + base[x_col].to_frame().astype(float), mean_rolling_periods=self.mean_rolling_periods, macd_periods=self.macd_periods, std_rolling_periods=self.std_rolling_periods, @@ -3485,7 +3489,7 @@ def fit( self.X = pd.concat( [ rolling_x_regressor_regressor( - base[x_col].to_frame(), + base[x_col].to_frame().astype(float), mean_rolling_periods=self.mean_rolling_periods, macd_periods=self.macd_periods, std_rolling_periods=self.std_rolling_periods, diff --git a/autots/tools/transform.py b/autots/tools/transform.py index 95f63805..dc8751e6 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -1200,14 +1200,14 @@ def get_new_params(method: str = "random", holiday_countries_used=None): else: polynomial_choice = None - if method == "all": + if method in ["all", "deep"]: choice = generate_regressor_params() elif method == "fast": choice = generate_regressor_params( model_dict={ "ElasticNet": 0.5, "DecisionTree": 0.25, - # "KNN": 0.02, + # "KNN": 0.002, # simply uses too much memory at scale } ) else: From c565d67bb999e6ed44c6037792f4feb8ed09f059 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 5 Dec 2024 12:28:09 -0600 Subject: [PATCH 12/16] 0.617 a12 --- TODO.md | 3 +- autots/evaluator/auto_model.py | 12 + autots/models/basics.py | 596 +++++++++++++++++++++++++++++++++ autots/models/model_list.py | 14 +- autots/models/statsmodels.py | 12 +- extended_tutorial.md | 1 + tests/test_autots.py | 1 + 7 files changed, 631 insertions(+), 8 deletions(-) diff --git a/TODO.md b/TODO.md index a2d0aa50..4f8fb179 100644 --- a/TODO.md +++ b/TODO.md @@ -14,7 +14,8 @@ * trimmed_mean to AverageValueNaive # 0.6.17 🇺🇦 🇺🇦 🇺🇦 -* minor adjustments and bug fixes +* minor adjustments and bug fixes for scalability +* added BallTreeRegressionMotif ### Unstable Upstream Pacakges (those that are frequently broken by maintainers) * Pytorch-Forecasting diff --git a/autots/evaluator/auto_model.py b/autots/evaluator/auto_model.py index 20277073..10aa4684 100644 --- a/autots/evaluator/auto_model.py +++ b/autots/evaluator/auto_model.py @@ -48,6 +48,7 @@ BallTreeMultivariateMotif, BasicLinearModel, TVVAR, + BallTreeRegressionMotif, ) from autots.models.statsmodels import ( GLS, @@ -732,6 +733,17 @@ def ModelMonster( n_jobs=n_jobs, **parameters, ) + elif model == 'BallTreeRegressionMotif': + return BallTreeRegressionMotif( + frequency=frequency, + prediction_interval=prediction_interval, + holiday_country=holiday_country, + random_seed=random_seed, + verbose=verbose, + forecast_length=forecast_length, + n_jobs=n_jobs, + **parameters, + ) elif model == "": raise AttributeError( ("Model name is empty. Likely this means AutoTS has not been fit.") diff --git a/autots/models/basics.py b/autots/models/basics.py index c733aba8..39dfa32e 100644 --- a/autots/models/basics.py +++ b/autots/models/basics.py @@ -38,6 +38,7 @@ from autots.tools.fft import fourier_extrapolation from autots.tools.impute import FillNA from autots.evaluator.metrics import wasserstein +from autots.models.sklearn import rolling_x_regressor_regressor # these are all optional packages @@ -4506,3 +4507,598 @@ def get_params(self): "mode": self.mode, "holiday_countries_used": self.holiday_countries_used, } + + +class BallTreeRegressionMotif(ModelObject): + """Forecasts using a nearest neighbors type model adapted for probabilistic time series. + This version uses a feature ala MultivariateRegression but with motifs instead of a regression ML model. + Many of these motifs will struggle when the forecast_length is large and history is short. + + Args: + frequency (str): String alias of datetime index frequency or else 'infer' + prediction_interval (float): Confidence interval for probabilistic forecast + n_jobs (int): how many parallel processes to run + random_seed (int): used in selecting windows if max_windows is less than total available + window (int): length of forecast history to match on + point_method (int): how to summarize the nearest neighbors to generate the point forecast + "weighted_mean", "mean", "median", "midhinge" + distance_metric (str): all valid values for scipy cdist + k (int): number of closest neighbors to consider + """ + + def __init__( + self, + frequency: str = 'infer', + prediction_interval: float = 0.9, + holiday_country: str = 'US', + random_seed: int = 2020, + verbose: int = 0, + n_jobs: int = 1, + window: int = 5, + point_method: str = "mean", + distance_metric: str = "canberra", + k: int = 10, + sample_fraction=None, + comparison_transformation: dict = None, + combination_transformation: dict = None, + extend_df: bool = True, + # multivar params + holiday: bool = False, + mean_rolling_periods: int = 30, + macd_periods: int = None, + std_rolling_periods: int = 7, + max_rolling_periods: int = 7, + min_rolling_periods: int = 7, + ewm_var_alpha: float = None, + quantile90_rolling_periods: int = None, + quantile10_rolling_periods: int = None, + ewm_alpha: float = 0.5, + additional_lag_periods: int = None, + abs_energy: bool = False, + rolling_autocorr_periods: int = None, + nonzero_last_n: int = None, + datepart_method: str = None, + polynomial_degree: int = None, + probabilistic: bool = False, + scale_full_X: bool = False, + cointegration: str = None, + cointegration_lag: int = None, + series_hash: bool = False, + frac_slice: float = None, + **kwargs, + ): + ModelObject.__init__( + self, + "BallTreeRegressionMotif", + frequency, + prediction_interval, + holiday_country=holiday_country, + random_seed=random_seed, + verbose=verbose, + n_jobs=n_jobs, + ) + self.window = window + self.point_method = point_method + self.distance_metric = distance_metric + self.k = k + self.sample_fraction = sample_fraction + self.comparison_transformation = comparison_transformation + self.combination_transformation = combination_transformation + self.extend_df = extend_df + # multivar params + self.holiday = holiday + self.mean_rolling_periods = mean_rolling_periods + if mean_rolling_periods is None: + self.macd_periods = None + else: + self.macd_periods = macd_periods + self.std_rolling_periods = std_rolling_periods + self.max_rolling_periods = max_rolling_periods + self.min_rolling_periods = min_rolling_periods + self.ewm_var_alpha = ewm_var_alpha + self.quantile90_rolling_periods = quantile90_rolling_periods + self.quantile10_rolling_periods = quantile10_rolling_periods + self.ewm_alpha = ewm_alpha + self.additional_lag_periods = additional_lag_periods + self.abs_energy = abs_energy + self.rolling_autocorr_periods = rolling_autocorr_periods + self.nonzero_last_n = nonzero_last_n + self.datepart_method = datepart_method + self.polynomial_degree = polynomial_degree + self.regressor_train = None + self.regressor_per_series_train = None + self.static_regressor = None + self.probabilistic = probabilistic + self.scale_full_X = scale_full_X + self.cointegration = cointegration + self.cointegration_lag = cointegration_lag + self.series_hash = series_hash + self.frac_slice = frac_slice + + def fit(self, df, future_regressor=None, static_regressor=None, regressor_per_series=None,): + """Train algorithm given data supplied. + + Args: + df (pandas.DataFrame): Datetime Indexed + """ + if self.regression_type is not None: + if future_regressor is None: + raise ValueError( + "regression_type='User' but not future_regressor supplied." + ) + else: + self.regressor_train = future_regressor.reindex(df.index) + if regressor_per_series is not None: + self.regressor_per_series_train = regressor_per_series + if static_regressor is not None: + self.static_regressor = static_regressor + + df = self.basic_profile(df) + self.df = df + self.fit_runtime = datetime.datetime.now() - self.startTime + return self + + def predict( + self, forecast_length: int, future_regressor=None, just_point_forecast=False, static_regressor=None, regressor_per_series=None, + ): + """Generates forecast data immediately following dates of index supplied to .fit() + + Args: + forecast_length (int): Number of periods of data to forecast ahead + regressor (numpy.Array): additional regressor, not used + just_point_forecast (bool): If True, return a pandas.DataFrame of just point forecasts + + Returns: + Either a PredictionObject of forecasts and metadata, or + if just_point_forecast == True, a dataframe of point forecasts + """ + predictStartTime = datetime.datetime.now() + # fit transform only, no need for inverse as this is only for finding windows + if self.comparison_transformation is not None: + self.comparison_transformer = GeneralTransformer( + **self.comparison_transformation + ) + compare_df = self.comparison_transformer.fit_transform(self.df) + else: + compare_df = self.df + # applied once, then inversed after windows combined as forecast + if self.combination_transformation is not None: + self.combination_transformer = GeneralTransformer( + **self.combination_transformation + ) + wind_arr = self.combination_transformer.fit_transform(self.df) + else: + wind_arr = self.df + + ############################ + # fractional slicing to reduce size + if self.frac_slice is not None: + slice_size = int(self.df.shape[0] * self.frac_slice) + self.slice_index = self.df.index[slice_size:] + else: + self.slice_index = None + # handle regressor + if self.regression_type is not None: + cut_regr = self.regressor_train + cut_regr.index = compare_df.index + else: + cut_regr = None + + parallel = True + if self.n_jobs in [0, 1] or compare_df.shape[1] < 20: + parallel = False + elif not joblib_present: + parallel = False + # joblib multiprocessing to loop through series + # this might be causing issues, TBD Key Error from Resource Tracker + if parallel: + self.Xa = Parallel( + n_jobs=self.n_jobs, verbose=self.verbose, timeout=36000 + )( + delayed(rolling_x_regressor_regressor)( + compare_df[x_col].to_frame(), + mean_rolling_periods=self.mean_rolling_periods, + macd_periods=self.macd_periods, + std_rolling_periods=self.std_rolling_periods, + max_rolling_periods=self.max_rolling_periods, + min_rolling_periods=self.min_rolling_periods, + ewm_var_alpha=self.ewm_var_alpha, + quantile90_rolling_periods=self.quantile90_rolling_periods, + quantile10_rolling_periods=self.quantile10_rolling_periods, + additional_lag_periods=self.additional_lag_periods, + ewm_alpha=self.ewm_alpha, + abs_energy=self.abs_energy, + rolling_autocorr_periods=self.rolling_autocorr_periods, + nonzero_last_n=self.nonzero_last_n, + add_date_part=self.datepart_method, + holiday=self.holiday, + holiday_country=self.holiday_country, + polynomial_degree=self.polynomial_degree, + window=self.window, + future_regressor=cut_regr, + # these rely the if part not being run if None + regressor_per_series=( + self.regressor_per_series_train[x_col] + if self.regressor_per_series_train is not None + else None + ), + static_regressor=( + self.static_regressor.loc[x_col].to_frame().T + if self.static_regressor is not None + else None + ), + cointegration=self.cointegration, + cointegration_lag=self.cointegration_lag, + series_id=x_col if self.series_hash else None, + slice_index=self.slice_index, + series_id_to_multiindex=x_col, + ) + for x_col in compare_df.columns + ) + self.Xa = pd.concat(self.Xa) + else: + self.Xa = pd.concat( + [ + rolling_x_regressor_regressor( + compare_df[x_col].to_frame(), + mean_rolling_periods=self.mean_rolling_periods, + macd_periods=self.macd_periods, + std_rolling_periods=self.std_rolling_periods, + max_rolling_periods=self.max_rolling_periods, + min_rolling_periods=self.min_rolling_periods, + ewm_var_alpha=self.ewm_var_alpha, + quantile90_rolling_periods=self.quantile90_rolling_periods, + quantile10_rolling_periods=self.quantile10_rolling_periods, + additional_lag_periods=self.additional_lag_periods, + ewm_alpha=self.ewm_alpha, + abs_energy=self.abs_energy, + rolling_autocorr_periods=self.rolling_autocorr_periods, + nonzero_last_n=self.nonzero_last_n, + add_date_part=self.datepart_method, + holiday=self.holiday, + holiday_country=self.holiday_country, + polynomial_degree=self.polynomial_degree, + window=self.window, + future_regressor=cut_regr, + # these rely the if part not being run if None + regressor_per_series=( + self.regressor_per_series_train[x_col] + if self.regressor_per_series_train is not None + else None + ), + static_regressor=( + self.static_regressor.loc[x_col].to_frame().T + if self.static_regressor is not None + else None + ), + cointegration=self.cointegration, + cointegration_lag=self.cointegration_lag, + series_id=x_col if self.series_hash else None, + slice_index=self.slice_index, + series_id_to_multiindex=x_col, + ) + for x_col in compare_df.columns + ] + ) + ############################ + test_index = self.create_forecast_index(forecast_length=forecast_length) + + # filter because we need that last bit + self.Xb = self.Xa[self.Xa.index.get_level_values(0) == self.Xa.index.get_level_values(0).max()] + # don't include a certain amount of the end as they won't have any usable history + self.Xa = self.Xa[self.Xa.index.get_level_values(0).isin(self.Xa.index.get_level_values(0).unique().sort_values()[:-self.window])] # int(self.forecast_length / 2) + + if self.distance_metric in ["euclidean", 'kdtree']: + from scipy.spatial import KDTree + + # Build a KDTree for Xb + tree = KDTree(self.Xa, leafsize=40) + else: + from sklearn.neighbors import BallTree + + tree = BallTree(self.Xa, metric=self.distance_metric) + # Query the KDTree to find k nearest neighbors for each point in Xa + A, self.windows = tree.query(self.Xb, k=self.k) + + + # extend data to future to assure full length for windows, forward fill + if self.extend_df: + extension = pd.DataFrame(np.nan, + index = pd.date_range(start=wind_arr.index[-1], periods=int(forecast_length/2) + 1, freq=self.frequency)[1:], + columns = self.column_names, + ) + wind_arr = pd.concat([wind_arr, extension], axis=0).ffill() + + dt_array = self.Xa.index.get_level_values(0).values # Datetime array + series_array = self.Xa.index.get_level_values(1).values # Series names array + + # Flatten windows array to work with 1D arrays + n_series, k = self.windows.shape + N = n_series * k + dt_selected_flat = dt_array[self.windows.flatten()] + series_selected_flat = series_array[self.windows.flatten()] + + # Find positions in df.index where dates are greater than selected datetimes + pos_in_df_index = wind_arr.index.searchsorted(dt_selected_flat, side='right') # Shape: (N,) + + # Create positions for forecast_length ahead + positions = pos_in_df_index[:, None] + np.arange(forecast_length)[None, :] # Shape: (N, forecast_length) + + # Handle positions exceeding the length of df.index + max_index = len(wind_arr.index) + valid_positions = (positions >= 0) & (positions < max_index) + + # Map series names to column indices + series_name_to_col_idx = {name: idx for idx, name in enumerate(wind_arr.columns)} + col_indices = np.array([series_name_to_col_idx[name] for name in series_selected_flat]) + + # Broadcast col_indices to match the shape of positions + col_indices_broadcasted = np.repeat(col_indices, forecast_length).reshape(N, forecast_length) + + # Use advanced indexing to extract data + data = np.full((N, forecast_length), np.nan) # Initialize data array with NaNs + positions_flat = positions.flatten() + col_indices_flat = col_indices_broadcasted.flatten() + valid_mask_flat = valid_positions.flatten() + + # Extract valid data + positions_flat_valid = positions_flat[valid_mask_flat] + col_indices_flat_valid = col_indices_flat[valid_mask_flat] + data_flat = data.flatten() + data_flat[valid_mask_flat] = wind_arr.values[positions_flat_valid, col_indices_flat_valid] + + # Reshape data back to original dimensions + data = data_flat.reshape(N, forecast_length) + # (k, forecast_length, n_series) + self.result_windows = data.reshape(n_series, k, forecast_length).transpose(1, 2, 0) + + # now aggregate results into point and bound forecasts + if self.point_method == "weighted_mean": + weights = np.repeat(A.T[..., np.newaxis, :], 14, axis=1) + if weights.sum() == 0: + weights = None + forecast = np.average(self.result_windows, axis=0, weights=weights) + elif self.point_method == "mean": + forecast = np.nanmean(self.result_windows, axis=0) + elif self.point_method == "median": + forecast = np.nanmedian(self.result_windows, axis=0) + elif self.point_method == "midhinge": + q1 = nan_quantile(self.result_windows, q=0.25, axis=0) + q2 = nan_quantile(self.result_windows, q=0.75, axis=0) + forecast = (q1 + q2) / q2 + elif self.point_method == 'closest': + # assumes the first K is the smallest distance (true when written) + forecast = self.result_windows[0] + + pred_int = round((1 - self.prediction_interval) / 2, 5) + upper_forecast = nan_quantile(self.result_windows, q=(1 - pred_int), axis=0) + lower_forecast = nan_quantile(self.result_windows, q=pred_int, axis=0) + + forecast = pd.DataFrame(forecast, index=test_index, columns=self.column_names) + lower_forecast = pd.DataFrame( + lower_forecast, index=test_index, columns=self.column_names + ) + upper_forecast = pd.DataFrame( + upper_forecast, index=test_index, columns=self.column_names + ) + if self.combination_transformation is not None: + forecast = self.combination_transformer.inverse_transform(forecast) + lower_forecast = self.combination_transformer.inverse_transform( + lower_forecast + ) + upper_forecast = self.combination_transformer.inverse_transform( + upper_forecast + ) + if just_point_forecast: + return forecast + else: + predict_runtime = datetime.datetime.now() - predictStartTime + prediction = PredictionObject( + model_name=self.name, + forecast_length=forecast_length, + forecast_index=forecast.index, + forecast_columns=forecast.columns, + # so it's producing float32 but pandas is better with float64 + lower_forecast=lower_forecast.astype(float), + forecast=forecast.astype(float), + upper_forecast=upper_forecast.astype(float), + prediction_interval=self.prediction_interval, + predict_runtime=predict_runtime, + fit_runtime=self.fit_runtime, + model_parameters=self.get_params(), + ) + + return prediction + + def get_new_params(self, method: str = 'random'): + """Returns dict of new parameters for parameter tuning""" + metric_list = [ + 'braycurtis', + 'canberra', + 'chebyshev', + 'cityblock', + 'euclidean', + 'hamming', + 'mahalanobis', + 'minkowski', + 'kdtree', + ] + metric_probabilities = [ + 0.05, + 0.05, + 0.05, + 0.05, + 