From 86ba253a20e0ffcbd1a3492c7099da67f52cb034 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Wed, 29 Nov 2023 12:26:40 -0600 Subject: [PATCH 01/26] 0.6.4 a1 --- TODO.md | 9 +++------ autots/evaluator/auto_ts.py | 3 ++- autots/models/basics.py | 2 +- autots/tools/shaping.py | 33 +++++++++++++++++++++++++++++++++ 4 files changed, 39 insertions(+), 8 deletions(-) diff --git a/TODO.md b/TODO.md index ef785b33..9efd8be0 100644 --- a/TODO.md +++ b/TODO.md @@ -12,12 +12,9 @@ * The most recent data will generally be the most important * Forecasts are desired for the future immediately following the most recent data. -# 0.6.3 🎐🎐🎐 -* energy datasets to load_live_daily -* improved the 'Scalable' transformer_list to reduce memory issues on larger datasets -* memory improvements to KalmanSmoother, HolidayTransformer, LocalLinearTrend -* added DiffSmoother -* added force_gc arg which can be tried if memory is in short supply relative to data (probably won't help much) +# 0.6.4 🔜🔜🔜 +* adjusted n_jobs back to minus 1 for multivariatemotif +* fixed bug with plot_validations not working with some frequencies ### Unstable Upstream Pacakges (those that are frequently broken by maintainers) * Pytorch-Forecasting diff --git a/autots/evaluator/auto_ts.py b/autots/evaluator/auto_ts.py index d91a8634..3e9df044 100644 --- a/autots/evaluator/auto_ts.py +++ b/autots/evaluator/auto_ts.py @@ -17,6 +17,7 @@ NumericTransformer, clean_weights, infer_frequency, + freq_to_timedelta, ) from autots.tools.transform import GeneralTransformer, RandomTransform from autots.evaluator.auto_model import ( @@ -3136,7 +3137,7 @@ def plot_validations( used_freq = self.used_frequency start_date = plot_df[plot_df.columns.difference(['actuals'])].dropna( how='all', axis=0 - ).index.min() - (pd.to_timedelta(used_freq) * int(self.forecast_length * 3)) + ).index.min() - (freq_to_timedelta(used_freq) * int(self.forecast_length * 3)) if end_date == "auto": end_date = plot_df[plot_df.columns.difference(['actuals'])].dropna( how='all', axis=0 diff --git a/autots/models/basics.py b/autots/models/basics.py index b36e7a08..3fb483cc 100644 --- a/autots/models/basics.py +++ b/autots/models/basics.py @@ -1269,7 +1269,7 @@ def predict( # joblib multiprocessing to loop through series if self.parallel: - df_list = Parallel(n_jobs=(self.n_jobs))( + df_list = Parallel(n_jobs=(self.n_jobs - 1))( delayed(looped_motif)( Xa=x.reshape(-1, x.shape[-1]) if self.multivariate else x[:, i], Xb=self.df.iloc[-self.window :, i].to_numpy().reshape(1, -1), diff --git a/autots/tools/shaping.py b/autots/tools/shaping.py index 1c5daaf3..28075a44 100644 --- a/autots/tools/shaping.py +++ b/autots/tools/shaping.py @@ -1,4 +1,5 @@ """Reshape data.""" +import re import numpy as np import pandas as pd @@ -29,6 +30,38 @@ def infer_frequency(df_wide, warn=True, **kwargs): return frequency +def split_digits_and_non_digits(s): + # Find all digit and non-digit sequences + all_parts = re.findall(r'\d+|\D+', s) + + # Separate digit and non-digit parts + digits = ''.join(part for part in all_parts if part.isdigit()) + nondigits = ''.join(part for part in all_parts if not part.isdigit()) + + return digits, nondigits + + +def freq_to_timedelta(freq): + """Working around pandas limitations.""" + freq = str(freq).split("-")[0] + digits, nondigits = split_digits_and_non_digits(freq) + # 'month start' is being recognized as miliseconds here + if freq == "MS": + return pd.to_timedelta(28, units='D') + if not digits: + freq = "1" + freq + try: + new_freq = pd.to_timedelta(freq) + except Exception: + if "M" in freq: + new_freq = pd.to_timedelta(28, unit='D') + elif 'y' in freq.lower(): + new_freq = pd.to_timedelta(364, unit='D') + else: + raise ValueError(f"freq {freq} not recognized for to_timedelta. Please report this issue to AutoTS if found") + return new_freq + + def df_cleanup( df_wide, frequency: str = "infer", From de2c27d95463faa6bb14227c8651fee406907108 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 30 Nov 2023 08:49:39 -0600 Subject: [PATCH 02/26] 0.6.4 a2 --- autots/tools/transform.py | 5 +- production_example.py | 191 ++++++++++++++++++++------------------ 2 files changed, 102 insertions(+), 94 deletions(-) diff --git a/autots/tools/transform.py b/autots/tools/transform.py index 8ced4beb..a8a96490 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -5165,8 +5165,9 @@ def transformer_list_to_dict(transformer_list): del transformer_list["KalmanSmoothing"] # potential kernel/RAM issues del transformer_list["SinTrend"] # no observed issues, but for efficiency # del transformer_list["HolidayTransformer"] # improved, should be good enough - # del transformer_list["RegressionFilter"] # improved, might be good enough - # del transformer_list["LocalLinearTrend"] # improved, might be good enough + # temporary removal for testing + del transformer_list["RegressionFilter"] # improved, might be good enough + del transformer_list["AnomalyRemoval"] # improved, might be good enough if isinstance(transformer_list, dict): transformer_prob = list(transformer_list.values()) diff --git a/production_example.py b/production_example.py index 043c4932..301b67cb 100644 --- a/production_example.py +++ b/production_example.py @@ -54,7 +54,7 @@ save_location = None # "C:/Users/Colin/Downloads" # directory to save templates to. Defaults to working dir template_filename = f"autots_forecast_template_{forecast_name}.csv" forecast_csv_name = None # f"autots_forecast_{forecast_name}.csv" # or None, point forecast only is written -model_list = "fast_parallel_no_arima" +model_list = "scalable" transformer_list = "fast" # 'superfast' transformer_max_depth = 5 models_mode = "default" # "deep", "regressor" @@ -68,6 +68,7 @@ }, } back_forecast = False +csv_load = False start_time = datetime.datetime.now() @@ -91,10 +92,10 @@ models_to_validate = 0.15 ensemble = ["horizontal-max", "dist", "simple"] # , "mosaic", "mosaic-window", 'mlensemble' elif evolve: - gens = 50 - generation_timeout = 480 # minutes + gens = 500 + generation_timeout = 300 # minutes models_to_validate = 0.15 - ensemble = ["horizontal-max", "dist", "simple"] # "mosaic", "mosaic-window", "subsample" + ensemble = ["horizontal-max"] # "mosaic", "mosaic-window", "subsample" else: gens = 0 generation_timeout = 60 # minutes @@ -110,89 +111,92 @@ """ Begin dataset retrieval """ -fred_series = [ - "DGS10", - "T5YIE", - "SP500", - "DCOILWTICO", - "DEXUSEU", - "BAMLH0A0HYM2", - "DAAA", - "DEXUSUK", - "T10Y2Y", -] -tickers = ["MSFT", "PG"] -trend_list = ["forecasting", "msft", "p&g"] -weather_event_types = ["%28Z%29+Winter+Weather", "%28Z%29+Winter+Storm"] -wikipedia_pages = ['all', 'Microsoft', "Procter_%26_Gamble", "YouTube", "United_States"] -df = load_live_daily( - long=False, - fred_key=fred_key, - fred_series=fred_series, - tickers=tickers, - trends_list=trend_list, - earthquake_min_magnitude=5, - weather_years=3, - london_air_days=700, - wikipedia_pages=wikipedia_pages, - gsa_key=gsa_key, - gov_domain_list=None, # ['usajobs.gov', 'usps.com', 'weather.gov'], - gov_domain_limit=700, - weather_event_types=weather_event_types, - sleep_seconds=15, -) -# be careful of very noisy, large value series mixed into more well-behaved data as they can skew some metrics such that they get most of the attention -# remove "volume" data as it skews MAE (other solutions are to adjust metric_weighting towards SMAPE, use series `weights`, or pre-scale data) -df = df[[x for x in df.columns if "_volume" not in x]] -# remove dividends and stock splits as it skews metrics -df = df[[x for x in df.columns if "_dividends" not in x]] -df = df[[x for x in df.columns if "stock_splits" not in x]] -# scale 'wiki_all' to millions to prevent too much skew of MAE -if 'wiki_all' in df.columns: - df['wiki_all_millions'] = df['wiki_all'] / 1000000 - df = df.drop(columns=['wiki_all']) - -# manual NaN cleaning where real values are easily approximated, this is the way -# although if you have 'no good idea' why it is random, auto is best -# note manual pre-cleaning affects VALIDATION significantly (for better or worse) -# as NaN times in history are skipped by metrics, but filled values, as added here, are evaluated -if trend_list is not None: - for tx in trend_list: - if tx in df.columns: - df[tx] = df[tx].interpolate('akima').fillna(method='ffill', limit=30).fillna(method='bfill', limit=30) -# fill weekends -if tickers is not None: - for fx in tickers: - for suffix in ["_high", "_low", "_open", "_close"]: - fxs = (fx + suffix).lower() - if fxs in df.columns: - df[fxs] = df[fxs].interpolate('akima') -if fred_series is not None: - for fx in fred_series: - if fx in df.columns: - df[fx] = df[fx].interpolate('akima') -if weather_event_types is not None: - wevnt = [x for x in df.columns if "_Events" in x] - df[wevnt] = df[wevnt].mask(df[wevnt].notnull().cummax(), df[wevnt].fillna(0)) -# most of the NaN here are just weekends, when financial series aren't collected, ffill of a few steps is fine -# partial forward fill, no back fill -df = df.fillna(method='ffill', limit=3) - -df = df[df.index.year > 1999] -# remove any data from the future -df = df[df.index <= start_time] -# remove series with no recent data -df = df.dropna(axis="columns", how="all") -min_cutoff_date = start_time - datetime.timedelta(days=180) -most_recent_date = df.notna()[::-1].idxmax() -drop_cols = most_recent_date[most_recent_date < min_cutoff_date].index.tolist() -df = df.drop(columns=drop_cols) -print( - f"Series with most NaN: {df.head(365).isnull().sum().sort_values(ascending=False).head(5)}" -) +if not csv_load: + fred_series = [ + "DGS10", + "T5YIE", + "SP500", + "DCOILWTICO", + "DEXUSEU", + "BAMLH0A0HYM2", + "DAAA", + "DEXUSUK", + "T10Y2Y", + ] + tickers = ["MSFT", "PG"] + trend_list = ["forecasting", "msft", "p&g"] + weather_event_types = ["%28Z%29+Winter+Weather", "%28Z%29+Winter+Storm"] + wikipedia_pages = ['all', 'Microsoft', "Procter_%26_Gamble", "YouTube", "United_States"] + df = load_live_daily( + long=False, + fred_key=fred_key, + fred_series=fred_series, + tickers=tickers, + trends_list=trend_list, + earthquake_min_magnitude=5, + weather_years=3, + london_air_days=700, + wikipedia_pages=wikipedia_pages, + gsa_key=gsa_key, + gov_domain_list=None, # ['usajobs.gov', 'usps.com', 'weather.gov'], + gov_domain_limit=700, + weather_event_types=weather_event_types, + sleep_seconds=15, + ) + # be careful of very noisy, large value series mixed into more well-behaved data as they can skew some metrics such that they get most of the attention + # remove "volume" data as it skews MAE (other solutions are to adjust metric_weighting towards SMAPE, use series `weights`, or pre-scale data) + df = df[[x for x in df.columns if "_volume" not in x]] + # remove dividends and stock splits as it skews metrics + df = df[[x for x in df.columns if "_dividends" not in x]] + df = df[[x for x in df.columns if "stock_splits" not in x]] + # scale 'wiki_all' to millions to prevent too much skew of MAE + if 'wiki_all' in df.columns: + df['wiki_all_millions'] = df['wiki_all'] / 1000000 + df = df.drop(columns=['wiki_all']) + + # manual NaN cleaning where real values are easily approximated, this is the way + # although if you have 'no good idea' why it is random, auto is best + # note manual pre-cleaning affects VALIDATION significantly (for better or worse) + # as NaN times in history are skipped by metrics, but filled values, as added here, are evaluated + if trend_list is not None: + for tx in trend_list: + if tx in df.columns: + df[tx] = df[tx].interpolate('akima').fillna(method='ffill', limit=30).fillna(method='bfill', limit=30) + # fill weekends + if tickers is not None: + for fx in tickers: + for suffix in ["_high", "_low", "_open", "_close"]: + fxs = (fx + suffix).lower() + if fxs in df.columns: + df[fxs] = df[fxs].interpolate('akima') + if fred_series is not None: + for fx in fred_series: + if fx in df.columns: + df[fx] = df[fx].interpolate('akima') + if weather_event_types is not None: + wevnt = [x for x in df.columns if "_Events" in x] + df[wevnt] = df[wevnt].mask(df[wevnt].notnull().cummax(), df[wevnt].fillna(0)) + # most of the NaN here are just weekends, when financial series aren't collected, ffill of a few steps is fine + # partial forward fill, no back fill + df = df.fillna(method='ffill', limit=3) + + df = df[df.index.year > 1999] + # remove any data from the future + df = df[df.index <= start_time] + # remove series with no recent data + df = df.dropna(axis="columns", how="all") + min_cutoff_date = start_time - datetime.timedelta(days=180) + most_recent_date = df.notna()[::-1].idxmax() + drop_cols = most_recent_date[most_recent_date < min_cutoff_date].index.tolist() + df = df.drop(columns=drop_cols) + print( + f"Series with most NaN: {df.head(365).isnull().sum().sort_values(ascending=False).head(5)}" + ) -df.to_csv(f"training_data_{forecast_name}.csv") -# df = pd.read_csv(f"training_data_{forecast_name}.csv", index_col=0, parse_dates=[0]) + # saving this to make it possible to rerun without waiting for download, but remove this in production + df.to_csv(f"training_data_{forecast_name}.csv") +else: + df = pd.read_csv(f"training_data_{forecast_name}.csv", index_col=0, parse_dates=[0]) # example future_regressor with some things we can glean from data and datetime index # note this only accepts `wide` style input dataframes @@ -222,15 +226,18 @@ """ metric_weighting = { - 'smape_weighting': 3, - 'mae_weighting': 2, - 'rmse_weighting': 1, + 'smape_weighting': 1, + 'mae_weighting': 3, + 'rmse_weighting': 2, 'made_weighting': 1, 'mage_weighting': 0, - 'mle_weighting': 0, + 'mate_weighting': 0.01, + 'mle_weighting': 1, 'imle_weighting': 0, - 'spl_weighting': 3, + 'spl_weighting': 5, 'dwae_weighting': 1, + 'uwmse_weighting': 1, + 'dwd_weighting': 0.1, 'runtime_weighting': 0.05, } @@ -258,7 +265,7 @@ # subset=100, # prefill_na=0, # remove_leading_zeroes=True, - current_model_file=f"current_model_{forecast_name}", + # current_model_file=f"current_model_{forecast_name}", generation_timeout=generation_timeout, n_jobs=n_jobs, verbose=1, From 800a51d76f209396f724d059777bc973541ea952 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 30 Nov 2023 08:56:31 -0600 Subject: [PATCH 03/26] 0.6.4 a3 --- autots/tools/transform.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/autots/tools/transform.py b/autots/tools/transform.py index a8a96490..fba637df 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -2865,7 +2865,7 @@ def get_new_params(method="random"): method_choice, method_params, transform_dict = anomaly_new_params(method=method) if transform_dict == "random": transform_dict = RandomTransform( - transformer_list="fast", transformer_max_depth=2 + transformer_list="scalable", transformer_max_depth=2 ) return { @@ -4681,8 +4681,8 @@ def __init__( ] @staticmethod - def get_new_params(method="random"): - return RandomTransform() + def get_new_params(method="fast"): + return RandomTransform(transformer_list=method) def fill_na(self, df, window: int = 10): """ @@ -5161,13 +5161,14 @@ def transformer_list_to_dict(transformer_list): elif transformer_list == "superfast": transformer_list = superfast_transformer_dict elif transformer_list == "scalable": + # "scalable" meant to be even smaller than "fast" subset of transformers transformer_list = fast_transformer_dict.copy() del transformer_list["KalmanSmoothing"] # potential kernel/RAM issues del transformer_list["SinTrend"] # no observed issues, but for efficiency # del transformer_list["HolidayTransformer"] # improved, should be good enough # temporary removal for testing del transformer_list["RegressionFilter"] # improved, might be good enough - del transformer_list["AnomalyRemoval"] # improved, might be good enough + # del transformer_list["AnomalyRemoval"] # improved, might be good enough if isinstance(transformer_list, dict): transformer_prob = list(transformer_list.values()) @@ -5377,6 +5378,6 @@ def random_cleaners(): )[0] if transform_dict == "random": transform_dict = RandomTransform( - transformer_list="fast", transformer_max_depth=2 + transformer_list="scalable", transformer_max_depth=2 ) return transform_dict From 521d8a2eb701354c2868a91decbe50d0715f371b Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 30 Nov 2023 20:53:48 -0600 Subject: [PATCH 04/26] 0.6.4 a4 --- autots/models/sklearn.py | 40 ++++++++++++++++++++++++++++++++++----- autots/tools/transform.py | 2 +- 2 files changed, 36 insertions(+), 6 deletions(-) diff --git a/autots/models/sklearn.py b/autots/models/sklearn.py index 7453d5f7..215dc31c 100644 --- a/autots/models/sklearn.py +++ b/autots/models/sklearn.py @@ -543,6 +543,32 @@ def retrieve_classifier( n_jobs=n_jobs, **model_param_dict, ) + elif model_class == "KNN": + from sklearn.neighbors import KNeighborsClassifier + return KNeighborsClassifier( + n_jobs=n_jobs, + **model_param_dict, + ) + elif model_class == "SGD": + from sklearn.linear_model import SGDClassifier + from sklearn.multioutput import MultiOutputClassifier + + if multioutput: + return MultiOutputClassifier( + SGDClassifier( + random_state=random_seed, + verbose=verbose_bool, + n_jobs=n_jobs, + **model_param_dict, + ) + ) + else: + return SGDClassifier( + random_state=random_seed, + verbose=verbose_bool, + n_jobs=n_jobs, + **model_param_dict, + ) else: raise ValueError(f"classifier {model_class} not a recognized option.") @@ -552,18 +578,18 @@ def retrieve_classifier( # 'RandomForest': 0.02, # crashes sometimes at scale for unclear reasons 'ElasticNet': 0.05, 'MLP': 0.05, - 'DecisionTree': 0.05, + 'DecisionTree': 0.02, 'KNN': 0.05, 'Adaboost': 0.03, 'SVM': 0.05, # was slow, LinearSVR seems much faster 'BayesianRidge': 0.05, 'xgboost': 0.05, 'KerasRNN': 0.01, - 'Transformer': 0.02, + 'Transformer': 0.01, 'HistGradientBoost': 0.03, 'LightGBM': 0.1, 'LightGBMRegressorChain': 0.03, - 'ExtraTrees': 0.05, + 'ExtraTrees': 0.01, 'RadiusNeighbors': 0.02, 'PoissonRegresssion': 0.03, 'RANSAC': 0.05, @@ -645,7 +671,7 @@ def retrieve_classifier( 'SVM': 0.05, 'KerasRNN': 0.05, 'Transformer': 0.02, # slow - 'ExtraTrees': 0.07, + 'ExtraTrees': 0.0001, # some params cause RAM crash? 'RadiusNeighbors': 0.05, 'MultioutputGPR': 0.0001, } @@ -739,7 +765,11 @@ def generate_classifier_params( method="default", ): if model_dict is None: - model_dict = {'xgboost': 1, 'ExtraTrees': 1, 'RandomForest': 1} + model_dict = { + 'xgboost': 1, 'ExtraTrees': 0.2, + 'RandomForest': 0.1, 'KNN': 1, + 'SGD': 0.1, + } regr_params = generate_regressor_params( model_dict=model_dict, method=method, diff --git a/autots/tools/transform.py b/autots/tools/transform.py index fba637df..3b0a2822 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -1118,7 +1118,7 @@ def get_new_params(method: str = "random", holiday_countries_used=None): choice = generate_regressor_params( model_dict={ "ElasticNet": 0.5, - "DecisionTree": 0.5, + "DecisionTree": 0.25, # 'ExtraTrees': 0.25, } ) From e8182165bc3eea5ad7c35b67ffb73c31cb4aa35b Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Fri, 1 Dec 2023 12:41:25 -0600 Subject: [PATCH 05/26] 0.6.4 a5 --- autots/models/cassandra.