From c728749d3427e310da953266db15f54c4b672e3e Mon Sep 17 00:00:00 2001 From: Olivier Sprangers Date: Thu, 7 Nov 2024 17:11:38 +0100 Subject: [PATCH] next_iter --- hierarchicalforecast/_modidx.py | 7 + hierarchicalforecast/core.py | 266 ++- hierarchicalforecast/evaluation.py | 83 +- hierarchicalforecast/utils.py | 107 +- nbs/src/core.ipynb | 925 +++++++-- nbs/src/evaluation.ipynb | 155 +- nbs/src/methods.ipynb | 3039 +--------------------------- nbs/src/utils.ipynb | 688 ++----- settings.ini | 2 +- setup.py | 9 +- 10 files changed, 1396 insertions(+), 3885 deletions(-) diff --git a/hierarchicalforecast/_modidx.py b/hierarchicalforecast/_modidx.py index dfb09938..be047a83 100644 --- a/hierarchicalforecast/_modidx.py +++ b/hierarchicalforecast/_modidx.py @@ -9,6 +9,8 @@ 'hierarchicalforecast/core.py'), 'hierarchicalforecast.core.HierarchicalReconciliation.__init__': ( 'src/core.html#hierarchicalreconciliation.__init__', 'hierarchicalforecast/core.py'), + 'hierarchicalforecast.core.HierarchicalReconciliation._prepare_Y': ( 'src/core.html#hierarchicalreconciliation._prepare_y', + 'hierarchicalforecast/core.py'), 'hierarchicalforecast.core.HierarchicalReconciliation._prepare_fit': ( 'src/core.html#hierarchicalreconciliation._prepare_fit', 'hierarchicalforecast/core.py'), 'hierarchicalforecast.core.HierarchicalReconciliation.bootstrap_reconcile': ( 'src/core.html#hierarchicalreconciliation.bootstrap_reconcile', @@ -202,12 +204,17 @@ 'hierarchicalforecast/utils.py'), 'hierarchicalforecast.utils.concat_str': ( 'src/utils.html#concat_str', 'hierarchicalforecast/utils.py'), + 'hierarchicalforecast.utils.cov2corr': ( 'src/utils.html#cov2corr', + 'hierarchicalforecast/utils.py'), + 'hierarchicalforecast.utils.df_constructor': ( 'src/utils.html#df_constructor', + 'hierarchicalforecast/utils.py'), 'hierarchicalforecast.utils.group_by_agg_named': ( 'src/utils.html#group_by_agg_named', 'hierarchicalforecast/utils.py'), 'hierarchicalforecast.utils.is_strictly_hierarchical': ( 'src/utils.html#is_strictly_hierarchical', 'hierarchicalforecast/utils.py'), 'hierarchicalforecast.utils.level_to_outputs': ( 'src/utils.html#level_to_outputs', 'hierarchicalforecast/utils.py'), + 'hierarchicalforecast.utils.pivot': ('src/utils.html#pivot', 'hierarchicalforecast/utils.py'), 'hierarchicalforecast.utils.quantiles_to_outputs': ( 'src/utils.html#quantiles_to_outputs', 'hierarchicalforecast/utils.py'), 'hierarchicalforecast.utils.samples_to_quantiles_df': ( 'src/utils.html#samples_to_quantiles_df', diff --git a/hierarchicalforecast/core.py b/hierarchicalforecast/core.py index 7d66679a..1bba793a 100644 --- a/hierarchicalforecast/core.py +++ b/hierarchicalforecast/core.py @@ -5,14 +5,17 @@ # %% ../nbs/src/core.ipynb 4 import re -import gc import time import copy from .methods import HReconciler +from .utils import pivot from inspect import signature from scipy.stats import norm from scipy import sparse from typing import Dict, List, Optional +from utilsforecast.compat import DFType +import utilsforecast.processing as ufp + import warnings import numpy as np @@ -39,7 +42,7 @@ def _build_fn_name(fn) -> str: return fn_name # %% ../nbs/src/core.ipynb 10 -def _reverse_engineer_sigmah(Y_hat_df, y_hat, model_name): +def _reverse_engineer_sigmah(Y_hat_df: DFType, y_hat: np.ndarray, model_name: str) -> np.ndarray: """ This function assumes that the model creates prediction intervals under a normality with the following the Equation: @@ -54,22 +57,22 @@ def _reverse_engineer_sigmah(Y_hat_df, y_hat, model_name): drop_cols.append('y') if model_name+'-median' in Y_hat_df.columns: drop_cols.append(model_name+'-median') - model_names = Y_hat_df.drop(columns=drop_cols, axis=1).columns.to_list() + model_names = ufp.drop_columns(Y_hat_df, drop_cols).columns pi_model_names = [name for name in model_names if ('-lo' in name or '-hi' in name)] pi_model_name = [pi_name for pi_name in pi_model_names if model_name in pi_name] pi = len(pi_model_name) > 0 - n_series = len(Y_hat_df.index.unique()) + n_series = len(Y_hat_df["unique_id"].unique()) if not pi: raise Exception(f'Please include `{model_name}` prediction intervals in `Y_hat_df`') pi_col = pi_model_name[0] sign = -1 if 'lo' in pi_col else 1 - level_col = re.findall('[\d]+[.,\d]+|[\d]*[.][\d]+|[\d]+', pi_col) - level_col = float(level_col[-1]) + level_cols = re.findall('[\d]+[.,\d]+|[\d]*[.][\d]+|[\d]+', pi_col) + level_col = float(level_cols[-1]) z = norm.ppf(0.5 + level_col / 200) - sigmah = Y_hat_df[pi_col].values.reshape(n_series,-1) + sigmah = Y_hat_df[pi_col].to_numpy().reshape(n_series,-1) sigmah = sign * (sigmah - y_hat) / z return sigmah @@ -97,99 +100,135 @@ def __init__(self, self.insample = any([method.insample for method in reconcilers]) def _prepare_fit(self, - Y_hat_df: pd.DataFrame, - S_df: pd.DataFrame, - Y_df: Optional[pd.DataFrame], + Y_hat_df: DFType, + S_df: DFType, + Y_df: Optional[DFType], tags: Dict[str, np.ndarray], level: Optional[List[int]] = None, intervals_method: str = 'normality', - sort_df: bool = True): + sort_df: bool = True, + id_col: str = "unique_id", + time_col: str = "ds", + target_col: str = "y", + ): """ Performs preliminary wrangling and protections """ + #-------------------------------- Match Y_hat/Y/S index order --------------------------------# - if sort_df: - Y_hat_df = Y_hat_df.reset_index() - Y_hat_df.unique_id = Y_hat_df.unique_id.astype('category') - Y_hat_df.unique_id = Y_hat_df.unique_id.cat.set_categories(S_df.index) - Y_hat_df = Y_hat_df.sort_values(by=['unique_id', 'ds']) - Y_hat_df = Y_hat_df.set_index('unique_id') - - if Y_df is not None: - Y_df = Y_df.reset_index() - Y_df.unique_id = Y_df.unique_id.astype('category') - Y_df.unique_id = Y_df.unique_id.cat.set_categories(S_df.index) - Y_df = Y_df.sort_values(by=['unique_id', 'ds']) - Y_df = Y_df.set_index('unique_id') - - S_df.index = pd.CategoricalIndex(S_df.index, categories=S_df.index) + # TODO: This is now a bit slow as we always sort. + S_df = ufp.assign_columns(S_df, f"{id_col}_id", np.arange(len(S_df))) + Y_hat_df = ufp.join(Y_hat_df, S_df[[id_col, f"{id_col}_id"]], on=id_col, how='left') + Y_hat_df = ufp.sort(Y_hat_df, by=[f"{id_col}_id", time_col]) + Y_hat_df = ufp.drop_columns(Y_hat_df, f"{id_col}_id") + if Y_df is not None: + Y_df = ufp.join(Y_df, S_df[[id_col, f"{id_col}_id"]], on=id_col, how='left') + Y_df = ufp.sort(Y_df, by=[f"{id_col}_id", time_col]) + Y_df = ufp.drop_columns(Y_df, f"{id_col}_id") + S_df = ufp.drop_columns(S_df, f"{id_col}_id") #----------------------------------- Check Input's Validity ----------------------------------# + # Check input's validity if intervals_method not in ['normality', 'bootstrap', 'permbu']: - raise ValueError(f'Unkwon interval method: {intervals_method}') + raise ValueError(f'Unknown interval method: {intervals_method}') if self.insample or (intervals_method in ['bootstrap', 'permbu']): if Y_df is None: - raise Exception('you need to pass `Y_df`') + raise Exception('You need to provide `Y_df`.') # Protect level list if (level is not None): - level_outside_domain = np.any((np.array(level) < 0)|(np.array(level) >= 100 )) + level_outside_domain = np.any((np.array(level) < 0) | (np.array(level) >= 100 )) if level_outside_domain and (intervals_method in ['normality', 'permbu']): - raise Exception('Level outside domain, send `level` list in [0,100)') + raise ValueError("Level must be a list containing floating values in the interval [0, 100).") # Declare output names - drop_cols = ['ds', 'y'] if 'y' in Y_hat_df.columns else ['ds'] - model_names = Y_hat_df.drop(columns=drop_cols, axis=1).columns.to_list() - - # Ensure numeric columns - if not len(Y_hat_df[model_names].select_dtypes(include='number').columns) == len(Y_hat_df[model_names].columns): - raise Exception('`Y_hat_df`s columns contain non numeric types') - - #Ensure no null values - if Y_hat_df[model_names].isnull().values.any(): - raise Exception('`Y_hat_df` contains null values') - - pi_model_names = [name for name in model_names if ('-lo' in name or '-hi' in name or '-median' in name)] - model_names = [name for name in model_names if name not in pi_model_names] + model_names = list(set(Y_hat_df.columns) - set([id_col, time_col, target_col])) + for model_name in model_names: + # Ensure numeric columns + ufp.validate_format(Y_hat_df[[id_col, time_col, model_name]], id_col=id_col, time_col=time_col, target_col=model_name) + + # Ensure no null values + assert not ufp.is_none(Y_hat_df[model_name]).any(), f"Column {model_name} in `Y_hat_df` contains null values. Make sure no column in `Y_hat_df` contains null values." # TODO: Complete y_hat_insample protection + model_names = [name for name in model_names if not ('-lo' in name or '-hi' in name or '-median' in name)] if intervals_method in ['bootstrap', 'permbu'] and Y_df is not None: if not (set(model_names) <= set(Y_df.columns)): - raise Exception('Check `Y_hat_df`s models are included in `Y_df` columns') + raise Exception(f"Check `Y_df` columns, {model_names} must be in `Y_df` columns.") - uids = Y_hat_df.index.unique() + # Assert S is an identity matrix at the bottom + S_np = ufp.to_numpy(ufp.drop_columns(S_df, id_col)) + if not np.allclose(S_np[-S_np.shape[1]:], np.eye(S_np.shape[1])): + raise ValueError(f"The bottom {S_np.shape[1]}x{S_np.shape[1]} part of S must be an identity matrix.") # Check Y_hat_df\S_df series difference - S_diff = len(S_df.index.difference(uids)) - Y_hat_diff = len(Y_hat_df.index.difference(S_df.index.unique())) - if S_diff > 0 or Y_hat_diff > 0: - raise Exception(f'Check `S_df`, `Y_hat_df` series difference, S\Y_hat={S_diff}, Y_hat\S={Y_hat_diff}') + # TODO: this logic should be method specific + S_diff = set(S_df[id_col]) - set(Y_hat_df[id_col]) + Y_hat_diff = set(Y_hat_df[id_col]) - set(S_df[id_col]) + if S_diff: + raise Exception(f'There are unique_ids in S_df that are not in Y_hat_df: {S_diff}') + if Y_hat_diff: + raise Exception(f'There are unique_ids in Y_hat_df that are not in S_df: {Y_hat_diff}') if Y_df is not None: - # Check Y_hat_df\Y_df series difference - Y_diff = len(Y_df.index.difference(uids)) - Y_hat_diff = len(Y_hat_df.index.difference(Y_df.index.unique())) - if Y_diff > 0 or Y_hat_diff > 0: - raise Exception(f'Check `Y_hat_df`, `Y_df` series difference, Y_hat\Y={Y_hat_diff}, Y\Y_hat={Y_diff}') - - # Same Y_hat_df/S_df/Y_df's unique_id order to prevent errors - S_df = S_df.loc[uids] + Y_diff = set(Y_df[id_col]) - set(Y_hat_df[id_col]) + Y_hat_diff = set(Y_hat_df[id_col]) - set(Y_df[id_col]) + if Y_diff: + raise Exception(f'There are unique_ids in Y_df that are not in Y_hat_df: {Y_diff}') + if Y_hat_diff: + raise Exception(f'There are unique_ids in Y_hat_df that are not in Y_df: {Y_hat_diff}') + + # Same Y_hat_df/S_df/Y_df's unique_ids. Order is guaranteed by the sort_df flag. + # TODO: this logic should be method specific + unique_ids = set(Y_hat_df[id_col]) + mask = ufp.is_in(S_df[id_col], unique_ids) + S_df = ufp.filter_with_mask(S_df, mask) return Y_hat_df, S_df, Y_df, model_names + def _prepare_Y(self, + Y_df: DFType, + S_df: DFType, + is_balanced: bool = True, + id_col: str = "unique_id", + time_col: str = "ds", + target_col: str = "y", + ) -> np.ndarray: + """ + Prepare Y data. + """ + if is_balanced: + Y = Y_df[target_col].to_numpy().reshape(len(S_df), -1) + else: + Y_pivot = pivot(Y_df, index=id_col, columns=time_col, values=target_col, sort=True) + + # TODO: check if this is the best way to do it + pos_in_Y = np.searchsorted(Y_pivot[id_col], S_df[id_col]) + Y_pivot = ufp.drop_columns(Y_pivot, id_col) + Y_pivot = ufp.take_rows(Y_pivot, pos_in_Y) + Y = Y_pivot.to_numpy() + + # TODO: the result is a Fortran contiguous array, see if we can avoid the below copy + Y = np.ascontiguousarray(Y, dtype=np.float64) + return Y + + def reconcile(self, - Y_hat_df: pd.DataFrame, - S: pd.DataFrame, + Y_hat_df: DFType, + S: DFType, tags: Dict[str, np.ndarray], - Y_df: Optional[pd.DataFrame] = None, + Y_df: Optional[DFType] = None, level: Optional[List[int]] = None, intervals_method: str = 'normality', num_samples: int = -1, seed: int = 0, sort_df: bool = True, is_balanced: bool = False, + id_col: str = "unique_id", + time_col: str = "ds", + target_col: str = "y", ): """Hierarchical Reconciliation Method. @@ -207,10 +246,10 @@ def reconcile(self, base predictions $\hat{\mathbf{y}}_{[a,b],\\tau}$. **Parameters:**
- `Y_hat_df`: pd.DataFrame, base forecasts with columns `ds` and models to reconcile indexed by `unique_id`.
- `Y_df`: pd.DataFrame, training set of base time series with columns `['ds', 'y']` indexed by `unique_id`.
+ `Y_hat_df`: DataFrame, base forecasts with columns `ds` and models to reconcile indexed by `unique_id`.
+ `Y_df`: DataFrame, training set of base time series with columns `['ds', 'y']` indexed by `unique_id`.
If a class of `self.reconciles` receives `y_hat_insample`, `Y_df` must include them as columns.
- `S`: pd.DataFrame with summing matrix of size `(base, bottom)`, see [aggregate method](https://nixtla.github.io/hierarchicalforecast/utils.html#aggregate).
+ `S`: DataFrame with summing matrix of size `(base, bottom)`, see [aggregate method](https://nixtla.github.io/hierarchicalforecast/utils.html#aggregate).
`tags`: Each key is a level and its value contains tags associated to that level.
`level`: positive float list [0,100), confidence levels for prediction intervals.
`intervals_method`: str, method used to calculate prediction intervals, one of `normality`, `bootstrap`, `permbu`.
@@ -218,10 +257,14 @@ def reconcile(self, `seed`: int=0, random seed for numpy generator's replicability.
`sort_df` : bool (default=True), if True, sort `df` by [`unique_id`,`ds`].
`is_balanced`: bool=False, wether `Y_df` is balanced, set it to True to speed things up if `Y_df` is balanced.
+ `id_col` : str='unique_id', column that identifies each serie.
+ `time_col` : str='ds', column that identifies each timestep, its values can be timestamps or integers.
+ `target_col` : str='y', column that contains the target. **Returns:**
- `Y_tilde_df`: pd.DataFrame, with reconciled predictions. + `Y_tilde_df`: DataFrame, with reconciled predictions. """ + # Check input's validity and sort dataframes Y_hat_df, S_df, Y_df, self.model_names = \ self._prepare_fit(Y_hat_df=Y_hat_df, @@ -230,30 +273,42 @@ def reconcile(self, tags=tags, level=level, intervals_method=intervals_method, - sort_df=sort_df) + sort_df=sort_df, + id_col=id_col, + time_col=time_col, + target_col=target_col, + ) # Initialize reconciler arguments reconciler_args = dict( - idx_bottom=S_df.index.get_indexer(S.columns), - tags={key: S_df.index.get_indexer(val) for key, val in tags.items()} + idx_bottom=np.arange(len(S_df))[-S_df.shape[1]:], + tags={key: ufp.is_in(S_df[id_col], val).to_numpy().nonzero()[0] for key, val in tags.items()} ) any_sparse = any([method.is_sparse_method for method in self.reconcilers]) if any_sparse: + if not isinstance(S_df, pd.DataFrame): + raise ValueError("You have one or more sparse reconciliation methods. Please convert `S_df` to a pandas DataFrame.") + if not isinstance(Y_hat_df, pd.DataFrame): + raise ValueError("You have one or more sparse reconciliation methods. Please convert `Y_hat_df` to a pandas DataFrame.") try: S_for_sparse = sparse.csr_matrix(S_df.sparse.to_coo()) except AttributeError: - warnings.warn('Using dense S matrix for sparse reconciliation method.') + warnings.warn("Using dense S matrix for sparse reconciliation method.") S_for_sparse = S_df.values.astype(np.float64, copy=False) if Y_df is not None: - if is_balanced: - y_insample = Y_df['y'].values.reshape(len(S_df), -1).astype(np.float64, copy=False) - else: - y_insample = Y_df.pivot(columns='ds', values='y').loc[S_df.index].values.astype(np.float64, copy=False) + if any_sparse and not isinstance(Y_df, pd.DataFrame): + raise ValueError("You have one or more sparse reconciliation methods. Please convert `Y_df` to a pandas DataFrame.") + y_insample = self._prepare_Y(Y_df=Y_df, + S_df=S_df, + is_balanced=is_balanced, + id_col=id_col, + time_col=time_col, + target_col=target_col) reconciler_args['y_insample'] = y_insample - Y_tilde_df= Y_hat_df.copy() + Y_tilde_df = ufp.copy_if_pandas(Y_hat_df) self.execution_times = {} self.level_names = {} self.sample_names = {} @@ -263,7 +318,8 @@ def reconcile(self, if reconciler.is_sparse_method: reconciler_args["S"] = S_for_sparse else: - reconciler_args["S"] = S_df.values.astype(np.float64, copy=False) + reconciler_args["S"] = ufp.to_numpy(ufp.drop_columns(S_df, id_col))\ + .astype(np.float64, copy=False) has_fitted = 'y_hat_insample' in signature(reconciler.fit_predict).parameters has_level = 'level' in signature(reconciler.fit_predict).parameters @@ -271,14 +327,23 @@ def reconcile(self, for model_name in self.model_names: start = time.time() recmodel_name = f'{model_name}/{reconcile_fn_name}' - y_hat = Y_hat_df[model_name].values.reshape(len(S_df), -1).astype(np.float64, copy=False) + + # TODO: the below should be method specific + y_hat = self._prepare_Y(Y_df=Y_hat_df[[id_col, time_col, model_name]], + S_df=S_df, + is_balanced=True, + id_col=id_col, + time_col=time_col, + target_col=model_name) reconciler_args['y_hat'] = y_hat if (self.insample and has_fitted) or intervals_method in ['bootstrap', 'permbu']: - if is_balanced: - y_hat_insample = Y_df[model_name].values.reshape(len(S_df), -1).astype(np.float64, copy=False) - else: - y_hat_insample = Y_df.pivot(columns='ds', values=model_name).loc[S_df.index].values.astype(np.float64, copy=False) + y_hat_insample = self._prepare_Y(Y_df=Y_df[[id_col, time_col, model_name]], + S_df=S_df, + is_balanced=is_balanced, + id_col=id_col, + time_col=time_col, + target_col=model_name) reconciler_args['y_hat_insample'] = y_hat_insample if has_level and (level is not None): @@ -305,30 +370,20 @@ def reconcile(self, fcsts_model = reconciler(**kwargs, level=level) # Parse final outputs - Y_tilde_df[recmodel_name] = fcsts_model['mean'].flatten() + Y_tilde_df = ufp.assign_columns(Y_tilde_df, recmodel_name, fcsts_model['mean'].flatten()) if intervals_method in ['bootstrap', 'normality', 'permbu'] and level is not None: level.sort() lo_names = [f'{recmodel_name}-lo-{lv}' for lv in reversed(level)] hi_names = [f'{recmodel_name}-hi-{lv}' for lv in level] self.level_names[recmodel_name] = lo_names + hi_names - sorted_quantiles = np.reshape(fcsts_model['quantiles'], (len(Y_tilde_df),-1)) - intervals_df = pd.DataFrame(sorted_quantiles, index=Y_tilde_df.index, - columns=self.level_names[recmodel_name]) - Y_tilde_df= pd.concat([Y_tilde_df, intervals_df], axis=1) + sorted_quantiles = np.reshape(fcsts_model['quantiles'], (len(Y_tilde_df), -1)) + Y_tilde_df = ufp.assign_columns(Y_tilde_df, self.level_names[recmodel_name], sorted_quantiles) if num_samples > 0: samples = reconciler.sample(num_samples=num_samples) self.sample_names[recmodel_name] = [f'{recmodel_name}-sample-{i}' for i in range(num_samples)] samples = np.reshape(samples, (len(Y_tilde_df),-1)) - samples_df = pd.DataFrame(samples, index=Y_tilde_df.index, - columns=self.sample_names[recmodel_name]) - Y_tilde_df= pd.concat([Y_tilde_df, samples_df], axis=1) - - del sorted_quantiles - del intervals_df - if self.insample and has_fitted: - del y_hat_insample - gc.collect() + Y_tilde_df = ufp.assign_columns(Y_tilde_df, self.sample_names[recmodel_name], samples) end = time.time() self.execution_times[f'{model_name}/{reconcile_fn_name}'] = (end - start) @@ -336,10 +391,10 @@ def reconcile(self, return Y_tilde_df def bootstrap_reconcile(self, - Y_hat_df: pd.DataFrame, - S_df: pd.DataFrame, + Y_hat_df: DFType, + S_df: DFType, tags: Dict[str, np.ndarray], - Y_df: Optional[pd.DataFrame] = None, + Y_df: Optional[DFType] = None, level: Optional[List[int]] = None, intervals_method: str = 'normality', num_samples: int = -1, @@ -351,19 +406,19 @@ def bootstrap_reconcile(self, for the different reconciliation techniques instantiated in the `reconcilers` list. **Parameters:**
- `Y_hat_df`: pd.DataFrame, base forecasts with columns `ds` and models to reconcile indexed by `unique_id`.