0.9, + 0.05, + 0.05, + 0.05, + 0.05, + ] + if method != "deep": + # evidence suggests 20 million can fit in 5 GB of RAM with a window of 28 + sample_fraction = random.choice([5000000, 50000000]) + else: + sample_fraction = random.choice([0.2, 0.5, 100000000, None]) + if method == "event_risk": + k_choice = random.choices( + [10, 15, 20, 50, 100], [0.3, 0.1, 0.1, 0.05, 0.05] + )[0] + else: + k_choice = random.choices( + [1, 3, 5, 10, 15, 20, 100], [0.02, 0.2, 0.2, 0.5, 0.1, 0.1, 0.1] + )[0] + transformers_none = random.choices([True, False], [0.7, 0.3])[0] + if transformers_none: + comparison_transformation = None + combination_transformation = None + else: + comparison_transformation = RandomTransform( + transformer_list=superfast_transformer_dict, + transformer_max_depth=1, + allow_none=True, + ) + combination_transformation = RandomTransform( + transformer_list=superfast_transformer_dict, + transformer_max_depth=1, + allow_none=True, + ) + # multivar params + if method == "deep": + window = random.choices( + [None, 3, 7, 10, 14, 28], [0.2, 0.2, 0.05, 0.05, 0.05, 0.05] + )[0] + # random.choices([2, 3, 5, 7, 10, 14, 28, 60], [0.01, 0.01, 0.01, 0.1, 0.5, 0.1, 0.1, 0.01])[0] + else: + window = random.choices([None, 3, 7, 10], [0.3, 0.3, 0.1, 0.05])[0] + mean_rolling_periods_choice = random.choices( + [None, 5, 7, 12, 30, 90, [2, 4, 6, 8, 12, (52, 2)], [7, 28, 364, (362, 4)]], + [0.3, 0.1, 0.1, 0.1, 0.1, 0.05, 0.05, 0.05], + )[0] + if mean_rolling_periods_choice is not None: + macd_periods_choice = seasonal_int(small=True) + if macd_periods_choice == mean_rolling_periods_choice: + macd_periods_choice = mean_rolling_periods_choice + 10 + else: + macd_periods_choice = None + std_rolling_periods_choice = random.choices( + [None, 5, 7, 10, 30, 90], [0.3, 0.1, 0.1, 0.1, 0.1, 0.05] + )[0] + ewm_var_alpha = random.choices([None, 0.2, 0.5, 0.8], [0.4, 0.1, 0.1, 0.05])[0] + quantile90_rolling_periods = random.choices( + [None, 5, 7, 10, 30, 90], [0.3, 0.1, 0.1, 0.1, 0.1, 0.05] + )[0] + quantile10_rolling_periods = random.choices( + [None, 5, 7, 10, 30, 90], [0.3, 0.1, 0.1, 0.1, 0.1, 0.05] + )[0] + max_rolling_periods_choice = random.choices( + [None, seasonal_int(small=True)], [0.2, 0.5] + )[0] + min_rolling_periods_choice = random.choices( + [None, seasonal_int(small=True)], [0.2, 0.5] + )[0] + lag_periods_choice = None + ewm_choice = random.choices( + [None, 0.1, 0.2, 0.5, 0.8], [0.4, 0.01, 0.1, 0.1, 0.05] + )[0] + abs_energy_choice = False + rolling_autocorr_periods_choice = random.choices( + [None, 2, 7, 12, 30], [0.99, 0.01, 0.01, 0.01, 0.01] + )[0] + nonzero_last_n = random.choices( + [None, 2, 7, 14, 30], [0.6, 0.01, 0.1, 0.1, 0.01] + )[0] + add_date_part_choice = random.choices( + [ + None, + 'simple', + 'expanded', + 'recurring', + "simple_2", + "simple_2_poly", + "simple_binarized", + "common_fourier", + "expanded_binarized", + "common_fourier_rw", + ["dayofweek", 365.25], + "simple_binarized2_poly", + ], + [0.2, 0.1, 0.025, 0.1, 0.05, 0.1, 0.05, 0.05, 0.05, 0.025, 0.05, 0.05], + )[0] + holiday_choice = random.choices([True, False], [0.1, 0.9])[0] + polynomial_degree_choice = random.choices([None, 2], [0.995, 0.005])[0] + if "regressor" in method: + regression_choice = "User" + else: + regression_choice = random.choices([None, 'User'], [0.7, 0.3])[0] + + return { + "window": window, + "point_method": random.choices( + ["weighted_mean", "mean", "median", "midhinge", "closest"], + [0.4, 0.2, 0.2, 0.2, 0.2], + )[0], + "distance_metric": random.choices(metric_list, metric_probabilities)[0], + "k": k_choice, + "sample_fraction": sample_fraction, + "comparison_transformation": comparison_transformation, + "combination_transformation": combination_transformation, + "extend_df": random.choices([True, False], [True, False])[0], + # multivar params + 'mean_rolling_periods': mean_rolling_periods_choice, + 'macd_periods': macd_periods_choice, + 'std_rolling_periods': std_rolling_periods_choice, + 'max_rolling_periods': max_rolling_periods_choice, + 'min_rolling_periods': min_rolling_periods_choice, + "quantile90_rolling_periods": quantile90_rolling_periods, + "quantile10_rolling_periods": quantile10_rolling_periods, + 'ewm_alpha': ewm_choice, + "ewm_var_alpha": ewm_var_alpha, + 'additional_lag_periods': lag_periods_choice, + 'abs_energy': abs_energy_choice, + 'rolling_autocorr_periods': rolling_autocorr_periods_choice, + 'nonzero_last_n': nonzero_last_n, + 'datepart_method': add_date_part_choice, + 'polynomial_degree': polynomial_degree_choice, + 'regression_type': regression_choice, + 'holiday': holiday_choice, + 'scale_full_X': random.choices([True, False], [0.2, 0.8])[0], + "series_hash": random.choices([True, False], [0.5, 0.5])[0], + "frac_slice": random.choices( + [None, 0.8, 0.5, 0.2, 0.1], [0.6, 0.1, 0.1, 0.1, 0.1] + )[0], + } + + def get_params(self): + """Return dict of current parameters""" + return { + "window": self.window, + "point_method": self.point_method, + "distance_metric": self.distance_metric, + "k": self.k, + "sample_fraction": self.sample_fraction, + "comparison_transformation": self.comparison_transformation, + "combination_transformation": self.combination_transformation, + "extend_df": self.extend_df, + # multivar params + 'mean_rolling_periods': self.mean_rolling_periods, + 'macd_periods': self.macd_periods, + 'std_rolling_periods': self.std_rolling_periods, + 'max_rolling_periods': self.max_rolling_periods, + 'min_rolling_periods': self.min_rolling_periods, + "quantile90_rolling_periods": self.quantile90_rolling_periods, + "quantile10_rolling_periods": self.quantile10_rolling_periods, + 'ewm_alpha': self.ewm_alpha, + "ewm_var_alpha": self.ewm_var_alpha, + 'additional_lag_periods': self.additional_lag_periods, + 'abs_energy': self.abs_energy, + 'rolling_autocorr_periods': self.rolling_autocorr_periods, + 'nonzero_last_n': self.nonzero_last_n, + 'datepart_method': self.datepart_method, + 'polynomial_degree': self.polynomial_degree, + 'regression_type': self.regression_type, + 'holiday': self.holiday, + 'scale_full_X': self.scale_full_X, + "series_hash": self.series_hash, + "frac_slice": self.frac_slice, + } + diff --git a/autots/models/model_list.py b/autots/models/model_list.py index f7b807d1..2b2b0830 100644 --- a/autots/models/model_list.py +++ b/autots/models/model_list.py @@ -50,6 +50,7 @@ "DMD", # 45 models "BasicLinearModel", "TVVAR", + "BallTreeRegressionMotif", ] # used for graphing, not for model selection model_classes = { @@ -100,6 +101,7 @@ 'SeasonalNaive': 'naive', 'ZeroesNaive': 'naive', "TVVAR": "stat", + "BallTreeRegressionMotif": "motif", } all_pragmatic = list((set(all_models) - set(['MLEnsemble', 'VARMAX', 'Greykite']))) # downweight slower models @@ -136,6 +138,8 @@ 'Cassandra': 0.8, 'BasicLinearModel': 0.8, 'TVVAR': 0.4, + "BallTreeMultivariateMotif": 0.4, + # "BallTreeRegressionMotif", } # fastest models at any scale superfast = [ @@ -174,6 +178,7 @@ 'FFT': 0.8, "BallTreeMultivariateMotif": 0.5, # keep an eye on RAM, not the fastest at scale but works... "BasicLinearModel": 0.6, + # "TVVAR": 0.6, } # models that can scale well if many CPU cores are available parallel = { @@ -212,7 +217,7 @@ best = list( set( list(fast_parallel_no_arima.keys()) - + ['MultivariateRegression', 'GluonTS', 'PytorchForecasting'] + + ['MultivariateRegression', 'NeuralForecast', 'PytorchForecasting'] ) ) @@ -261,6 +266,7 @@ "NeuralForecast", # mostly "BasicLinearModel", "TVVAR", + "BallTreeRegressionMotif", ] # models that use the shared information of multiple series to improve accuracy multivariate = [ @@ -287,6 +293,7 @@ "NeuralForecast", "DMD", "TVVAR", + "BallTreeRegressionMotif", ] univariate = list((set(all_models) - set(multivariate)) - set(experimental)) # USED IN AUTO_MODEL, models with no parameters @@ -337,6 +344,7 @@ "DMD", "BasicLinearModel", "TVVAR", + "BallTreeRegressionMotif", ] # USED IN AUTO_MODEL for models that don't share information among series no_shared = [ @@ -388,6 +396,7 @@ "NeuralForecast", "BasicLinearModel", "TVVAR", + "BallTreeRegressionMotif", ] motifs = [ 'UnivariateMotif', @@ -397,6 +406,7 @@ 'MetricMotif', 'SeasonalityMotif', 'BallTreeMultivariateMotif', + "BallTreeRegressionMotif", ] regressions = [ 'RollingRegression', @@ -417,6 +427,8 @@ "Motif", "ARCH", # simulations not motifs but similar "PytorchForecasting", + # "BallTreeRegressionMotif", + # "BallTreeMultivariateMotif", ] # these are those that require a parameter, and return a dict diff_window_motif_list = [ diff --git a/autots/models/statsmodels.py b/autots/models/statsmodels.py index fd240cc1..e59789dd 100644 --- a/autots/models/statsmodels.py +++ b/autots/models/statsmodels.py @@ -388,7 +388,7 @@ def predict( parallel = False # joblib multiprocessing to loop through series if parallel: - df_list = Parallel(n_jobs=self.n_jobs, verbose=pool_verbose, timeout=3600)( + df_list = Parallel(n_jobs=self.n_jobs, verbose=pool_verbose, timeout=7200)( delayed(glm_forecast_by_column)( current_series=df[col], X=X, @@ -616,7 +616,7 @@ def ets_forecast_by_column(current_series, args): parallel = False # joblib multiprocessing to loop through series if parallel: - df_list = Parallel(n_jobs=self.n_jobs)( + df_list = Parallel(n_jobs=self.n_jobs, timeout=36000)( # 10 hour timeout, should be enough... delayed(ets_forecast_by_column)(self.df_train[col].astype(float), args) for (col) in cols ) @@ -853,7 +853,7 @@ def predict( # joblib multiprocessing to loop through series if parallel: verbs = 0 if self.verbose < 1 else self.verbose - 1 - df_list = Parallel(n_jobs=self.n_jobs, verbose=(verbs))( + df_list = Parallel(n_jobs=self.n_jobs, verbose=(verbs), timeout=36000)( delayed(arima_seek_the_oracle)( current_series=self.df_train[col], args=args, series=col ) @@ -1137,7 +1137,7 @@ def uc_forecast_by_column(current_series, args): # joblib multiprocessing to loop through series if parallel: verbs = 0 if self.verbose < 1 else self.verbose - 1 - df_list = Parallel(n_jobs=self.n_jobs, verbose=(verbs))( + df_list = Parallel(n_jobs=self.n_jobs, verbose=(verbs), timeout=36000)( delayed(uc_forecast_by_column)( current_series=self.df_train[col], args=args, @@ -2062,7 +2062,7 @@ def theta_forecast_by_column(current_series, args): # joblib multiprocessing to loop through series if parallel: verbs = 0 if self.verbose < 1 else self.verbose - 1 - df_list = Parallel(n_jobs=self.n_jobs, verbose=(verbs))( + df_list = Parallel(n_jobs=self.n_jobs, verbose=(verbs), timeout=36000)( delayed(theta_forecast_by_column)( current_series=self.df_train[col], args=args ) @@ -2308,7 +2308,7 @@ def ardl_per_column(current_series, args): # joblib multiprocessing to loop through series if parallel: verbs = 0 if self.verbose < 1 else self.verbose - 1 - df_list = Parallel(n_jobs=self.n_jobs, verbose=(verbs))( + df_list = Parallel(n_jobs=self.n_jobs, verbose=(verbs), timeout=72000)( delayed(ardl_per_column)( current_series=self.df_train[col], args=args, diff --git a/extended_tutorial.md b/extended_tutorial.md index 44c0c660..b65a1154 100644 --- a/extended_tutorial.md +++ b/extended_tutorial.md @@ -878,6 +878,7 @@ Currently `MultivariateRegression` has the (slower) option to utilize a stock Gr | TiDE | tensorflow | | | | yes | True | | | | NeuralForecast | NeuralForecast | | True | | yes | True | | True | | TVVAR | | | True | | | True | | True | +| BallTreeRegressionMotif | sklearn | | True | joblib | | True | | True | | MotifSimulation | sklearn.metrics.pairwise | | True | joblib | | True | True | | | Greykite | (deprecated) | | True | joblib | | | True | | | TensorflowSTS | (deprecated) | | True | | yes | True | True | | diff --git a/tests/test_autots.py b/tests/test_autots.py index 2aaeac16..05e89de5 100644 --- a/tests/test_autots.py +++ b/tests/test_autots.py @@ -646,6 +646,7 @@ def test_models(self): 'BallTreeMultivariateMotif', 'FFT', "DMD", # 0.6.12 "BasicLinearModel", "TVVAR", # 0.6.16 + # "BallTreeRegressionMotif", # 0.6.17 ] # models that for whatever reason arne't consistent across test sessions run_only_no_score = ['FBProphet', 'RRVAR', "TMF"] From ce90f49cc8d15325e63bb4a74ae88ffe15c62ba2 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 5 Dec 2024 13:05:50 -0600 Subject: [PATCH 13/16] 0.6.17 a13 --- autots/models/basics.py | 14 +++++++++----- autots/models/sklearn.py | 7 +++---- 2 files changed, 12 insertions(+), 9 deletions(-) diff --git a/autots/models/basics.py b/autots/models/basics.py index 39dfa32e..b767d66d 100644 --- a/autots/models/basics.py +++ b/autots/models/basics.py @@ -3278,7 +3278,7 @@ def predict( # now aggregate results into point and bound forecasts if self.point_method == "weighted_mean": - weights = np.repeat(A.T[..., np.newaxis, :], 14, axis=1) + weights = np.repeat(A.T[..., np.newaxis, :], forecast_length, axis=1) if weights.sum() == 0: weights = None forecast = np.average(self.result_windows, axis=0, weights=weights) @@ -3289,7 +3289,7 @@ def predict( elif self.point_method == "midhinge": q1 = nan_quantile(self.result_windows, q=0.25, axis=0) q2 = nan_quantile(self.result_windows, q=0.75, axis=0) - forecast = (q1 + q2) / q2 + forecast = (q1 + q2) / 2 elif self.point_method == 'closest': # assumes the first K is the smallest distance (true when written) forecast = self.result_windows[0] @@ -4786,7 +4786,11 @@ def predict( # filter because we need that last bit self.Xb = self.Xa[self.Xa.index.get_level_values(0) == self.Xa.index.get_level_values(0).max()] # don't include a certain amount of the end as they won't have any usable history - self.Xa = self.Xa[self.Xa.index.get_level_values(0).isin(self.Xa.index.get_level_values(0).unique().sort_values()[:-self.window])] # int(self.forecast_length / 2) + if self.window is not None: + toss_bit = self.window + else: + toss_bit = int(forecast_length / 2) + self.Xa = self.Xa[self.Xa.index.get_level_values(0).isin(self.Xa.index.get_level_values(0).unique().sort_values()[:-toss_bit])] # int(self.forecast_length / 2) if self.distance_metric in ["euclidean", 'kdtree']: from scipy.spatial import KDTree @@ -4854,7 +4858,7 @@ def predict( # now aggregate results into point and bound forecasts if self.point_method == "weighted_mean": - weights = np.repeat(A.T[..., np.newaxis, :], 14, axis=1) + weights = np.repeat(A.T[..., np.newaxis, :], forecast_length, axis=1) if weights.sum() == 0: weights = None forecast = np.average(self.result_windows, axis=0, weights=weights) @@ -4865,7 +4869,7 @@ def predict( elif self.point_method == "midhinge": q1 = nan_quantile(self.result_windows, q=0.25, axis=0) q2 = nan_quantile(self.result_windows, q=0.75, axis=0) - forecast = (q1 + q2) / q2 + forecast = (q1 + q2) / 2 elif self.point_method == 'closest': # assumes the first K is the smallest distance (true when written) forecast = self.result_windows[0] diff --git a/autots/models/sklearn.py b/autots/models/sklearn.py index ca110962..3a07ba04 100644 --- a/autots/models/sklearn.py +++ b/autots/models/sklearn.py @@ -328,6 +328,8 @@ def rolling_x_regressor_regressor( X['series_id'] = df.columns[0] X = X.merge(static_regressor, left_on="series_id", right_index=True, how='left') X = X.drop(columns=['series_id']) + if slice_index is not None: + X = X[X.index.isin(slice_index)] if series_id_to_multiindex is not None: X["series_id"] = str(series_id_to_multiindex) X = X.set_index("series_id", append=True) @@ -337,10 +339,7 @@ def rolling_x_regressor_regressor( % 10**16 ) X['series_id'] = hashed - if slice_index is not None: - return X[X.index.isin(slice_index)] - else: - return X + return X def retrieve_regressor( From 04e14e652e6b3c8a97c25c042ff14a82bbd30881 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 5 Dec 2024 15:35:23 -0600 Subject: [PATCH 14/16] 0.6.17 a14 --- autots/models/basics.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/autots/models/basics.py b/autots/models/basics.py index b767d66d..11356871 100644 --- a/autots/models/basics.py +++ b/autots/models/basics.py @@ -3343,7 +3343,7 @@ def get_new_params(self, method: str = 'random'): 'cityblock', 'euclidean', 'hamming', - 'mahalanobis', + # 'mahalanobis', 'minkowski', 'kdtree', ] @@ -3354,7 +3354,7 @@ def get_new_params(self, method: str = 'random'): 0.05, 0.9, 0.05, - 0.05, + # 0.05, 0.05, 0.05, ] @@ -4840,7 +4840,7 @@ def predict( col_indices_broadcasted = np.repeat(col_indices, forecast_length).reshape(N, forecast_length) # Use advanced indexing to extract data - data = np.full((N, forecast_length), np.nan) # Initialize data array with NaNs + data = np.full((N, forecast_length), np.nan) positions_flat = positions.flatten() col_indices_flat = col_indices_broadcasted.flatten() valid_mask_flat = valid_positions.flatten() @@ -4923,7 +4923,7 @@ def get_new_params(self, method: str = 'random'): 'cityblock', 'euclidean', 'hamming', - 'mahalanobis', + # 'mahalanobis', 'minkowski', 'kdtree', ] @@ -4934,7 +4934,7 @@ def get_new_params(self, method: str = 'random'): 0.05, 0.9, 0.05, - 0.05, + # 0.05, 0.05, 0.05, ] From 7b40208386ba98759f6f7db884b9c2ab3ebc7853 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 5 Dec 2024 15:55:05 -0600 Subject: [PATCH 15/16] 0.6.17 a15 --- autots/tools/transform.