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/autots/models/cassandra.py b/autots/models/cassandra.py index 7836d37d..7d25b984 100644 --- a/autots/models/cassandra.py +++ b/autots/models/cassandra.py @@ -1849,7 +1849,8 @@ def get_new_params(self, method='fast'): if anomaly_intervention is not None: anomaly_detector_params = AnomalyRemoval.get_new_params(method=method) if anomaly_intervention == "model": - anomaly_intervention = general_template.sample(1).to_dict("records")[ + model_list, model_prob = model_list_to_dict("scalable") + anomaly_intervention = general_template[general_template['Model'].isin(model_list)].sample(1).to_dict("records")[ 0 ] # placeholder, probably else: @@ -1888,7 +1889,7 @@ def get_new_params(self, method='fast'): method=method ) trend_transformation = RandomTransform( - transformer_list="fast", + transformer_list="scalable", transformer_max_depth=3, # probably want some more usable defaults first as many random are senseless ) elif trend_base == 'pb1': @@ -1982,7 +1983,7 @@ def get_new_params(self, method='fast'): trend_anomaly_detector_params = AnomalyRemoval.get_new_params(method=method) else: trend_anomaly_detector_params = None - if method == 'deep': + if method in ['deep', 'all_linear']: linear_model = random.choices( [ 'lstsq', @@ -2030,7 +2031,7 @@ def get_new_params(self, method='fast'): )[0], 'method': random.choices( [None, 'L-BFGS-B', 'Nelder-Mead', 'TNC', 'Powell'], - [0.9, 0.02, 0.02, 0.02, 0.02], + [0.95, 0.02, 0.02, 0.02, 0.02], )[0], } elif linear_model == "bayesian_linear": From 21e1c9e74f4b3c65913f584456a533d7546c7c9c Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Fri, 1 Dec 2023 13:22:22 -0600 Subject: [PATCH 06/26] 0.6.4 a6 --- autots/evaluator/auto_model.py | 3 +++ autots/models/sklearn.py | 19 ++++++++++++++----- autots/tools/anomaly_utils.py | 4 ++-- autots/tools/transform.py | 2 ++ tests/test_autots.py | 2 ++ 5 files changed, 23 insertions(+), 7 deletions(-) diff --git a/autots/evaluator/auto_model.py b/autots/evaluator/auto_model.py index da042ea5..ead13051 100644 --- a/autots/evaluator/auto_model.py +++ b/autots/evaluator/auto_model.py @@ -1007,6 +1007,9 @@ def concat(self, another_eval): ignore_index=True, sort=False, ).reset_index(drop=True) + self.per_series_metrics = pd.concat( + [self.per_series_metrics, another_eval.per_series_metrics], axis=0, sort=False + ) self.per_series_mae = pd.concat( [self.per_series_mae, another_eval.per_series_mae], axis=0, sort=False ) diff --git a/autots/models/sklearn.py b/autots/models/sklearn.py index 215dc31c..8ef90b34 100644 --- a/autots/models/sklearn.py +++ b/autots/models/sklearn.py @@ -765,11 +765,20 @@ def generate_classifier_params( method="default", ): if model_dict is None: - model_dict = { - 'xgboost': 1, 'ExtraTrees': 0.2, - 'RandomForest': 0.1, 'KNN': 1, - 'SGD': 0.1, - } + if method == "fast": + model_dict = { + 'xgboost': 1, + # 'ExtraTrees': 0.2, # crashes sometimes + # 'RandomForest': 0.1, + 'KNN': 1, + 'SGD': 0.1, + } + else: + model_dict = { + 'xgboost': 1, 'ExtraTrees': 0.2, + 'RandomForest': 0.1, 'KNN': 1, + 'SGD': 0.1, + } regr_params = generate_regressor_params( model_dict=model_dict, method=method, diff --git a/autots/tools/anomaly_utils.py b/autots/tools/anomaly_utils.py index d1686c83..fecfa73d 100644 --- a/autots/tools/anomaly_utils.py +++ b/autots/tools/anomaly_utils.py @@ -455,14 +455,14 @@ def anomaly_new_params(method='random'): "LOF", "EE", "zscore", - "rolling_zscore", + "rolling_zscore", # Matt likes this one best "mad", "minmax", "IQR", "nonparametric", "IsolationForest", ], # Isolation Forest is good but slower (parallelized also) - [0.05, 0.1, 0.25, 0.25, 0.1, 0.1, 0.2, 0.1, 0.05], + [0.05, 0.1, 0.25, 0.3, 0.1, 0.1, 0.2, 0.1, 0.05], )[0] if method_choice == "IsolationForest": diff --git a/autots/tools/transform.py b/autots/tools/transform.py index 3b0a2822..c721dff9 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -5154,6 +5154,8 @@ def transformer_list_to_dict(transformer_list): del fast_transformer_dict["Cointegration"] del fast_transformer_dict["BTCD"] + if transformer_list is None: + transformer_list = "superfast" if not transformer_list or transformer_list == "all": transformer_list = transformer_dict elif transformer_list in ["fast", "default", "Fast", "auto"]: diff --git a/tests/test_autots.py b/tests/test_autots.py index ce1325de..31c9ca68 100644 --- a/tests/test_autots.py +++ b/tests/test_autots.py @@ -174,6 +174,8 @@ def test_autots(self): # self.assertTrue((back_forecast.index == model.df_wide_numeric.index).all(), msg="Back forecasting failed to have equivalent index to train.") self.assertFalse(np.any(back_forecast.isnull())) self.assertEqual(long_form.shape[0], forecasts_df.shape[0] * forecasts_df.shape[1] * 3) + # results present + self.assertGreater(model.initial_results.per_series_metrics.shape[0], 1) # TEST EXPORTING A TEMPLATE THEN USING THE BEST MODEL AS A PREDICTION df_train = df.iloc[:-forecast_length] From 36c5917496852edd5dc547b6ec5cec5ae8e6828e Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Sat, 2 Dec 2023 11:27:11 -0600 Subject: [PATCH 07/26] 0.6.4 a7 --- autots/evaluator/auto_model.py | 23 +++++++++++++---------- autots/models/model_list.py | 2 +- autots/models/sklearn.py | 4 ++-- 3 files changed, 16 insertions(+), 13 deletions(-) diff --git a/autots/evaluator/auto_model.py b/autots/evaluator/auto_model.py index ead13051..21a3175a 100644 --- a/autots/evaluator/auto_model.py +++ b/autots/evaluator/auto_model.py @@ -2376,15 +2376,17 @@ def NewGeneticTemplate( ) -def validation_aggregation(validation_results, df_train=None): +def validation_aggregation( + validation_results, df_train=None, + groupby_cols = [ + 'ID', + 'Model', + 'ModelParameters', + 'TransformationParameters', + 'Ensemble', + ], + ): """Aggregate a TemplateEvalObject.""" - groupby_cols = [ - 'ID', - 'Model', - 'ModelParameters', - 'TransformationParameters', - 'Ensemble', - ] col_aggs = { 'Runs': 'sum', 'smape': 'mean', @@ -2446,9 +2448,10 @@ def validation_aggregation(validation_results, df_train=None): validation_results.model_results = validation_results.model_results.replace( [np.inf, -np.inf], np.nan ) - validation_results.model_results = validation_results.model_results.groupby( + grouped = validation_results.model_results.groupby( groupby_cols - ).agg(col_aggs) + ) + validation_results.model_results = grouped.agg(col_aggs) validation_results.model_results = validation_results.model_results.reset_index( drop=False ) diff --git a/autots/models/model_list.py b/autots/models/model_list.py index a4a1b88d..af82dcda 100644 --- a/autots/models/model_list.py +++ b/autots/models/model_list.py @@ -103,7 +103,7 @@ # 'MultivariateMotif': 0.8, # RAM issues at scale it seems 'SectionalMotif': 1, 'NVAR': 0.3, - 'MAR': 0.5, + 'MAR': 0.25, 'RRVAR': 1, 'KalmanStateSpace': 0.4, 'MetricMotif': 1, diff --git a/autots/models/sklearn.py b/autots/models/sklearn.py index 8ef90b34..70f61ac0 100644 --- a/autots/models/sklearn.py +++ b/autots/models/sklearn.py @@ -671,9 +671,9 @@ def retrieve_classifier( 'SVM': 0.05, 'KerasRNN': 0.05, 'Transformer': 0.02, # slow - 'ExtraTrees': 0.0001, # some params cause RAM crash? + 'ExtraTrees': 0.00001, # some params cause RAM crash? 'RadiusNeighbors': 0.05, - 'MultioutputGPR': 0.0001, + 'MultioutputGPR': 0.00001, } gpu = ['Transformer', 'KerasRNN', 'MLP'] # or more accurately, no dnn gradient_boosting = { From 4effc72d4b6c2a2337262630d6c2d622fc7447e0 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Sun, 3 Dec 2023 22:04:35 -0600 Subject: [PATCH 08/26] 0.6.4 a8 --- autots/models/sklearn.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/autots/models/sklearn.py b/autots/models/sklearn.py index 70f61ac0..8252032f 100644 --- a/autots/models/sklearn.py +++ b/autots/models/sklearn.py @@ -584,8 +584,8 @@ def retrieve_classifier( 'SVM': 0.05, # was slow, LinearSVR seems much faster 'BayesianRidge': 0.05, 'xgboost': 0.05, - 'KerasRNN': 0.01, - 'Transformer': 0.01, + 'KerasRNN': 0.001, # slow at scale + 'Transformer': 0.001, 'HistGradientBoost': 0.03, 'LightGBM': 0.1, 'LightGBMRegressorChain': 0.03, @@ -665,11 +665,12 @@ def retrieve_classifier( datepart_model_dict: dict = { # 'RandomForest': 0.05, # crashes sometimes at scale for unclear reasons 'ElasticNet': 0.05, + 'xgboost': 0.01, 'MLP': 0.05, - 'DecisionTree': 0.05, + 'DecisionTree': 0.02, 'Adaboost': 0.05, - 'SVM': 0.05, - 'KerasRNN': 0.05, + 'SVM': 0.01, + 'KerasRNN': 0.02, 'Transformer': 0.02, # slow 'ExtraTrees': 0.00001, # some params cause RAM crash? 'RadiusNeighbors': 0.05, From 9b8f6fcddd81fe55b75184cdce2b80fe73eb8cbb Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Tue, 5 Dec 2023 12:53:56 -0600 Subject: [PATCH 09/26] 0.6.4 a9 --- 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 c721dff9..92be92dc 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -5169,8 +5169,8 @@ def transformer_list_to_dict(transformer_list): del transformer_list["SinTrend"] # no observed issues, but for efficiency # del transformer_list["HolidayTransformer"] # improved, should be good enough # temporary removal for testing - del transformer_list["RegressionFilter"] # improved, might be good enough - # del transformer_list["AnomalyRemoval"] # improved, might be good enough + del transformer_list["RegressionFilter"] + del transformer_list["LocalLinearTrend"] if isinstance(transformer_list, dict): transformer_prob = list(transformer_list.values()) From 6a5a6ebf5b9299dd8dcc368f8919c6ed22406adf Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Tue, 5 Dec 2023 23:40:27 -0600 Subject: [PATCH 10/26] 0.6.4 a10 --- autots/evaluator/auto_model.py | 9 ++++++++- autots/evaluator/auto_ts.py | 16 ++++++++++++++++ autots/models/base.py | 4 +++- autots/tools/transform.py | 17 ++++++++++++++--- 4 files changed, 41 insertions(+), 5 deletions(-) diff --git a/autots/evaluator/auto_model.py b/autots/evaluator/auto_model.py index 21a3175a..c620abc4 100644 --- a/autots/evaluator/auto_model.py +++ b/autots/evaluator/auto_model.py @@ -2145,6 +2145,7 @@ def NewGeneticTemplate( models_mode: str = "default", score_per_series=None, recursive_count=0, + model_list=None, # UPDATE RECURSIVE section if adding or removing params ): """ @@ -2158,6 +2159,8 @@ def NewGeneticTemplate( """ new_template_list = [] + if model_list is None: + model_list = model_results['Model'].unique().tolist() # filter existing templates sorted_results = model_results[ @@ -2213,8 +2216,11 @@ def NewGeneticTemplate( sidx = {name: i for i, name in enumerate(list(sorted_results), start=1)} for row in sorted_results.itertuples(name=None): n = n_list[counter] - counter += 1 model_type = row[sidx["Model"]] + # skip models not in the model_list + if model_type not in model_list: + continue + counter += 1 model_params = row[sidx["ModelParameters"]] try: trans_params = json.loads(row[sidx["TransformationParameters"]]) @@ -2359,6 +2365,7 @@ def NewGeneticTemplate( models_mode=models_mode, score_per_series=score_per_series, recursive_count=recursive_count, + model_list=model_list, ) # enjoy the privilege elif new_template.shape[0] < max_results: diff --git a/autots/evaluator/auto_ts.py b/autots/evaluator/auto_ts.py index 3e9df044..2b094442 100644 --- a/autots/evaluator/auto_ts.py +++ b/autots/evaluator/auto_ts.py @@ -239,6 +239,7 @@ def __init__( self.models_mode = models_mode self.current_model_file = current_model_file self.force_gc = force_gc + self.validate_import = None random.seed(self.random_seed) if self.max_generations is None and self.generation_timeout is not None: self.max_generations = 99999 @@ -1286,6 +1287,7 @@ def fit( transformer_max_depth=self.transformer_max_depth, models_mode=self.models_mode, score_per_series=self.score_per_series, + model_list=self.model_list, ) submitted_parameters = pd.concat( [submitted_parameters, new_template], @@ -1407,6 +1409,12 @@ def fit( subset=['Model', 'ModelParameters', 'TransformationParameters'] ) self.validation_template = validation_template[self.template_cols] + if self.validate_import is not None: + self.validation_template = pd.concat( + [self.validation_template, self.validate_import] + ).drop_duplicates( + subset=['Model', 'ModelParameters', 'TransformationParameters'] + ) # run validations if self.num_validations > 0: @@ -2450,6 +2458,7 @@ def import_template( enforce_model_list: bool = True, include_ensemble: bool = False, include_horizontal: bool = False, + force_validation: bool = False, ): """Import a previously exported template of model parameters. Must be done before the AutoTS object is .fit(). @@ -2460,6 +2469,7 @@ def import_template( enforce_model_list (bool): if True, remove model types not in model_list include_ensemble (bool): if enforce_model_list is True, this specifies whether to allow ensembles anyway (otherwise they are unpacked and parts kept) include_horizontal (bool): if enforce_model_list is True, this specifies whether to allow ensembles except horizontal (overridden by keep_ensemble) + force_validation (bool): if True, all models imported here will automatically get sent to full cross validation (regardless of first eval performance) """ if method.lower() in ['add on', 'addon', 'add_on']: addon_flag = True @@ -2497,6 +2507,12 @@ def import_template( else: return ValueError("method must be 'addon' or 'only'") + if force_validation: + if self.validate_import is None: + self.validate_import = import_template + else: + self.validate_import = pd.concat([self.validate_import, import_template]) + return self def export_best_model(self, filename, **kwargs): diff --git a/autots/models/base.py b/autots/models/base.py index 9a73b73c..f6ada43b 100644 --- a/autots/models/base.py +++ b/autots/models/base.py @@ -333,6 +333,7 @@ def plot_distributions( y_col='TotalRuntimeSeconds', xlim=None, xlim_right=None, + title_suffix="", ): import matplotlib.pyplot as plt import seaborn as sns @@ -391,7 +392,7 @@ def plot_distributions( plt.legend(handles, labels, title=group_col) # , bbox_to_anchor=(1.05, 1), loc=2 # Adding titles and labels - plt.title(f'Distribution of {y_col} by {group_col}', fontsize=16) + plt.title(f'Distribution of {y_col} by {group_col}{title_suffix}', fontsize=16) plt.xlabel(f'{y_col}', fontsize=14) plt.ylabel('Density', fontsize=14) @@ -570,6 +571,7 @@ def plot_ensemble_runtimes(self, xlim_right=None): y_col='TotalRuntimeSeconds', xlim=0, xlim_right=xlim_right, + title_suffix=" in Chosen Ensemble" ) def plot_df( diff --git a/autots/tools/transform.py b/autots/tools/transform.py index 92be92dc..0b93dee0 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -4340,6 +4340,7 @@ def __init__( fillna=None, n_jobs=1, adjustment: int = 2, + reverse_alignment=True, ): """Detect anomalies on a historic dataset in the DIFFS then cumsum back to origin space. No inverse_transform available. @@ -4350,6 +4351,7 @@ def __init__( transform_dict (dict): option but helpful, often datepart, differencing, or other standard AutoTS transformer params method_params (dict): parameters specific to the method, use `.get_new_params()` to see potential models fillna (str): how to fill anomaly values removed + reverse_alighment (bool): if True, remove diffs then cumsum n_jobs (int): multiprocessing jobs, used by some methods """ super().__init__(name="DiffSmoother") @@ -4360,6 +4362,7 @@ def __init__( self.n_jobs = n_jobs self.fillna = fillna self.adjustment = adjustment + self.reverse_alignment = reverse_alignment def fit(self, df): """Fit. @@ -4394,11 +4397,18 @@ def transform(self, df): method_params=self.method_params, n_jobs=self.n_jobs, ) - diffs = diffs.where(self.anomalies != -1, np.nan) + if self.reverse_alignment: + diffs = diffs.where(self.anomalies != -1, np.nan) + else: + # also removes any -1 in your first row of data, for giggles + diffs = df.where(pd.concat([df.iloc[0:1], self.anomalies]) != -1, np.nan) if self.fillna is not None: diffs = FillNA(diffs, method=self.fillna, window=10) - return pd.concat([diffs, df.tail(1)]).iloc[::-1].cumsum().iloc[::-1] + if self.reverse_alignment: + return pd.concat([diffs, df.tail(1)]).iloc[::-1].cumsum().iloc[::-1] + else: + return diffs def fit_transform(self, df): """Fits and Returns Magical DataFrame @@ -4408,7 +4418,7 @@ def fit_transform(self, df): return self.transform(df) @staticmethod - def get_new_params(method="random"): + def get_new_params(method="fast"): fillna = random.choices( [ None, @@ -4438,6 +4448,7 @@ def get_new_params(method="random"): "method": method_choice, "method_params": method_params, "transform_dict": None, + "reverse_alignment": random.choice([True, False]), "fillna": fillna, } From 05dc09f7312b21a3c10cda8dacb019d6ef6377e9 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Wed, 6 Dec 2023 00:09:48 -0600 Subject: [PATCH 11/26] 0.6.4 a11 --- autots/evaluator/auto_ts.py | 12 +++++++++++- autots/tools/transform.py | 11 ++++++++--- 2 files changed, 19 insertions(+), 4 deletions(-) diff --git a/autots/evaluator/auto_ts.py b/autots/evaluator/auto_ts.py index 2b094442..32f063a0 100644 --- a/autots/evaluator/auto_ts.py +++ b/autots/evaluator/auto_ts.py @@ -727,9 +727,19 @@ def get_new_params(method='random'): }, }, }, + { + "fillna": None, + "transformations": {"0": "CenterSplit"}, + "transformation_params": { + "0": { + 'fillna': 'ffill', + 'center': 'zero', + }, + }, + }, 'random', ], - [0.9, 0.1, 0.05, 0.1, 0.1, 0.1, 0.1, 0.05, 0.15, 0.1], + [0.9, 0.1, 0.05, 0.1, 0.1, 0.1, 0.1, 0.05, 0.15, 0.05, 0.1], )[0] if preclean_choice == "random": preclean_choice = RandomTransform( diff --git a/autots/tools/transform.py b/autots/tools/transform.py index 0b93dee0..cf21465d 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -4401,7 +4401,10 @@ def transform(self, df): diffs = diffs.where(self.anomalies != -1, np.nan) else: # also removes any -1 in your first row of data, for giggles - diffs = df.where(pd.concat([df.iloc[0:1], self.anomalies]) != -1, np.nan) + full_anom = pd.concat([df.iloc[0:1], self.anomalies]) + full_anom.index = df.index + full_anom.columns = df.columns + diffs = df.where(full_anom != -1, np.nan) if self.fillna is not None: diffs = FillNA(diffs, method=self.fillna, window=10) @@ -4433,22 +4436,24 @@ def get_new_params(method="fast"): 2.5, 3.0, ], - [0.0, 0.3, 0.1, 0.3, 0.3, 0.1, 0.1, 0.1, 0.2, 0.1, 0.1], + [0.0, 0.3, 0.1, 0.3, 0.3, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], )[0] if isinstance(fillna, (float, int)): method_choice = None method_params = None transform_dict = None + reverse_alignment = True else: method_choice, method_params, transform_dict = anomaly_new_params( method=method ) + reverse_alignment = random.choice([True, False]) return { "method": method_choice, "method_params": method_params, "transform_dict": None, - "reverse_alignment": random.choice([True, False]), + "reverse_alignment": reverse_alignment, "fillna": fillna, } From 567f3d65dd982db5ba9e0ec0e687402b92bf8d5d Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Wed, 6 Dec 2023 21:45:03 -0600 Subject: [PATCH 12/26] 0.6.4 a12 --- autots/models/sklearn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/autots/models/sklearn.py b/autots/models/sklearn.py index 8252032f..27719e1b 100644 --- a/autots/models/sklearn.py +++ b/autots/models/sklearn.py @@ -580,7 +580,7 @@ def retrieve_classifier( 'MLP': 0.05, 'DecisionTree': 0.02, 'KNN': 0.05, - 'Adaboost': 0.03, + 'Adaboost': 0.01, 'SVM': 0.05, # was slow, LinearSVR seems much faster 'BayesianRidge': 0.05, 'xgboost': 0.05, From 9ef4bcea9f6be8c2cf4f057ee34c73557e165bba Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 7 Dec 2023 08:53:48 -0600 Subject: [PATCH 13/26] 0.6.4 a13 --- autots/tools/regressor.