o 0j*@sddlmZmZmZddlZddlZddlm Z m Z ddl m Z ddl mZmZe d r0ddlZgd Ze jGd d d Ze je d Gd ddZe jGdddZe jGdddZe jGdddZe jGdddZdS))AnyDictListN)class_requires_depsis_dep_available) benchmark)load_from_dataframe time_featurejoblib)BuildTSDatasetTSCutOff TSNormalize TimeFeature TStoArray TStoBatchcsReZdZdZdeeefffdd ZdedefddZ d e de fd d Z Z S) ra;Truncates time series data to a specified length for training. This class provides a method to truncate or cut off time series data to a specified input length, optionally skipping some initial data points. This is useful for preparing data for training models that require a fixed input size. sizect||_dS)aKInitializes the TSCutOff with size configurations. Args: size (Dict[str, int]): Dictionary containing size configurations, including 'in_chunk_len' for the input chunk length and optionally 'skip_chunk_len' for the number of initial data points to skip. N)super__init__r)selfr __class__n/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddlex/inference/models/common/ts/processors.pyr/s zTSCutOff.__init__ts_listreturncfdd|DS)zApplies the cut off operation to a list of time series. Args: ts_list (List): List of time series data frames to be truncated. Returns: List: List of truncated time series data frames. cg|]}|qSr)cutoff.0tsrrr Dz%TSCutOff.__call__..rrrrr%r__call__; zTSCutOff.__call__r$cCsj|jdd}t||jd|kr'tdt|d|jd|jdd||jd| d}|S)a\Truncates a single time series data frame to the specified length. This method truncates the time series data to the specified input chunk length, optionally skipping some initial data points. It raises a ValueError if the time series is too short. Args: ts: A single time series data frame to be truncated. Returns: Any: The truncated time series data frame. Raises: ValueError: If the time series length is less than the required minimum length (input chunk length plus any skip chunk length). Zskip_chunk_lenrZ in_chunk_lenz The length of the input data is z, but it should be at least z for training.N)rgetlen ValueError)rr$Zskip_lents_datarrrr!Fs$zTSCutOff.cutoff) __name__ __module__ __qualname____doc__rstrintrrr)rr! __classcell__rrrrr%s   rcsfeZdZdZdedeeefffdd Zdee j dee j fdd Z d e j de j fd d Z Z S) raNormalizes time series data using a pre-fitted scaler. This class normalizes specified columns of time series data using a pre-fitted scaler loaded from a specified path. It supports normalization of both target and feature columns as specified in the parameters. scale_path params_infocs tt||_||_dS)abInitializes the TSNormalize with a scaler and normalization parameters. Args: scale_path (str): Path to the pre-fitted scaler object file. params_info (Dict[str, Any]): Dictionary containing information about which columns to normalize, including 'target_cols' and 'feature_cols'. N)rrr loadscalerr7)rr6r7rrrrjs  zTSNormalize.__init__rrcr)a Applies normalization to a list of time series data frames. Args: ts_list (List[pd.DataFrame]): List of time series data frames to be normalized. Returns: List[pd.DataFrame]: List of normalized time series data frames. cr r)tsnormr"r%rrr&r'z(TSNormalize.__call__..rr(rr%rr)wr*zTSNormalize.__call__r$cCsh|jdddur|j||jd||jd<|jdddur2|j||jd||jd<|S)avNormalizes specified columns of a single time series data frame. This method applies the scaler to normalize the specified target and feature columns of the time series. Args: ts (pd.DataFrame): A single time series data frame to be normalized. Returns: pd.DataFrame: The normalized time series data frame. Z target_colsNZ feature_cols)r7r+r9 transformrr$rrrr:s   zTSNormalize.tsnorm)r/r0r1r2r3rrrrpdZ DataFramer)r:r5rrrrr`s   rcsNeZdZdZdeeefffdd ZdedefddZ defd d Z Z S) rzHConstructs a time series dataset from a list of time series data frames.r7cr)zInitializes the BuildTSDataset with parameters for dataset construction. Args: params_info (Dict[str, Any]): Dictionary containing parameters for constructing the time series dataset. N)rrr7)rr7rrrrs  zBuildTSDataset.__init__rrcr)zApplies the dataset construction to a list of time series. Args: ts_list (List): List of time series data frames. Returns: List: List of constructed time series datasets. cr r) buildtsdatar"r%rrr&r'z+BuildTSDataset.__call__..rr(rr%rr)r*zBuildTSDataset.__call__cCst|fi|j}|S)zBuilds a time series dataset from a single time series data frame. Args: ts: A single time series data frame. Returns: Any: A constructed time series dataset. )r r7)rr$r.rrrr>s zBuildTSDataset.buildtsdata) r/r0r1r2rr3rrrr)r>r5rrrrrs   rcsneZdZdZ ddeeefdeeefdeffdd Z de d e fd d Z d eeefd efd dZ Z S)rz=Extracts time features from time series data for forecasting.Fr7rholidaycs$t|d|_||_||_dS)aEInitializes the TimeFeature extractor. Args: params_info (Dict[str, Any]): Dictionary containing frequency information. size (Dict[str, int]): Dictionary containing the output chunk length. holiday (bool, optional): Whether to include holiday features. Defaults to False. freqN)rrr@rr?)rr7rr?rrrrs  zTimeFeature.__init__rrcr)zApplies time feature extraction to a list of time series. Args: ts_list (List): List of time series data frames. Returns: List: List of time series with extracted time features. cr r)timefeatr"r%rrr&r'z(TimeFeature.__call__..rr(rr%rr)r*zTimeFeature.__call__r$cCsB|jst||jgd|jd}|St||jgd|jd}|S)zExtracts time features from a single time series data frame. 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Args: ts (Dict[str, Any]): A single time series data frame. Returns: List[np.ndarray]: List of arrays representing the time series data. Zfloat32)listrGkeyssortappendnparrayZastype)rr$rZ input_namekeyrrrrHs  zTStoArray.tstoarray)r/r0r1r2rr3rrrrMndarrayr)rHr5rrrrrs ( ( rc@s.eZdZdZdeejdeejfddZdS)rzConvert a list of time series into batches for processing. This class provides a method to convert a list of time series data into batches. Each time series in the list is assumed to be a sequence of equal-length arrays or DataFrames. rrcs"td}fddt|DS)a#Convert a list of time series into batches. This method stacks time series data along a new axis to create batches. It assumes that each time series in the list has the same length. Args: ts_list (List[np.ndarray]): A list of time series, where each time series is represented as a list or array of equal length. Returns: List[np.ndarray]: A list of batches, where each batch is a stacked array of time series data at the same index across all series. rcs(g|]tjfddDddqS)csg|]}|qSrrr"irrr&Bsz1TStoBatch.__call__...r)Zaxis)rMstack)r#rrQrr&Bs(z&TStoBatch.__call__..)r,range)rrnrrTrr)3s zTStoBatch.__call__N)r/r0r1r2rrMrPr)rrrrr*s"r)typingrrrnumpyrMZpandasr=Z utils.depsrrZutils.benchmarkr funcsr r r __all__Ztimeitrrrrrrrrrrs,  :7%@)