import json
import hdbscan
import pandas as pd
import joblib
import os
from ad_model.processing import preprocess
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split

# Ranges for input features based on excellent, good, average, & poor category
UEKeyList = ['MeasTimestampRF', 'UEPDCPBytesDL', 'UEPDCPBytesUL', 'UEPRBUsageDL', 'UEPRBUsageUL', 'S_RSRP', 'S_RSRQ', 'S_SINR', 'UEID']

sigstr = {'S_RSRP': {'Excellent Signal': [-80, 10000000000000000], 'Good Signal': [-90, -80], 'Average Signal': [-100, -90], 'Poor Signal': [-100000000000000000, -100]}, 'S_RSRQ': {'Excellent Signal': [-10, 10000000000000000], 'Good Signal': [-15, -10], 'Average Signal': [-20, -15], 'Poor Signal': [-100000000000000000, -20]}, 'S_SINR': {'Excellent Signal': [20, 10000000000000000], 'Good Signal': [13, 20], 'Average Signal': [0, 13], 'Poor Signal': [-100000000000000000, 0]}}

PRB = {'UEPRBUsageDL': {'Excellent Signal': [25, 10000000000000000], 'Good Signal': [20, 25], 'Average Signal': [10, 20], 'Poor Signal': [-100000000000000000, 10]}, 'UEPRBUsageUL': {'Excellent Signal': [15, 10000000000000000], 'Good Signal': [10, 15], 'Average Signal': [5, 10], 'Poor Signal': [-100000000000000000, 5]}}

tput = {'UEPDCPBytesDL': {'Excellent Signal': [300000, 10000000000000000], 'Good Signal': [200000, 300000], 'Average Signal': [100000, 200000], 'Poor Signal': [-100000000000000000, 100000]}, 'UEPDCPBytesUL': {'Excellent Signal': [125000, 10000000000000000], 'Good Signal': [100000, 125000], 'Average Signal': [10000, 100000], 'Poor Signal': [-100000000000000000, 10000]}}


def category(df, ranges):
    # Based on ranges, each sample is return with category(excellent, good, average, & poor category).
    data = df.copy()
    for block in ranges:
        df = data[list(block.keys())].copy()
        for key, value in block.items():
            temp = data[list(block.keys())].copy()
            for cat, bounds in value.items():
                ind = temp[(temp[key] <= bounds[1]) & (temp[key] > bounds[0])].index
                df.loc[ind, key] = cat
        data[df.columns] = df
    # Maximum category value is considered as final category value.
    category = data[['UEPDCPBytesDL', 'UEPDCPBytesUL', 'UEPRBUsageDL', 'UEPRBUsageUL', 'S_RSRP', 'S_RSRQ', 'S_SINR']].mode(axis=1)[0]
    return category


class modelling(object):
    def __init__(self, data):
        self.time = data.MeasTimestampRF
        self.id = data.UEID
        self.data = data.drop(['UEID', 'MeasTimestampRF'], axis=1)

    def dbscan(self):
        """
         Train hdbscan for the input dataframe
         save the hdbscan model
        """

        df = self.data.copy()
        hdb = hdbscan.HDBSCAN(min_cluster_size=16000, min_samples=5, prediction_data=True).fit(df)
        joblib.dump(hdb, 'ad/hdbscan')
        self.data['Category'] = hdb.labels_  # Stores the labels into category field

    def RandomForest(self, y):
        """
         Transform categorical label into numeric(Save the LabelEncoder).
         Create Train and Test split for Random Forest Classifier and Save the model
        """
        df = self.data.copy()
        le = LabelEncoder()
        y = le.fit_transform(y)
        joblib.dump(le, 'ad/LabelEncoder')
        X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.20, stratify=y, random_state=42)
        rf = RandomForestClassifier(max_depth=9, random_state=0)
        rf.fit(X_train, y_train)  # Fit the RFC model
        print("X_train cols:", X_train.columns)
        joblib.dump(rf, 'ad/RF')  # Save the RF model


def train():
    """
     Main function to perform training on input files
     Read all the csv file in the current path and create trained model
    """
    print('Training Starts : ')
    path = 'ad/ue_data/'
    df = pd.DataFrame()
    # Read all the csv files and store the combined data into df
    for file in os.listdir(path):
        df = df.append(pd.read_csv(path + file))
    df = df[UEKeyList]
    df.index = range(len(df))
    y = category(df, [sigstr, PRB, tput])
    seg = {}
    # Save the category of each UEID and save it as json file
    for ue in df.UEID.unique():
        seg[str(ue)] = list(set(y[df[df['UEID'] == ue].index]))

    with open('ue_seg.json', 'w') as outfile:
        json.dump(seg, outfile)

    # Do a preprocessing, processing and save the model
    ps = preprocess(df)
    ps.process()
    df = ps.data
    db = modelling(df)
    # db.dbscan()
    db.RandomForest(y)