o #j.I@sUdZddlZddlZddlZddlZddlmZmZmZm Z m Z m Z ddl m Z m Z ddlZddlZddlmZddlZddlZddlZddlmZddlmZddlmZdd d d d Zeeefe d <Gd ddZ!dS)zT Prophet Service Module Handles Facebook Prophet time series forecasting operations N)DictListOptionalAnyTupleUnion)datetime timedelta)Prophet) get_logger) MySQLModelMEQEYE)MQYA _FREQ_ALIASESc @seZdZUdZiZeeefed<e Z dZ e Z ddZdefddZd=d d Zd=d d ZdedefddZdededefddZdededefddZddZdedeefddZdedefddZd>deed ed!edejfd"d#Z d?deedeed$eedeeeffd%d&Z ' ( )d@ded*ed+ed,edeeeff d-d.Z ' ( / 0dAded*ed+ed1ed2edef d3d4Z!dedeeeeffd5d6Z"deeeeffd7d8Z#dedefd9d:Z$deede%eeffd;d<Z&d S)BProphetServicezOs&z2ProphetService._model_file_path..z.joblib)joinr(pathr&)rr.safe_idrrr_model_file_pathNszProphetService._model_file_pathpayloadcCs||}tj||dd|S)N)compress)r9joblibdump)rr.r: file_pathrrr _save_to_diskRs zProphetService._save_to_diskr?cCs,tj|std|d|dt|S)NzModel file for 'z' not found at '')r(r7isfileFileNotFoundErrorr=load)rr.r?rrr_load_from_diskWs  zProphetService._load_from_diskc Cs(d}|j|||t||||fdS)Na INSERT INTO ml_prophet_models (model_id, file_path, config, data_points, train_start, train_end) VALUES (%s, %s, %s, %s, %s, %s) ON DUPLICATE KEY UPDATE file_path=VALUES(file_path), config=VALUES(config), data_points=VALUES(data_points), train_start=VALUES(train_start), train_end=VALUES(train_end), updated_at=CURRENT_TIMESTAMP )rr,jsondumps)rr.r?r% data_points train_start train_endsqlrrr_upsert_metadata\s$zProphetService._upsert_metadatacCsF|jd|f}|s dS|d}t|dtr!t|d|d<|S)Nz;SELECT * FROM ml_prophet_models WHERE model_id = %s LIMIT 1rr%)rr, isinstancegetstrrFloads)rr.rowsrowrrr_fetch_metadatagszProphetService._fetch_metadatacCstj|tjvrtj|WdSWdn1swY||}|dur5td|dz ||||}WntyW|j d|ft d|dwtj|tj|<Wd|S1slwY|S)u8Three-layer fetch: process cache → DB metadata + disk.NzModel 'z ' not found1DELETE FROM ml_prophet_models WHERE model_id = %sz<' file is missing. Orphaned record removed. Please re-train.) r _cache_lockrrS ValueErrorrEr9rCrr, RuntimeError)rr.metar:rrr_resolve_modelrs.      zProphetService._resolve_modeldsydata date_column value_columnc Csz;t|}||jvrt||||<||}||jvr)tj||dd||<|}|jdt |d|WSt y[}z|j dt |t dt |d}~ww)a+ Prepare data for Prophet model Args: data: List of dictionaries with date and value columns date_column: Name of the date column value_column: Name of the value column Returns: Pandas DataFrame formatted for Prophet coerce)errorszPrepared DataFrame with z rows for ProphetzError preparing DataFrame: zInvalid data format: N)pd DataFramecolumns to_datetime sort_values to_numericdropnarinfolen ExceptionerrorrOrV)rr\r]r^dferrr_prepare_dataframes    z!ProphetService._prepare_dataframer%c Csz||}t|dkrtddddddddddd }|r#||tdi|}|||s;d td }|d  d }|d  d }|||tt|||d} t j | || } ||| |t|||| t j|<Wdn1swY|jd|dt|d|dt|||d  |d  ddWSty} z |jdt| d} ~ ww)a  Train a Prophet model Args: data: Time series data model_id: Optional model identifier for storage config: Prophet configuration parameters Returns: Training results with model info z?Insufficient data for training (minimum 2 data points required)additiveTFg?g$@g?) seasonality_modeyearly_seasonalityweekly_seasonalitydaily_seasonalitychangepoint_prior_scaleseasonality_prior_scaleholidays_prior_scalechangepoint_rangeinterval_widthprophet_z %Y%m%d_%H%M%SrZ%Y-%m-%d)modelr% training_data created_atrHrIrJNzTrained Prophet model z with z data pointstrained)startend)r.statusrHr%training_date_rangezError training Prophet model: r)rnrirVupdater