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from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
import pandas as pd
import tensorflow as tf
import numpy as np
from sklearn.preprocessing import LabelEncoder
import argparse
import shutil
from sacred.observers import FileStorageObserver, MongoObserver
from sacred import Experiment

ex = Experiment('s487197-train', save_git_info=False)

def get_x_y(data):

    lb = LabelEncoder()
    data = data.drop(["Location 1"], axis=1)
    data = data.drop(columns=["Longitude", "Latitude", "Location", "Total Incidents", "CrimeTime", "Neighborhood", "Post", "CrimeDate", "Inside/Outside"], axis=1)
    for column_name in data.columns:
        data[column_name] = lb.fit_transform(data[column_name])
    x = data.drop('Weapon', axis=1)
    y = data['Weapon']



    return data, x, y

@ex.config
def my_config():
    parser = argparse.ArgumentParser(description='Train')

    parser.add_argument('-epochs', type=int, default=20)
    parser.add_argument('-lr', type=float, default=0.01)
    parser.add_argument('-validation_split', type=float, default=0.2)
    args = parser.parse_args()
    epochs=args.epochs
    lr=args.lr
    validation_split=args.validation_split
    return args

@ex.automain
def train_model(config):

   # parser = argparse.ArgumentParser(description='Train')

   # parser.add_argument('-epochs', type=int, default=20)
   # parser.add_argument('-lr', type=float, default=0.01)
    #parser.add_argument('-validation_split', type=float, default=0.2)
    ex.observers.append(FileStorageObserver('s487197'))

    ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
    args = config
    
    train = pd.read_csv('baltimore_train.csv')

    data_train, x_train, y_train = get_x_y(train)
    normalizer = tf.keras.layers.Normalization(axis=1)
    normalizer.adapt(np.array(x_train))
    model = Sequential(normalizer)
    model.add(Dense(64, activation="relu"))
    model.add(Dense(10, activation='relu'))
    model.add(Dense(10, activation='relu'))
    model.add(Dense(10, activation='relu'))
    model.add(Dense(5, activation="softmax"))
    model.compile(Adam(learning_rate=args.lr), loss='sparse_categorical_crossentropy', metrics = ['accuracy'] )
    model.summary()

    history = model.fit(
        x_train,
        y_train,
        epochs=args.epochs,
        validation_split=args.validation_split)
    hist = pd.DataFrame(history.history)
    hist['epoch'] = history.epoch
    model.save('baltimore_model')
    shutil.make_archive('baltimore', 'zip', 'baltimore_model')


train_model(my_config())