DjXddlmZddlZddlZddlmZmZddlm Z ddl m Z ddl m Z ddlmZmZdd lmZdd lmZmZdd lmZd gZd ZGdd eZy))isliceN) BaseEstimatorclone) BaseARIMA)BaseTransformer)BaseEndogTransformer)BaseExogTransformerBaseExogFeaturizer) check_endog)DTYPEcheck_is_fitted)if_delegate_has_methodPipelinec tdD]2}|j|dstjd|zt4|S)N)typz:'%s' is deprecated and will be removed in a future release)popwarningswarnDeprecationWarning)kwargsks UC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\pmdarima/pipeline.py_warn_for_deprecatedrsB . ::a  MM$&'(, .. MceZdZdZdZdZdZddZdZdZ e dZ e d Z d Z dd Zd ZddZ ddZ ddZdZeddZddZy )raL A pipeline of transformers with an optional final estimator stage The pipeline object chains together an arbitrary number of named, ordered transformations, passing the output from one as the input to the next. As the last stage, an ``ARIMA`` or ``AutoARIMA`` object will be fit. This pipeline takes after the scikit-learn ``sklearn.Pipeline`` object, which behaves similarly but does not share the same time-series interface that ``pmdarima`` follows. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a `'__'`, as in the example below. Parameters ---------- steps : list List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an ARIMA or AutoARIMA estimator. Examples -------- >>> from pmdarima.datasets import load_wineind >>> from pmdarima.arima import AutoARIMA >>> from pmdarima.pipeline import Pipeline >>> from pmdarima.preprocessing import FourierFeaturizer >>> >>> wineind = load_wineind() >>> pipeline = Pipeline([ ... ("fourier", FourierFeaturizer(m=12, k=3)), ... ("arima", AutoARIMA(seasonal=False, stepwise=True, ... suppress_warnings=True, ... error_action='ignore')) ... ]) >>> pipeline.fit(wineind) Pipeline(steps=[('fourier', FourierFeaturizer(k=3, m=12)), ('arima', AutoARIMA(D=None, alpha=0.05, callback=None, d=None, disp=0, error_action='ignore', information_criterion='aic', m=1, max_D=1, max_P=2, max_Q=2, max_d=2, max_order=10, max_p=5, max_q=5, maxiter=None, method=None, n_fits=10, n...s_warnings=True, test='kpss', trace=False, transparams=True, trend=None, with_intercept=True))]) c2||_|jyN)steps_validate_steps)selfrs r__init__zPipeline.__init__Qs  rctt|t|k7r#tdjt |t|j |j d}|r#tdjt||Dcgc] }d|vs| }}|rtdj|ycc}w)Nz$Names provided are not unique: {0!r}F)deepz:Estimator names conflict with constructor arguments: {0!r}__z.Estimator names must not contain __: got {0!r})lenset ValueErrorformatlist intersection get_paramssorted)r!names invalid_namesnames r_validate_nameszPipeline._validate_namesUs s5z?c%j (%%+VDK%8: :E //U0KL 006vm7L0MO O*/@$44<@ @ %%+VM%:< < As  C!Cc Ft|j\}}|j||dd}|d}|D]-}t|trt d|dt |dt|tst d|dt |dt|jS)NzDAll intermediate steps should be instances of BaseTransformer, but 'z' (type z) is notz4Last step of Pipeline should be of type BaseARIMA. 'z) isn't) ziprr1 isinstancer TypeErrortyperr*)r!r. estimators transformers estimatorts rr zPipeline._validate_stepsbs,z U#"#2 rN  HAa1; -KE4++dA.KD%(,L u % - -sAc,t|jS)z4 Returns the length of the Pipeline )r&rr!s r__len__zPipeline.