- `Y_df`: pd.DataFrame, training set of base time series with columns `['ds', 'y']` indexed by `unique_id`.
+ `Y_hat_df`: DataFrame, base forecasts with columns `ds` and models to reconcile indexed by `unique_id`.
+ `Y_df`: DataFrame, training set of base time series with columns `['ds', 'y']` indexed by `unique_id`.
If a class of `self.reconciles` receives `y_hat_insample`, `Y_df` must include them as columns.
- `S`: pd.DataFrame with summing matrix of size `(base, bottom)`, see [aggregate method](https://nixtla.github.io/hierarchicalforecast/utils.html#aggregate).
+ `S`: DataFrame with summing matrix of size `(base, bottom)`, see [aggregate method](https://nixtla.github.io/hierarchicalforecast/utils.html#aggregate).
`tags`: Each key is a level and its value contains tags associated to that level.
`level`: positive float list [0,100), confidence levels for prediction intervals.
`intervals_method`: str, method used to calculate prediction intervals, one of `normality`, `bootstrap`, `permbu`.
`num_samples`: int=-1, if positive return that many probabilistic coherent samples. `num_seeds`: int=1, random seed for numpy generator's replicability.
- `sort_df` : bool (default=True), if True, sort `df` by [`unique_id`,`ds`].
+ `sort_df` : deprecated.
**Returns:**
- `Y_bootstrap_df`: pd.DataFrame, with bootstraped reconciled predictions. + `Y_bootstrap_df`: DataFrame, with bootstraped reconciled predictions. """ # Check input's validity and sort dataframes @@ -387,15 +442,16 @@ def bootstrap_reconcile(self, num_samples=num_samples, seed=seed, sort_df=False) - Y_tilde_df['seed'] = seed + Y_tilde_df = ufp.assign_columns(Y_tilde_df, 'seed', seed) + # TODO: fix broken recmodel_names if seed==0: first_columns = Y_tilde_df.columns Y_tilde_df.columns = first_columns Y_tilde_list.append(Y_tilde_df) - Y_bootstrap_df = pd.concat(Y_tilde_list, axis=0) - del Y_tilde_list - gc.collect() + Y_bootstrap_df = ufp.vertical_concat(Y_tilde_list) + # del Y_tilde_list + # gc.collect() return Y_bootstrap_df diff --git a/hierarchicalforecast/evaluation.py b/hierarchicalforecast/evaluation.py index 15531a02..061d36b6 100644 --- a/hierarchicalforecast/evaluation.py +++ b/hierarchicalforecast/evaluation.py @@ -8,7 +8,10 @@ from typing import Callable, Dict, List, Optional, Union import numpy as np -import pandas as pd +import utilsforecast.processing as ufp + +from .utils import pivot, df_constructor +from utilsforecast.compat import DFType from scipy.stats import multivariate_normal # %% ../nbs/src/evaluation.ipynb 6 @@ -338,55 +341,89 @@ def __init__(self, self.evaluators = evaluators def evaluate(self, - Y_hat_df: pd.DataFrame, - Y_test_df: pd.DataFrame, + Y_hat_df: DFType, + Y_test_df: DFType, tags: Dict[str, np.ndarray], - Y_df: Optional[pd.DataFrame] = None, - benchmark: Optional[str] = None): + Y_df: Optional[DFType] = None, + benchmark: Optional[str] = None, + id_col: str = "unique_id", + time_col: str = "ds", + target_col: str = "y", + ): """Hierarchical Evaluation Method. **Parameters:**
- `Y_hat_df`: pd.DataFrame, Forecasts indexed by `'unique_id'` with column `'ds'` and models to evaluate.
- `Y_test_df`: pd.DataFrame, True values with columns `['ds', 'y']`.
+ `Y_hat_df`: DataFrame, Forecasts indexed by `'unique_id'` with column `'ds'` and models to evaluate.
+ `Y_test_df`: DataFrame, True values with columns `['ds', 'y']`.
`tags`: np.array, each str key is a level and its value contains tags associated to that level.
- `Y_df`: pd.DataFrame, Training set of base time series with columns `['ds', 'y']` indexed by `unique_id`.
+ `Y_df`: DataFrame, Training set of base time series with columns `['ds', 'y']` indexed by `unique_id`.
`benchmark`: str, If passed, evaluators are scaled by the error of this benchark.
+ `id_col` : str='unique_id', column that identifies each serie.
+ `time_col` : str='ds', column that identifies each timestep, its values can be timestamps or integers.
+ `target_col` : str='y', column that contains the target. **Returns:**
- `evaluation`: pd.DataFrame with accuracy measurements across hierarchical levels. + `evaluation`: DataFrame with accuracy measurements across hierarchical levels. """ - drop_cols = ['ds', 'y'] if 'y' in Y_hat_df.columns else ['ds'] - h = len(Y_hat_df.loc[[Y_hat_df.index[0]]]) - model_names = Y_hat_df.drop(columns=drop_cols, axis=1).columns.to_list() + n_series = len(set(Y_hat_df[id_col])) + h = len(set(Y_hat_df[time_col])) + if len(Y_hat_df) != n_series * h: + raise Exception('Y_hat_df should have a forecast for each series and horizon') + fn_names = [fn.__name__ for fn in self.evaluators] has_y_insample = any(['y_insample' in signature(fn).parameters for fn in self.evaluators]) if has_y_insample and Y_df is None: - raise Exception('At least one evaluator needs y insample, please pass `Y_df`') + raise Exception('At least one evaluator needs y_insample, please pass `Y_df`') + if benchmark is not None: fn_names = [f'{fn_name}-scaled' for fn_name in fn_names] + tags_ = {'Overall': np.concatenate(list(tags.values()))} tags_ = {**tags_, **tags} - index = pd.MultiIndex.from_product([tags_.keys(), fn_names], names=['level', 'metric']) - evaluation = pd.DataFrame(columns=model_names, index=index) - for level, cats in tags_.items(): - Y_h_cats = Y_hat_df.loc[cats] - y_test_cats = Y_test_df.loc[cats, 'y'].values.reshape(-1, h) + + model_names = list(set(Y_hat_df.columns) - set([time_col, target_col, id_col])) + evaluation_np = np.empty((len(tags_), len(fn_names), len(model_names)), dtype=np.float64) + evaluation_index_np = np.empty((len(tags_) * len(fn_names), 2), dtype=object) + for i_level, (level, cats) in enumerate(tags_.items()): + mask = ufp.is_in(Y_hat_df[id_col], cats) + Y_h_cats = ufp.filter_with_mask(Y_hat_df, mask) + + mask = ufp.is_in(Y_test_df[id_col], cats) + y_test_cats = ufp.filter_with_mask(Y_test_df, mask)[target_col]\ + .to_numpy()\ + .reshape(-1, h) + if has_y_insample and Y_df is not None: - y_insample = Y_df.pivot(columns='ds', values='y').loc[cats].values + y_insample = pivot(Y_df, index = id_col, columns = time_col, values = target_col) + mask = ufp.is_in(y_insample[id_col], cats) + y_insample = ufp.filter_with_mask(y_insample, mask) + y_insample = ufp.drop_columns(y_insample, id_col) + y_insample = y_insample.to_numpy() + for i_fn, fn in enumerate(self.evaluators): if 'y_insample' in signature(fn).parameters: kwargs = {'y_insample': y_insample} else: kwargs = {} fn_name = fn_names[i_fn] - for model in model_names: - loss = fn(y_test_cats, Y_h_cats[model].values.reshape(-1, h), **kwargs) + for i_model, model in enumerate(model_names): + loss = fn(y_test_cats, Y_h_cats[model].to_numpy().reshape(-1, h), **kwargs) if benchmark is not None: - scale = fn(y_test_cats, Y_h_cats[benchmark].values.reshape(-1, h), **kwargs) + scale = fn(y_test_cats, Y_h_cats[benchmark].to_numpy().reshape(-1, h), **kwargs) if np.isclose(scale, 0., atol=np.finfo(float).eps): scale += np.finfo(float).eps if np.isclose(scale, loss, atol=1e-8): scale = 1. loss /= scale - evaluation.loc[(level, fn_name), model] = loss + + evaluation_np[i_level, i_fn, i_model] = loss + evaluation_index_np[i_level * len(fn_names) + i_fn, 0] = level + evaluation_index_np[i_level * len(fn_names) + i_fn, 1] = fn_name + + evaluation_np = evaluation_np.reshape(-1, len(model_names)) + evaluation = df_constructor(dftype=type(Y_hat_df), + X=evaluation_index_np, + columns=["level", "metric"]) + evaluation = ufp.assign_columns(evaluation, model_names, evaluation_np) + return evaluation diff --git a/hierarchicalforecast/utils.py b/hierarchicalforecast/utils.py index 9559226f..17b304ee 100644 --- a/hierarchicalforecast/utils.py +++ b/hierarchicalforecast/utils.py @@ -1,28 +1,39 @@ # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/src/utils.ipynb. # %% auto 0 -__all__ = ['concat_str', 'group_by_agg_named', 'aggregate', 'HierarchicalPlot'] +__all__ = ['concat_str', 'group_by_agg_named', 'df_constructor', 'pivot', 'aggregate', 'HierarchicalPlot'] # %% ../nbs/src/utils.ipynb 3 import sys import timeit -from typing import Dict, List, Optional, Iterable, Union, Sequence +import warnings +from typing import Dict, List, Optional, Iterable, Union, Sequence, TypeVar import matplotlib.pyplot as plt import numpy as np from numba import njit, prange import pandas as pd from sklearn.preprocessing import OneHotEncoder -from utilsforecast.compat import DFType +from utilsforecast.compat import DataFrame import utilsforecast.processing as ufp plt.rcParams['font.family'] = 'serif' # %% ../nbs/src/utils.ipynb 5 # This code should be moved to utilsforecast -from utilsforecast.compat import DataFrame -import polars as pl +try: + import polars + import polars as pl + from polars import DataFrame as pl_DataFrame + + DFType = TypeVar("DFType", pd.DataFrame, polars.DataFrame) +except ImportError: + class pl_DataFrame: ... # type: ignore + DFType = pd.DataFrame # type: ignore + +# %% ../nbs/src/utils.ipynb 6 +# This code should be moved to utilsforecast def concat_str( df: DataFrame, cols: List[str], @@ -54,14 +65,55 @@ def group_by_agg_named(df: DataFrame, by, aggs, maintain_order=False) -> DataFra ) return out -# %% ../nbs/src/utils.ipynb 6 +def df_constructor(dftype: DFType, X: Optional[np.ndarray] = None, columns: Optional[List[str]] = None, sparse: bool = False) -> DataFrame: + """ + Create a DataFrame of type DFType from a numpy array. + """ + if dftype is pd.DataFrame: + if sparse: + df_constructor = pd.DataFrame.sparse.from_spmatrix + else: + df_constructor = pd.DataFrame + df = df_constructor(X, columns=columns) + else: + if sparse: + warnings.warn("Sparse DataFrames are not supported in Polars.") + + df = pl_DataFrame(X, schema=columns) + + return df + +def pivot(df: DataFrame, index: str = "unique_id", columns: str = "ds", values: str = "y", sort: bool = True) -> DataFrame: + """ + Pivot a DataFrame. + """ + if isinstance(df, pd.DataFrame): + pivot_args = {'values': values, + 'index': index, + 'columns': columns, + 'sort': sort, + 'dropna': False} + df_pivot = df.pivot_table(**pivot_args) + df_pivot = df_pivot.reset_index() + else: + # Polars + pivot_args = {'values': values, + 'index': index, + 'on': columns, + 'maintain_order': sort} + df_pivot = df.pivot(**pivot_args) + if sort: + df_pivot = df_pivot.sort(by=index) + return df_pivot + +# %% ../nbs/src/utils.ipynb 7 # Global variables NUMBA_NOGIL = True NUMBA_CACHE = True NUMBA_PARALLEL = True NUMBA_FASTMATH = True -# %% ../nbs/src/utils.ipynb 7 +# %% ../nbs/src/utils.ipynb 8 class CodeTimer: def __init__(self, name=None, verbose=True): self.name = " '" + name + "'" if name else '' @@ -76,7 +128,7 @@ def __exit__(self, exc_type, exc_value, traceback): print('Code block' + self.name + \ ' took:\t{0:.5f}'.format(self.took) + ' seconds') -# %% ../nbs/src/utils.ipynb 8 +# %% ../nbs/src/utils.ipynb 9 def is_strictly_hierarchical(S: np.ndarray, tags: Dict[str, np.ndarray]): # main idea: @@ -95,6 +147,23 @@ def is_strictly_hierarchical(S: np.ndarray, return paths == nodes # %% ../nbs/src/utils.ipynb 10 +def cov2corr(cov, return_std=False): + """ convert covariance matrix to correlation matrix + **Parameters:**
+ `cov`: array_like, 2d covariance matrix.
+ `return_std`: bool=False, if True returned std.
+ **Returns:**
+ `corr`: ndarray (subclass) correlation matrix + """ + cov = np.asanyarray(cov) + std_ = np.sqrt(np.diag(cov)) + corr = cov / np.outer(std_, std_) + if return_std: + return corr, std_ + else: + return corr + +# %% ../nbs/src/utils.ipynb 12 def _to_upper_hierarchy(bottom_split, bottom_values, upper_key): upper_split = upper_key.split('/') upper_idxs = [bottom_split.index(i) for i in upper_split] @@ -105,7 +174,7 @@ def join_upper(bottom_value): return [join_upper(val) for val in bottom_values] -# %% ../nbs/src/utils.ipynb 12 +# %% ../nbs/src/utils.ipynb 15 def aggregate( df: DFType, spec: List[List[str]], @@ -198,20 +267,14 @@ def aggregate( except TypeError: # sklearn < 1.2 encoder = OneHotEncoder(categories=categories, sparse=sparse_s, dtype=np.float64) S = encoder.fit_transform(S).T - if isinstance(df, pl.DataFrame): - S_df = pl.DataFrame(S, schema=list(bottom_levels)) - else: - df_constructor = pd.DataFrame - if sparse_s: - df_constructor = pd.DataFrame.sparse.from_spmatrix - S_df = df_constructor(S, columns=bottom_levels) + S_df = df_constructor(type(df), S, columns=list(bottom_levels), sparse=sparse_s) S_df = ufp.assign_columns(S_df, names="unique_id", values=np.hstack(categories)) S_df = S_df[["unique_id"] + list(bottom_levels)] return Y_df, S_df, tags -# %% ../nbs/src/utils.ipynb 27 +# %% ../nbs/src/utils.ipynb 30 class HierarchicalPlot: """ Hierarchical Plot @@ -431,7 +494,7 @@ def plot_hierarchical_predictions_gap(self, plt.grid() plt.show() -# %% ../nbs/src/utils.ipynb 42 +# %% ../nbs/src/utils.ipynb 45 # convert levels to output quantile names def level_to_outputs(level:Iterable[int]): """ Converts list of levels into output names matching StatsForecast and NeuralForecast methods. @@ -475,7 +538,7 @@ def quantiles_to_outputs(quantiles:Iterable[float]): output_names.append('-median') return quantiles, output_names -# %% ../nbs/src/utils.ipynb 43 +# %% ../nbs/src/utils.ipynb 46 # given input array of sample forecasts and inptut quantiles/levels, # output a Pandas Dataframe with columns of quantile predictions def samples_to_quantiles_df(samples: np.ndarray, @@ -533,7 +596,7 @@ def samples_to_quantiles_df(samples: np.ndarray, return _quantiles, pd.concat([data,df], axis=1).set_index('unique_id') -# %% ../nbs/src/utils.ipynb 49 +# %% ../nbs/src/utils.ipynb 52 # Masked empirical covariance matrix @njit("Array(float64, 2, 'F')(Array(float64, 2, 'C'), Array(bool, 2, 'C'))", nogil=NUMBA_NOGIL, cache=NUMBA_CACHE, parallel=NUMBA_PARALLEL, fastmath=NUMBA_FASTMATH, error_model="numpy") # @njit(nogil=NOGIL, cache=CACHE, parallel=True, fastmath=True, error_model="numpy") @@ -565,7 +628,7 @@ def _ma_cov(residuals: np.ndarray, not_nan_mask: np.ndarray): return W -# %% ../nbs/src/utils.ipynb 50 +# %% ../nbs/src/utils.ipynb 53 # Shrunk covariance matrix using the Schafer-Strimmer method @njit("Array(float64, 2, 'F')(Array(float64, 2, 'C'), float64)", nogil=NUMBA_NOGIL, cache=NUMBA_CACHE, parallel=NUMBA_PARALLEL, fastmath=NUMBA_FASTMATH, error_model="numpy") @@ -689,7 +752,7 @@ def _shrunk_covariance_schaferstrimmer_with_nans(residuals: np.ndarray, not_nan_ return W -# %% ../nbs/src/utils.ipynb 52 +# %% ../nbs/src/utils.ipynb 55 # Lasso cyclic coordinate descent @njit("Array(float64, 1, 'C')(Array(float64, 2, 'C'), Array(float64, 1, 'C'), float64, int64, float64)", nogil=NUMBA_NOGIL, cache=NUMBA_CACHE, fastmath=NUMBA_FASTMATH, error_model="numpy") def _lasso(X: np.ndarray, y: np.ndarray, diff --git a/nbs/src/core.ipynb b/nbs/src/core.ipynb index 387cef97..899eeec3 100644 --- a/nbs/src/core.ipynb +++ b/nbs/src/core.ipynb @@ -44,14 +44,17 @@ "source": [ "#| export\n", "import re\n", - "import gc\n", "import time\n", "import copy\n", "from hierarchicalforecast.methods import HReconciler\n", + "from hierarchicalforecast.utils import pivot\n", "from inspect import signature\n", "from scipy.stats import norm\n", "from scipy import sparse\n", "from typing import Dict, List, Optional\n", + "from utilsforecast.compat import DFType\n", + "import utilsforecast.processing as ufp\n", + "\n", "import warnings\n", "\n", "import numpy as np\n", @@ -143,7 +146,7 @@ "outputs": [], "source": [ "#| exporti\n", - "def _reverse_engineer_sigmah(Y_hat_df, y_hat, model_name):\n", + "def _reverse_engineer_sigmah(Y_hat_df: DFType, y_hat: np.ndarray, model_name: str) -> np.ndarray:\n", " \"\"\"\n", " This function assumes that the model creates prediction intervals\n", " under a normality with the following the Equation:\n", @@ -158,22 +161,22 @@ " drop_cols.append('y')\n", " if model_name+'-median' in Y_hat_df.columns:\n", " drop_cols.append(model_name+'-median')\n", - " model_names = Y_hat_df.drop(columns=drop_cols, axis=1).columns.to_list()\n", + " model_names = ufp.drop_columns(Y_hat_df, drop_cols).columns\n", " pi_model_names = [name for name in model_names if ('-lo' in name or '-hi' in name)]\n", " pi_model_name = [pi_name for pi_name in pi_model_names if model_name in pi_name]\n", " pi = len(pi_model_name) > 0\n", "\n", - " n_series = len(Y_hat_df.index.unique())\n", + " n_series = len(Y_hat_df[\"unique_id\"].unique())\n", "\n", " if not pi:\n", " raise Exception(f'Please include `{model_name}` prediction intervals in `Y_hat_df`')\n", "\n", " pi_col = pi_model_name[0]\n", " sign = -1 if 'lo' in pi_col else 1\n", - " level_col = re.findall('[\\d]+[.,\\d]+|[\\d]*[.][\\d]+|[\\d]+', pi_col)\n", - " level_col = float(level_col[-1])\n", + " level_cols = re.findall('[\\d]+[.,\\d]+|[\\d]*[.][\\d]+|[\\d]+', pi_col)\n", + " level_col = float(level_cols[-1])\n", " z = norm.ppf(0.5 + level_col / 200)\n", - " sigmah = Y_hat_df[pi_col].values.reshape(n_series,-1)\n", + " sigmah = Y_hat_df[pi_col].to_numpy().reshape(n_series,-1)\n", " sigmah = sign * (sigmah - y_hat) / z\n", "\n", " return sigmah" @@ -208,99 +211,135 @@ " self.insample = any([method.insample for method in reconcilers])\n", " \n", " def _prepare_fit(self,\n", - " Y_hat_df: pd.DataFrame,\n", - " S_df: pd.DataFrame,\n", - " Y_df: Optional[pd.DataFrame],\n", + " Y_hat_df: DFType,\n", + " S_df: DFType,\n", + " Y_df: Optional[DFType],\n", " tags: Dict[str, np.ndarray],\n", " level: Optional[List[int]] = None,\n", " intervals_method: str = 'normality',\n", - " sort_df: bool = True):\n", + " sort_df: bool = True,\n", + " id_col: str = \"unique_id\",\n", + " time_col: str = \"ds\", \n", + " target_col: str = \"y\", \n", + " ):\n", " \"\"\"\n", " Performs preliminary wrangling and protections\n", " \"\"\"\n", + "\n", " #-------------------------------- Match Y_hat/Y/S index order --------------------------------#\n", - " if sort_df:\n", - " Y_hat_df = Y_hat_df.reset_index()\n", - " Y_hat_df.unique_id = Y_hat_df.unique_id.astype('category')\n", - " Y_hat_df.unique_id = Y_hat_df.unique_id.cat.set_categories(S_df.index)\n", - " Y_hat_df = Y_hat_df.sort_values(by=['unique_id', 'ds'])\n", - " Y_hat_df = Y_hat_df.set_index('unique_id')\n", - "\n", - " if Y_df is not None:\n", - " Y_df = Y_df.reset_index()\n", - " Y_df.unique_id = Y_df.unique_id.astype('category')\n", - " Y_df.unique_id = Y_df.unique_id.cat.set_categories(S_df.index)\n", - " Y_df = Y_df.sort_values(by=['unique_id', 'ds'])\n", - " Y_df = Y_df.set_index('unique_id')\n", - "\n", - " S_df.index = pd.CategoricalIndex(S_df.index, categories=S_df.index)\n", + " # TODO: This is now a bit slow as we always sort.