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/autots/tools/regressor.py b/autots/tools/regressor.py index 28c1d29a..1988b61d 100644 --- a/autots/tools/regressor.py +++ b/autots/tools/regressor.py @@ -325,10 +325,12 @@ def create_lagged_regressor( ) regressor_forecast = df_inner.tail(forecast_length) - # also dates.shift(forecast_length)[-forecast_length:] - regressor_forecast.index = pd.date_range( - dates[-1], periods=(forecast_length + 1), freq=frequency - )[1:] + try: + regressor_forecast.index = pd.date_range( + dates[-1], periods=(forecast_length + 1), freq=frequency + )[1:] + except Exception: + raise ValueError("create_regressor doesn't work on data where forecast_length > historical data length") regressor_train = df_inner.shift(forecast_length) if backfill == "ets": model_flag = True From ae65cf191c8446be5c128364b81566343782c22e Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 7 Dec 2023 18:05:12 -0600 Subject: [PATCH 14/26] 0.6.4 a14 --- TODO.md | 2 + test.py | 121 +++++++++++++++++++++++++++++++------------------------- 2 files changed, 70 insertions(+), 53 deletions(-) diff --git a/TODO.md b/TODO.md index 9efd8be0..44f1924e 100644 --- a/TODO.md +++ b/TODO.md @@ -15,6 +15,8 @@ # 0.6.4 🔜🔜🔜 * adjusted n_jobs back to minus 1 for multivariatemotif * fixed bug with plot_validations not working with some frequencies +* force_validation added to import_template +* model_list now enforced in new generations ### Unstable Upstream Pacakges (those that are frequently broken by maintainers) * Pytorch-Forecasting diff --git a/test.py b/test.py index f40ba304..8f1e4e10 100644 --- a/test.py +++ b/test.py @@ -37,7 +37,7 @@ name = template_filename.replace('.csv', '').replace("autots_forecast_template_", "") random_seed = 2023 forecast_length = 90 -long = False +long = True # df = load_linear(long=long, shape=(400, 1000), introduce_nan=None) # df = load_sine(long=long, shape=(400, 1000), start_date="2021-01-01", introduce_random=100).iloc[:, 2:] # df = load_artificial(long=long, date_start="2018-01-01") @@ -65,8 +65,8 @@ validation_method = "backwards" # "similarity" frequency = "infer" drop_most_recent = 0 -generations = 150 -generation_timeout = 2 +generations = 100 +generation_timeout = 5 num_validations = 2 # "auto" initial_template = "Random" # "General+Random" if use_template: @@ -109,29 +109,30 @@ }, } ensemble = [ - "simple", + # "simple", # 'mlensemble', 'horizontal-max', # "mosaic-window", # 'mosaic-crosshair', -] # "dist", "subsample", "mosaic-window", "horizontal-max" +] # "dist", "subsample", "mosaic-window", "horizontal" # ensemble = None metric_weighting = { - 'smape_weighting': 3, + 'smape_weighting': 5, 'mae_weighting': 2, - 'rmse_weighting': 2, + 'rmse_weighting': 1, 'made_weighting': 1, 'mage_weighting': 0, 'mate_weighting': 1, 'mle_weighting': 0, # avoid underestimate 'imle_weighting': 0, # avoid overestimate - 'spl_weighting': 0, + 'spl_weighting': 3, 'containment_weighting': 0.1, 'contour_weighting': 0, - 'runtime_weighting': 0.01, + 'runtime_weighting': 0.05, 'maxe_weighting': 0, 'oda_weighting': 0, 'mqae_weighting': 0, + 'uwmse_weighting': 1, 'wasserstein_weighting': 0, 'dwd_weighting': 1, 'smoothness_weighting': -0.5, @@ -145,17 +146,20 @@ "lower_constraint": 0.1, "bounds": True, } -forecast_index = pd.date_range(start=df.index[-1], periods=forecast_length + 1, freq=df.index.freq)[1:] -# sets an extremely high value for the cap, one that should never actually be reached by the data normally -if isinstance(df, pd.Series): - cols = [df.name] -else: - cols = df.columns -upper_constraint = pd.DataFrame(9999999999, index=forecast_index, columns=cols) -# in this case also assuming negatives won't happen so setting a lower constraint of 0 -lower_constraint = pd.DataFrame(0, index=forecast_index, columns=cols) -# add in your dates you want as definitely 0 -upper_constraint.loc["2022-10-31"] = 0 +if not long: + forecast_index = pd.date_range(start=df.index[-1], periods=forecast_length + 1, freq=df.index.freq)[1:] + # sets an extremely high value for the cap, one that should never actually be reached by the data normally + if isinstance(df, pd.Series): + cols = [df.name] + else: + cols = df.columns + upper_constraint = pd.DataFrame(9999999999, index=forecast_index, columns=cols) + # in this case also assuming negatives won't happen so setting a lower constraint of 0 + lower_constraint = pd.DataFrame(0, index=forecast_index, columns=cols) + # add in your dates you want as definitely 0 + upper_constraint.loc["2022-10-31"] = 0 +upper_constraint = 0 +lower_constraint = 0 constraint = { "constraint_method": "absolute", "upper_constraint": upper_constraint, @@ -198,40 +202,50 @@ verbose=verbose, models_mode=models_mode, random_seed=random_seed, - current_model_file=f"current_model_{name}", + # current_model_file=f"current_model_{name}", ) - -regr_train, regr_fcst = create_regressor( - df, - forecast_length=forecast_length, - frequency=frequency, - drop_most_recent=drop_most_recent, - scale=True, - summarize="auto", - backfill="bfill", - fill_na="pchip", - holiday_countries=["US"], - datepart_method="recurring", - preprocessing_params={ - "fillna": None, - "transformations": {"0": "LocalLinearTrend"}, - "transformation_params": { - "0": { - 'rolling_window': 30, - 'n_tails': 0.1, - 'n_future': 0.2, - 'method': 'mean', - 'macro_micro': True - }, +if not long: + regr_train, regr_fcst = create_regressor( + df, + forecast_length=forecast_length, + frequency=frequency, + drop_most_recent=drop_most_recent, + scale=True, + summarize="auto", + backfill="bfill", + fill_na="pchip", + holiday_countries=["US"], + datepart_method="recurring", + preprocessing_params={ + "fillna": None, + "transformations": {"0": "LocalLinearTrend"}, + "transformation_params": { + "0": { + 'rolling_window': 30, + 'n_tails': 0.1, + 'n_future': 0.2, + 'method': 'mean', + 'macro_micro': True + }, + }, }, - }, -) + ) +else: + regr_train = None + regr_fcst = None # model = model.import_results('test.pickle') if use_template: model = model.import_template( - template_filename, method=template_import_method, enforce_model_list=True + template_filename, method=template_import_method, + enforce_model_list=False, force_validation=True, + ) + model = model.import_template( + "/Users/colincatlin/Downloads/template_categories_1.csv", method=template_import_method, enforce_model_list=True + ) + model = model.import_template( + "/Users/colincatlin/Downloads/test_import.csv", method=template_import_method, enforce_model_list=False, force_validation=True, ) start_time_for = timeit.default_timer() @@ -254,12 +268,13 @@ max_per_model_class=5, include_results=True, ) - model.export_template( - "slowest_models_template.csv", - models="slowest", - n=10, - include_results=True, - ) + if False: + model.export_template( + "slowest_models_template.csv", + models="slowest", + n=10, + include_results=True, + ) elapsed_for = timeit.default_timer() - start_time_for From 059e0c5aba0ae5255692240c2abbea651b68c1c8 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 7 Dec 2023 18:07:10 -0600 Subject: [PATCH 15/26] 0.6.4 a15 --- test.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/test.py b/test.py index 8f1e4e10..0ad0b297 100644 --- a/test.py +++ b/test.py @@ -237,16 +237,16 @@ # model = model.import_results('test.pickle') if use_template: - model = model.import_template( - template_filename, method=template_import_method, - enforce_model_list=False, force_validation=True, - ) - model = model.import_template( - "/Users/colincatlin/Downloads/template_categories_1.csv", method=template_import_method, enforce_model_list=True - ) - model = model.import_template( - "/Users/colincatlin/Downloads/test_import.csv", method=template_import_method, enforce_model_list=False, force_validation=True, - ) + if os.path.exists(template_filename): + model = model.import_template( + template_filename, method=template_import_method, + enforce_model_list=False, force_validation=True, + ) + file2 = "/Users/colincatlin/Downloads/test_import.csv" + if os.path.exists(file2): + model = model.import_template( + file2, method=template_import_method, enforce_model_list=False, force_validation=True, + ) start_time_for = timeit.default_timer() model = model.fit( From b80411d19ba1b50cbbc6848ccd8821e323a019ec Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 7 Dec 2023 23:22:18 -0600 Subject: [PATCH 16/26] 0.6.4 16 --- autots/evaluator/auto_ts.py | 3 +- autots/models/neural_forecast.py | 301 +++++++++++++++++++++++++++++++ 2 files changed, 303 insertions(+), 1 deletion(-) create mode 100644 autots/models/neural_forecast.py diff --git a/autots/evaluator/auto_ts.py b/autots/evaluator/auto_ts.py index 32f063a0..38de3938 100644 --- a/autots/evaluator/auto_ts.py +++ b/autots/evaluator/auto_ts.py @@ -1112,6 +1112,7 @@ def fit( """ self.model = None self.grouping_ids = grouping_ids + self.fitStart = pd.Timestamp.now() # convert class variables to local variables (makes testing easier) if self.validation_method == "custom": @@ -1129,7 +1130,6 @@ def fit( else: self.validation_indexes = [] - prediction_interval = self.prediction_interval random_seed = self.random_seed metric_weighting = self.metric_weighting verbose = self.verbose @@ -1759,6 +1759,7 @@ def fit( # clean up any remaining print statements sys.stdout.flush() + self.fitRuntime = pd.Timestamp.now() - self.fitStart return self def validation_agg(self): diff --git a/autots/models/neural_forecast.py b/autots/models/neural_forecast.py new file mode 100644 index 00000000..9ed3baa6 --- /dev/null +++ b/autots/models/neural_forecast.py @@ -0,0 +1,301 @@ +""" +Nixtla's NeuralForecast. Be warned, as of writing, their package has commercial restrictions. +""" +import logging +import random +from datetime import datetime +import pandas as pd +from autots.models.base import ModelObject, PredictionObject +from autots.tools.probabilistic import Point_to_Probability + + +class NeuralForecast(ModelObject): + """See NeuralForecast documentation for details. + + Args: + name (str): String to identify class + frequency (str): String alias of datetime index frequency or else 'infer' + prediction_interval (float): Confidence interval for probabilistic forecast + regression_type: str = None, + model_args (dict): for all model args that aren't in default list, run get_new_params for default + """ + + def __init__( + self, + name: str = "NeuralForecast", + frequency: str = 'infer', + prediction_interval: float = 0.9, + holiday_country: str = 'US', + random_seed: int = 2023, + verbose: int = 0, + forecast_length: int = 28, + regression_type: str = None, + n_jobs: int = -1, + models = "LSTM", + loss = "MQLoss", + input_size = "2ForecastLength", + max_steps = 1000, + learning_rate = 0.001, + early_stop_patience_steps = 3, + activation = 'ReLU', + scaler_type = 'robust', + model_args = {}, + **kwargs, + ): + ModelObject.__init__( + self, + name, + frequency, + prediction_interval, + holiday_country=holiday_country, + random_seed=random_seed, + regression_type=regression_type, + verbose=verbose, + n_jobs=n_jobs, + ) + self.models = models + self.loss = loss + self.input_size = input_size + self.max_steps = max_steps + self.learning_rate = learning_rate + self.early_stop_patience_steps = early_stop_patience_steps + self.activation = activation + self.scaler_type = scaler_type + self.forecast_length = forecast_length + self.df_train = None + + def fit(self, df, future_regressor=None): + """Train algorithm given data supplied. + + Args: + df (pandas.DataFrame): Datetime Indexed + """ + self.basic_profile(df) + if self.regression_type in ["User", "user"]: + if future_regressor is None: + raise ValueError( + "regression_type='User' but no future_regressor passed" + ) + + from neuralforecast import NeuralForecast + from neuralforecast.losses.pytorch import SMAPE, MQLoss, DistributionLoss, MAE, HuberLoss + from neuralforecast.models import TFT, LSTM, NHITS, MLP, NBEATS, TimesNet, PatchTST, DeepAR + + prediction_interval = self.prediction_interval + forecast_length = self.forecast_length + freq = self.frequency + + # Split data and declare panel dataset + if isinstance(prediction_interval, list): + levels = prediction_interval + elif isinstance(prediction_interval, dict): + levels = list(prediction_interval.values()) + else: + levels = [int(prediction_interval * 100)] + + logging.getLogger("pytorch_lightning").setLevel(logging.CRITICAL) + loss = self.loss + if loss == "MQLoss": + loss = MQLoss(level=levels) + elif loss == "Poisson": + loss = DistributionLoss(distribution='Poisson', level=levels, return_params=False) + elif loss == "Bernoulli": + loss = DistributionLoss(distribution='Bernoulli', level=levels, return_params=False) + elif loss == "StudentT": + loss = DistributionLoss(distribution='StudentT', level=levels, return_params=False) + elif loss == "NegativeBinomial": + loss = DistributionLoss(distribution='NegativeBinomial', level=levels, return_params=False) + elif loss == "Normal": + loss = DistributionLoss(distribution='Normal', level=levels, return_params=False) + elif loss == "Tweedie": + loss = DistributionLoss(distribution='Tweedie', level=levels, return_params=False) + elif loss == "MAE": + self.df_train = df + loss = MAE() + elif loss == "HuberLoss": + self.df_train = df + loss = HuberLoss() + elif loss == "SMAPE": + self.df_train = df + loss = SMAPE() + elif isinstance(loss, str): + raise ValueError(f"loss not recognized: {loss}") + else: + # allow custom input + pass + + str_input = str(self.input_size).lower() + if "forecastlength" in str_input: + input_size = forecast_length * int(''.join([x for x in str_input if x.isdigit()])) + else: + input_size = int(self.input_size) + base_args = { + "h": forecast_length, + "input_size": input_size, + "max_steps": self.max_steps, + "num_workers_loader": self.self.n_jobs, + "random_seed": self.random_seed, + "learning_rate": self.learning_rate, + "loss": self.loss, + "early_stop_patience_steps": self.early_stop_patience_steps, + 'scaler_type': self.scaler_type, + "activation": self.activation, + } + models = self.models + model_args = self.model_args + if self.regression_type in ['User', 'user']: + base_args["futr_exog_list"] = future_regressor.columns.tolist() + if isinstance(models, list): + # User inputs classes directly + pass + elif models == 'LSTM': + models = [LSTM(**{**base_args, **model_args})] + elif models == "NHITS": + models = [NHITS(**{**base_args, **model_args})] + elif models == "NBEATS": + models = [NBEATS(**{**base_args, **model_args})] + elif models == "MLP": + models = [MLP(**{**base_args, **model_args})] + elif models == "TimesNet": + models = [TimesNet(**{**base_args, **model_args})] + elif models == "TFT": + models = [TFT(**{**base_args, **model_args})] + elif models == "PatchTST": + models = [PatchTST(**{**base_args, **model_args})] + elif models == "DeepAR": + models = [DeepAR(**{**base_args, **model_args})] + else: + raise ValueError(f"models not recognized: {models}") + + # model params + + silly_format = df.reset_index(names='ds').melt(id_vars='ds', value_name='y', var_name='unique_id') + if self.regression_type in ['User', 'user']: + silly_format.merge(future_regressor, left_on='ds', right_index=True) + self.nf = NeuralForecast(models=models, freq=freq) + self.nf.fit(df=silly_format) + self.fit_runtime = datetime.datetime.now() - self.startTime + return self + + def predict(self, future_regressor=None, just_point_forecast=False): + predictStartTime = datetime.datetime.now() + if self.regression_type in ['User', 'user']: + index = self.create_forecast_index(forecast_length=self.forecast_length) + futr_df = pd.concat([pd.Series(col, index=index, name='temp') for col in self.column_names]) + futr_df = futr_df.merge(future_regressor, left_index=True, right_index=True) + futr_df = futr_df.reset_index(names='ds') + long_forecast = self.nf.predict(futr_df=futr_df) + else: + long_forecast = self.nf.predict() + target_col = [x for x in long_forecast.columns if "median" in x][0] + forecast = long_forecast.reset_index().pivot_table(index='ds', columns='unique_id', values=target_col)[self.column_names] + + if just_point_forecast: + return forecast + if self.df_train is not None: + upper_forecast, lower_forecast = Point_to_Probability( + self.df_train, + forecast, + method='inferred_normal', + prediction_interval=self.prediction_interval, + ) + else: + target_col = [x for x in long_forecast.columns if "hi-" in x][0] + upper_forecast = long_forecast.reset_index().pivot_table(index='ds', columns='unique_id', values=target_col)[self.column_names] + target_col = [x for x in long_forecast.columns if "lo-" in x][0] + lower_forecast = long_forecast.reset_index().pivot_table(index='ds', columns='unique_id', values=target_col)[self.column_names] + + predict_runtime = datetime.datetime.now() - predictStartTime + prediction = PredictionObject( + model_name=self.name, + forecast_length=self.forecast_length, + forecast_index=forecast.index, + forecast_columns=forecast.columns, + lower_forecast=lower_forecast, + forecast=forecast, + upper_forecast=upper_forecast, + 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'): + """Return dict of new parameters for parameter tuning.""" + model_list = ['DeepAR', 'MLP', "LSTM", "PatchTST", "NHITS", "TFT", "TimesNet"] + if method in model_list: + models = method + else: + models = random.choice(model_list) + if "regressor" in method: + regression_type_choice = "User" + else: + regression_type_choice = random.choices([None, "User"], weights=[0.8, 0.2])[ + 0 + ] + if models == "TFT": + model_args = { + "n_head": random.choice([2, 4]), + "dropout": random.choice([0.05, 0.2, 0.3, 0.6]), + "batch_size": random.choice([32, 64, 128, 256]), + "hidden_size": random.choice([4, 64, 128, 256]), + "windows_batch_size": random.choice([128, 256, 512, 1024]), + } + elif models == "NHITS": + model_args = { + "input_size": random.choice([28, 28*2, 28*3, 28*5]), + "n_blocks": 5*[1], + "mlp_units": 5 * [[512, 512]], + "n_pool_kernel_size": random.choice([5*[1], 5*[2], 5*[4], + [8, 4, 2, 1, 1]]), + "n_freq_downsample": random.choice([[8, 4, 2, 1, 1], + [1, 1, 1, 1, 1]]), + "batch_size": random.choice([32, 64, 128, 256]), + "windows_batch_size": random.choice([128, 256, 512, 1024]), + } + else: + model_args = {} + + return { + 'models': models, + 'scaler_type': random.choices([None, 'robust', 'minmax', 'standard'], [0.5, 0.5, 0.2, 0.2])[0], + 'activation': random.choices( + ['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid'], + [0.5, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] + )[0], + 'loss': random.choices( + ['MQLoss', 'Poisson', 'Bernoulli', 'NegativeBinomial', 'Normal', 'Tweedie', 'HuberLoss', "MAE", "SMAPE", "StudentT"], + [0.3, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] + )[0], + 'learning_rate': random.choices( + [0.001, 0.1, 0.01, 0.0003, 0.00001], + [0.4, 0.1, 0.1, 0.1, 0.1] + )[0], + "max_steps": random.choices( + [40, 100, 1000, 10000], + [0.2, 