from pathlib import Path
from core.Settings import (
    RDSAP_RESPONSE,
    FLOOR_LEVEL_MAP,
    BUILT_FORM_REMAP,
    EARLIEST_EPC_DATE,
    FULLY_GLAZED_DESCRIPTIONS,
    FIXED_FEATURES,
    LATEST_FIELD,
    COMPONENT_FEATURES
)
from model_data.BaseUtility import Definitions
from tqdm import tqdm
import pandas as pd
import numpy as np

from autogluon.tabular import TabularDataset, TabularPredictor

RANDOM_SEED = 0

DATA_DIRECTORY = Path(__file__).parent / 'data' / 'all-domestic-certificates'

FLOAT_COLUMNS = [
    'NUMBER_OPEN_FIREPLACES',
    'EXTENSION_COUNT',
    'TOTAL_FLOOR_AREA',
    'PHOTO_SUPPLY',
    'FIXED_LIGHTING_OUTLETS_COUNT',
    'FLOOR_HEIGHT',
    'NUMBER_HABITABLE_ROOMS',
    'LOW_ENERGY_LIGHTING',
    'MULTI_GLAZE_PROPORTION',
    'NUMBER_HEATED_ROOMS'
]


def create_raw_data():
    """
    Extract all information to do a simple predictor for RDSAP
    """

    directories = [entry for entry in DATA_DIRECTORY.iterdir() if entry.is_dir()]
    # directories = directories[0:10]
    dfs = []
    for directory in tqdm(directories):
        filepath = directory / "certificates.csv"
        df = pd.read_csv(filepath, low_memory=False)

        # Remove any bad uprns and ignore old/bad data
        df = df[~pd.isnull(df["UPRN"])]
        df = df[df["LODGEMENT_DATE"] >= EARLIEST_EPC_DATE]
        df = df[df["TRANSACTION_TYPE"] != "new dwelling"]
        df = df[~df["FLOOR_LEVEL"].isin(["top floor", "mid floor"])]

        # Change multi glaze proportion
        no_multi_glaze_proportion_index = pd.isnull(df["MULTI_GLAZE_PROPORTION"]) & (
            df["WINDOWS_DESCRIPTION"].isin(FULLY_GLAZED_DESCRIPTIONS))
        df.loc[no_multi_glaze_proportion_index, 'MULTI_GLAZE_PROPORTION'] = 100

        # Recast 
        df["UPRN"] = df["UPRN"].astype(int).astype(str)
        df['MAIN_HEATING_CONTROLS'] = df['MAIN_HEATING_CONTROLS'].astype(float)

        # Sort Data
        df = df.sort_values(["UPRN", "LODGEMENT_DATE"], ascending=True)

        # Map all anomaly values to None
        data_anomaly_map = dict(zip(Definitions.DATA_ANOMALY_MATCHES, [None] * len(Definitions.DATA_ANOMALY_MATCHES)))

        # Use replace function to map data (if exists in key), to corresponding value - i.e. Remove invalid values
        df = df.replace(data_anomaly_map)
        df = df.replace(np.NAN, None)

        # Remap certain columns
        df['FLOOR_LEVEL'] = df['FLOOR_LEVEL'].replace(FLOOR_LEVEL_MAP)
        df['BUILT_FROM'] = df['BUILT_FORM'].replace(BUILT_FORM_REMAP)

        # Keep only possible modelling columns
        df = df[[RDSAP_RESPONSE] + list(set(FIXED_FEATURES + LATEST_FIELD + COMPONENT_FEATURES))]

        # Reduce memory usage

        # df.memory_usage()
        # df.dtypes
        df[RDSAP_RESPONSE] = pd.to_numeric(df[RDSAP_RESPONSE], downcast='unsigned')
        df[FLOAT_COLUMNS] = df[FLOAT_COLUMNS].apply(pd.to_numeric, downcast='float')

        dfs.append(df)

    data = pd.concat(dfs)
    data.to_parquet('./energy_predictor_data.parquet')

    cleaned_data = data.dropna()
    # GIves you primarily flats
    cleaned_data.to_parquet('./energy_predictor_cleaned_data.parquet')


def main():
    data = TabularDataset(data='./model_build_data/energy_data/cleaned_data/train_validation_data.parquet')

    subsample_size = round(len(data) / 100)
    data = data.sample(subsample_size, random_state=RANDOM_SEED)

    predictor_RDSAP = TabularPredictor(
        label=RDSAP_RESPONSE,
        path="agModels-predictENERGY",
        problem_type="regression",
        eval_metric='mean_absolute_error'
    ).fit(data, time_limit=800, presets='high_quality', excluded_model_types=['KNN', 'CAT'])

    test_data = TabularDataset('./model_build_data/energy_data/cleaned_data/test_data.parquet')
    performance = predictor_RDSAP.evaluate(test_data)
    predictions = predictor_RDSAP.predict(test_data)
    predictor_RDSAP.feature_importance(test_data)


if __name__ == "__main__":
    main()