\n", + " S_df = ufp.assign_columns(S_df, f\"{id_col}_id\", np.arange(len(S_df)))\n", + " Y_hat_df = ufp.join(Y_hat_df, S_df[[id_col, f\"{id_col}_id\"]], on=id_col, how='left')\n", + " Y_hat_df = ufp.sort(Y_hat_df, by=[f\"{id_col}_id\", time_col])\n", + " Y_hat_df = ufp.drop_columns(Y_hat_df, f\"{id_col}_id\")\n", + " if Y_df is not None:\n", + " Y_df = ufp.join(Y_df, S_df[[id_col, f\"{id_col}_id\"]], on=id_col, how='left')\n", + " Y_df = ufp.sort(Y_df, by=[f\"{id_col}_id\", time_col])\n", + " Y_df = ufp.drop_columns(Y_df, f\"{id_col}_id\")\n", + " S_df = ufp.drop_columns(S_df, f\"{id_col}_id\")\n", "\n", " #----------------------------------- Check Input's Validity ----------------------------------#\n", + "\n", " # Check input's validity\n", " if intervals_method not in ['normality', 'bootstrap', 'permbu']:\n", - " raise ValueError(f'Unkwon interval method: {intervals_method}')\n", + " raise ValueError(f'Unknown interval method: {intervals_method}')\n", "\n", " if self.insample or (intervals_method in ['bootstrap', 'permbu']):\n", " if Y_df is None:\n", - " raise Exception('you need to pass `Y_df`')\n", + " raise Exception('You need to provide `Y_df`.')\n", " \n", " # Protect level list\n", " if (level is not None):\n", - " level_outside_domain = np.any((np.array(level) < 0)|(np.array(level) >= 100 ))\n", + " level_outside_domain = np.any((np.array(level) < 0) | (np.array(level) >= 100 ))\n", " if level_outside_domain and (intervals_method in ['normality', 'permbu']):\n", - " raise Exception('Level outside domain, send `level` list in [0,100)')\n", + " raise ValueError(\"Level must be a list containing floating values in the interval [0, 100).\")\n", "\n", " # Declare output names\n", - " drop_cols = ['ds', 'y'] if 'y' in Y_hat_df.columns else ['ds']\n", - " model_names = Y_hat_df.drop(columns=drop_cols, axis=1).columns.to_list()\n", - "\n", - " # Ensure numeric columns\n", - " if not len(Y_hat_df[model_names].select_dtypes(include='number').columns) == len(Y_hat_df[model_names].columns):\n", - " raise Exception('`Y_hat_df`s columns contain non numeric types')\n", - " \n", - " #Ensure no null values\n", - " if Y_hat_df[model_names].isnull().values.any():\n", - " raise Exception('`Y_hat_df` contains null values')\n", - " \n", - " pi_model_names = [name for name in model_names if ('-lo' in name or '-hi' in name or '-median' in name)]\n", - " model_names = [name for name in model_names if name not in pi_model_names]\n", + " model_names = list(set(Y_hat_df.columns) - set([id_col, time_col, target_col]))\n", + " for model_name in model_names:\n", + " # Ensure numeric columns\n", + " ufp.validate_format(Y_hat_df[[id_col, time_col, model_name]], id_col=id_col, time_col=time_col, target_col=model_name)\n", + "\n", + " # Ensure no null values\n", + " assert not ufp.is_none(Y_hat_df[model_name]).any(), f\"Column {model_name} in `Y_hat_df` contains null values. Make sure no column in `Y_hat_df` contains null values.\"\n", " \n", " # TODO: Complete y_hat_insample protection\n", + " model_names = [name for name in model_names if not ('-lo' in name or '-hi' in name or '-median' in name)] \n", " if intervals_method in ['bootstrap', 'permbu'] and Y_df is not None:\n", " if not (set(model_names) <= set(Y_df.columns)):\n", - " raise Exception('Check `Y_hat_df`s models are included in `Y_df` columns')\n", + " raise Exception(f\"Check `Y_df` columns, {model_names} must be in `Y_df` columns.\")\n", "\n", - " uids = Y_hat_df.index.unique()\n", + " # Assert S is an identity matrix at the bottom\n", + " S_np = ufp.to_numpy(ufp.drop_columns(S_df, id_col))\n", + " if not np.allclose(S_np[-S_np.shape[1]:], np.eye(S_np.shape[1])):\n", + " raise ValueError(f\"The bottom {S_np.shape[1]}x{S_np.shape[1]} part of S must be an identity matrix.\")\n", "\n", " # Check Y_hat_df\\S_df series difference\n", - " S_diff = len(S_df.index.difference(uids))\n", - " Y_hat_diff = len(Y_hat_df.index.difference(S_df.index.unique()))\n", - " if S_diff > 0 or Y_hat_diff > 0:\n", - " raise Exception(f'Check `S_df`, `Y_hat_df` series difference, S\\Y_hat={S_diff}, Y_hat\\S={Y_hat_diff}')\n", + " # TODO: this logic should be method specific\n", + " S_diff = set(S_df[id_col]) - set(Y_hat_df[id_col])\n", + " Y_hat_diff = set(Y_hat_df[id_col]) - set(S_df[id_col])\n", + " if S_diff:\n", + " raise Exception(f'There are unique_ids in S_df that are not in Y_hat_df: {S_diff}')\n", + " if Y_hat_diff:\n", + " raise Exception(f'There are unique_ids in Y_hat_df that are not in S_df: {Y_hat_diff}')\n", "\n", " if Y_df is not None:\n", - " # Check Y_hat_df\\Y_df series difference\n", - " Y_diff = len(Y_df.index.difference(uids))\n", - " Y_hat_diff = len(Y_hat_df.index.difference(Y_df.index.unique()))\n", - " if Y_diff > 0 or Y_hat_diff > 0:\n", - " raise Exception(f'Check `Y_hat_df`, `Y_df` series difference, Y_hat\\Y={Y_hat_diff}, Y\\Y_hat={Y_diff}')\n", - "\n", - " # Same Y_hat_df/S_df/Y_df's unique_id order to prevent errors\n", - " S_df = S_df.loc[uids]\n", + " Y_diff = set(Y_df[id_col]) - set(Y_hat_df[id_col])\n", + " Y_hat_diff = set(Y_hat_df[id_col]) - set(Y_df[id_col])\n", + " if Y_diff:\n", + " raise Exception(f'There are unique_ids in Y_df that are not in Y_hat_df: {Y_diff}')\n", + " if Y_hat_diff:\n", + " raise Exception(f'There are unique_ids in Y_hat_df that are not in Y_df: {Y_hat_diff}')\n", + "\n", + " # Same Y_hat_df/S_df/Y_df's unique_ids. Order is guaranteed by the sort_df flag.\n", + " # TODO: this logic should be method specific\n", + " unique_ids = set(Y_hat_df[id_col])\n", + " mask = ufp.is_in(S_df[id_col], unique_ids)\n", + " S_df = ufp.filter_with_mask(S_df, mask)\n", "\n", " return Y_hat_df, S_df, Y_df, model_names\n", "\n", + " def _prepare_Y(self, \n", + " Y_df: DFType, \n", + " S_df: DFType, \n", + " is_balanced: bool = True,\n", + " id_col: str = \"unique_id\",\n", + " time_col: str = \"ds\", \n", + " target_col: str = \"y\", \n", + " ) -> np.ndarray:\n", + " \"\"\"\n", + " Prepare Y data.\n", + " \"\"\"\n", + " if is_balanced:\n", + " Y = Y_df[target_col].to_numpy().reshape(len(S_df), -1)\n", + " else:\n", + " Y_pivot = pivot(Y_df, index=id_col, columns=time_col, values=target_col, sort=True)\n", + "\n", + " # TODO: check if this is the best way to do it\n", + " pos_in_Y = np.searchsorted(Y_pivot[id_col], S_df[id_col])\n", + " Y_pivot = ufp.drop_columns(Y_pivot, id_col)\n", + " Y_pivot = ufp.take_rows(Y_pivot, pos_in_Y)\n", + " Y = Y_pivot.to_numpy()\n", + "\n", + " # TODO: the result is a Fortran contiguous array, see if we can avoid the below copy\n", + " Y = np.ascontiguousarray(Y, dtype=np.float64)\n", + " return Y\n", + "\n", + "\n", " def reconcile(self, \n", - " Y_hat_df: pd.DataFrame,\n", - " S: pd.DataFrame,\n", + " Y_hat_df: DFType,\n", + " S: DFType,\n", " tags: Dict[str, np.ndarray],\n", - " Y_df: Optional[pd.DataFrame] = None,\n", + " Y_df: Optional[DFType] = None,\n", " level: Optional[List[int]] = None,\n", " intervals_method: str = 'normality',\n", " num_samples: int = -1,\n", " seed: int = 0,\n", " sort_df: bool = True,\n", " is_balanced: bool = False,\n", + " id_col: str = \"unique_id\",\n", + " time_col: str = \"ds\", \n", + " target_col: str = \"y\", \n", " ):\n", " \"\"\"Hierarchical Reconciliation Method.\n", "\n", @@ -318,10 +357,10 @@ " base predictions $\\hat{\\mathbf{y}}_{[a,b],\\\\tau}$.\n", "\n", " **Parameters:**
\n", - " `Y_hat_df`: pd.DataFrame, base forecasts with columns `ds` and models to reconcile indexed by `unique_id`.
\n", - " `Y_df`: pd.DataFrame, training set of base time series with columns `['ds', 'y']` indexed by `unique_id`.
\n", + " `Y_hat_df`: DataFrame, base forecasts with columns `ds` and models to reconcile indexed by `unique_id`.
\n", + " `Y_df`: DataFrame, training set of base time series with columns `['ds', 'y']` indexed by `unique_id`.
\n", " If a class of `self.reconciles` receives `y_hat_insample`, `Y_df` must include them as columns.
\n", - " `S`: pd.DataFrame with summing matrix of size `(base, bottom)`, see [aggregate method](https://nixtla.github.io/hierarchicalforecast/utils.html#aggregate).
\n", + " `S`: DataFrame with summing matrix of size `(base, bottom)`, see [aggregate method](https://nixtla.github.io/hierarchicalforecast/utils.html#aggregate).
\n", " `tags`: Each key is a level and its value contains tags associated to that level.
\n", " `level`: positive float list [0,100), confidence levels for prediction intervals.
\n", " `intervals_method`: str, method used to calculate prediction intervals, one of `normality`, `bootstrap`, `permbu`.
\n", @@ -329,10 +368,14 @@ " `seed`: int=0, random seed for numpy generator's replicability.
\n", " `sort_df` : bool (default=True), if True, sort `df` by [`unique_id`,`ds`].
\n", " `is_balanced`: bool=False, wether `Y_df` is balanced, set it to True to speed things up if `Y_df` is balanced.
\n", + " `id_col` : str='unique_id', column that identifies each serie.
\n", + " `time_col` : str='ds', column that identifies each timestep, its values can be timestamps or integers.
\n", + " `target_col` : str='y', column that contains the target. \n", "\n", " **Returns:**
\n", - " `Y_tilde_df`: pd.DataFrame, with reconciled predictions.\n", + " `Y_tilde_df`: DataFrame, with reconciled predictions.\n", " \"\"\"\n", + "\n", " # Check input's validity and sort dataframes\n", " Y_hat_df, S_df, Y_df, self.model_names = \\\n", " self._prepare_fit(Y_hat_df=Y_hat_df,\n", @@ -341,30 +384,42 @@ " tags=tags,\n", " level=level,\n", " intervals_method=intervals_method,\n", - " sort_df=sort_df)\n", + " sort_df=sort_df,\n", + " id_col=id_col,\n", + " time_col=time_col,\n", + " target_col=target_col, \n", + " )\n", "\n", " # Initialize reconciler arguments\n", " reconciler_args = dict(\n", - " idx_bottom=S_df.index.get_indexer(S.columns),\n", - " tags={key: S_df.index.get_indexer(val) for key, val in tags.items()}\n", + " idx_bottom=np.arange(len(S_df))[-S_df.shape[1]:],\n", + " tags={key: ufp.is_in(S_df[id_col], val).to_numpy().nonzero()[0] for key, val in tags.items()}\n", " )\n", "\n", " any_sparse = any([method.is_sparse_method for method in self.reconcilers])\n", " if any_sparse:\n", + " if not isinstance(S_df, pd.DataFrame):\n", + " raise ValueError(\"You have one or more sparse reconciliation methods. Please convert `S_df` to a pandas DataFrame.\")\n", + " if not isinstance(Y_hat_df, pd.DataFrame):\n", + " raise ValueError(\"You have one or more sparse reconciliation methods. Please convert `Y_hat_df` to a pandas DataFrame.\")\n", " try:\n", " S_for_sparse = sparse.csr_matrix(S_df.sparse.to_coo())\n", " except AttributeError:\n", - " warnings.warn('Using dense S matrix for sparse reconciliation method.')\n", + " warnings.warn(\"Using dense S matrix for sparse reconciliation method.\")\n", " S_for_sparse = S_df.values.astype(np.float64, copy=False)\n", "\n", " if Y_df is not None:\n", - " if is_balanced:\n", - " y_insample = Y_df['y'].values.reshape(len(S_df), -1).astype(np.float64, copy=False)\n", - " else:\n", - " y_insample = Y_df.pivot(columns='ds', values='y').loc[S_df.index].values.astype(np.float64, copy=False)\n", + " if any_sparse and not isinstance(Y_df, pd.DataFrame):\n", + " raise ValueError(\"You have one or more sparse reconciliation methods. Please convert `Y_df` to a pandas DataFrame.\") \n", + " y_insample = self._prepare_Y(Y_df=Y_df, \n", + " S_df=S_df, \n", + " is_balanced=is_balanced, \n", + " id_col=id_col, \n", + " time_col=time_col, \n", + " target_col=target_col) \n", " reconciler_args['y_insample'] = y_insample\n", "\n", - " Y_tilde_df= Y_hat_df.copy()\n", + " Y_tilde_df = ufp.copy_if_pandas(Y_hat_df)\n", " self.execution_times = {}\n", " self.level_names = {}\n", " self.sample_names = {}\n", @@ -374,7 +429,8 @@ " if reconciler.is_sparse_method:\n", " reconciler_args[\"S\"] = S_for_sparse\n", " else:\n", - " reconciler_args[\"S\"] = S_df.values.astype(np.float64, copy=False)\n", + " reconciler_args[\"S\"] = ufp.to_numpy(ufp.drop_columns(S_df, id_col))\\\n", + " .astype(np.float64, copy=False)\n", "\n", " has_fitted = 'y_hat_insample' in signature(reconciler.fit_predict).parameters\n", " has_level = 'level' in signature(reconciler.fit_predict).parameters\n", @@ -382,14 +438,23 @@ " for model_name in self.model_names:\n", " start = time.time()\n", " recmodel_name = f'{model_name}/{reconcile_fn_name}'\n", - " y_hat = Y_hat_df[model_name].values.reshape(len(S_df), -1).astype(np.float64, copy=False)\n", + "\n", + " # TODO: the below should be method specific\n", + " y_hat = self._prepare_Y(Y_df=Y_hat_df[[id_col, time_col, model_name]], \n", + " S_df=S_df, \n", + " is_balanced=True, \n", + " id_col=id_col, \n", + " time_col=time_col, \n", + " target_col=model_name)\n", " reconciler_args['y_hat'] = y_hat\n", "\n", " if (self.insample and has_fitted) or intervals_method in ['bootstrap', 'permbu']:\n", - " if is_balanced:\n", - " y_hat_insample = Y_df[model_name].values.reshape(len(S_df), -1).astype(np.float64, copy=False)\n", - " else:\n", - " y_hat_insample = Y_df.pivot(columns='ds', values=model_name).loc[S_df.index].values.astype(np.float64, copy=False)\n", + " y_hat_insample = self._prepare_Y(Y_df=Y_df[[id_col, time_col, model_name]], \n", + " S_df=S_df, \n", + " is_balanced=is_balanced, \n", + " id_col=id_col, \n", + " time_col=time_col, \n", + " target_col=model_name) \n", " reconciler_args['y_hat_insample'] = y_hat_insample\n", "\n", " if has_level and (level is not None):\n", @@ -416,30 +481,20 @@ " fcsts_model = reconciler(**kwargs, level=level)\n", "\n", " # Parse final outputs\n", - " Y_tilde_df[recmodel_name] = fcsts_model['mean'].flatten()\n", + " Y_tilde_df = ufp.assign_columns(Y_tilde_df, recmodel_name, fcsts_model['mean'].flatten())\n", " if intervals_method in ['bootstrap', 'normality', 'permbu'] and level is not None:\n", " level.sort()\n", " lo_names = [f'{recmodel_name}-lo-{lv}' for lv in reversed(level)]\n", " hi_names = [f'{recmodel_name}-hi-{lv}' for lv in level]\n", " self.level_names[recmodel_name] = lo_names + hi_names\n", - " sorted_quantiles = np.reshape(fcsts_model['quantiles'], (len(Y_tilde_df),-1))\n", - " intervals_df = pd.DataFrame(sorted_quantiles, index=Y_tilde_df.index,\n", - " columns=self.level_names[recmodel_name])\n", - " Y_tilde_df= pd.concat([Y_tilde_df, intervals_df], axis=1)\n", + " sorted_quantiles = np.reshape(fcsts_model['quantiles'], (len(Y_tilde_df), -1))\n", + " Y_tilde_df = ufp.assign_columns(Y_tilde_df, self.level_names[recmodel_name], sorted_quantiles)\n", "\n", " if num_samples > 0:\n", " samples = reconciler.sample(num_samples=num_samples)\n", " self.sample_names[recmodel_name] = [f'{recmodel_name}-sample-{i}' for i in range(num_samples)]\n", " samples = np.reshape(samples, (len(Y_tilde_df),-1))\n", - " samples_df = pd.DataFrame(samples, index=Y_tilde_df.index,\n", - " columns=self.sample_names[recmodel_name])\n", - " Y_tilde_df= pd.concat([Y_tilde_df, samples_df], axis=1)\n", - "\n", - " del sorted_quantiles\n", - " del intervals_df\n", - " if self.insample and has_fitted:\n", - " del y_hat_insample\n", - " gc.collect()\n", + " Y_tilde_df = ufp.assign_columns(Y_tilde_df, self.sample_names[recmodel_name], samples)\n", "\n", " end = time.time()\n", " self.execution_times[f'{model_name}/{reconcile_fn_name}'] = (end - start)\n", @@ -447,10 +502,10 @@ " return Y_tilde_df\n", "\n", " def bootstrap_reconcile(self,\n", - " Y_hat_df: pd.DataFrame,\n", - " S_df: pd.DataFrame,\n", + " Y_hat_df: DFType,\n", + " S_df: DFType,\n", " tags: Dict[str, np.ndarray],\n", - " Y_df: Optional[pd.DataFrame] = None,\n", + " Y_df: Optional[DFType] = None,\n", " level: Optional[List[int]] = None,\n", " intervals_method: str = 'normality',\n", " num_samples: int = -1,\n", @@ -462,19 +517,19 @@ " for the different reconciliation techniques instantiated in the `reconcilers` list. \n", "\n", " **Parameters:**
\n", - " `Y_hat_df`: pd.DataFrame, base forecasts with columns `ds` and models to reconcile indexed by `unique_id`.
\n", - " `Y_df`: pd.DataFrame, training set of base time series with columns `['ds', 'y']` indexed by `unique_id`.
\n", + " `Y_hat_df`: DataFrame, base forecasts with columns `ds` and models to reconcile indexed by `unique_id`.
\n", + " `Y_df`: DataFrame, training set of base time series with columns `['ds', 'y']` indexed by `unique_id`.
\n", " If a class of `self.reconciles` receives `y_hat_insample`, `Y_df` must include them as columns.
\n", - " `S`: pd.DataFrame with summing matrix of size `(base, bottom)`, see [aggregate method](https://nixtla.github.io/hierarchicalforecast/utils.html#aggregate).
\n", + " `S`: DataFrame with summing matrix of size `(base, bottom)`, see [aggregate method](https://nixtla.github.io/hierarchicalforecast/utils.html#aggregate).
\n", " `tags`: Each key is a level and its value contains tags associated to that level.
\n", " `level`: positive float list [0,100), confidence levels for prediction intervals.
\n", " `intervals_method`: str, method used to calculate prediction intervals, one of `normality`, `bootstrap`, `permbu`.
\n", " `num_samples`: int=-1, if positive return that many probabilistic coherent samples.\n", " `num_seeds`: int=1, random seed for numpy generator's replicability.
\n", - " `sort_df` : bool (default=True), if True, sort `df` by [`unique_id`,`ds`].
\n", + " `sort_df` : deprecated.
