from datetime import datetime

import pandas as pd
import numpy as np
from sacred.observers import FileStorageObserver, MongoObserver
from sacred import Experiment
from sklearn.metrics import mean_squared_error

from tensorflow import keras

ex = Experiment("sacred_scopes", interactive=True)
# ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017',
#                                   db_name='sacred'))
ex.observers.append(FileStorageObserver('my_runs'))


@ex.config
def my_config():
    epochs_amount = 30


def normalize_data(data):
    return (data - np.min(data)) / (np.max(data) - np.min(data))


@ex.capture
def prepare_model(epochs_amount, _run):
    _run.info["prepare_message_ts"] = str(datetime.now())

    data = pd.read_csv("data_train", sep=',', skip_blank_lines=True, nrows=1087, error_bad_lines=False,
                       names=["vipip install sacreddeo_id", "last_trending_date", "publish_date", "publish_hour",
                              "category_id",
                              "channel_title", "views", "likes", "dislikes", "comment_count"]).dropna()

    X = data.loc[:, data.columns == "views"].astype(int)
    y = data.loc[:, data.columns == "likes"].astype(int)

    min_val_sub = np.min(X)
    max_val_sub = np.max(X)
    X = (X - min_val_sub) / (max_val_sub - min_val_sub)
    print(min_val_sub)
    print(max_val_sub)

    min_val_like = np.min(y)
    max_val_like = np.max(y)
    y = (y - min_val_like) / (max_val_like - min_val_like)

    print(min_val_like)
    print(max_val_like)

    model = keras.Sequential([
        keras.layers.Dense(512, input_dim=X.shape[1], activation='relu'),
        keras.layers.Dense(256, activation='relu'),
        keras.layers.Dense(256, activation='relu'),
        keras.layers.Dense(128, activation='relu'),
        keras.layers.Dense(1, activation='linear'),
    ])

    model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absolute_error'])

    model.fit(X, y, epochs=int(epochs_amount), validation_split=0.3)

    data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
                       skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
                                                                "publish_date", "publish_hour", "category_id",
                                                                "channel_title", "views", "likes", "dislikes",
                                                                "comment_count"]).dropna()
    X_test = data.loc[:, data.columns == "views"].astype(int)
    y_test = data.loc[:, data.columns == "likes"].astype(int)

    min_val_sub = np.min(X_test)
    max_val_sub = np.max(X_test)
    X_test = (X_test - min_val_sub) / (max_val_sub - min_val_sub)
    print(min_val_sub)
    print(max_val_sub)

    min_val_like = np.min(y_test)
    max_val_like = np.max(y_test)
    print(min_val_like)
    print(max_val_like)

    prediction = model.predict(X_test)

    prediction_denormalized = []
    for pred in prediction:
        denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
        prediction_denormalized.append(denorm)

    f = open("predictions.txt", "w")
    for (pred, test) in zip(prediction_denormalized, y_test.values):
        f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))

    error = mean_squared_error(y_test, prediction_denormalized)
    print(error)

    model.save('model')

    _run.log_scalar("training.metrics", error)
    return error


@ex.main
def my_main(epochs_amount):
    print(prepare_model())


ex.run()
ex.add_artifact("model.pb")