__len__s4::rc,t|jS)zMap the steps to a dictionary)dictrrOs r named_stepszPipeline.named_stepssDJJrc*|jdd}|S)Nr3r)r)r!r:s r_final_estimatorzPipeline._final_estimatorsJJrN1% rc<|t||k7r t|}|Sr)r&)r! n_periodsexogs r_check_n_periodszPipeline._check_n_periodss"  D Y 6D IrNc |jx}|_t|tdd}|}|jd i|}|j d|_|jdD]4\}} } t| } || } | j||fi| \}}| | f||<6t|tjr|jjnd|_||dd} |j j"|fd|i| |S) aFit the pipeline of transformers and the ARIMA model Chain the time-series and X array through a series of transformations, fitting each stage along the way, finally fitting an ARIMA or AutoARIMA model. Parameters ---------- y : array-like or iterable, shape=(n_samples,) The time-series to which to fit the ``ARIMA`` estimator. This may either be a Pandas ``Series`` object (statsmodels can internally use the dates in the index), or a numpy array. This should be a one-dimensional array of floats, and should not contain any ``np.nan`` or ``np.inf`` values. X : array-like, shape=[n_obs, n_vars], optional (default=None) An optional 2-d array of exogenous variables. If provided, these variables are used as additional features in the regression operation. This should not include a constant or trend. Note that if an ``ARIMA`` is fit on exogenous features, it must be provided exogenous features for making predictions. **fit_kwargs : keyword args Extra keyword arguments used for each stage's ``fit`` stage. Similar to scikit-learn pipeline keyword args, the keys are compound, comprised of the stage name and the argument name separated by a "__". For instance, if fitting an ARIMA in stage "arima", your kwargs may resemble:: {"arima__maxiter": 10} FT)dtypecopypreserve_seriesrr?Nr3X)r r=r r rMshape n_samples_rCr fit_transformr5pd DataFramecolumnstolistx_feats_rUfit) r!yr_ fit_kwargsrytXt named_kwargsstep_idxr0 transformercloned_transformerrs rriz Pipeline.fitsB#2244 %eT J 't''5*5 ((1++/:::+G 9 'HdK!&{!3 !$'F5'55b"GGFB $%78E(O 9"bll+ ))+15 eBil+!!!"55f5 rc t|d|}|jd i|}|jdD]v\}}}t|ts||} t|t r3| j dd} | | |k7rtd|d| d|d || d<|jd|fi| \} }x|j||j}|jd \} } || || fS) z3Runs transformation steps before predicting on datar=Fr^rWNz)Manually set 'n_periods' kwarg for step 'z&' differs from forecasting n_periods (z != )r3r`) rrMrCr5r r getr( transformrhr=)r!rWr_rrmrnror0rpkwnum_p_nmests r _pre_predictzPipeline._pre_predictsh''t''1&1 +/:::+G > 'HdK+':;!$'k+=>FF;5E(Ui-?(-1%*DEE'0B{O. --dB="=2! >* == $DMM"B++b/C3 R(((rc p|j||}tdi|}|j||fi|\}}}|S)aGet the transformed X array Generate the X array ``n_periods`` in the future. This passes the provided exog variables through all transformation steps, returning it before it's passed into the model. Parameters ---------- n_periods : int, optional (default=10) The number of periods in the future to forecast. X : array-like, shape=[n_obs, n_vars], optional (default=None) An optional 2-d array of exogenous variables. If provided, these variables are used as additional features in the regression operation. This should not include a constant or trend. Note that if an ``ARIMA`` is fit on exogenous features, it must be provided exogenous features for making predictions. **kwargs : keyword args Extra keyword arguments used for each stage's ``transform`` stage and the estimator's ``predict`` stage. Similar to scikit-learn pipeline keyword args, the keys are compound, comprised of the stage name and the argument name separated by a "__". For instance, if you have a FourierFeaturizer whose stage is named "fourier", your transform kwargs could resemble:: {"fourier__n_periods": 50} Returns ------- X_prime : pd.DataFrame The transformed exog array. r`)rYrr{)r!rWr_rrmrxs rruzPipeline.transformsHD)))Q7 %//$4$$Y.//0/4;r) itertoolsrpandasrdr sklearn.baserrbaserpreprocessing.baserpreprocessing.endog.baser preprocessing.exog.baser r utilsr compatr rcompat.sklearnr__all__rrr`rrrsB-/:L*2 ,B;}B;r