import warnings

import pandas as pd
import numpy as np

from tensorflow import keras
import sys
import mlflow.sklearn

import logging

from evaluate_network import evaluate_model

logging.basicConfig(level=logging.WARN)
logger = logging.getLogger(__name__)

mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("s434765")

warnings.filterwarnings("ignore")
np.random.seed(40)

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


data = pd.read_csv("data_train", sep=',',  skip_blank_lines=True, nrows=1087, error_bad_lines=False,
                   names=["video_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)

with mlflow.start_run() as run:
    print("MLflow run experiment_id: {0}".format(run.info.experiment_id))
    print("MLflow run artifact_uri: {0}".format(run.info.artifact_uri))

    mlflow.keras.autolog()
    mlflow.log_param("epochs", int(sys.argv[1]))
    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(sys.argv[1]), validation_split = 0.3)

    model.save('model')

    error = evaluate_model()
    mlflow.log_metric("rmse", error)