\n", "\n", " **Returns:**
\n", - " `Y_bootstrap_df`: pd.DataFrame, with bootstraped reconciled predictions.\n", + " `Y_bootstrap_df`: DataFrame, with bootstraped reconciled predictions.\n", " \"\"\"\n", "\n", " # Check input's validity and sort dataframes\n", @@ -498,16 +553,17 @@ " num_samples=num_samples,\n", " seed=seed,\n", " sort_df=False)\n", - " Y_tilde_df['seed'] = seed\n", + " Y_tilde_df = ufp.assign_columns(Y_tilde_df, 'seed', seed)\n", + "\n", " # TODO: fix broken recmodel_names\n", " if seed==0:\n", " first_columns = Y_tilde_df.columns\n", " Y_tilde_df.columns = first_columns\n", " Y_tilde_list.append(Y_tilde_df)\n", "\n", - " Y_bootstrap_df = pd.concat(Y_tilde_list, axis=0)\n", - " del Y_tilde_list\n", - " gc.collect()\n", + " Y_bootstrap_df = ufp.vertical_concat(Y_tilde_list)\n", + " # del Y_tilde_list\n", + " # gc.collect()\n", "\n", " return Y_bootstrap_df" ] @@ -572,6 +628,8 @@ "df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/tourism.csv')\n", "df = df.rename({'Trips': 'y', 'Quarter': 'ds'}, axis=1)\n", "df.insert(0, 'Country', 'Australia')\n", + "df['ds'] = df['ds'].str.replace(r'(\\d+) (Q\\d)', r'\\1-\\2', regex=True)\n", + "df['ds'] = pd.to_datetime(df['ds'])\n", "\n", "# non strictly hierarchical structure\n", "hierS_grouped_df = [\n", @@ -606,6 +664,45 @@ " assert all(np.array_equal(tags_orig[k], tags_cat[k]) for k in tags_orig.keys())" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "import polars as pl\n", + "import polars.testing as pltest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "df_pl = pl.DataFrame(df)\n", + "\n", + "# getting df\n", + "hier_grouped_df_pl, S_grouped_df_pl, tags_grouped_pl = aggregate(df_pl, hierS_grouped_df)\n", + "hier_strict_df_pl, S_strict_pl, tags_strict_pl = aggregate(df_pl, hiers_strictly)\n", + "\n", + "# check categorical input produces same output\n", + "df2_pl = df_pl.clone()\n", + "for col in ['Country', 'State', 'Purpose', 'Region']:\n", + " df2_pl = df2_pl.with_columns(pl.col(col).cast(pl.Categorical))\n", + "\n", + "for spec in [hierS_grouped_df, hiers_strictly]:\n", + " Y_orig_pl, S_orig_pl, tags_orig_pl = aggregate(df_pl, spec)\n", + " Y_cat_pl, S_cat_pl, tags_cat_pl = aggregate(df2_pl, spec)\n", + " pltest.assert_frame_equal(Y_cat_pl, Y_orig_pl)\n", + " pltest.assert_frame_equal(S_cat_pl, S_orig_pl)\n", + " assert all(np.array_equal(tags_orig_pl[k], tags_cat_pl[k]) for k in tags_orig_pl.keys())\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -615,11 +712,11 @@ "#| hide\n", "hier_grouped_df['y_model'] = hier_grouped_df['y']\n", "# we should be able to recover y using the methods\n", - "hier_grouped_hat_df = hier_grouped_df.groupby('unique_id').tail(12)\n", + "hier_grouped_hat_df = hier_grouped_df.groupby('unique_id').tail(12).reset_index(drop=True)\n", "ds_h = hier_grouped_hat_df['ds'].unique()\n", - "hier_grouped_df = hier_grouped_df.query('~(ds in @ds_h)')\n", - "#adding noise to `y_model` to avoid perfect fited values\n", - "hier_grouped_df['y_model'] += np.random.uniform(-1, 1, len(hier_grouped_df))\n", + "hier_grouped_df_filtered = hier_grouped_df.query('~(ds in @ds_h)').copy()\n", + "# adding noise to `y_model` to avoid perfect fited values\n", + "hier_grouped_df_filtered['y_model'] += np.random.uniform(-1, 1, len(hier_grouped_df_filtered))\n", "\n", "#hierachical reconciliation\n", "hrec = HierarchicalReconciliation(reconcilers=[\n", @@ -633,12 +730,11 @@ " MinTrace(method='wls_struct', nonnegative=True),\n", " MinTrace(method='wls_var', nonnegative=True),\n", " MinTrace(method='mint_shrink', nonnegative=True),\n", - " # ERM recovers but needs bigger eps\n", - " #ERM(method='reg_bu', lambda_reg=None),\n", "])\n", - "reconciled = hrec.reconcile(Y_hat_df=hier_grouped_hat_df, Y_df=hier_grouped_df, \n", + "reconciled = hrec.reconcile(Y_hat_df=hier_grouped_hat_df, \n", + " Y_df=hier_grouped_df_filtered, \n", " S=S_grouped_df, tags=tags_grouped)\n", - "for model in reconciled.drop(columns=['ds', 'y']).columns:\n", + "for model in ufp.drop_columns(reconciled, ['ds', 'y', 'unique_id']).columns:\n", " if 'ERM' in model:\n", " eps = 3\n", " elif 'nonnegative' in model:\n", @@ -648,6 +744,33 @@ " test_close(reconciled['y'], reconciled[model], eps=eps)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "hier_grouped_hat_df_pl = pl.from_pandas(hier_grouped_hat_df)\n", + "hier_grouped_df_filtered_pl = pl.from_pandas(hier_grouped_df_filtered)\n", + "S_grouped_df_pl = pl.from_pandas(S_grouped_df)\n", + "\n", + "reconciled_pl = hrec.reconcile(Y_hat_df=hier_grouped_hat_df_pl, \n", + " Y_df=hier_grouped_df_filtered_pl, \n", + " S=S_grouped_df_pl, \n", + " tags=tags_grouped)\n", + "\n", + "for model in ufp.drop_columns(reconciled_pl, ['ds', 'y', 'unique_id']).columns:\n", + " if 'ERM' in model:\n", + " eps = 3\n", + " elif 'nonnegative' in model:\n", + " eps = 1e-1\n", + " else:\n", + " eps = 1e-1\n", + " test_close(reconciled_pl['y'], reconciled_pl[model], eps=eps)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -657,7 +780,8 @@ "#| hide\n", "# test incorrect Y_hat_df datatypes\n", "hier_grouped_hat_df_nan = hier_grouped_hat_df.copy()\n", - "hier_grouped_hat_df_nan.loc['Australia', 'y_model'] = float('nan')\n", + "hier_grouped_hat_df_idx_changed = hier_grouped_hat_df_nan.query(\"unique_id == 'Australia'\").index\n", + "hier_grouped_hat_df_nan.loc[hier_grouped_hat_df_idx_changed, 'y_model'] = float('nan')\n", "test_fail(\n", " hrec.reconcile,\n", " contains='null values',\n", @@ -665,7 +789,8 @@ ")\n", "\n", "hier_grouped_hat_df_none = hier_grouped_hat_df.copy()\n", - "hier_grouped_hat_df_none.loc['Australia', 'y_model'] = None\n", + "hier_grouped_hat_df_idx_changed = hier_grouped_hat_df_none.query(\"unique_id == 'Australia'\").index\n", + "hier_grouped_hat_df_none.loc[hier_grouped_hat_df_idx_changed, 'y_model'] = None\n", "test_fail(\n", " hrec.reconcile,\n", " contains='null values',\n", @@ -676,11 +801,42 @@ "hier_grouped_hat_df_str['y_model'] = hier_grouped_hat_df_str['y_model'].astype(str)\n", "test_fail(\n", " hrec.reconcile,\n", - " contains='numeric types',\n", + " contains='numeric data type',\n", " args=(hier_grouped_hat_df_str, S_grouped_df, tags_grouped, hier_grouped_df),\n", ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "# test incorrect Y_hat_df datatypes\n", + "hier_grouped_hat_df_nan_pl = pl.from_pandas(hier_grouped_hat_df_nan)\n", + "test_fail(\n", + " hrec.reconcile,\n", + " contains='null values',\n", + " args=(hier_grouped_hat_df_nan_pl, S_grouped_df_pl, tags_grouped_pl, hier_grouped_df_pl),\n", + ")\n", + "\n", + "hier_grouped_hat_df_none_pl = pl.from_pandas(hier_grouped_hat_df_none)\n", + "test_fail(\n", + " hrec.reconcile,\n", + " contains='null values',\n", + " args=(hier_grouped_hat_df_none_pl, S_grouped_df_pl, tags_grouped_pl, hier_grouped_df_pl),\n", + ")\n", + "\n", + "hier_grouped_hat_df_str_pl = pl.from_pandas(hier_grouped_hat_df_str)\n", + "test_fail(\n", + " hrec.reconcile,\n", + " contains='numeric data type',\n", + " args=(hier_grouped_hat_df_str_pl, S_grouped_df_pl, tags_grouped_pl, hier_grouped_df_pl),\n", + ")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -690,23 +846,55 @@ "#| hide\n", "# test expected error\n", "# different series S and Y_hat_df\n", + "drop_idx = hier_grouped_hat_df.query(\"unique_id == 'Australia'\").index\n", "test_fail(\n", " hrec.reconcile,\n", - " contains='series difference',\n", - " args=(hier_grouped_hat_df.drop('Australia'), S_grouped_df, tags_grouped, hier_grouped_df),\n", + " contains='There are unique_ids in S_df that are not in Y_hat_df',\n", + " args=(hier_grouped_hat_df.drop(index=drop_idx), S_grouped_df, tags_grouped, hier_grouped_df),\n", " \n", ")\n", + "\n", + "drop_idx = S_grouped_df.query(\"unique_id == 'Australia'\").index\n", "test_fail(\n", " hrec.reconcile,\n", - " contains='series difference',\n", - " args=(hier_grouped_hat_df, S_grouped_df.drop('Australia'), tags_grouped, hier_grouped_df),\n", - " \n", + " contains='There are unique_ids in Y_hat_df that are not in S_df',\n", + " args=(hier_grouped_hat_df, S_grouped_df.drop(index=drop_idx), tags_grouped, hier_grouped_df),\n", ")\n", + "\n", + "drop_idx = hier_grouped_df.query(\"unique_id == 'Australia'\").index\n", "test_fail(\n", " hrec.reconcile,\n", - " contains='series difference',\n", - " args=(hier_grouped_hat_df, S_grouped_df, tags_grouped, hier_grouped_df.drop('Australia')),\n", - " \n", + " contains='There are unique_ids in Y_hat_df that are not in Y_df',\n", + " args=(hier_grouped_hat_df, S_grouped_df, tags_grouped, hier_grouped_df.drop(index=drop_idx)), \n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "# test expected error\n", + "# different series S and Y_hat_df\n", + "test_fail(\n", + " hrec.reconcile,\n", + " contains='There are unique_ids in S_df that are not in Y_hat_df',\n", + " args=(hier_grouped_hat_df_pl.filter(pl.col(\"unique_id\") != \"Australia\"), S_grouped_df_pl, tags_grouped_pl, hier_grouped_df_pl),\n", + ")\n", + "\n", + "test_fail(\n", + " hrec.reconcile,\n", + " contains='There are unique_ids in Y_hat_df that are not in S_df',\n", + " args=(hier_grouped_hat_df_pl, S_grouped_df_pl.filter(pl.col(\"unique_id\") != \"Australia\"), tags_grouped_pl, hier_grouped_df_pl),\n", + ")\n", + "\n", + "test_fail(\n", + " hrec.reconcile,\n", + " contains='There are unique_ids in Y_hat_df that are not in Y_df',\n", + " args=(hier_grouped_hat_df_pl, S_grouped_df_pl, tags_grouped_pl, hier_grouped_df_pl.filter(pl.col(\"unique_id\") != \"Australia\")), \n", ")" ] }, @@ -729,6 +917,28 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# | hide\n", + "# polars\n", + "# test expected error\n", + "# different columns Y_df and Y_hat_df\n", + "hier_grouped_hat_df_pl = pl.from_pandas(hier_grouped_hat_df)\n", + "hier_grouped_df_pl = pl.from_pandas(hier_grouped_df)\n", + "S_grouped_df_pl = pl.from_pandas(S_grouped_df)\n", + "\n", + "test_fail(\n", + " hrec.reconcile,\n", + " contains='Please include ',\n", + " args=(hier_grouped_hat_df_pl, S_grouped_df_pl, tags_grouped_pl, \n", + " hier_grouped_df_pl, [80], 'permbu'), # permbu needs y_hat_insample\n", + ")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -747,7 +957,37 @@ "])\n", "reconciled = hrec.reconcile(Y_hat_df=hier_grouped_hat_df,\n", " S=S_grouped_df, tags=tags_grouped)\n", - "for model in reconciled.drop(columns=['ds', 'y']).columns:\n", + "for model in reconciled.drop(columns=['ds', 'y', 'unique_id']).columns:\n", + " if 'ERM' in model:\n", + " eps = 3\n", + " elif 'nonnegative' in model:\n", + " eps = 1e-1\n", + " else:\n", + " eps = 1e-1\n", + " test_close(reconciled['y'], reconciled[model], eps=eps)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "# test reconcile method without insample\n", + "hrec = HierarchicalReconciliation(reconcilers=[\n", + " #these methods should reconstruct the original y\n", + " BottomUp(),\n", + " MinTrace(method='ols'),\n", + " MinTrace(method='wls_struct'),\n", + " MinTrace(method='ols', nonnegative=True),\n", + " MinTrace(method='wls_struct', nonnegative=True),\n", + "])\n", + "reconciled = hrec.reconcile(Y_hat_df=hier_grouped_hat_df_pl,\n", + " S=S_grouped_df_pl, \n", + " tags=tags_grouped_pl)\n", + "for model in ufp.drop_columns(reconciled, ['ds', 'y', 'unique_id']).columns:\n", " if 'ERM' in model:\n", " eps = 3\n", " elif 'nonnegative' in model:\n", @@ -781,9 +1021,25 @@ "outputs": [], "source": [ "#| hide\n", - "# methods should work with\n", - "# srtictly hierarchical structures\n", + "# polars\n", + "# top down should break\n", + "# with non strictly hierarchical structures\n", + "hrec = HierarchicalReconciliation([TopDown(method='average_proportions')])\n", + "test_fail(\n", + " hrec.reconcile,\n", + " contains='requires strictly hierarchical structures',\n", + " args=(hier_grouped_hat_df_pl, S_grouped_df_pl, tags_grouped_pl, hier_grouped_df_pl,)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "#| hide\n", + "# methods should work with strictly hierarchical structures\n", "hier_strict_df['y_model'] = hier_strict_df['y']\n", "# we should be able to recover y using the methods\n", "hier_strict_df_h = hier_strict_df.groupby('unique_id').tail(12)\n", @@ -824,7 +1080,7 @@ " S=S_strict, \n", " tags=tags_strict\n", ")\n", - "for model in reconciled.drop(columns=['ds', 'y']).columns:\n", + "for model in reconciled.drop(columns=['ds', 'y', 'unique_id']).columns:\n", " if 'ERM' in model:\n", " eps = 3\n", " elif 'nonnegative' in model:\n", @@ -842,8 +1098,8 @@ " )\n", " # but it should recover the total level\n", " total_tag = tags_strict['Country']\n", - " test_close(reconciled['y'].loc[total_tag], \n", - " reconciled[model].loc[total_tag], 1e-2)\n", + " test_close(reconciled[[\"unique_id\", \"y\"]].query(\"unique_id == @total_tag[0]\")[\"y\"], \n", + " reconciled[[\"unique_id\", model]].query(\"unique_id == @total_tag[0]\")[model], 1e-2)\n", " elif 'MiddleOut' in model:\n", " if 'forecast_proportions' in model:\n", " test_close(reconciled['y'], reconciled[model], eps)\n", @@ -855,12 +1111,98 @@ " )\n", " # but it should recover the total level\n", " total_tag = tags_strict[middle_out_level]\n", - " test_close(reconciled['y'].loc[total_tag], \n", - " reconciled[model].loc[total_tag], 1e-2)\n", + " test_close(reconciled[[\"unique_id\", \"y\"]].query(\"unique_id == @total_tag[0]\")[\"y\"], \n", + " reconciled[[\"unique_id\", model]].query(\"unique_id == @total_tag[0]\")[model], 1e-2)\n", " else:\n", " test_close(reconciled['y'], reconciled[model], eps)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "# methods should work with strictly hierarchical structures\n", + "hier_strict_df_pl = ufp.assign_columns(hier_strict_df_pl, 'y_model', hier_strict_df_pl['y'])\n", + "# we should be able to recover y using the methods\n", + "hier_strict_df_h_pl = hier_strict_df_pl.group_by('unique_id').tail(12)\n", + "ds_h = set(hier_strict_df_h_pl['ds'])\n", + "hier_strict_df_pl = hier_strict_df_pl.filter(~pl.col(\"ds\").is_in(ds_h))\n", + "#adding noise to `y_model` to avoid perfect fited values\n", + "hier_strict_df_pl = hier_strict_df_pl.with_columns(pl.col('y_model') + np.random.uniform(-1, 1, len(hier_strict_df_pl)))\n", + "\n", + "middle_out_level = 'Country/State'\n", + "# hierarchical reconciliation\n", + "hrec = HierarchicalReconciliation(reconcilers=[\n", + " #these methods should reconstruct the original y\n", + " BottomUp(),\n", + " MinTrace(method='ols'),\n", + " MinTrace(method='wls_struct'),\n", + " MinTrace(method='wls_var'),\n", + " MinTrace(method='mint_shrink'),\n", + " MinTrace(method='ols', nonnegative=True),\n", + " MinTrace(method='wls_struct', nonnegative=True),\n", + " MinTrace(method='wls_var', nonnegative=True),\n", + " MinTrace(method='mint_shrink', nonnegative=True),\n", + " # top down doesnt recover the original y\n", + " # but it should recover the total level\n", + " TopDown(method='forecast_proportions'),\n", + " TopDown(method='average_proportions'),\n", + " TopDown(method='proportion_averages'),\n", + " # middle out doesnt recover the original y\n", + " # but it should recover the total level\n", + " MiddleOut(middle_level=middle_out_level, top_down_method='forecast_proportions'),\n", + " MiddleOut(middle_level=middle_out_level, top_down_method='average_proportions'),\n", + " MiddleOut(middle_level=middle_out_level, top_down_method='proportion_averages'),\n", + " # ERM recovers but needs bigger eps\n", + " #ERM(method='reg_bu', lambda_reg=None),\n", + "])\n", + "reconciled_pl = hrec.reconcile(\n", + " Y_hat_df=hier_strict_df_h_pl, \n", + " Y_df=hier_strict_df_pl, \n", + " S=S_strict_pl, \n", + " tags=tags_strict_pl\n", + ")\n", + "for model in ufp.drop_columns(reconciled_pl, ['ds', 'y', 'unique_id']).columns:\n", + " if 'ERM' in model:\n", + " eps = 3\n", + " elif 'nonnegative' in model:\n", + " eps = 1e-1\n", + " else:\n", + " eps = 1e-1\n", + " if 'TopDown' in model:\n", + " if 'forecast_proportions' in model:\n", + " test_close(reconciled_pl['y'], reconciled_pl[model], eps)\n", + " else:\n", + " # top down doesnt recover the original y\n", + " test_fail(\n", + " test_close,\n", + " args=(reconciled_pl['y'], reconciled_pl[model], eps),\n", + " )\n", + " # but it should recover the total level\n", + " total_tag = tags_strict['Country']\n", + " test_close(reconciled_pl[[\"unique_id\", \"y\"]].filter(pl.col(\"unique_id\") == total_tag[0])[\"y\"], \n", + " reconciled_pl[[\"unique_id\", model]].filter(pl.col(\"unique_id\") == total_tag[0])[model], 1e-2)\n", + " elif 'MiddleOut' in model:\n", + " if 'forecast_proportions' in model:\n", + " test_close(reconciled_pl['y'], reconciled_pl[model], eps)\n", + " else:\n", + " # top down doesnt recover the original y\n", + " test_fail(\n", + " test_close,\n", + " args=(reconciled_pl['y'], reconciled_pl[model], eps),\n", + " )\n", + " # but it should recover the total level\n", + " total_tag = tags_strict[middle_out_level]\n", + " test_close(reconciled_pl[[\"unique_id\", \"y\"]].filter(pl.col(\"unique_id\") == total_tag[0])[\"y\"], \n", + " reconciled_pl[[\"unique_id\", model]].filter(pl.col(\"unique_id\") == total_tag[0])[model], 1e-2)\n", + " else:\n", + " test_close(reconciled_pl['y'], reconciled_pl[model], eps)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -876,7 +1218,26 @@ " tags=tags_strict,\n", " is_balanced=True,\n", ")\n", - "test_close(reconciled.drop(columns='ds').values, reconciled_balanced.drop(columns='ds').values, eps=1e-10)" + "test_close(reconciled.drop(columns=[\"unique_id\", \"ds\"]).values, reconciled_balanced.drop(columns=[\"unique_id\", \"ds\"]).values, eps=1e-10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "# test is_balanced behaviour\n", + "reconciled_balanced = hrec.reconcile(\n", + " Y_hat_df=hier_strict_df_h_pl, \n", + " Y_df=hier_strict_df_pl, \n", + " S=S_strict_pl, \n", + " tags=tags_strict_pl,\n", + " is_balanced=True,\n", + ")\n", + "test_close(reconciled_pl.drop([\"unique_id\", \"ds\"]).to_numpy(), reconciled_balanced.drop([\"unique_id\", \"ds\"]).to_numpy(), eps=1e-10)" ] }, { @@ -949,9 +1310,50 @@ "fitted_df = fcst.forecast_fitted_values()\n", "\n", "fcst_df = hrec.reconcile(\n", + " Y_hat_df=fcst_df.reset_index(),\n", + " Y_df=fitted_df.reset_index(),\n", + " S=S_df,\n", + " tags=tags,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "# test unbalanced dataset\n", + "df_pl = pl.from_pandas(df)\n", + "hier_df_pl, S_df_pl, tags_pl = aggregate(df=df_pl, spec=hier_levels)\n", + "\n", + "train_df = hier_df_pl.filter(pl.col(\"ds\") <= pl.lit('2019-12-31').str.to_date())\n", + "test_df = hier_df_pl.filter(pl.col(\"ds\") > pl.lit('2019-12-31').str.to_date())\n", + "\n", + "fcst = StatsForecast(\n", + " models=[\n", + " RandomWalkWithDrift(),\n", + " ],\n", + " freq='1mo',\n", + " n_jobs=1,\n", + ")\n", + "\n", + "hrec = HierarchicalReconciliation(\n", + " reconcilers=[\n", + " BottomUp(),\n", + " MinTrace(method='mint_shrink'),\n", + " ]\n", + ")\n", + "\n", + "fcst_df = fcst.forecast(df=train_df, h=12, fitted=True)\n", + "fitted_df = fcst.forecast_fitted_values()\n", + "\n", + "fcst_df = hrec.reconcile(\n", " Y_hat_df=fcst_df,\n", " Y_df=fitted_df,\n", - " S=S_df,\n", + " S=S_df_pl,\n", " tags=tags,\n", ")" ] @@ -965,7 +1367,6 @@ "#| hide\n", "# MinTrace should break\n", "# with extremely overfitted model, y_model==y\n", - "\n", "zero_df = hier_grouped_df.copy()\n", "zero_df['y'] = 0\n", "zero_df['y_model'] = 0\n", @@ -977,6 +1378,25 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "# MinTrace should break\n", + "# with extremely overfitted model, y_model==y\n", + "zero_df_pl = pl.from_pandas(zero_df) \n", + "hrec = HierarchicalReconciliation([MinTrace(method='mint_shrink')])\n", + "test_fail(\n", + " hrec.reconcile,\n", + " contains='Insample residuals',\n", + " args=(hier_grouped_hat_df_pl, S_grouped_df_pl, tags_grouped_pl, zero_df_pl)\n", + ")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -994,7 +1414,7 @@ " S=S_grouped_df, \n", " tags=tags_grouped\n", ")\n", - "for model in reconciled.drop(columns=['ds', 'y']).columns:\n", + "for model in reconciled.drop(columns=['ds', 'y', 'unique_id']).columns:\n", " test_close(reconciled['y'], reconciled[model], eps=1e-1)" ] }, @@ -1005,7 +1425,19 @@ "outputs": [], "source": [ "#| hide\n", - "reconciled.loc[tags_grouped['Country/State']]" + "# polars\n", + "#test methods that dont use residuals\n", + "#even if their signature includes\n", + "#that argument\n", + "hrec = HierarchicalReconciliation([MinTrace(method='ols')])\n", + "reconciled = hrec.reconcile(\n", + " Y_hat_df=hier_grouped_hat_df_pl, \n", + " Y_df=hier_grouped_df_pl.drop(['y_model']), \n", + " S=S_grouped_df_pl, \n", + " tags=tags_grouped_pl\n", + ")\n", + "for model in ufp.drop_columns(reconciled, ['ds', 'y', 'unique_id']).columns:\n", + " test_close(reconciled['y'], reconciled[model], eps=1e-1)" ] }, { @@ -1021,10 +1453,34 @@ " Y_df=hier_grouped_df, S=S_grouped_df, tags=tags_grouped,\n", " level=[80, 90], \n", " intervals_method='bootstrap')\n", - "total = reconciled.loc[tags_grouped['Country/State/Region/Purpose']].groupby('ds').sum().reset_index()\n", + "total = reconciled.query(\"unique_id in @tags_grouped['Country/State/Region/Purpose']\").groupby('ds').sum().reset_index()\n", "pd.testing.assert_frame_equal(\n", " total[['ds', 'y_model/BottomUp']],\n", - " reconciled.loc['Australia'][['ds', 'y_model/BottomUp']].reset_index(drop=True)\n", + " reconciled.query(\"unique_id == 'Australia'\")[['ds', 'y_model/BottomUp']].reset_index(drop=True)\n", + ")\n", + "assert 'y_model/BottomUp-lo-80' in reconciled.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "# test methods with bootstrap prediction intervals\n", + "hrec = HierarchicalReconciliation([BottomUp()])\n", + "reconciled = hrec.reconcile(Y_hat_df=hier_grouped_hat_df_pl, \n", + " Y_df=hier_grouped_df_pl, \n", + " S=S_grouped_df_pl, \n", + " tags=tags_grouped_pl,\n", + " level=[80, 90], \n", + " intervals_method='bootstrap')\n", + "total = reconciled.filter(pl.col(\"unique_id\").is_in(tags_grouped['Country/State/Region/Purpose'])).group_by('ds', maintain_order=True).sum()\n", + "pltest.assert_frame_equal(\n", + " total[['ds', 'y_model/BottomUp']],\n", + " reconciled.filter(pl.col(\"unique_id\") == 'Australia')[['ds', 'y_model/BottomUp']]\n", ")\n", "assert 'y_model/BottomUp-lo-80' in reconciled.columns" ] @@ -1044,10 +1500,35 @@ " Y_df=hier_grouped_df, S=S_grouped_df, tags=tags_grouped,\n", " level=[80, 90], \n", " intervals_method='normality')\n", - "total = reconciled.loc[tags_grouped['Country/State/Region/Purpose']].groupby('ds').sum().reset_index()\n", + "total = reconciled.query(\"unique_id in @tags_grouped['Country/State/Region/Purpose']\").groupby('ds').sum().reset_index()\n", "pd.testing.assert_frame_equal(\n", " total[['ds', 'y_model/BottomUp']],\n", - " reconciled.loc['Australia'][['ds', 'y_model/BottomUp']].reset_index(drop=True)\n", + " reconciled.query(\"unique_id == 'Australia'\")[['ds', 'y_model/BottomUp']].reset_index(drop=True)\n", + ")\n", + "assert 'y_model/BottomUp-lo-80' in reconciled.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "# test methods with normality prediction intervals\n", + "hier_grouped_hat_df_pl = pl.from_pandas(hier_grouped_hat_df)\n", + "hrec = HierarchicalReconciliation([BottomUp()])\n", + "reconciled = hrec.reconcile(Y_hat_df=hier_grouped_hat_df_pl,\n", + " Y_df=hier_grouped_df_pl, \n", + " S=S_grouped_df_pl, \n", + " tags=tags_grouped_pl,\n", + " level=[80, 90], \n", + " intervals_method='normality')\n", + "total = reconciled.filter(pl.col(\"unique_id\").is_in(tags_grouped['Country/State/Region/Purpose'])).group_by('ds', maintain_order=True).sum()\n", + "pltest.assert_frame_equal(\n", + " total[['ds', 'y_model/BottomUp']],\n", + " reconciled.filter(pl.col(\"unique_id\") == 'Australia')[['ds', 'y_model/BottomUp']]\n", ")\n", "assert 'y_model/BottomUp-lo-80' in reconciled.columns" ] @@ -1077,14 +1558,53 @@ "hier_strict_df_h['y_model-hi-80'] = hier_strict_df_h['y_model'] + 1.96\n", "hrec = HierarchicalReconciliation([BottomUp()])\n", "reconciled = hrec.reconcile(Y_hat_df=hier_strict_df_h,\n", - " Y_df=hier_strict_df, S=S_strict, \n", - " tags=tags_grouped,\n", + " Y_df=hier_strict_df, \n", + " S=S_strict, \n", + " tags=tags_strict,\n", " level=[80, 90], \n", " intervals_method='permbu')\n", - "total = reconciled.loc[tags_grouped['Country/State/Region']].groupby('ds').sum().reset_index()\n", + "total = reconciled.query(\"unique_id in @tags_grouped['Country/State/Region']\").groupby('ds').sum().reset_index()\n", "pd.testing.assert_frame_equal(\n", " total[['ds', 'y_model/BottomUp']],\n", - " reconciled.loc['Australia'][['ds', 'y_model/BottomUp']].reset_index(drop=True)\n", + " reconciled.query(\"unique_id == 'Australia'\")[['ds', 'y_model/BottomUp']].reset_index(drop=True)\n", + ")\n", + "assert 'y_model/BottomUp-lo-80' in reconciled.