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/autots/tools/transform.py b/autots/tools/transform.py index dc8751e6..013d5755 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -516,7 +516,7 @@ def _hpfilter_one_return(series, lamb=1600, part="trend"): def get_new_params(method: str = "random"): part = random.choices(["trend", "cycle"], weights=[0.98, 0.02])[0] lamb = random.choices( - [1600, 6.25, 129600, 104976000000], weights=[0.5, 0.2, 0.2, 0.1] + [1600, 6.25, 129600, 104976000000, 4, 16, 62.5, 1049760000000], weights=[0.5, 0.2, 0.2, 0.1, 0.025, 0.025, 0.025, 0.025] )[0] return {"part": part, "lamb": lamb} @@ -6510,7 +6510,7 @@ def get_transformer_params(transformer: str = "EmptyTransformer", method: str = "DifferencedTransformer": 0.05, "PositiveShift": 0.02, "Log": 0.01, - "SeasonalDifference": 0.1, + "SeasonalDifference": 0.05, "bkfilter": 0.05, "ClipOutliers": 0.05, # "Discretize": 0.01, # excessive memory use for some of this @@ -6519,13 +6519,13 @@ def get_transformer_params(transformer: str = "EmptyTransformer", method: str = "AlignLastValue": 0.05, "AlignLastDiff": 0.05, "HistoricValues": 0.005, # need to test more - "CenterSplit": 0.005, # need to test more + "CenterSplit": 0.0005, # need to test more "Round": 0.01, "CenterLastValue": 0.01, "ShiftFirstValue": 0.005, "Constraint": 0.005, # not well tested yet on speed/ram - # "BKBandpassFilter": 0.01, # seems feasible, untested - # "DiffSmoother": 0.005, # seems feasible, untested + "BKBandpassFilter": 0.01, # seems feasible, untested + "DiffSmoother": 0.005, # seems feasible, untested # "FIRFilter": 0.005, # seems feasible, untested # "FFTFilter": 0.01, # seems feasible, untested # "FFTDecomposition": 0.01, # seems feasible, untested From 71be485590878fafe647c9383af9e87abdfc7b94 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 5 Dec 2024 16:16:09 -0600 Subject: [PATCH 16/16] 0.6.17 --- autots/__init__.py | 2 +- autots/evaluator/auto_model.py | 19 +++- autots/evaluator/auto_ts.py | 26 +++--- autots/models/base.py | 12 +-- autots/models/basics.py | 83 +++++++++++++----- autots/models/ensemble.py | 18 ++-- autots/models/sklearn.py | 11 ++- autots/models/statsmodels.py | 8 +- autots/tools/profile.py | 6 +- autots/tools/seasonal.py | 6 +- autots/tools/transform.py | 11 ++- docs/build/doctrees/environment.pickle | Bin 10529510 -> 10643325 bytes .../doctrees/source/autots.evaluator.doctree | Bin 968861 -> 969877 bytes .../doctrees/source/autots.models.doctree | Bin 2711664 -> 2787256 bytes docs/build/doctrees/source/tutorial.doctree | Bin 211047 -> 212922 bytes docs/build/html/.buildinfo | 2 +- .../html/_static/documentation_options.js | 2 +- docs/build/html/genindex.html | 14 ++- docs/build/html/index.html | 4 +- docs/build/html/objects.inv | Bin 8810 -> 8830 bytes docs/build/html/py-modindex.html | 4 +- docs/build/html/search.html | 4 +- docs/build/html/searchindex.js | 2 +- docs/build/html/source/autots.datasets.html | 4 +- docs/build/html/source/autots.evaluator.html | 6 +- docs/build/html/source/autots.html | 11 ++- docs/build/html/source/autots.models.html | 74 +++++++++++++++- docs/build/html/source/autots.templates.html | 4 +- docs/build/html/source/autots.tools.html | 4 +- docs/build/html/source/intro.html | 4 +- docs/build/html/source/modules.html | 4 +- docs/build/html/source/tutorial.html | 25 ++++-- docs/conf.py | 2 +- pyproject.toml | 2 +- setup.py | 2 +- 35 files changed, 265 insertions(+), 111 deletions(-) diff --git a/autots/__init__.py b/autots/__init__.py index b320baef..333690ce 100644 --- a/autots/__init__.py +++ b/autots/__init__.py @@ -27,7 +27,7 @@ from autots.models.cassandra import Cassandra -__version__ = '0.6.16' +__version__ = '0.6.17' TransformTS = GeneralTransformer diff --git a/autots/evaluator/auto_model.py b/autots/evaluator/auto_model.py index 10aa4684..c4b322ad 100644 --- a/autots/evaluator/auto_model.py +++ b/autots/evaluator/auto_model.py @@ -2033,9 +2033,22 @@ def _eval_prediction_for_template( "fillna": "quadratic", "transformations": {"0": "AlignLastValue", "1": "ChangepointDetrend"}, "transformation_params": { - "0": {"rows": 1, "lag": 1, "method": "multiplicative", "strength": 1.0, "first_value_only": False, "threshold": None, "threshold_method": "mean"}, - "1": {"model": "Linear", "changepoint_spacing": 180, "changepoint_distance_end": 360, "datepart_method": None} - } + "0": { + "rows": 1, + "lag": 1, + "method": "multiplicative", + "strength": 1.0, + "first_value_only": False, + "threshold": None, + "threshold_method": "mean", + }, + "1": { + "model": "Linear", + "changepoint_spacing": 180, + "changepoint_distance_end": 360, + "datepart_method": None, + }, + }, }, ] diff --git a/autots/evaluator/auto_ts.py b/autots/evaluator/auto_ts.py index bbd9ea2b..26b9d551 100644 --- a/autots/evaluator/auto_ts.py +++ b/autots/evaluator/auto_ts.py @@ -428,9 +428,9 @@ def __init__( full_params['transformations'] = transformations full_params['transformation_params'] = transformation_params - self.initial_template.loc[ - index, 'TransformationParameters' - ] = json.dumps(full_params) + self.initial_template.loc[index, 'TransformationParameters'] = ( + json.dumps(full_params) + ) self.regressor_used = False self.subset_flag = False @@ -1974,10 +1974,10 @@ def _run_template( self.model_count = template_result.model_count # capture results from lower-level template run if "TotalRuntime" in template_result.model_results.columns: - template_result.model_results[ - 'TotalRuntime' - ] = template_result.model_results['TotalRuntime'].fillna( - pd.Timedelta(seconds=60) + template_result.model_results['TotalRuntime'] = ( + template_result.model_results['TotalRuntime'].fillna( + pd.Timedelta(seconds=60) + ) ) else: # trying to catch a rare and sneaky bug (perhaps some variety of beetle?) @@ -2094,9 +2094,9 @@ def _run_validations( frac=0.8, random_state=self.random_seed ).reindex(idx) nan_frac = val_df_train.shape[1] / num_validations - val_df_train.iloc[ - -2:, int(nan_frac * y) : int(nan_frac * (y + 1)) - ] = np.nan + val_df_train.iloc[-2:, int(nan_frac * y) : int(nan_frac * (y + 1))] = ( + np.nan + ) # run validation template on current slice result = self._run_template( @@ -4753,9 +4753,9 @@ def diagnose_params(self, target='runtime', waterfall_plots=True): ) y = pd.json_normalize(json.loads(row["ModelParameters"])) y.index = [row['ID']] - y[ - 'Model' - ] = x # might need to remove this and do analysis independently for each + y['Model'] = ( + x # might need to remove this and do analysis independently for each + ) res.append( pd.DataFrame( { diff --git a/autots/models/base.py b/autots/models/base.py index ceec7ddb..d0c79777 100644 --- a/autots/models/base.py +++ b/autots/models/base.py @@ -490,18 +490,18 @@ def long_form_results( value_name=value_name, id_vars="datetime", ).set_index("datetime") - upload_upper[ - interval_name - ] = f"{round(100 - ((1- self.prediction_interval)/2) * 100, 0)}%" + upload_upper[interval_name] = ( + f"{round(100 - ((1- self.prediction_interval)/2) * 100, 0)}%" + ) upload_lower = pd.melt( self.lower_forecast.rename_axis(index='datetime').reset_index(), var_name=id_name, value_name=value_name, id_vars="datetime", ).set_index("datetime") - upload_lower[ - interval_name - ] = f"{round(((1- self.prediction_interval)/2) * 100, 0)}%" + upload_lower[interval_name] = ( + f"{round(((1- self.prediction_interval)/2) * 100, 0)}%" + ) upload = pd.concat([upload, upload_upper, upload_lower], axis=0) if datetime_column is not None: diff --git a/autots/models/basics.py b/autots/models/basics.py index 11356871..e0610157 100644 --- a/autots/models/basics.py +++ b/autots/models/basics.py @@ -3263,7 +3263,10 @@ def predict( # Query the KDTree to find k nearest neighbors for each point in Xa Xb = compare_df.iloc[-self.window :].to_numpy().T A, self.windows = tree.query(Xb, k=self.k) # dualtree=True - if self.combination_transformation is not None or self.comparison_transformation is not None: + if ( + self.combination_transformation is not None + or self.comparison_transformation is not None + ): del Xa Xc = chunk_reshape( wind_arr.to_numpy(dtype=np.float32), @@ -4615,7 +4618,13 @@ def __init__( self.series_hash = series_hash self.frac_slice = frac_slice - def fit(self, df, future_regressor=None, static_regressor=None, regressor_per_series=None,): + def fit( + self, + df, + future_regressor=None, + static_regressor=None, + regressor_per_series=None, + ): """Train algorithm given data supplied. Args: @@ -4639,7 +4648,12 @@ def fit(self, df, future_regressor=None, static_regressor=None, regressor_per_se return self def predict( - self, forecast_length: int, future_regressor=None, just_point_forecast=False, static_regressor=None, regressor_per_series=None, + self, + forecast_length: int, + future_regressor=None, + just_point_forecast=False, + static_regressor=None, + regressor_per_series=None, ): """Generates forecast data immediately following dates of index supplied to .fit() @@ -4692,9 +4706,7 @@ def predict( # joblib multiprocessing to loop through series # this might be causing issues, TBD Key Error from Resource Tracker if parallel: - self.Xa = Parallel( - n_jobs=self.n_jobs, verbose=self.verbose, timeout=36000 - )( + self.Xa = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, timeout=36000)( delayed(rolling_x_regressor_regressor)( compare_df[x_col].to_frame(), mean_rolling_periods=self.mean_rolling_periods, @@ -4784,13 +4796,19 @@ def predict( test_index = self.create_forecast_index(forecast_length=forecast_length) # filter because we need that last bit - self.Xb = self.Xa[self.Xa.index.get_level_values(0) == self.Xa.index.get_level_values(0).max()] + self.Xb = self.Xa[ + self.Xa.index.get_level_values(0) == self.Xa.index.get_level_values(0).max() + ] # don't include a certain amount of the end as they won't have any usable history if self.window is not None: toss_bit = self.window else: toss_bit = int(forecast_length / 2) - self.Xa = self.Xa[self.Xa.index.get_level_values(0).isin(self.Xa.index.get_level_values(0).unique().sort_values()[:-toss_bit])] # int(self.forecast_length / 2) + self.Xa = self.Xa[ + self.Xa.index.get_level_values(0).isin( + self.Xa.index.get_level_values(0).unique().sort_values()[:-toss_bit] + ) + ] # int(self.forecast_length / 2) if self.distance_metric in ["euclidean", 'kdtree']: from scipy.spatial import KDTree @@ -4803,13 +4821,17 @@ def