U Kv f@s*ddlmZddlmZmZmZddlZddlZddlZ ddl m Z m Z m Z mZmZmZmZmZmZmZddlmZddlmZddlmmZddlmmZddlm Z dZ!d Z"ej#Z$ej%Z%d d Z&d d Z'dddddZ(Gdddej)Z*Gdddej+Z,Gdddej-Z.e/e.e,dS)) annotations)is_float_index is_int_indexis_numeric_dtypeN) DatetimeIndexIndexPeriod PeriodIndex RangeIndexSeries Timestamp date_range period_range to_datetime) to_offset) PandasData) ValueWarninga %(model)s Parameters ---------- %(params)s dates : array_like, optional An array-like object of datetime objects. If a pandas object is given for endog or exog, it is assumed to have a DateIndex. freq : str, optional The frequency of the time-series. A Pandas offset or 'B', 'D', 'W', 'M', 'A', or 'Q'. This is optional if dates are given. %(extra_params)s %(extra_sections)szTimeseries model base classc Cs|}|}t|ttf}t|}t|t}t|}t|}|rt|ttj fr|dkrh| |krh||}n\||dkrz|j }|j } Wn t k r|j }|j} YnX||d| } t|| | d}|s2|r2|s2t|ttj fr2|dkr| |kr||}n*||dkr2tt|dt|d}|r:|tkrHt} nt} t|ttj fr|dkr| |kr|||}n@|t|dkr| |dt|d|jd}|d}n||}nv|tkrt||jd} nt|} | |dkr:| |d| |jd}|d| ks2| |dt|d|jd}|d}|rL||} n^|sX|rz ||Wn4ttfk r}ztt|W5d}~XYnX|} n ||} ||k }t| tr| jd}n| }| |d|d|fS) a Get the location of a specific key in an index Parameters ---------- key : label The key for which to find the location if the underlying index is a DateIndex or a location if the underlying index is a RangeIndex or an Index with an integer dtype. index : pd.Index The index to search. Returns ------- loc : int The location of the key index : pd.Index The index including the key; this is a copy of the original index unless the index had to be expanded to accommodate `key`. index_was_expanded : bool Whether or not the index was expanded to accommodate `key`. Notes ----- If `key` is past the end of of the given index, and the index is either an Index with an integral dtype or a date index, this function extends the index up to and including key, and then returns the location in the new index. r)startstopstep)rZperiodsfreq)rrendrN) isinstancer rrr typelenintnpintegerrrAttributeError_startZ_steprZaranger rrrr get_loc IndexError ValueErrorKeyErrorstrslicer)keyindex base_index date_index int_index range_indexZ index_classnobsZbase_index_startZbase_index_steprZindex_fnZdate_keyloceindex_was_expandedrr3B/tmp/pip-unpacked-wheel-2v6byqio/statsmodels/tsa/base/tsa_model.py get_index_loc5s                  r5c Cszt||\}}}Wntk r}zz~t|ttjfsD||}nt|trV|j}t|tj r|j t krxt |}n|d}t|t js|d|d}d}Wn|YnXW5d}~XYnX|||fS)a Get the location of a specific key in an index or model row labels Parameters ---------- key : label The key for which to find the location if the underlying index is a DateIndex or is only being used as row labels, or a location if the underlying index is a RangeIndex or a NumericIndex. index : pd.Index The index to search. row_labels : pd.Index Row labels to search if key not found in index Returns ------- loc : int The location of the key index : pd.Index The index including the key; this is a copy of the original index unless the index had to be expanded to accommodate `key`. index_was_expanded : bool Whether or not the index was expanded to accommodate `key`. Notes ----- This function expands on `get_index_loc` by first trying the given base index (or the model's index if the base index was not given) and then falling back to try again with the model row labels as the base index. rNrF)r5r&rrrr r#r(rZndarrayZdtypeboolZargmaxnumbersIntegral)r)r* row_labelsr0r2r1r3r3r4get_index_label_locs(       r:F"tuple[int, int, int, Index | None]returnc Cszt|||j\}} } Wntk r4tdYnX|dkrPt|t|d}zt|||j\}} } Wntk rtdYnXt|tr|j}t|tr|jd}| |d} ||krt d|dk rt| t|kst dt|t s |s t j dt dd t|} nj|rt|st|jdk rL| sL| sL|j||d} n&|sbt j d t dd t j d tdd n |r~d} | dk r| d |_| d |_| |_nd|_d|_d|_t||dd }||8}|||| fS)a) Get the location of a specific key in an index or model row labels Parameters ---------- start : label The key at which to start prediction. Depending on the underlying model's index, may