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "# test methods with PERMBU prediction intervals\n", + "\n", + "# test expect error with grouped structure\n", + "# (non strictly hierarchical)\n", + "hier_grouped_hat_df_pl = pl.from_pandas(hier_grouped_hat_df)\n", + "\n", + "hrec = HierarchicalReconciliation([BottomUp()])\n", + "test_fail(\n", + " hrec.reconcile,\n", + " contains='requires strictly hierarchical structures',\n", + " args=(hier_grouped_hat_df_pl, S_grouped_df_pl, tags_grouped_pl, hier_grouped_df_pl, [80, 90], 'permbu',)\n", + ")\n", + "\n", + "# test PERMBU\n", + "hier_strict_df_h_pl = pl.from_pandas(hier_strict_df_h)\n", + "hrec = HierarchicalReconciliation([BottomUp()])\n", + "reconciled = hrec.reconcile(Y_hat_df=hier_strict_df_h_pl,\n", + " Y_df=hier_strict_df_pl, \n", + " S=S_strict_pl, \n", + " tags=tags_strict_pl,\n", + " level=[80, 90], \n", + " intervals_method='permbu')\n", + "total = reconciled.filter(pl.col(\"unique_id\").is_in(tags_grouped['Country/State/Region'])).group_by('ds', maintain_order=True).sum()\n", + "pltest.assert_frame_equal(\n", + " total[['ds', 'y_model/BottomUp']],\n", + " reconciled.filter(pl.col(\"unique_id\") == 'Australia')[['ds', 'y_model/BottomUp']]\n", ")\n", "assert 'y_model/BottomUp-lo-80' in reconciled.columns" ] @@ -1105,8 +1625,29 @@ " num_seeds=2)\n", "assert 'y_model/BottomUp-lo-80' in bootstrap_df.columns\n", "assert 'seed' in bootstrap_df.columns\n", - "assert len(bootstrap_df.seed.unique())==2\n", - "bootstrap_df" + "assert len(set(bootstrap_df[\"seed\"]))==2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "# test methods with Bootraped Bootstap prediction intervals\n", + "hrec = HierarchicalReconciliation([BottomUp()])\n", + "bootstrap_df = hrec.bootstrap_reconcile(Y_hat_df=hier_grouped_hat_df_pl,\n", + " Y_df=hier_grouped_df_pl, \n", + " S_df=S_grouped_df_pl, \n", + " tags=tags_grouped_pl,\n", + " level=[80, 90],\n", + " intervals_method='bootstrap',\n", + " num_seeds=2)\n", + "assert 'y_model/BottomUp-lo-80' in bootstrap_df.columns\n", + "assert 'seed' in bootstrap_df.columns\n", + "assert len(set(bootstrap_df[\"seed\"]))==2" ] }, { @@ -1120,16 +1661,38 @@ "hrec = HierarchicalReconciliation([BottomUp()])\n", "test_fail(\n", " hrec.reconcile,\n", - " contains='Level outside domain',\n", + " contains='Level must be a list containing floating values in the interval [0, 100',\n", " args=(hier_grouped_hat_df, S_grouped_df, tags_grouped, hier_grouped_df, [-1, 80, 90], 'permbu',)\n", ")\n", "test_fail(\n", " hrec.reconcile,\n", - " contains='Level outside domain',\n", + " contains='Level must be a list containing floating values in the interval [0, 100',\n", " args=(hier_grouped_hat_df, S_grouped_df, tags_grouped, hier_grouped_df, [80, 90, 101], 'normality',)\n", ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "# test level protection for PERMBU and Normality probabilistic methods\n", + "hrec = HierarchicalReconciliation([BottomUp()])\n", + "test_fail(\n", + " hrec.reconcile,\n", + " contains='Level must be a list containing floating values in the interval [0, 100',\n", + " args=(hier_grouped_hat_df_pl, S_grouped_df_pl, tags_grouped_pl, hier_grouped_df_pl, [-1, 80, 90], 'permbu',)\n", + ")\n", + "test_fail(\n", + " hrec.reconcile,\n", + " contains='Level must be a list containing floating values in the interval [0, 100',\n", + " args=(hier_grouped_hat_df_pl, S_grouped_df_pl, tags_grouped_pl, hier_grouped_df_pl, [80, 90, 101], 'normality',)\n", + ")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1171,7 +1734,6 @@ "Y_df, S_df, tags = aggregate(df=df, spec=hierarchy_levels)\n", "qs = Y_df['ds'].str.replace(r'(\\d+) (Q\\d)', r'\\1-\\2', regex=True)\n", "Y_df['ds'] = pd.PeriodIndex(qs, freq='Q').to_timestamp()\n", - "Y_df = Y_df.reset_index()\n", "\n", "# Split train/test sets\n", "Y_test_df = Y_df.groupby('unique_id').tail(4)\n", @@ -1179,13 +1741,13 @@ "\n", "# Compute base auto-ETS predictions\n", "# Careful identifying correct data freq, this data quarterly 'Q'\n", - "fcst = StatsForecast(models=[Naive()], freq='Q', n_jobs=-1)\n", + "fcst = StatsForecast(models=[Naive()], freq='QE', n_jobs=-1)\n", "Y_hat_df = fcst.forecast(df=Y_train_df, h=4, fitted=True)\n", "Y_fitted_df = fcst.forecast_fitted_values()\n", "\n", "# Reconcile the base predictions\n", - "Y_train_df = Y_train_df.reset_index().set_index('unique_id')\n", - "Y_hat_df = Y_hat_df.reset_index().set_index('unique_id')\n", + "Y_train_df = Y_train_df.reset_index()\n", + "Y_hat_df = Y_hat_df.reset_index()\n", "reconcilers = [BottomUp(),\n", " MinTrace(method='mint_shrink')]\n", "hrec = HierarchicalReconciliation(reconcilers=reconcilers)\n", @@ -1194,6 +1756,63 @@ " S=S_df, tags=tags)\n", "Y_rec_df.groupby('unique_id', observed=True).head(2)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "#| eval: false\n", + "import numpy as np\n", + "import polars as pl\n", + "\n", + "from datetime import date\n", + "from statsforecast.core import StatsForecast\n", + "from statsforecast.models import ETS, Naive\n", + "\n", + "from hierarchicalforecast.utils import aggregate\n", + "from hierarchicalforecast.core import HierarchicalReconciliation\n", + "from hierarchicalforecast.methods import BottomUp, MinTrace\n", + "\n", + "# Load TourismSmall dataset\n", + "df = pl.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/tourism.csv')\n", + "df = df.rename({'Trips': 'y', 'Quarter': 'ds'})\n", + "df = df.drop(\"ds\")\n", + "df = df.with_columns([pl.concat( [pl.date_range(date(1998, 3, 31), date(2017, 12, 31), interval=\"1q\", eager=True) for _ in range(304)]).alias(\"ds\"),\n", + " pl.lit(\"Australia\").alias(\"Country\")])\n", + "\n", + "# Create hierarchical seires based on geographic levels and purpose\n", + "# And Convert quarterly ds string to pd.datetime format\n", + "hierarchy_levels = [['Country'],\n", + " ['Country', 'State'], \n", + " ['Country', 'Purpose'], \n", + " ['Country', 'State', 'Region'], \n", + " ['Country', 'State', 'Purpose'], \n", + " ['Country', 'State', 'Region', 'Purpose']]\n", + "\n", + "Y_df, S_df, tags = aggregate(df=df, spec=hierarchy_levels)\n", + "\n", + "# Split train/test sets\n", + "Y_test_df = Y_df.group_by('unique_id').tail(4)\n", + "Y_train_df = Y_df.group_by('unique_id').head(76)\n", + "\n", + "# Compute base auto-ETS predictions\n", + "# Careful identifying correct data freq, this data quarterly 'Q'\n", + "fcst = StatsForecast(models=[Naive()], freq='1q', n_jobs=-1)\n", + "Y_hat_df = fcst.forecast(df=Y_train_df, h=4, fitted=True)\n", + "Y_fitted_df = fcst.forecast_fitted_values()\n", + "\n", + "# Reconcile the base predictions\n", + "reconcilers = [BottomUp(),\n", + " MinTrace(method='mint_shrink')]\n", + "hrec = HierarchicalReconciliation(reconcilers=reconcilers)\n", + "Y_rec_df = hrec.reconcile(Y_hat_df=Y_hat_df, \n", + " Y_df=Y_fitted_df,\n", + " S=S_df, tags=tags)\n", + "Y_rec_df.group_by('unique_id').head(2)" + ] } ], "metadata": { diff --git a/nbs/src/evaluation.ipynb b/nbs/src/evaluation.ipynb index 89d23568..b3eddd3a 100644 --- a/nbs/src/evaluation.ipynb +++ b/nbs/src/evaluation.ipynb @@ -38,7 +38,10 @@ "from typing import Callable, Dict, List, Optional, Union\n", "\n", "import numpy as np\n", - "import pandas as pd\n", + "import utilsforecast.processing as ufp\n", + "\n", + "from hierarchicalforecast.utils import pivot, df_constructor\n", + "from utilsforecast.compat import DFType\n", "from scipy.stats import multivariate_normal" ] }, @@ -49,6 +52,7 @@ "outputs": [], "source": [ "#| hide\n", + "import pandas as pd\n", "from fastcore.test import test_close, test_fail\n", "from nbdev.showdoc import add_docs, show_doc" ] @@ -529,57 +533,91 @@ " self.evaluators = evaluators\n", "\n", " def evaluate(self, \n", - " Y_hat_df: pd.DataFrame,\n", - " Y_test_df: pd.DataFrame,\n", + " Y_hat_df: DFType,\n", + " Y_test_df: DFType,\n", " tags: Dict[str, np.ndarray],\n", - " Y_df: Optional[pd.DataFrame] = None,\n", - " benchmark: Optional[str] = None):\n", + " Y_df: Optional[DFType] = None,\n", + " benchmark: Optional[str] = None,\n", + " id_col: str = \"unique_id\",\n", + " time_col: str = \"ds\", \n", + " target_col: str = \"y\", \n", + " ):\n", " \"\"\"Hierarchical Evaluation Method.\n", "\n", " **Parameters:**
\n", - " `Y_hat_df`: pd.DataFrame, Forecasts indexed by `'unique_id'` with column `'ds'` and models to evaluate.
\n", - " `Y_test_df`: pd.DataFrame, True values with columns `['ds', 'y']`.
\n", + " `Y_hat_df`: DataFrame, Forecasts indexed by `'unique_id'` with column `'ds'` and models to evaluate.
\n", + " `Y_test_df`: DataFrame, True values with columns `['ds', 'y']`.
\n", " `tags`: np.array, each str key is a level and its value contains tags associated to that level.
\n", - " `Y_df`: pd.DataFrame, Training set of base time series with columns `['ds', 'y']` indexed by `unique_id`.
\n", + " `Y_df`: DataFrame, Training set of base time series with columns `['ds', 'y']` indexed by `unique_id`.
\n", " `benchmark`: str, If passed, evaluators are scaled by the error of this benchark.
\n", + " `id_col` : str='unique_id', column that identifies each serie.
\n", + " `time_col` : str='ds', column that identifies each timestep, its values can be timestamps or integers.
\n", + " `target_col` : str='y', column that contains the target.\n", "\n", " **Returns:**
\n", - " `evaluation`: pd.DataFrame with accuracy measurements across hierarchical levels.\n", + " `evaluation`: DataFrame with accuracy measurements across hierarchical levels.\n", " \"\"\"\n", - " drop_cols = ['ds', 'y'] if 'y' in Y_hat_df.columns else ['ds']\n", - " h = len(Y_hat_df.loc[[Y_hat_df.index[0]]])\n", - " model_names = Y_hat_df.drop(columns=drop_cols, axis=1).columns.to_list()\n", + " n_series = len(set(Y_hat_df[id_col]))\n", + " h = len(set(Y_hat_df[time_col]))\n", + " if len(Y_hat_df) != n_series * h:\n", + " raise Exception('Y_hat_df should have a forecast for each series and horizon')\n", + "\n", " fn_names = [fn.__name__ for fn in self.evaluators]\n", " has_y_insample = any(['y_insample' in signature(fn).parameters for fn in self.evaluators])\n", " if has_y_insample and Y_df is None:\n", - " raise Exception('At least one evaluator needs y insample, please pass `Y_df`')\n", + " raise Exception('At least one evaluator needs y_insample, please pass `Y_df`')\n", + "\n", " if benchmark is not None:\n", " fn_names = [f'{fn_name}-scaled' for fn_name in fn_names]\n", + "\n", " tags_ = {'Overall': np.concatenate(list(tags.values()))}\n", " tags_ = {**tags_, **tags}\n", - " index = pd.MultiIndex.from_product([tags_.keys(), fn_names], names=['level', 'metric'])\n", - " evaluation = pd.DataFrame(columns=model_names, index=index)\n", - " for level, cats in tags_.items():\n", - " Y_h_cats = Y_hat_df.loc[cats]\n", - " y_test_cats = Y_test_df.loc[cats, 'y'].values.reshape(-1, h)\n", + "\n", + " model_names = list(set(Y_hat_df.columns) - set([time_col, target_col, id_col]))\n", + " evaluation_np = np.empty((len(tags_), len(fn_names), len(model_names)), dtype=np.float64)\n", + " evaluation_index_np = np.empty((len(tags_) * len(fn_names), 2), dtype=object)\n", + " for i_level, (level, cats) in enumerate(tags_.items()):\n", + " mask = ufp.is_in(Y_hat_df[id_col], cats)\n", + " Y_h_cats = ufp.filter_with_mask(Y_hat_df, mask)\n", + "\n", + " mask = ufp.is_in(Y_test_df[id_col], cats)\n", + " y_test_cats = ufp.filter_with_mask(Y_test_df, mask)[target_col]\\\n", + " .to_numpy()\\\n", + " .reshape(-1, h)\n", + "\n", " if has_y_insample and Y_df is not None:\n", - " y_insample = Y_df.pivot(columns='ds', values='y').loc[cats].values\n", + " y_insample = pivot(Y_df, index = id_col, columns = time_col, values = target_col)\n", + " mask = ufp.is_in(y_insample[id_col], cats)\n", + " y_insample = ufp.filter_with_mask(y_insample, mask)\n", + " y_insample = ufp.drop_columns(y_insample, id_col)\n", + " y_insample = y_insample.to_numpy()\n", + "\n", " for i_fn, fn in enumerate(self.evaluators):\n", " if 'y_insample' in signature(fn).parameters:\n", " kwargs = {'y_insample': y_insample}\n", " else:\n", " kwargs = {}\n", " fn_name = fn_names[i_fn]\n", - " for model in model_names:\n", - " loss = fn(y_test_cats, Y_h_cats[model].values.reshape(-1, h), **kwargs)\n", + " for i_model, model in enumerate(model_names):\n", + " loss = fn(y_test_cats, Y_h_cats[model].to_numpy().reshape(-1, h), **kwargs)\n", " if benchmark is not None:\n", - " scale = fn(y_test_cats, Y_h_cats[benchmark].values.reshape(-1, h), **kwargs)\n", + " scale = fn(y_test_cats, Y_h_cats[benchmark].to_numpy().reshape(-1, h), **kwargs)\n", " if np.isclose(scale, 0., atol=np.finfo(float).eps):\n", " scale += np.finfo(float).eps\n", " if np.isclose(scale, loss, atol=1e-8):\n", " scale = 1.\n", " loss /= scale\n", - " evaluation.loc[(level, fn_name), model] = loss\n", + "\n", + " evaluation_np[i_level, i_fn, i_model] = loss\n", + " evaluation_index_np[i_level * len(fn_names) + i_fn, 0] = level\n", + " evaluation_index_np[i_level * len(fn_names) + i_fn, 1] = fn_name\n", + "\n", + " evaluation_np = evaluation_np.reshape(-1, len(model_names))\n", + " evaluation = df_constructor(dftype=type(Y_hat_df), \n", + " X=evaluation_index_np, \n", + " columns=[\"level\", \"metric\"])\n", + " evaluation = ufp.assign_columns(evaluation, model_names, evaluation_np)\n", + "\n", " return evaluation" ] }, @@ -646,6 +684,8 @@ "df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/tourism.csv')\n", "df = df.rename({'Trips': 'y', 'Quarter': 'ds'}, axis=1)\n", "df.insert(0, 'Country', 'Australia')\n", + "df['ds'] = df['ds'].str.replace(r'(\\d+) (Q\\d)', r'\\1-\\2', regex=True)\n", + "df['ds'] = pd.to_datetime(df['ds'])\n", "\n", "# non strictly hierarchical structure\n", "hiers_grouped = [\n", @@ -686,7 +726,8 @@ " # ERM recovers but needs bigger eps\n", " ERM(method='reg_bu', lambda_reg=None),\n", "])\n", - "reconciled = hrec.reconcile(Y_hat_df=hier_grouped_df_h, Y_df=hier_grouped_df, \n", + "reconciled = hrec.reconcile(Y_hat_df=hier_grouped_df_h, \n", + " Y_df=hier_grouped_df, \n", " S=S_grouped, tags=tags_grouped)" ] }, @@ -704,7 +745,33 @@ "\n", "evaluator = HierarchicalEvaluation([mse, rmse])\n", "evaluator.evaluate(Y_hat_df=reconciled.drop(columns='y'), \n", - " Y_test_df=reconciled[['ds', 'y']], \n", + " Y_test_df=reconciled[['unique_id', 'ds', 'y']], \n", + " tags=tags_grouped,\n", + " benchmark='y_model')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "import polars as pl" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "reconciled_pl = pl.from_pandas(reconciled)\n", + "evaluator.evaluate(Y_hat_df=reconciled_pl.drop('y'), \n", + " Y_test_df=reconciled_pl[['unique_id', 'ds', 'y']], \n", " tags=tags_grouped,\n", " benchmark='y_model')" ] @@ -723,12 +790,30 @@ "\n", "evaluator = HierarchicalEvaluation([mase])\n", "evaluator.evaluate(Y_hat_df=reconciled.drop(columns='y'), \n", - " Y_test_df=reconciled[['ds', 'y']], \n", + " Y_test_df=reconciled[['unique_id', 'ds', 'y']], \n", " tags=tags_grouped,\n", " Y_df=hier_grouped_df,\n", " benchmark='y_model')" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "reconciled_pl = pl.from_pandas(reconciled)\n", + "hier_grouped_df_pl = pl.from_pandas(hier_grouped_df)\n", + "\n", + "evaluator.evaluate(Y_hat_df=reconciled_pl.drop('y'), \n", + " Y_test_df=reconciled_pl[['unique_id', 'ds', 'y']], \n", + " tags=tags_grouped,\n", + " Y_df=hier_grouped_df_pl,\n", + " benchmark='y_model')" + ] + }, { "cell_type": "code", "execution_count": null, @@ -739,12 +824,28 @@ "# test work for h=1\n", "evaluator = HierarchicalEvaluation([mase])\n", "evaluator.evaluate(Y_hat_df=reconciled.groupby('unique_id').tail(1).drop(columns='y'), \n", - " Y_test_df=reconciled.groupby('unique_id').tail(1)[['ds', 'y']], \n", + " Y_test_df=reconciled.groupby('unique_id').tail(1)[['unique_id', 'ds', 'y']], \n", " tags=tags_grouped,\n", " Y_df=hier_grouped_df,\n", " benchmark='y_model')" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# polars\n", + "# test work for h=1\n", + "evaluator.evaluate(Y_hat_df=reconciled_pl.group_by('unique_id').tail(1).drop('y'), \n", + " Y_test_df=reconciled_pl.group_by('unique_id').tail(1)[['unique_id', 'ds', 'y']], \n", + " tags=tags_grouped,\n", + " Y_df=hier_grouped_df_pl,\n", + " benchmark='y_model')" + ] + }, { "attachments": {}, "cell_type": "markdown", diff --git a/nbs/src/methods.ipynb b/nbs/src/methods.ipynb index 13d98f70..2ba0ba01 100644 --- a/nbs/src/methods.ipynb +++ b/nbs/src/methods.ipynb @@ -324,59 +324,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L146){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUp\n", - "\n", - "> BottomUp ()\n", - "\n", - "*Bottom Up Reconciliation Class.\n", - "The most basic hierarchical reconciliation is performed using an Bottom-Up strategy. It was proposed for \n", - "the first time by Orcutt in 1968.\n", - "The corresponding hierarchical \"projection\" matrix is defined as:\n", - "$$\\mathbf{P}_{\\text{BU}} = [\\mathbf{0}_{\\mathrm{[b],[a]}}\\;|\\;\\mathbf{I}_{\\mathrm{[b][b]}}]$$\n", - "\n", - "**Parameters:**
\n", - "None\n", - "\n", - "**References:**
\n", - "- [Orcutt, G.H., Watts, H.W., & Edwards, J.B.(1968). \"Data aggregation and information loss\". The American \n", - "Economic Review, 58 , 773(787)](http://www.jstor.org/stable/1815532).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L146){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUp\n", - "\n", - "> BottomUp ()\n", - "\n", - "*Bottom Up Reconciliation Class.\n", - "The most basic hierarchical reconciliation is performed using an Bottom-Up strategy. It was proposed for \n", - "the first time by Orcutt in 1968.\n", - "The corresponding hierarchical \"projection\" matrix is defined as:\n", - "$$\\mathbf{P}_{\\text{BU}} = [\\mathbf{0}_{\\mathrm{[b],[a]}}\\;|\\;\\mathbf{I}_{\\mathrm{[b][b]}}]$$\n", - "\n", - "**Parameters:**
\n", - "None\n", - "\n", - "**References:**
\n", - "- [Orcutt, G.H., Watts, H.W., & Edwards, J.B.(1968). \"Data aggregation and information loss\". The American \n", - "Economic Review, 58 , 773(787)](http://www.jstor.org/stable/1815532).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(BottomUp, title_level=3)" ] @@ -385,73 +333,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L171){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUp.fit\n", - "\n", - "> BottomUp.fit (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None, seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*Bottom Up Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L171){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUp.fit\n", - "\n", - "> BottomUp.fit (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None, seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*Bottom Up Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(BottomUp.fit, name='BottomUp.fit', title_level=3)" ] @@ -460,59 +342,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUp.predict\n", - "\n", - "> BottomUp.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUp.predict\n", - "\n", - "> BottomUp.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(BottomUp.predict, name='BottomUp.predict', title_level=3)" ] @@ -521,77 +351,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L211){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUp.fit_predict\n", - "\n", - "> BottomUp.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*BottomUp Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the Bottom Up approach.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L211){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUp.fit_predict\n", - "\n", - "> BottomUp.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*BottomUp Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the Bottom Up approach.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(BottomUp.fit_predict, name='BottomUp.fit_predict', title_level=3)" ] @@ -600,59 +360,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUp.sample\n", - "\n", - "> BottomUp.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUp.sample\n", - "\n", - "> BottomUp.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(BottomUp.sample, name='BottomUp.sample', title_level=3)" ] @@ -691,53 +399,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L253){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUpSparse\n", - "\n", - "> BottomUpSparse ()\n", - "\n", - "*BottomUpSparse Reconciliation Class.\n", - "\n", - "This is the implementation of a Bottom Up reconciliation using the sparse\n", - "matrix approach. It works much more efficient on datasets with many time series.\n", - "[makoren: At least I hope so, I only checked up until ~20k time series, and\n", - "there's no real improvement, it would be great to check for smth like 1M time\n", - "series, where the dense S matrix really stops fitting in memory]\n", - "\n", - "See the parent class for more details.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L253){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUpSparse\n", - "\n", - "> BottomUpSparse ()\n", - "\n", - "*BottomUpSparse Reconciliation Class.\n", - "\n", - "This is the implementation of a Bottom Up reconciliation using the sparse\n", - "matrix approach. It works much more efficient on datasets with many time series.\n", - "[makoren: At least I hope so, I only checked up until ~20k time series, and\n", - "there's no real improvement, it would be great to check for smth like 1M time\n", - "series, where the dense S matrix really stops fitting in memory]\n", - "\n", - "See the parent class for more details.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(BottomUpSparse, title_level=3)" ] @@ -746,75 +408,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L171){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUpSparse.fit\n", - "\n", - "> BottomUpSparse.fit (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*Bottom Up Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L171){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUpSparse.fit\n", - "\n", - "> BottomUpSparse.fit (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*Bottom Up Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(BottomUpSparse.fit, name='BottomUpSparse.fit', title_level=3)" ] @@ -823,59 +417,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUpSparse.predict\n", - "\n", - "> BottomUpSparse.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUpSparse.predict\n", - "\n", - "> BottomUpSparse.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(BottomUpSparse.predict, name='BottomUpSparse.predict', title_level=3)" ] @@ -884,77 +426,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L211){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUpSparse.fit_predict\n", - "\n", - "> BottomUpSparse.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*BottomUp Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the Bottom Up approach.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L211){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUpSparse.fit_predict\n", - "\n", - "> BottomUpSparse.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*BottomUp Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the Bottom Up approach.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(BottomUpSparse.fit_predict, name='BottomUpSparse.fit_predict', title_level=3)" ] @@ -963,59 +435,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUpSparse.sample\n", - "\n", - "> BottomUpSparse.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### BottomUpSparse.sample\n", - "\n", - "> BottomUpSparse.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(BottomUpSparse.sample, name='BottomUpSparse.sample', title_level=3)" ] @@ -1383,67 +803,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L315){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDown\n", - "\n", - "> TopDown (method:str)\n", - "\n", - "*Top Down Reconciliation Class.\n", - "\n", - "The Top Down hierarchical reconciliation method, distributes the total aggregate predictions and decomposes \n", - "it down the hierarchy using proportions $\\mathbf{p}_{\\mathrm{[b]}}$ that can be actual historical values \n", - "or estimated.\n", - "\n", - "$$\\mathbf{P}=[\\mathbf{p}_{\\mathrm{[b]}}\\;|\\;\\mathbf{0}_{\\mathrm{[b][a,b\\;-1]}}]$$\n", - "**Parameters:**
\n", - "`method`: One of `forecast_proportions`, `average_proportions` and `proportion_averages`.
