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from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout
from keras.optimizers import Adam
import pandas as pd
import tensorflow as tf
import numpy as np
from sklearn.preprocessing import LabelEncoder


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


def train_model():
    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=0.01), loss='sparse_categorical_crossentropy', metrics = ['accuracy'] )
    model.summary()

    history = model.fit(
        x_train,
        y_train,
        epochs=20,
        validation_split=0.2)
    hist = pd.DataFrame(history.history)
    hist['epoch'] = history.epoch
    model.save('baltimore_model3')


train_model()