be an integer, a date (string, datetime object, pd.Timestamp, or pd.Period object), or some other object in the model's row labels. end : label The key at which to end prediction (note that this key will be *included* in prediction). Depending on the underlying model's index, may be an integer, a date (string, datetime object, pd.Timestamp, or pd.Period object), or some other object in the model's row labels. nobs : int base_index : pd.Index index : pd.Index, optional Optionally an index to associate the predicted results to. If None, an attempt is made to create an index for the predicted results from the model's index or model's row labels. silent : bool, optional Argument to silence warnings. Returns ------- start : int The index / observation location at which to begin prediction. end : int The index / observation location at which to end in-sample prediction. The maximum value for this is nobs-1. out_of_sample : int The number of observations to forecast after the end of the sample. prediction_index : pd.Index or None The index associated with the prediction results. This index covers the range [start, end + out_of_sample]. If the model has no given index and no given row labels (i.e. endog/exog is not Pandas), then this will be None. Notes ----- The arguments `start` and `end` behave differently, depending on if they are integer or not. If either is an integer, then it is assumed to refer to a *location* in the index, not to an index value. On the other hand, if it is a date string or some other type of object, then it is assumed to refer to an index *value*. In all cases, the returned `start` and `end` values refer to index *locations* (so in the former case, the given location is validated and returned whereas in the latter case a location is found that corresponds to the given index value). This difference in behavior is necessary to support `RangeIndex`. This is because integers for a RangeIndex could refer either to index values or to index locations in an ambiguous way (while for `NumericIndex`, since we have required them to be full indexes, there is no ambiguity). zYThe `start` argument could not be matched to a location related to the index of the data.NrzWThe `end` argument could not be matched to a location related to the index of the data.z)Prediction must have `end` after `start`.zeInvalid `index` provided in prediction. Must have length consistent with `start` and `end` arguments.zBecause the model data (`endog`, `exog`) were not given as Pandas objects, the prediction output will be Numpy arrays, and the given `index` argument will only be used internally. stacklevelzmNo supported index is available. Prediction results will be given with an integer index beginning at `start`.zNo supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.rr)r:r9r&maxrrr(rrr%rwarningswarnrr FutureWarningZ predict_startZ predict_endZ predict_dates)rrr/r+r*silent index_noneindex_generateddata_Z start_oosZ end_indexZend_oosZprediction_indexZ out_of_sampler3r3r4get_prediction_indexsH            rJcs|eZdZeeeeddZdfdd ZdddZ dd d Z dd d Z dddddZ ddZ ddZee eddZZS)TimeSeriesModel)modelparamsZ extra_paramsZextra_sectionsNnonec s*tj||fd|i||||dS)Nmissing)super__init__ _init_dates)selfendogZexogdatesrrPkwargs __class__r3r4rRszTimeSeriesModel.__init__cCs|dk r|}n|jj}|dkr.|dk r.tdd}|dk rt|ttfszbt|}t|stt |stt|dt r|tdt|t r|j }t |}t|tstd|}Wn|dk rtdYnXt|ttfr|dkr"|jdkr"|j}|dk r"d}|dk r"tjd |td d |dk r4t|}|dkr^|jdkr^|dk rtd np|dk r|jdkrt|d|d |d}|s||std|}n$|dk r|s|j|kstdn|dk rtd|dk }t|ttf}t|t} t|} t|t} |r$|jdk nd} tt|jjd} | rL|| nd}|rz |j}Wntk r||j}YnXnd}|r|s| s|stjdtd d |r| stjdtd d |r|stjdtd d d}|r| r|p| r|p| }|r|}n| }d}||_||_ |dk|_!