fitrnowstrftimeminmaxrrUr@rLrrrh isoformatrjrkrO) rr\r.r%rlprophet_configr|rIrJr:r?rmrrr train_modelsZ        zProphetService.train_modelDTperiodsfreqinclude_historyc Csvz||}|d}t||}|j||d}||}|s$t|dnd} g} t| t|D];} | |dj|  dt dt t |dj| d t dt t |d j| d t dt t |d j| d d q/|||| t d } |r|d} | dj d| d d d| d<|jd|d|d| WSty}z |jdt|d}~ww)ap Generate forecast using trained model Args: model_id: ID of the trained model periods: Number of periods to forecast freq: Frequency of forecast ('D' for daily, 'H' for hourly, etc.) include_history: Whether to include historical data in response Returns: Forecast results r|rrr}rrZr{gyhat yhat_lower yhat_upper)rZrrr)r.forecast_periods frequencyforecast generated_atr[rZr[historical_datazGenerated forecast for model z: z periodszError generating forecast: N)rYrrNmake_future_dataframepredictrirangeappendilocrrroundfloatrrrdttolistrrhrjrkrO)rr.rrr model_infor|futurer start_idx forecast_rowsiresultr}rmrrrgenerate_forecastsB      z ProphetService.generate_forecast Xwidthheightc Csz\||}|d}t||}|j||d}||} |j| |d|dfd} t} | j| dddd| dt |  d } | t| |jd |d | WStyt} z |jd t| d } ~ ww)aa Generate forecast plot as base64 encoded image Args: model_id: ID of the trained model periods: Number of periods to forecast freq: Frequency of forecast width: Plot width in pixels height: Plot height in pixels Returns: Base64 encoded PNG image r|rd)figsizepngtight)formatdpi bbox_inchesrzutf-8zGenerated plot for model zdata:image/png;base64,zError generating plot: N)rYrrNrrplotioBytesIOsavefigseekbase64 b64encodegetvaluedecodeclosepltrrhrjrkrO)rr.rrrrrr|rrfigbuffer image_base64rmrrr generate_plotJs(      zProphetService.generate_plotcCstj/|tjvr+tj|}|d|d|d|d|ddWdSWdn1s5wY||}|durEdS|d|dt|dt|dt|ddS)z Get information about a trained model Args: model_id: ID of the model Returns: Model information or None if not found rHr%r~rIrJ)rHr%r~rIrJN)rrUrrrNrSrO)rr.r4rXrrrget_model_info}s"       zProphetService.get_model_infoc Csz|jd}g}|D]0}t|dtr|dnt|d}||d|d|t|dt|dt|ddq |S) z_ List all trained models Returns: List of model summaries zxSELECT model_id, data_points, config, train_start, train_end, created_at FROM ml_prophet_models ORDER BY created_at DESCr%r.rHrIrJr~)r.rHr%rIrJr~)rr,rMdictrFrPrrO)rrQrrRr%rrr list_modelss$   zProphetService.list_modelsc Cs||}tj |tjv}Wdn1swY|dur%|s%dStjtj|dWdn1s:wY|roz||}tj|rRt |Wnt yn}z|j d|WYd}~nd}~ww|j d|fdS)z Delete a trained model Args: model_id: ID of the model to delete Returns: True if deleted, False if not found NFzCould not delete model file: rTT)rSrrUrpopr9r(r7rBremoveOSErrorrwarningrr,)rr.rXin_cacher?rmrrr delete_models*      zProphetService.delete_modelcCst|tr t|dkr dSddh}t|D]R\}}t|ts(dd|dfS||s9dd|dfSz t|dWndd |fYSzt |dWqdd |fYSd S) z Validate input data format for Prophet Args: data: Input data to validate Returns: Tuple of (is_valid, error_message) ro)Fz/Data must be a list with at least 2 data pointsrZr[Fz Data point z must be a dictionaryz must contain 'ds' and 'y' keysz"Invalid date format in data point z$Invalid numeric value in data point )Tr/) rMlistri enumeraterissubsetkeysrardr)rr\ required_keysritemrrrvalidate_data_formats"  z#ProphetService.validate_data_format)r!Nr)NN)rrT)rrrr)'__name__ __module__ __qualname____doc__rrrOr__annotations__ threadingLockrUr*r+r r&rrr9r@rErLrrSrYrrarbrnrintboolrrrrrrrrrrrrs~     " )  Q  G 3"r)"rr(rFloggingrtypingrrrrrrrr pandasranumpynpprophetr rrr=matplotlib.pyplotpyplotrapp.utils.helpersr app.models.mysql_modelr rrOrrrrrrs$