predict( tree = BallTree(self.Xa, metric=self.distance_metric) # Query the KDTree to find k nearest neighbors for each point in Xa A, self.windows = tree.query(self.Xb, k=self.k) - - + # extend data to future to assure full length for windows, forward fill if self.extend_df: - extension = pd.DataFrame(np.nan, - index = pd.date_range(start=wind_arr.index[-1], periods=int(forecast_length/2) + 1, freq=self.frequency)[1:], - columns = self.column_names, + extension = pd.DataFrame( + np.nan, + index=pd.date_range( + start=wind_arr.index[-1], + periods=int(forecast_length / 2) + 1, + freq=self.frequency, + )[1:], + columns=self.column_names, ) wind_arr = pd.concat([wind_arr, extension], axis=0).ffill() @@ -4823,21 +4845,31 @@ def predict( series_selected_flat = series_array[self.windows.flatten()] # Find positions in df.index where dates are greater than selected datetimes - pos_in_df_index = wind_arr.index.searchsorted(dt_selected_flat, side='right') # Shape: (N,) + pos_in_df_index = wind_arr.index.searchsorted( + dt_selected_flat, side='right' + ) # Shape: (N,) # Create positions for forecast_length ahead - positions = pos_in_df_index[:, None] + np.arange(forecast_length)[None, :] # Shape: (N, forecast_length) - + positions = ( + pos_in_df_index[:, None] + np.arange(forecast_length)[None, :] + ) # Shape: (N, forecast_length) + # Handle positions exceeding the length of df.index max_index = len(wind_arr.index) valid_positions = (positions >= 0) & (positions < max_index) - + # Map series names to column indices - series_name_to_col_idx = {name: idx for idx, name in enumerate(wind_arr.columns)} - col_indices = np.array([series_name_to_col_idx[name] for name in series_selected_flat]) - + series_name_to_col_idx = { + name: idx for idx, name in enumerate(wind_arr.columns) + } + col_indices = np.array( + [series_name_to_col_idx[name] for name in series_selected_flat] + ) + # Broadcast col_indices to match the shape of positions - col_indices_broadcasted = np.repeat(col_indices, forecast_length).reshape(N, forecast_length) + col_indices_broadcasted = np.repeat(col_indices, forecast_length).reshape( + N, forecast_length + ) # Use advanced indexing to extract data data = np.full((N, forecast_length), np.nan) @@ -4849,12 +4881,16 @@ def predict( positions_flat_valid = positions_flat[valid_mask_flat] col_indices_flat_valid = col_indices_flat[valid_mask_flat] data_flat = data.flatten() - data_flat[valid_mask_flat] = wind_arr.values[positions_flat_valid, col_indices_flat_valid] + data_flat[valid_mask_flat] = wind_arr.values[ + positions_flat_valid, col_indices_flat_valid + ] # Reshape data back to original dimensions data = data_flat.reshape(N, forecast_length) # (k, forecast_length, n_series) - self.result_windows = data.reshape(n_series, k, forecast_length).transpose(1, 2, 0) + self.result_windows = data.reshape(n_series, k, forecast_length).transpose( + 1, 2, 0 + ) # now aggregate results into point and bound forecasts if self.point_method == "weighted_mean": @@ -5105,4 +5141,3 @@ def get_params(self): "series_hash": self.series_hash, "frac_slice": self.frac_slice, } - diff --git a/autots/models/ensemble.py b/autots/models/ensemble.py index ebd062a2..123c81d4 100644 --- a/autots/models/ensemble.py +++ b/autots/models/ensemble.py @@ -2188,15 +2188,15 @@ def _buildup_mosaics( f"Mosaic Ensemble failed on model {row[3]} series {row[2]} and period {row[1]} due to missing model: {e} " + mi ) from e - melted[ - 'forecast' - ] = fore # [forecasts[row[3]][row[2]].iloc[row[1]] for row in melted.itertuples()] - melted[ - 'upper_forecast' - ] = u_fore # [upper_forecasts[row[3]][row[2]].iloc[row[1]] for row in melted.itertuples()] - melted[ - 'lower_forecast' - ] = l_fore # [lower_forecasts[row[3]][row[2]].iloc[row[1]] for row in melted.itertuples()] + melted['forecast'] = ( + fore # [forecasts[row[3]][row[2]].iloc[row[1]] for row in melted.itertuples()] + ) + melted['upper_forecast'] = ( + u_fore # [upper_forecasts[row[3]][row[2]].iloc[row[1]] for row in melted.itertuples()] + ) + melted['lower_forecast'] = ( + l_fore # [lower_forecasts[row[3]][row[2]].iloc[row[1]] for row in melted.itertuples()] + ) forecast_df = melted.pivot( values="forecast", columns="series_id", index="forecast_period" diff --git a/autots/models/sklearn.py b/autots/models/sklearn.py index 3a07ba04..558b7dc4 100644 --- a/autots/models/sklearn.py +++ b/autots/models/sklearn.py @@ -335,8 +335,7 @@ def rolling_x_regressor_regressor( X = X.set_index("series_id", append=True) if series_id is not None: hashed = ( - int(hashlib.sha256(str(series_id).encode('utf-8')).hexdigest(), 16) - % 10**16 + int(hashlib.sha256(str(series_id).encode('utf-8')).hexdigest(), 16) % 10**16 ) X['series_id'] = hashed return X @@ -1151,7 +1150,9 @@ def generate_regressor_params( "n_neighbors": random.choices([3, 5, 10, 14], [0.2, 0.7, 0.1, 0.1])[ 0 ], - "weights": random.choices(['uniform', 'distance'], [0.999, 0.001])[0], + "weights": random.choices(['uniform', 'distance'], [0.999, 0.001])[ + 0 + ], 'p': random.choices([2, 1, 1.5], [0.7, 0.1, 0.1])[0], 'leaf_size': random.choices([30, 10, 50], [0.8, 0.1, 0.1])[0], }, @@ -3936,9 +3937,7 @@ def _rbf_kernel(self, x1, x2, gamma): if gamma is None: gamma = 1.0 / x1.shape[1] distance = ( - np.sum(x1**2, 1).reshape(-1, 1) - + np.sum(x2**2, 1) - - 2 * np.dot(x1, x2.T) + np.sum(x1**2, 1).reshape(-1, 1) + np.sum(x2**2, 1) - 2 * np.dot(x1, x2.T) ) return np.exp(-gamma * distance) diff --git a/autots/models/statsmodels.py b/autots/models/statsmodels.py index e59789dd..c4970fbb 100644 --- a/autots/models/statsmodels.py +++ b/autots/models/statsmodels.py @@ -616,7 +616,9 @@ def ets_forecast_by_column(current_series, args): parallel = False # joblib multiprocessing to loop through series if parallel: - df_list = Parallel(n_jobs=self.n_jobs, timeout=36000)( # 10 hour timeout, should be enough... + df_list = Parallel( + n_jobs=self.n_jobs, timeout=36000 + )( # 10 hour timeout, should be enough... delayed(ets_forecast_by_column)(self.df_train[col].astype(float), args) for (col) in cols ) @@ -666,7 +668,9 @@ def get_new_params(self, method: str = 'random'): seasonal_probability = [0.2, 0.2, 0.6] seasonal_choice = random.choices(seasonal_list, seasonal_probability)[0] if seasonal_choice in ["additive", "multiplicative"]: - seasonal_period_choice = seasonal_int(small=True if method != "deep" else False) + seasonal_period_choice = seasonal_int( + small=True if method != "deep" else False + ) else: seasonal_period_choice = None parameter_dict = { diff --git a/autots/tools/profile.py b/autots/tools/profile.py index 72b6ddec..df59d17d 100644 --- a/autots/tools/profile.py +++ b/autots/tools/profile.py @@ -129,9 +129,9 @@ def profile_time_series( & (metrics_df['cv_squared'] >= cvar_threshold), 'PROFILE', ] = 'lumpy' - metrics_df.loc[ - metrics_df['zero_diff_proportion'] >= flat_threshold, 'PROFILE' - ] = 'flat' + metrics_df.loc[metrics_df['zero_diff_proportion'] >= flat_threshold, 'PROFILE'] = ( + 'flat' + ) metrics_df.loc[ metrics_df['null_percentage'] >= new_product_threshold, 'PROFILE' ] = 'new_product' diff --git a/autots/tools/seasonal.py b/autots/tools/seasonal.py index 02a4e63f..1e3c1769 100644 --- a/autots/tools/seasonal.py +++ b/autots/tools/seasonal.py @@ -751,7 +751,11 @@ def create_seasonality_feature(DTindex, t, seasonality, history_days=None): ["dayofweek", (365.25, 4)], ["dayofweek", (365.25, 14)], ["dayofweek", (365.25, 24)], - ["dayofweek", (365.25, 14), (354.37, 10)], # 354.37 should be islamic calendar avg length + [ + "dayofweek", + (365.25, 14), + (354.37, 10), + ], # 354.37 should be islamic calendar avg length "other", ] diff --git a/autots/tools/transform.py b/autots/tools/transform.py index 013d5755..d81e54f8 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -516,7 +516,8 @@ def _hpfilter_one_return(series, lamb=1600, part="trend"): def get_new_params(method: str = "random"): part = random.choices(["trend", "cycle"], weights=[0.98, 0.02])[0] lamb = random.choices( - [1600, 6.25, 129600, 104976000000, 4, 16, 62.5, 1049760000000], weights=[0.5, 0.2, 0.2, 0.1, 0.025, 0.025, 0.025, 0.025] + [1600, 6.25, 129600, 104976000000, 4, 16, 62.5, 1049760000000], + weights=[0.5, 0.2, 0.2, 0.1, 0.025, 0.025, 0.025, 0.025], )[0] return {"part": part, "lamb": lamb} @@ -2849,8 +2850,12 @@ def __init__( @staticmethod def get_new_params(method: str = "random"): return { - "rows": random.choices([1, 2, 4, 7, 24, 84, 168], [0.83, 0.02, 0.05, 0.1, 0.01, 0.05, 0.05])[0], - "lag": random.choices([1, 2, 7, 28, 84, 168], [0.8, 0.05, 0.1, 0.05, 0.05, 0.01])[0], + "rows": random.choices( + [1, 2, 4, 7, 24, 84, 168], [0.83, 0.02, 0.05, 0.1, 0.01, 0.05, 0.05] + )[0], + "lag": random.choices( + [1, 2, 7, 28, 84, 168], [0.8, 0.05, 0.1, 0.05, 0.05, 0.01] + )[0], "method": random.choices(["additive", "multiplicative"], [0.9, 0.1])[0], "strength": random.choices( [1.0, 0.9, 0.7, 0.5, 0.2], [0.8, 0.05, 0.05, 0.05, 0.05] diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index 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Check how many validations are possible given the length of the data. Beyond initial eval split which is always assumed.