| oF| oF| |_"|oT| |_#|j#rh|jjnd|_$||_%|j#r|jnd|j_&|j#r|jj'nd|j_dS)a Initialize dates Parameters ---------- dates : array_like, optional An array like object containing dates. freq : str, tuple, datetime.timedelta, DateOffset or None, optional A frequency specification for either `dates` or the row labels from the endog / exog data. Notes ----- Creates `self._index` and related attributes. `self._index` is always a Pandas index, and it is always NumericIndex, DatetimeIndex, or PeriodIndex. If Pandas objects, endog / exog may have any type of index. If it is an NumericIndex with values 0, 1, ..., nobs-1 or if it is (coerceable to) a DatetimeIndex or PeriodIndex *with an associated frequency*, then it is called a "supported" index. Otherwise it is called an "unsupported" index. Supported indexes are standardized (i.e. a list of date strings is converted to a DatetimeIndex) and the result is put in `self._index`. Unsupported indexes are ignored, and a supported NumericIndex is generated and put in `self._index`. Warnings are issued in this case to alert the user if the returned index from some operation (e.g. forecasting) is different from the original data's index. However, whenever possible (e.g. purely in-sample prediction), the original index is returned. The benefit of supported indexes is that they allow *forecasting*, i.e. it is possible to extend them in a reasonable way. Thus every model must have an underlying supported index, even if it is just a generated NumericIndex. Nz,Frequency provided without associated index.FrzNumeric index givenzCould not coerce to date indexz2Non-date index index provided to `dates` argument.TzMNo frequency information was provided, so inferred frequency %s will be used.r>r?zYNo frequency information was provided with date index and no frequency could be inferred.rrzEThe given frequency argument could not be matched to the given index.zBThe given frequency argument is incompatible with the given index.zKGiven index could not be coerced to dates but `freq` argument was provided.zLAn unsupported index was provided and will be ignored when e.g. forecasting.z|A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.zeA date index has been provided, but it is not monotonic and so will be ignored when e.g. forecasting.)(rHr9r%rrr rZasarrayrrfloatr valuesrrr inferred_freqrBrCrrr equalsrr rangerUshapeZis_monotonic_increasingr! is_monotonic_index_index_generated _index_noneZ _index_int64Z _index_datesZ _index_freqZ_index_inferred_freqrVZfreqstr)rTrVrr*r\raZresampled_indexZ has_indexr,Z period_indexr-r.Zhas_freq incrementZ is_incrementr`rGZ valid_indexr3r3r4rSs*                  zTimeSeriesModel._init_datescCs|dkr|j}t||S)a9 Get the location of a specific key in an index Parameters ---------- key : label The key for which to find the location if the underlying index is a DateIndex or a location if the underlying index is a RangeIndex or an NumericIndex. base_index : pd.Index, optional Optionally the base index to search. If None, the model's index is searched. Returns ------- loc : int The location of the key index : pd.Index The index including the key; this is a copy of the original index unless the index had to be expanded to accommodate `key`. index_was_expanded : bool Whether or not the index was expanded to accommodate `key`. Notes ----- If `key` is past the end of of the given index, and the index is either an NumericIndex or a date index, this function extends the index up to and including key, and then returns the location in the new index. N)rar5rTr)r+r3r3r4_get_index_locszTimeSeriesModel._get_index_loccCs|dkr|j}t|||jjS)a| Get the location of a specific key in an index or model row labels Parameters ---------- key : label The key for which to find the location if the underlying index is a DateIndex or is only being used as row labels, or a location if the underlying index is a RangeIndex or an NumericIndex. base_index : pd.Index, optional Optionally the base index to search. If None, the model's index is searched. Returns ------- loc : int The location of the key index : pd.Index The index including the key; this is a copy of the original index unless the index