0.2, 0.8, 0.01], + )[0], + 'input_size': random.choices( + [10, 28, "2ForecastLength", "3ForecastLength"], + [0.2, 0.2, 0.2, 0.2] + ), + "early_stop_patience_steps": random.choice([1, 3, 5]), + "model_args": model_args, + 'regression_type': regression_type_choice, + } + + def get_params(self): + """Return dict of current parameters.""" + return { + 'models': self.models, + 'scaler_type': self.scaler_type, + 'activation': self.activation, + 'loss': self.loss, + 'learning_rate': self.learning_rate, + "max_steps": self.max_steps, + 'input_size': self.input_size, + "early_stop_patience_steps": self.early_stop_patience_steps, + "model_args": self.model_args, + 'regression_type': self.regression_type, + } From 65613410fdb275f0caf9926926229fb994f04f4c Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Thu, 7 Dec 2023 23:46:17 -0600 Subject: [PATCH 17/26] 0.6.4 a17 --- TODO.md | 1 + autots/evaluator/auto_model.py | 13 +++++++ autots/models/model_list.py | 7 +++- autots/models/neural_forecast.py | 60 ++++++++++++++++++++------------ extended_tutorial.md | 5 +-- pyproject.toml | 1 + 6 files changed, 62 insertions(+), 25 deletions(-) diff --git a/TODO.md b/TODO.md index 44f1924e..0aba10d9 100644 --- a/TODO.md +++ b/TODO.md @@ -17,6 +17,7 @@ * fixed bug with plot_validations not working with some frequencies * force_validation added to import_template * model_list now enforced in new generations +* added NeuralForecast ### 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 c620abc4..5a35c860 100644 --- a/autots/evaluator/auto_model.py +++ b/autots/evaluator/auto_model.py @@ -683,6 +683,19 @@ def ModelMonster( n_jobs=n_jobs, **parameters, ) + elif model in ["NeuralForecast", "neuralforecast"]: + from autots.models.neural_forecast import NeuralForecast + + return NeuralForecast( + frequency=frequency, + forecast_length=forecast_length, + prediction_interval=prediction_interval, + holiday_country=holiday_country, + random_seed=random_seed, + verbose=verbose, + n_jobs=n_jobs, + **parameters, + ) else: raise AttributeError( ("Model String '{}' not a recognized model type").format(model) diff --git a/autots/models/model_list.py b/autots/models/model_list.py index af82dcda..9ab1a663 100644 --- a/autots/models/model_list.py +++ b/autots/models/model_list.py @@ -45,6 +45,7 @@ 'FFT', "BallTreeMultivariateMotif", "TiDE", + "NeuralForecast", ] all_pragmatic = list((set(all_models) - set(['MLEnsemble', 'VARMAX', 'Greykite']))) # downweight slower models @@ -162,7 +163,7 @@ # models that perform slowly at scale slow = list((set(all_models) - set(fast.keys())) - set(experimental)) # use GPU -gpu = ['GluonTS', 'WindowRegression', 'PytorchForecasting', "TiDE"] +gpu = ['GluonTS', 'WindowRegression', 'PytorchForecasting', "TiDE", "NeuralForecast", "NeuralProphet"] # models with model-based upper/lower forecasts probabilistic = [ 'ARIMA', @@ -187,6 +188,7 @@ 'MetricMotif', 'Cassandra', 'SeasonalityMotif', + "NeuralForecast", # mostly ] # models that use the shared information of multiple series to improve accuracy multivariate = [ @@ -210,6 +212,7 @@ 'Cassandra', # depends 'BallTreeMultivariateMotif', "TiDE", + "NeuralForecast", ] univariate = list((set(all_models) - set(multivariate)) - set(experimental)) # USED IN AUTO_MODEL, models with no parameters @@ -256,6 +259,7 @@ 'FFT', 'BallTreeMultivariateMotif', "TiDE", + "NeuralForecast", ] # USED IN AUTO_MODEL for models that don't share information among series no_shared = [ @@ -303,6 +307,7 @@ 'ARCH', 'Cassandra', 'PreprocessingRegression', + "NeuralForecast", ] motifs = [ 'UnivariateMotif', diff --git a/autots/models/neural_forecast.py b/autots/models/neural_forecast.py index 9ed3baa6..a469ba89 100644 --- a/autots/models/neural_forecast.py +++ b/autots/models/neural_forecast.py @@ -30,13 +30,13 @@ def __init__( verbose: int = 0, forecast_length: int = 28, regression_type: str = None, - n_jobs: int = -1, + n_jobs: int = 1, models = "LSTM", loss = "MQLoss", input_size = "2ForecastLength", max_steps = 1000, learning_rate = 0.001, - early_stop_patience_steps = 3, + early_stop_patience_steps = -1, activation = 'ReLU', scaler_type = 'robust', model_args = {}, @@ -61,6 +61,7 @@ def __init__( self.early_stop_patience_steps = early_stop_patience_steps self.activation = activation self.scaler_type = scaler_type + self.model_args = model_args self.forecast_length = forecast_length self.df_train = None @@ -108,7 +109,7 @@ def fit(self, df, future_regressor=None): elif loss == "Normal": loss = DistributionLoss(distribution='Normal', level=levels, return_params=False) elif loss == "Tweedie": - loss = DistributionLoss(distribution='Tweedie', level=levels, return_params=False) + loss = DistributionLoss(distribution='Tweedie', level=levels, return_params=False, rho=1.5) elif loss == "MAE": self.df_train = df loss = MAE() @@ -129,41 +130,42 @@ def fit(self, df, future_regressor=None): input_size = forecast_length * int(''.join([x for x in str_input if x.isdigit()])) else: input_size = int(self.input_size) - base_args = { + self.base_args = { "h": forecast_length, "input_size": input_size, "max_steps": self.max_steps, - "num_workers_loader": self.self.n_jobs, + "num_workers_loader": self.n_jobs, "random_seed": self.random_seed, "learning_rate": self.learning_rate, - "loss": self.loss, - "early_stop_patience_steps": self.early_stop_patience_steps, + "loss": loss, + # "early_stop_patience_steps": self.early_stop_patience_steps, 'scaler_type': self.scaler_type, - "activation": self.activation, + # "activation": self.activation, } models = self.models model_args = self.model_args if self.regression_type in ['User', 'user']: - base_args["futr_exog_list"] = future_regressor.columns.tolist() + self.base_args["futr_exog_list"] = future_regressor.columns.tolist() + if isinstance(models, list): # User inputs classes directly pass elif models == 'LSTM': - models = [LSTM(**{**base_args, **model_args})] + models = [LSTM(**{**self.base_args, **model_args})] elif models == "NHITS": - models = [NHITS(**{**base_args, **model_args})] + models = [NHITS(**{**self.base_args, **model_args})] elif models == "NBEATS": - models = [NBEATS(**{**base_args, **model_args})] + models = [NBEATS(**{**self.base_args, **model_args})] elif models == "MLP": - models = [MLP(**{**base_args, **model_args})] + models = [MLP(**{**self.base_args, **model_args})] elif models == "TimesNet": - models = [TimesNet(**{**base_args, **model_args})] + models = [TimesNet(**{**self.base_args, **model_args})] elif models == "TFT": - models = [TFT(**{**base_args, **model_args})] + models = [TFT(**{**self.base_args, **model_args})] elif models == "PatchTST": - models = [PatchTST(**{**base_args, **model_args})] + models = [PatchTST(**{**self.base_args, **model_args})] elif models == "DeepAR": - models = [DeepAR(**{**base_args, **model_args})] + models = [DeepAR(**{**self.base_args, **model_args})] else: raise ValueError(f"models not recognized: {models}") @@ -177,7 +179,7 @@ def fit(self, df, future_regressor=None): self.fit_runtime = datetime.datetime.now() - self.startTime return self - def predict(self, future_regressor=None, just_point_forecast=False): + def predict(self, forecast_length=None, future_regressor=None, just_point_forecast=False): predictStartTime = datetime.datetime.now() if self.regression_type in ['User', 'user']: index = self.create_forecast_index(forecast_length=self.forecast_length) @@ -187,7 +189,7 @@ def predict(self, future_regressor=None, just_point_forecast=False): long_forecast = self.nf.predict(futr_df=futr_df) else: long_forecast = self.nf.predict() - target_col = [x for x in long_forecast.columns if "median" in x][0] + target_col = [x for x in long_forecast.columns.tolist() if "median" in str(x)][0] forecast = long_forecast.reset_index().pivot_table(index='ds', columns='unique_id', values=target_col)[self.column_names] if just_point_forecast: @@ -279,8 +281,8 @@ def get_new_params(self, method: str = 'random'): 'input_size': random.choices( [10, 28, "2ForecastLength", "3ForecastLength"], [0.2, 0.2, 0.2, 0.2] - ), - "early_stop_patience_steps": random.choice([1, 3, 5]), + )[0], + # "early_stop_patience_steps": random.choice([1, 3, 5]), "model_args": model_args, 'regression_type': regression_type_choice, } @@ -295,7 +297,21 @@ def get_params(self): 'learning_rate': self.learning_rate, "max_steps": self.max_steps, 'input_size': self.input_size, - "early_stop_patience_steps": self.early_stop_patience_steps, + # "early_stop_patience_steps": self.early_stop_patience_steps, "model_args": self.model_args, 'regression_type': self.regression_type, } + + +from autots import load_daily + +df = load_daily(long=False) + +params = NeuralForecast().get_new_params() +print(params) +mod = NeuralForecast() +mod = NeuralForecast(**params) +mod.fit(df) +prediction = mod.predict() +prediction.plot_grid() + diff --git a/extended_tutorial.md b/extended_tutorial.md index 2b6cdb2d..8c50be73 100644 --- a/extended_tutorial.md +++ b/extended_tutorial.md @@ -868,9 +868,10 @@ Currently `MultivariateRegression` has the (slower) option to utilize a stock Gr | Cassandra | scipy | | True | | | True | | True | | KalmanStateSpace | | | True | | | | | | | FFT | | | True | | | | | | -| TiDE | tensorflow | | | | yes | True | c | | +| TiDE | tensorflow | | | | yes | True | | | +| NeuralForecast | NeuralForecast | | True | | yes | True | True | True | | MotifSimulation | sklearn.metrics.pairwise | | True | joblib | | True | True | | -| Greykite | (deprecated) | | True | joblib | | | True | nyi | +| Greykite | (deprecated) | | True | joblib | | | True | | | TensorflowSTS | (deprecated) | | True | | yes | True | True | | | TFPRegression | (deprecated) | | True | | yes | True | True | True | | ComponentAnalysis | (deprecated) | | | | | True | True | _ | diff --git a/pyproject.toml b/pyproject.toml index b42b58b4..c331402a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,6 +40,7 @@ additional = [ 'pytrends', 'matplotlib', 'requests', + 'seaborn', ] [project.urls] From 59ac672e529c4a2314eecbe8d9ce3666133094c1 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Fri, 8 Dec 2023 09:08:42 -0600 Subject: [PATCH 18/26] 0.6.4 a18 --- autots/models/neural_forecast.py | 56 ++++++++++++++++++-------------- 1 file changed, 32 insertions(+), 24 deletions(-) diff --git a/autots/models/neural_forecast.py b/autots/models/neural_forecast.py index a469ba89..e87dde61 100644 --- a/autots/models/neural_forecast.py +++ b/autots/models/neural_forecast.py @@ -3,7 +3,7 @@ """ import logging import random -from datetime import datetime +import datetime import pandas as pd from autots.models.base import ModelObject, PredictionObject from autots.tools.probabilistic import Point_to_Probability @@ -34,7 +34,7 @@ def __init__( models = "LSTM", loss = "MQLoss", input_size = "2ForecastLength", - max_steps = 1000, + max_steps = 100, learning_rate = 0.001, early_stop_patience_steps = -1, activation = 'ReLU', @@ -138,9 +138,10 @@ def fit(self, df, future_regressor=None): "random_seed": self.random_seed, "learning_rate": self.learning_rate, "loss": loss, - # "early_stop_patience_steps": self.early_stop_patience_steps, 'scaler_type': self.scaler_type, - # "activation": self.activation, + # trying to suppress the stupid file logging lightning does + "logger": False, + "log_every_n_steps": 0, } models = self.models model_args = self.model_args @@ -189,6 +190,7 @@ def predict(self, forecast_length=None, future_regressor=None, just_point_foreca long_forecast = self.nf.predict(futr_df=futr_df) else: long_forecast = self.nf.predict() + self.long_forecast = long_forecast target_col = [x for x in long_forecast.columns.tolist() if "median" in str(x)][0] forecast = long_forecast.reset_index().pivot_table(index='ds', columns='unique_id', values=target_col)[self.column_names] @@ -229,13 +231,17 @@ def get_new_params(self, method: str = 'random'): if method in model_list: models = method else: - models = random.choice(model_list) + models = random.choices(model_list, [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2])[0] if "regressor" in method: regression_type_choice = "User" else: regression_type_choice = random.choices([None, "User"], weights=[0.8, 0.2])[ 0 ] + activation = random.choices( + ['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid'], + [0.5, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] + )[0] if models == "TFT": model_args = { "n_head": random.choice([2, 4]), @@ -255,17 +261,19 @@ def get_new_params(self, method: str = 'random'): [1, 1, 1, 1, 1]]), "batch_size": random.choice([32, 64, 128, 256]), "windows_batch_size": random.choice([128, 256, 512, 1024]), + "activation": activation, + } + elif models == "MLP": + model_args = { + 'num_layers': random.choice([1, 2, 3, 4]), + 'activation': activation, } else: model_args = {} return { 'models': models, - 'scaler_type': random.choices([None, 'robust', 'minmax', 'standard'], [0.5, 0.5, 0.2, 0.2])[0], - 'activation': random.choices( - ['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid'], - [0.5, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] - )[0], + 'scaler_type': random.choices(["identity", 'robust', 'minmax', 'standard'], [0.5, 0.5, 0.2, 0.2])[0], 'loss': random.choices( ['MQLoss', 'Poisson', 'Bernoulli', 'NegativeBinomial', 'Normal', 'Tweedie', 'HuberLoss', "MAE", "SMAPE", "StudentT"], [0.3, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] @@ -275,8 +283,8 @@ def get_new_params(self, method: str = 'random'): [0.4, 0.1, 0.1, 0.1, 0.1] )[0], "max_steps": random.choices( - [40, 100, 1000, 10000], - [0.2, 0.2, 0.8, 0.01], + [40, 80, 100, 1000], + [0.2, 0.2, 0.2, 0.05], )[0], 'input_size': random.choices( [10, 28, "2ForecastLength", "3ForecastLength"], @@ -292,7 +300,7 @@ def get_params(self): return { 'models': self.models, 'scaler_type': self.scaler_type, - 'activation': self.activation, + # 'activation': self.activation, 'loss': self.loss, 'learning_rate': self.learning_rate, "max_steps": self.max_steps, @@ -303,15 +311,15 @@ def get_params(self): } -from autots import load_daily - -df = load_daily(long=False) - -params = NeuralForecast().get_new_params() -print(params) -mod = NeuralForecast() -mod = NeuralForecast(**params) -mod.fit(df) -prediction = mod.predict() -prediction.plot_grid() +if False: + from autots import load_daily + + df = load_daily(long=False) + + params = NeuralForecast().get_new_params() + print(params) + mod = NeuralForecast(**params) + mod.fit(df) + prediction = mod.predict() + prediction.plot_grid(df) From 9efa99845eeca331bf32bde342ea34d3ac30d069 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Fri, 8 Dec 2023 09:16:24 -0600 Subject: [PATCH 19/26] 0.6.4 a18 pandas deciding to change fillna was kinda stupid, broke so many lines of code that I am still finding --- autots/tools/transform.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/autots/tools/transform.py b/autots/tools/transform.py index cf21465d..7c2123a5 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -2421,9 +2421,7 @@ def fit_transform(self, df): df (pandas.DataFrame): input dataframe """ self.fit(df) - return (df - self.means.shift(self.lag).values[..., None]).fillna( - method="bfill" - ) + return (df - self.means.shift(self.lag).values[..., None]).bfill() def inverse_transform(self, df, trans_method: str = "forecast"): """Returns data to original *or* forecast form From 6b7271e1c8c6bd073ec087bce1932c808c17f7ac Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Fri, 8 Dec 2023 22:41:36 -0600 Subject: [PATCH 20/26] 0.6.4 a19 --- autots/__init__.py | 3 +- autots/models/neural_forecast.py | 58 +++++++++++++++++++++++++------- 2 files changed, 47 insertions(+), 14 deletions(-) diff --git a/autots/__init__.py b/autots/__init__.py index de6d92e9..16202077 100644 --- a/autots/__init__.py +++ b/autots/__init__.py @@ -19,7 +19,7 @@ from autots.evaluator.auto_ts import AutoTS from autots.evaluator.event_forecasting import EventRiskForecast from autots.tools.transform import GeneralTransformer, RandomTransform -from autots.tools.shaping import long_to_wide +from autots.tools.shaping import long_to_wide, infer_frequency from autots.tools.regressor import create_lagged_regressor, create_regressor from autots.evaluator.auto_model import model_forecast from autots.evaluator.anomaly_detector import AnomalyDetector, HolidayDetector @@ -53,4 +53,5 @@ 'AnomalyDetector', 'HolidayDetector', 'Cassandra', + 'infer_frequency', ] diff --git a/autots/models/neural_forecast.py b/autots/models/neural_forecast.py index e87dde61..89640f58 100644 --- a/autots/models/neural_forecast.py +++ b/autots/models/neural_forecast.py @@ -134,7 +134,7 @@ def fit(self, df, future_regressor=None): "h": forecast_length, "input_size": input_size, "max_steps": self.max_steps, - "num_workers_loader": self.n_jobs, + # "num_workers_loader": self.n_jobs, "random_seed": self.random_seed, "learning_rate": self.learning_rate, "loss": loss, @@ -174,7 +174,7 @@ def fit(self, df, future_regressor=None): silly_format = df.reset_index(names='ds').melt(id_vars='ds', value_name='y', var_name='unique_id') if self.regression_type in ['User', 'user']: - silly_format.merge(future_regressor, left_on='ds', right_index=True) + silly_format = silly_format.merge(future_regressor, left_on='ds', right_index=True) self.nf = NeuralForecast(models=models, freq=freq) self.nf.fit(df=silly_format) self.fit_runtime = datetime.datetime.now() - self.startTime @@ -184,14 +184,19 @@ def predict(self, forecast_length=None, future_regressor=None, just_point_foreca predictStartTime = datetime.datetime.now() if self.regression_type in ['User', 'user']: index = self.create_forecast_index(forecast_length=self.forecast_length) - futr_df = pd.concat([pd.Series(col, index=index, name='temp') for col in self.column_names]) - futr_df = futr_df.merge(future_regressor, left_index=True, right_index=True) + futr_df = pd.concat([pd.Series(col, index=index, name='unique_id') for col in self.column_names]) + futr_df = futr_df.to_frame().merge(future_regressor, left_index=True, right_index=True) futr_df = futr_df.reset_index(names='ds') + self.futr_df = futr_df long_forecast = self.nf.predict(futr_df=futr_df) else: long_forecast = self.nf.predict() - self.long_forecast = long_forecast - target_col = [x for x in long_forecast.columns.tolist() if "median" in str(x)][0] + # self.long_forecast = long_forecast + target_col = [x for x in long_forecast.columns.tolist() if "median" in str(x)] + if len(target_col) < 1: + target_col = long_forecast.columns[-1] + else: + target_col = target_col[0] forecast = long_forecast.reset_index().pivot_table(index='ds', columns='unique_id', values=target_col)[self.column_names] if just_point_forecast: @@ -266,7 +271,6 @@ def get_new_params(self, method: str = 'random'): elif models == "MLP": model_args = { 'num_layers': random.choice([1, 2, 3, 4]), - 'activation': activation, } else: model_args = {} @@ -312,14 +316,42 @@ def get_params(self): if False: - from autots import load_daily - + from autots.models.neural_forecast import NeuralForecast + from autots import load_daily, create_regressor, infer_frequency + df = load_daily(long=False) - + forecast_length = 28 + frequency = infer_frequency(df) + regr_train, regr_fcst = create_regressor( + df, + forecast_length=forecast_length, + frequency=frequency, + drop_most_recent=0, + scale=True, + summarize="auto", + backfill="bfill", + fill_na="pchip", + holiday_countries=["US"], + datepart_method="recurring", + preprocessing_params={ + "fillna": None, + "transformations": {"0": "LocalLinearTrend"}, + "transformation_params": { + "0": { + 'rolling_window': 30, + 'n_tails': 0.1, + 'n_future': 0.2, + 'method': 'mean', + 'macro_micro': True + }, + }, + }, + ) + params = NeuralForecast().get_new_params() print(params) - mod = NeuralForecast(**params) - mod.fit(df) - prediction = mod.predict() + mod = NeuralForecast(forecast_length=forecast_length, frequency=frequency, **params) + mod.fit(df, future_regressor=regr_train) + prediction = mod.predict(future_regressor=regr_fcst) prediction.plot_grid(df) From f42519c81efafefc19966685953c2a44ec51bc98 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Sat, 9 Dec 2023 10:31:55 -0600 Subject: [PATCH 21/26] 0.6.4 a20 --- autots/models/model_list.py | 1 - autots/models/neural_forecast.py | 27 ++++++++++++++------------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/autots/models/model_list.py b/autots/models/model_list.py index 9ab1a663..e6b07fca 100644 --- a/autots/models/model_list.py +++ b/autots/models/model_list.py @@ -259,7 +259,6 @@ 'FFT', 'BallTreeMultivariateMotif', "TiDE", - "NeuralForecast", ] # USED IN AUTO_MODEL for models that don't share information among series no_shared = [ diff --git a/autots/models/neural_forecast.py b/autots/models/neural_forecast.py index 89640f58..506e82e1 100644 --- a/autots/models/neural_forecast.py +++ b/autots/models/neural_forecast.py @@ -17,6 +17,7 @@ class NeuralForecast(ModelObject): frequency (str): String alias of datetime index frequency or else 'infer' prediction_interval (float): Confidence interval for probabilistic forecast regression_type: str = None, + model (str or object): string aliases or passed to the models arg of NeuralForecast model_args (dict): for all model args that aren't in default list, run get_new_params for default """ @@ -31,7 +32,7 @@ def __init__( forecast_length: int = 28, regression_type: str = None, n_jobs: int = 1, - models = "LSTM", + model = "LSTM", loss = "MQLoss", input_size = "2ForecastLength", max_steps = 100, @@ -53,7 +54,7 @@ def __init__( verbose=verbose, n_jobs=n_jobs, ) - self.models = models + self.model = model self.loss = loss self.input_size = input_size self.max_steps = max_steps @@ -105,7 +106,7 @@ def fit(self, df, future_regressor=None): elif loss == "StudentT": loss = DistributionLoss(distribution='StudentT', level=levels, return_params=False) elif loss == "NegativeBinomial": - loss = DistributionLoss(distribution='NegativeBinomial', level=levels, return_params=False) + loss = DistributionLoss(distribution='NegativeBinomial', level=levels, total_count=3, return_params=False) elif loss == "Normal": loss = DistributionLoss(distribution='Normal', level=levels, return_params=False) elif loss == "Tweedie": @@ -143,7 +144,7 @@ def fit(self, df, future_regressor=None): "logger": False, "log_every_n_steps": 0, } - models = self.models + models = self.model model_args = self.model_args if self.regression_type in ['User', 'user']: self.base_args["futr_exog_list"] = future_regressor.columns.tolist() @@ -236,7 +237,7 @@ def get_new_params(self, method: str = 'random'): if method in model_list: models = method else: - models = random.choices(model_list, [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2])[0] + models = random.choices(model_list, [0.05, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2])[0] if "regressor" in method: regression_type_choice = "User" else: @@ -247,6 +248,10 @@ def get_new_params(self, method: str = 'random'): ['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid'], [0.5, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] )[0] + loss = random.choices( + ['MQLoss', 'Poisson', 'Bernoulli', 'NegativeBinomial', 'Normal', 'Tweedie', 'HuberLoss', "MAE", "SMAPE", "StudentT"], + [0.3, 0.1, 0.01, 0.1, 0.1, 0.01, 0.1, 0.1, 0.1, 0.01] + )[0] if models == "TFT": model_args = { "n_head": random.choice([2, 4]), @@ -276,12 +281,9 @@ def get_new_params(self, method: str = 'random'): model_args = {} return { - 'models': models, + 'model': models, 'scaler_type': random.choices(["identity", 'robust', 'minmax', 'standard'], [0.5, 0.5, 0.2, 0.2])[0], - 'loss': random.choices( - ['MQLoss', 'Poisson', 'Bernoulli', 'NegativeBinomial', 'Normal', 'Tweedie', 'HuberLoss', "MAE", "SMAPE", "StudentT"], - [0.3, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] - )[0], + 'loss': loss, 'learning_rate': random.choices( [0.001, 0.1, 0.01, 0.0003, 0.00001], [0.4, 0.1, 0.1, 0.1, 0.1] @@ -302,14 +304,13 @@ def get_new_params(self, method: str = 'random'): def get_params(self): """Return dict of current parameters.""" return { - 'models': self.models, + # NOTE: `models` (plural) conflicts with AutoTS ensemble setup + 'model': self.model, 'scaler_type': self.scaler_type, - # 'activation': self.activation, 'loss': self.loss, 'learning_rate': self.learning_rate, "max_steps": self.max_steps, 'input_size': self.input_size, - # "early_stop_patience_steps": self.early_stop_patience_steps, "model_args": self.model_args, 'regression_type': self.regression_type, } From 9777aedace3b4b5777579be114102f14c54bade8 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Sat, 9 Dec 2023 13:33:22 -0600 Subject: [PATCH 22/26] 0.6.4 a21 --- autots/models/base.py | 45 ++++++++++++++++++++++--------------------- test.py | 14 +++++--------- 2 files changed, 28 insertions(+), 31 deletions(-) diff --git a/autots/models/base.py b/autots/models/base.py index f6ada43b..2c1642af 100644 --- a/autots/models/base.py +++ b/autots/models/base.py @@ -295,9 +295,12 @@ def extract_single_transformer( ) return ", ".join(allz) else: - trans_dict = transformation_params.get("transformations") - if isinstance(trans_dict, dict): - return ", ".join(list(trans_dict.values())) + if isinstance(transformation_params, dict): + trans_dict = transformation_params.get("transformations") + if isinstance(trans_dict, dict): + return ", ".join(list(trans_dict.values())) + else: + return "None" else: return "None" @@ -492,6 +495,7 @@ def long_form_results( value_name="Value", interval_name='PredictionInterval', update_datetime_name=None, + datetime_column=None, ): """Export forecasts (including upper and lower) as single 'long' format output @@ -499,41 +503,38 @@ def long_form_results( id_name (str): name of column containing ids value_name (str): name of column containing numeric values interval_name (str): name of column telling you what is upper/lower + datetime_column (str): if None, is index, otherwise, name of column for datetime update_datetime_name (str): if not None, adds column with current timestamp and this name Returns: pd.DataFrame """ - try: - upload = pd.melt( - self.forecast, - var_name=id_name, - value_name=value_name, - ignore_index=False, - ) - except Exception: - raise ImportError("Requires pandas>=1.1.0") + upload = pd.melt( + self.forecast.reset_index(names='datetime'), + var_name="SeriesID", value_name="Value", + id_vars="datetime", + ).set_index("datetime") upload[interval_name] = "50%" upload_upper = pd.melt( - self.upper_forecast, - var_name=id_name, - value_name=value_name, - ignore_index=False, - ) + self.upper_forecast.reset_index(names='datetime'), + var_name="SeriesID", value_name="Value", + id_vars="datetime", + ).set_index("datetime") upload_upper[ interval_name ] = f"{round(100 - ((1- self.prediction_interval)/2) * 100, 0)}%" upload_lower = pd.melt( - self.lower_forecast, - var_name=id_name, - value_name=value_name, - ignore_index=False, - ) + self.lower_forecast.reset_index(names='datetime'), + var_name="SeriesID", value_name="Value", + id_vars="datetime", + ).set_index("datetime") 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: + upload = upload.reset_index(drop=False, names=datetime_column) if update_datetime_name is not None: upload[update_datetime_name] = datetime.datetime.utcnow() return upload diff --git a/test.py b/test.py index 0ad0b297..dc840e5e 100644 --- a/test.py +++ b/test.py @@ -147,12 +147,12 @@ "bounds": True, } if not long: - forecast_index = pd.date_range(start=df.index[-1], periods=forecast_length + 1, freq=df.index.freq)[1:] - # sets an extremely high value for the cap, one that should never actually be reached by the data normally if isinstance(df, pd.Series): cols = [df.name] else: cols = df.columns + forecast_index = pd.date_range(start=df.index[-1], periods=forecast_length + 1, freq=df.index.freq)[1:] + # sets an extremely high value for the cap, one that should never actually be reached by the data normally upper_constraint = pd.DataFrame(9999999999, index=forecast_index, columns=cols) # in this case also assuming negatives won't happen so setting a lower constraint of 0 lower_constraint = pd.DataFrame(0, index=forecast_index, columns=cols) @@ -281,19 +281,15 @@ prediction = model.predict( future_regressor=regr_fcst, verbose=1, fail_on_forecast_nan=True ) +print(prediction.long_form_results().sample(5)) # point forecasts dataframe forecasts_df = prediction.forecast # accuracy of all tried model results (not including cross validation) initial_results = model.results() # validation results validation_results = model.results("validation") - -""" -initial_results["TransformationRuntime"] = initial_results["TransformationRuntime"].dt.total_seconds() -initial_results["FitRuntime"] = initial_results["FitRuntime"].dt.total_seconds() -initial_results["PredictRuntime"] = initial_results["PredictRuntime"].dt.total_seconds() -initial_results["TotalRuntime"] = initial_results["TotalRuntime"].dt.total_seconds() -""" +if long: + cols = model.df_wide_numeric.columns.tolist() sleep(5) print(model) From 2a9fac39f7c376ee3d20a5f3e2bb790860848669 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Sat, 9 Dec 2023 22:42:04 -0600 Subject: [PATCH 23/26] 0.6.4 a22 --- autots/models/model_list.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/autots/models/model_list.py b/autots/models/model_list.py index e6b07fca..9c5b0430 100644 --- a/autots/models/model_list.py +++ b/autots/models/model_list.py @@ -149,7 +149,7 @@ best = list( set( list(fast_parallel_no_arima.keys()) - + ['MultivariateRegression', 'GluonTS', 'PytorchForecasting'] + + ['MultivariateRegression', 'GluonTS', 'PytorchForecasting', "MAR"] ) ) From 7a31842f3ff20ec8c50a7cc1fa20ee2b9fd81ab4 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Sun, 10 Dec 2023 12:08:46 -0600 Subject: [PATCH 24/26] 0.6.4 a23 --- autots/models/cassandra.py | 1 + autots/templates/general.py | 12 ++++++++++++ 2 files changed, 13 insertions(+) diff --git a/autots/models/cassandra.py b/autots/models/cassandra.py index 7d25b984..f676bf2c 100644 --- a/autots/models/cassandra.py +++ b/autots/models/cassandra.py @@ -1924,6 +1924,7 @@ def get_new_params(self, method='fast'): }, } elif trend_base == 'pb2': + # REPLACE THIS WITHOUT EXTRA TREES trend_model = {'Model': 'WindowRegression'} trend_model['ModelParameters'] = { "window_size": 12, diff --git a/autots/templates/general.py b/autots/templates/general.py index 6e8d58ad..b9b1554a 100644 --- a/autots/templates/general.py +++ b/autots/templates/general.py @@ -440,6 +440,18 @@ ''', 'Ensemble': 0, }, + "70": { + 'Model': 'Cassandra', + 'ModelParameters': '''{"preprocessing_transformation": {"fillna": "ffill", "transformations": {"0": "SeasonalDifference", "1": "SeasonalDifference"}, "transformation_params": {"0": {"lag_1": 12, "method": "Median"}, "1": {"lag_1": 7, "method": "LastValue"}}}, "scaling": "BaseScaler", "past_impacts_intervention": null, "seasonalities": ["dayofmonthofyear", "weekend"], "ar_lags": null, "ar_interaction_seasonality": null, "anomaly_detector_params": {"method": "zscore", "method_params": {"distribution": "norm", "alpha": 0.05}, "fillna": "rolling_mean_24", "transform_dict": {"transformations": {"0": "DatepartRegression"}, "transformation_params": {"0": {"datepart_method": "simple_3", "regression_model": {"model": "DecisionTree", "model_params": {"max_depth": null, "min_samples_split": 0.1}}}}}}, "anomaly_intervention": "remove", "holiday_detector_params": {"threshold": 0.8, "splash_threshold": 0.85, "use_dayofmonth_holidays": true, "use_wkdom_holidays": true, "use_wkdeom_holidays": false, "use_lunar_holidays": false, "use_lunar_weekday": false, "use_islamic_holidays": false, "use_hebrew_holidays": false, "anomaly_detector_params": {"method": "mad", "method_params": {"distribution": "norm", "alpha": 0.05}, "fillna": "fake_date", "transform_dict": {"fillna": null, "transformations": {"0": "EWMAFilter"}, "transformation_params": {"0": {"span": 7}}}}, "remove_excess_anomalies": false, "impact": null, "regression_params": null, "output": "multivariate"}, "holiday_countries_used": false, "multivariate_feature": null, "multivariate_transformation": null, "regressor_transformation": null, "regressors_used": false, "linear_model": {"model": "lstsq", "lambda": 1, "recency_weighting": null}, "randomwalk_n": 10, "trend_window": 15, "trend_standin": "rolling_trend", "trend_anomaly_detector_params": {"method": "nonparametric", "method_params": {"p": null, "z_init": 1.5, "z_limit": 12, "z_step": 0.25, "inverse": false, "max_contamination": 0.25, "mean_weight": 25, "sd_weight": 25, "anomaly_count_weight": 1.0}, "fillna": "ffill", "transform_dict": {"fillna": "time", "transformations": {"0": "MinMaxScaler"}, "transformation_params": {"0": {}}}}, "trend_transformation": {"fillna": "ffill", "transformations": {"0": "AlignLastDiff", "1": "HPFilter"}, "transformation_params": {"0": {"rows": 1, "displacement_rows": 1, "quantile": 0.7, "decay_span": 3}, "1": {"part": "trend", "lamb": 129600}}}, "trend_model": {"Model": "LastValueNaive", "ModelParameters": {}}, "trend_phi": null}''', + 'TransformationParameters': '{"fillna": "linear", "transformations": {"0": "Round"}, "transformation_params": {"0": {"decimals": 0, "on_transform": true, "on_inverse": true}}}', + 'Ensemble': 0, + }, + "71": { + 'Model': 'Cassandra', + 'ModelParameters': '''{"preprocessing_transformation": {"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "bkfilter"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {}}}, "scaling": "BaseScaler", "past_impacts_intervention": null, "seasonalities": [7, 365.25], "ar_lags": [7], "ar_interaction_seasonality": null, "anomaly_detector_params": null, "anomaly_intervention": null, "holiday_detector_params": {"threshold": 0.9, "splash_threshold": null, "use_dayofmonth_holidays": true, "use_wkdom_holidays": true, "use_wkdeom_holidays": false, "use_lunar_holidays": false, "use_lunar_weekday": false, "use_islamic_holidays": false, "use_hebrew_holidays": true, "anomaly_detector_params": {"method": "mad", "method_params": {"distribution": "norm", "alpha": 0.05}, "fillna": "rolling_mean_24", "transform_dict": {"fillna": null, "transformations": {"0": "EWMAFilter"}, "transformation_params": {"0": {"span": 7}}}}, "remove_excess_anomalies": false, "impact": null, "regression_params": null, "output": "multivariate"}, "holiday_countries_used": true, "multivariate_feature": null, "multivariate_transformation": null, "regressor_transformation": null, "regressors_used": false, "linear_model": {"model": "linalg_solve", "lambda": 1, "recency_weighting": 0.1}, "randomwalk_n": null, "trend_window": 365, "trend_standin": "rolling_trend", "trend_anomaly_detector_params": {"method": "mad", "method_params": {"distribution": "uniform", "alpha": 0.05}, "fillna": "rolling_mean_24", "transform_dict": {"fillna": null, "transformations": {"0": "EWMAFilter"}, "transformation_params": {"0": {"span": 7}}}}, "trend_transformation": {"fillna": "nearest", "transformations": {"0": "StandardScaler", "1": "AnomalyRemoval"}, "transformation_params": {"0": {}, "1": {"method": "IQR", "transform_dict": {"fillna": null, "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 6}}}, "method_params": {"iqr_threshold": 2.5, "iqr_quantiles": [0.25, 0.75]}, "fillna": "ffill"}}}, "trend_model": {"Model": "SectionalMotif", "ModelParameters": {"window": 50, "point_method": "mean", "distance_metric": "dice", "include_differenced": true, "k": 5, "stride_size": 1, "regression_type": null}}, "trend_phi": null}''', + 'TransformationParameters': '{"fillna": "pad", "transformations": {"0": "EWMAFilter", "1": "LevelShiftTransformer", "2": "StandardScaler", "3": "DatepartRegression"}, "transformation_params": {"0": {"span": 7}, "1": {"window_size": 7, "alpha": 2.0, "grouping_forward_limit": 2, "max_level_shifts": 5, "alignment": "average"}, "2": {}, "3": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": "recurring", "polynomial_degree": null, "transform_dict": {"fillna": null, "transformations": {"0": "ScipyFilter"}, "transformation_params": {"0": {"method": "savgol_filter", "method_args": {"window_length": 31, "polyorder": 3, "deriv": 0, "mode": "interp"}}}}, "holiday_countries_used": false}}}', + 'Ensemble': 0, + }, } general_template = pd.DataFrame.from_dict(general_template_dict, orient='index') From c05ac0b87fdb8dec6c254432cac4cb03b5807dda Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Sun, 10 Dec 2023 12:43:40 -0600 Subject: [PATCH 25/26] 0.6.4 a24 --- autots/models/matrix_var.