diff --git a/docs/build/html/source/autots.html b/docs/build/html/source/autots.html index 9971b449..7dc24817 100644 --- a/docs/build/html/source/autots.html +++ b/docs/build/html/source/autots.html @@ -14,10 +14,10 @@ gtag('config', 'G-P2KLF8302E'); - autots package — AutoTS 0.6.16 documentation + autots package — AutoTS 0.6.17 documentation - + @@ -343,6 +343,13 @@

SubpackagesBallTreeMultivariateMotif.predict() +
  • BallTreeRegressionMotif +
  • BasicLinearModel

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When it is not found, a full rebuild will be done. -config: c0235afd30c9c9583639f9b41c307b3d +config: 344d20e947fc722055a81658eb431371 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js index 9c357c8a..1d51443c 100644 --- a/docs/build/html/_static/documentation_options.js +++ b/docs/build/html/_static/documentation_options.js @@ -1,5 +1,5 @@ const DOCUMENTATION_OPTIONS = { - VERSION: '0.6.16', + VERSION: '0.6.17', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 28cd5e77..44ff187b 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -13,10 +13,10 @@ gtag('config', 'G-P2KLF8302E'); - Index — AutoTS 0.6.16 documentation + Index — AutoTS 0.6.17 documentation - + @@ -544,6 +544,8 @@