\n", - "\n", - "**References:**
\n", - "- [CW. Gross (1990). \"Disaggregation methods to expedite product line forecasting\". Journal of Forecasting, 9 , 233–254. \n", - "doi:10.1002/for.3980090304](https://onlinelibrary.wiley.com/doi/abs/10.1002/for.3980090304).
\n", - "- [G. Fliedner (1999). \"An investigation of aggregate variable time series forecast strategies with specific subaggregate \n", - "time series statistical correlation\". Computers and Operations Research, 26 , 1133–1149. \n", - "doi:10.1016/S0305-0548(99)00017-9](https://doi.org/10.1016/S0305-0548(99)00017-9).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L315){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDown\n", - "\n", - "> TopDown (method:str)\n", - "\n", - "*Top Down Reconciliation Class.\n", - "\n", - "The Top Down hierarchical reconciliation method, distributes the total aggregate predictions and decomposes \n", - "it down the hierarchy using proportions $\\mathbf{p}_{\\mathrm{[b]}}$ that can be actual historical values \n", - "or estimated.\n", - "\n", - "$$\\mathbf{P}=[\\mathbf{p}_{\\mathrm{[b]}}\\;|\\;\\mathbf{0}_{\\mathrm{[b][a,b\\;-1]}}]$$\n", - "**Parameters:**
\n", - "`method`: One of `forecast_proportions`, `average_proportions` and `proportion_averages`.
\n", - "\n", - "**References:**
\n", - "- [CW. Gross (1990). \"Disaggregation methods to expedite product line forecasting\". Journal of Forecasting, 9 , 233–254. \n", - "doi:10.1002/for.3980090304](https://onlinelibrary.wiley.com/doi/abs/10.1002/for.3980090304).
\n", - "- [G. Fliedner (1999). \"An investigation of aggregate variable time series forecast strategies with specific subaggregate \n", - "time series statistical correlation\". Computers and Operations Research, 26 , 1133–1149. \n", - "doi:10.1016/S0305-0548(99)00017-9](https://doi.org/10.1016/S0305-0548(99)00017-9).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(TopDown, title_level=3)" ] @@ -1452,75 +812,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L377){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDown.fit\n", - "\n", - "> TopDown.fit (S, y_hat, y_insample:numpy.ndarray,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None, seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None,\n", - "> idx_bottom:Optional[numpy.ndarray]=None)\n", - "\n", - "*TopDown Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L377){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDown.fit\n", - "\n", - "> TopDown.fit (S, y_hat, y_insample:numpy.ndarray,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None, seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None,\n", - "> idx_bottom:Optional[numpy.ndarray]=None)\n", - "\n", - "*TopDown Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(TopDown.fit, name='TopDown.fit', title_level=3)" ] @@ -1529,59 +821,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDown.predict\n", - "\n", - "> TopDown.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDown.predict\n", - "\n", - "> TopDown.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(TopDown.predict, name='TopDown.predict', title_level=3)" ] @@ -1590,81 +830,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L420){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDown.fit_predict\n", - "\n", - "> TopDown.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> tags:Dict[str,numpy.ndarray],\n", - "> idx_bottom:numpy.ndarray=None,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None)\n", - "\n", - "*Top Down Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the Top Down approach.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L420){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDown.fit_predict\n", - "\n", - "> TopDown.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> tags:Dict[str,numpy.ndarray],\n", - "> idx_bottom:numpy.ndarray=None,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None)\n", - "\n", - "*Top Down Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the Top Down approach.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(TopDown.fit_predict, name='TopDown.fit_predict', title_level=3)" ] @@ -1673,59 +839,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDown.sample\n", - "\n", - "> TopDown.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDown.sample\n", - "\n", - "> TopDown.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(TopDown.sample, name='TopDown.sample', title_level=3)" ] @@ -1803,47 +917,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L477){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDownSparse\n", - "\n", - "> TopDownSparse (method:str)\n", - "\n", - "*TopDownSparse Reconciliation Class.\n", - "\n", - "This is an implementation of top-down reconciliation using the sparse matrix\n", - "approach. It works much more efficiently on data sets with many time series.\n", - "\n", - "See the parent class for more details.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L477){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDownSparse\n", - "\n", - "> TopDownSparse (method:str)\n", - "\n", - "*TopDownSparse Reconciliation Class.\n", - "\n", - "This is an implementation of top-down reconciliation using the sparse matrix\n", - "approach. It works much more efficiently on data sets with many time series.\n", - "\n", - "See the parent class for more details.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(TopDownSparse, title_level=3)" ] @@ -1852,77 +926,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L377){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDownSparse.fit\n", - "\n", - "> TopDownSparse.fit (S, y_hat, y_insample:numpy.ndarray,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None,\n", - "> idx_bottom:Optional[numpy.ndarray]=None)\n", - "\n", - "*TopDown Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L377){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDownSparse.fit\n", - "\n", - "> TopDownSparse.fit (S, y_hat, y_insample:numpy.ndarray,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None,\n", - "> idx_bottom:Optional[numpy.ndarray]=None)\n", - "\n", - "*TopDown Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(TopDownSparse.fit, name='TopDownSparse.fit', title_level=3)" ] @@ -1931,59 +935,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDownSparse.predict\n", - "\n", - "> TopDownSparse.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDownSparse.predict\n", - "\n", - "> TopDownSparse.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(TopDownSparse.predict, name='TopDownSparse.predict', title_level=3)" ] @@ -1992,81 +944,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L420){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDownSparse.fit_predict\n", - "\n", - "> TopDownSparse.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> tags:Dict[str,numpy.ndarray],\n", - "> idx_bottom:numpy.ndarray=None,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None)\n", - "\n", - "*Top Down Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the Top Down approach.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L420){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDownSparse.fit_predict\n", - "\n", - "> TopDownSparse.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> tags:Dict[str,numpy.ndarray],\n", - "> idx_bottom:numpy.ndarray=None,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None)\n", - "\n", - "*Top Down Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the Top Down approach.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(TopDownSparse.fit_predict, name='TopDownSparse.fit_predict', title_level=3)" ] @@ -2075,59 +953,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDownSparse.sample\n", - "\n", - "> TopDownSparse.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### TopDownSparse.sample\n", - "\n", - "> TopDownSparse.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(TopDownSparse.sample, name='TopDownSparse.sample', title_level=3)" ] @@ -2372,63 +1198,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L539){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOut\n", - "\n", - "> MiddleOut (middle_level:str, top_down_method:str)\n", - "\n", - "*Middle Out Reconciliation Class.\n", - "\n", - "This method is only available for **strictly hierarchical structures**. It anchors the base predictions \n", - "in a middle level. The levels above the base predictions use the Bottom-Up approach, while the levels \n", - "below use a Top-Down.\n", - "\n", - "**Parameters:**
\n", - "`middle_level`: Middle level.
\n", - "`top_down_method`: One of `forecast_proportions`, `average_proportions` and `proportion_averages`.
\n", - "\n", - "**References:**
\n", - "- [Hyndman, R.J., & Athanasopoulos, G. (2021). \"Forecasting: principles and practice, 3rd edition:\n", - "Chapter 11: Forecasting hierarchical and grouped series.\". OTexts: Melbourne, Australia. OTexts.com/fpp3 \n", - "Accessed on July 2022.](https://otexts.com/fpp3/hierarchical.html)*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L539){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOut\n", - "\n", - "> MiddleOut (middle_level:str, top_down_method:str)\n", - "\n", - "*Middle Out Reconciliation Class.\n", - "\n", - "This method is only available for **strictly hierarchical structures**. It anchors the base predictions \n", - "in a middle level. The levels above the base predictions use the Bottom-Up approach, while the levels \n", - "below use a Top-Down.\n", - "\n", - "**Parameters:**
\n", - "`middle_level`: Middle level.
\n", - "`top_down_method`: One of `forecast_proportions`, `average_proportions` and `proportion_averages`.
\n", - "\n", - "**References:**
\n", - "- [Hyndman, R.J., & Athanasopoulos, G. (2021). \"Forecasting: principles and practice, 3rd edition:\n", - "Chapter 11: Forecasting hierarchical and grouped series.\". OTexts: Melbourne, Australia. OTexts.com/fpp3 \n", - "Accessed on July 2022.](https://otexts.com/fpp3/hierarchical.html)*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MiddleOut, title_level=3)" ] @@ -2437,33 +1207,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L566){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOut.fit\n", - "\n", - "> MiddleOut.fit (**kwargs)" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L566){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOut.fit\n", - "\n", - "> MiddleOut.fit (**kwargs)" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MiddleOut.fit, name='MiddleOut.fit', title_level=3)" ] @@ -2472,57 +1216,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L569){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOut.predict\n", - "\n", - "> MiddleOut.predict (**kwargs)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L569){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOut.predict\n", - "\n", - "> MiddleOut.predict (**kwargs)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MiddleOut.predict, name='MiddleOut.predict', title_level=3)" ] @@ -2531,63 +1225,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L572){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOut.fit_predict\n", - "\n", - "> MiddleOut.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> tags:Dict[str,numpy.ndarray],\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None)\n", - "\n", - "*Middle Out Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Only used for `forecast_proportions`
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the Middle Out approach.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L572){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOut.fit_predict\n", - "\n", - "> MiddleOut.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> tags:Dict[str,numpy.ndarray],\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None)\n", - "\n", - "*Middle Out Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Only used for `forecast_proportions`
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the Middle Out approach.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MiddleOut.fit_predict, title_level=3)" ] @@ -2596,59 +1234,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOut.sample\n", - "\n", - "> MiddleOut.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOut.sample\n", - "\n", - "> MiddleOut.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MiddleOut.sample, name='MiddleOut.sample', title_level=3)" ] @@ -2755,47 +1341,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L655){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOutSparse\n", - "\n", - "> MiddleOutSparse (middle_level:str, top_down_method:str)\n", - "\n", - "*MiddleOutSparse Reconciliation Class.\n", - "\n", - "This is an implementation of middle-out reconciliation using the sparse matrix\n", - "approach. It works much more efficiently on data sets with many time series.\n", - "\n", - "See the parent class for more details.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L655){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOutSparse\n", - "\n", - "> MiddleOutSparse (middle_level:str, top_down_method:str)\n", - "\n", - "*MiddleOutSparse Reconciliation Class.\n", - "\n", - "This is an implementation of middle-out reconciliation using the sparse matrix\n", - "approach. It works much more efficiently on data sets with many time series.\n", - "\n", - "See the parent class for more details.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MiddleOutSparse, title_level=3)" ] @@ -2804,33 +1350,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L566){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOutSparse.fit\n", - "\n", - "> MiddleOutSparse.fit (**kwargs)" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L566){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOutSparse.fit\n", - "\n", - "> MiddleOutSparse.fit (**kwargs)" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MiddleOutSparse.fit, name='MiddleOutSparse.fit', title_level=3)" ] @@ -2839,57 +1359,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L569){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOutSparse.predict\n", - "\n", - "> MiddleOutSparse.predict (**kwargs)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L569){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOutSparse.predict\n", - "\n", - "> MiddleOutSparse.predict (**kwargs)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MiddleOutSparse.predict, name='MiddleOutSparse.predict', title_level=3)" ] @@ -2898,63 +1368,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L670){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOutSparse.fit_predict\n", - "\n", - "> MiddleOutSparse.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> tags:Dict[str,numpy.ndarray],\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None)\n", - "\n", - "*Middle Out Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Only used for `forecast_proportions`
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the Middle Out approach.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L670){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOutSparse.fit_predict\n", - "\n", - "> MiddleOutSparse.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> tags:Dict[str,numpy.ndarray],\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None)\n", - "\n", - "*Middle Out Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Only used for `forecast_proportions`
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the Middle Out approach.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MiddleOutSparse.fit_predict, title_level=3)" ] @@ -2963,59 +1377,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOutSparse.sample\n", - "\n", - "> MiddleOutSparse.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MiddleOutSparse.sample\n", - "\n", - "> MiddleOutSparse.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MiddleOutSparse.sample, name='MiddleOutSparse.sample', title_level=3)" ] @@ -3394,87 +1756,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L746){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTrace\n", - "\n", - "> MinTrace (method:str, nonnegative:bool=False,\n", - "> mint_shr_ridge:Optional[float]=2e-08, num_threads:int=1)\n", - "\n", - "*MinTrace Reconciliation Class.\n", - "\n", - "This reconciliation algorithm proposed by Wickramasuriya et al. depends on a generalized least squares estimator \n", - "and an estimator of the covariance matrix of the coherency errors $\\mathbf{W}_{h}$. The Min Trace algorithm \n", - "minimizes the squared errors for the coherent forecasts under an unbiasedness assumption; the solution has a \n", - "closed form.
\n", - "\n", - "$$\n", - "\\mathbf{P}_{\\text{MinT}}=\\left(\\mathbf{S}^{\\intercal}\\mathbf{W}_{h}\\mathbf{S}\\right)^{-1}\n", - "\\mathbf{S}^{\\intercal}\\mathbf{W}^{-1}_{h}\n", - "$$\n", - "\n", - "**Parameters:**
\n", - "`method`: str, one of `ols`, `wls_struct`, `wls_var`, `mint_shrink`, `mint_cov`.
\n", - "`nonnegative`: bool, reconciled forecasts should be nonnegative?
\n", - "`mint_shr_ridge`: float=2e-8, ridge numeric protection to MinTrace-shr covariance estimator.
\n", - "`num_threads`: int=1, number of threads to use for solving the optimization problems (when nonnegative=True).\n", - "\n", - "**References:**
\n", - "- [Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2019). \"Optimal forecast reconciliation for\n", - "hierarchical and grouped time series through trace minimization\". Journal of the American Statistical Association, \n", - "114 , 804–819. doi:10.1080/01621459.2018.1448825.](https://robjhyndman.com/publications/mint/).\n", - "- [Wickramasuriya, S.L., Turlach, B.A. & Hyndman, R.J. (2020). \"Optimal non-negative\n", - "forecast reconciliation\". Stat Comput 30, 1167–1182,\n", - "https://doi.org/10.1007/s11222-020-09930-0](https://robjhyndman.com/publications/nnmint/).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L746){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTrace\n", - "\n", - "> MinTrace (method:str, nonnegative:bool=False,\n", - "> mint_shr_ridge:Optional[float]=2e-08, num_threads:int=1)\n", - "\n", - "*MinTrace Reconciliation Class.\n", - "\n", - "This reconciliation algorithm proposed by Wickramasuriya et al. depends on a generalized least squares estimator \n", - "and an estimator of the covariance matrix of the coherency errors $\\mathbf{W}_{h}$. The Min Trace algorithm \n", - "minimizes the squared errors for the coherent forecasts under an unbiasedness assumption; the solution has a \n", - "closed form.
\n", - "\n", - "$$\n", - "\\mathbf{P}_{\\text{MinT}}=\\left(\\mathbf{S}^{\\intercal}\\mathbf{W}_{h}\\mathbf{S}\\right)^{-1}\n", - "\\mathbf{S}^{\\intercal}\\mathbf{W}^{-1}_{h}\n", - "$$\n", - "\n", - "**Parameters:**
\n", - "`method`: str, one of `ols`, `wls_struct`, `wls_var`, `mint_shrink`, `mint_cov`.
\n", - "`nonnegative`: bool, reconciled forecasts should be nonnegative?
\n", - "`mint_shr_ridge`: float=2e-8, ridge numeric protection to MinTrace-shr covariance estimator.
