o 0jH$@sddlmZmZmZmZmZddlZddlZ ddl m Z ddl m ZddlmZmZmZddlmZmZmZedr>ddlZed rHdd lmZd Zejd d d eedgdZejdd d dZejddd eed ddZejddd eedddZ ejddddZ!ejddddZ"ejddddZ#ejddddZ$ejddd e je%dded gdZ&ejd dd e je%ddedgdZ'ej(ej)ej*ej+ej,ej-ej.ej/eeee e!e"e#e$e&e'gZ0d!ej1fd"d#Z2d!ej1fd$d%Z3d!ej1fd&d'Z4d!ej1fd(d)Z5d!ej1fd*d+Z6d!ej1fd,d-Z7d!ej1fd.d/Z8d!ej1fd0d1Z9d!ej1fd2d3Z:d!ej1fd4d5Z;d!ej1fd6d7Zd!ej1fdd?Z@e2e3e4e5e6e7e?e@e8e9e:e;ed@ZA    A dadBee jBe jCfdCeeDdDeeeeDeDfdEeeeDeEfdFeFdGeeeGeeDeGffdHe jBfdIdJZH          A K LdbdMe jBdNeeDdCeeDdOeeeeDeDfdPeeeeDeDfdQeeeeDeDfdReeeeDeDfdSeeeeDeDfdTeeeeDeDfdEeeeDeEfdUeFdVeDdWeEdHeeDeee jBeeDeIffffdXdYZJdHee jKgeLffdZd[ZMed  Adcd\edEeeeDeEfdReeDd]eEd^eFdHef d_d`ZNdS)d)CallableDictListOptionalUnionN)Versionholiday) DateOffsetDayEaster)function_requires_depsget_dep_versionis_dep_availablezchinese-calendarz scikit-learn)StandardScalerz Easter Sunday)monthdayoffsetz New Years Day)rrZ Superbowl)weekdayz Mothers DayzIndependence DayZ Christmas z New Years Evez Black Friday z Cyber MondayxcC|jSN)yearr r%i/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddlex/inference/models/common/ts/funcs.py _cal_yearMr'cCr!r"rr$r%r%r& _cal_monthSr(r*cCr!r"rr$r%r%r&_cal_dayYr(r,cCr!r"hourr$r%r%r& _cal_hour_r(r/cCr!r" dayofweekr$r%r%r& _cal_weekdayer(r2cCr!r")quarterr$r%r%r& _cal_quarterkr(r4cC|jddS)Ng7@?r-r$r%r%r&_cal_hourofdayqr7cCr5)Ng@r6r0r$r%r%r&_cal_dayofweekwr8r9cCr5)Ng>@r6r+r$r%r%r&_cal_dayofmonth}r8r:cCr5)Ngv@r6) dayofyearr$r%r%r&_cal_dayofyearr8r<cCr5)NgI@r6) weekofyearr$r%r%r&_cal_weekofyearr8r>cCtt|Sr")floatchinese_calendar is_holidayr$r%r%r& _cal_holidayrCcCr?r")r@rA is_workdayr$r%r%r& _cal_workdayrDrFcCr5)N;r6)minuter$r%r%r&_cal_minuteofhourr8rIcCr5)Ng&@r6r)r$r%r%r&_cal_monthofyearr8rJ)r#rrr.rr3Z minuteofhourZ monthofyearZ hourofdayr1Z dayofmonthr;r=rBrEFdatatime_col value_colsfreq drop_tail_nandtypereturnc Csd}|dur t|tjr|}n|jdd|j|kf}n |jdd|f}|rC||jvr9td||jdd|f}n|j}tj j |j rXt|t rX|t }tj j |j r|rpt|trk|dkrotdnd}t|t||}} | ||t|krtdtj|| |d} nDtj j |j stj j |j rt|}t|} |rt|t stdnt| }|durtd|d d kr|dd}ntd t|tjr|}|j| d d |jd d |S)aoTransforms a DataFrame or Series into a time-indexed DataFrame. Args: data (Union[pd.DataFrame, pd.Series]): The input data containing time series information. time_col (Optional[str]): The column name representing time information. If None, uses the index. value_cols (Optional[Union[List[str], str]]): Columns to extract as values. If None, uses all except time_col. freq (Optional[Union[str, int]]): The frequency of the time series data. drop_tail_nan (bool): If True, drop trailing NaN values from the data. dtype (Optional[Union[type, Dict[str, type]]]): Enforce a specific data type on the resulting DataFrame. Returns: pd.DataFrame: A DataFrame with time as the index and specified value columns. Raises: ValueError: If the time column doesn't exist, or if frequency cannot be inferred. Nz0The time column: {} doesn't exist in the `data`!rzOThe type of `freq` should be `int` when the type of `time_col` is `RangeIndex`.z5The number of rows doesn't match with the RangeIndex!)startstopstepzRThe type of `freq` should be `str` when the type of `time_col` is `DatetimeIndex`.z7Failed to infer the `freq`. A valid `freq` is required.r-z"The type of `time_col` is invalid.T)inplace) isinstancepdSeriescopyloccolumns ValueErrorformatindexapitypesis_integer_dtyperPstrZastypeintminmaxlenZ RangeIndexZis_string_dtypeZis_datetime64_any_dtype to_datetimeZ DatetimeIndex infer_freqto_frameZ set_indexZ sort_index) rKrLrMrNrOrPZ series_dataZ time_col_valsZ start_idxZstop_idxZ time_indexr%r%r&load_from_one_dataframesj            rkpre dfgroup_id target_cols label_colobserved_cov_cols feature_colsknown_cov_colsstatic_cov_colsfill_missing_dates fillna_methodfillna_window_sizec  slg}|dur||}|D]}|||||gqn|g}g}|r7t|tr5t|dkr5td|}|r;|}|D]t}d}d}d}t}t||||gs`t |fdd|j D| }nG|rit ||| }|rrt ||| }|r{t ||| }|rt|tr|g}|D]}||j vstt ||dkrtd||j d||<q|||||dq=|dS) aLoads and processes time series data from a DataFrame. This function extracts and organizes time series data from a given DataFrame. It supports optional grouping and extraction of specific columns as features. Args: df (pd.DataFrame): The input DataFrame containing time series data. group_id (Optional[str]): Column name used for grouping the data. time_col (Optional[str]): Name of the time column. target_cols (Optional[Union[List[str], str]]): Columns to be used as target. label_col (Optional[Union[List[str], str]]): Columns to be used as label. observed_cov_cols (Optional[Union[List[str], str]]): Columns for observed covariates. feature_cols (Optional[Union[List[str], str]]): Columns to be used as features. known_cov_cols (Optional[Union[List[str], str]]): Columns for known covariates. static_cov_cols (Optional[Union[List[str], str]]): Columns for static covariates. freq (Optional[Union[str, int]]): Frequency of the time series data. fill_missing_dates (bool): Whether to fill missing dates in the time series. fillna_method (str): Method to fill missing values ('pre' or 'post'). fillna_window_size (int): Window size for filling missing values. **kwargs: Additional keyword arguments. Returns: Dict[str, Optional[Union[pd.DataFrame, Dict[str, any]]]]: A dictionary containing processed time series data. Nrz"The length of label_col must be 1.csg|]}|kr|qSr%r%).0arLr%r& ksz'load_from_dataframe..zIStatic covariate columns data is not in columns or schema is not correct!r) past_targetZobserved_cov_numericknown_cov_numericZstatic_cov_numeric) uniqueappendisinrWrcrgr]dictanyrkr\npZiloc)rnrorLrprqrrrsrtrurNrvrwrxkwargsdfsZ group_uniquecolumnrestargetZ observed_covZ known_covZ static_covcolr%r{r&load_from_dataframes)    rcsdtjdtffdd }|S)aCreates a function to calculate the distance in days to the nearest holiday. This function generates a closure that computes the number of days from a given date index to the nearest holiday within a defined window. Args: holiday: An object that provides a `dates` method, which returns the dates of holidays within a specified range. Returns: Callable[[pd.Timestamp], float]: A function that takes a date index as input and returns the distance in days to the nearest holiday. r_rQcsR|tjtd|tjtd}t|dks Jd|dt||djS)aCalculates the distance in days from a given date index to the nearest holiday. Args: index (pd.Timestamp): The date index for which the distance to the nearest holiday should be calculated. Returns: float: The number of days to the nearest holiday. Raises: AssertionError: If no holiday is found within the specified window. )daysrz&No closest holiday for the date index z found.)datesrXZ Timedelta MAX_WINDOWrgr@r)r_Z holiday_daterr%r&_distance_to_days  z._distance_to_holiday.._distance_to_day)rX Timestampr@)r rr%rr&_distance_to_holidaysrdataset extend_pointsrVcs@|}|s|}|d}|s|dj}n|j}|jd}tjj||jr.t d|su|dur6|nt ||}t d} t | t dkr\tj ||d||d d |d } ntj ||d||d d |d } t|| g}|D]d kr||fdd} ||| _|ddurtj| | jd|d<qw| |dj|d<qwg} ttD]L\} }||t|} ||| _| dt| |ddurtj| dt| | jd|d<q| |dj|ddt| <qt}||d| ||d| |d| <qw|S)a_Transforms the time column of a dataset into time features. This function extracts time-related features from the time column in a dataset, optionally extending the time series for future points and normalizing holiday distances. Args: dataset (Dict): Dataset to be transformed. freq: Optional[Union[str, int]]: Frequency of the time series data. If not provided, the frequency will be inferred. feature_cols (List[str]): List of feature columns to be extracted. extend_points (int): Number of future points to extend the time series. inplace (bool): Whether to perform the transformation inplace. Default is False. Returns: Dict: The transformed dataset with time features added. 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