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/autots/models/matrix_var.py b/autots/models/matrix_var.py index e8242ea8..e0c75a9e 100644 --- a/autots/models/matrix_var.py +++ b/autots/models/matrix_var.py @@ -361,7 +361,7 @@ def get_new_params(self, method: str = 'random'): 'seasonality': seasonal_int(include_one=False, very_small=True), 'family': random.choices( ['gaussian', 'poisson', 'negativebinomial', 'gamma', 'chi2', 'uniform'], - [0.6, 0.05, 0.05, 0.2, 0.05, 0.05], + [0.6, 0.05, 0.02, 0.2, 0.05, 0.05], )[0], 'maxiter': 200, } From a799a0fb03831cd0e5512a29d51af74aea69dce7 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Sun, 10 Dec 2023 20:25:14 -0600 Subject: [PATCH 26/26] 0.6.4 --- autots/__init__.py | 2 +- autots/evaluator/auto_model.py | 27 +-- autots/evaluator/auto_ts.py | 8 +- autots/models/base.py | 11 +- autots/models/cassandra.py | 8 +- autots/models/model_list.py | 9 +- autots/models/neural_forecast.py | 165 ++++++++++++------ autots/models/sklearn.py | 17 +- autots/tools/regressor.py | 4 +- autots/tools/shaping.py | 6 +- docs/build/doctrees/environment.pickle | Bin 9176249 -> 9241276 bytes docs/build/doctrees/source/autots.doctree | Bin 898360 -> 904920 bytes .../doctrees/source/autots.evaluator.doctree | Bin 889174 -> 892520 bytes .../doctrees/source/autots.models.doctree | Bin 2388173 -> 2422616 bytes .../doctrees/source/autots.templates.doctree | Bin 13711 -> 13711 bytes .../doctrees/source/autots.tools.doctree | Bin 1615745 -> 1619074 bytes docs/build/doctrees/source/tutorial.doctree | Bin 205854 -> 207042 bytes docs/build/html/.buildinfo | 2 +- .../_sources/source/autots.models.rst.txt | 8 + .../html/_static/documentation_options.js | 2 +- docs/build/html/genindex.html | 35 +++- docs/build/html/index.html | 2 +- docs/build/html/objects.inv | Bin 7628 -> 7705 bytes docs/build/html/py-modindex.html | 7 +- docs/build/html/search.html | 2 +- docs/build/html/searchindex.js | 2 +- docs/build/html/source/autots.datasets.html | 3 +- docs/build/html/source/autots.evaluator.html | 10 +- docs/build/html/source/autots.html | 33 +++- docs/build/html/source/autots.models.html | 58 +++++- docs/build/html/source/autots.templates.html | 5 +- docs/build/html/source/autots.tools.html | 19 +- docs/build/html/source/intro.html | 3 +- docs/build/html/source/modules.html | 5 +- docs/build/html/source/tutorial.html | 26 ++- docs/conf.py | 2 +- docs/source/autots.models.rst | 8 + pyproject.toml | 2 +- setup.py | 2 +- 39 files changed, 369 insertions(+), 124 deletions(-) diff --git a/autots/__init__.py b/autots/__init__.py index 16202077..85b0f1e4 100644 --- a/autots/__init__.py +++ b/autots/__init__.py @@ -26,7 +26,7 @@ from autots.models.cassandra import Cassandra -__version__ = '0.6.3' +__version__ = '0.6.4' TransformTS = GeneralTransformer diff --git a/autots/evaluator/auto_model.py b/autots/evaluator/auto_model.py index 5a35c860..7c14d338 100644 --- a/autots/evaluator/auto_model.py +++ b/autots/evaluator/auto_model.py @@ -1021,7 +1021,9 @@ def concat(self, another_eval): sort=False, ).reset_index(drop=True) self.per_series_metrics = pd.concat( - [self.per_series_metrics, another_eval.per_series_metrics], axis=0, sort=False + [self.per_series_metrics, another_eval.per_series_metrics], + axis=0, + sort=False, ) self.per_series_mae = pd.concat( [self.per_series_mae, another_eval.per_series_mae], axis=0, sort=False @@ -2397,15 +2399,16 @@ def NewGeneticTemplate( def validation_aggregation( - validation_results, df_train=None, - groupby_cols = [ - 'ID', - 'Model', - 'ModelParameters', - 'TransformationParameters', - 'Ensemble', - ], - ): + validation_results, + df_train=None, + groupby_cols=[ + 'ID', + 'Model', + 'ModelParameters', + 'TransformationParameters', + 'Ensemble', + ], +): """Aggregate a TemplateEvalObject.""" col_aggs = { 'Runs': 'sum', @@ -2468,9 +2471,7 @@ def validation_aggregation( validation_results.model_results = validation_results.model_results.replace( [np.inf, -np.inf], np.nan ) - grouped = validation_results.model_results.groupby( - groupby_cols - ) + grouped = validation_results.model_results.groupby(groupby_cols) validation_results.model_results = grouped.agg(col_aggs) validation_results.model_results = validation_results.model_results.reset_index( drop=False diff --git a/autots/evaluator/auto_ts.py b/autots/evaluator/auto_ts.py index 38de3938..6ad44aa2 100644 --- a/autots/evaluator/auto_ts.py +++ b/autots/evaluator/auto_ts.py @@ -2522,7 +2522,9 @@ def import_template( if self.validate_import is None: self.validate_import = import_template else: - self.validate_import = pd.concat([self.validate_import, import_template]) + self.validate_import = pd.concat( + [self.validate_import, import_template] + ) return self @@ -3164,7 +3166,9 @@ def plot_validations( used_freq = self.used_frequency start_date = plot_df[plot_df.columns.difference(['actuals'])].dropna( how='all', axis=0 - ).index.min() - (freq_to_timedelta(used_freq) * int(self.forecast_length * 3)) + ).index.min() - ( + freq_to_timedelta(used_freq) * int(self.forecast_length * 3) + ) if end_date == "auto": end_date = plot_df[plot_df.columns.difference(['actuals'])].dropna( how='all', axis=0 diff --git a/autots/models/base.py b/autots/models/base.py index 2c1642af..649d6a19 100644 --- a/autots/models/base.py +++ b/autots/models/base.py @@ -511,13 +511,15 @@ def long_form_results( """ upload = pd.melt( self.forecast.reset_index(names='datetime'), - var_name="SeriesID", value_name="Value", + var_name="SeriesID", + value_name="Value", id_vars="datetime", ).set_index("datetime") upload[interval_name] = "50%" upload_upper = pd.melt( self.upper_forecast.reset_index(names='datetime'), - var_name="SeriesID", value_name="Value", + var_name="SeriesID", + value_name="Value", id_vars="datetime", ).set_index("datetime") upload_upper[ @@ -525,7 +527,8 @@ def long_form_results( ] = f"{round(100 - ((1- self.prediction_interval)/2) * 100, 0)}%" upload_lower = pd.melt( self.lower_forecast.reset_index(names='datetime'), - var_name="SeriesID", value_name="Value", + var_name="SeriesID", + value_name="Value", id_vars="datetime", ).set_index("datetime") upload_lower[ @@ -572,7 +575,7 @@ def plot_ensemble_runtimes(self, xlim_right=None): y_col='TotalRuntimeSeconds', xlim=0, xlim_right=xlim_right, - title_suffix=" in Chosen Ensemble" + title_suffix=" in Chosen Ensemble", ) def plot_df( diff --git a/autots/models/cassandra.py b/autots/models/cassandra.py index f676bf2c..91c78d47 100644 --- a/autots/models/cassandra.py +++ b/autots/models/cassandra.py @@ -1850,9 +1850,11 @@ def get_new_params(self, method='fast'): anomaly_detector_params = AnomalyRemoval.get_new_params(method=method) if anomaly_intervention == "model": model_list, model_prob = model_list_to_dict("scalable") - anomaly_intervention = general_template[general_template['Model'].isin(model_list)].sample(1).to_dict("records")[ - 0 - ] # placeholder, probably + anomaly_intervention = ( + general_template[general_template['Model'].isin(model_list)] + .sample(1) + .to_dict("records")[0] + ) # placeholder, probably else: anomaly_detector_params = None diff --git a/autots/models/model_list.py b/autots/models/model_list.py index 9c5b0430..e128dec7 100644 --- a/autots/models/model_list.py +++ b/autots/models/model_list.py @@ -163,7 +163,14 @@ # models that perform slowly at scale slow = list((set(all_models) - set(fast.keys())) - set(experimental)) # use GPU -gpu = ['GluonTS', 'WindowRegression', 'PytorchForecasting', "TiDE", "NeuralForecast", "NeuralProphet"] +gpu = [ + 'GluonTS', + 'WindowRegression', + 'PytorchForecasting', + "TiDE", + "NeuralForecast", + "NeuralProphet", +] # models with model-based upper/lower forecasts probabilistic = [ 'ARIMA', diff --git a/autots/models/neural_forecast.py b/autots/models/neural_forecast.py index 506e82e1..1c497eb4 100644 --- a/autots/models/neural_forecast.py +++ b/autots/models/neural_forecast.py @@ -32,15 +32,15 @@ def __init__( forecast_length: int = 28, regression_type: str = None, n_jobs: int = 1, - model = "LSTM", - loss = "MQLoss", - input_size = "2ForecastLength", - max_steps = 100, - learning_rate = 0.001, - early_stop_patience_steps = -1, - activation = 'ReLU', - scaler_type = 'robust', - model_args = {}, + model="LSTM", + loss="MQLoss", + input_size="2ForecastLength", + max_steps=100, + learning_rate=0.001, + early_stop_patience_steps=-1, + activation='ReLU', + scaler_type='robust', + model_args={}, **kwargs, ): ModelObject.__init__( @@ -80,8 +80,23 @@ def fit(self, df, future_regressor=None): ) from neuralforecast import NeuralForecast - from neuralforecast.losses.pytorch import SMAPE, MQLoss, DistributionLoss, MAE, HuberLoss - from neuralforecast.models import TFT, LSTM, NHITS, MLP, NBEATS, TimesNet, PatchTST, DeepAR + from neuralforecast.losses.pytorch import ( + SMAPE, + MQLoss, + DistributionLoss, + MAE, + HuberLoss, + ) + from neuralforecast.models import ( + TFT, + LSTM, + NHITS, + MLP, + NBEATS, + TimesNet, + PatchTST, + DeepAR, + ) prediction_interval = self.prediction_interval forecast_length = self.forecast_length @@ -94,23 +109,38 @@ def fit(self, df, future_regressor=None): levels = list(prediction_interval.values()) else: levels = [int(prediction_interval * 100)] - + logging.getLogger("pytorch_lightning").setLevel(logging.CRITICAL) loss = self.loss if loss == "MQLoss": loss = MQLoss(level=levels) elif loss == "Poisson": - loss = DistributionLoss(distribution='Poisson', level=levels, return_params=False) + loss = DistributionLoss( + distribution='Poisson', level=levels, return_params=False + ) elif loss == "Bernoulli": - loss = DistributionLoss(distribution='Bernoulli', level=levels, return_params=False) + loss = DistributionLoss( + distribution='Bernoulli', level=levels, return_params=False + ) elif loss == "StudentT": - loss = DistributionLoss(distribution='StudentT', level=levels, return_params=False) + loss = DistributionLoss( + distribution='StudentT', level=levels, return_params=False + ) elif loss == "NegativeBinomial": - loss = DistributionLoss(distribution='NegativeBinomial', level=levels, total_count=3, return_params=False) + loss = DistributionLoss( + distribution='NegativeBinomial', + level=levels, + total_count=3, + return_params=False, + ) elif loss == "Normal": - loss = DistributionLoss(distribution='Normal', level=levels, return_params=False) + loss = DistributionLoss( + distribution='Normal', level=levels, return_params=False + ) elif loss == "Tweedie": - loss = DistributionLoss(distribution='Tweedie', level=levels, return_params=False, rho=1.5) + loss = DistributionLoss( + distribution='Tweedie', level=levels, return_params=False, rho=1.5 + ) elif loss == "MAE": self.df_train = df loss = MAE() @@ -128,7 +158,9 @@ def fit(self, df, future_regressor=None): str_input = str(self.input_size).lower() if "forecastlength" in str_input: - input_size = forecast_length * int(''.join([x for x in str_input if x.isdigit()])) + input_size = forecast_length * int( + ''.join([x for x in str_input if x.isdigit()]) + ) else: input_size = int(self.input_size) self.base_args = { @@ -170,23 +202,36 @@ def fit(self, df, future_regressor=None): models = [DeepAR(**{**self.base_args, **model_args})] else: raise ValueError(f"models not recognized: {models}") - - # model params - silly_format = df.reset_index(names='ds').melt(id_vars='ds', value_name='y', var_name='unique_id') + # model params + + silly_format = df.reset_index(names='ds').melt( + id_vars='ds', value_name='y', var_name='unique_id' + ) if self.regression_type in ['User', 'user']: - silly_format = silly_format.merge(future_regressor, left_on='ds', right_index=True) + silly_format = silly_format.merge( + future_regressor, left_on='ds', right_index=True + ) self.nf = NeuralForecast(models=models, freq=freq) self.nf.fit(df=silly_format) self.fit_runtime = datetime.datetime.now() - self.startTime return self - def predict(self, forecast_length=None, future_regressor=None, just_point_forecast=False): + def predict( + self, forecast_length=None, future_regressor=None, just_point_forecast=False + ): predictStartTime = datetime.datetime.now() if self.regression_type in ['User', 'user']: index = self.create_forecast_index(forecast_length=self.forecast_length) - futr_df = pd.concat([pd.Series(col, index=index, name='unique_id') for col in self.column_names]) - futr_df = futr_df.to_frame().merge(future_regressor, left_index=True, right_index=True) + futr_df = pd.concat( + [ + pd.Series(col, index=index, name='unique_id') + for col in self.column_names + ] + ) + futr_df = futr_df.to_frame().merge( + future_regressor, left_index=True, right_index=True + ) futr_df = futr_df.reset_index(names='ds') self.futr_df = futr_df long_forecast = self.nf.predict(futr_df=futr_df) @@ -198,7 +243,9 @@ def predict(self, forecast_length=None, future_regressor=None, just_point_foreca target_col = long_forecast.columns[-1] else: target_col = target_col[0] - forecast = long_forecast.reset_index().pivot_table(index='ds', columns='unique_id', values=target_col)[self.column_names] + forecast = long_forecast.reset_index().pivot_table( + index='ds', columns='unique_id', values=target_col + )[self.column_names] if just_point_forecast: return forecast @@ -211,9 +258,13 @@ def predict(self, forecast_length=None, future_regressor=None, just_point_foreca ) else: target_col = [x for x in long_forecast.columns if "hi-" in x][0] - upper_forecast = long_forecast.reset_index().pivot_table(index='ds', columns='unique_id', values=target_col)[self.column_names] + upper_forecast = long_forecast.reset_index().pivot_table( + index='ds', columns='unique_id', values=target_col + )[self.column_names] target_col = [x for x in long_forecast.columns if "lo-" in x][0] - lower_forecast = long_forecast.reset_index().pivot_table(index='ds', columns='unique_id', values=target_col)[self.column_names] + lower_forecast = long_forecast.reset_index().pivot_table( + index='ds', columns='unique_id', values=target_col + )[self.column_names] predict_runtime = datetime.datetime.now() - predictStartTime prediction = PredictionObject( @@ -246,11 +297,22 @@ def get_new_params(self, method: str = 'random'): ] activation = random.choices( ['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid'], - [0.5, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] + [0.5, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], )[0] loss = random.choices( - ['MQLoss', 'Poisson', 'Bernoulli', 'NegativeBinomial', 'Normal', 'Tweedie', 'HuberLoss', "MAE", "SMAPE", "StudentT"], - [0.3, 0.1, 0.01, 0.1, 0.1, 0.01, 0.1, 0.1, 0.1, 0.01] + [ + 'MQLoss', + 'Poisson', + 'Bernoulli', + 'NegativeBinomial', + 'Normal', + 'Tweedie', + 'HuberLoss', + "MAE", + "SMAPE", + "StudentT", + ], + [0.3, 0.1, 0.01, 0.1, 0.1, 0.01, 0.1, 0.1, 0.1, 0.01], )[0] if models == "TFT": model_args = { @@ -262,16 +324,16 @@ def get_new_params(self, method: str = 'random'): } elif models == "NHITS": model_args = { - "input_size": random.choice([28, 28*2, 28*3, 28*5]), - "n_blocks": 5*[1], + "input_size": random.choice([28, 28 * 2, 28 * 3, 28 * 5]), + "n_blocks": 5 * [1], "mlp_units": 5 * [[512, 512]], - "n_pool_kernel_size": random.choice([5*[1], 5*[2], 5*[4], - [8, 4, 2, 1, 1]]), - "n_freq_downsample": random.choice([[8, 4, 2, 1, 1], - [1, 1, 1, 1, 1]]), + "n_pool_kernel_size": random.choice( + [5 * [1], 5 * [2], 5 * [4], [8, 4, 2, 1, 1]] + ), + "n_freq_downsample": random.choice([[8, 4, 2, 1, 1], [1, 1, 1, 1, 1]]), "batch_size": random.choice([32, 64, 128, 256]), "windows_batch_size": random.choice([128, 256, 512, 1024]), - "activation": activation, + "activation": activation, } elif models == "MLP": model_args = { @@ -282,20 +344,20 @@ def get_new_params(self, method: str = 'random'): return { 'model': models, - 'scaler_type': random.choices(["identity", 'robust', 'minmax', 'standard'], [0.5, 0.5, 0.2, 0.2])[0], + 'scaler_type': random.choices( + ["identity", 'robust', 'minmax', 'standard'], [0.5, 0.5, 0.2, 0.2] + )[0], 'loss': loss, 'learning_rate': random.choices( - [0.001, 0.1, 0.01, 0.0003, 0.00001], - [0.4, 0.1, 0.1, 0.1, 0.1] + [0.001, 0.1, 0.01, 0.0003, 0.00001], [0.4, 0.1, 0.1, 0.1, 0.1] )[0], "max_steps": random.choices( [40, 80, 100, 1000], [0.2, 0.2, 0.2, 0.05], )[0], 'input_size': random.choices( - [10, 28, "2ForecastLength", "3ForecastLength"], - [0.2, 0.2, 0.2, 0.2] - )[0], + [10, 28, "2ForecastLength", "3ForecastLength"], [0.2, 0.2, 0.2, 0.2] + )[0], # "early_stop_patience_steps": random.choice([1, 3, 5]), "model_args": model_args, 'regression_type': regression_type_choice, @@ -310,7 +372,7 @@ def get_params(self): 'loss': self.loss, 'learning_rate': self.learning_rate, "max_steps": self.max_steps, - 'input_size': self.input_size, + 'input_size': self.input_size, "model_args": self.model_args, 'regression_type': self.regression_type, } @@ -340,11 +402,11 @@ def get_params(self): "transformation_params": { "0": { 'rolling_window': 30, - 'n_tails': 0.1, - 'n_future': 0.2, - 'method': 'mean', - 'macro_micro': True - }, + 'n_tails': 0.1, + 'n_future': 0.2, + 'method': 'mean', + 'macro_micro': True, + }, }, }, ) @@ -355,4 +417,3 @@ def get_params(self): mod.fit(df, future_regressor=regr_train) prediction = mod.predict(future_regressor=regr_fcst) prediction.plot_grid(df) - diff --git a/autots/models/sklearn.py b/autots/models/sklearn.py index 27719e1b..e499626d 100644 --- a/autots/models/sklearn.py +++ b/autots/models/sklearn.py @@ -545,6 +545,7 @@ def retrieve_classifier( ) elif model_class == "KNN": from sklearn.neighbors import KNeighborsClassifier + return KNeighborsClassifier( n_jobs=n_jobs, **model_param_dict, @@ -552,7 +553,7 @@ def retrieve_classifier( elif model_class == "SGD": from sklearn.linear_model import SGDClassifier from sklearn.multioutput import MultiOutputClassifier - + if multioutput: return MultiOutputClassifier( SGDClassifier( @@ -776,8 +777,10 @@ def generate_classifier_params( } else: model_dict = { - 'xgboost': 1, 'ExtraTrees': 0.2, - 'RandomForest': 0.1, 'KNN': 1, + 'xgboost': 1, + 'ExtraTrees': 0.2, + 'RandomForest': 0.1, + 'KNN': 1, 'SGD': 0.1, } regr_params = generate_regressor_params( @@ -2298,9 +2301,13 @@ def predict( index, method=self.datepart_method, polynomial_degree=self.polynomial_degree ) if self.regression_type in ['User', 'user']: - self.X_pred = pd.concat([self.X_pred, future_regressor], axis=1) + self.X_pred = pd.concat( + [self.X_pred, future_regressor.reindex(index)], axis=1 + ) if self.X_pred.shape[0] > index.shape[0]: - raise ValueError("future_regressor and X index failed to align") + raise ValueError( + f"future_regressor {future_regressor.index} and X {self.X_pred.index} index failed to align" + ) self.X_pred.columns = [str(xc) for xc in self.X_pred.columns] forecast = pd.DataFrame( diff --git a/autots/tools/regressor.py b/autots/tools/regressor.py index 1988b61d..299f38f4 100644 --- a/autots/tools/regressor.py +++ b/autots/tools/regressor.py @@ -330,7 +330,9 @@ def create_lagged_regressor( dates[-1], periods=(forecast_length + 1), freq=frequency )[1:] except Exception: - raise ValueError("create_regressor doesn't work on data where forecast_length > historical data length") + raise ValueError( + "create_regressor doesn't work on data where forecast_length > historical data length" + ) regressor_train = df_inner.shift(forecast_length) if backfill == "ets": model_flag = True diff --git a/autots/tools/shaping.py b/autots/tools/shaping.py index 28075a44..c416c750 100644 --- a/autots/tools/shaping.py +++ b/autots/tools/shaping.py @@ -33,7 +33,7 @@ def infer_frequency(df_wide, warn=True, **kwargs): def split_digits_and_non_digits(s): # Find all digit and non-digit sequences all_parts = re.findall(r'\d+|\D+', s) - + # Separate digit and non-digit parts digits = ''.join(part for part in all_parts if part.isdigit()) nondigits = ''.join(part for part in all_parts if not part.isdigit()) @@ -58,7 +58,9 @@ def freq_to_timedelta(freq): elif 'y' in freq.lower(): new_freq = pd.to_timedelta(364, unit='D') else: - raise ValueError(f"freq {freq} not recognized for to_timedelta. Please report this issue to AutoTS if found") + raise ValueError( + f"freq {freq} not recognized for to_timedelta. Please report this issue to AutoTS if found" + ) return new_freq diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index 82e013c6d62323b440039a47e026b22d5dc8f449..73d584b3fcae7b134c5bf4a0a7c7fb0c3fff757c 100644 GIT binary patch literal 9241276 zcmeFa37j2AbuX-4qtPx)w&k@gFEg^G8OaNai1;il+c>giBN-6H=DO$Jp1JqxZoZ2~ zV`E}Sz*gu-9Jug70zU|lkVo>u7S@D7SOPo}$b*FJ3j~svyyOwGKmsIu|8uIUyQ}+l z&#me^!hG027|nETr%s*vpHrt!RXx1=)ytMGUq=7=+d8dsrEz-7Yn-aIn~l2H==S|B z&*)WZrJEz4Ue(|I;{HSZNq?o=YW0u$ZD((T&M4^>9mVn{EA_UBY&~kn61q9{f&}j_4k^a zL_$F0y5=db-L90-%0qpBb;s-Wm}c*E*K2et%|@qxFS>G$>iX&KRKsmGYhJU_4;g~5 zF31PLeScN`+V-`5|J+E4xk|U(n`yaoXvOo_w%X0AR|N0Yh(7oIb;V|Dp^GobLPU zi_KaMt#McU^>?;QUfU}juAqG|;TZb1YO3AoVw_e^)k}SUtnRi?mYVa8e$U_3_3Evf z+x0qymfJ0Z6aIK1_||v*E#B#3tyl6Wx$Cthv(^-N{Lrnt%f0$cXR0)V-=_{AFC6bS z+a6djUhdXwg>KoagYtE5&7E;OC=Y4kgihD(R*FHJYYXB78r;-cm}7Clr-d2{uMunp z+rlaf&6emc3E}2WxjA2GHRq|@g_(u^8GnP@aBB-_p;M@!#~W_ld)Ku`ZolOz*X_IZ 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A