\n", - "`num_threads`: int=1, number of threads to use for solving the optimization problems (when nonnegative=True).\n", - "\n", - "**References:**
\n", - "- [Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2019). \"Optimal forecast reconciliation for\n", - "hierarchical and grouped time series through trace minimization\". Journal of the American Statistical Association, \n", - "114 , 804–819. doi:10.1080/01621459.2018.1448825.](https://robjhyndman.com/publications/mint/).\n", - "- [Wickramasuriya, S.L., Turlach, B.A. & Hyndman, R.J. (2020). \"Optimal non-negative\n", - "forecast reconciliation\". Stat Comput 30, 1167–1182,\n", - "https://doi.org/10.1007/s11222-020-09930-0](https://robjhyndman.com/publications/nnmint/).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MinTrace, title_level=3)" ] @@ -3483,75 +1765,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L858){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTrace.fit\n", - "\n", - "> MinTrace.fit (S, y_hat, y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None, seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None,\n", - "> idx_bottom:Optional[numpy.ndarray]=None)\n", - "\n", - "*MinTrace Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L858){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTrace.fit\n", - "\n", - "> MinTrace.fit (S, y_hat, y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None, seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None,\n", - "> idx_bottom:Optional[numpy.ndarray]=None)\n", - "\n", - "*MinTrace Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MinTrace.fit, name='MinTrace.fit', title_level=3)" ] @@ -3560,59 +1774,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTrace.predict\n", - "\n", - "> MinTrace.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTrace.predict\n", - "\n", - "> MinTrace.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MinTrace.predict, name='MinTrace.predict', title_level=3)" ] @@ -3621,79 +1783,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L947){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTrace.fit_predict\n", - "\n", - "> MinTrace.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray=None,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*MinTrace Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Only used by `wls_var`, `mint_cov`, `mint_shrink`
\n", - "`y_hat_insample`: Insample fitted values of size (`base`, `insample_size`). Only used by `wls_var`, `mint_cov`, `mint_shrink`
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`sampler`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the MinTrace approach.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L947){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTrace.fit_predict\n", - "\n", - "> MinTrace.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray=None,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*MinTrace Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Only used by `wls_var`, `mint_cov`, `mint_shrink`
\n", - "`y_hat_insample`: Insample fitted values of size (`base`, `insample_size`). Only used by `wls_var`, `mint_cov`, `mint_shrink`
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`sampler`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the MinTrace approach.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MinTrace.fit_predict, name='MinTrace.fit_predict', title_level=3)" ] @@ -3702,59 +1792,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTrace.sample\n", - "\n", - "> MinTrace.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTrace.sample\n", - "\n", - "> MinTrace.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MinTrace.sample, name='MinTrace.sample', title_level=3)" ] @@ -3940,65 +1978,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L996){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTraceSparse\n", - "\n", - "> MinTraceSparse (method:str, nonnegative:bool=False,\n", - "> mint_shr_ridge:Optional[float]=2e-08, num_threads:int=1)\n", - "\n", - "*MinTraceSparse Reconciliation Class.\n", - "\n", - "This is the implementation of a subset of MinTrace features using the sparse\n", - "matrix approach. It works much more efficient on datasets with many time series.\n", - "\n", - "See the parent class for more details.\n", - "\n", - "Currently supported:\n", - "* Methods using diagonal W matrix, i.e. \"ols\", \"wls_struct\", \"wls_var\",\n", - "* The standard MinT version (non-negative is not supported).\n", - "\n", - "Note: due to the numerical instability of the matrix inversion when creating the\n", - "P matrix, the method is NOT guaranteed to give identical results to the non-sparse\n", - "version.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L996){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTraceSparse\n", - "\n", - "> MinTraceSparse (method:str, nonnegative:bool=False,\n", - "> mint_shr_ridge:Optional[float]=2e-08, num_threads:int=1)\n", - "\n", - "*MinTraceSparse Reconciliation Class.\n", - "\n", - "This is the implementation of a subset of MinTrace features using the sparse\n", - "matrix approach. It works much more efficient on datasets with many time series.\n", - "\n", - "See the parent class for more details.\n", - "\n", - "Currently supported:\n", - "* Methods using diagonal W matrix, i.e. \"ols\", \"wls_struct\", \"wls_var\",\n", - "* The standard MinT version (non-negative is not supported).\n", - "\n", - "Note: due to the numerical instability of the matrix inversion when creating the\n", - "P matrix, the method is NOT guaranteed to give identical results to the non-sparse\n", - "version.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MinTraceSparse, title_level=3)" ] @@ -4007,79 +1987,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L1100){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTraceSparse.fit\n", - "\n", - "> MinTraceSparse.fit (S:scipy.sparse._csr.csr_matrix, y_hat:numpy.ndarray,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None,\n", - "> idx_bottom:Optional[numpy.ndarray]=None)\n", - "\n", - "*MinTrace Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L1100){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTraceSparse.fit\n", - "\n", - "> MinTraceSparse.fit (S:scipy.sparse._csr.csr_matrix, y_hat:numpy.ndarray,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None,\n", - "> idx_bottom:Optional[numpy.ndarray]=None)\n", - "\n", - "*MinTrace Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MinTraceSparse.fit, name='MinTraceSparse.fit', title_level=3)" ] @@ -4088,59 +1996,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTraceSparse.predict\n", - "\n", - "> MinTraceSparse.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTraceSparse.predict\n", - "\n", - "> MinTraceSparse.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MinTraceSparse.predict, name='MinTraceSparse.predict', title_level=3)" ] @@ -4149,79 +2005,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L947){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTraceSparse.fit_predict\n", - "\n", - "> MinTraceSparse.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray=None,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*MinTrace Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Only used by `wls_var`, `mint_cov`, `mint_shrink`
\n", - "`y_hat_insample`: Insample fitted values of size (`base`, `insample_size`). Only used by `wls_var`, `mint_cov`, `mint_shrink`
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`sampler`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the MinTrace approach.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L947){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTraceSparse.fit_predict\n", - "\n", - "> MinTraceSparse.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray=None,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*MinTrace Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Only used by `wls_var`, `mint_cov`, `mint_shrink`
\n", - "`y_hat_insample`: Insample fitted values of size (`base`, `insample_size`). Only used by `wls_var`, `mint_cov`, `mint_shrink`
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`sampler`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the MinTrace approach.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MinTraceSparse.fit_predict, name='MinTraceSparse.fit_predict', title_level=3)" ] @@ -4230,59 +2014,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTraceSparse.sample\n", - "\n", - "> MinTraceSparse.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### MinTraceSparse.sample\n", - "\n", - "> MinTraceSparse.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(MinTraceSparse.sample, name='MinTraceSparse.sample', title_level=3)" ] @@ -4291,16 +2023,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\ospra\\AppData\\Local\\Temp\\ipykernel_35220\\3919364200.py:41: UserWarning: `num_threads` is only used when `nonnegative=True`\n", - " warnings.warn('`num_threads` is only used when `nonnegative=True`')\n" - ] - } - ], + "outputs": [], "source": [ "#| hide\n", "for method in ['ols', 'wls_struct', 'wls_var', 'mint_shrink']:\n", @@ -4465,79 +2188,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L1166){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### OptimalCombination\n", - "\n", - "> OptimalCombination (method:str, nonnegative:bool=False,\n", - "> num_threads:int=1)\n", - "\n", - "*Optimal Combination Reconciliation Class.\n", - "\n", - "This reconciliation algorithm was proposed by Hyndman et al. 2011, the method uses generalized least squares \n", - "estimator using the coherency errors covariance matrix. Consider the covariance of the base forecast \n", - "$\\textrm{Var}(\\epsilon_{h}) = \\Sigma_{h}$, the $\\mathbf{P}$ matrix of this method is defined by:\n", - "$$ \\mathbf{P} = \\left(\\mathbf{S}^{\\intercal}\\Sigma_{h}^{\\dagger}\\mathbf{S}\\right)^{-1}\\mathbf{S}^{\\intercal}\\Sigma^{\\dagger}_{h}$$\n", - "where $\\Sigma_{h}^{\\dagger}$ denotes the variance pseudo-inverse. The method was later proven equivalent to \n", - "`MinTrace` variants.\n", - "\n", - "**Parameters:**
\n", - "`method`: str, allowed optimal combination methods: 'ols', 'wls_struct'.
\n", - "`nonnegative`: bool, reconciled forecasts should be nonnegative?
\n", - "\n", - "**References:**
\n", - "- [Rob J. Hyndman, Roman A. Ahmed, George Athanasopoulos, Han Lin Shang (2010). \"Optimal Combination Forecasts for \n", - "Hierarchical Time Series\".](https://robjhyndman.com/papers/Hierarchical6.pdf).
\n", - "- [Shanika L. Wickramasuriya, George Athanasopoulos and Rob J. Hyndman (2010). \"Optimal Combination Forecasts for \n", - "Hierarchical Time Series\".](https://robjhyndman.com/papers/MinT.pdf).\n", - "- [Wickramasuriya, S.L., Turlach, B.A. & Hyndman, R.J. (2020). \"Optimal non-negative\n", - "forecast reconciliation\". Stat Comput 30, 1167–1182, \n", - "https://doi.org/10.1007/s11222-020-09930-0](https://robjhyndman.com/publications/nnmint/).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L1166){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### OptimalCombination\n", - "\n", - "> OptimalCombination (method:str, nonnegative:bool=False,\n", - "> num_threads:int=1)\n", - "\n", - "*Optimal Combination Reconciliation Class.\n", - "\n", - "This reconciliation algorithm was proposed by Hyndman et al. 2011, the method uses generalized least squares \n", - "estimator using the coherency errors covariance matrix. Consider the covariance of the base forecast \n", - "$\\textrm{Var}(\\epsilon_{h}) = \\Sigma_{h}$, the $\\mathbf{P}$ matrix of this method is defined by:\n", - "$$ \\mathbf{P} = \\left(\\mathbf{S}^{\\intercal}\\Sigma_{h}^{\\dagger}\\mathbf{S}\\right)^{-1}\\mathbf{S}^{\\intercal}\\Sigma^{\\dagger}_{h}$$\n", - "where $\\Sigma_{h}^{\\dagger}$ denotes the variance pseudo-inverse. The method was later proven equivalent to \n", - "`MinTrace` variants.\n", - "\n", - "**Parameters:**
\n", - "`method`: str, allowed optimal combination methods: 'ols', 'wls_struct'.
\n", - "`nonnegative`: bool, reconciled forecasts should be nonnegative?
\n", - "\n", - "**References:**
\n", - "- [Rob J. Hyndman, Roman A. Ahmed, George Athanasopoulos, Han Lin Shang (2010). \"Optimal Combination Forecasts for \n", - "Hierarchical Time Series\".](https://robjhyndman.com/papers/Hierarchical6.pdf).
\n", - "- [Shanika L. Wickramasuriya, George Athanasopoulos and Rob J. Hyndman (2010). \"Optimal Combination Forecasts for \n", - "Hierarchical Time Series\".](https://robjhyndman.com/papers/MinT.pdf).\n", - "- [Wickramasuriya, S.L., Turlach, B.A. & Hyndman, R.J. (2020). \"Optimal non-negative\n", - "forecast reconciliation\". Stat Comput 30, 1167–1182, \n", - "https://doi.org/10.1007/s11222-020-09930-0](https://robjhyndman.com/publications/nnmint/).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(OptimalCombination, title_level=3)" ] @@ -4546,79 +2197,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L858){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### OptimalCombination.fit\n", - "\n", - "> OptimalCombination.fit (S, y_hat,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None,\n", - "> idx_bottom:Optional[numpy.ndarray]=None)\n", - "\n", - "*MinTrace Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L858){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### OptimalCombination.fit\n", - "\n", - "> OptimalCombination.fit (S, y_hat,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None,\n", - "> idx_bottom:Optional[numpy.ndarray]=None)\n", - "\n", - "*MinTrace Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`tags`: Each key is a level and each value its `S` indices.
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Optional for `forecast_proportions` method.
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(OptimalCombination.fit, name='OptimalCombination.fit', title_level=3)" ] @@ -4627,59 +2206,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### OptimalCombination.predict\n", - "\n", - "> OptimalCombination.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### OptimalCombination.predict\n", - "\n", - "> OptimalCombination.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(OptimalCombination.predict, name='OptimalCombination.predict', title_level=3)" ] @@ -4688,79 +2215,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L947){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### OptimalCombination.fit_predict\n", - "\n", - "> OptimalCombination.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray=None,\n", - "> y_insample:Optional[numpy.ndarray]=None, \n", - "> y_hat_insample:Optional[numpy.ndarray]=No\n", - "> ne, sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None, tags:Optional[Di\n", - "> ct[str,numpy.ndarray]]=None)\n", - "\n", - "*MinTrace Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Only used by `wls_var`, `mint_cov`, `mint_shrink`
\n", - "`y_hat_insample`: Insample fitted values of size (`base`, `insample_size`). Only used by `wls_var`, `mint_cov`, `mint_shrink`
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`sampler`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the MinTrace approach.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L947){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### OptimalCombination.fit_predict\n", - "\n", - "> OptimalCombination.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray=None,\n", - "> y_insample:Optional[numpy.ndarray]=None, \n", - "> y_hat_insample:Optional[numpy.ndarray]=No\n", - "> ne, sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None,\n", - "> seed:Optional[int]=None, tags:Optional[Di\n", - "> ct[str,numpy.ndarray]]=None)\n", - "\n", - "*MinTrace Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`y_insample`: Insample values of size (`base`, `insample_size`). Only used by `wls_var`, `mint_cov`, `mint_shrink`
\n", - "`y_hat_insample`: Insample fitted values of size (`base`, `insample_size`). Only used by `wls_var`, `mint_cov`, `mint_shrink`
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`sampler`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the MinTrace approach.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(OptimalCombination.fit_predict, name='OptimalCombination.fit_predict', title_level=3)" ] @@ -4769,59 +2224,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### OptimalCombination.sample\n", - "\n", - "> OptimalCombination.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### OptimalCombination.sample\n", - "\n", - "> OptimalCombination.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(OptimalCombination.sample, name='OptimalCombination.sample', title_level=3)" ] @@ -5040,77 +2443,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L1200){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### ERM\n", - "\n", - "> ERM (method:str, lambda_reg:float=0.01)\n", - "\n", - "*Optimal Combination Reconciliation Class.\n", - "\n", - "The Empirical Risk Minimization reconciliation strategy relaxes the unbiasedness assumptions from\n", - "previous reconciliation methods like MinT and optimizes square errors between the reconciled predictions\n", - "and the validation data to obtain an optimal reconciliation matrix P.\n", - "\n", - "The exact solution for $\\mathbf{P}$ (`method='closed'`) follows the expression:\n", - "$$\\mathbf{P}^{*} = \\left(\\mathbf{S}^{\\intercal}\\mathbf{S}\\right)^{-1}\\mathbf{Y}^{\\intercal}\\hat{\\mathbf{Y}}\\left(\\hat{\\mathbf{Y}}\\hat{\\mathbf{Y}}\\right)^{-1}$$\n", - "\n", - "The alternative Lasso regularized $\\mathbf{P}$ solution (`method='reg_bu'`) is useful when the observations \n", - "of validation data is limited or the exact solution has low numerical stability.\n", - "$$\\mathbf{P}^{*} = \\text{argmin}_{\\mathbf{P}} ||\\mathbf{Y}-\\mathbf{S} \\mathbf{P} \\hat{Y} ||^{2}_{2} + \\lambda ||\\mathbf{P}-\\mathbf{P}_{\\text{BU}}||_{1}$$\n", - "\n", - "**Parameters:**
\n", - "`method`: str, one of `closed`, `reg` and `reg_bu`.
\n", - "`lambda_reg`: float, l1 regularizer for `reg` and `reg_bu`.
\n", - "\n", - "**References:**
\n", - "- [Ben Taieb, S., & Koo, B. (2019). Regularized regression for hierarchical forecasting without \n", - "unbiasedness conditions. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge \n", - "Discovery & Data Mining KDD '19 (p. 1337-1347). New York, NY, USA: Association for Computing Machinery.](https://doi.org/10.1145/3292500.3330976).
*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L1200){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### ERM\n", - "\n", - "> ERM (method:str, lambda_reg:float=0.01)\n", - "\n", - "*Optimal Combination Reconciliation Class.\n", - "\n", - "The Empirical Risk Minimization reconciliation strategy relaxes the unbiasedness assumptions from\n", - "previous reconciliation methods like MinT and optimizes square errors between the reconciled predictions\n", - "and the validation data to obtain an optimal reconciliation matrix P.\n", - "\n", - "The exact solution for $\\mathbf{P}$ (`method='closed'`) follows the expression:\n", - "$$\\mathbf{P}^{*} = \\left(\\mathbf{S}^{\\intercal}\\mathbf{S}\\right)^{-1}\\mathbf{Y}^{\\intercal}\\hat{\\mathbf{Y}}\\left(\\hat{\\mathbf{Y}}\\hat{\\mathbf{Y}}\\right)^{-1}$$\n", - "\n", - "The alternative Lasso regularized $\\mathbf{P}$ solution (`method='reg_bu'`) is useful when the observations \n", - "of validation data is limited or the exact solution has low numerical stability.\n", - "$$\\mathbf{P}^{*} = \\text{argmin}_{\\mathbf{P}} ||\\mathbf{Y}-\\mathbf{S} \\mathbf{P} \\hat{Y} ||^{2}_{2} + \\lambda ||\\mathbf{P}-\\mathbf{P}_{\\text{BU}}||_{1}$$\n", - "\n", - "**Parameters:**
\n", - "`method`: str, one of `closed`, `reg` and `reg_bu`.
\n", - "`lambda_reg`: float, l1 regularizer for `reg` and `reg_bu`.
\n", - "\n", - "**References:**
\n", - "- [Ben Taieb, S., & Koo, B. (2019). Regularized regression for hierarchical forecasting without \n", - "unbiasedness conditions. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge \n", - "Discovery & Data Mining KDD '19 (p. 1337-1347). New York, NY, USA: Association for Computing Machinery.](https://doi.org/10.1145/3292500.3330976).
*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(ERM, title_level=3)" ] @@ -5119,73 +2452,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L1289){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### ERM.fit\n", - "\n", - "> ERM.fit (S, y_hat, y_insample, y_hat_insample,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None, seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None,\n", - "> idx_bottom:Optional[numpy.ndarray]=None)\n", - "\n", - "*ERM Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`y_insample`: Train values of size (`base`, `insample_size`).
\n", - "`y_hat_insample`: Insample train predictions of size (`base`, `insample_size`).
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L1289){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### ERM.fit\n", - "\n", - "> ERM.fit (S, y_hat, y_insample, y_hat_insample,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None, seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None,\n", - "> idx_bottom:Optional[numpy.ndarray]=None)\n", - "\n", - "*ERM Fit Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`y_insample`: Train values of size (`base`, `insample_size`).
\n", - "`y_hat_insample`: Insample train predictions of size (`base`, `insample_size`).
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "`**sampler_kwargs`: Coherent sampler instantiation arguments.
\n", - "\n", - "**Returns:**
\n", - "`self`: object, fitted reconciler.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(ERM.fit, name='ERM.fit', title_level=3)" ] @@ -5194,59 +2461,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### ERM.predict\n", - "\n", - "> ERM.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L86){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### ERM.predict\n", - "\n", - "> ERM.predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Predict using reconciler.\n", - "\n", - "Predict using fitted mean and probabilistic reconcilers.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated predictions.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(ERM.predict, name='ERM.predict', title_level=3)" ] @@ -5255,77 +2470,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L1334){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### ERM.fit_predict\n", - "\n", - "> ERM.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray=None,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None, seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*ERM Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`y_insample`: Train values of size (`base`, `insample_size`).
\n", - "`y_hat_insample`: Insample train predictions of size (`base`, `insample_size`).
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the ERM approach.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L1334){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### ERM.fit_predict\n", - "\n", - "> ERM.fit_predict (S:numpy.ndarray, y_hat:numpy.ndarray,\n", - "> idx_bottom:numpy.ndarray=None,\n", - "> y_insample:Optional[numpy.ndarray]=None,\n", - "> y_hat_insample:Optional[numpy.ndarray]=None,\n", - "> sigmah:Optional[numpy.ndarray]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> intervals_method:Optional[str]=None,\n", - "> num_samples:Optional[int]=None, seed:Optional[int]=None,\n", - "> tags:Optional[Dict[str,numpy.ndarray]]=None)\n", - "\n", - "*ERM Reconciliation Method.\n", - "\n", - "**Parameters:**
\n", - "`S`: Summing matrix of size (`base`, `bottom`).
\n", - "`y_hat`: Forecast values of size (`base`, `horizon`).
\n", - "`y_insample`: Train values of size (`base`, `insample_size`).
\n", - "`y_hat_insample`: Insample train predictions of size (`base`, `insample_size`).
\n", - "`idx_bottom`: Indices corresponding to the bottom level of `S`, size (`bottom`).
\n", - "`level`: float list 0-100, confidence levels for prediction intervals.
\n", - "`intervals_method`: Sampler for prediction intevals, one of `normality`, `bootstrap`, `permbu`.
\n", - "\n", - "**Returns:**
\n", - "`y_tilde`: Reconciliated y_hat using the ERM approach.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(ERM.fit_predict, name='ERM.fit_predict', title_level=3)" ] @@ -5334,59 +2479,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### ERM.sample\n", - "\n", - "> ERM.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/methods.py#L108){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### ERM.sample\n", - "\n", - "> ERM.sample (num_samples:int)\n", - "\n", - "*Sample probabilistic coherent distribution.\n", - "\n", - "Generates n samples from a probabilistic coherent distribution.\n", - "The method uses fitted mean and probabilistic reconcilers, defined by\n", - "the `intervals_method` selected during the reconciler's\n", - "instantiation. Currently available: `normality`, `bootstrap`, `permbu`.\n", - "\n", - "**Parameters:**
\n", - "`num_samples`: int, number of samples generated from coherent distribution.
\n", - "\n", - "**Returns:**
\n", - "`samples`: Coherent samples of size (`num_series`, `horizon`, `num_samples`).*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(ERM.sample, name='ERM.sample', title_level=3)" ] @@ -5416,24 +2509,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ nan, 100., 50., 40., 20.],\n", - " [ nan, 30., 15., 12., 6.],\n", - " [ nan, 70., 35., 28., 14.],\n", - " [ nan, 10., 5., 4., 2.],\n", - " [ nan, 20., 10., 8., 4.],\n", - " [ nan, 30., 15., 12., 6.],\n", - " [ nan, 40., 20., 16., 8.]])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "S @ y_hat_bottom_insample" ] @@ -5505,16 +2581,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\ospra\\AppData\\Local\\Temp\\ipykernel_35220\\2002835881.py:138: UserWarning: Prediction intervals not implement for `forecast_proportions`\n", - " warnings.warn('Prediction intervals not implement for `forecast_proportions`')\n" - ] - } - ], + "outputs": [], "source": [ "#| hide\n", "# test TopDown + intervals\n", diff --git a/nbs/src/utils.ipynb b/nbs/src/utils.ipynb index bd142c9e..05ec52a0 100644 --- a/nbs/src/utils.ipynb +++ b/nbs/src/utils.ipynb @@ -41,14 +41,15 @@ "#| export\n", "import sys\n", "import timeit\n", - "from typing import Dict, List, Optional, Iterable, Union, Sequence\n", + "import warnings\n", + "from typing import Dict, List, Optional, Iterable, Union, Sequence, TypeVar\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from numba import njit, prange\n", "import pandas as pd\n", "from sklearn.preprocessing import OneHotEncoder\n", - "from utilsforecast.compat import DFType\n", + "from utilsforecast.compat import DataFrame\n", "import utilsforecast.processing as ufp\n", "\n", "plt.rcParams['font.family'] = 'serif'" @@ -71,16 +72,33 @@ { "cell_type": "code", "execution_count": null, - "id": "f8b168ad", + "id": "2d547778", "metadata": {}, "outputs": [], "source": [ "#| export\n", - "\n", "# This code should be moved to utilsforecast\n", - "from utilsforecast.compat import DataFrame\n", - "import polars as pl\n", + "try:\n", + " import polars\n", + " import polars as pl\n", + " from polars import DataFrame as pl_DataFrame\n", "\n", + " DFType = TypeVar(\"DFType\", pd.DataFrame, polars.DataFrame)\n", + "except ImportError:\n", + " class pl_DataFrame: ... # type: ignore\n", + "\n", + " DFType = pd.DataFrame # type: ignore" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8b168ad", + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "# This code should be moved to utilsforecast\n", "def concat_str(\n", " df: DataFrame,\n", " cols: List[str],\n", @@ -110,7 +128,48 @@ " out = ufp.group_by(df, by, maintain_order).agg(\n", " *[getattr(pl.col(col), agg)().alias(col_name) for col_name, (col, agg) in aggs.items()]\n", " )\n", - " return out" + " return out\n", + "\n", + "def df_constructor(dftype: DFType, X: Optional[np.ndarray] = None, columns: Optional[List[str]] = None, sparse: bool = False) -> DataFrame:\n", + " \"\"\"\n", + " Create a DataFrame of type DFType from a numpy array.\n", + " \"\"\"\n", + " if dftype is pd.DataFrame:\n", + " if sparse:\n", + " df_constructor = pd.DataFrame.sparse.from_spmatrix\n", + " else:\n", + " df_constructor = pd.DataFrame\n", + " df = df_constructor(X, columns=columns) \n", + " else:\n", + " if sparse:\n", + " warnings.warn(\"Sparse DataFrames are not supported in Polars.\")\n", + " \n", + " df = pl_DataFrame(X, schema=columns)\n", + " \n", + " return df\n", + "\n", + "def pivot(df: DataFrame, index: str = \"unique_id\", columns: str = \"ds\", values: str = \"y\", sort: bool = True) -> DataFrame:\n", + " \"\"\"\n", + " Pivot a DataFrame.\n", + " \"\"\"\n", + " if isinstance(df, pd.DataFrame):\n", + " pivot_args = {'values': values, \n", + " 'index': index,\n", + " 'columns': columns,\n", + " 'sort': sort,\n", + " 'dropna': False}\n", + " df_pivot = df.pivot_table(**pivot_args)\n", + " df_pivot = df_pivot.reset_index()\n", + " else:\n", + " # Polars\n", + " pivot_args = {'values': values, \n", + " 'index': index,\n", + " 'on': columns,\n", + " 'maintain_order': sort} \n", + " df_pivot = df.pivot(**pivot_args)\n", + " if sort:\n", + " df_pivot = df_pivot.sort(by=index)\n", + " return df_pivot" ] }, { @@ -177,6 +236,31 @@ " return paths == nodes" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "da433b2e", + "metadata": {}, + "outputs": [], + "source": [ + "#| exporti\n", + "def cov2corr(cov, return_std=False):\n", + " \"\"\" convert covariance matrix to correlation matrix\n", + " **Parameters:**
\n", + " `cov`: array_like, 2d covariance matrix.