    B

  • BallTreeMultivariateMotif (class in autots.models.basics) +
    • +
  • BallTreeRegressionMotif (class in autots.models.basics)
  • base_scaler() (autots.Cassandra method) @@ -1089,6 +1091,8 @@

    F

  • (autots.models.basics.AverageValueNaive method)
  • (autots.models.basics.BallTreeMultivariateMotif method) +
    • +
  • (autots.models.basics.BallTreeRegressionMotif method)
  • (autots.models.basics.BasicLinearModel method)
    • @@ -1579,6 +1583,8 @@

    G

  • (autots.models.basics.AverageValueNaive method)
  • (autots.models.basics.BallTreeMultivariateMotif method) +
    • +
  • (autots.models.basics.BallTreeRegressionMotif method)
  • (autots.models.basics.BasicLinearModel method)
    • @@ -1775,6 +1781,8 @@

    G

  • (autots.models.basics.AverageValueNaive method)
  • (autots.models.basics.BallTreeMultivariateMotif method) +
    • +
  • (autots.models.basics.BallTreeRegressionMotif method)
  • (autots.models.basics.BasicLinearModel method)
    • @@ -2784,6 +2792,8 @@

    P

  • (autots.models.basics.AverageValueNaive method)
  • (autots.models.basics.BallTreeMultivariateMotif method) +
    • +
  • (autots.models.basics.BallTreeRegressionMotif method)
  • (autots.models.basics.BasicLinearModel method)
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"plot_horizontal_model_count() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_horizontal_model_count"]], "plot_horizontal_per_generation() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_horizontal_per_generation"]], "plot_horizontal_transformers() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_horizontal_transformers"]], "plot_metric_corr() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_metric_corr"]], "plot_model_failure_rate() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_model_failure_rate"]], "plot_mosaic() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_mosaic"]], "plot_per_series_error() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_per_series_error"]], "plot_per_series_mape() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_per_series_mape"]], "plot_per_series_smape() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_per_series_smape"]], "plot_series_corr() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_series_corr"]], "plot_transformer_by_class() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_transformer_by_class"]], "plot_transformer_failure_rate() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_transformer_failure_rate"]], "plot_unpredictability() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_unpredictability"]], "plot_validations() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_validations"]], "precomp_wasserstein() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.precomp_wasserstein"]], "predict() (autots.evaluator.auto_model.modelprediction method)": [[3, "autots.evaluator.auto_model.ModelPrediction.predict"]], "predict() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.predict"]], "predict() (autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.predict"], [3, "id11"]], "predict_historic() (autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.predict_historic"], [3, "id12"]], "qae() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.qae"]], "random_model() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.random_model"]], "regression_check (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.regression_check"]], "remove_leading_zeros() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.remove_leading_zeros"]], "results() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.results"]], "retrieve_validation_forecasts() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.retrieve_validation_forecasts"]], "rmse() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.rmse"]], "root_mean_square_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.root_mean_square_error"]], "rps() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.rps"]], "run() (autots.evaluator.benchmark.benchmark method)": [[3, "autots.evaluator.benchmark.Benchmark.run"]], "save() (autots.evaluator.auto_model.templateevalobject method)": [[3, "autots.evaluator.auto_model.TemplateEvalObject.save"]], "save_template() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.save_template"]], "scaled_pinball_loss() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.scaled_pinball_loss"]], "score_per_series (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.score_per_series"]], "score_to_anomaly() (autots.evaluator.anomaly_detector.anomalydetector method)": [[3, "autots.evaluator.anomaly_detector.AnomalyDetector.score_to_anomaly"]], "set_limit() (autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.set_limit"]], "set_limit() (autots.evaluator.event_forecasting.eventriskforecast static method)": [[3, "id13"]], "set_limit_forecast() (in module autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.set_limit_forecast"]], "set_limit_forecast_historic() (in module autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.set_limit_forecast_historic"]], "smape() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.smape"]], "smoothness() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.smoothness"]], "spl() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.spl"]], "symmetric_mean_absolute_percentage_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.symmetric_mean_absolute_percentage_error"]], "threshold_loss() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.threshold_loss"]], "trans_dict_recomb() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.trans_dict_recomb"]], "unpack_ensemble_models() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.unpack_ensemble_models"]], "unsorted_wasserstein() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.unsorted_wasserstein"]], "validate_num_validations() (in module autots.evaluator.validation)": [[3, "autots.evaluator.validation.validate_num_validations"]], "validation_agg() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.validation_agg"]], "validation_aggregation() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.validation_aggregation"]], "wasserstein() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.wasserstein"]], "arch (class in autots.models.arch)": [[4, "autots.models.arch.ARCH"]], "ardl (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARDL"]], "arima (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARIMA"]], "averagevaluenaive (class in autots.models.basics)": [[4, "autots.models.basics.AverageValueNaive"]], "balltreemultivariatemotif (class in autots.models.basics)": [[4, "autots.models.basics.BallTreeMultivariateMotif"]], "basiclinearmodel (class in autots.models.basics)": [[4, "autots.models.basics.BasicLinearModel"]], "bayesianmultioutputregression (class in autots.models.cassandra)": [[4, "autots.models.cassandra.BayesianMultiOutputRegression"]], "bestnensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.BestNEnsemble"]], "cassandra (class in autots.models.cassandra)": [[4, "autots.models.cassandra.Cassandra"]], "componentanalysis (class in autots.models.sklearn)": [[4, "autots.models.sklearn.ComponentAnalysis"]], "constantnaive (class in autots.models.basics)": [[4, "autots.models.basics.ConstantNaive"]], "dmd (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.DMD"]], "datepartregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.DatepartRegression"]], "distensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.DistEnsemble"]], "dynamicfactor (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactor"]], "dynamicfactormq (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactorMQ"]], "ets (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ETS"]], "elasticnetwork (class in autots.models.dnn)": [[4, "autots.models.dnn.ElasticNetwork"]], "ensembleforecast() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleForecast"]], "ensembletemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleTemplateGenerator"]], "fbprophet (class in autots.models.prophet)": [[4, "autots.models.prophet.FBProphet"]], "fft (class in autots.models.basics)": [[4, "autots.models.basics.FFT"]], "glm (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLM"]], "gls (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLS"]], "gluonts (class in autots.models.gluonts)": [[4, "autots.models.gluonts.GluonTS"]], "greykite (class in autots.models.greykite)": [[4, "autots.models.greykite.Greykite"]], "hdistensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HDistEnsemble"]], "horizontalensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalEnsemble"]], "horizontaltemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalTemplateGenerator"]], "kalmanstatespace (class in autots.models.basics)": [[4, "autots.models.basics.KalmanStateSpace"]], "kerasrnn (class in autots.models.dnn)": [[4, "autots.models.dnn.KerasRNN"]], "latc (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.LATC"]], "lastvaluenaive (class in autots.models.basics)": [[4, "autots.models.basics.LastValueNaive"]], "mar (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.MAR"]], "mlensemble (class in autots.models.mlensemble)": [[4, "autots.models.mlensemble.MLEnsemble"]], "metricmotif (class in autots.models.basics)": [[4, "autots.models.basics.MetricMotif"]], "modelobject (class in autots.models.base)": [[4, "autots.models.base.ModelObject"]], "mosaicensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.MosaicEnsemble"]], "motif (class in autots.models.basics)": [[4, "autots.models.basics.Motif"]], "motifsimulation (class in autots.models.basics)": [[4, "autots.models.basics.MotifSimulation"]], "multivariateregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.MultivariateRegression"]], "nvar (class in autots.models.basics)": [[4, "autots.models.basics.NVAR"]], "neuralforecast (class in autots.models.neural_forecast)": [[4, "autots.models.neural_forecast.NeuralForecast"]], "neuralprophet (class in autots.models.prophet)": [[4, "autots.models.prophet.NeuralProphet"]], "predictionobject (class in autots.models.base)": [[4, "autots.models.base.PredictionObject"]], "preprocessingregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.PreprocessingRegression"]], "pytorchforecasting (class in autots.models.pytorch)": [[4, "autots.models.pytorch.PytorchForecasting"]], "rrvar (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.RRVAR"]], "randomfourierencoding (class in autots.models.sklearn)": [[4, "autots.models.sklearn.RandomFourierEncoding"]], "rollingregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.RollingRegression"]], "seasonalnaive (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalNaive"]], "seasonalitymotif (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalityMotif"]], "sectionalmotif (class in autots.models.basics)": [[4, "autots.models.basics.SectionalMotif"]], "tfpregression (class in autots.models.tfp)": [[4, "autots.models.tfp.TFPRegression"]], "tfpregressor (class in autots.models.tfp)": [[4, "autots.models.tfp.TFPRegressor"]], "tmf (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.TMF"]], "tvvar (class in autots.models.basics)": [[4, "autots.models.basics.TVVAR"]], "tensorflowsts (class in autots.models.tfp)": [[4, "autots.models.tfp.TensorflowSTS"]], "theta (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.Theta"]], "tide (class in autots.models.tide)": [[4, "autots.models.tide.TiDE"]], "timecovariates (class in autots.models.tide)": [[4, "autots.models.tide.TimeCovariates"]], "timeseriesdata (class in autots.models.tide)": [[4, "autots.models.tide.TimeSeriesdata"]], "transformer (class in autots.models.dnn)": [[4, "autots.models.dnn.Transformer"]], "univariateregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.UnivariateRegression"]], "unobservedcomponents (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.UnobservedComponents"]], "var (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.VAR"]], "varmax (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.VARMAX"]], "vecm (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.VECM"]], "vectorizedmultioutputgpr (class in autots.models.sklearn)": [[4, "autots.models.sklearn.VectorizedMultiOutputGPR"]], "windowregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.WindowRegression"]], "zeroesnaive (in module autots.models.basics)": [[4, "autots.models.basics.ZeroesNaive"]], "analyze_trend() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.analyze_trend"]], "anomalies (autots.models.cassandra.cassandra..anomaly_detector attribute)": [[4, "autots.models.cassandra.Cassandra..anomaly_detector.anomalies"]], "apply_beta_threshold() (autots.models.basics.tvvar method)": [[4, "autots.models.basics.TVVAR.apply_beta_threshold"]], "apply_constraints() (autots.models.base.predictionobject method)": [[4, "autots.models.base.PredictionObject.apply_constraints"], [4, "id0"]], "apply_constraints() (in module autots.models.base)": [[4, "autots.models.base.apply_constraints"]], "arima_seek_the_oracle() (in module autots.models.statsmodels)": [[4, "autots.models.statsmodels.arima_seek_the_oracle"]], "auto_fit() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.auto_fit"]], "auto_model_list() (in module autots.models.model_list)": [[4, "autots.models.model_list.auto_model_list"]], "autots.models": [[4, "module-autots.models"]], "autots.models.arch": [[4, "module-autots.models.arch"]], "autots.models.base": [[4, "module-autots.models.base"]], "autots.models.basics": [[4, "module-autots.models.basics"]], "autots.models.cassandra": [[4, "module-autots.models.cassandra"]], "autots.models.dnn": [[4, "module-autots.models.dnn"]], "autots.models.ensemble": [[4, "module-autots.models.ensemble"]], "autots.models.gluonts": [[4, "module-autots.models.gluonts"]], "autots.models.greykite": [[4, "module-autots.models.greykite"]], "autots.models.matrix_var": [[4, "module-autots.models.matrix_var"]], "autots.models.mlensemble": [[4, "module-autots.models.mlensemble"]], "autots.models.model_list": [[4, "module-autots.models.model_list"]], "autots.models.neural_forecast": [[4, "module-autots.models.neural_forecast"]], "autots.models.prophet": [[4, "module-autots.models.prophet"]], "autots.models.pytorch": [[4, "module-autots.models.pytorch"]], "autots.models.sklearn": [[4, "module-autots.models.sklearn"]], "autots.models.statsmodels": [[4, "module-autots.models.statsmodels"]], "autots.models.tfp": [[4, "module-autots.models.tfp"]], "autots.models.tide": [[4, "module-autots.models.tide"]], "base_scaler() (autots.models.basics.basiclinearmodel method)": [[4, "autots.models.basics.BasicLinearModel.base_scaler"]], "base_scaler() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.base_scaler"]], "base_scaler() (autots.models.sklearn.multivariateregression method)": [[4, "autots.models.sklearn.MultivariateRegression.base_scaler"]], "basic_profile() (autots.models.base.modelobject method)": [[4, "autots.models.base.ModelObject.basic_profile"]], "calculate_peak_density() (in module autots.models.base)": [[4, "autots.models.base.calculate_peak_density"]], "clean_regressor() (in module autots.models.cassandra)": [[4, "autots.models.cassandra.clean_regressor"]], "coefficient_summary() (autots.models.basics.basiclinearmodel method)": [[4, "autots.models.basics.BasicLinearModel.coefficient_summary"]], "compare_actual_components() (autots.models.cassandra.cassandra method)": [[4, "autots.models.cassandra.Cassandra.compare_actual_components"]], "conj_grad_w() (in module autots.models.matrix_var)": [[4, "autots.models.matrix_var.conj_grad_w"]], "conj_grad_x() (in module autots.models.matrix_var)": [[4, "autots.models.matrix_var.conj_grad_x"]], "cost_function() (autots.models.basics.kalmanstatespace method)": [[4, "autots.models.basics.KalmanStateSpace.cost_function"]], "cost_function_dwae() (in module autots.models.cassandra)": [[4, "autots.models.cassandra.cost_function_dwae"]], "cost_function_l1() (in module autots.models.cassandra)": [[4, "autots.models.cassandra.cost_function_l1"]], "cost_function_l1_positive() (in module autots.models.cassandra)": [[4, 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autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.set_limit_forecast"]], "set_limit_forecast_historic() (in module autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.set_limit_forecast_historic"]], "smape() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.smape"]], "smoothness() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.smoothness"]], "spl() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.spl"]], "symmetric_mean_absolute_percentage_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.symmetric_mean_absolute_percentage_error"]], "threshold_loss() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.threshold_loss"]], "trans_dict_recomb() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.trans_dict_recomb"]], "unpack_ensemble_models() (in module autots.evaluator.auto_model)": [[3, 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(class in autots.models.sklearn)": [[4, "autots.models.sklearn.DatepartRegression"]], "distensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.DistEnsemble"]], "dynamicfactor (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactor"]], "dynamicfactormq (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactorMQ"]], "ets (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ETS"]], "elasticnetwork (class in autots.models.dnn)": [[4, "autots.models.dnn.ElasticNetwork"]], "ensembleforecast() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleForecast"]], "ensembletemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleTemplateGenerator"]], "fbprophet (class in autots.models.prophet)": [[4, "autots.models.prophet.FBProphet"]], "fft (class in autots.models.basics)": [[4, "autots.models.basics.FFT"]], "glm (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLM"]], "gls (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLS"]], "gluonts (class in autots.models.gluonts)": [[4, "autots.models.gluonts.GluonTS"]], "greykite (class in autots.models.greykite)": [[4, "autots.models.greykite.Greykite"]], "hdistensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HDistEnsemble"]], "horizontalensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalEnsemble"]], "horizontaltemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalTemplateGenerator"]], "kalmanstatespace (class in autots.models.basics)": [[4, "autots.models.basics.KalmanStateSpace"]], "kerasrnn (class in autots.models.dnn)": [[4, "autots.models.dnn.KerasRNN"]], "latc (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.LATC"]], "lastvaluenaive (class in autots.models.basics)": [[4, "autots.models.basics.LastValueNaive"]], "mar (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.MAR"]], "mlensemble (class in autots.models.mlensemble)": [[4, "autots.models.mlensemble.MLEnsemble"]], "metricmotif (class in autots.models.basics)": [[4, "autots.models.basics.MetricMotif"]], "modelobject (class in autots.models.base)": [[4, "autots.models.base.ModelObject"]], "mosaicensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.MosaicEnsemble"]], "motif (class in autots.models.basics)": [[4, "autots.models.basics.Motif"]], "motifsimulation (class in autots.models.basics)": [[4, "autots.models.basics.MotifSimulation"]], "multivariateregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.MultivariateRegression"]], "nvar (class in autots.models.basics)": [[4, "autots.models.basics.NVAR"]], "neuralforecast (class in autots.models.neural_forecast)": [[4, "autots.models.neural_forecast.NeuralForecast"]], "neuralprophet (class in autots.models.prophet)": [[4, "autots.models.prophet.NeuralProphet"]], "predictionobject (class in autots.models.base)": [[4, "autots.models.base.PredictionObject"]], "preprocessingregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.PreprocessingRegression"]], "pytorchforecasting (class in autots.models.pytorch)": [[4, "autots.models.pytorch.PytorchForecasting"]], "rrvar (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.RRVAR"]], "randomfourierencoding (class in autots.models.sklearn)": [[4, "autots.models.sklearn.RandomFourierEncoding"]], "rollingregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.RollingRegression"]], "seasonalnaive (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalNaive"]], "seasonalitymotif (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalityMotif"]], "sectionalmotif (class in autots.models.basics)": [[4, "autots.models.basics.SectionalMotif"]], "tfpregression 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b/docs/build/html/source/autots.evaluator.html @@ -14,10 +14,10 @@ gtag('config', 'G-P2KLF8302E'); - autots.evaluator package — AutoTS 0.6.16 documentation + autots.evaluator package — AutoTS 0.6.17 documentation - + @@ -1966,7 +1966,7 @@

    Submodules
    -autots.evaluator.validation.validate_num_validations(validation_method, num_validations, df_wide_numeric, forecast_length, min_allowed_train_percent=0.5, verbose=0)
    +autots.evaluator.validation.validate_num_validations(validation_method='backwards', num_validations=2, df_wide_numeric=None, forecast_length=None, min_allowed_train_percent=0.5, verbose=0)

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