+
  • + autots.models.neural_forecast + +
  • @@ -1003,6 +1010,8 @@

    F

  • (autots.models.matrix_var.TMF method)
  • (autots.models.mlensemble.MLEnsemble method) +
  • +
  • (autots.models.neural_forecast.NeuralForecast method)
  • (autots.models.prophet.FBProphet method)
  • @@ -1277,6 +1286,8 @@

    F

  • (in module autots.tools.seasonal)
  • +
  • freq_to_timedelta() (in module autots.tools.shaping) +
  • full_mae_errors (autots.evaluator.auto_model.TemplateEvalObject attribute)
  • full_mae_ids (autots.evaluator.auto_model.TemplateEvalObject attribute) @@ -1419,6 +1430,8 @@

    G

  • (autots.models.matrix_var.TMF method)
  • (autots.models.mlensemble.MLEnsemble method) +
  • +
  • (autots.models.neural_forecast.NeuralForecast method)
  • (autots.models.prophet.FBProphet method)
  • @@ -1589,6 +1602,8 @@

    G

  • (autots.models.matrix_var.TMF method)
  • (autots.models.mlensemble.MLEnsemble method) +
  • +
  • (autots.models.neural_forecast.NeuralForecast method)
  • (autots.models.prophet.FBProphet method)
  • @@ -1757,8 +1772,12 @@

    I

  • (autots.tools.transform.DatepartRegressionTransformer method)
  • -
  • infer_frequency() (in module autots.tools.shaping) +
  • infer_frequency() (in module autots) + +
  • inferred_normal() (in module autots.tools.probabilistic)
  • IntermittentOccurrence (class in autots.tools.transform) @@ -2102,6 +2121,8 @@

    M

  • autots.models.mlensemble
  • autots.models.model_list +
  • +
  • autots.models.neural_forecast
  • autots.models.prophet
  • @@ -2199,6 +2220,8 @@

    N

  • nan_percentile() (in module autots.tools.percentile)
  • nan_quantile() (in module autots.tools.percentile) +
  • +
  • NeuralForecast (class in autots.models.neural_forecast)
  • NeuralProphet (class in autots.models.prophet)
  • @@ -2468,6 +2491,8 @@

    P

  • (autots.models.matrix_var.TMF method)
  • (autots.models.mlensemble.MLEnsemble method) +
  • +
  • (autots.models.neural_forecast.NeuralForecast method)
  • (autots.models.prophet.FBProphet method)
  • @@ -2772,8 +2797,6 @@

    S

  • seek_the_oracle() (in module autots.models.greykite)
  • - - +
    • set_limit_forecast() (in module autots.evaluator.event_forecasting)
    • set_limit_forecast_historic() (in module autots.evaluator.event_forecasting) @@ -2821,6 +2846,8 @@

      S

    • smoothness() (in module autots.evaluator.metrics)
    • spl() (in module autots.evaluator.metrics) +
    • +
    • split_digits_and_non_digits() (in module autots.tools.shaping)
    • StatsmodelsFilter (class in autots.tools.transform)
    • diff --git a/docs/build/html/index.html b/docs/build/html/index.html index 3efe10c4..4cd64e69 100644 --- a/docs/build/html/index.html +++ b/docs/build/html/index.html @@ -6,7 +6,7 @@ - AutoTS — AutoTS 0.6.2 documentation + AutoTS — AutoTS 0.6.4 documentation diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv index 83554f1e3f352022df2c1a71ac49979bec318922..534d21ba0345b726466d54bab4e683f68002344d 100644 GIT binary patch delta 7603 zcmV;k9ZcfPJDEI?i+?Xk&RCAd(qwk`1qUM0-H1rAO@b}UpMC+}$tJr&)F1nhNLDv~ z1)vH*;Zgvvbgs)+$Ctllc~Yg~)t_FKc~!*XKY0B~bkJG)Z_Q`l|NE=&zia<|qwuUr!h%^)Gb>gg)fSX24DoQ#0QDjepezkUJ>~0&p)hQ{tH& zdBL-i06L~QrC?>87lP@$E8P~9`SEbSL8bNgqemV;v7M0`^HOgX7d?tnGh=8XwQ0H5 zg?y~E*q)pTRf_D<*fNo$ct&OnWqGTCA3QFaUS+$LjV7lO{G&;|?mbs#o zHrcUfnFyw;%ztDq_G3uCS9vKs8eantUypgA+0+!h5L#r%lxvUL-Eo;#rs)=bsipQx z#1gLg_LR1_GbxP1n6ah858&5bHt7#}A;0BH^Tx3ELW!cbMH0$%o5^lFL1tw0X%U4e zt5lcX>HW=5DygNZf;INO^~VPhKY4X)-d%KIEkT^bsPKJ{ 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index 1d4fda7c..92a2fc44 100644 --- a/docs/build/html/py-modindex.html +++ b/docs/build/html/py-modindex.html @@ -5,7 +5,7 @@ - Python Module Index — AutoTS 0.6.2 documentation + Python Module Index — AutoTS 0.6.4 documentation @@ -159,6 +159,11 @@