\n", + " `return_std`: bool=False, if True returned std.
\n", + " **Returns:**
\n", + " `corr`: ndarray (subclass) correlation matrix\n", + " \"\"\"\n", + " cov = np.asanyarray(cov)\n", + " std_ = np.sqrt(np.diag(cov))\n", + " corr = cov / np.outer(std_, std_)\n", + " if return_std:\n", + " return corr, std_\n", + " else:\n", + " return corr" + ] + }, { "cell_type": "markdown", "id": "3a1f4267", @@ -204,6 +288,17 @@ " return [join_upper(val) for val in bottom_values]" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9fdc577", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "import warnings" + ] + }, { "cell_type": "code", "execution_count": null, @@ -266,7 +361,6 @@ " \"y\": (\"y\", \"sum\")\n", " }\n", "\n", - "\n", " # Check if exog_vars are present in df & add to the aggregation dictionary if it is not None\n", " if exog_vars is not None:\n", " missing_vars = [var for var in exog_vars.keys() if var not in df.columns]\n", @@ -314,7 +408,6 @@ " encoder = OneHotEncoder(categories=categories, sparse_output=sparse_s, dtype=np.float64)\n", " except TypeError: # sklearn < 1.2\n", " encoder = OneHotEncoder(categories=categories, sparse=sparse_s, dtype=np.float64) \n", - " # print(S)\n", " S = encoder.fit_transform(S).T\n", " if sparse_s:\n", " df_constructor = pd.DataFrame.sparse.from_spmatrix\n", @@ -424,13 +517,7 @@ " except TypeError: # sklearn < 1.2\n", " encoder = OneHotEncoder(categories=categories, sparse=sparse_s, dtype=np.float64) \n", " S = encoder.fit_transform(S).T\n", - " if isinstance(df, pl.DataFrame):\n", - " S_df = pl.DataFrame(S, schema=list(bottom_levels))\n", - " else:\n", - " df_constructor = pd.DataFrame\n", - " if sparse_s:\n", - " df_constructor = pd.DataFrame.sparse.from_spmatrix\n", - " S_df = df_constructor(S, columns=bottom_levels)\n", + " S_df = df_constructor(type(df), S, columns=list(bottom_levels), sparse=sparse_s)\n", "\n", " S_df = ufp.assign_columns(S_df, names=\"unique_id\", values=np.hstack(categories))\n", " S_df = S_df[[\"unique_id\"] + list(bottom_levels)]\n", @@ -443,61 +530,7 @@ "execution_count": null, "id": "75cea2f7", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L97){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### aggregate\n", - "\n", - "> aggregate (df:~DFType, spec:List[List[str]],\n", - "> exog_vars:Optional[Dict[str,Union[str,List[str]]]]=None,\n", - "> sparse_s:bool=False)\n", - "\n", - "*Utils Aggregation Function.\n", - "Aggregates bottom level series contained in the pandas DataFrame `df` according\n", - "to levels defined in the `spec` list.*\n", - "\n", - "| | **Type** | **Default** | **Details** |\n", - "| -- | -------- | ----------- | ----------- |\n", - "| df | DFType | | Dataframe with columns `['ds', 'y']` and columns to aggregate. |\n", - "| spec | List | | List of levels. Each element of the list should contain a list of columns of `df` to aggregate. |\n", - "| exog_vars | Optional | None | |\n", - "| sparse_s | bool | False | Return `S_df` as a sparse Pandas dataframe. |\n", - "| **Returns** | **pandas or polars DataFrame** | | **Hierarchically structured series.** |" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L97){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### aggregate\n", - "\n", - "> aggregate (df:~DFType, spec:List[List[str]],\n", - "> exog_vars:Optional[Dict[str,Union[str,List[str]]]]=None,\n", - "> sparse_s:bool=False)\n", - "\n", - "*Utils Aggregation Function.\n", - "Aggregates bottom level series contained in the pandas DataFrame `df` according\n", - "to levels defined in the `spec` list.*\n", - "\n", - "| | **Type** | **Default** | **Details** |\n", - "| -- | -------- | ----------- | ----------- |\n", - "| df | DFType | | Dataframe with columns `['ds', 'y']` and columns to aggregate. |\n", - "| spec | List | | List of levels. Each element of the list should contain a list of columns of `df` to aggregate. |\n", - "| exog_vars | Optional | None | |\n", - "| sparse_s | bool | False | Return `S_df` as a sparse Pandas dataframe. |\n", - "| **Returns** | **pandas or polars DataFrame** | | **Hierarchically structured series.** |" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(aggregate, title_level=3)" ] @@ -614,24 +647,7 @@ "execution_count": null, "id": "5ae76480-44d9-45ec-b50a-f8b666cc0200", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\ospra\\AppData\\Local\\Temp\\ipykernel_24168\\1804625729.py:4: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " dates = pd.date_range(start='2019-01-31', freq='M', periods=max_tenure)\n", - "c:\\Users\\ospra\\miniconda3\\envs\\hierarchicalforecast\\lib\\site-packages\\utilsforecast\\data.py:104: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " freq = pd.tseries.frequencies.to_offset(freq)\n", - "c:\\Users\\ospra\\miniconda3\\envs\\hierarchicalforecast\\lib\\site-packages\\utilsforecast\\data.py:104: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " freq = pd.tseries.frequencies.to_offset(freq)\n", - "c:\\Users\\ospra\\miniconda3\\envs\\hierarchicalforecast\\lib\\site-packages\\utilsforecast\\data.py:104: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " freq = pd.tseries.frequencies.to_offset(freq)\n", - "c:\\Users\\ospra\\miniconda3\\envs\\hierarchicalforecast\\lib\\site-packages\\utilsforecast\\data.py:104: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " freq = pd.tseries.frequencies.to_offset(freq)\n" - ] - } - ], + "outputs": [], "source": [ "#| hide\n", "# test unbalanced dataset\n", @@ -809,18 +825,7 @@ "execution_count": null, "id": "f4b3828f-bbcc-4116-a969-49c78c33bf72", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Code block 'strict aggregation old' took:\t0.02568 seconds\n", - "Code block 'strict aggregation new' took:\t0.05351 seconds\n", - "Code block 'grouped aggregation old' took:\t0.06828 seconds\n", - "Code block 'grouped aggregation new' took:\t0.18641 seconds\n" - ] - } - ], + "outputs": [], "source": [ "#| hide\n", "# Test equality of aggregation and aggregation_old\n", @@ -848,18 +853,7 @@ "execution_count": null, "id": "f7688d6e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Code block 'strict non-sparse aggregate' took:\t0.05451 seconds\n", - "Code block 'strict sparse aggregate' took:\t0.05386 seconds\n", - "Code block 'grouped non-sparse aggregate' took:\t0.18228 seconds\n", - "Code block 'grouped sparse aggregate' took:\t0.18737 seconds\n" - ] - } - ], + "outputs": [], "source": [ "#| hide\n", "# Test equality of sparse and non-sparse aggregation\n", @@ -1135,53 +1129,7 @@ "execution_count": null, "id": "d27c4cff-ebbe-41f9-920b-7bbc997d0adc", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L203){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### HierarchicalPlot\n", - "\n", - "> HierarchicalPlot (S:~DFType, tags:Dict[str,numpy.ndarray])\n", - "\n", - "*Hierarchical Plot\n", - "\n", - "This class contains a collection of matplotlib visualization methods, suited for small\n", - "to medium sized hierarchical series.\n", - "\n", - "**Parameters:**
\n", - "`S`: DataFrame with summing matrix of size `(base, bottom)`, see [aggregate function](https://nixtla.github.io/hierarchicalforecast/utils.html#aggregate).
\n", - "`tags`: np.ndarray, with hierarchical aggregation indexes, where \n", - " each key is a level and its value contains tags associated to that level.

*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L203){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### HierarchicalPlot\n", - "\n", - "> HierarchicalPlot (S:~DFType, tags:Dict[str,numpy.ndarray])\n", - "\n", - "*Hierarchical Plot\n", - "\n", - "This class contains a collection of matplotlib visualization methods, suited for small\n", - "to medium sized hierarchical series.\n", - "\n", - "**Parameters:**
\n", - "`S`: DataFrame with summing matrix of size `(base, bottom)`, see [aggregate function](https://nixtla.github.io/hierarchicalforecast/utils.html#aggregate).
\n", - "`tags`: np.ndarray, with hierarchical aggregation indexes, where \n", - " each key is a level and its value contains tags associated to that level.

*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(HierarchicalPlot, title_level=3)" ] @@ -1191,43 +1139,7 @@ "execution_count": null, "id": "45d1872c-7979-44f7-972b-2031729b04b0", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L224){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### plot_summing_matrix\n", - "\n", - "> plot_summing_matrix ()\n", - "\n", - "*Summation Constraints plot\n", - "\n", - "This method simply plots the hierarchical aggregation\n", - "constraints matrix $\\mathbf{S}$.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L224){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### plot_summing_matrix\n", - "\n", - "> plot_summing_matrix ()\n", - "\n", - "*Summation Constraints plot\n", - "\n", - "This method simply plots the hierarchical aggregation\n", - "constraints matrix $\\mathbf{S}$.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(HierarchicalPlot.plot_summing_matrix, \n", " name='plot_summing_matrix', title_level=3)" @@ -1238,59 +1150,7 @@ "execution_count": null, "id": "920de36f-e7fe-4ea4-81bb-d0f897e1f469", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L235){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### plot_series\n", - "\n", - "> plot_series (series:str, Y_df:~DFType, models:Optional[List[str]]=None,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Single Series plot\n", - "\n", - "**Parameters:**
\n", - "`series`: str, string identifying the `'unique_id'` any-level series to plot.
\n", - "`Y_df`: DataFrame, hierarchically structured series ($\\mathbf{y}_{[a,b]}$). \n", - " It contains columns `['unique_id', 'ds', 'y']`, it may have `'models'`.
\n", - "`models`: List[str], string identifying filtering model columns.\n", - "`level`: float list 0-100, confidence levels for prediction intervals available in `Y_df`.
\n", - "\n", - "**Returns:**
\n", - "Single series plot with filtered models and prediction interval level.

*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L235){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### plot_series\n", - "\n", - "> plot_series (series:str, Y_df:~DFType, models:Optional[List[str]]=None,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Single Series plot\n", - "\n", - "**Parameters:**
\n", - "`series`: str, string identifying the `'unique_id'` any-level series to plot.
\n", - "`Y_df`: DataFrame, hierarchically structured series ($\\mathbf{y}_{[a,b]}$). \n", - " It contains columns `['unique_id', 'ds', 'y']`, it may have `'models'`.
\n", - "`models`: List[str], string identifying filtering model columns.\n", - "`level`: float list 0-100, confidence levels for prediction intervals available in `Y_df`.
\n", - "\n", - "**Returns:**
\n", - "Single series plot with filtered models and prediction interval level.

*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(HierarchicalPlot.plot_series, \n", " name='plot_series', title_level=3)" @@ -1301,63 +1161,7 @@ "execution_count": null, "id": "9fa265cd-0c05-4617-a40a-f2c62513f7a2", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L296){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### plot_hierarchically_linked_series\n", - "\n", - "> plot_hierarchically_linked_series (bottom_series:str, Y_df:~DFType,\n", - "> models:Optional[List[str]]=None,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Hierarchically Linked Series plot\n", - "\n", - "**Parameters:**
\n", - "`bottom_series`: str, string identifying the `'unique_id'` bottom-level series to plot.
\n", - "`Y_df`: DataFrame, hierarchically structured series ($\\mathbf{y}_{[a,b]}$). \n", - " It contains columns ['unique_id', 'ds', 'y'] and models.
\n", - "`models`: List[str], string identifying filtering model columns.\n", - "`level`: float list 0-100, confidence levels for prediction intervals available in `Y_df`.
\n", - "\n", - "**Returns:**
\n", - "Collection of hierarchilly linked series plots associated with the `bottom_series`\n", - "and filtered models and prediction interval level.

*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L296){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### plot_hierarchically_linked_series\n", - "\n", - "> plot_hierarchically_linked_series (bottom_series:str, Y_df:~DFType,\n", - "> models:Optional[List[str]]=None,\n", - "> level:Optional[List[int]]=None)\n", - "\n", - "*Hierarchically Linked Series plot\n", - "\n", - "**Parameters:**
\n", - "`bottom_series`: str, string identifying the `'unique_id'` bottom-level series to plot.
\n", - "`Y_df`: DataFrame, hierarchically structured series ($\\mathbf{y}_{[a,b]}$). \n", - " It contains columns ['unique_id', 'ds', 'y'] and models.
\n", - "`models`: List[str], string identifying filtering model columns.\n", - "`level`: float list 0-100, confidence levels for prediction intervals available in `Y_df`.
\n", - "\n", - "**Returns:**
\n", - "Collection of hierarchilly linked series plots associated with the `bottom_series`\n", - "and filtered models and prediction interval level.

*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(HierarchicalPlot.plot_hierarchically_linked_series, \n", " name='plot_hierarchically_linked_series', title_level=3)" @@ -1368,67 +1172,7 @@ "execution_count": null, "id": "d8621cba", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L366){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### plot_hierarchical_predictions_gap\n", - "\n", - "> plot_hierarchical_predictions_gap (Y_df:~DFType,\n", - "> models:Optional[List[str]]=None,\n", - "> xlabel:Optional[str]=None,\n", - "> ylabel:Optional[str]=None)\n", - "\n", - "*Hierarchically Predictions Gap plot\n", - "\n", - "**Parameters:**
\n", - "`Y_df`: DataFrame, hierarchically structured series ($\\mathbf{y}_{[a,b]}$). \n", - " It contains columns ['unique_id', 'ds', 'y'] and models.
\n", - "`models`: List[str], string identifying filtering model columns.\n", - "`xlabel`: str, string for the plot's x axis label.\n", - "`ylable`: str, string for the plot's y axis label.\n", - "\n", - "**Returns:**
\n", - "Plots of aggregated predictions at different levels of the hierarchical structure.\n", - "The aggregation is performed according to the tag levels see \n", - "[aggregate function](https://nixtla.github.io/hierarchicalforecast/utils.html).

*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L366){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### plot_hierarchical_predictions_gap\n", - "\n", - "> plot_hierarchical_predictions_gap (Y_df:~DFType,\n", - "> models:Optional[List[str]]=None,\n", - "> xlabel:Optional[str]=None,\n", - "> ylabel:Optional[str]=None)\n", - "\n", - "*Hierarchically Predictions Gap plot\n", - "\n", - "**Parameters:**
\n", - "`Y_df`: DataFrame, hierarchically structured series ($\\mathbf{y}_{[a,b]}$). \n", - " It contains columns ['unique_id', 'ds', 'y'] and models.
\n", - "`models`: List[str], string identifying filtering model columns.\n", - "`xlabel`: str, string for the plot's x axis label.\n", - "`ylable`: str, string for the plot's y axis label.\n", - "\n", - "**Returns:**
\n", - "Plots of aggregated predictions at different levels of the hierarchical structure.\n", - "The aggregation is performed according to the tag levels see \n", - "[aggregate function](https://nixtla.github.io/hierarchicalforecast/utils.html).

*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(HierarchicalPlot.plot_hierarchical_predictions_gap,\n", " name='plot_hierarchical_predictions_gap', title_level=3)" @@ -1439,18 +1183,7 @@ "execution_count": null, "id": "662bdbce-cb13-4ba2-a794-1cd1bc3b96a8", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#| hide\n", "hier_df['Model'] = hier_df['y'] * 1.1\n", @@ -1493,18 +1215,7 @@ "execution_count": null, "id": "88068d1a-b670-410a-975e-a92e22ea9948", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#| hide\n", "hplots.plot_series(series='Australia', \n", @@ -1516,26 +1227,7 @@ "execution_count": null, "id": "60b90ed3-e1c3-47da-850b-ccda3319f630", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\ospra\\AppData\\Local\\Temp\\ipykernel_24168\\4257372374.py:126: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", - " cmap = plt.cm.get_cmap(\"tab10\", 10)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#| hide\n", "hplots.plot_hierarchically_linked_series(\n", @@ -1550,26 +1242,7 @@ "execution_count": null, "id": "f45855eb-e800-40db-a00b-5ddb956ae348", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\ospra\\AppData\\Local\\Temp\\ipykernel_24168\\4257372374.py:126: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", - " cmap = plt.cm.get_cmap(\"tab10\", 10)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#| hide\n", "hplots.plot_hierarchically_linked_series(\n", @@ -1583,26 +1256,7 @@ "execution_count": null, "id": "96c9263e-6d07-4527-88ea-40153435f44f", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\ospra\\AppData\\Local\\Temp\\ipykernel_24168\\4257372374.py:126: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", - " cmap = plt.cm.get_cmap(\"tab10\", 10)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#| hide\n", "# test series with just one value\n", @@ -1617,18 +1271,7 @@ "execution_count": null, "id": "fb7cc841-73db-4e00-99e4-b123b9d09db2", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#| hide\n", "hplots.plot_hierarchical_predictions_gap(Y_df=hier_df.drop(columns='y'), models=['Model'])" @@ -1639,32 +1282,7 @@ "execution_count": null, "id": "82c2b84f", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\ospra\\miniconda3\\envs\\hierarchicalforecast\\lib\\site-packages\\statsforecast\\models.py:887: FutureWarning: `ETS` will be deprecated in future versions of `StatsForecast`. Please use `AutoETS` instead.\n", - " ETS._warn()\n", - "c:\\Users\\ospra\\miniconda3\\envs\\hierarchicalforecast\\lib\\site-packages\\statsforecast\\core.py:476: FutureWarning: The `df` argument of the StatsForecast constructor as well as reusing stored dfs from other methods is deprecated and will raise an error in a future version. Please provide the `df` argument to the corresponding method instead, e.g. fit/forecast.\n", - " warnings.warn(\n", - "c:\\Users\\ospra\\miniconda3\\envs\\hierarchicalforecast\\lib\\site-packages\\statsforecast\\core.py:476: FutureWarning: The `df` argument of the StatsForecast constructor as well as reusing stored dfs from other methods is deprecated and will raise an error in a future version. Please provide the `df` argument to the corresponding method instead, e.g. fit/forecast.\n", - " warnings.warn(\n", - "c:\\Users\\ospra\\miniconda3\\envs\\hierarchicalforecast\\lib\\site-packages\\statsforecast\\core.py:494: FutureWarning: In a future version the predictions will have the id as a column. You can set the `NIXTLA_ID_AS_COL` environment variable to adopt the new behavior and to suppress this warning.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#| eval: false\n", "from statsforecast.core import StatsForecast\n", @@ -1830,71 +1448,7 @@ "execution_count": null, "id": "18e4fcc4", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L469){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### samples_to_quantiles_df\n", - "\n", - "> samples_to_quantiles_df (samples:numpy.ndarray, unique_ids:Sequence[str],\n", - "> dates:List[str],\n", - "> quantiles:Optional[List[float]]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> model_name:Optional[str]='model')\n", - "\n", - "*Transform Random Samples into HierarchicalForecast input.\n", - "Auxiliary function to create compatible HierarchicalForecast input `Y_hat_df` dataframe.\n", - "\n", - "**Parameters:**
\n", - "`samples`: numpy array. Samples from forecast distribution of shape [n_series, n_samples, horizon].
\n", - "`unique_ids`: string list. Unique identifiers for each time series.
\n", - "`dates`: datetime list. List of forecast dates.
\n", - "`quantiles`: float list in [0., 1.]. Alternative to level, quantiles to estimate from y distribution.
\n", - "`level`: int list in [0,100]. Probability levels for prediction intervals.
\n", - "`model_name`: string. Name of forecasting model.
\n", - "\n", - "**Returns:**
\n", - "`quantiles`: float list in [0., 1.]. quantiles to estimate from y distribution .
\n", - "`Y_hat_df`: pd.DataFrame. With base quantile forecasts with columns ds and models to reconcile indexed by unique_id.*" - ], - "text/plain": [ - "---\n", - "\n", - "[source](https://github.com/Nixtla/hierarchicalforecast/blob/main/hierarchicalforecast/utils.py#L469){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", - "\n", - "### samples_to_quantiles_df\n", - "\n", - "> samples_to_quantiles_df (samples:numpy.ndarray, unique_ids:Sequence[str],\n", - "> dates:List[str],\n", - "> quantiles:Optional[List[float]]=None,\n", - "> level:Optional[List[int]]=None,\n", - "> model_name:Optional[str]='model')\n", - "\n", - "*Transform Random Samples into HierarchicalForecast input.\n", - "Auxiliary function to create compatible HierarchicalForecast input `Y_hat_df` dataframe.\n", - "\n", - "**Parameters:**
\n", - "`samples`: numpy array. Samples from forecast distribution of shape [n_series, n_samples, horizon].
\n", - "`unique_ids`: string list. Unique identifiers for each time series.
\n", - "`dates`: datetime list. List of forecast dates.
\n", - "`quantiles`: float list in [0., 1.]. Alternative to level, quantiles to estimate from y distribution.
\n", - "`level`: int list in [0,100]. Probability levels for prediction intervals.
\n", - "`model_name`: string. Name of forecasting model.
\n", - "\n", - "**Returns:**
\n", - "`quantiles`: float list in [0., 1.]. quantiles to estimate from y distribution .
\n", - "`Y_hat_df`: pd.DataFrame. With base quantile forecasts with columns ds and models to reconcile indexed by unique_id.*" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "show_doc(samples_to_quantiles_df, title_level=3)" ] diff --git a/settings.ini b/settings.ini index f398b3df..07f1ac61 100644 --- a/settings.ini +++ b/settings.ini @@ -17,7 +17,7 @@ license = apache2 status = 2 requirements = numpy, numba, pandas, scikit-learn, quadprog, matplotlib, intel-cmplr-lib-rt ; platform_system!="Darwin" and platform_machine=="x86_64" dev_requirements = datasetsforecast nbdev statsforecast>=1.0.0 requests scipy pre-commit ruff black pytest pytest-benchmark polars -polars_requirements = polars[numpy] +polars_requirements = polars nbs_path = nbs doc_path = _docs recursive = True diff --git a/setup.py b/setup.py index 7b9703a8..9b14ae40 100644 --- a/setup.py +++ b/setup.py @@ -29,6 +29,11 @@ min_python = cfg['min_python'] lic = licenses.get(cfg['license'].lower(), (cfg['license'], None)) dev_requirements = (cfg.get('dev_requirements') or '').split() +polars_requirements = (cfg.get('polars_requirements') or '').split() +all_requirements = [ + *polars_requirements, + *dev_requirements, +] setuptools.setup( name = 'hierarchicalforecast', @@ -42,7 +47,9 @@ packages = setuptools.find_packages(), include_package_data = True, install_requires = requirements, - extras_require={ 'dev': dev_requirements }, + extras_require={'dev': dev_requirements, + 'polars': polars_requirements, + 'all': all_requirements }, dependency_links = cfg.get('dep_links','').split(), python_requires = '>=' + cfg['min_python'], long_description = open('README.md', encoding='utf8').read(),