Dj;#lddlZddlmZddlmZddlmZddlmZd gZ d Z d Z d Z Gd d eeZ y)N)BaseExogFeaturizer)UpdatableMixin)check_is_fitted)C_fourier_termsFourierFeaturizercNtjtj|zSN)npsinpixs gC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\pmdarima/preprocessing/exog/fourier.pyr 266"%%!)$cNtjtj|zSr )r cosrrs rrr rrcXt||}tj|jSr )r r asarrayT)ptimesXs r_fourier_termsrs" 5!A ::a=??rcHeZdZdZdfd ZdZdZd dZd dZd dZ xZ S) r a Fourier terms for modeling seasonality This transformer creates an exogenous matrix containing terms from a Fourier series, up to order ``k``. It is based on ``R::forecast code`` [1]. In practice, it permits us to fit a seasonal time series *without* seasonal order (i.e., ``seasonal=False``) by supplying decomposed seasonal Fourier terms as an exogenous array. The advantages of this technique, per Hyndman [2]: * It allows any length seasonality * The seasonal pattern is smooth for small values of K (but more wiggly seasonality can be handled by increasing K) * The short-term dynamics are easily handled with a simple ARMA error The disadvantage is that the seasonal periodicity of the time series is assumed to be fixed. Functionally, this is a featurizer. This means that exogenous features are *derived* from ``y``, as opposed to transforming an existing exog array. It also behaves slightly differently in the :func:`transform` stage than most other exogenous transformers in that ``exog`` is not a required arg, and it takes ``**kwargs``. See the :func:`transform` docstr for more info. Parameters ---------- m : int The seasonal periodicity of the endogenous vector, y. k : int, optional (default=None) The number of sine and cosine terms (each) to include. I.e., if ``k`` is 2, 4 new features will be generated. ``k`` must not exceed ``m/2``, which is the default value if not set. The value of ``k`` can be selected by minimizing the AIC. prefix : str or None, optional (default=None) The feature prefix Examples -------- >>> import pandas as pd >>> from pmdarima.preprocessing import FourierFeaturizer >>> from pmdarima.datasets import load_wineind >>> y = load_wineind() >>> trans = FourierFeaturizer(12, 4) >>> y_prime, X = trans.fit_transform(y) >>> X.head() FOURIER_S12-0 FOURIER_C12-0 ... FOURIER_S12-3 FOURIER_C12-3 0 0.500000 8.660254e-01 ... 8.660254e-01 -0.5 1 0.866025 5.000000e-01 ... -8.660255e-01 -0.5 2 1.000000 -4.371139e-08 ... 1.748456e-07 1.0 3 0.866025 -5.000001e-01 ... 8.660253e-01 -0.5 4 0.500000 -8.660254e-01 ... -8.660255e-01 -0.5 Notes ----- * Helpful for long seasonal periods (large ``m``) where ``seasonal=True`` seems to take a very long time to fit a model. References ---------- .. [1] https://github.com/robjhyndman/forecast/blob/master/R/season.R .. [2] https://robjhyndman.com/hyndsight/longseasonality/ c@||_||_t| |yr )mksuper__init__)selfr!r"prefix __class__s rr$zFourierFeaturizer.__init__\s  rc&|j}|d}|S)NFOURIER)r&)r%pfxs r _get_prefixzFourierFeaturizer._get_prefixbskk ;C rc |j}t|jdDcgc]"}d||dzdk(rdnd|j|dzfz$c}Scc}w)Nrz %s_%s%i-%irrSC)r+rangeshaper!)r%rr*is r_get_feature_namesz$FourierFeaturizer._get_feature_nameshsk 1771:& (  1uzsQ  ( ((s'AcV|j||d\}}|j}|j}||dz}d|z|kDs|dkr tdt j |dz|z j tj}||_||_ |jd|_ |S)aFit the transformer Computes the periods of all the Fourier terms. The values of ``y`` are not actually used; only the periodicity is used when computing Fourier terms. Parameters ---------- y : array-like or None, shape=(n_samples,) The endogenous (time-series) array. X : array-like or None, shape=(n_samples, n_features), optional The exogenous array of additional covariates. If specified, the Fourier terms will be column-bound on the right side of the matrix. Otherwise, the Fourier terms will be returned as the new exogenous array. T null_allowedrrz2k must be a positive integer not greater than m//2r) _check_y_Xr!r" ValueErrorr arangeastypefloat64p_k_r0n_)r%yr_r!r"rs rfitzFourierFeaturizer.fitss&q!$71 FF FF 9QA q519A)* * iilQ! # + +BJJ 7 ''!* rc t|d|j||d\}}|r3|1||jdk7rtd|d|jddt j |j |ztj d z}t|j|}|r || dddf}|j||}||fS) aCreate Fourier term features When an ARIMA is fit with an exogenous array, it must be forecasted with one also. Since at ``predict`` time in a pipeline we won't have ``y`` (and we may not yet have an ``exog`` array), we have to know how far into the future for which to compute Fourier terms (hence ``n_periods``). This method will compute the Fourier features for a given frequency and ``k`` term. Note that the ``y`` values are not used to compute these, so this does not pose a risk of data leakage. Parameters ---------- y : array-like or None, shape=(n_samples,) The endogenous (time-series) array. This is unused and technically optional for the Fourier terms, since it uses the pre-computed ``n`` to calculate the seasonal Fourier terms. X : array-like or None, shape=(n_samples, n_features), optional The exogenous array of additional covariates. If specified, the Fourier terms will be column-bound on the right side of the matrix. Otherwise, the Fourier terms will be returned as the new exogenous array. n_periods : int, optional (default=0) The number of periods in the future to forecast. If ``n_periods`` is 0, will compute the Fourier features for the training set. ``n_periods`` corresponds to the number of samples that will be returned. r;Tr4NrzBIf n_periods and X are specified, n_periods must match dims of X (z != ))dtyper) rr6r0r7r r8r=r:rr; _safe_hstack)r%r>r n_periodskwargsr?r X_fouriers r transformzFourierFeaturizer.transforms@ d#q!$71 AGGAJ& ""+D A?  $''I-RZZ@1D"477E2  !9*+q.1I   a +!t rc t|d|j||j||fdt|i|\}}|xjt|z c_||fS)aMUpdate the params and return the transformed arrays Since no parameters really get updated in the Fourier featurizer, all we do is compose forecasts for ``n_periods=len(y)`` and then update ``n_``. Parameters ---------- y : array-like or None, shape=(n_samples,) The endogenous (time-series) array. X : array-like or None, shape=(n_samples, n_features) The exogenous array of additional covariates. **kwargs : keyword args Keyword arguments required by the transform function. r;rE)r _check_endogrHlenr=)r%r>rrFr?Xts rupdate_and_transformz&FourierFeaturizer.update_and_transforms]$ d# !q!@s1v@@2 3q6"u r)NNr )Nr) __name__ __module__ __qualname____doc__r$r+r2r@rHrM __classcell__)r's@rr r s)?B!  ((T3nr)numpyr baserrcompatr_fourierr __all__sinpicospirr rrr[s>$!%%   $ $S*NSr