      Python Module Index

          autots.models.model_list + + +     + autots.models.neural_forecast +     diff --git a/docs/build/html/search.html b/docs/build/html/search.html index 881deec9..976f5ce3 100644 --- a/docs/build/html/search.html +++ b/docs/build/html/search.html @@ -5,7 +5,7 @@ - Search — AutoTS 0.6.2 documentation + Search — AutoTS 0.6.4 documentation diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index b4327c87..ba08a395 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["index", "source/autots", "source/autots.datasets", "source/autots.evaluator", "source/autots.models", "source/autots.templates", "source/autots.tools", "source/intro", "source/modules", "source/tutorial"], "filenames": ["index.rst", "source\\autots.rst", "source\\autots.datasets.rst", "source\\autots.evaluator.rst", "source\\autots.models.rst", "source\\autots.templates.rst", "source\\autots.tools.rst", "source\\intro.rst", 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[6, "module-autots.tools.probabilistic"], [6, "module-autots.tools.profile"], [6, "module-autots.tools.regressor"], [6, "module-autots.tools.seasonal"], [6, "module-autots.tools.shaping"], [6, "module-autots.tools.thresholding"], [6, "module-autots.tools.transform"], [6, "module-autots.tools.window_functions"]], "mosaic_to_df() (autots.autots method)": [[1, "autots.AutoTS.mosaic_to_df"]], "next_fit() (autots.cassandra method)": [[1, "autots.Cassandra.next_fit"]], "params (autots.cassandra. attribute)": [[1, "autots.Cassandra..params"]], "parse_best_model() (autots.autots method)": [[1, "autots.AutoTS.parse_best_model"]], "plot() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.plot"]], "plot() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.plot"], [1, "id13"]], "plot() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.plot"]], "plot_anomaly() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.plot_anomaly"]], "plot_back_forecast() (autots.autots method)": [[1, "autots.AutoTS.plot_back_forecast"]], "plot_backforecast() (autots.autots method)": [[1, "autots.AutoTS.plot_backforecast"]], "plot_components() (autots.cassandra method)": [[1, "autots.Cassandra.plot_components"], [1, "id2"]], "plot_eval() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.plot_eval"]], "plot_forecast() (autots.cassandra method)": [[1, "autots.Cassandra.plot_forecast"], [1, "id3"]], "plot_generation_loss() (autots.autots method)": [[1, "autots.AutoTS.plot_generation_loss"]], "plot_horizontal() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal"]], "plot_horizontal_model_count() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal_model_count"]], "plot_horizontal_per_generation() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal_per_generation"]], "plot_horizontal_transformers() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal_transformers"]], "plot_metric_corr() (autots.autots method)": [[1, "autots.AutoTS.plot_metric_corr"]], "plot_per_series_error() (autots.autots method)": [[1, "autots.AutoTS.plot_per_series_error"]], "plot_per_series_mape() (autots.autots method)": [[1, "autots.AutoTS.plot_per_series_mape"]], "plot_per_series_smape() (autots.autots method)": [[1, "autots.AutoTS.plot_per_series_smape"]], "plot_things() (autots.cassandra method)": [[1, "autots.Cassandra.plot_things"]], "plot_trend() (autots.cassandra method)": [[1, "autots.Cassandra.plot_trend"], [1, "id4"]], "plot_validations() (autots.autots method)": [[1, "autots.AutoTS.plot_validations"]], "predict() (autots.autots method)": [[1, "autots.AutoTS.predict"]], "predict() (autots.cassandra method)": [[1, "autots.Cassandra.predict"], [1, "id5"]], "predict() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.predict"], [1, "id14"]], "predict_historic() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.predict_historic"], [1, "id15"]], "predict_new_product() (autots.cassandra method)": [[1, "autots.Cassandra.predict_new_product"]], "predict_x_array (autots.cassandra. attribute)": [[1, "autots.Cassandra..predict_x_array"]], "predicted_trend (autots.cassandra. attribute)": [[1, "autots.Cassandra..predicted_trend"]], "process_components() (autots.cassandra method)": [[1, "autots.Cassandra.process_components"]], "regression_check (autots.autots attribute)": [[1, "autots.AutoTS.regression_check"]], "results() (autots.autots method)": [[1, "autots.AutoTS.results"]], "retrieve_transformer() (autots.generaltransformer class method)": [[1, "autots.GeneralTransformer.retrieve_transformer"]], "retrieve_validation_forecasts() (autots.autots method)": [[1, "autots.AutoTS.retrieve_validation_forecasts"]], "return_components() (autots.cassandra method)": [[1, "autots.Cassandra.return_components"], [1, "id6"]], "rolling_trend() (autots.cassandra method)": [[1, "autots.Cassandra.rolling_trend"]], "save_template() (autots.autots method)": [[1, "autots.AutoTS.save_template"]], "scale_data() (autots.cassandra method)": [[1, "autots.Cassandra.scale_data"]], "score_per_series (autots.autots attribute)": [[1, "autots.AutoTS.score_per_series"]], "score_to_anomaly() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.score_to_anomaly"]], "scores (autots.cassandra..anomaly_detector attribute)": [[1, "autots.Cassandra..anomaly_detector.scores"]], "set_limit() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.set_limit"]], "set_limit() (autots.eventriskforecast static method)": [[1, "id16"]], "to_origin_space() (autots.cassandra method)": [[1, "autots.Cassandra.to_origin_space"]], "transform() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.transform"]], "treatment_causal_impact() (autots.cassandra method)": [[1, "autots.Cassandra.treatment_causal_impact"]], "trend_train (autots.cassandra. attribute)": [[1, "autots.Cassandra..trend_train"]], "validation_agg() (autots.autots method)": [[1, "autots.AutoTS.validation_agg"]], "x_array (autots.cassandra. attribute)": [[1, "autots.Cassandra..x_array"]], "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"]], "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 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(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, 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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"]], "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"]], "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_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.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.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"]], "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, "autots.models.cassandra.cost_function_l1_positive"]], "cost_function_l2() (in module autots.models.cassandra)": [[4, 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"autots.tools.cpu_count module": [[6, "module-autots.tools.cpu_count"]], "autots.tools.fast_kalman module": [[6, "module-autots.tools.fast_kalman"]], "Usage example": [[6, "usage-example"]], "autots.tools.fft module": [[6, "module-autots.tools.fft"]], "autots.tools.hierarchial module": [[6, "module-autots.tools.hierarchial"]], "autots.tools.holiday module": [[6, "module-autots.tools.holiday"]], "autots.tools.impute module": [[6, "module-autots.tools.impute"]], "autots.tools.lunar module": [[6, "module-autots.tools.lunar"]], "autots.tools.percentile module": [[6, "module-autots.tools.percentile"]], "autots.tools.probabilistic module": [[6, "module-autots.tools.probabilistic"]], "autots.tools.profile module": [[6, "module-autots.tools.profile"]], "autots.tools.regressor module": [[6, "module-autots.tools.regressor"]], "autots.tools.seasonal module": [[6, "module-autots.tools.seasonal"]], "autots.tools.shaping module": [[6, "module-autots.tools.shaping"]], "autots.tools.thresholding 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Model": [[9, "id5"]], "Metrics": [[9, "id6"]], "Hierarchial and Grouped Forecasts": [[9, "hierarchial-and-grouped-forecasts"]], "Ensembles": [[9, "id7"]], "Installation and Dependency Versioning": [[9, "installation-and-dependency-versioning"]], "Requirements:": [[9, "requirements"]], "Optional Packages": [[9, "optional-packages"]], "Safest bet for installation:": [[9, "safest-bet-for-installation"]], "Intel conda channel installation (sometime faster, also, more prone to bugs)": [[9, "intel-conda-channel-installation-sometime-faster-also-more-prone-to-bugs"]], "Speed Benchmark": [[9, "speed-benchmark"]], "Caveats and Advice": [[9, "caveats-and-advice"]], "Mysterious crashes": [[9, "mysterious-crashes"]], "Series IDs really need to be unique (or column names need to be all unique in wide data)": [[9, "series-ids-really-need-to-be-unique-or-column-names-need-to-be-all-unique-in-wide-data"]], "Short Training History": [[9, "short-training-history"]], "Adding regressors and other information": [[9, "adding-regressors-and-other-information"]], "Simulation Forecasting": [[9, "id8"]], "Event Risk Forecasting and Anomaly Detection": [[9, "event-risk-forecasting-and-anomaly-detection"]], "A Hack for Passing in Parameters (that aren\u2019t otherwise available)": [[9, "a-hack-for-passing-in-parameters-that-aren-t-otherwise-available"]], "Categorical Data": [[9, "categorical-data"]], "Custom and Unusual Frequencies": [[9, "custom-and-unusual-frequencies"]], "Using the Transformers independently": [[9, "using-the-transformers-independently"]], "Note on ~Regression Models": [[9, "note-on-regression-models"]], "Models": [[9, "id9"]]}, "indexentries": {"anomalydetector (class in autots)": [[1, "autots.AnomalyDetector"]], "autots (class in autots)": [[1, "autots.AutoTS"]], "cassandra (class in autots)": [[1, "autots.Cassandra"]], "eventriskforecast (class in autots)": [[1, "autots.EventRiskForecast"]], "generaltransformer (class in autots)": [[1, "autots.GeneralTransformer"]], "holidaydetector (class in autots)": [[1, "autots.HolidayDetector"]], "randomtransform() (in module autots)": [[1, "autots.RandomTransform"]], "transformts (in module autots)": [[1, "autots.TransformTS"]], "analyze_trend() (autots.cassandra method)": [[1, "autots.Cassandra.analyze_trend"]], "anomalies (autots.cassandra..anomaly_detector attribute)": [[1, "autots.Cassandra..anomaly_detector.anomalies"]], "auto_fit() (autots.cassandra method)": [[1, "autots.Cassandra.auto_fit"]], "autots": [[1, "module-autots"]], "back_forecast() (autots.autots method)": [[1, "autots.AutoTS.back_forecast"]], "base_scaler() (autots.cassandra method)": [[1, "autots.Cassandra.base_scaler"]], "best_model (autots.autots attribute)": [[1, "autots.AutoTS.best_model"]], "best_model_ensemble (autots.autots attribute)": [[1, "autots.AutoTS.best_model_ensemble"]], "best_model_name (autots.autots attribute)": [[1, "autots.AutoTS.best_model_name"]], "best_model_params (autots.autots attribute)": [[1, "autots.AutoTS.best_model_params"]], "best_model_per_series_mape() (autots.autots method)": [[1, "autots.AutoTS.best_model_per_series_mape"]], "best_model_per_series_score() (autots.autots method)": [[1, "autots.AutoTS.best_model_per_series_score"]], "best_model_transformation_params (autots.autots attribute)": [[1, "autots.AutoTS.best_model_transformation_params"]], "compare_actual_components() (autots.cassandra method)": [[1, "autots.Cassandra.compare_actual_components"]], "create_forecast_index() (autots.cassandra method)": [[1, "autots.Cassandra.create_forecast_index"]], "create_lagged_regressor() (in module autots)": [[1, "autots.create_lagged_regressor"]], "create_regressor() (in module autots)": [[1, "autots.create_regressor"]], "create_t() (autots.cassandra method)": [[1, "autots.Cassandra.create_t"]], "cross_validate() (autots.cassandra method)": [[1, "autots.Cassandra.cross_validate"]], "dates_to_holidays() (autots.cassandra.holiday_detector method)": [[1, "autots.Cassandra.holiday_detector.dates_to_holidays"]], "dates_to_holidays() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.dates_to_holidays"]], "detect() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.detect"]], "detect() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.detect"]], "df_wide_numeric (autots.autots attribute)": [[1, "autots.AutoTS.df_wide_numeric"]], "diagnose_params() (autots.autots method)": [[1, "autots.AutoTS.diagnose_params"]], "export_best_model() (autots.autots method)": [[1, "autots.AutoTS.export_best_model"]], "export_template() (autots.autots method)": [[1, "autots.AutoTS.export_template"]], "failure_rate() (autots.autots method)": [[1, "autots.AutoTS.failure_rate"]], "feature_importance() (autots.cassandra method)": [[1, "autots.Cassandra.feature_importance"]], "fill_na() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.fill_na"]], "fit() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.fit"]], "fit() (autots.autots method)": [[1, "autots.AutoTS.fit"]], "fit() (autots.cassandra method)": [[1, "autots.Cassandra.fit"], [1, "id0"]], "fit() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.fit"], [1, "id9"]], "fit() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.fit"]], "fit() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.fit"]], "fit_anomaly_classifier() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.fit_anomaly_classifier"]], "fit_data() (autots.autots method)": [[1, "autots.AutoTS.fit_data"]], "fit_data() (autots.cassandra method)": [[1, "autots.Cassandra.fit_data"]], "fit_transform() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.fit_transform"]], "generate_historic_risk_array() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.generate_historic_risk_array"]], "generate_historic_risk_array() (autots.eventriskforecast static method)": [[1, "id10"]], "generate_result_windows() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.generate_result_windows"], [1, "id11"]], "generate_risk_array() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.generate_risk_array"]], "generate_risk_array() (autots.eventriskforecast static method)": [[1, "id12"]], "get_metric_corr() (autots.autots method)": [[1, "autots.AutoTS.get_metric_corr"]], "get_new_params() (autots.anomalydetector static method)": [[1, "autots.AnomalyDetector.get_new_params"]], "get_new_params() (autots.autots static method)": [[1, "autots.AutoTS.get_new_params"]], "get_new_params() (autots.cassandra method)": [[1, "autots.Cassandra.get_new_params"], [1, "id1"]], "get_new_params() (autots.generaltransformer static method)": [[1, "autots.GeneralTransformer.get_new_params"]], "get_new_params() (autots.holidaydetector static method)": [[1, "autots.HolidayDetector.get_new_params"]], "get_params() (autots.cassandra method)": [[1, "autots.Cassandra.get_params"]], "holiday_count (autots.cassandra. attribute)": [[1, "autots.Cassandra..holiday_count"]], "holidays (autots.cassandra. attribute)": [[1, "autots.Cassandra..holidays"]], "horizontal_per_generation() (autots.autots method)": [[1, "autots.AutoTS.horizontal_per_generation"]], "horizontal_to_df() (autots.autots method)": [[1, "autots.AutoTS.horizontal_to_df"]], "import_best_model() (autots.autots method)": [[1, "autots.AutoTS.import_best_model"]], "import_results() (autots.autots method)": [[1, "autots.AutoTS.import_results"]], "import_template() (autots.autots method)": [[1, "autots.AutoTS.import_template"]], "infer_frequency() (in module autots)": [[1, "autots.infer_frequency"]], "inverse_transform() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.inverse_transform"]], "list_failed_model_types() (autots.autots method)": [[1, "autots.AutoTS.list_failed_model_types"]], "load_artificial() (in module autots)": [[1, "autots.load_artificial"]], "load_daily() (in module autots)": [[1, "autots.load_daily"]], "load_hourly() (in module autots)": [[1, "autots.load_hourly"]], "load_linear() (in module autots)": [[1, "autots.load_linear"]], "load_live_daily() (in module autots)": [[1, "autots.load_live_daily"]], "load_monthly() (in module autots)": [[1, "autots.load_monthly"]], "load_sine() (in module autots)": [[1, "autots.load_sine"]], "load_template() (autots.autots method)": [[1, "autots.AutoTS.load_template"]], "load_weekdays() (in module autots)": [[1, "autots.load_weekdays"]], "load_weekly() (in module autots)": [[1, "autots.load_weekly"]], "load_yearly() (in module autots)": [[1, "autots.load_yearly"]], "long_to_wide() (in module autots)": [[1, "autots.long_to_wide"]], "model_forecast() (in module autots)": [[1, "autots.model_forecast"]], "model_results (autots.autots.initial_results attribute)": [[1, "autots.AutoTS.initial_results.model_results"]], "module": [[1, "module-autots"], [2, "module-autots.datasets"], [2, "module-autots.datasets.fred"], [3, "module-autots.evaluator"], [3, "module-autots.evaluator.anomaly_detector"], [3, "module-autots.evaluator.auto_model"], [3, "module-autots.evaluator.auto_ts"], [3, "module-autots.evaluator.benchmark"], [3, "module-autots.evaluator.event_forecasting"], [3, "module-autots.evaluator.metrics"], [3, "module-autots.evaluator.validation"], [4, "module-autots.models"], [4, "module-autots.models.arch"], [4, "module-autots.models.base"], [4, "module-autots.models.basics"], [4, "module-autots.models.cassandra"], [4, "module-autots.models.dnn"], [4, "module-autots.models.ensemble"], [4, "module-autots.models.gluonts"], [4, "module-autots.models.greykite"], [4, "module-autots.models.matrix_var"], [4, "module-autots.models.mlensemble"], [4, "module-autots.models.model_list"], [4, "module-autots.models.neural_forecast"], [4, "module-autots.models.prophet"], [4, "module-autots.models.pytorch"], [4, "module-autots.models.sklearn"], [4, "module-autots.models.statsmodels"], [4, "module-autots.models.tfp"], [4, "module-autots.models.tide"], [5, "module-autots.templates"], [5, "module-autots.templates.general"], [6, "module-autots.tools"], [6, "module-autots.tools.anomaly_utils"], [6, "module-autots.tools.calendar"], [6, "module-autots.tools.cointegration"], [6, "module-autots.tools.cpu_count"], [6, "module-autots.tools.fast_kalman"], [6, "module-autots.tools.fft"], [6, "module-autots.tools.hierarchial"], [6, "module-autots.tools.holiday"], [6, "module-autots.tools.impute"], [6, "module-autots.tools.lunar"], [6, "module-autots.tools.percentile"], [6, "module-autots.tools.probabilistic"], [6, "module-autots.tools.profile"], [6, "module-autots.tools.regressor"], [6, "module-autots.tools.seasonal"], [6, "module-autots.tools.shaping"], [6, "module-autots.tools.thresholding"], [6, "module-autots.tools.transform"], [6, "module-autots.tools.window_functions"]], "mosaic_to_df() (autots.autots method)": [[1, "autots.AutoTS.mosaic_to_df"]], "next_fit() (autots.cassandra method)": [[1, "autots.Cassandra.next_fit"]], "params (autots.cassandra. attribute)": [[1, "autots.Cassandra..params"]], "parse_best_model() (autots.autots method)": [[1, "autots.AutoTS.parse_best_model"]], "plot() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.plot"]], "plot() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.plot"], [1, "id13"]], "plot() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.plot"]], "plot_anomaly() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.plot_anomaly"]], "plot_back_forecast() (autots.autots method)": [[1, "autots.AutoTS.plot_back_forecast"]], "plot_backforecast() (autots.autots method)": [[1, "autots.AutoTS.plot_backforecast"]], "plot_components() (autots.cassandra method)": [[1, "autots.Cassandra.plot_components"], [1, "id2"]], "plot_eval() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.plot_eval"]], "plot_forecast() (autots.cassandra method)": [[1, "autots.Cassandra.plot_forecast"], [1, "id3"]], "plot_generation_loss() (autots.autots method)": [[1, "autots.AutoTS.plot_generation_loss"]], "plot_horizontal() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal"]], "plot_horizontal_model_count() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal_model_count"]], "plot_horizontal_per_generation() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal_per_generation"]], "plot_horizontal_transformers() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal_transformers"]], "plot_metric_corr() (autots.autots method)": [[1, "autots.AutoTS.plot_metric_corr"]], "plot_per_series_error() (autots.autots method)": [[1, "autots.AutoTS.plot_per_series_error"]], "plot_per_series_mape() (autots.autots method)": [[1, "autots.AutoTS.plot_per_series_mape"]], "plot_per_series_smape() (autots.autots method)": [[1, "autots.AutoTS.plot_per_series_smape"]], "plot_things() (autots.cassandra method)": [[1, "autots.Cassandra.plot_things"]], "plot_trend() (autots.cassandra method)": [[1, "autots.Cassandra.plot_trend"], [1, "id4"]], "plot_validations() (autots.autots method)": [[1, "autots.AutoTS.plot_validations"]], "predict() (autots.autots method)": [[1, "autots.AutoTS.predict"]], "predict() (autots.cassandra method)": [[1, "autots.Cassandra.predict"], [1, "id5"]], "predict() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.predict"], [1, "id14"]], "predict_historic() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.predict_historic"], [1, "id15"]], "predict_new_product() (autots.cassandra method)": [[1, "autots.Cassandra.predict_new_product"]], "predict_x_array (autots.cassandra. attribute)": [[1, "autots.Cassandra..predict_x_array"]], "predicted_trend (autots.cassandra. attribute)": [[1, "autots.Cassandra..predicted_trend"]], "process_components() (autots.cassandra method)": [[1, "autots.Cassandra.process_components"]], "regression_check (autots.autots attribute)": [[1, "autots.AutoTS.regression_check"]], "results() (autots.autots method)": [[1, "autots.AutoTS.results"]], "retrieve_transformer() (autots.generaltransformer class method)": [[1, "autots.GeneralTransformer.retrieve_transformer"]], "retrieve_validation_forecasts() (autots.autots method)": [[1, "autots.AutoTS.retrieve_validation_forecasts"]], "return_components() (autots.cassandra method)": [[1, "autots.Cassandra.return_components"], [1, "id6"]], "rolling_trend() (autots.cassandra method)": [[1, "autots.Cassandra.rolling_trend"]], "save_template() (autots.autots method)": [[1, "autots.AutoTS.save_template"]], "scale_data() (autots.cassandra method)": [[1, "autots.Cassandra.scale_data"]], "score_per_series (autots.autots attribute)": [[1, "autots.AutoTS.score_per_series"]], "score_to_anomaly() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.score_to_anomaly"]], "scores (autots.cassandra..anomaly_detector attribute)": [[1, "autots.Cassandra..anomaly_detector.scores"]], "set_limit() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.set_limit"]], "set_limit() (autots.eventriskforecast static method)": [[1, "id16"]], "to_origin_space() (autots.cassandra method)": [[1, "autots.Cassandra.to_origin_space"]], "transform() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.transform"]], "treatment_causal_impact() (autots.cassandra method)": [[1, "autots.Cassandra.treatment_causal_impact"]], "trend_train (autots.cassandra. attribute)": [[1, "autots.Cassandra..trend_train"]], "validation_agg() (autots.autots method)": [[1, "autots.AutoTS.validation_agg"]], "x_array (autots.cassandra. attribute)": [[1, "autots.Cassandra..x_array"]], "autots.datasets": [[2, "module-autots.datasets"]], "autots.datasets.fred": [[2, "module-autots.datasets.fred"]], "get_fred_data() (in module autots.datasets.fred)": [[2, "autots.datasets.fred.get_fred_data"]], "load_artificial() (in module autots.datasets)": [[2, "autots.datasets.load_artificial"]], "load_daily() (in module autots.datasets)": [[2, "autots.datasets.load_daily"]], "load_hourly() (in module autots.datasets)": [[2, "autots.datasets.load_hourly"]], "load_linear() (in module autots.datasets)": [[2, "autots.datasets.load_linear"]], "load_live_daily() (in module autots.datasets)": [[2, "autots.datasets.load_live_daily"]], "load_monthly() (in module autots.datasets)": [[2, "autots.datasets.load_monthly"]], "load_sine() (in module autots.datasets)": [[2, "autots.datasets.load_sine"]], "load_weekdays() (in module autots.datasets)": [[2, "autots.datasets.load_weekdays"]], "load_weekly() (in module autots.datasets)": [[2, "autots.datasets.load_weekly"]], "load_yearly() (in module autots.datasets)": [[2, "autots.datasets.load_yearly"]], "load_zeroes() (in module autots.datasets)": [[2, "autots.datasets.load_zeroes"]], "anomalydetector (class in autots.evaluator.anomaly_detector)": [[3, "autots.evaluator.anomaly_detector.AnomalyDetector"]], "autots (class in autots.evaluator.auto_ts)": [[3, "autots.evaluator.auto_ts.AutoTS"]], "benchmark (class in autots.evaluator.benchmark)": [[3, "autots.evaluator.benchmark.Benchmark"]], "eventriskforecast (class in autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast"]], "holidaydetector (class in autots.evaluator.anomaly_detector)": [[3, "autots.evaluator.anomaly_detector.HolidayDetector"]], "modelmonster() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.ModelMonster"]], "modelprediction (class in autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.ModelPrediction"]], "newgenetictemplate() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.NewGeneticTemplate"]], "randomtemplate() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.RandomTemplate"]], "templateevalobject (class in autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.TemplateEvalObject"]], "templatewizard() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.TemplateWizard"]], "uniquetemplates() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.UniqueTemplates"]], "array_last_val() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.array_last_val"]], "autots.evaluator": [[3, "module-autots.evaluator"]], "autots.evaluator.anomaly_detector": [[3, "module-autots.evaluator.anomaly_detector"]], "autots.evaluator.auto_model": [[3, "module-autots.evaluator.auto_model"]], "autots.evaluator.auto_ts": [[3, "module-autots.evaluator.auto_ts"]], "autots.evaluator.benchmark": [[3, "module-autots.evaluator.benchmark"]], "autots.evaluator.event_forecasting": [[3, "module-autots.evaluator.event_forecasting"]], "autots.evaluator.metrics": [[3, "module-autots.evaluator.metrics"]], "autots.evaluator.validation": [[3, "module-autots.evaluator.validation"]], "back_forecast() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.back_forecast"]], "back_forecast() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.back_forecast"]], "best_model (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model"]], "best_model_ensemble (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_ensemble"]], "best_model_name (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_name"]], "best_model_params (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_params"]], "best_model_per_series_mape() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_per_series_mape"]], "best_model_per_series_score() (autots.evaluator.auto_ts.autots 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"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, 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(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, 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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"]], "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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autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_maker_3"]], "window_sum_mean() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_sum_mean"]], "window_sum_mean_nan_tail() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_sum_mean_nan_tail"]], "window_sum_nan_mean() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_sum_nan_mean"]], "zscore_survival_function() (in module autots.tools.anomaly_utils)": [[6, "autots.tools.anomaly_utils.zscore_survival_function"]]}}) \ No newline at end of file diff --git a/docs/build/html/source/autots.datasets.html b/docs/build/html/source/autots.datasets.html index 4169d17a..b68d27dd 100644 --- a/docs/build/html/source/autots.datasets.html +++ b/docs/build/html/source/autots.datasets.html @@ -6,7 +6,7 @@ - autots.datasets package — AutoTS 0.6.2 documentation + autots.datasets package — AutoTS 0.6.4 documentation @@ -249,6 +249,7 @@

      Navigation

    • Intro
    • AutoTS
    • Tutorial
    • +
    • Extended Tutorial

    Return new template given old template with model accuracies.

    “No mating!” - Pattern, Sanderson

    @@ -483,7 +483,7 @@

    Submodules
    -autots.evaluator.auto_model.validation_aggregation(validation_results, df_train=None)
    +autots.evaluator.auto_model.validation_aggregation(validation_results, df_train=None, groupby_cols=['ID', 'Model', 'ModelParameters', 'TransformationParameters', 'Ensemble'])

    Aggregate a TemplateEvalObject.

    @@ -870,7 +870,7 @@

    Submodules
    -import_template(filename: str, method: str = 'add_on', enforce_model_list: bool = True, include_ensemble: bool = False, include_horizontal: bool = False)
    +import_template(filename: str, method: str = 'add_on', enforce_model_list: bool = True, include_ensemble: bool = False, include_horizontal: bool = False, force_validation: bool = False)

    Import a previously exported template of model parameters. Must be done before the AutoTS object is .fit().

    @@ -881,6 +881,7 @@

    SubmodulesIntro
  • AutoTS
  • Tutorial
  • +
  • Extended Tutorial
  • +
    autots.tools.shaping.infer_frequency(df_wide, warn=True, **kwargs)
    @@ -1589,6 +1595,11 @@

    Usage example +
    +autots.tools.shaping.split_digits_and_non_digits(s)
    +

    +
    autots.tools.shaping.subset_series(df, weights, n: int = 1000, random_state: int = 2020)
    @@ -2398,7 +2409,7 @@

    Usage example
    -class autots.tools.transform.DiffSmoother(output='multivariate', method=None, transform_dict=None, method_params=None, fillna=None, n_jobs=1, adjustment: int = 2)
    +class autots.tools.transform.DiffSmoother(output='multivariate', method=None, transform_dict=None, method_params=None, fillna=None, n_jobs=1, adjustment: int = 2, reverse_alignment=True)

    Bases: EmptyTransformer

    @@ -2418,7 +2429,7 @@

    Usage example
    -static get_new_params(method='random')
    +static get_new_params(method='fast')

    Generate new random parameters

    @@ -2989,7 +3000,7 @@

    Usage example
    -static get_new_params(method='random')
    +static get_new_params(method='fast')

    diff --git a/docs/build/html/source/intro.html b/docs/build/html/source/intro.html index 38fcc286..f5fbadb9 100644 --- a/docs/build/html/source/intro.html +++ b/docs/build/html/source/intro.html @@ -6,7 +6,7 @@ - Intro — AutoTS 0.6.2 documentation + Intro — AutoTS 0.6.4 documentation @@ -229,6 +229,7 @@

    Navigation

  • Tutorial
  • +
  • Extended Tutorial
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