had to be expanded to accommodate `key`. index_was_expanded : bool Whether or not the index was expanded to accommodate `key`. Notes ----- This method expands on `_get_index_loc` by first trying the given base index (or the model's index if the base index was not given) and then falling back to try again with the model row labels as the base index. N)rar:rHr9rer3r3r4_get_index_label_locsz$TimeSeriesModel._get_index_label_locFr;r<c Cs,t|j}t||||j|||j|j|jd S)a Get the location of a specific key in an index or model row labels Parameters ---------- start : label The key at which to start prediction. Depending on the underlying model's index, may be an integer, a date (string, datetime object, pd.Timestamp, or pd.Period object), or some other object in the model's row labels. end : label The key at which to end prediction (note that this key will be *included* in prediction). Depending on the underlying model's index, may be an integer, a date (string, datetime object, pd.Timestamp, or pd.Period object), or some other object in the model's row labels. index : pd.Index, optional Optionally an index to associate the predicted results to. If None, an attempt is made to create an index for the predicted results from the model's index or model's row labels. silent : bool, optional Argument to silence warnings. Returns ------- start : int The index / observation location at which to begin prediction. end : int The index / observation location at which to end in-sample prediction. The maximum value for this is nobs-1. out_of_sample : int The number of observations to forecast after the end of the sample. prediction_index : pd.Index or None The index associated with the prediction results. This index covers the range [start, end + out_of_sample]. If the model has no given index and no given row labels (i.e. endog/exog is not Pandas), then this will be None. Notes ----- The arguments `start` and `end` behave differently, depending on if they are integer or not. If either is an integer, then it is assumed to refer to a *location* in the index, not to an index value. On the other hand, if it is a date string or some other type of object, then it is assumed to refer to an index *value*. In all cases, the returned `start` and `end` values refer to index *locations* (so in the former case, the given location is validated and returned whereas in the latter case a location is found that corresponds to the given index value). This difference in behavior is necessary to support `RangeIndex`. This is because integers for a RangeIndex could refer either to index values or to index locations in an ambiguous way (while for `NumericIndex`, since we have required them to be full indexes, there is no ambiguity). )r+r*rErFrGrH)rrUrJrarcrbrH)rTrrr*rEr/r3r3r4_get_prediction_index s8 z%TimeSeriesModel._get_prediction_indexcCs|jjSN)rHxnames)rTr3r3r4_get_exog_namesPszTimeSeriesModel._get_exog_namescCst|ts|g}||j_dSri)rlistrHrj)rTvalsr3r3r4_set_exog_namesSs zTimeSeriesModel._set_exog_namesz%The names of the exogenous variables.)NNNrO)NN)N)N)NF)__name__ __module__ __qualname___tsa_doc _model_doc_generic_params_missing_param_doc__doc__rRrSrfrgrhrkrnpropertyZ exog_names __classcell__r3r3rXr4rKs0 k # #ErKcseZdZdfdd ZZS)TimeSeriesModelResults?cs|j|_t||||dSri)rHrQrR)rTrMrNZnormalized_cov_paramsZscalerXr3r4rRcszTimeSeriesModelResults.__init__)rz)rorprqrRrxr3r3rXr4rybsryc@s8eZdZiZeejjeZddiZ eejj e Z dS)TimeSeriesResultsWrapperZpredictrVN) rorprq_attrswrapZ union_dictsbaseZLikelihoodResultsWrapperZ _wrap_attrsZ_methodsZ _wrap_methodsr3r3r3r4r{hsr{)NFFNN)0 __future__rZstatsmodels.compat.pandasrrrr7rBZnumpyrZpandasrrrr r r r r rrZpandas.tseries.frequenciesrZstatsmodels.base.datarZstatsmodels.base.modelr~rMZstatsmodels.base.wrapperwrapperr}Zstatsmodels.tools.sm_exceptionsrrrrsZ_model_params_docrtrur5r:rJZLikelihoodModelrKZLikelihoodModelResultsryZResultsWrapperr{Zpopulate_wrapperr3r3r3r4sD 0    N/